Merge pull request #26 from albarema/main

updates news and cards

.gitignore

@@ -42,4 +42,5 @@ vignettes/*.pdf
 *.DS_Store
 /.quarto/
 _site
-site_libs
+site_libs
+.codegpt

contact/contact.qmd

@@ -28,7 +28,6 @@ at [hds-sandbox](https://github.com/hds-sandbox/).
 | Jakob Skelmose                | Data Scientist                      | Department of Clinical Medicine, AAU|Martin Boegsted                    |
 | Samuele Soraggi               | Data Scientist                      | Bioinformatics Research Centre, AU |Mikkel Schierup                     |
 | Jesper Roy Christiansen       | Data Scientist                      | Computerome, DTU                   |Peter Løngreen                     |
-| Jacob Fredegaard Hansen       | Data Scientist                      | Department of Biochemistry and Molecular Biology, SDU|Ole Nørregaard Jensen & <br> Veit Schvämmle|
 
 <style>
 table {
@@ -43,7 +42,7 @@ table tbody tr:hover {
 
 
 
-We appreciate the contributions of previous team members José Alejandro Romero Herrera (KU), Conor O'Hare (KU), Sander Boisen Valentin (AAU) and Peter Husen (SDU).
+We appreciate the contributions of previous team members Jacob Fredegaard Hansen (STU), José Alejandro Romero Herrera (KU), Conor O'Hare (KU), Sander Boisen Valentin (AAU) and Peter Husen (SDU).
 
 
 Find all the team members and their contacts below. Click on their names for more information about each member of the team:

news/past/2024-09-30-hpcLaunch.qmd

@@ -9,6 +9,4 @@ categories: [HPC, RDM, KU, course]
 description: "HPC & RDM intro workshop"
 ---
 
-[Sign-up](https://eventsignup.ku.dk/hpc-launch)
-
 The goal of the course HPC-Launch is to support the launch (and/or reconfiguration) of health data projects from an efficient and modern computing and data management perspective. Targeting trainees and researchers in bioinformatics and large-scale health records, the course will consist of two modules: High-Performance Computing (HPC) and Research Data management (RDM). With the HPC module, we want to expand understanding and efficient use of HPC resources for complex health data science projects. We will fill gaps in technical understanding for beginner to intermediate users of supercomputing platforms and share up-to-date information on computing resources available to Danish researchers and how to get access. With the RDM module, we will introduce the importance of research data management practices and demonstrate practical tips and tools for its implementation at a local research group level. Overall, the course will be a mix of theory, discussion of real-world use cases and participant needs, and active practice/exercises conducted on the HPC platform UCloud (SDU) using bash and relevant IDEs.

news/past/2024-11-04-hpcPipes.qmd

@@ -9,8 +9,6 @@ categories: [snakemake, conda, KU, course]
 description: "Click to sign-up"
 ---
 
-[Sign-up](https://eventsignup.ku.dk/hpc-pipes)
-
 The course HPC-Pipes introduces best practices for setting up, running, and sharing reproducible bioinformatics pipelines and workflows. Rather than instruct on the whys and wherefores of using particular tools for a bioinformatics analysis, we will cover the general process of building a robust pipeline (regardless of data type) using workflow languages, environment/package managers, optimized HPC resources, and FAIRly managed data and tools. On course completion, participants will be able to  use this knowledge to design their own custom pipelines with tools appropriate for their individual analysis needs.
 
 The course will provide guidance on how to automate data analysis using common workflow languages such as Snakemake or Nextflow. Subsequently, we will delve into ensuring the reproducibility of pipelines and explore available options. Participants will learn how to share their data analysis and software with the research community. We will also delve into different strategies for managing the produced research data. This includes addressing the challenges posed by large volumes of data and exploring computational approaches that aid in data organization, documentation, processing, analysis, storing, sharing, and preservation. These discussions will encompass the reasons behind the increasing popularity of Docker and other containers, along with demonstrations on how to effectively utilize package and environment managers like Conda to control the software environment within a workflow. Finally, participants will learn how to manage and optimize their pipeline projects on HPC platforms, using compute resources efficiently.

news/upcoming/2024-11-18-bulk.qmd → news/past/2024-11-18-bulk.qmd

@@ -4,12 +4,12 @@ image: "../../images/transcriptomics.png"
 author: HeaDS
 Organizers: HeaDS
 date: 2024-11-18
+Dates: "18-20 November 2024"
+Location: "University of Copenhagen"
 categories: [Bulk RNAseq, nf-core, DESeq2]
 description: "Click to sign-up"
 ---
 
-[Sign-up](https://eventsignup.ku.dk/rnaseq-nov2024/conference)
-
 This workshop contains a basic tutorial on how to approach bioinformatics analyses of bulk RNAseq data, starting from the count matrix.
 
 By the end of this workshop, you will be able to:

news/upcoming/2025-05-12-hpcPipes.qmd

@@ -2,7 +2,7 @@
 title: "HPC Pipes"
 author: "ARM and JB"
 Organizers: A.Refoyo and J.Bartell
-date: 2025-05-12
+date: 2025-08-25
 categories: [snakemake, conda, KU, course]
 description: "Opening for signup soon"
 ---
@@ -11,4 +11,4 @@ Opening for signup soon.
 
 The course HPC-Pipes introduces best practices for setting up, running, and sharing reproducible bioinformatics pipelines and workflows, with a strong emphasis on Snakemake for practical exercises. Rather than focusing on specific tools for bioinformatics analysis, we will cover the entire process of building a robust pipeline—applicable to any data type—using workflow languages, environment/package managers, optimized HPC resources, and FAIR principles for data and tool management. By the end of the course, participants will be equipped to design custom pipelines tailored to their analysis needs.
 
-We will guide participants in automating data analysis with popular workflow languages like Snakemake and Nextflow. From there, we’ll explore how to ensure reproducibility within pipelines and the available options for sharing data analysis and software within the research community. Participants will also learn strategies for managing and organizing large datasets, from documentation and processing to storage, sharing, and preservation. We’ll cover tools like Docker and other containers, with demonstrations on using package and environment managers such as Conda to control the software environment within workflows and containers (Docker and Apptainer). Finally, we’ll provide insights into managing and optimizing pipeline projects on HPC platforms, using resources efficiently.
+We will guide participants in automating data analysis with popular workflow languages like Snakemake (primarily) and Nextflow. From there, we’ll explore how to ensure reproducibility within pipelines and the available options for sharing data analysis and software within the research community. Participants will also learn strategies for managing and organizing large datasets, from documentation and processing to storage, sharing, and preservation. We’ll cover tools like Docker and other containers, with demonstrations on using package and environment managers such as Conda to control the software environment within workflows and containers (Docker and Apptainer). Finally, we’ll provide insights into managing and optimizing pipeline projects on HPC platforms, using resources efficiently.