Bioinformatics Training & Education Program

You share your data because you have to, but is it actually working for you? Six years after a landmark NIH workshop asked what role generalist repositories should play in the research landscape, the Generalist Repository Ecosystem Initiative (GREI) is bringing the conversation back to hear real evidence and perspectives from the researchers living it.

Over two half-days, you'll hear directly from researchers, repository leaders, and data experts discussing the questions that matter most to your work: What does it really take for data to be findable and reusable across repositories? Is data sharing translating into citations, reuse, and recognition, and how can that impact be measured and credited? What is "AI-ready" data starting to mean in practice, and where are researchers already seeing generalist repositories play a role? And when a project's data spans a generalist repository and a discipline-specific one, what does good collaboration between the two actually look like?

Through four panel discussions, researcher lightning talks, and a fireside chat, you'll hear real-world experiences and perspectives, along with a look at where generalist repositories are headed in the future.

This workshop is built for NIH-funded researchers and trainees at any career stage, and it's equally valuable for the librarians, repository teams, and NIH program staff who support that work.

Explore the full agenda and speaker lineup below, and reserve your spot today.

View the full workshop program here.

Partek Flow is a point-and-click platform for building analysis workflows for Next Generation Sequences (NGS), including DNA, bulk and single-cell RNA, spatial transcriptomics, ATAC, and ChIP, helping scientists avoid the steep learning curve of code-based NGS analysis. In this demonstration-only class, an Illumina scientist will show steps for spatial transcriptomics analysis including QC, exploratory analysis, batch effect removal, integration of spatial and gene expression information, as well as differential expression and pathway analysis.  No prior experience or access to Partek Flow is required. Attendance is limited to NIH staff.

Data Science Seminar Series

Finding cancer imaging data sets shouldn't require advanced technical skills. In this Data Science Seminar Series session, Dr. Andrey Fedorov introduces two new tools (enabled by Large Language Models [LLMs]) that make it easier to search, explore, and work with 100+ terabytes of data in NCI’s Imaging Data Commons (IDC). 
During the session Dr. Fedorov will demonstrate the latest IDC Model Context Protocol (MCP) server and the agent skill that can integrate with LLM-enabled assistants. He will show you how to:

• set up the IDC MCP server and agent skill to connect your assistant to IDC data
• find and access DICOM imaging data sets, or cohorts, by describing your research needs in everyday language
• use your assistant to brainstorm research questions, or hypotheses, for a specific data set

Whether you're a cancer researcher new to working with imaging data or a data scientist looking to simplify access to a large repository, this session shows you a more direct path to accessing data from the IDC.

This one-hour and thirty minute online training is part one of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation.   

By the end of part one of this training series, attendees will be able to:   

• Understand data management best practices   

• Become familiar with data management tools  

• Have a solid knowledge of the resources, enabling data sharing  

During Part 2, attendees will learn about sharing and archiving data. You must register separately for Part 2 of this training. This training is introductory, no prior knowledge required.  

This hour and half online training is part two of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation.  

By the end of part two of this training series, attendees will be able to:   

• Have a solid knowledge of the resources, enabling data sharing  

• Understand how data is archived and preserved  

Part 1 of this training covers understanding research data, how to manage research data, and how to work with data. During Part 2, attendees learn about sharing and archiving data. This training is introductory, no prior knowledge required.  

You must register separately for Part 1 of this training.