Practical Training on Using NIDM Tools to Annotate General Tabular Data and BIDS Datasets
Organizer: David Keator
Dates: 1 Sep 2021
Time: 12:00-15:00 EDT / 18:00-21:00 CEST (3 hrs)
Target audience: Anyone interested in tools for annotating BIDS datasets and tabular data and how these techniques, through NIDM, enable improved search across datasets and reusability. The practical training will focus on graphical tools for annotation so no specialized programming skills are needed.
Registration deadline: 25 July 2021
Register for this session: CLOSED
Maximum participants: 60
About this tutorial
This training session focuses on the latest developments in the NIDM community and how the tools can be used to conform to the FAIR principles. The training will begin with a brief introduction to the new graphical tools for managing your community’s terminologies and/or study data dictionaries and the associated interface to produce improved data dictionaries that incorporate concept-level annotations to improve search. We will use the ENIGMA schizophrenia working group as a running example to provide attendees with experience using the tools, understanding the outputs, and how they can be utilized in your own studies. The final section of the training focuses on your own datasets, where you will be able to use the techniques taught in the first part of the training and apply them to your specific use-cases with support from the trainers.
Part 1 (~30-45 min.):
What is dataset annotation in this context and why is it important?
How do dataset annotations enable more reproducible science?
How does the Neuroimaging Data Model (NIDM) use annotated datasets for improved search?
Introduction to graphical tools for dataset annotation and terminology management.
Part 2 (~60 min.):
Using example data from the ENIGMA schizophrenia working group, use the tools from Part 1 to annotate these data.
Discussion of the outputs and how to use them to query with pynidm python tools.
Part 3 (~ 60 min):
Annotate your own datasets
This can be BIDS data or tabular (CSV/TSV) data.
This could be simply a CSV/TSV file with a list of the variables you’ve collected from your study.
Experiment with querying across example datasets brought by attendees for Part 3 using NIDM and pynidm python tools.