diff --git a/.gitignore b/.gitignore
index 6ac4afac..eade2167 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,4 +42,5 @@ vignettes/*.pdf
 *.DS_Store
 /.quarto/
 _site
-site_libs
\ No newline at end of file
+site_libs
+.codegpt
\ No newline at end of file
diff --git a/cards/JacobHansen.qmd b/cards/JacobHansen.qmd
deleted file mode 100644
index d8cfb8dc..00000000
--- a/cards/JacobHansen.qmd
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: "Jacob Fredegaard Hansen"
-description: "Data Scientist, University of Southern Denmark"
-#https://emojidb.org/quarto-emojis for emojis copypaste
----
-
-![](jaCob.jpg){width=300}
-
-[![](LinkedIN.png){width=50}](https://www.linkedin.com/in/jacobfh)   [![](Github_black.png){width=50}](https://github.com/jacobfh1)   [![](Outlook.png){width=50}](jfredegaard@bmb.sdu.dk?subject=Test)   [![](mapPin.png){width=50}](https://www.sdu.dk/en/forskning/biomedical-ms)   [![](www.png){width=50}](https://jacobfh.com/)   
-
-Jacob is a Sandbox data scientist based at the University of Southern Denmark, part of the [Protein Research Group](https://www.sdu.dk/en/forskning/protein) in the [Department of Biochemistry and Molecular Biology](https://www.sdu.dk/en/om_sdu/institutter_centre/bmb_biokemi_og_molekylaer_biologi). He specializes in proteomics applications and protein structure prediction tools. Jacob has a background in biochemistry and molecular biology (BSc) and computational biomedicine (MSc). Before delving into health data science at Sandbox, he focused on transcription factor binding specificity for his master's thesis, analyzing and evaluating it as a biophysical process.
\ No newline at end of file
diff --git a/cards/jaCob.jpg b/cards/jaCob.jpg
deleted file mode 100644
index 57e09708..00000000
Binary files a/cards/jaCob.jpg and /dev/null differ
diff --git a/contact/contact.qmd b/contact/contact.qmd
index 1a9e7079..7193c1f7 100644
--- a/contact/contact.qmd
+++ b/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:
\ No newline at end of file
diff --git a/news/past/2024-09-30-hpcLaunch.qmd b/news/past/2024-09-30-hpcLaunch.qmd
index 0b544850..bfc036f7 100644
--- a/news/past/2024-09-30-hpcLaunch.qmd
+++ b/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.
\ No newline at end of file
diff --git a/news/past/2024-11-04-hpcPipes.qmd b/news/past/2024-11-04-hpcPipes.qmd
index f55f94fd..5fdda53d 100644
--- a/news/past/2024-11-04-hpcPipes.qmd
+++ b/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.
\ No newline at end of file
diff --git a/news/upcoming/2024-11-18-bulk.qmd b/news/past/2024-11-18-bulk.qmd
similarity index 90%
rename from news/upcoming/2024-11-18-bulk.qmd
rename to news/past/2024-11-18-bulk.qmd
index cbe18a1e..997aac5f 100644
--- a/news/upcoming/2024-11-18-bulk.qmd
+++ b/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:
diff --git a/news/upcoming/2025-05-12-hpcPipes.qmd b/news/upcoming/2025-05-12-hpcPipes.qmd
index 118623c8..ddf8cbad 100644
--- a/news/upcoming/2025-05-12-hpcPipes.qmd
+++ b/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.
\ No newline at end of file
+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.
\ No newline at end of file