diff --git a/_data/modified.yml b/_data/modified.yml
index 9ef8b5c1a..24f613262 100644
--- a/_data/modified.yml
+++ b/_data/modified.yml
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+ modified: Wed Oct 30 13:57:41 2024 -0400
+ authored: 2024-10-30T13:57:41-04:00
+ committed: 2024-10-30T13:57:41-04:00
+ filename: sn_collections/_workshops/2023-geospatial-workshop/2023-9-27-Geospatial-Workshop-3.md
+ - commit: bcb981d002bbf872dafdd029e1d4ea49dd783601
+ modified: Wed Oct 30 13:57:41 2024 -0400
+ authored: 2024-10-30T13:57:41-04:00
+ committed: 2024-10-30T13:57:41-04:00
+ filename: sn_collections/_workshops/2023-geospatial-workshop/2023-9-28-Geospatial-Workshop-4.md
+ - commit: bcb981d002bbf872dafdd029e1d4ea49dd783601
+ modified: Wed Oct 30 13:57:41 2024 -0400
+ authored: 2024-10-30T13:57:41-04:00
+ committed: 2024-10-30T13:57:41-04:00
+ filename: sn_collections/_workshops/2023-geospatial-workshop/2023-9-28-Geospatial-Workshop-5.md
+ - commit: bcb981d002bbf872dafdd029e1d4ea49dd783601
+ modified: Wed Oct 30 13:57:41 2024 -0400
+ authored: 2024-10-30T13:57:41-04:00
+ committed: 2024-10-30T13:57:41-04:00
+ filename: sn_collections/_workshops/2024-07-spemw/2024-07-19-package-env-workshop-python.md
+ - commit: bcb981d002bbf872dafdd029e1d4ea49dd783601
+ modified: Wed Oct 30 13:57:41 2024 -0400
+ authored: 2024-10-30T13:57:41-04:00
+ committed: 2024-10-30T13:57:41-04:00
+ filename: sn_collections/_workshops/2024-07-spemw/2024-07-19-package-env-workshop-r.md
+ - commit: bcb981d002bbf872dafdd029e1d4ea49dd783601
+ modified: Wed Oct 30 13:57:41 2024 -0400
+ authored: 2024-10-30T13:57:41-04:00
+ committed: 2024-10-30T13:57:41-04:00
+ filename: sn_collections/_workshops/2024-10-spemw/2024-10-03-package-env-workshop-python.md
+ - commit: bcb981d002bbf872dafdd029e1d4ea49dd783601
+ modified: Wed Oct 30 13:57:41 2024 -0400
+ authored: 2024-10-30T13:57:41-04:00
+ committed: 2024-10-30T13:57:41-04:00
+ filename: sn_collections/_workshops/2024-10-spemw/2024-10-04-package-env-workshop-r.md
+ - commit: f84346a10eb70f188e47f65dcfd0f544a1de3c2d
+ modified: Mon Oct 21 09:43:24 2024 -0400
+ authored: 2024-10-21T09:43:24-04:00
+ committed: 2024-10-21T09:43:24-04:00
+ filename: sn_collections/_workshops/2024-ai-user-forum/20-a-ml-intro.md
+ - commit: f9103cf0fc9ee8622448894652afed2d0a428cdb
+ modified: Mon Oct 21 09:41:51 2024 -0400
+ authored: 2024-10-21T09:41:51-04:00
+ committed: 2024-10-21T09:41:51-04:00
+ filename: sn_collections/_workshops/2024-ai-user-forum/20-data-prep.md
+ - commit: f9103cf0fc9ee8622448894652afed2d0a428cdb
+ modified: Mon Oct 21 09:41:51 2024 -0400
+ authored: 2024-10-21T09:41:51-04:00
+ committed: 2024-10-21T09:41:51-04:00
+ filename: sn_collections/_workshops/2024-ai-user-forum/20-project-management.md
+ - commit: f9103cf0fc9ee8622448894652afed2d0a428cdb
+ modified: Mon Oct 21 09:41:51 2024 -0400
+ authored: 2024-10-21T09:41:51-04:00
+ committed: 2024-10-21T09:41:51-04:00
+ filename: sn_collections/_workshops/2024-ai-user-forum/21-bioinformatics.md
+ - commit: f9103cf0fc9ee8622448894652afed2d0a428cdb
+ modified: Mon Oct 21 09:41:51 2024 -0400
+ authored: 2024-10-21T09:41:51-04:00
+ committed: 2024-10-21T09:41:51-04:00
+ filename: sn_collections/_workshops/2024-ai-user-forum/21-computer-vision-2.md
+ - commit: f9103cf0fc9ee8622448894652afed2d0a428cdb
+ modified: Mon Oct 21 09:41:51 2024 -0400
+ authored: 2024-10-21T09:41:51-04:00
+ committed: 2024-10-21T09:41:51-04:00
+ filename: sn_collections/_workshops/2024-ai-user-forum/21-computer-vision.md
+ - commit: f9103cf0fc9ee8622448894652afed2d0a428cdb
+ modified: Mon Oct 21 09:41:51 2024 -0400
+ authored: 2024-10-21T09:41:51-04:00
+ committed: 2024-10-21T09:41:51-04:00
+ filename: sn_collections/_workshops/2024-ai-user-forum/21-data-management.md
+ - commit: f9103cf0fc9ee8622448894652afed2d0a428cdb
+ modified: Mon Oct 21 09:41:51 2024 -0400
+ authored: 2024-10-21T09:41:51-04:00
+ committed: 2024-10-21T09:41:51-04:00
+ filename: sn_collections/_workshops/2024-ai-user-forum/21-deepvariant.md
+ - commit: f9103cf0fc9ee8622448894652afed2d0a428cdb
+ modified: Mon Oct 21 09:41:51 2024 -0400
+ authored: 2024-10-21T09:41:51-04:00
+ committed: 2024-10-21T09:41:51-04:00
+ filename: sn_collections/_workshops/2024-ai-user-forum/21-protein-structure.md
+ - commit: f9103cf0fc9ee8622448894652afed2d0a428cdb
+ modified: Mon Oct 21 09:41:51 2024 -0400
+ authored: 2024-10-21T09:41:51-04:00
+ committed: 2024-10-21T09:41:51-04:00
+ filename: sn_collections/_workshops/2024-ai-user-forum/21-spatial-modeling.md
+ - commit: c77d7a85f0363c795f6fdff378915643455823b7
+ modified: Mon Oct 28 09:19:49 2024 -0400
+ authored: 2024-10-28T09:19:49-04:00
+ committed: 2024-10-28T09:19:49-04:00
+ filename: sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-0.md
+ - commit: c77d7a85f0363c795f6fdff378915643455823b7
+ modified: Mon Oct 28 09:19:49 2024 -0400
+ authored: 2024-10-28T09:19:49-04:00
+ committed: 2024-10-28T09:19:49-04:00
+ filename: sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-1.md
+ - commit: dcb3aa58c5643162767f1867c81025b5fad92642
+ modified: Mon Oct 28 15:23:27 2024 -0400
+ authored: 2024-10-28T15:23:27-04:00
+ committed: 2024-10-28T15:23:27-04:00
+ filename: sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-2.md
+ - commit: c77d7a85f0363c795f6fdff378915643455823b7
+ modified: Mon Oct 28 09:19:49 2024 -0400
+ authored: 2024-10-28T09:19:49-04:00
+ committed: 2024-10-28T09:19:49-04:00
+ filename: sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-3.md
+ - commit: c77d7a85f0363c795f6fdff378915643455823b7
+ modified: Mon Oct 28 09:19:49 2024 -0400
+ authored: 2024-10-28T09:19:49-04:00
+ committed: 2024-10-28T09:19:49-04:00
+ filename: sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-4.md
+ - commit: c77d7a85f0363c795f6fdff378915643455823b7
+ modified: Mon Oct 28 09:19:49 2024 -0400
+ authored: 2024-10-28T09:19:49-04:00
+ committed: 2024-10-28T09:19:49-04:00
+ filename: sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-5.md
+ - commit: 2db6f105f2aaa1c2f823652af87a5861aa526da1
+ modified: Thu Oct 17 18:46:46 2024 -0400
+ authored: 2024-10-17T18:46:46-04:00
+ committed: 2024-10-17T18:46:46-04:00
+ filename: sn_collections/_workshops/practicum-ai/computer-vision.md
+ - commit: 2db6f105f2aaa1c2f823652af87a5861aa526da1
+ modified: Thu Oct 17 18:46:46 2024 -0400
+ authored: 2024-10-17T18:46:46-04:00
+ committed: 2024-10-17T18:46:46-04:00
+ filename: sn_collections/_workshops/practicum-ai/computing.md
+ - commit: 2db6f105f2aaa1c2f823652af87a5861aa526da1
+ modified: Thu Oct 17 18:46:46 2024 -0400
+ authored: 2024-10-17T18:46:46-04:00
+ committed: 2024-10-17T18:46:46-04:00
+ filename: sn_collections/_workshops/practicum-ai/deep-learning.md
+ - commit: 2db6f105f2aaa1c2f823652af87a5861aa526da1
+ modified: Thu Oct 17 18:46:46 2024 -0400
+ authored: 2024-10-17T18:46:46-04:00
+ committed: 2024-10-17T18:46:46-04:00
+ filename: sn_collections/_workshops/practicum-ai/getting-started.md
+ - commit: 2db6f105f2aaa1c2f823652af87a5861aa526da1
+ modified: Thu Oct 17 18:46:46 2024 -0400
+ authored: 2024-10-17T18:46:46-04:00
+ committed: 2024-10-17T18:46:46-04:00
+ filename: sn_collections/_workshops/practicum-ai/python.md
diff --git a/_data/navigation.yml b/_data/navigation.yml
index e726a17f8..2b5fcfcea 100644
--- a/_data/navigation.yml
+++ b/_data/navigation.yml
@@ -83,14 +83,14 @@ primary:
url: /research/working-groups/ag100pest
- title: Arthropod Genomics
url: /research/working-groups/arthropods
+ - title: Breeding AI and ML
+ url: /research/working-groups/breeding
- title: Geospatial Research
url: /research/working-groups/geospatial
- title: Microbiome
url: /research/working-groups/microbiome
- title: LTAR Phenology
url: /research/working-groups/LTARphenology
- - title: Pollinator
- url: /research/working-groups/pollinator
- title: Protein Function and Phenotype Prediction
url: /research/working-groups/proteinfunction
- title: Translational Omics
@@ -136,8 +136,10 @@ primary:
url: /opportunities/fellowships
- title: AI Innovation Fund
url: /opportunities/ai-innovation/
- - title: SCINet and AI-COE fellowship proposals
- url: /opportunities/scinet-aicoe-fellowships
+ - title: Postdoctoral Fellowship Mentor Proposals
+ url: /opportunities/fellowship-mentors
+ - title: Graduate Student Internship Mentor Applications
+ url: /opportunities/internship-mentors
# - title: Open Positions
# url: /opportunities/open_positions
diff --git a/_data/tables/fellowships.csv b/_data/tables/fellowships.csv
index 43623f8d9..90eb4fdb4 100644
--- a/_data/tables/fellowships.csv
+++ b/_data/tables/fellowships.csv
@@ -1,16 +1,5 @@
-Opportunity,Location,Keywords,url
-"Computational Tools & Pipeline Development for Metagenomic Data Analysis Fellowship, USDA-ARS-2022-0027","Ames, IA","Metagenomics, Bioinformatics",https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0027
-"High Performance Computing and Prediction of Geospatial Dynamics Fellowship, USDA-ARS-2022-0029",TBD,"Geospatial, Times Series Data",https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0029
-"USDA-ARS Remote Sensing of Agro-ecosystems & High-performance Computing Fellowship, USDA-ARS-2022-0031","Beltsville, MD","Geosptial, Remote Sensing, ML",https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0031
-"USDA-ARS High Performance Computing Fellowship, USDA-ARS-2022-0071",TBD,"Agro-ecosystem dynamics, Natural Language Processing, Remote Sensing, Geospatial",https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0071
-"USDA-ARS SCINet AI Machine Learning in Maize Genomics Postdoctoral Fellowship, USDA-ARS-2022-0158","Dubois, ID","Image Analysis, AI, ML",https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0158
-"USDA-ARS SCINet Postdoctoral Fellowship in Machine Learning for Influenza A Virus Pandemic Prevention, USDA-ARS-2022-0162","Ames, IA","Genomics, ML",https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0162
-"USDA-ARS SCINet Fellowship for Developing AI and ML Techniques to Advance Understanding of How Dietary Patterns Influence Human Health, USDA-ARS-2022-0369","Beltsville, MD","Human Health, Dietary Patterns, AI, ML",https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0369
-"USDA-ARS SCINet Postdoctoral Fellowship in AI Methods for Predicting Protein Function and Disease Susceptibility in Crops, USDA-ARS-2022-0436","Stuttgart, AR","Genomics, AI",https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0436
-"USDA-ARS SCINet/AI-COE postdoctoral fellowship in AI/Machining Learning for Animal Behavior Research","Boise, ID","Geospatial, ML, AI",https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINET-2023-0202
-"USDA-ARS SCINet/AI-COE postdoctoral fellowship in Using AI to address large, complex datasets in microbiome-based integrated pest management","Colombia, MO","Microbiome Science, Genetics, AI, ML",https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2023-0223
-"USDA-ARS SCINet/AI-COE postdoctoral fellowship in Machine Learning to Distinguish Pest from Non-Pest Weevils","College Station, TX","Genomics, Pest Management, Genomics",https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2023-0224
-"USDA-ARS SCINet/AI-COE postdoctoral fellowship in Food Security Agency in Alaska","Tifton, GA","Geospatial, Remote Sensing, ML",https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2023-0228
-"USDA-ARS SCINet/AI-COE postdoctoral fellowship in bridging local measurements to management scales using machine learning","Beltsville, MD","Soil Moisture, Hydrology, AI, Geospatial",https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2023-0229
-"USDA-ARS SCINet/AI-COE postdoctoral fellowship in using AI to develop a cross kingdom gene editing tool kit","Colombia, MO","Genomics, AI, ML",https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2023-0245
-"USDA-ARS SCINet/AI-COE postdoctoral fellowship in taxon-specific model training to improve accuracy of variant calling in non-model systems","Hilo, HI","Genomics, AI, ML",https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2023-0260
+Opportunity,Location,Deadline,url
+USDA-ARS Postdoctoral Fellowship in Fish Functional Genomics and Epigenetics,"Auburn, AL",2025-01-03 03:00 PM ET,https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2024-0285
+USDA-ARS SCINet/AI-COE Postdoctoral Fellowship in Comparative Chemosensory Genomics of Stored Product Insects,"Manhattan, KS",2025-07-31 03:00 PM ET,https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2024-0299
+USDA-ARS SCINet/AI-COE Postdoctoral Fellowship to Modernize Hydrologic Models for High-Performance Computing,"Temple, Texas",2025-10-25 03:00 PM ET,https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2024-0307
+USDA-ARS SCINet/AI-COE Postdoctoral Fellowship in Fungal Secondary Metabolomes,"Ithaca, New York",2024-10-25 03:00 PM ET,https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2024-0308
diff --git a/_data/tables/funded_FY22.csv b/_data/tables/funded_FY22.csv
new file mode 100644
index 000000000..226592979
--- /dev/null
+++ b/_data/tables/funded_FY22.csv
@@ -0,0 +1,20 @@
+Title,Mentor,Co-mentor(s)
+Developing tools for the real-time monitoring and query of all the world's publicly available sequence data.,Adam Rivers,
+Determining the structure and function of proteins of foodborne and plant pathogens using Alphafold2 and top-down proteomic analysis,Clifton K. Fagerquist,
+AI-driven phenotype extraction from UAS imagery for crop genetics and breeding,Jacob Washburn,"Alisa Coffin, Max Feldman"
+Machine learning approaches to gain structural and functional insights of genes regulating climate adaptability,Carson Andorf,Taner Sen
+Inferring Protein Function for Enhanced Breeding using Machine Learning and Protein Structure Prediction,Taner Sen,Carson Andorf
+A MATLAB toolbox for dynamic prediction of meteorological drought across Southern Great Plains,Daniel Moriasi,Ali Mirchi
+Optimization of AI-based microscope image analysis with the Blackbird imaging robot,Lance Cadle-Davidson,Yu Jiang
+Automated Detection of Insect Species in Food and Processing Environments using Artificial Intelligence,Paul R Armstrong,Lester Pordesimo
+Improving the accuracy and scope of machine learning tools for camera trap-based ecological data analysis across US grazinglands,Hailey Wilmer,
+"A broadly deployable deep learning workflow for image feature identification, segmentation, and data extraction.",Devin A Rippner,"Jeff Neyhart , Kayla Altendorf, Garett Heineck, Andrew McElrone"
+An AI approach for discovering novel blast disease resistance sources in rice.,Jeremy Edwards,Yulin Jia
+Predicting interspecies transmission of influenza A virus from swine to humans with machine learning,Tavis Anderson,Amy Vincent
+Modeling the Spread and Adaptation of Stored-Product Insect Pests in the Face of Climate Change,Alison Gerken,Rob Morrison
+Using AI to Analyze Climate Effects on Crop Performance,Xianran Li,
+AI/ML and Deep Learning to Enhance Understanding of Dietary Patterns that Promote Human Health,David Baer,Lauren O'Connor
+The evolutionary genomics of Macrophomina phaseolina.,Peter Montgomery Henry,
+Spatial multi-criterion optimization of agricultural ecosystem services at the landscape scale,Sarah Goslee,
+Application of machine learning in livestock genomics,George Liu,
+Determining the pervasiveness of hybridization and introgression in agriculture and the driving mechanisms,Christopher Owen,
\ No newline at end of file
diff --git a/_data/tables/funded.csv b/_data/tables/funded_FY23.csv
similarity index 100%
rename from _data/tables/funded.csv
rename to _data/tables/funded_FY23.csv
diff --git a/_data/tables/funded_FY24.csv b/_data/tables/funded_FY24.csv
new file mode 100644
index 000000000..5a5a972bc
--- /dev/null
+++ b/_data/tables/funded_FY24.csv
@@ -0,0 +1,6 @@
+Title,Mentor,Co-mentor(s)
+Application of machine learning to reproductive epigenetics for precision aquaculture,Jason Abernathy,
+Secondary Metabolome of Nematode Parasitic Fungi for Biocontrol,Kathryn Bushley,Ted Thannhauser and Catherine Wram
+Developing AI-empowered climate mitigation solutions in agricultural soils,Michael Cosh,
+Integrating Multiple Data Streams into Forecasts of Vector-borne Livestock Disease Emergence and Spread,Amy Hudson,
+Modernizing the Soil and Water Assessment Tool (SWAT+) for use on SCINet computing infrastructure,Kelly Thorp,"Jeff Arnold, Mike White, and Merilynn Schantz"
\ No newline at end of file
diff --git a/_includes/collect/components/calendar b/_includes/collect/components/calendar
index 73543404b..ff1d520ff 100644
--- a/_includes/collect/components/calendar
+++ b/_includes/collect/components/calendar
@@ -17,9 +17,9 @@
{% if include.filter == "true" %}
-{% assign tags = allevents | map: 'tags' | uniq %}
-{% assign types = allevents | map: 'type' | uniq %}
-{% assign providers = allevents | map: 'provider' | uniq %}
+{% assign tags = allevents | map: 'tags' | uniq | compact | sort %}
+{% assign types = allevents | map: 'type' | uniq | compact | sort %}
+{% assign providers = allevents | map: 'provider' | uniq | compact | sort %}
{% assign sort_list = true %}
{% endif %}
@@ -58,7 +58,7 @@
{% assign sortings = page.sorted %}
-
+
{% if flow == 'normal' %}
{% for _events in allevents %}
@@ -71,6 +71,6 @@
{% endif %}
-
+
{% endif %}
{% endif %}
diff --git a/_includes/collect/components/session-ul b/_includes/collect/components/session-ul
index f21b4483a..0af860efd 100644
--- a/_includes/collect/components/session-ul
+++ b/_includes/collect/components/session-ul
@@ -44,7 +44,7 @@
{% endif %}
{% if session.details %}
{% for _detail in session.details %}
- - {% include components/link.html url=_detail.url text=_detail.text external=true %}
+ - {{_detail.text}}
{% endfor %}
{% endif %}
diff --git a/_includes/collect/process/event b/_includes/collect/process/event
index b73169b4e..b32acdc21 100644
--- a/_includes/collect/process/event
+++ b/_includes/collect/process/event
@@ -29,8 +29,7 @@
{%- assign materials = session.materials | default: parent.materials %}
{%- assign details = session.details | default: parent.details %}
-{%- capture stags %}{% if include.session['tags'] %}{{ session.tags }} {% endif %}{{ parent.tags }}{% endcapture %}
-{% assign tags = stags | split: ' ' %}
+{% assign tags = session.tags | concat: parent.tags %}
{% if end_date %}
{% assign end_date = end_date | date: '%s' | plus: 0 %}
@@ -63,6 +62,6 @@
{% endif %}
-{% include collect/process/hashed date=date date-start=date-start duration=duration time=time end_date=end_date caldate=cal_date multiday=multiday url=url title=title section=section parenttitle=parenttitle excerpt=excerpt type=type provider=provider registration=registration sessions=sessions prerequisites=prerequisites duration=duration system=system location=location reason=reason assets=assets link=link latest=latest instructor=instructor presenter=presenter lead=lead location=location materials=materials details=details %}
+{% include collect/process/hashed date=date date-start=date-start duration=duration time=time end_date=end_date caldate=cal_date multiday=multiday url=url title=title section=section parenttitle=parenttitle excerpt=excerpt type=type provider=provider registration=registration sessions=sessions prerequisites=prerequisites duration=duration system=system location=location reason=reason assets=assets link=link latest=latest instructor=instructor presenter=presenter lead=lead location=location materials=materials details=details tags=tags %}
{% assign event = hashed %}
\ No newline at end of file
diff --git a/_includes/components/table.html b/_includes/components/table.html
index 09805806c..7314c5d7f 100644
--- a/_includes/components/table.html
+++ b/_includes/components/table.html
@@ -55,8 +55,12 @@
{% endfor %}
+ {% if table.sort %}
+
+ {% endif %}
{% if table.announcement %}
-
+
{{ table.announcement | markdownify }}
{% endif %}
diff --git a/_layouts/downtime.html b/_layouts/downtime.html
index a431210b5..ad4810472 100644
--- a/_layouts/downtime.html
+++ b/_layouts/downtime.html
@@ -77,7 +77,7 @@
Further detail for each outage is posted in the announcements below if availible.
{% endif %}
-{% for _down in downtime reversed %}
+{% for _down in downtime %}
{% include components/collection-downtime.html post=_down %}
{% endfor %}
diff --git a/_layouts/feed.ics b/_layouts/feed.ics
index f9a6befbc..5929ca7e2 100644
--- a/_layouts/feed.ics
+++ b/_layouts/feed.ics
@@ -90,7 +90,7 @@ DURATION:{{ post.duration }}{% elsif post.end_date %}
DTEND{{ tzid }}:{{ post.end_date | date: "%Y%m%dT%H%M00" }}{{ endtzid }}{% else %}
DTEND:{{ post.date | date: "%Y%m%d" }}{% endif %}{% if post.rrule %}
RRULE:{{post.rrule}}{% endif %}
-SUMMARY:{% include feed.html content=fulltitle %}
+SUMMARY:{% include collect/feed.html content=fulltitle %}
DESCRIPTION:{%- capture mydescription -%}
{% if post.cal-text %}{{post.cal-text}}{% elsif post.excerpt %}{{ post.excerpt }}{% else %}{{ post.text }}{% endif %}
{% if post.time %}
- Outage time: {{ post.time }}
{% endif %}{% if post.systems %}
diff --git a/_layouts/nav.html b/_layouts/nav.html
index 94151329a..28192dc69 100644
--- a/_layouts/nav.html
+++ b/_layouts/nav.html
@@ -8,7 +8,16 @@
-
+{% if page.sidenav_basic %}
+ {% assign sidenav = page.sidenav_basic %}
+ {% assign bc_sidenav = sidenav %}
+ {% if page.alt_nav %}
+ {% assign alt_nav = page.alt_nav %}
+ {% else %}
+ {% assign firstnav = sidenav | first %}
+ {% assign alt_nav = firstnav.title %}
+ {% endif %}
+{% else %}
{% if page.sidenav_link %}
{% assign pagelink = page.sidenav_link %}
{% else %}
@@ -139,6 +148,8 @@
{% assign sidenav = bc_sidenav %}
{% endif %}
+{% endif %}
+
{% if page.sidenav_append %}
{% assign sidenav = sidenav | concat: page.sidenav_append %}
{% endif %}
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diff --git a/pages/about/about.md b/pages/about/about.md
index 5999b79a5..36237381c 100644
--- a/pages/about/about.md
+++ b/pages/about/about.md
@@ -8,7 +8,7 @@ breadcrumb_title: About
The SCINet initiative is an effort by the USDA Agricultural Research Service to improve the USDA’s research capacity by providing scientists with access to high-performance computing (HPC) clusters, high-speed networking for data transfer, and training in scientific computing.
-SCINet supports a growing community of nearly 2,000 USDA research scientists and university partners to accelerate agricultural discovery through advanced computational infrastructure and scientific computing.
+SCINet supports a growing community of over 2,000 USDA research scientists and university partners to accelerate agricultural discovery through advanced computational infrastructure and scientific computing.
Current uses of SCINet span multiple disciplines, including genomics, plant breeding, hydrology, crop production, plant and animal disease modeling, and natural resource management. SCINet users include ARS and other federal scientists, as well as partners external to the federal government.
@@ -40,4 +40,4 @@ To get started using SCINet, [sign up for an account](/about/signup) and visit o
-
\ No newline at end of file
+
diff --git a/pages/about/organization.md b/pages/about/organization.md
index b2671261e..b51c505a6 100644
--- a/pages/about/organization.md
+++ b/pages/about/organization.md
@@ -130,7 +130,7 @@ SAC-table:
Term expires: ""
---
-The ARS Scientific Computing Initiative is led by an Executive Committee comprised of ARS leadership including Dr. Marlen Eve – Acting Associate Administrator for National Programs, Dr. Brian Stucky – Acting Chief Scientific Information Officer, Rob Butler – Acting SCINet Project Manager, ARS scientists representing IT and security, and users of the HPC systems. SCINet committees report to the Executive Committee, including the Scientific Advisory Committee (SAC), an HPC Policy Committee, and an HPC Software Committee.
+The ARS Scientific Computing Initiative is led by an Executive Committee comprised of ARS leadership including Dr. Jeff Silverstein – Associate Administrator for National Programs, Dr. Brian Stucky – Acting Chief Scientific Information Officer, Rob Butler – Acting SCINet Project Manager, ARS scientists representing IT and security, and users of the HPC systems. SCINet committees report to the Executive Committee, including the Scientific Advisory Committee (SAC), an HPC Policy Committee, and an HPC Software Committee.
The program runs two high performance computer clusters and a storage unit. The Ceres cluster is housed at the National Animal Disease Center in Ames, IA and operated by Iowa State University. The Atlas cluster is located and ran by Mississippi State University in Starkville, MS, and the Juno storage unit is located at the National Agricultural Library in Beltsville, MD.
@@ -141,7 +141,7 @@ User support at SCINet is provided by the Virtual Support Research Core (VSRC)
{: .usa-table .usa-table--compact }
Person | Position
---|---
-Dr. Marlen Eve | Acting Associate Administrator for National Programs
+Dr. Jeff Silverstein | Associate Administrator for National Programs
Dr. Brian Stucky | Acting Chief Scientific Information Officer, ARS
## Executive Committee
@@ -151,7 +151,7 @@ The ARS Scientific Computing Initiative is led by an Executive Committee.
{: .usa-table .usa-table--compact }
Person | Position
---|---
-Dr. Marlen Eve | Acting Associate Administrator for National Programs
+Dr. Jeff Silverstein | Associate Administrator for National Programs
Dr. Brian Stucky | Acting Chief Scientific Information Officer, ARS
Ms. Lorna Drennen | Assistant Chief Information officer
Mr. Stan Kosecki | Deputy Assistant Chief Information Officer
@@ -212,7 +212,7 @@ Rob Butler | Acting SCINet Project Manager
The software committee evaluates requests by users to add or delete software to/from one of the HPCs. This committee is composed of scientific experts in the use of HPCs from across agricultural disciplines, and that represent our two HPCs. Users can make software requests by completing the [Software Request Form]({{ site.baseurl }}/support/request#software-request).
-
+{% comment %}
+{% endcomment %}
diff --git a/pages/home.md b/pages/home.md
index 509944b6a..76dbcd8d0 100644
--- a/pages/home.md
+++ b/pages/home.md
@@ -4,11 +4,12 @@ permalink: /
type: future
description: SCINet is a USDA initiative to provide agricultural scientists access to high performance computing, networking and training.
-#alerts:
-# - alert:
-# title: Atlas Update
-# type: warning
-# text: There have been several changes to the Atlas compute cluster. Read more about these updates here.
+# alerts:
+# - alert:
+# title: Ceres Update
+# type: success
+# title: Ceres maintenance completed
+# text: Ceres' new storage system is now fully deployed and available for use! Read more about these updates here.
layout: scinet_home
diff --git a/pages/news/newsletter.md b/pages/news/newsletter.md
index 25ae91e3e..ea2227de4 100644
--- a/pages/news/newsletter.md
+++ b/pages/news/newsletter.md
@@ -17,6 +17,8 @@ Research highlights from researchers who use SCINet resources are added quarterl
+### [USDA ARS SCINet Newsletter: October 2024](/assets/pdf/newsletters/SCINet-Newsletter-October-2024.pdf){:target="_blank"}
+
### [USDA ARS SCINet Newsletter: July 2024](/assets/pdf/newsletters/SCINet-Newsletter-July-2024.pdf){:target="_blank"}
### [USDA ARS SCINet Newsletter: April 2024](/assets/pdf/newsletters/SCINet-Newsletter-April-2024.pdf){:target="_blank"}
diff --git a/pages/opportunities/ai-innovation.md b/pages/opportunities/ai-innovation.md
index 3255aa802..d8c04632b 100644
--- a/pages/opportunities/ai-innovation.md
+++ b/pages/opportunities/ai-innovation.md
@@ -1,5 +1,5 @@
---
-title: ARS AI Innovation Fund (FY24)
+title: ARS AI Innovation Fund (FY25)
description: Internal USDA-ARS grants for artificial intelligence and machine learning projects
permalink: /opportunities/ai-innovation/
author: Brian Stucky
@@ -20,16 +20,16 @@ subnav:
- title: Proposal format and submission
url: '#proposal-format-and-submission'
- title: Definition of AI/ML technologies
- url: '#definition-of-ai/ml-technologies'
+ url: '#definition-of-aiml-technologies'
---
## Overview
-The goal of the ARS Artificial Intelligence Center of Excellence (AI-COE) is to enable innovative ARS science by promoting the adoption and use of AI and machine learning (ML) tools and methods in agricultural research. For FY24, the AI-COE expects to fund 4 to 6 proposals at up to $100,000 each for activities to encourage and promote AI-related research in agriculture. For examples of successful proposal topics, please see the abstracts of the AI Innovation Fund proposals funded in [FY2023]({{ site.baseurl }}/opportunities/ai-innovation/fy23-awards), [FY2022]({{ site.baseurl }}/opportunities/ai-innovation/fy22-awards), and [FY2021]({{ site.baseurl }}/opportunities/ai-innovation/fy21-awards).
+The goal of the ARS Artificial Intelligence Center of Excellence (AI-COE) is to enable innovative ARS science by promoting the adoption and use of AI and machine learning (ML) tools and methods in agricultural research. For FY25, the AI-COE expects to fund 4 to 6 proposals at up to $100,000 each for activities to encourage and promote AI-related research in agriculture. For examples of successful proposal topics, please see the abstracts of the AI Innovation Fund proposals funded in [FY2024]({{ site.baseurl }}/opportunities/ai-innovation/fy24-awards), [FY2023]({{ site.baseurl }}/opportunities/ai-innovation/fy23-awards), [FY2022]({{ site.baseurl }}/opportunities/ai-innovation/fy22-awards), and [FY2021]({{ site.baseurl }}/opportunities/ai-innovation/fy21-awards).
-Proposals must be submitted using the [online submission form](https://forms.office.com/g/xLJMnYUt13) and are due **by close of business on Friday, December 15, 2023**. All submitted proposals must be approved by a relevant RL or supervisor. We expect that funds will be available for use some time in early 2024 and will need to be spent or placed in collaborative agreements by the end of FY2024. For questions, contact Dr. Brian Stucky, Computational Biologist in the SCINet Office and Acting ARS CSIO.
+Proposals must be submitted using the [online submission form](https://forms.office.com/g/EznVdueHHE) and are due **by close of business on Friday, December 6, 2024**. All submitted proposals must be approved by a relevant RL or supervisor. We expect that funds will be available for use some time in early 2025 and will need to be spent or placed in collaborative agreements by the end of FY2025. For questions, contact Dr. Brian Stucky, Computational Biologist with the SCINet Office and Acting ARS CSIO.
## Proposal guidelines
@@ -39,7 +39,7 @@ Projects of high priority for funding are those that:
1. Develop or adapt an AI/ML method that empowers ARS scientists to answer a specific question/problem or test a hypothesis of agricultural importance.
2. Develop or adapt AI/ML technologies to create a prototype digital product that solves a need for producers or agricultural researchers.
-Proposals should be primarily focused on developing, adapting, or applying methods that fall into the category of AI or ML (see definitions below). Please refer to the abstracts of the AI Innovation Fund proposals funded in [FY2023]({{ site.baseurl }}/opportunities/ai-innovation/fy23-awards), [FY2022]({{ site.baseurl }}/opportunities/ai-innovation/fy22-awards), and [FY2021]({{ site.baseurl }}/opportunities/ai-innovation/fy21-awards) for examples of successful proposal topics.
+Proposals should be primarily focused on developing, adapting, or applying methods that fall into the category of AI or ML (see definitions below). Please refer to the abstracts of the AI Innovation Fund proposals funded in [FY2024]({{ site.baseurl }}/opportunities/ai-innovation/fy24-awards), [FY2023]({{ site.baseurl }}/opportunities/ai-innovation/fy23-awards), [FY2022]({{ site.baseurl }}/opportunities/ai-innovation/fy22-awards), and [FY2021]({{ site.baseurl }}/opportunities/ai-innovation/fy21-awards) for examples of successful proposal topics.
Researchers developing a method or digital product are encouraged to define a minimum viable product as a deliverable.
@@ -55,11 +55,11 @@ Training, workshop, and working group activities are not supported by this call.
### Project Funding
-We expect to fund 4 to 6 proposals up to $100,000 each. Funds must be spent in fiscal year 2024, which may require an agreement with a university partner or the Oak Ridge Institute for Science and Education (ORISE).
+We expect to fund 4 to 6 proposals up to $100,000 each. Funds must be spent in fiscal year 2025, which may require an agreement with a university partner or the Oak Ridge Institute for Science and Education (ORISE).
## Proposal format and submission
-All proposals must be submitted using the [online submission form](https://forms.office.com/g/xLJMnYUt13). The PI's RL or supervisor must approve the proposal prior to submission (approval will be indicated on the submission form). A complete proposal will include:
+All proposals must be submitted using the [online submission form](https://forms.office.com/g/EznVdueHHE). The PI's RL or supervisor must approve the proposal prior to submission (approval will be indicated on the submission form). A complete proposal will include:
* A proposal abstract of 300 words or less.
* Proposal text (project description) of up to 2 pages in length that clearly lays out a specific challenge or question, proposes a method or tool to be developed or applied to solve the challenge or to answer the question, and demonstrates that the project team has the capability to complete the project. Deliverables for the project should be defined.
* A detailed project budget provided as an Excel spreadsheet. Please use [REE budget form 455](https://www.ars.usda.gov/ARSUserFiles/FMAD/Agreements/ree-455-112018.xlsx).
@@ -67,16 +67,16 @@ All proposals must be submitted using the [online submission form](https://forms
The proposal text and budget justification should be submitted as PDF documents with margins of no less than 1 inch and font size of no less than 11. References are not included in the 2-page limit for the proposal text. Only one proposal as the lead investigator responsible for project completion can be submitted by a scientist, although a scientist can be a member of multiple proposals. We encourage teams of investigators collaborating on a problem.
-**Deadline for proposal submission:** Close of business on Friday, December 15, 2023.
+**Deadline for proposal submission:** Close of business on Friday, December 6, 2024.
-**Eligibility:** ARS Category 1, 4, or 6 scientists with RL or supervisor approval. Please note that PIs on an FY23 AI Innovation Fund award will not be eligible for an FY24 award.
+**Eligibility:** ARS Category 1, 4, or 6 scientists with RL or supervisor approval. Please note that PIs on an FY24 AI Innovation Fund award will not be eligible for an FY25 award.
## Definition of AI/ML technologies
(These are examples and not inclusive of all possible methods and tools.)
-_AI methods_ involve automated decision-making or inference from data, and use methods in the subfields:
+_AI methods_ involve automated decision-making or inference from data and use methods from the subfields of:
* machine learning (including deep learning)
* mathematical optimization (integer programming and operations research)
* machine reasoning and logic programming
@@ -86,4 +86,4 @@ _AI methods_ involve automated decision-making or inference from data, and use m
_Machine learning_ involves training a model with data and then making decisions or answering questions using that model. ML methods include:
* tasks like classification, regression, dimensionality reduction, and clustering;
* domain areas like natural language processing, computer vision, and time-series analyses;
-* methods like decision trees and random forests, neural networks, Bayesian networks, and support vector machines.
+* methods like decision trees and random forests, neural networks (including deep learning), Bayesian networks, and support vector machines.
diff --git a/pages/opportunities/ai-innovation/ai-innovation-fy21-awards.md b/pages/opportunities/ai-innovation/ai-innovation-fy21-awards.md
index 50d177b7f..bdd51d7e3 100644
--- a/pages/opportunities/ai-innovation/ai-innovation-fy21-awards.md
+++ b/pages/opportunities/ai-innovation/ai-innovation-fy21-awards.md
@@ -11,6 +11,10 @@ sidenav_append:
url: /opportunities/ai-innovation/fy21-awards
- title: ARS AI Innovation Fund - FY2022 Awards
url: /opportunities/ai-innovation/fy22-awards
+ - title: ARS AI Innovation Fund - FY2023 Awards
+ url: /opportunities/ai-innovation/fy23-awards
+ - title: ARS AI Innovation Fund - FY2024 Awards
+ url: /opportunities/ai-innovation/fy24-awards
subnav:
- title: Funded proposals
diff --git a/pages/opportunities/ai-innovation/ai-innovation-fy22-awards.md b/pages/opportunities/ai-innovation/ai-innovation-fy22-awards.md
index 9c338deab..dfddd0d23 100644
--- a/pages/opportunities/ai-innovation/ai-innovation-fy22-awards.md
+++ b/pages/opportunities/ai-innovation/ai-innovation-fy22-awards.md
@@ -11,6 +11,10 @@ sidenav_append:
url: /opportunities/ai-innovation/fy21-awards
- title: ARS AI Innovation Fund - FY2022 Awards
url: /opportunities/ai-innovation/fy22-awards
+ - title: ARS AI Innovation Fund - FY2023 Awards
+ url: /opportunities/ai-innovation/fy23-awards
+ - title: ARS AI Innovation Fund - FY2024 Awards
+ url: /opportunities/ai-innovation/fy24-awards
subnav:
- title: Funded proposals
diff --git a/pages/opportunities/ai-innovation/ai-innovation-fy23-awards.md b/pages/opportunities/ai-innovation/ai-innovation-fy23-awards.md
index f8ffa8cbd..2e5e82bfb 100644
--- a/pages/opportunities/ai-innovation/ai-innovation-fy23-awards.md
+++ b/pages/opportunities/ai-innovation/ai-innovation-fy23-awards.md
@@ -13,6 +13,8 @@ sidenav_append:
url: /opportunities/ai-innovation/fy22-awards
- title: ARS AI Innovation Fund - FY2023 Awards
url: /opportunities/ai-innovation/fy23-awards
+ - title: ARS AI Innovation Fund - FY2024 Awards
+ url: /opportunities/ai-innovation/fy24-awards
subnav:
- title: Funded proposals
diff --git a/pages/opportunities/ai-innovation/ai-innovation-fy24-awards.md b/pages/opportunities/ai-innovation/ai-innovation-fy24-awards.md
new file mode 100644
index 000000000..707db2631
--- /dev/null
+++ b/pages/opportunities/ai-innovation/ai-innovation-fy24-awards.md
@@ -0,0 +1,57 @@
+---
+title: ARS AI Innovation Fund - FY2024 Awards
+description: Abstracts of AI Innovation Fund proposals funded in FY2024.
+permalink: /opportunities/ai-innovation/fy24-awards
+author: Brian Stucky
+layout: page
+
+sidenav_link: /opportunities/ai-innovation/
+sidenav_append:
+ - title: ARS AI Innovation Fund - FY2021 Awards
+ url: /opportunities/ai-innovation/fy21-awards
+ - title: ARS AI Innovation Fund - FY2022 Awards
+ url: /opportunities/ai-innovation/fy22-awards
+ - title: ARS AI Innovation Fund - FY2023 Awards
+ url: /opportunities/ai-innovation/fy23-awards
+ - title: ARS AI Innovation Fund - FY2024 Awards
+ url: /opportunities/ai-innovation/fy24-awards
+
+subnav:
+ - title: Funded proposals
+ url: '#funded-proposals'
+---
+
+The ARS AI Center of Excellence (AI-COE) funded four AI Innovation Fund proposals in FY2024. The program was again very competitive, with many more proposals submitted than we could support. Information about the funded projects is provided below.
+
+## Funded proposals
+
+### Deep learning based high-resolution field level soil moisture mapper from UAVs
+
+* **PI and Co-PIs:** Ardeshir Adeli and Yanbo Huang
+* **Amount of award:** $99,978
+* **Abstract:** A deep learning (DL) based high-resolution, and field-level soil moisture (SM) mapping system will be developed by utilizing UAV-based global navigation satellite system reflectometry (GNSS-R) observation signals together with multispectral camera images, LIDAR point clouds, and other sensory data. This system will be a digital product prototyped to provide more useful information to enhance SM research and used as a decision support tool for producers for better crop management. Our proposed system fundamentally uses the existing radio frequency (RF) signals transmitted by satellites opportunistically to measure SM by leveraging penetration capabilities of RF signals into the vegetation and soil. GNSS transmissions are in L-band RF frequencies are highly sensitive to changes in SM content, particularly over the top 5 cm portion of the soil.
+
+ A comprehensive dataset has been collected for the last four years utilizing multiple UAV flight campaigns with visual, multispectral, hyperspectral, LIDAR, and microwave sensors over the cotton and corn plots under different management practices from the RR Foil Plant Science Research Center at Mississippi State University along with ground-based SM observations. Observed UAV-based sensory data is dependent not only on the SM but also on the vegetation, surface roughness, topography, soil texture, GNSS satellites' positions, transmitter characteristics, receiver orientation, and flight parameters through a combination of linear and nonlinear relations. We propose to leverage the SCINet high-performance computing infrastructure to aid in the development of a machine learning architecture to learn this complex and nonlinear relationship. As the UAV scans the field, the developed DL model will produce the SM map of the area at 5-10 cm resolution levels. The created map will be visualized for the final users, such as producers or agricultural researchers, along with the proposed system to not only help manage irrigation, improve yields and product quality, but also protect the environment.
+
+
+### Rapid prediction of _Salmonella enterica_ virulence from MALDI-TOF mass spectra using machine learning
+
+* **PI and Co-PIs:** Christopher Anderson, Shawn Bearson, and Paul Villanueva
+* **Amount of award:** $100,000
+* **Abstract:** _Salmonella_ causes ~1.35 million infections yearly in the United States. Despite mitigation efforts that have reduced contamination of some food sources, salmonella-related illnesses have not readily decreased. Interventions have typically focused on serovar-level control of _Salmonella_. Still, a more proactive approach would focus on the early detection of strains with high virulence in humans. However, assays to assess virulence are expensive and time intensive, and whole genome sequencing alone cannot predict _Salmonella_ pathogenicity. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) can characterize the protein composition of bacterial cells in only a few minutes and allow for the characterization of strains beyond gene composition. Recent work demonstrates the potential of MALDI-TOF MS beyond bacterial identification by pairing MALDI-TOF spectra with machine learning to predict bacterial phenotypes of interest, such as antimicrobial resistance. Training machine learning models from MALDI-TOF spectra requires sophisticated data preprocessing and a database of isolates with associated phenotypes. To address the need for more timely monitoring strategies to accurately identify _Salmonella_ strains with high potential for human health risk, we propose developing a machine learning model that predicts _Salmonella_ virulence directly from MALDI-TOF spectra. We will use the invasion of a human cell line to measure virulence for a collection of ~1,000 _Salmonella enterica_ isolates to train the model. The deliverables of this project include a Python package with the preprocessing and prediction workflow as well as a web-based interface for users to submit MALDI-TOF spectra for virulence prediction. The outcomes of this proposal should accelerate the utility of MALDI-TOF MS for rapid and accurate phenotypic predictions and fill the technical gap of a lack of tools for ARS and other agricultural researchers to identify _Salmonella_ isolates of public health concern.
+
+
+### The _Fusarium_-Host Interactome Discovery App
+
+* **PI and Co-PIs:** Carson Andorf, Hye-Seon Kim, and Taner Sen
+* **Amount of award:** $100,000
+* **Abstract:** _Fusarium_ is a pervasive fungal pathogen that poses significant threats to global food security and causes billions of dollars in economic loss annually. Climate change is predicted to enhance susceptibility to crop pathogens, demanding new resources to empower researchers and breeders to develop resilient strategies against _Fusarium_. Here we propose to develop the _Fusarium_-Host Interactome Discovery App, a digital application to identify genetic and proteomic interactions between _Fusarium_ and its cereal crop hosts: wheat, maize, barley, oat, and rye. The project will create an innovative artificial intelligence (AI) workflow that will predict host-pathogen protein-protein interactions, identify the functional consequences of missense mutations across all the species, provide protein functional and structural annotations, and create a web-based application. This application will utilize advanced protein language and protein diffusion models to elucidate the interactions of _Fusarium_ and cereal proteins that result in susceptibility or resistance. Visualization tools will enable users to explore how genetic mutations across 22 _Fusarium_ genomes and 135 cereal genomes impact protein interactions and identify potential targets for developing disease-resistant varieties. This collaborative project combines the expertise from multiple USDA-ARS Area locations, fostering synergy among maize, small grains, and _Fusarium_ research communities. The collective effort aims to aid researchers, breeders, and farmers in safeguarding cereal crop health against biotic threats by providing valuable foresight into the potential risks of emerging pathogens, their virulence levels, and the extent of variation in resistant germplasm. The application and datasets will be easily accessible through the GrainGenes and MaizeGDB databases.
+
+
+### Bad apples? Next generation postharvest risk assessment tools
+
+* **PI and Co-PIs:** Loren Honaas, Stephen Ficklin (Washington State University), and Meg Staton (University of Tennessee Knoxville)
+* **Amount of award:** $98,936
+* **Abstract:** Three billion pounds (~30%) of fresh pome fruit is diverted to the processing supply chain in large part because fruit lose quality during storage and the supply chain. The current risk assessment tools in use by the industry are inadequate to reliably estimate risk for such losses in fruit quality. Hence the prioritization by stakeholders, including the WA Tree Fruit Research Commission (WTFRC), of research projects that develop new and better tools to estimate risk for postharvest fruit quality losses. We have been funded by the WTFRC (>$1M) to develop such technology; our projects include development of transcriptome-based biosignature AI models (Next Generation Maturity Indices - NGMIs) that predict optimum harvest dates and AI/computer vision software (Granny) that improves the granularity and accuracy for the scoring of traits that are currently rated/scored by hand. As these projects mature, we aim to integrate them for enhanced risk assessment, and also to begin building a user-interface to promote adoption by producers - these are the aims of our proposal.
+We have amassed 5+ years of data on many cultivars of apple that includes upwards of 1,000 transcriptomes, rich fruit physiological data, and postharvest storage trial outcomes from collectively >30,000 individual apples. Our NGMIs can accurately predict harvest dates. Granny outputs are fine-grained, highly-accurate, and concordant with ratings of fruit made by experts. Collaborators, industry partners, and growers are all in place to take the next steps towards next generation risk assessment tools for postharvest pome fruit quality. Specifically, we propose to integrate NGMIs and Granny to improve harvest date predictions (with regard to retaining fruit quality during storage) and to build a software user interface for these tools that growers can use (and test) in the near term.
+
diff --git a/pages/opportunities/scinet-aicoe-fellowships.md b/pages/opportunities/fellowship-mentors.md
similarity index 68%
rename from pages/opportunities/scinet-aicoe-fellowships.md
rename to pages/opportunities/fellowship-mentors.md
index 42366cec9..9729f1fc3 100644
--- a/pages/opportunities/scinet-aicoe-fellowships.md
+++ b/pages/opportunities/fellowship-mentors.md
@@ -1,23 +1,21 @@
---
-title: ARS SCINet and AI Center of Excellence Postdoctoral Fellowships Program (FY24)
+title: ARS SCINet and AI Center of Excellence Postdoctoral Fellowships Program (FY25)
description: Internal USDA-ARS funding for SCINet and AI-COE postdoctoral fellowships.
-permalink: /opportunities/scinet-aicoe-fellowships
+permalink: /opportunities/fellowship-mentors
# author: Brian Stucky
layout: page
-
-table:
- position: back
- source: funded
- announcement: ""
-
-
+subnav:
+ - title: Overview
+ url: '#overview'
+ - title: Proposal format and submission
+ url: '#proposal-format-and-submission'
---
## Overview
-The SCINet Program, in collaboration with the ARS Artificial Intelligence Center of Excellence (AI-COE), is calling for proposals for funding to support postdoctoral fellows to be mentored by ARS scientists. The goal of the fellowships program is to develop the next generation of ARS scientists with expertise in conducting and leading individual and collaborative research using computationally intensive approaches.
+The SCINet Program, in collaboration with the ARS Artificial Intelligence Center of Excellence (AI-COE), is calling for proposals for funding to support postdoctoral fellows to be mentored by ARS scientists. The goal of the fellowships program is to develop the next generation of ARS scientists with expertise in conducting and leading individual and collaborative research using computationally intensive approaches. For examples of successful proposal topics, please see the titles of the postdoctoral fellowship proposals funded in [FY2024]({{ site.baseurl }}/opportunities/fellowship-mentors/fy24-awards), [FY2023]({{ site.baseurl }}/opportunities/fellowship-mentors/fy23-awards), and [FY2022]({{ site.baseurl }}/opportunities/fellowship-mentors/fy22-awards).
Each fellow should: (1) be involved in individual and collaborative, multi-unit research that includes substantial computational work and that will leverage SCINet's computing infrastructure; (2) have training and leadership opportunities; and (3) contribute to the overall success of SCNet or the AI-COE and the SCINet/AI-COE Fellowships Program. In addition, each fellow will have the opportunity to take advantage of training courses that build computational literacy, such as in data science, AI, bioinformatics, and geospatial analyses on SCINet's high-performance computing clusters (Ceres, Atlas).
@@ -26,14 +24,11 @@ All fellows will receive a competitive stipend and travel support. Fellowships a
## Proposal format and submission
-All proposals must be submitted using the [online submission form](https://forms.office.com/g/D0PtZC7nS0). The PI's RL or supervisor must approve the proposal prior to submission (approval will be indicated on the submission form).
+All proposals must be submitted using the [online submission form](https://forms.office.com/g/XvP71bTAia). The PI's RL or supervisor must approve the proposal prior to submission (approval will be indicated on the submission form).
-All proposals must use [the fellowships proposal template](https://usdagcc.sharepoint.com/:w:/s/REE-ARS-SCINetOffice/EcHJ_mpmo59CvP5Kv6OvOWsBgScm4E-sUTNC478EzbvEMg?e=Z7Ro0H). Please follow the instructions in the template and do not change the section headings or document formatting. The final proposal must be **no more than 750 words, including the headings**.
+All proposals must use [the fellowships proposal template](https://usdagcc.sharepoint.com/:w:/s/REE-ARS-SCINetOffice/EXfbBIAM9vpPrFGXowAYsSwBYJBAwCtwI3UDWWprN0yM-w?e=uznaVH). Please follow the instructions in the template and do not change the section headings or document formatting. The final proposal must be **no more than 750 words, including the headings**.
-**Deadline for proposal submission:** Close of business on Friday, December 15, 2023.
+**Deadline for proposal submission:** Close of business on Friday, December 6, 2024.
-**Eligibility:** ARS Category 1, 4, or 6 scientists with RL or supervisor approval. Please note that lead mentors on FY23 awards are not eligible for an FY24 award.
-
-
-## Proposals funded in FY23
+**Eligibility:** ARS Category 1, 4, or 6 scientists with RL or supervisor approval. Please note that lead mentors on FY24 awards are not eligible for an FY25 award.
diff --git a/pages/opportunities/fellowships.md b/pages/opportunities/fellowships.md
index 3b5a88296..cfadf0b4d 100644
--- a/pages/opportunities/fellowships.md
+++ b/pages/opportunities/fellowships.md
@@ -9,28 +9,27 @@ layout: page
subnav:
- title: Postdoc and Masters Fellowships
url: '#postdoc-and-masters-fellowships'
- - title: Other Opportunities
- url: '#other-opportunities'
-table:
- position: front
+fellowships:
source: fellowships
sort: true
- title: Postdoc and Masters Fellowships
- caption: Interested applicants are encouraged to visit the ORISE link for more information about a position and how to apply. To see the most up to date list of all SCINet and AI-COE opportunities, visit https://www.zintellect.com/Catalog and enter keyword “SCINet”
- announcement: "About the USDA ARS: The ARS mission involves problem-solving research in the widely diverse food and agricultural areas encompassing plant production and protection; animal production and protection; natural resources and sustainable agricultural systems; and nutrition; food safety and quality. The programs are conducted in 46 of the 50 States, Puerto Rico, and the U.S. Virgin Islands. Programs are also carried out in cooperation with several foreign countries. For ARS to maintain its standing as a premier scientific organization, major investments in computing, networking, and storage infrastructure are required as well as trained scientific personnel. Training in data and information management are integral to the integrity, security, and accessibility of research findings, results, and outcomes within the ARS research enterprise. The USDA ARS Chief Science Information Officer (ARS-CSIO at usda dot gov) can be contacted for additional information."
+ caption: Available Fellowships
+# announcement: "About the USDA ARS: The ARS mission involves problem-solving research in the widely diverse food and agricultural areas encompassing plant production and protection; animal production and protection; natural resources and sustainable agricultural systems; and nutrition; food safety and quality. The programs are conducted in 46 of the 50 States, Puerto Rico, and the U.S. Virgin Islands. Programs are also carried out in cooperation with several foreign countries. For ARS to maintain its standing as a premier scientific organization, major investments in computing, networking, and storage infrastructure are required as well as trained scientific personnel. Training in data and information management are integral to the integrity, security, and accessibility of research findings, results, and outcomes within the ARS research enterprise. The USDA ARS Chief Science Information Officer (ARS-CSIO at usda dot gov) can be contacted for additional information."
-summarybox:
- header: About the program
- text: "The U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS) SCINet and AI Center of Excellence offer exciting collaborative research opportunities to motivated participants interested in solving agricultural-natural resource related problems. One of the goals of the ORISE Fellowship program is to develop and apply new and emerging technologies including artificial intelligence (AI) and machine learning. Many of these questions rely on the synthesis, integration, and analysis of large, diverse datasets that benefit from high performance computers (HPC) or a cloud computing environment. Fellows will have the opportunity to collaborate on problems of high priority to the USDA ARS, while being trained across a range of skills including AI, machine learning, deep learning, data science, and/or statistical software as needed for the success of the position."
+# summarybox:
+# header: About the program
+# text: "The U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS) SCINet and AI Center of Excellence offer exciting collaborative research opportunities to motivated participants interested in solving agricultural-natural resource related problems. One of the goals of the ORISE Fellowship program is to develop and apply new and emerging technologies including artificial intelligence (AI) and machine learning. Many of these questions rely on the synthesis, integration, and analysis of large, diverse datasets that benefit from high performance computers (HPC) or a cloud computing environment. Fellows will have the opportunity to collaborate on problems of high priority to the USDA ARS, while being trained across a range of skills including AI, machine learning, deep learning, data science, and/or statistical software as needed for the success of the position."
---
+The U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS) SCINet and AI Center of Excellence offer exciting collaborative research opportunities to motivated participants interested in solving agricultural-natural resource related problems. One of the goals of the ORISE Fellowship program is to develop and apply new and emerging technologies including artificial intelligence (AI) and machine learning. Many of these questions rely on the synthesis, integration, and analysis of large, diverse datasets that benefit from high performance computers (HPC) or a cloud computing environment. Fellows will have the opportunity to collaborate on problems of high priority to the USDA ARS, while being trained across a range of skills including AI, machine learning, deep learning, data science, and/or statistical software as needed for the success of the position.
-## Other Opportunities:
+## Postdoc and Masters Fellowships
-**Mississippi State University’s Geosystems Research Institute Summer Graduate Research Experience:** As part of the MSU/USDA Advancing Agricultural Research through High-Performance Computing project, MSU will be hosting a multi-disciplinary program designed for graduate students with interest in agriculture productivity, environmental ecology, geospatial analysis, AI/ML, epidemiology, and bioinformatics. Selected students will spend 9-weeks this summer working at Mississippi State University side-by-side with leading faculty conducting research in a high-performance computing environment. Review of applicants will begin March 15, 2022, and those accepted will be notified on or before April 15, 2022. [Learn more and apply today](https://www.gri.msstate.edu/research/aar/SREP/)!
+Interested applicants are encouraged to visit the ORISE link for more information about a position and how to apply. To see the most up to date list of all SCINet and AI-COE opportunities, visit [https://www.zintellect.com/Catalog](https://www.zintellect.com/Catalog) and enter keyword "SCINet"
+
+{% include table.html table=page.fellowships %}
diff --git a/pages/opportunities/fellowships/fellowships-fy22-awards.md b/pages/opportunities/fellowships/fellowships-fy22-awards.md
new file mode 100644
index 000000000..fecb4bbd1
--- /dev/null
+++ b/pages/opportunities/fellowships/fellowships-fy22-awards.md
@@ -0,0 +1,29 @@
+---
+title: ARS SCINet and AI Center of Excellence Postdoctoral Fellowships Program - FY2022 Awards
+description: Abstracts of SCINet and AI-COE postdoctoral fellowships proposals funded in FY2022.
+permalink: /opportunities/fellowship-mentors/fy22-awards
+# author: Brian Stucky
+layout: page
+
+sidenav_basic:
+ - title: Postdoctoral Fellowships Program
+ url: /opportunities/fellowship-mentors
+ class: "guide-nav"
+ - title: FY2022 Awards
+ url: /opportunities/fellowship-mentors/fy22-awards
+ - title: FY2023 Awards
+ url: /opportunities/fellowship-mentors/fy23-awards
+ - title: FY2024 Awards
+ url: /opportunities/fellowship-mentors/fy24-awards
+
+
+
+table:
+ position: back
+ source: funded_FY22
+ caption: Funded proposals
+---
+
+The ARS SCINet and AI Center of Excellence (AI-COE) funded 19 postdoctoral fellowship proposals in FY2022. The program was again very competitive, with many more proposals submitted than we could support. Information about the funded projects is provided below.
+
+
diff --git a/pages/opportunities/fellowships/fellowships-fy23-awards.md b/pages/opportunities/fellowships/fellowships-fy23-awards.md
new file mode 100644
index 000000000..0717b407d
--- /dev/null
+++ b/pages/opportunities/fellowships/fellowships-fy23-awards.md
@@ -0,0 +1,29 @@
+---
+title: ARS SCINet and AI Center of Excellence Postdoctoral Fellowships Program - FY2023 Awards
+description: Abstracts of SCINet and AI-COE postdoctoral fellowships proposals funded in FY2023.
+permalink: /opportunities/fellowship-mentors/fy23-awards
+# author: Brian Stucky
+layout: page
+
+sidenav_basic:
+ - title: Postdoctoral Fellowships Program
+ url: /opportunities/fellowship-mentors
+ class: "guide-nav"
+ - title: FY2022 Awards
+ url: /opportunities/fellowship-mentors/fy22-awards
+ - title: FY2023 Awards
+ url: /opportunities/fellowship-mentors/fy23-awards
+ - title: FY2024 Awards
+ url: /opportunities/fellowship-mentors/fy24-awards
+
+
+table:
+ position: back
+ source: funded_FY23
+ caption: Funded proposals
+---
+
+The ARS SCINet and AI Center of Excellence (AI-COE) funded 22 postdoctoral fellowship proposals in FY2023. The program was again very competitive, with many more proposals submitted than we could support. Information about the funded projects is provided below.
+
+
+
diff --git a/pages/opportunities/fellowships/fellowships-fy24-awards.md b/pages/opportunities/fellowships/fellowships-fy24-awards.md
new file mode 100644
index 000000000..85d3ae32e
--- /dev/null
+++ b/pages/opportunities/fellowships/fellowships-fy24-awards.md
@@ -0,0 +1,30 @@
+---
+title: ARS SCINet and AI Center of Excellence Postdoctoral Fellowships Program - FY2024 Awards
+description: Abstracts of SCINet and AI-COE postdoctoral fellowships proposals funded in FY2024.
+permalink: /opportunities/fellowship-mentors/fy24-awards
+# author: Brian Stucky
+layout: page
+
+sidenav_basic:
+ - title: Postdoctoral Fellowships Program
+ url: /opportunities/fellowship-mentors
+ class: "guide-nav"
+ - title: FY2022 Awards
+ url: /opportunities/fellowship-mentors/fy22-awards
+ - title: FY2023 Awards
+ url: /opportunities/fellowship-mentors/fy23-awards
+ - title: FY2024 Awards
+ url: /opportunities/fellowship-proposals/fy24-awards
+
+
+
+table:
+ position: back
+ source: funded_FY24
+ caption: Funded proposals
+---
+
+The ARS SCINet and AI Center of Excellence (AI-COE) funded five postdoctoral fellowship proposals in FY2024. The program was again very competitive, with many more proposals submitted than we could support. Information about the funded projects is provided below.
+
+
+
diff --git a/pages/opportunities/internship-mentors.md b/pages/opportunities/internship-mentors.md
new file mode 100644
index 000000000..3bd517695
--- /dev/null
+++ b/pages/opportunities/internship-mentors.md
@@ -0,0 +1,46 @@
+---
+title: ARS AI Center of Excellence and SCINet Graduate Student Internships Program (FY25)
+description: Internal USDA-ARS funding for AI-COE and SCINet graduate student internships.
+permalink: /opportunities/internship-mentors
+# author: Brian Stucky
+layout: page
+
+subnav:
+ - title: Overview
+ url: '#overview'
+ - title: Program logistics
+ url: '#program-logistics'
+ - title: Application format and submission
+ url: '#application-format-and-submission'
+
+---
+
+
+## Overview
+
+The ARS Artificial Intelligence Center of Excellence (AI-COE) and SCINet are seeking ARS scientists who wish to serve as graduate student internship mentors in the spring or summer of 2025. We want each of our interns to have an outstanding experience, which means we need outstanding ARS mentors and research projects! (Application instructions are below.)
+
+This program has three primary goals:
+
+* Bring new AI, machine learning (ML), data science, and/or computer science (CS) expertise to ARS research.
+* Enhance the educational experience of our student participants.
+* Increase awareness of agricultural research with ARS as an exciting career path for data scientists and computer scientists.
+
+We work with partner universities to recruit students with AI, ML, data science, or CS expertise and pair them with ARS research projects and mentors, based on the interests of the students and available research opportunities. ARS researchers who have previously participated as mentors have overwhelmingly reported that serving as an internship mentor was a rewarding experience that benefited their research program.
+
+## Program logistics
+ARS scientists who wish to serve as a mentor must submit an application by Friday, December 6, 2024 (see detailed instructions below). When the application deadline closes, all applications are reviewed by ARS leadership. Applications that are accepted are placed into a pool of available internship opportunities. The SCINet Office then works with our partner universities to recruit graduate student participants, but our university collaborators are responsible for choosing the final set of interns. We then use an algorithmic process to optimally match students with internship opportunities based on the students' interests. Again, please note that ARS mentors are not responsible for recruiting interns, and we will not be able to accept or fund interns who are not recruited through our partner universities. ARS mentors also are not responsible for managing stipend or travel funding payments. The SCINet Office, in collaboration with our partner institutions, will take care of all funds management.
+
+Most internships are 10-week internships that take place over the summer, but there are typically also a small number of spring semester internships. Each internship includes a competitive stipend and travel funding for the participant to spend time onsite with their ARS mentor(s). Internships are intended to be hybrid remote/in-person experiences with most of the internship completed remotely but up to two weeks spent onsite at the ARS mentor’s research location. Please note that geographic location will not be a factor in selecting ARS mentors for these internships. Our intent is for these internships to be available for any ARS research location.
+
+At the end of the summer, all interns are expected to participate in our day-long internships symposium. This is a virtual event that gives the students an opportunity to present their research and practice answering questions from a live audience.
+
+## Application format and submission
+
+All applications must be submitted using the [online submission form](https://forms.office.com/g/ZMY46KTQnT). The lead mentor's RL or supervisor must approve the application prior to submission (approval will be indicated on the submission form).
+
+All applications must use the [application template](https://usdagcc.sharepoint.com/:w:/s/REE-ARS-SCINetOffice/EQaVFicsiVZAphaYty7Sh7EBeb-UKo3qQ1hVyybwk3jIBQ). Please follow the instructions in the template and do not change the section headings or document formatting. The final application must be **no more than one page in length**.
+
+**Deadline for application submission:** Close of business on Friday, December 6, 2024.
+
+**Eligibility:** ARS Category 1, 4, or 6 scientists with RL or supervisor approval. Please note that ARS scientists who served as an internship mentor in 2024 are eligible to serve as a mentor again in 2025 and are welcome to apply.
diff --git a/pages/research/overview.md b/pages/research/overview.md
index 3cb240beb..efdb1621e 100644
--- a/pages/research/overview.md
+++ b/pages/research/overview.md
@@ -4,7 +4,7 @@ description: Working groups and use cases for SCINet
permalink: /research/overview
author:
layout: page
-
+published: false
---
## This is a placeholder page for the summary of this section
diff --git a/pages/research/working-groups.md b/pages/research/working-groups.md
index 276c4dafa..907c87aa3 100644
--- a/pages/research/working-groups.md
+++ b/pages/research/working-groups.md
@@ -8,44 +8,61 @@ layout: page
# sidenav: Research
subnav:
- - title: Ag100Pest
- url: /research/working-groups/ag100pest
- internal: link
- - title: Arthropod Genomics
- url: /research/working-groups/arthropods
- internal: link
- - title: Geospatial Research
- url: /research/working-groups/geospatial
- internal: link
- - title: Microbiome
- url: /research/working-groups/microbiome
- internal: link
- - title: LTAR Phenology
- url: /research/working-groups/LTARphenology
- internal: link
- - title: Pollinator
- url: /research/working-groups/pollinator
- internal: link
- - title: Protein Function and Phenotype Prediction
- url: /research/working-groups/proteinfunction
- internal: link
- - title: Translational Omics
- url: /research/working-groups/omics
- internal: link
+ - title: Current Working Groups
+ url: "#current-working-groups"
+ - title: Creating a working group
+ url: "#creating-a-working-group"
+ # - title: Ag100Pest
+ # url: /research/working-groups/ag100pest
+ # internal: link
+ # - title: Arthropod Genomics
+ # url: /research/working-groups/arthropods
+ # internal: link
+ # - title: Geospatial Research
+ # url: /research/working-groups/geospatial
+ # internal: link
+ # - title: Microbiome
+ # url: /research/working-groups/microbiome
+ # internal: link
+ # - title: LTAR Phenology
+ # url: /research/working-groups/LTARphenology
+ # internal: link
+ # - title: Pollinator
+ # url: /research/working-groups/pollinator
+ # internal: link
+ # - title: Protein Function and Phenotype Prediction
+ # url: /research/working-groups/proteinfunction
+ # internal: link
+ # - title: Translational Omics
+ # url: /research/working-groups/omics
+ # internal: link
---
-The SCINet initiative contributes funding to various working groups that use SCINet computational resources for research. The funding is generally used to convene in-person group workshops. There are also two working groups that develop SCINet learning/training materials.
+SCINet working groups (WGs) support ARS researchers and their collaborators in using scientific computing methods and SCINet computational resources in their research. Common WG activities include hosting recurring virtual meetings and webinars, organizing training events, and participating in collaborative research or software development projects. The SCINet Office facilitates WGs by:
+* Advertising information about the WG and how to join the group on the SCINet website.
+* Sharing WG updates with the ARS community via the quarterly SCINet Newsletter.
+* Assisting in training workshop coordination (e.g., advertising the event to the SCINet community, handling registrations, providing temporary SCINet project space for instructors and participants, and reserving computational resources).
+* Inviting WG representatives to VRSC meetings to stay up-to-date with changes to SCINet’s computing resources and to share WG updates with the group if desired.
-The current working groups are:
+
+## Current Working Groups
* [Ag100Pest Initiative (subgroup of AGR)]({{ site.baseurl }}/research/working-groups/ag100pest)
* [Arthropod Genomics Research (AGR) Working Group]({{ site.baseurl }}/research/working-groups/arthropods)
+* [Breeding AI and ML Working Group]({{ site.baseurl }}/research/working-groups/breeding)
* [Geospatial Research Working Group]({{ site.baseurl }}/research/working-groups/geospatial)
* [Microbiome Working Group]({{ site.baseurl }}/research/working-groups/microbiome)
-* [Pollinator Working Group]({{ site.baseurl }}/research/working-groups/pollinator)
* [SCINet-Longterm Agroecosystem Research (LTAR) Phenology Working Group]({{ site.baseurl }}/research/working-groups/LTARphenology)
* [Protein Function and Phenotype Prediction Working Group]({{ site.baseurl }}/research/working-groups/proteinfunction)
* [Translational Omics Working Group]({{ site.baseurl }}/research/working-groups/omics)
+## Creating a working group
+
+If you are interested in creating a working group, please compile the following:
+* The working group's name
+* A description of the working group including its purpose and goals
+* Contact information for people to reach out to if they want to learn more about or join the working group.
+
+Send this information to the SCINet office at [ARS-SCINet-Office@usda.gov](mailto:ARS-SCINet-Office@usda.gov).
diff --git a/pages/research/working_groups/breeding.md b/pages/research/working_groups/breeding.md
new file mode 100644
index 000000000..09e4b3325
--- /dev/null
+++ b/pages/research/working_groups/breeding.md
@@ -0,0 +1,35 @@
+---
+title: Breeding AI and ML Working Group
+description: A space where researchers working on addressing problems in breeding using artificial intelligence and machine learning methods can exchange knowledge and build community support
+permalink: /research/working-groups/breeding
+layout: page
+# cal-titles: excerpt
+# hide-provider: true
+# no-event-links: true
+# no-tags: true
+
+# filter:
+# provider: Breeding AI
+
+# collect: events-recurring
+# #reverse_collection: true
+
+# archive-label: Meeting Recordings
+# cal-label: Upcoming Webinars
+
+# subnav:
+# - title: Meeting Materials
+# url: '#meeting-materials'
+
+# remove-excerpt:
+---
+
+The focus of this group is to create a space where researchers working on addressing problems in breeding using artificial intelligence (AI) and machine learning (ML) methods can exchange knowledge and build community support. This group uses a “train the trainers” approach towards addressing the need for data standardization and protocols for transfer of research material to SCINet. The group is composed of both scientists already using SCINet resources and scientists interested in expanding utilization of SCINet in their research. This group will investigate, discover, and share learning resources for ARS scientists to promote both the development of AI/ML data and models. Eventually a repository will be made available for material developed in this space.
+
+
+
+This working group supports the Digital Ag Science Hub (DASH) effort within USDA focused at enterprising phenotyping solutions across breeding and production research space.
+
+
+
+Please contact [Amanda Hulse-Kemp](mailto:amanda.hulse-kemp@usda.gov) for more information.
\ No newline at end of file
diff --git a/pages/research/working_groups/pollinator.md b/pages/research/working_groups/pollinator.md
index 0e180e793..5592d59dc 100644
--- a/pages/research/working_groups/pollinator.md
+++ b/pages/research/working_groups/pollinator.md
@@ -4,7 +4,7 @@ description: The SCINet Pollinator working group was initiated in February 2021
permalink: /research/working-groups/pollinator
author: Melanie Kammerer
layout: page
-
+published: false
subnav:
- title: Workshops
url: '#workshops'
diff --git a/sn_collections/_announcements/2024-10-16-ceres.md b/sn_collections/_announcements/2024-10-16-ceres.md
new file mode 100644
index 000000000..5693eac61
--- /dev/null
+++ b/sn_collections/_announcements/2024-10-16-ceres.md
@@ -0,0 +1,11 @@
+---
+title: Ceres storage upgrade
+
+---
+SCINet's Ceres supercomputer has a new data storage system that offers substantial advantages over the old storage system. These advantages include nearly 75% more usable storage space (increased from ~5.5 PB to ~9.6 PB), an ~80% reduction in power and cooling requirements, and faster read/write performance. Ceres users will automatically benefit from these improvements because all file systems (/project, /90daydata, and /home) have been moved to the new storage system.
+
+With the new storage system, please note that there is a change in how quota enforcement is managed which may affect some quotas. Under the previous system, quotas were enforced based on the physical space required to store data. For example, if 100GB of files only required 80GB of physical storage space, those files would only be counted as 80GB against your storage quota. With the new system, quotas are enforced based on raw file sizes. In the same example above, even if the storage system only needs 80GB of physical space to store 100GB of files, the files will still be counted as 100GB of data against the quota. If you find that you are now exceeding or approaching your /project quota, the Project PI or Manager can [request a quota increase](/support/request#to-request-a-quota-increase-for-an-existing-scinet-project) for your project.
+
+Any compute jobs that were not able to complete by October 11 at 4:00 PM CT were held in the job queues after the maintenance completed. If you have a job that is being held, you will need to explicitly release it by running `scontrol release `. If you wish to cancel the job instead, please run `scancel `. (You can find the ID of your queued jobs by running `squeue --me`.)
+
+If you believe you are missing any files, feel free to open a support ticket (email [scinet_vrsc@usda.gov](scinet_vrsc@usda.gov)). Please note that the old `/project` filesystem will be available for a limited time from Ceres' data transfer node (ceres-dtn) as `/project-old`. In the near future, this will be removed to free up space, power, and cooling for Ceres compute resources.
diff --git a/sn_collections/_downtime/2024-12-12-juno.md b/sn_collections/_downtime/2024-12-12-juno.md
new file mode 100644
index 000000000..1baf8209f
--- /dev/null
+++ b/sn_collections/_downtime/2024-12-12-juno.md
@@ -0,0 +1,12 @@
+---
+date-start: 2024-12-12 07:00:00
+tzid: 'America/Chicago'
+duration: PT9H
+time: 8am-5pm ET
+systems: Juno
+locations: All
+reason: Network Updates
+---
+
+A network switch at Beltsville/NAL will be updated December 12. The upgrade should only take 1-2 hours, but network engineers are reserving the whole day just in case there are any unexpected issues.
+Juno will be unavailable intermittently during the network upgrade.
\ No newline at end of file
diff --git a/sn_collections/_downtime/2024-12-18-ceres.md b/sn_collections/_downtime/2024-12-18-ceres.md
new file mode 100644
index 000000000..21551faed
--- /dev/null
+++ b/sn_collections/_downtime/2024-12-18-ceres.md
@@ -0,0 +1,12 @@
+---
+date-start: 2024-12-18 07:00:00
+tzid: 'America/Chicago'
+duration: PT9H
+time: 8am-5pm ET
+systems: Ceres
+locations: All
+reason: Network Updates
+---
+
+The primary ARS SCINet network switch at Ames will be upgraded on Wednesday December 18th between 8am-5pm Eastern. The upgrade should only take 1-2 hours, but the whole day is reserved in case there are any unexpected issues.
+Connections to Ceres cluster may be affected.
\ No newline at end of file
diff --git a/sn_collections/_downtime/2025-01-14-Atlas.md b/sn_collections/_downtime/2025-01-14-Atlas.md
new file mode 100644
index 000000000..d46e082ec
--- /dev/null
+++ b/sn_collections/_downtime/2025-01-14-Atlas.md
@@ -0,0 +1,18 @@
+---
+date-start: 2025-01-14 06:00:00
+tzid: 'America/Chicago'
+duration: PT11H
+time: 6 am - 5 pm CT
+systems: Atlas
+locations: All
+reason: Maintenance
+---
+The Mississippi State University High Performance
+Computing Collaboratory’s (MSU/HPC2) Computing Office has scheduled
+maintenance for the atlas compute cluster.
+
+All associated systems (login, devel, dtn, ood, compute, storage, etc…) and
+services (cron, globus, ood, etc…) will be disabled during this maintenance.
+
+A notification will be posted once maintenance is complete.
+For any associated problems, submit a help desk ticket.
\ No newline at end of file
diff --git a/sn_collections/_downtime/2025-04-08-Atlas.md b/sn_collections/_downtime/2025-04-08-Atlas.md
new file mode 100644
index 000000000..fd7f33396
--- /dev/null
+++ b/sn_collections/_downtime/2025-04-08-Atlas.md
@@ -0,0 +1,18 @@
+---
+date-start: 2025-04-08 06:00:00
+tzid: 'America/Chicago'
+duration: PT11H
+time: 6 am - 5 pm CT
+systems: Atlas
+locations: All
+reason: Maintenance
+---
+The Mississippi State University High Performance
+Computing Collaboratory’s (MSU/HPC2) Computing Office has scheduled
+maintenance for the atlas compute cluster.
+
+All associated systems (login, devel, dtn, ood, compute, storage, etc…) and
+services (cron, globus, ood, etc…) will be disabled during this maintenance.
+
+A notification will be posted once maintenance is complete.
+For any associated problems, submit a help desk ticket.
\ No newline at end of file
diff --git a/sn_collections/_downtime/2025-07-08-Atlas.md b/sn_collections/_downtime/2025-07-08-Atlas.md
new file mode 100644
index 000000000..fff48040f
--- /dev/null
+++ b/sn_collections/_downtime/2025-07-08-Atlas.md
@@ -0,0 +1,18 @@
+---
+date-start: 2025-07-08 06:00:00
+tzid: 'America/Chicago'
+duration: PT11H
+time: 6 am - 5 pm CT
+systems: Atlas
+locations: All
+reason: Maintenance
+---
+The Mississippi State University High Performance
+Computing Collaboratory’s (MSU/HPC2) Computing Office has scheduled
+maintenance for the atlas compute cluster.
+
+All associated systems (login, devel, dtn, ood, compute, storage, etc…) and
+services (cron, globus, ood, etc…) will be disabled during this maintenance.
+
+A notification will be posted once maintenance is complete.
+For any associated problems, submit a help desk ticket.
\ No newline at end of file
diff --git a/sn_collections/_downtime/2025-10-14-Atlas.md b/sn_collections/_downtime/2025-10-14-Atlas.md
new file mode 100644
index 000000000..a080b06d3
--- /dev/null
+++ b/sn_collections/_downtime/2025-10-14-Atlas.md
@@ -0,0 +1,18 @@
+---
+date-start: 2025-10-14 06:00:00
+tzid: 'America/Chicago'
+duration: PT11H
+time: 6 am - 5 pm CT
+systems: Atlas
+locations: All
+reason: Maintenance
+---
+The Mississippi State University High Performance
+Computing Collaboratory’s (MSU/HPC2) Computing Office has scheduled
+maintenance for the atlas compute cluster.
+
+All associated systems (login, devel, dtn, ood, compute, storage, etc…) and
+services (cron, globus, ood, etc…) will be disabled during this maintenance.
+
+A notification will be posted once maintenance is complete.
+For any associated problems, submit a help desk ticket.
\ No newline at end of file
diff --git a/sn_collections/_events-recurring/omics.md b/sn_collections/_events-recurring/omics.md
index 48f029dc6..5e3a5e2ab 100644
--- a/sn_collections/_events-recurring/omics.md
+++ b/sn_collections/_events-recurring/omics.md
@@ -19,6 +19,19 @@ excerpt: To recieve an invitation to upcoming webinars, fill out the [Translatio
archive-label: Webinar Recordings
sessions:
+ - session:
+ title: "Comparative analysis of adaptive immune systems in agriculturally important species"
+ date: 2024-12-12
+ time: 11am-12pm ET
+ presenter: Dr. Yana Safonova
+ - session:
+ title: "Harnessing Transposons for Precise and Efficient Genome Editing in Plants"
+ date: 2024-11-14
+ time: 11am-12pm ET
+ presenter: Dr. Peng Liu
+ materials:
+ - text: webinar recording
+ url: https://usdagcc-my.sharepoint.com/:v:/r/personal/george_liu_usda_gov/Documents/publication/Hu%20Zhenbin/TOmics%20WG/20241114Recording/GMT20241114-160115_Recording_1920x1200.mp4?csf=1&web=1
- session:
title: "The genomic and metabolic making of yeast ecological diversity"
date: 2024-10-10 11:00:00
diff --git a/sn_collections/_events-recurring/practicum-ai.md b/sn_collections/_events-recurring/practicum-ai.md
index e729f610f..fbd8c363d 100644
--- a/sn_collections/_events-recurring/practicum-ai.md
+++ b/sn_collections/_events-recurring/practicum-ai.md
@@ -2,7 +2,7 @@
title: Practicum AI
description: Practicum AI is a hands-on applied artificial intelligence (AI) curriculum intended for learners with limited coding and math background.
categories: [Practicum AI]
-
+permalink: /events/practicum-ai/
registration:
text: Practicum AI Registration
diff --git a/sn_collections/_events-recurring/scinet-corner.md b/sn_collections/_events-recurring/scinet-corner.md
index 429759702..8216862f6 100644
--- a/sn_collections/_events-recurring/scinet-corner.md
+++ b/sn_collections/_events-recurring/scinet-corner.md
@@ -16,11 +16,19 @@ filter-archive: materials
archive-label: SCINet Corner Recordings
-sessions:
+sessions:
+ - session:
+ title: HPC Software Module System and Placing Requests for Software
+ date: 2024-12-12
+ time: 1-2 pm ET
+ excerpt: This presentation will cover modules in the context of Ceres and Atlas, how to call and use them, examples of frequently used modules, and what to do if your software isn't available.
- session:
title: Tidyverse for data cleaning and wrangling
date: 2024-10-17
time: 1-2 pm ET
+ materials:
+ - text: Session Recording
+ url: https://usdagcc.sharepoint.com/:v:/s/REE-ARS-SCINetOffice/EQ1SQRyluzdDon-gQBzTRu0BoEXwAjmXE5CRw-IhAO0j7g?e=XDuiHi
- session:
title: "Introduction to plotting with ggplot2"
date: 2024-09-19 13:00:00
@@ -165,7 +173,7 @@ This presentation will cover how to log on to Open OnDemand for Ceres and Atlas,
url: https://usdagcc.sharepoint.com/:v:/s/REE-ARS-SCINetOffice/Efccvnjx5ilPhokEl1kp-ggBnIrYRYkDpWKEykBF09hDqA?e=usBHyT #main link to video
date: 2022-12-01 13:00:00
time: 1-2 pm ET
- tags: R-Project dplyr
+ tags: [R-Project,dplyr]
instructor: Viswanathan Satheesh
excerpt: "Viswanathan Satheesh continues exploration of R after the previous SCINet Corner (November 3, 2022) and provides an introduction to dplyr."
materials:
@@ -204,7 +212,7 @@ This presentation will cover how to log on to Open OnDemand for Ceres and Atlas,
---
-The SCINet Corner is a recurring virtual gathering to provide a space for people to meet and discuss SCINet-related items. The main idea is that SCINet users with similar interests can get help from each other. This virtual meeting will provide space for, and facilitate, these interactions on the first or second Thursday of the month.
+The SCINet Corner is a recurring virtual gathering to provide a space for people to meet and discuss SCINet-related items. The main idea is that SCINet users with similar interests can get help from each other. This virtual meeting will provide space for, and facilitate, these interactions once a month.
Location: [SpatialChat](https://app.spatial.chat/s/scinet-corner)
diff --git a/sn_collections/_events/2024-11-13-intro-slurm.md b/sn_collections/_events/2024-11-13-intro-slurm.md
new file mode 100644
index 000000000..b300d0cde
--- /dev/null
+++ b/sn_collections/_events/2024-11-13-intro-slurm.md
@@ -0,0 +1,22 @@
+---
+title: "Introduction to the Command line and Slurm"
+type: training
+description: Event - introduction to the Linux command line and managing computing tasks on a supercomputer
+
+tags: slurm command-line
+time: 1-5 PM ET
+
+details:
+ - text: Registration reserved for AI User Forum participants
+
+materials:
+ - text: Workshop Recording
+ url: https://usdagcc.sharepoint.com/:v:/s/REE-ARS-SCINetOffice/EVWdBWm8rsROgyk1I-gZIfoB2XuH4bGkZSC6v6jquU38Sw?e=0Ififv
+
+# registration:
+# text: Register for the AI User Forum
+# url: https://events.tti.tamu.edu/conference/2024-forum-on-ai-applications-to-usda-science/
+
+---
+
+This hands-on workshop provides an introduction to the Linux command line and managing computing tasks on a supercomputer. Participants will gain a foundational understanding of supercomputing concepts and essential commands for managing files and directories via the command-line shell. The workshop will also cover basic compute job management using the Slurm job scheduler, including how to submit and monitor jobs on SCINet's supercomputers.
diff --git a/sn_collections/_events/2024-11-14-intro-python.md b/sn_collections/_events/2024-11-14-intro-python.md
new file mode 100644
index 000000000..27048d8b9
--- /dev/null
+++ b/sn_collections/_events/2024-11-14-intro-python.md
@@ -0,0 +1,85 @@
+---
+title: Introduction to Jupyter Notebooks and Python
+type: training
+description: Training - learning tools and techniques that are fundamental for building, testing, and deploying AI models in Jupyter Notebooks with Python.
+
+tags: python jupyter artificial-intelligence
+time: 1-5 PM ET
+
+# details:
+# - text: Registration reserved for AI User Forum participants
+
+materials:
+ - text: Workshop recording
+ url: https://usdagcc.sharepoint.com/:v:/s/REE-ARS-SCINetOffice/EVh7jdve53JApdx89HA2Ay4BTjC-t9aAneNJzchDe_VEkQ?e=xtc6hV&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D
+ - text: Workshop Materials
+ url: https://github.com/PracticumAI/python-crash-course-ars
+
+# registration:
+# text: Register for the AI User Forum
+# url: https://events.tti.tamu.edu/conference/2024-forum-on-ai-applications-to-usda-science/
+
+---
+
+In this workshop, which assumes no prior coding experience, you will begin to learn the tools and techniques that are fundamental for building, testing, and deploying AI models in Jupyter Notebooks with Python. Participants will explore Jupyter Notebooks as an interactive platform for coding and data analysis. The workshop will introduce participants to Python, including popular data exploration and visualization libraries such as pandas and plotnine.
+
+## Learning Objectives:
+By the end of this workshop, participants will be able to:
+* Use Jupyter Notebooks for coding Python.
+* Understand the basics of Python code.
+* Begin troubleshooting common coding errors.
+* Practice data manipulation and visualization using Python libraries.
+
+## Tutorial setup instruction
+
+Steps to prepare for the tutorial:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on “Clusters” -> “Atlas Shell Access” on the top menu. This will open a new tab with a command-line session on Atlas's login node.
+
+1. **Request resources on a compute node** by running the following command:
+
+ {:.copy-code}
+ ```bash
+ srun --reservation=preforum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash
+ ```
+
+1. **Create and/or update your workshop working directory** and copy the tutorial materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the `$USER` variable.
+
+ {:.copy-code}
+ ```bash
+ mkdir -p /90daydata/shared/$USER/intro_python
+ cd /90daydata/shared/$USER/intro_python
+ cp -r /project/ai_forum/intro_python/python-crash-course-ars/* .
+ ```
+
+1. **Setup the kernel for JupyterLab.** You will create a kernel called *intro_python_env* to access from JupyterLab Server. Run the following commands to activate the workshop's virtual environment and create a new kernelspec from it:
+
+ {:.copy-code}
+ ```bash
+ source /project/ai_forum/intro_python/intro_python_env/bin/activate
+ ipython kernel install --name "intro_python_env" --user
+ ```
+
+1. **Stop the interactive job** on the compute node by running the command:
+
+ {:.copy-code}
+ ```bash
+ exit
+ ```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *JupyterLab Server*. Specify the following input values on the page:
+
+ * Account: scinet_workshop1
+ * Partition: atlas
+ * QOS: normal 14-00:00:00
+ * Number of hours: 4
+ * Number of nodes: 1
+ * Number of tasks: 1
+ * Additional Slurm Parameters: \-\-reservation=preforum \-\-mem=16G
+ * Working Directory: /90daydata/shared/${USER}
+
+ Click *Launch*. The screen will update to the *Interactive Sessions* page. When your Jupyter session is ready, the top card will update from *Queued* to *Running* and a *Connect to JupyterLab Server* button will appear. Click *Connect to JupyterLab Server*.
+
+1. **Select the *intro_python_env* kernel** for each notebook.
diff --git a/sn_collections/_events/geospatial/2024-Geospatial-Workshop.md b/sn_collections/_events/geospatial/2024-Geospatial-Workshop.md
index 94385c283..75ed7e727 100644
--- a/sn_collections/_events/geospatial/2024-Geospatial-Workshop.md
+++ b/sn_collections/_events/geospatial/2024-Geospatial-Workshop.md
@@ -8,6 +8,10 @@ excerpt: Provides hands-on tutorials on workflows to access the SCINet HPC syste
categories: [2024 Geospatial Workshop]
tags: Geospatial
+layout_type: workshop
+cal-titles: show
+post_type: calendar
+
provider: Geospatial Working Group
sessions:
- session:
@@ -37,10 +41,10 @@ The two overarching goals of the GRWG Annual Workshop are to:
1. Provide hands-on tutorials on workflows to access the SCINet high-performance computing (HPC) systems and conduct geospatial research at scale.
1. Foster research efforts that had previously been unattainable due to computational limitations or technical bottlenecks.
-# Organizing Committee
+## Organizing Committee
{:.border-bottom}
-The organizing committee for the 2024 Annual Workship comprises:
+The organizing committee for the 2024 Annual Workshop comprises:
* Andrea Albright, SCINet Postdoctoral Fellow
* Amira Burns, SCINet Fellow
@@ -52,10 +56,43 @@ The organizing committee for the 2024 Annual Workship comprises:
* Melanie Veron, SCINet Fellow
-# How to Participate
+## How to Participate
{:.border-bottom}
To participate in the workshop, please register by submitting [the registration form](https://forms.office.com/g/XqvSkCMeM2). A calendar event with the Zoom call-in information will be sent after you register.
The form asks for your SCINet account since full participation in hands-on activites will require an account. **If you do not have a SCINet account already, [please apply for one]({{ site.baseurl }}/about/signup).** We recommend applying for an account as soon as possible, as the process can take time for final approval. All registrants will be invited to a pre-workshop support session the week before the workshop to test logging into their SCINet account.
+-----
+
+
+### Pre-Workshop Instructions:
+
+To help minimize technical issues and delays at the start of the workshop, please try the following four tests prior to the workshop.
+
+* **Logging on to [Ceres Open OnDemand (OOD)](https://ceres-ood.scinet.usda.gov/):** Please confirm you can successfully log in to Ceres OOD with your SCINet account [(see instructions here)]({{site.baseurl}}/guides/access/web-based-login). If you are successful, you will be able to see the Ceres OOD home page.
+* **Ceres Shell Access:** When on Ceres OOD, click on the top navigation bar: “Clusters” > “Ceres Shell Access”. A new tab will appear that looks like a shell terminal (e.g., like PowerShell). Please confirm you do not receive any error messages or requests to re-authenticate and that the final line looks like "[firstname.lastname@ceres ~]$".
+* **RStudio Server:** Back on the main Ceres OOD tab, click on the top or side navigation bar: "Interactive Apps" > "RStudio Server".
+ * Fill the input fields with the following (input fields not listed below can be left at their default values):
+ * Queue: short
+ * Number of hours: 1
+ * Number of cores: 2
+ * Memory required: 6G
+ * Optional Slurm Arguments: (leave empty)
+ * Click the "Launch" button.
+ * Wait a moment for the job card to update from "Queued" to "Running".
+ * Please confirm that clicking on the "Connect to RStudio Server" button opens a new tab with the RStudio Server interface.
+* **JupyterLab Server:** Back on the main Ceres OOD tab, click on the top or side navigation bar: "Interactive Apps" > "Jupyter".
+ * Fill the input fields with the following (input fields not listed below can be left at their default values):
+ * Queue: short
+ * Number of hours: 1
+ * Number of cores: 2
+ * Memory required: 6G
+ * Optional Slurm Arguments: (leave empty)
+ * Jupyter Notebook vs Lab: Lab
+ * Working Directory: $HOME
+ * Click the "Launch" button.
+ * Wait a moment for the job card to update from "Queued" to "Running".
+ * Please confirm that clicking on the "Connect to Jupyter" button opens a new tab with the JupyterLab Server interface.
+
+## Workshop Sessions
\ No newline at end of file
diff --git a/sn_collections/_events/package-env/2024-07-package-env-workshop.md b/sn_collections/_events/package-env/2024-07-package-env-workshop.md
index 0e5990ae1..5022e5e3f 100644
--- a/sn_collections/_events/package-env/2024-07-package-env-workshop.md
+++ b/sn_collections/_events/package-env/2024-07-package-env-workshop.md
@@ -9,6 +9,9 @@ duplicate: true ## Added with the expectation of the Oct one using the same work
tags: software package-management
layout_type: workshop
provider: SCINet
+
+cal-titles: show
+
sessions:
- session:
time: 12 PM – 5 PM ET
@@ -56,14 +59,6 @@ Steps to prepare for the tutorial sessions:
```
-### Python (and conda)
-
-The first half of the workshop will focus on [environment and package management using Python and conda](/workshops/2024-07-19-package-env-workshop-python).
-
-### R
-
-The second half of the workshop will focus on [environment and package management using R](/workshops/2024-07-19-package-env-workshop-r).
-
## Recording
diff --git a/sn_collections/_events/package-env/2024-10-package-env-workshop.md b/sn_collections/_events/package-env/2024-10-package-env-workshop.md
index 7ba668818..8fc5ebdf0 100644
--- a/sn_collections/_events/package-env/2024-10-package-env-workshop.md
+++ b/sn_collections/_events/package-env/2024-10-package-env-workshop.md
@@ -9,6 +9,9 @@ categories: [2024 10 SPEMW]
tags: software package-management
layout_type: workshop
provider: SCINet
+
+cal-titles: show
+
sessions:
- session:
time: 1:00 PM - 4:30 PM ET
diff --git a/sn_collections/_guides/access/ssh-login.md b/sn_collections/_guides/access/ssh-login.md
index 6f01d44f0..77eff6ddf 100644
--- a/sn_collections/_guides/access/ssh-login.md
+++ b/sn_collections/_guides/access/ssh-login.md
@@ -41,7 +41,7 @@ This guide gives step-by-step instructions for accessing SCINet systems via SSH,
If you do wish to access SCINet systems via SSH, you will need to have software called "SmallStepCLI" installed on your computer. The instructions below detail how to install and configure SmallStepCLI and use it for SSH access to SCINet systems.
-**If you are a LincPass User and still encountering errors after completing the below steps, see [SmallStepCLI Install Troubleshooting for LincPass Users](/guides/access/login/smallstepscli)**
+**If you are a LincPass User and still encountering errors after completing the below steps, see [SmallStepCLI Install Troubleshooting for LincPass Users](/guides/access/login/smallsteps)**
## Small Step Installation Instructions
@@ -71,7 +71,7 @@ If you do wish to access SCINet systems via SSH, you will need to have software
- Installing SmallStepsCLI:
- **If you are on a USDA-managed Windows laptop or workstation:**
- If your workstation has CEC support, you can [install **SmallStepsCLI** directly from the Software Center]({{ site.baseurl}}/guides/access/login/smallstepscli-download). If Software Center fails to install SmallStepCLI, please contact your IT Specialist prior to continuing.
- - If you are encountering errors after completing the install, see [SmallStepCLI Install Troubleshooting for LincPass Users](/guides/access/login/smallstepscli).
+ - If you are encountering errors after completing the install, see [SmallStepCLI Install Troubleshooting for LincPass Users](/guides/access/login/smallsteps).
- After installing, you may need to restart your terminal.
- If your workstation has "status quo" support (i.e., without CEC support), you will need to have your local IT Specialist install the software for you.
- **If you are not on a USDA-managed laptop:**
diff --git a/sn_collections/_guides/data/assets/data_management_sop-fig_1.png b/sn_collections/_guides/data/assets/data_management_sop-fig_1.png
index e8ac1ecf1..eb42c7eb2 100644
Binary files a/sn_collections/_guides/data/assets/data_management_sop-fig_1.png and b/sn_collections/_guides/data/assets/data_management_sop-fig_1.png differ
diff --git a/sn_collections/_guides/data/storage.md b/sn_collections/_guides/data/storage.md
index e57e28fe9..0fe4ff29f 100644
--- a/sn_collections/_guides/data/storage.md
+++ b/sn_collections/_guides/data/storage.md
@@ -63,9 +63,6 @@ Home directories have 10GB quotas and are intended to be mainly used for configu
should be run from project directories in `/90daydata` or in `/project`. Software installs that require a lot of space,
such as conda virtual environments, should be done in [`/project`](#project-directories).
-Files in home directories are automatically compressed and backed up. Due to backup method used on Ceres, space freed
-after deleting files in home directories, becomes available only after 6 days.
-
## Project Directories
Project directories are usually associated with ARS Research Projects. While it's possible to run simulations on Ceres or Atlas using only home directories and [Large Short-term Storage](#large-short-term-storage) in `/90daydata/shared`, it is recommended to request a project directory. Having a project directory will allow to install software packages in /project and keep important data on [Juno Archive Storage](#juno-archive-storage).
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-00-Geospatial-Workshop-Itinerary.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-00-Geospatial-Workshop-Itinerary.md
index 76b52b7d1..8816c6b04 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-00-Geospatial-Workshop-Itinerary.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-00-Geospatial-Workshop-Itinerary.md
@@ -9,7 +9,7 @@ provider: Geospatial Working Group
type: itinerary
tags: Geospatial
-layout: page
+
collect: workshops
no-tags: true
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-00-Geospatial-Workshop-Premeeting.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-00-Geospatial-Workshop-Premeeting.md
index 382e54943..c0dfd6cc8 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-00-Geospatial-Workshop-Premeeting.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-00-Geospatial-Workshop-Premeeting.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: orientation
tags: Geospatial
-layout: page
+
subnav:
- title: Tutorials
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-25-Geospatial-Workshop-0.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-25-Geospatial-Workshop-0.md
index a8ba22c90..d00fa410d 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-25-Geospatial-Workshop-0.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-25-Geospatial-Workshop-0.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-29-Geospatial-Workshop-1.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-29-Geospatial-Workshop-1.md
index 21c8706f2..bda0c6f62 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-29-Geospatial-Workshop-1.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-29-Geospatial-Workshop-1.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-29-Geospatial-Workshop-2.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-29-Geospatial-Workshop-2.md
index 87b0fdec3..40b0db6b2 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-29-Geospatial-Workshop-2.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-29-Geospatial-Workshop-2.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-3.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-3.md
index 433a1add9..190791d48 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-3.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-3.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-4.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-4.md
index e0a76c06d..7cf749acd 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-4.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-4.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-5.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-5.md
index 3c66a1482..f86459abb 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-5.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-30-Geospatial-Workshop-5.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-31-Geospatial-Workshop-6.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-31-Geospatial-Workshop-6.md
index a3e6add82..97e8a0ddb 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-31-Geospatial-Workshop-6.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-31-Geospatial-Workshop-6.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial R-project
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-31-Geospatial-Workshop-7.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-31-Geospatial-Workshop-7.md
index 9260d30b8..0e99c5e31 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-8-31-Geospatial-Workshop-7.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-8-31-Geospatial-Workshop-7.md
@@ -7,7 +7,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-9-1-Geospatial-Workshop-8.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-9-1-Geospatial-Workshop-8.md
index dbe9f84fb..9dc0e703c 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-9-1-Geospatial-Workshop-8.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-9-1-Geospatial-Workshop-8.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial, python
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-9-1-Geospatial-Workshop-9.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-9-1-Geospatial-Workshop-9.md
index 3920b3816..0777add4f 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-9-1-Geospatial-Workshop-9.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-9-1-Geospatial-Workshop-9.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-9-2-Geospatial-Workshop-10.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-9-2-Geospatial-Workshop-10.md
index 40355da01..9e1fb902f 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-9-2-Geospatial-Workshop-10.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-9-2-Geospatial-Workshop-10.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2022-geospatial-workshop/2022-9-2-Geospatial-Workshop-11.md b/sn_collections/_workshops/2022-geospatial-workshop/2022-9-2-Geospatial-Workshop-11.md
index 41c0ee9af..bcc9b8681 100644
--- a/sn_collections/_workshops/2022-geospatial-workshop/2022-9-2-Geospatial-Workshop-11.md
+++ b/sn_collections/_workshops/2022-geospatial-workshop/2022-9-2-Geospatial-Workshop-11.md
@@ -8,7 +8,7 @@ categories: [2022 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2023-geospatial-workshop/2023-00-Geospatial-Workshop-Itinerary.md b/sn_collections/_workshops/2023-geospatial-workshop/2023-00-Geospatial-Workshop-Itinerary.md
index 3d21939ff..2a276f041 100644
--- a/sn_collections/_workshops/2023-geospatial-workshop/2023-00-Geospatial-Workshop-Itinerary.md
+++ b/sn_collections/_workshops/2023-geospatial-workshop/2023-00-Geospatial-Workshop-Itinerary.md
@@ -9,7 +9,7 @@ provider: Geospatial Working Group
type: itinerary
tags: Geospatial
-layout: page
+
collect: workshops
no-tags: true
diff --git a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-21-Geospatial-Workshop-0.md b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-21-Geospatial-Workshop-0.md
index f93183a9d..37eb5c56f 100644
--- a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-21-Geospatial-Workshop-0.md
+++ b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-21-Geospatial-Workshop-0.md
@@ -8,7 +8,7 @@ categories: [2023-Geospatial-Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-25-Geospatial-Workshop-1.md b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-25-Geospatial-Workshop-1.md
index 95c512ee8..34640929a 100644
--- a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-25-Geospatial-Workshop-1.md
+++ b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-25-Geospatial-Workshop-1.md
@@ -8,7 +8,7 @@ categories: [2023 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-26-Geospatial-Workshop-2.md b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-26-Geospatial-Workshop-2.md
index 41a080eaa..1d518dd0d 100644
--- a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-26-Geospatial-Workshop-2.md
+++ b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-26-Geospatial-Workshop-2.md
@@ -8,7 +8,7 @@ categories: [2023 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-27-Geospatial-Workshop-3.md b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-27-Geospatial-Workshop-3.md
index 83faf4d7c..9bf67da39 100644
--- a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-27-Geospatial-Workshop-3.md
+++ b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-27-Geospatial-Workshop-3.md
@@ -8,7 +8,7 @@ categories: [2023 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-28-Geospatial-Workshop-4.md b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-28-Geospatial-Workshop-4.md
index 82ddd107c..334e3dc98 100644
--- a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-28-Geospatial-Workshop-4.md
+++ b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-28-Geospatial-Workshop-4.md
@@ -8,7 +8,7 @@ categories: [2023 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-28-Geospatial-Workshop-5.md b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-28-Geospatial-Workshop-5.md
index e3c047aa8..5cc3a129d 100644
--- a/sn_collections/_workshops/2023-geospatial-workshop/2023-9-28-Geospatial-Workshop-5.md
+++ b/sn_collections/_workshops/2023-geospatial-workshop/2023-9-28-Geospatial-Workshop-5.md
@@ -8,7 +8,7 @@ categories: [2023 Geospatial Workshop]
provider: Geospatial Working Group
type: workshop
tags: Geospatial
-layout: page
+
sessions:
- session:
diff --git a/sn_collections/_workshops/2024-07-spemw/2024-07-19-package-env-workshop-python.md b/sn_collections/_workshops/2024-07-spemw/2024-07-19-package-env-workshop-python.md
index d688183be..951a79465 100644
--- a/sn_collections/_workshops/2024-07-spemw/2024-07-19-package-env-workshop-python.md
+++ b/sn_collections/_workshops/2024-07-spemw/2024-07-19-package-env-workshop-python.md
@@ -2,7 +2,7 @@
title: "Software Package/Environment Management Workshop: Python and conda"
description: In this workshop presented by the SCINet Office, we will cover best practices for managing software packages and computing environments on SCINet's supercomputers.
excerpt: "We will begin by focusing on package and environment management with the standard Python toolset: the `venv` and `pip` modules that are usually included with Python. Later, we will learn package and environment management with conda."
-layout: page
+
categories: [2024 07 SPEMW]
sidenav_link: /training/resources
diff --git a/sn_collections/_workshops/2024-07-spemw/2024-07-19-package-env-workshop-r.md b/sn_collections/_workshops/2024-07-spemw/2024-07-19-package-env-workshop-r.md
index 5f6226bc8..18a6c5dd5 100644
--- a/sn_collections/_workshops/2024-07-spemw/2024-07-19-package-env-workshop-r.md
+++ b/sn_collections/_workshops/2024-07-spemw/2024-07-19-package-env-workshop-r.md
@@ -2,7 +2,7 @@
title: "Software Package/Environment Management Workshop: R"
description: In this workshop presented by the SCINet Office, we will cover best practices for managing software packages and computing environments on SCINet's supercomputers.
excerpt: "In this session, we will begin by using R from the command line. Later, we will cover similar steps using RStudio Server available on Open OnDemand. We will primarily focus on using the `renv` package for package management, but we will also note alternatives at the end."
-layout: page
+
categories: [2024 07 SPEMW]
sidenav_link: /training/resources
diff --git a/sn_collections/_workshops/2024-10-spemw/2024-10-03-package-env-workshop-python.md b/sn_collections/_workshops/2024-10-spemw/2024-10-03-package-env-workshop-python.md
index d978ff244..5f2479f19 100644
--- a/sn_collections/_workshops/2024-10-spemw/2024-10-03-package-env-workshop-python.md
+++ b/sn_collections/_workshops/2024-10-spemw/2024-10-03-package-env-workshop-python.md
@@ -2,20 +2,19 @@
title: "Software Package/Environment Management Workshop: Python and conda"
description: In this workshop presented by the SCINet Office, we will cover best practices for managing software packages and computing environments on SCINet's supercomputers.
excerpt: "We will begin by focusing on package and environment management with the standard Python toolset: the `venv` and `pip` modules that are usually included with Python. Later, we will learn package and environment management with conda."
-layout: page
categories: [2024 10 SPEMW]
sidenav_link: /training/resources
-details:
+materials:
- text: Session Recording
url: https://usdagcc.sharepoint.com/:v:/s/REE-ARS-SCINetOffice/Ef55DPFjTMdOqzMwxs4UAtYBDwq4xzLT5qp4M9jzOSZLIg
---
-# Managing packages and environments in Python
+## Managing packages and environments in Python
We will begin by focusing on package and environment management with the standard Python toolset: the `venv` and `pip` modules that are usually included with Python. Later, we will learn package and environment management with conda.
-## Choosing which version of Python to use
+### Choosing which version of Python to use
1. First, use the cluster's environment module system to find and load the version of Python you want to use for your project: `module spider python` or `ml spider python`.
1. Load the version of Python you'd like to use. E.g., `module load python/3.12.5` or `ml load python/3.12.5`. (Note that you can use tab completion for module names!)
@@ -23,7 +22,7 @@ We will begin by focusing on package and environment management with the standar
Note: After you create your virtual environment, you no longer need to load the associated Python environment module. You can simply activate the virtual environment!
-## Creating and managing virtual environments with `venv`
+### Creating and managing virtual environments with `venv`
1. If you are not already in your workshop directory, change to it by running `cd /90daydata/shared/$USER/`.
1. Use the `venv` module and command to create a new virtual environment: `python -m venv demo_venv`.
@@ -34,7 +33,7 @@ Note: After you create your virtual environment, you no longer need to load the
That is pretty much everything you need to know about how to use and manage Python virtual environments! The `venv` command does have [more options](https://docs.python.org/3/library/venv.html), but you most likely won't need them.
-## Installing and managing Python packages in a virtual environment
+### Installing and managing Python packages in a virtual environment
The standard software tool for managing Python packages is `pip`, which is included with Python. If a Python virtual environment is activated, `pip` commands will automatically be applied to the active virtual environment.
@@ -65,7 +64,7 @@ for i in range(n):
print_fake_data()
```
-## Using `requirements.txt` to automate package management
+### Using `requirements.txt` to automate package management
In order to make virtual environments and package management _truly_ useful, we need a mechanism to easily and precisely record all of the packages an environment requires. `pip` can use a special "requirements file", usually named `requirements.txt`, to do this. In its simplest form, `requirements.txt` simply lists the names of packages that are needed for an environment, with one package on each line. For example:
```
@@ -91,7 +90,7 @@ It is a good practice to include a `requirements.txt` file along with the code a
> **Exercise 3:** Create a _new_ virtual environment and install all packages from `requirements.txt` from Exercise 2 into the virtual environment. Confirm that the program from Exercise 1 runs in your new virtual environment.
-## Using virtual environments with Jupyter notebooks
+### Using virtual environments with Jupyter notebooks
What we've learned so far is all you need for using Python from the command line. How, though, do you access your virtual environment from a Jupyter notebook? For this, we need to create a "Jupyter kernel" to make our environment available in notebooks.
@@ -140,7 +139,7 @@ df = pd.DataFrame({
> Create a suitable virtual environment for this code, then create a Jupyter kernel for your notebook from the environment. Verify that the code runs.
-# Managing packages and environments with Anaconda
+## Managing packages and environments with Anaconda
If all of the software components you need to manage in your virtual environment are Python packages, we strongly recommend using the `venv` and `pip` workflow described in detail above. Why? Because `pip` and `venv`:
* Are included with Python and therefore available pretty much anywhere Python is available. This helps ensure that your workflow is portable and easy to share.
@@ -149,7 +148,7 @@ If all of the software components you need to manage in your virtual environment
However, if you need to manage other kinds of software, too, `conda` can provide a useful alternative. Conceptually, the process of managing software using `conda` is the same: you create a virtual environment and then manage software packages within that environment. We will go over the basics in this workshop; please see [the official documentation](https://conda.io/projects/conda/en/latest/user-guide/index.html) for more information!
-## Load and initialize miniconda
+### Load and initialize miniconda
First, load the environment module for miniconda so that you have access to the `conda` command:
* On Ceres: `module load miniconda` or `ml load miniconda`.
@@ -164,7 +163,7 @@ Note: `mamba` is a drop-in replacement for `conda` that is generally faster and
1. Run `conda config --add channels conda-forge` to add the "conda-forge" channel.
1. Run `conda config --remove channels defaults` to remove the "defaults" channel.
-## Creating and managing environments with `conda`
+### Creating and managing environments with `conda`
First, let's cover what _not_ to do! Most online documentation will tell you to create a new conda environment by running `conda create -n ENVNAME`, where "ENVNAME" is the name of the new environment. E.g., `conda create -n conda_env`. The problem with this is that all packages will be installed into a hidden directory inside your home directory (typically `~/.conda/envs`) and you will quickly run out of space!
@@ -175,7 +174,7 @@ Instead, we need to tell `conda` to create the environment in a location that we
1. To deactivate the environment and return to the "normal" command environment you had before, run `conda deactivate`.
1. To remove the environment, delete its directory: e.g., `rm -I -r conda_venv`. Alternatively, you can run the command `conda remove --prefix /path/to/ENVNAME --all`.
-## Installing and managing software in a conda environment
+### Installing and managing software in a conda environment
The `conda` command is also used to install and remove software from a conda environment.
@@ -191,7 +190,7 @@ Conda provides an alternative way to manage Python packages. Although you can st
> **Exercise 6:** Modify your conda environment so that you can run the Python script you created for Exercise 1.
-## Using `environment.yml` to automate package management
+### Using `environment.yml` to automate package management
Just as we can use `requirements.txt` to specify the packages to include in a Python virtual environment, we can use a file typically called `environment.yml` to specify the packages to include in a conda environment.
@@ -201,7 +200,7 @@ To create a new conda environment that matches the contents of an environment fi
> **Exercise 7:** Save the configuration of the conda environment you created for Exercise 6 and use it to create a new conda environment. Verify that you have the correct version of Python in the new environment and are able to run the Python script you created for Exercise 1.
-## Using conda environments with Jupyter notebooks
+### Using conda environments with Jupyter notebooks
The process to make a conda environment available to Jupyter notebooks is nearly the same as for Python virtual environments.
diff --git a/sn_collections/_workshops/2024-10-spemw/2024-10-04-package-env-workshop-r.md b/sn_collections/_workshops/2024-10-spemw/2024-10-04-package-env-workshop-r.md
index e87b72fee..bd6ae5a99 100644
--- a/sn_collections/_workshops/2024-10-spemw/2024-10-04-package-env-workshop-r.md
+++ b/sn_collections/_workshops/2024-10-spemw/2024-10-04-package-env-workshop-r.md
@@ -2,21 +2,20 @@
title: "Software Package/Environment Management Workshop: R"
description: In this workshop presented by the SCINet Office, we will cover best practices for managing software packages and computing environments on SCINet's supercomputers.
excerpt: "In this session, we will begin by using R from the command line. Later, we will cover similar steps using RStudio Server available on Open OnDemand. We will primarily focus on using the `renv` package for package management, but we will also note alternatives at the end."
-layout: page
categories: [2024 10 SPEMW]
sidenav_link: /training/resources
-details:
+materials:
- text: Session Recording
url: https://usdagcc.sharepoint.com/:v:/s/REE-ARS-SCINetOffice/EXDitJDkH8JBoPwUwmZryoABJ40oa6XL2K6CDOv4FgWlkA?e=BATq8f
---
-# Managing packages and environments in command-line R
+## Managing packages and environments in command-line R
In this session, we will begin by using R from the command line. Later, we will cover similar steps using RStudio Server available on Open OnDemand. We will primarily focus on using the `renv` package for package management, but we will also note alternatives at the end.
-## Choosing which version of R to use
+### Choosing which version of R to use
Multiple R versions are available in the environment module system. Note that modules are named with 'r' and the program available after the module is loaded is 'R'. With each new minor version of R you use, the `renv` package will need to be installed.
@@ -26,7 +25,7 @@ Multiple R versions are available in the environment module system. Note that mo
1. Run the R command `install.packages('renv')` to install `renv` for this version of R.
1. Run `q()` to exit R, and enter `n` when prompted to save the workspace image.
-## Creating and managing R environments with the `renv` package
+### Creating and managing R environments with the `renv` package
To use `renv` for package management, it needs to be associated with an R project. This could be an empty directory if you are just starting a project or it could be one or more R scripts within a directory. Either way, the scope of the `renv` environment will be dictated by the working directory in which it is initialized. To initialize an `renv` environment, you use the `renv::init()` command. **For getting started and for this workshop, we recommend passing two arguments when initializing the environment: `renv::init(settings = list(use.cache = FALSE, ppm.enabled = FALSE))`.** These arguments keep package installations within the project directory instead of your home directory and prevent some potentially faulty URL translations from happening when packages are downloaded from repositories.
@@ -66,7 +65,7 @@ R will have to be restarted before the project library is setup, i.e., our `exer
Now we are set up with a project with an `renv` environment!
-## Installing and managing R packages in your project library
+### Installing and managing R packages in your project library
Next, we will expand our project with additional packages!
@@ -98,7 +97,7 @@ for(i in c(1:N,N:1)){
1. For example, if we install the `MASS` library because we think we may need it but later don't, `renv::restore(clean=TRUE)` will help remove the unused package from the project library.
1. `renv::restore()` can also be used to revert package version discrepancies like for `cli` above.
-## Reproduce renv projects
+### Reproduce renv projects
In order to make environments and package management _truly_ useful, we need a mechanism to easily reproduce environments. With the `renv` project files we looked at before, you can have `renv` reproduce the same environment in a new project directory.
@@ -111,11 +110,11 @@ In order to make environments and package management _truly_ useful, we need a m
-# Managing packages and environments in RStudio Server
+## Managing packages and environments in RStudio Server
The approach of using `renv` in RStudio is very similar to using `renv` with command-line R. For completeness, we will create another environment in RStudio Server.
-## Choosing which version of R to use
+### Choosing which version of R to use
Multiple R versions are available when requesting RStudio Server sessions on Open OnDemand. From the [Open OnDemand](https://atlas-ood.hpc.msstate.edu/) page, select "Interactive Apps" > "RStudio Server". You will be taken to a page with multiple input fields to configure your RStudio Server session and one of those is "R Version".
@@ -131,7 +130,7 @@ Multiple R versions are available when requesting RStudio Server sessions on Ope
1. When you are in RStudio Server, install `renv`. Note, we only need to install `renv` because we chose a different version of R.
-## Creating and managing R environments with the `renv` package
+### Creating and managing R environments with the `renv` package
Since `renv` is project specific, you need to change the working directory to the project directory in which you would like to use `renv`. Once in the project directory, you can run `renv::init(settings = list(use.cache = FALSE, ppm.enabled = FALSE))` to start managing the project environment with `renv`.
@@ -163,7 +162,7 @@ string <- x$matches$Title[correct_length] %>%
}
```
-# If you don't want to use `renv`
+## If you don't want to use `renv`
`renv` is the main solution if you want a handful of commands to manage a project's packages with a project-specific library and also document that process to increase reproducibility. If, for any reason, you are not a fan of `renv`, there are some commands and R-related files that can help you at least manage the locations of package installations.
diff --git a/sn_collections/_workshops/2024-ai-user-forum/20-a-ml-intro.md b/sn_collections/_workshops/2024-ai-user-forum/20-a-ml-intro.md
index d53b4ce74..0ee2987eb 100644
--- a/sn_collections/_workshops/2024-ai-user-forum/20-a-ml-intro.md
+++ b/sn_collections/_workshops/2024-ai-user-forum/20-a-ml-intro.md
@@ -14,3 +14,72 @@ prerequisites:
Machine learning underlies the vast majority of modern AI methods, including the ever-expanding applications of deep learning and generative AI. This workshop will give participants a hands-on introduction to the basic concepts and techniques needed to understand machine learning and to apply machine learning methods to scientific research.
Participants will learn how to train, evaluate, and use a variety of machine learning models for data analysis tasks. This session will also help participants critically evaluate the use and application of machine learning in science.
+
+
+## Tutorial setup instruction
+
+Steps to prepare for the tutorial:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on "Clusters" -> "Atlas Shell Access" on the top menu. This will open a new tab with a command-line session on Atlas's login node.
+
+1. **Request resources on a compute node** by running the following command:
+
+ {:.copy-code}
+ ```bash
+srun --reservation=forum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash
+```
+
+1. **Create and/or update your workshop working directory** and copy the tutorial materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the `$USER` variable.
+
+ {:.copy-code}
+ ```bash
+mkdir -p /90daydata/shared/$USER/intro_ml
+cd /90daydata/shared/$USER/intro_ml
+cp -r /project/ai_forum/intro_ml/intro_ml.ipynb .
+```
+
+1. **Setup the kernel for JupyterLab.** You will create a kernel called *intro_ml_env* to access from JupyterLab Server. Run the following commands to activate the workshop's virtual environment and create a new kernelspec from it:
+
+ {:.copy-code}
+ ```bash
+source /project/ai_forum/intro_ml/intro_ml_env/bin/activate
+ipython kernel install --name "intro_ml_env" --user
+```
+
+1. **Stop the interactive job** on the compute node by running the command:
+
+ {:.copy-code}
+ ```bash
+exit
+```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *JupyterLab Server*. Specify the following input values on the page:
+
+ * Account: scinet_workshop1
+ * Partition: atlas
+ * QOS: normal 14-00:00:00
+ * Number of hours: 4
+ * Number of nodes: 1
+ * Number of tasks: 6
+ * Additional Slurm Parameters:
+
+ {: .copy-code }
+ ```
+--reservation=forum --mem=32G
+```
+ * Working Directory:
+
+ {: .copy-code }
+ ```
+/90daydata/shared/${USER}/intro_ml
+```
+
+ Click *Launch*. The screen will update to the *Interactive Sessions* page. When your Jupyter session is ready, the top card will update from *Queued* to *Running* and a *Connect to JupyterLab Server* button will appear. Click *Connect to JupyterLab Server*.
+
+1. **Open the `intro_ml.ipynb` notebook.**
+
+1. **Select the `intro_ml_env` kernel** for the notebook.
+
+
diff --git a/sn_collections/_workshops/2024-ai-user-forum/20-data-prep.md b/sn_collections/_workshops/2024-ai-user-forum/20-data-prep.md
index 8970b18f5..e9d24d8e3 100644
--- a/sn_collections/_workshops/2024-ai-user-forum/20-data-prep.md
+++ b/sn_collections/_workshops/2024-ai-user-forum/20-data-prep.md
@@ -7,6 +7,14 @@ lead: Genome Informatics Facility at Iowa State University
section: November 20, Afternoon — Foundational Skills and Concepts
+materials:
+ - text: Data Prep Markdown File
+ url: https://github.com/ISUgenomics/genome_assembly_workshop/blob/main/AIUserForum_Workshop_Nov2024/01_DataPrep.md
+ - text: Assembly and Assessment Markdown File
+ url: https://github.com/ISUgenomics/genome_assembly_workshop/blob/main/AIUserForum_Workshop_Nov2024/02_AssemblyAssessment.md
+ - text: Genome Annotation Markdown File
+ url: https://github.com/ISUgenomics/genome_assembly_workshop/blob/main/AIUserForum_Workshop_Nov2024/03_GenomeAnnotation.md
+
prerequisites:
- text: Familiarity with basic command-line concepts. We will offer virtual training for these skills before the Forum begins.
---
@@ -14,3 +22,30 @@ prerequisites:
In this workshop, participants will explore techniques for evaluating the accuracy and completeness of genome assemblies and annotations, helping attendees understand key metrics and statistical methods used to assess the quality of genomic data.
Knowing how to evaluate a genome will ensure reliable results for downstream, AI-based analyses like gene model prediction, variant detection, or comparative studies. Participants will also learn how to extract the transcripts and proteins from their genomes, to enable a variety of downstream AI-based applications, such as protein structure prediction. By the end of the workshop, attendees will be better equipped with the practical skills necessary to evaluate genomes and annotations for a range of bioinformatics applications.
+
+## Tutorial Setup Instructions
+
+Steps to prepare for the tutorial session:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide](https://scinet.usda.gov/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on "Clusters" -> "Atlas Shell Access" on the top menu. This will open a new tab with a command-line session on Atlas' login node.
+
+1. **Request resources on a compute node** by running the following command:
+
+ {: .copy-code }
+ ```
+ srun --reservation=forum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash
+ ```
+
+1. **Create a workshop working directory** and copy the workshop materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the $USER variable.
+
+ {: .copy-code }
+ ```
+ mkdir -p /90daydata/shared/$USER
+ cd /90daydata/shared/$USER
+ cp -r /project/ai_forum/assembly_prep .
+ ```
+
+
+1. **Stop the interactive job** on the compute node by running the command exit.
\ No newline at end of file
diff --git a/sn_collections/_workshops/2024-ai-user-forum/21-bioinformatics.md b/sn_collections/_workshops/2024-ai-user-forum/21-bioinformatics.md
index 3a9a7323a..5735dd2a5 100644
--- a/sn_collections/_workshops/2024-ai-user-forum/21-bioinformatics.md
+++ b/sn_collections/_workshops/2024-ai-user-forum/21-bioinformatics.md
@@ -14,3 +14,43 @@ prerequisites:
In this hands-on workshop, participants will learn how to predict the functional roles of proteins by analyzing their sequence data using state-of-the-art bioinformatics tools powered by AI. The focus will be on understanding how AI-based methods are applied to predict protein characteristics and other downstream uses for gene annotations.
Two such examples will be predicting signal peptides (indicators of protein secretion) and subcellular localization (where the protein operates in the cell). Participants will use sample datasets to explore how computational models can interpret protein sequences and provide insights into their biological roles. By the end of the session, attendees will have the knowledge and skills to functionally annotate proteins in any gene annotation.
+
+## Tutorial Setup Instructions
+
+Steps to prepare for the tutorial session:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide](https://scinet.usda.gov/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on "Clusters" -> "Atlas Shell Access" on the top menu. This will open a new tab with a command-line session on Atlas' login node.
+
+1. **Request resources on a compute node** by running the following command:
+
+ {: .copy-code }
+ ```
+ salloc --reservation=forum-gpu -A scinet_workshop1 -p gpu-a100-mig7 -n1 --gres=gpu:1 -A scinet_workshop1 -t 3:00:00
+ ```
+ `salloc: Granted job allocation `
+ `salloc: Nodes atlas-0245 are ready for job`
+
+ {: .copy-code }
+ ```
+ srun --jobid= --pty bash
+ ```
+
+1. **Create a workshop working directory** and copy the workshop materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the $USER variable.
+
+ {: .copy-code }
+ ```
+mkdir -p /90daydata/shared/$USER
+cd /90daydata/shared/$USER
+cp -r /project/ai_forum/functional_annotation .
+```
+
+1. **Stop the interactive job** on the compute node by running the command exit.
+
+
+## Tutorials
+
+* [Identifying secreted proteins and predicting their subcellular localization](https://bioinformaticsworkbook.org/dataAnalysis/GenomeAnnotation/Secreted_Protein_Prediction_with_SignalP_and_TMHMM)
+* [DeepGoPlus - Using AI to associate GO terms with novel proteins](https://bioinformaticsworkbook.org/dataAnalysis/GenomeAnnotation/DeepGoPlus_AI_Functional_Prediction_of_Proteins)
+* [A ProtTrans Pipeline to Differentiate Transmembrane Domains from Other Proteins](https://bioinformaticsworkbook.org/dataAnalysis/GenomeAnnotation/Protein_Classification_with_ProtTrans)
diff --git a/sn_collections/_workshops/2024-ai-user-forum/21-computer-vision-2.md b/sn_collections/_workshops/2024-ai-user-forum/21-computer-vision-2.md
index f11146f4a..4a33ed75b 100644
--- a/sn_collections/_workshops/2024-ai-user-forum/21-computer-vision-2.md
+++ b/sn_collections/_workshops/2024-ai-user-forum/21-computer-vision-2.md
@@ -16,3 +16,71 @@ prerequisites:
In this workshop, participants will learn the key concepts and techniques needed to use modern, deep learning-based computer vision methods for object detection and semantic segmentation. Learners will practice training and evaluating state-of-the-art computer vision models on custom image datasets.
This workshop is intended as a continuation of “Computer vision I: introduction and image classification”, but participants do not need to take the earlier workshop if they already have a basic knowledge of machine learning and computer vision concepts.
+
+
+## Tutorial setup instruction
+
+Steps to prepare for the tutorial:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on "Clusters" -> "Atlas Shell Access" on the top menu. This will open a new tab with a command-line session on Atlas's login node.
+
+1. **Request resources on a compute node** by running the following command:
+
+ {:.copy-code}
+ ```bash
+ srun --reservation=forum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash
+ ```
+
+1. **Create and/or update your workshop working directory** and copy the tutorial materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the `$USER` variable.
+
+ {:.copy-code}
+ ```bash
+ mkdir -p /90daydata/shared/$USER/computer_vision2
+ cd /90daydata/shared/$USER/computer_vision2
+ cp /project/ai_forum/computer_vision2/*.ipynb .
+ cp /project/ai_forum/computer_vision2/*.py .
+ ```
+
+1. **Setup the kernel for JupyterLab.** You will create a kernel called *computer_vision_2_env* to access from JupyterLab Server. Run the following commands to activate the workshop's virtual environment and create a new kernelspec from it:
+
+ {:.copy-code}
+ ```bash
+ source /project/ai_forum/computer_vision2/computer_vision_2_env/bin/activate
+ ipython kernel install --name "computer_vision_2_env" --user
+ ```
+
+1. **Stop the interactive job** on the compute node by running the command:
+
+ {:.copy-code}
+ ```bash
+ exit
+ ```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *JupyterLab Server*. Specify the following input values on the page:
+
+ * Account: scinet_workshop1
+ * Partition: gpu-a100-mig7
+ * QOS: normal 14-00:00:00
+ * Number of hours: 4
+ * Number of nodes: 1
+ * Number of tasks: 2
+ * Additional Slurm Parameters:
+
+ {: .copy-code }
+ ```
+--reservation=forum-gpu --gres=gpu:1 --mem=32G --ntasks-per-node=2
+```
+ * Working Directory:
+
+ {: .copy-code }
+ ```
+/90daydata/shared/${USER}/computer_vision2
+```
+
+ Click *Launch*. The screen will update to the *Interactive Sessions* page. When your Jupyter session is ready, the top card will update from *Queued* to *Running* and a *Connect to JupyterLab Server* button will appear. Click *Connect to JupyterLab Server*.
+
+1. **Open the `computer_vision_2.ipynb` notebook.**
+
+1. **Select the `computer_vision_2_env` kernel** for the notebook.
diff --git a/sn_collections/_workshops/2024-ai-user-forum/21-computer-vision.md b/sn_collections/_workshops/2024-ai-user-forum/21-computer-vision.md
index ca183d46e..0d4cc48f5 100644
--- a/sn_collections/_workshops/2024-ai-user-forum/21-computer-vision.md
+++ b/sn_collections/_workshops/2024-ai-user-forum/21-computer-vision.md
@@ -14,3 +14,70 @@ prerequisites:
---
This workshop will teach participants the concepts and tools they need to begin applying modern, deep learning-based computer vision methods to their own scientific research. This will be an interactive, hands-on workshop that will offer plenty of opportunities for practice and experiential learning. By the end of the session, participants will have trained and evaluated a state-of-the-art image classification model on a custom image dataset.
+
+## Tutorial setup instruction
+
+Steps to prepare for the tutorial:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on "Clusters" -> "Atlas Shell Access" on the top menu. This will open a new tab with a command-line session on Atlas's login node.
+
+1. **Request resources on a compute node** by running the following command:
+
+ {:.copy-code}
+ ```bash
+ srun --reservation=forum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash
+ ```
+
+1. **Create and/or update your workshop working directory** and copy the tutorial materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the `$USER` variable.
+
+ {:.copy-code}
+ ```bash
+ mkdir -p /90daydata/shared/$USER/computer_vision1
+ cd /90daydata/shared/$USER/computer_vision1
+ cp -r /project/ai_forum/computer_vision1/computer_vision_1.ipynb .
+ cp -r /project/ai_forum/computer_vision1/*.py .
+ ```
+
+1. **Setup the kernel for JupyterLab.** You will create a kernel called *computer_vision_1_env* to access from JupyterLab Server. Run the following commands to activate the workshop's virtual environment and create a new kernelspec from it:
+
+ {:.copy-code}
+ ```bash
+ source /project/ai_forum/computer_vision1/computer_vision_1_env/bin/activate
+ ipython kernel install --name "computer_vision_1_env" --user
+ ```
+
+1. **Stop the interactive job** on the compute node by running the command:
+
+ {:.copy-code}
+ ```bash
+ exit
+ ```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *JupyterLab Server*. Specify the following input values on the page:
+
+ * Account: scinet_workshop1
+ * Partition: gpu-a100-mig7
+ * QOS: normal 14-00:00:00
+ * Number of hours: 4
+ * Number of nodes: 1
+ * Number of tasks: 2
+ * Additional Slurm Parameters:
+
+ {: .copy-code }
+ ```
+--reservation=forum-gpu --gres=gpu:1 --mem=32G --ntasks-per-node=2
+```
+ * Working Directory:
+
+ {: .copy-code }
+ ```
+/90daydata/shared/${USER}/computer_vision1
+```
+
+ Click *Launch*. The screen will update to the *Interactive Sessions* page. When your Jupyter session is ready, the top card will update from *Queued* to *Running* and a *Connect to JupyterLab Server* button will appear. Click *Connect to JupyterLab Server*.
+
+1. **Open the `computer_vision_1.ipynb` notebook.**
+
+1. **Select the `computer_vision_1_env` kernel** for the notebook.
diff --git a/sn_collections/_workshops/2024-ai-user-forum/21-deepvariant.md b/sn_collections/_workshops/2024-ai-user-forum/21-deepvariant.md
index 8c633064e..dd92ce6c1 100644
--- a/sn_collections/_workshops/2024-ai-user-forum/21-deepvariant.md
+++ b/sn_collections/_workshops/2024-ai-user-forum/21-deepvariant.md
@@ -14,3 +14,118 @@ Prerequisites:
---
DeepVariant is a DNA sequence variant caller that uses a convolutional neural network (CNN) to call genotypes relative to a reference genome assembly. In this workshop, we will discuss a workflow for calling variants from whole-genome data for multiple individuals. This workflow involves trimming and filtering raw reads, mapping them to a reference assembly, calling variants for each individual, merging the variants of all individuals into a single variant call format file (.vcf), and filtering the resulting variant file. We will guide participants through this pipeline step by step, providing generalized commands for each phase of the process, as well as strategies for optimizing cluster usage and reducing compute time. The final product will be a .vcf containing variants for all individuals which can be used for downstream analyses, along with a solid understanding for performing variant detection using DeepVariant.
+
+
+## Tutorial Setup Instructions
+
+Steps to prepare for the tutorial session:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide](https://scinet.usda.gov/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on "Clusters" -> "Atlas Shell Access" on the top menu. This will open a new tab with a command-line session on Atlas' login node.
+
+1. **Request resources on a compute node** by running the following command:
+
+ {: .copy-code }
+ ```
+ srun --reservation=forum -A scinet_workshop1 -t 03:00:00 -n 1 --mem 8G --pty bash
+ ```
+
+1. **Create a workshop working directory** and copy the workshop materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the $USER variable.
+
+ {: .copy-code }
+ ```
+ mkdir -p /90daydata/shared/$USER/deepvariant
+ cd /90daydata/shared/$USER/deepvariant
+
+ cp /project/ai_forum/deepvariant/Sample_Data/assembly.fasta .
+
+ mkdir PE_directory
+ cp -r /project/ai_forum/deepvariant/Sample_Data/samplename_R*.fastq.gz .
+
+ # Create a directory for trimmed reads
+ mkdir Trimmed
+
+ # Create a directory for mapped reads
+ mkdir Mapped
+
+ # Create a directory for variants
+ mkdir Variants
+
+ # Create a directory for intermediate files
+ mkdir Int
+
+ # Activate your conda environment
+ module load miniconda3
+ module load samtools
+ module load apptainer
+ source activate /project/ai_forum/deepvariant/Software/condaenvs/deepvariant
+
+ # Prepare for a fun time
+
+ # Step 1: trimming
+ # Trim adapter artifacts from your reads
+ trim_galore --paired \
+ --basename samplename \
+ --output_dir Trimmed \
+ --cores 24 \
+ PE_directory/samplename_R1.fastq.gz \
+ PE_directory/samplename_R2.fastq.gz
+
+ # Step 2: mapping
+ # Index your reference assembly
+ bwa-mem2 index assembly.fasta
+
+ # Index reference assembly using Samtools (for later)
+ samtools faidx assembly.fasta >assembly.fasta.fai
+
+ # For each of your trimmed and paired reads:
+ bwa-mem2 mem –t 48 assembly.fasta \
+ Trimmed/samplename_val_1.fq.gz \
+ Trimmed/samplename_val_2.fq.gz | \
+ samblaster | \
+ samtools sort -@ 48 –o Mapped/samplename.bam
+
+ # Step 3: call variants
+ apptainer exec Software/sifs/deepvariant_1.6.0.sif \
+ python3 Software/deepvariant/scripts/run_deepvariant.py \
+ --num_shards=48 \
+ --model_type WGS \
+ --output_vcf Variants/samplename.vcf.gz \
+ --output_gvcf Variants/samplename.g.vcf.gz \
+ --ref assembly.fasta \
+ --reads Mapped/samplename.bam \
+ --sample_name samplename \
+ --intermediate_results_dir Int/samplename_int \
+ --make_examples_extra_args "normalize_reads=true” \\
+ --dry_run
+
+ # Merge and Filter
+ module load miniconda3
+ module load jemalloc
+ module load bcftools
+ module load htslib
+ conda activate /project/ai_forum/deepvariant/Software/condaenvs/glnexus
+
+ # Use GLNexus for joint calling .g.vcf samples:
+ glnexus_cli --threads 48 --config DeepVariantWGS *.g.vcf.gz > cohort.bcf
+
+ # Convert raw bcf results to vcf format:
+ bcftools convert -Oz -o cohort.vcf.gz cohort.bcf
+
+ # Fill tags and drop DP<=1 calls
+ bcftools +setGT cohort.bcf --threads 46 -Ob -- -t q -n . -e 'FMT/DP>=1' | \
+ bcftools +fill-tags --threads 46 - -Ob -- -t AF,AN,AC | \
+ bcftools annotate --threads 46 - -Ov -x FORMAT/RNC -o cohort.clean.vcf
+
+ # Filter
+ plink2 --vcf cohort.clean.vcf --geno 0.5 --vcf-min-qual 20 --min-alleles 2 --max-alleles 2 --vcf-half-call missing --allow-extra-chr --recode vcf --out cohort.clean.diploid
+
+ # Make numeric (0,1,2)
+ plink2 --vcf cohort.clean.diploid.vcf --allow-extra-chr --recode A-transpose –out cohort.clean.diploid.Atranspose
+
+ # See results
+ head -n 20 cohort.clean.diploid.Atranspose.traw
+ ```
+
+1. **Stop the interactive job** on the compute node by running the command exit.
\ No newline at end of file
diff --git a/sn_collections/_workshops/2024-ai-user-forum/21-protein-structure.md b/sn_collections/_workshops/2024-ai-user-forum/21-protein-structure.md
index 8d11e794f..1faeca837 100644
--- a/sn_collections/_workshops/2024-ai-user-forum/21-protein-structure.md
+++ b/sn_collections/_workshops/2024-ai-user-forum/21-protein-structure.md
@@ -15,3 +15,140 @@ In this workshop, participants will learn how to use cutting-edge, AI-based tool
The workshop will start by exploring 3D protein structure prediction using AlphaFold for alignment-based structure prediction and ESMFold for single-sequence structure prediction. Participants will then learn how to use FoldSeek for structure-based protein similarity search. The last part of the workshop will bring all of these concepts together by using PanEffect to explore how genetic variations in protein sequence can influence an organism’s phenotype.
+## Tutorial Setup Instructions
+
+Steps to prepare for the tutorial session:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide](https://scinet.usda.gov/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on "Clusters" -> "Atlas Shell Access" on the top menu. This will open a new tab with a command-line session on Atlas' login node.
+
+1. **Request resources on a compute node** by running the following command:
+
+ {: .copy-code }
+ ```
+ salloc --reservation=forum-gpu -A scinet_workshop1 -p gpu-a100-mig7 -n1 --gres=gpu:1 -A scinet_workshop1 -t 3:00:00
+ ```
+ `salloc: Granted job allocation `
+ `salloc: Nodes atlas-0245 are ready for job`
+
+ {: .copy-code }
+ ```
+ srun --jobid= --pty bash
+ ```
+
+1. **Create a workshop working directory** and copy the workshop materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the $USER variable.
+
+ {: .copy-code }
+ ```
+ mkdir -p /90daydata/shared/$USER/
+ cd /90daydata/shared/$USER/
+ cp -r /project/ai_forum/protein_structure .
+ ```
+
+
+1. **Stop the interactive job** on the compute node by running the command exit.
+
+## Schedule
+
+
+
+
+Additional Resources:
+* Tool Descriptions
+* Conda Environments
diff --git a/sn_collections/_workshops/2024-ai-user-forum/21-spatial-modeling.md b/sn_collections/_workshops/2024-ai-user-forum/21-spatial-modeling.md
index 51d32bdce..9b9510643 100644
--- a/sn_collections/_workshops/2024-ai-user-forum/21-spatial-modeling.md
+++ b/sn_collections/_workshops/2024-ai-user-forum/21-spatial-modeling.md
@@ -17,3 +17,71 @@ This workshop will explore examples of spatial modeling tasks (e.g., spatial int
The goals of this session are to
1) introduce key concepts about incorporating spatial data in machine learning and
2) provide examples in Python on how to manipulate spatial datasets to use in machine learning functions, compare the performance of machine learning approaches for spatial prediction, and visualize observed spatial data and the prediction results.
+
+
+## Tutorial setup instruction
+
+Steps to prepare for the tutorial:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on "Clusters" -> "Atlas Shell Access" on the top menu. This will open a new tab with a command-line session on Atlas's login node.
+
+1. **Request resources on a compute node** by running the following command:
+
+ {:.copy-code}
+ ```bash
+ srun --reservation=forum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash
+ ```
+
+1. **Create and/or update your workshop working directory** and copy the tutorial materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the `$USER` variable.
+
+ {:.copy-code}
+ ```bash
+ mkdir -p /90daydata/shared/$USER/spatial_modeling
+ cd /90daydata/shared/$USER/spatial_modeling
+ cp -r /project/ai_forum/spatial_modeling/spatial_modeling.ipynb .
+ cp -r /project/ai_forum/spatial_modeling/data .
+ ```
+
+1. **Setup the kernel for JupyterLab.** You will create a kernel called *spatial_modeling_env* to access from JupyterLab Server. Run the following commands to activate the workshop's virtual environment and create a new kernelspec from it:
+
+ {:.copy-code}
+ ```bash
+ source /project/ai_forum/spatial_modeling/spatial_modeling_env/bin/activate
+ ipython kernel install --name "spatial_modeling_env" --user
+ ```
+
+1. **Stop the interactive job** on the compute node by running the command:
+
+ {:.copy-code}
+ ```bash
+ exit
+ ```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *JupyterLab Server*. Specify the following input values on the page:
+
+ * Account: scinet_workshop1
+ * Partition: atlas
+ * QOS: normal 14-00:00:00
+ * Number of hours: 4
+ * Number of nodes: 1
+ * Number of tasks: 16
+ * Additional Slurm Parameters:
+
+ {: .copy-code }
+ ```
+--reservation=forum --mem=32G
+```
+ * Working Directory:
+
+ {: .copy-code }
+ ```
+/90daydata/shared/${USER}/spatial_modeling
+```
+
+ Click *Launch*. The screen will update to the *Interactive Sessions* page. When your Jupyter session is ready, the top card will update from *Queued* to *Running* and a *Connect to JupyterLab Server* button will appear. Click *Connect to JupyterLab Server*.
+
+1. **Select the `spatial_modeling_env` kernel** for the `spatial_modeling.ipynb` notebook.
+
+
diff --git a/sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-0.md b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-0.md
new file mode 100644
index 000000000..ba5167cc2
--- /dev/null
+++ b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-0.md
@@ -0,0 +1,69 @@
+---
+title: "Kickoff with lightning talks"
+description: Provides hands-on tutorials on workflows to access the SCINet HPC systems and conduct geospatial research at scale and fosters geospatial research efforts.
+excerpt: "This session will have an introductory presentation about the working group and workshop goals plus lightning talks by ARS researchers and SCINet fellows about their research."
+
+categories: [2024 Geospatial Workshop]
+
+sidenav_link: /training/resources
+
+lead: Heather Savoy, Amy Hudson
+time: 12:00 PM - 1:15 PM ET
+
+
+---
+
+This session will have an introductory presentation about the working group and workshop goals plus lightning talks by ARS researchers and SCINet fellows about their research.
+
+## Lightning talk presenters
+
+* **Georgia Harrison**: *An Independent Accuracy Assessment of Satellite-Derived Rangeland Fractional Cover*
+* **Efrain Duarte**: *Application of remote sensing tools for monitoring soil moisture in semi-arid ecosystems*
+* **Mahesh Lal Maskey**: *A Tool to Extract Actual Evapotranspiration from the USGS MODIS Data Portal*
+* **Amitava Chatterjee**: *Soil Health Classification Framework for Florida Soils using K-Means Clustering*
+* **Andrea Albright**: *Irrigation pond water storage variability using in situ and UAS data*
+* **Kossi Nouwakpo**: *A deep learning approach for irrigation methods mapping*
+
+A recording of the session will be added after the workshop concludes.
+
+
+## Tutorial setup instructions
+
+Steps to prepare for the tutorial sessions:
+
+1. **Login to Ceres Open OnDemand** at [https://ceres-ood.scinet.usda.gov/](https://ceres-ood.scinet.usda.gov/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on “Clusters” -> “Ceres Shell Access” on the top menu. This will open a new tab with a command-line session on Ceres' login node.
+
+1. **Request resources on a compute node** to avoid using the login node for data transfers by running the following command.
+
+ {:.copy-code}
+ ```bash
+ srun --reservation=workshop -A geospatialworkshop -t 00:30:00 -n 1 --mem 8G --pty bash
+ ```
+
+1. **Create a workshop working directory** and copy the workshop materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the `$USER` variable.
+
+ {:.copy-code}
+ ```bash
+ mkdir -p /90daydata/shared/$USER/
+ cd /90daydata/shared/$USER/
+ cp -r /project/geospatialworkshop/2024/tutorial* .
+ ```
+
+1. **Create a symbolic link to the virtual environment.** In the workshop project space, there is a `grwg_2024_env` virtual environment for the Python packages we will be using during the workshop tutorials.You will create a symbolic link to that virtual environment from your workshop working directory. You will then have a shortcut called `my_grwg_2024_env` in your workshop working directory that points to the virtual environment so you can easily access the virtual environment from VS Code.
+
+ {:.copy-code}
+ ```bash
+ ln -s /project/geospatialworkshop/2024/grwg_2024_env/ /90daydata/shared/$USER/my_grwg_2024_env
+ ```
+
+1. **Setup the kernel for JupyterLab.** You will create a kernel called *grwg_2024_env* to access from JupyterLab Server. Run the following commands to activate the workshop's virtual environment and create a new kernelspec from it:
+
+ {:.copy-code}
+ ```bash
+ source /project/geospatialworkshop/2024/grwg_2024_env/bin/activate
+ ipython kernel install --name "grwg_2024_env" --user
+ ```
+
+1. **Stop the interactive job** on the compute node by running the command `exit`.
diff --git a/sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-1.md b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-1.md
new file mode 100644
index 000000000..8c83f1576
--- /dev/null
+++ b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-1.md
@@ -0,0 +1,23 @@
+---
+title: "Demonstration: Species distribution modeling with MaxEnt"
+description: Provides hands-on tutorials on workflows to access the SCINet HPC systems and conduct geospatial research at scale and fosters geospatial research efforts.
+excerpt: "During this session, we will explore the topic of species distribution / environmental niche modeling (SDM/ENM), with a particular focus on MaxEnt (maximum entropy), a popular SDM/ENM algorithm used to estimate species habitats from presence-only occurrence records."
+
+categories: [2024 Geospatial Workshop]
+
+sidenav_link: /training/resources
+
+time: 1:30 PM - 2:30 PM ET
+lead: Melanie Veron
+prerequisites:
+ - text: Have a SCINet account and be able to login
+ url: /about/signup
+ - text: Familiarity with R and the RStudio environment.
+
+---
+
+
+During this session, we will explore the topic of species distribution / environmental niche modeling (SDM/ENM), with a particular focus on MaxEnt (maximum entropy), a popular SDM/ENM algorithm used to estimate species habitats from presence-only occurrence records.
+
+A recording of the demonstration will be added after the workshop concludes.
+
diff --git a/sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-2.md b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-2.md
new file mode 100644
index 000000000..80c7082c9
--- /dev/null
+++ b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-6-Geospatial-Workshop-2.md
@@ -0,0 +1,75 @@
+---
+title: "Tutorial: Geospatial code development using VS Code and Python"
+description: Provides hands-on tutorials on workflows to access the SCINet HPC systems and conduct geospatial research at scale and fosters geospatial research efforts.
+excerpt: "This tutorial will illustrate how to use Visual Studio (VS) Code, a flexible development environment available on SCINet's Ceres cluster, to develop Python scripts. By the end of the tutorial, participants will know how to access VS Code in Open OnDemand and use it to navigate files on Ceres, access Python packages, and develop and execute Python scripts."
+
+categories: [2024 Geospatial Workshop]
+
+sidenav_link: /training/resources
+
+time: 2:45 PM - 3:45 PM ET
+lead: Andrea Albright
+prerequisites:
+ - text: Have a SCINet account and be able to login
+ url: /about/signup
+ - text: Experience with scripting languages, e.g., Python.
+---
+
+This tutorial will illustrate how to use Visual Studio (VS) Code, a flexible development environment available on SCINet's Ceres cluster, to develop Python scripts. By the end of the tutorial, participants will know how to access VS Code in Open OnDemand and use it to navigate files on Ceres, access Python packages, and develop and execute Python scripts.
+
+Additional details and instructions for the tutorial will be added closer to the event. A recording of the tutorial will be added after the workshop concludes.
+
+
+## Tutorial setup instructions
+
+Steps to prepare for the tutorial sessions:
+
+1. **Login to Ceres Open OnDemand** at [https://ceres-ood.scinet.usda.gov/](https://ceres-ood.scinet.usda.gov/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Launch a VS Code session.** Under the *Interactive Apps* menu, select *VS Code*. Specify the following input values on the page:
+
+ * Account: geospatialworkshop
+ * Queue: short---------Max Time: 2-00:00:00
+ * QOS: 400thread
+ * Number of cores: 2
+ * Memory required: 16GB
+ * Number of hours: 2
+ * Optional Slurm Arguments: \-\-reservation=workshop
+ * Working Directory: /90daydata/shared/${USER}
+ * Codeserver Version: 4.17
+
+ Click *Launch*. The screen will update to the *Interactive Sessions* page. When your Jupyter session is ready, the top card will update from *Queued* to *Running* and a *Connect to VS Code* button will appear. Click *Connect to VS Code*.
+
+1. **Open the terminal**
+ * Select the Menu (upper left)
+ * Select 'Terminal'
+ * Select 'New Terminal'
+
+1. **Install Python and Jupyter extensions** Find the Extensions tab on the left side. Search for 'Python' and 'Jupyter', and install each extension.
+
+1. **Activate the virtual environment** Select the pre-installed environment, so that the interactive window automatically loads the correct environment.
+ * Select the Menu (upper left)
+ * Select 'View'
+ * Select 'Command Palette'
+ * Type 'Python: Select Interpreter' into the command palette window
+ * Select '+Enter interpreter path...'
+ * Enter:
+
+ {:.copy-code}
+ ```bash
+ /project/geospatialworkshop/2024/grwg_2024_env/bin/python
+ ```
+
+ 1. **Activate the environment:** by pasting the following command in the terminal:
+
+ {:.copy-code}
+ ```bash
+ source /project/geospatialworkshop/2024/grwg_2024_env/bin/activate
+ ```
+
+1. **Open 'VSCode_geospatial_demo.py'** from `/90daydata/shared/$USER/tutorial2`.
+
+
+
+
+
diff --git a/sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-3.md b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-3.md
new file mode 100644
index 000000000..53e5f42cb
--- /dev/null
+++ b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-3.md
@@ -0,0 +1,61 @@
+---
+title: "Tutorial: Applying geographically informed Graph CNNs in disease ecology modeling"
+description: Provides hands-on tutorials on workflows to access the SCINet HPC systems and conduct geospatial research at scale and fosters geospatial research efforts.
+excerpt: "This tutorial presents a workflow using a Graph Convolutional Neural Network with LSTM layers (GLSTM model) to classify West Nile Virus disease presence or absence in horses across counties in a subset of states in the US"
+
+categories: [2024 Geospatial Workshop]
+
+sidenav_link: /training/resources
+
+time: 12:00 PM - 1:00 PM ET
+lead: Amber Mooney
+prerequisites:
+ - text: Have a SCINet account and be able to login
+ url: /about/signup
+ - text: Basic understanding of neural networks.
+ - text: Familiarity with Python and Jupyter notebooks.
+ - text: Familiarity with handling data in geopandas.
+---
+
+
+
+This tutorial presents a workflow using a Graph Convolutional Neural Network with LSTM layers (GLSTM model) to classify West Nile Virus disease presence or absence in horses across counties in a subset of states in the US. Leveraging geospatial data, the tutorial will guide you through constructing the model, processing spatial and temporal inputs, and interpreting results for spatially informed disease predictions.
+
+Additional details and instructions for the tutorial will be added closer to the event. A recording of the tutorial will be added after the workshop concludes.
+
+
+## Tutorial setup instructions
+
+Steps to prepare for the tutorial sessions:
+
+1. **Login to Ceres Open OnDemand** at [https://ceres-ood.scinet.usda.gov/](https://ceres-ood.scinet.usda.gov/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on “Clusters” -> “Ceres Shell Access” on the top menu. This will open a new tab with a command-line session on Ceres' login node.
+
+1. **Create and/or update your workshop working directory** and copy the tutorial materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the `$USER` variable. These commands will work for everyone, including if you attending previous workshop sessions or not.
+
+ {:.copy-code}
+ ```bash
+ mkdir -p /90daydata/shared/$USER/
+ cd /90daydata/shared/$USER/
+ cp -r /project/geospatialworkshop/2024/tutorial3 .
+ source /project/geospatialworkshop/2024/grwg_2024_env/bin/activate
+ ipython kernel install --name "grwg_2024_env" --user
+ ```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *Jupyter*. Specify the following input values on the page:
+
+ * Account: geospatialworkshop
+ * Queue: short---------Max Time: 2-00:00:00
+ * QOS: 400thread
+ * Number of hours: 2
+ * Number of cores: 8
+ * Memory required: 16GB
+ * Optional Slurm Arguments: \-\-reservation=workshop
+ * Jupyter Notebook vs Lab: Lab
+ * Working Directory: /90daydata/shared/${USER}
+
+ Click *Launch*. The screen will update to the *Interactive Sessions* page. When your Jupyter session is ready, the top card will update from *Queued* to *Running* and a *Connect to Jupyter* button will appear. Click *Connect to Jupyter*.
+
+1. For the notebook used in the tutorial, select the *grwg_2024_env* kernel.
+
diff --git a/sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-4.md b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-4.md
new file mode 100644
index 000000000..ee0c0372d
--- /dev/null
+++ b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-4.md
@@ -0,0 +1,57 @@
+---
+title: "Tutorial: Using xarray to work with multidimensional geospatial datasets"
+description: Provides hands-on tutorials on workflows to access the SCINet HPC systems and conduct geospatial research at scale and fosters geospatial research efforts.
+excerpt: "In this tutorial you will learn the basic concepts of the Python library `xarray` while exploring its capabilities when working with multidimensional datasets with dimensions of time, latitude, and longitude. We will also touch on the capabilities of parallel processing using another Python library called `dask`."
+
+categories: [2024 Geospatial Workshop]
+
+sidenav_link: /training/resources
+
+time: 1:15 PM - 2:45 PM ET
+lead: Erika Peirce
+prerequisites:
+ - text: Have a SCINet account and be able to login
+ url: /about/signup
+ - text: Familiarity with Python and Jupyter notebooks.
+ - text: Familiarity with handling data using pandas.
+---
+
+In this tutorial you will learn the basic concepts of the Python library `xarray` while exploring its capabilities when working with multidimensional datasets with dimensions of time, latitude, and longitude. We will also touch on the capabilities of parallel processing using another Python library called `dask`.
+
+Additional details and instructions for the tutorial will be added closer to the event. A recording of the tutorial will be added after the workshop concludes.
+
+## Tutorial setup instructions
+
+Steps to prepare for the tutorial sessions:
+
+1. **Login to Ceres Open OnDemand** at [https://ceres-ood.scinet.usda.gov/](https://ceres-ood.scinet.usda.gov/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on “Clusters” -> “Ceres Shell Access” on the top menu. This will open a new tab with a command-line session on Ceres' login node.
+
+1. **Create and/or update your workshop working directory** and copy the tutorial materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the `$USER` variable. These commands will work for everyone, including if you attending previous workshop sessions or not.
+
+ {:.copy-code}
+ ```bash
+ mkdir -p /90daydata/shared/$USER/
+ cd /90daydata/shared/$USER/
+ cp -r /project/geospatialworkshop/2024/tutorial4 .
+ source /project/geospatialworkshop/2024/grwg_2024_env/bin/activate
+ ipython kernel install --name "grwg_2024_env" --user
+ ```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *Jupyter*. Specify the following input values on the page:
+
+ * Account: geospatialworkshop
+ * Queue: short---------Max Time: 2-00:00:00
+ * QOS: 400thread
+ * Number of hours: 2
+ * Number of cores: 8
+ * Memory required: 16GB
+ * Optional Slurm Arguments: \-\-reservation=workshop
+ * Jupyter Notebook vs Lab: Lab
+ * Working Directory: /90daydata/shared/${USER}
+
+ Click *Launch*. The screen will update to the *Interactive Sessions* page. When your Jupyter session is ready, the top card will update from *Queued* to *Running* and a *Connect to Jupyter* button will appear. Click *Connect to Jupyter*.
+
+1. For the notebook used in the tutorial, select the *grwg_2024_env* kernel.
+
diff --git a/sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-5.md b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-5.md
new file mode 100644
index 000000000..ac47080e8
--- /dev/null
+++ b/sn_collections/_workshops/2024-geospatial-workshop/2024-11-7-Geospatial-Workshop-5.md
@@ -0,0 +1,18 @@
+---
+title: "Discussions"
+description: Provides hands-on tutorials on workflows to access the SCINet HPC systems and conduct geospatial research at scale and fosters geospatial research efforts.
+excerpt: "This session will have discussions on how to collaborate on SCINet, desired training topics and collaboration opportunities in the upcoming year, and how someone can contribute to the working group."
+
+categories: [2024 Geospatial Workshop]
+
+sidenav_link: /training/resources
+lead: Heather Savoy, Amy Hudson
+time: 3:00 PM - 4:00 PM ET
+---
+
+
+This session will have discussions on how to collaborate on SCINet, desired training topics and collaboration opportunities in the upcoming year, and how someone can contribute to the working group.
+
+A summary of the session will be added after the workshop concludes.
+
+