NIDM-Terms: community-based terminology management for improved neuroimaging dataset descriptions and query

NIDM-Terms: community-based terminology management for improved neuroimaging dataset descriptions and query

The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts tha...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in neuroinformatics Vol. 17; p. 1174156
Main Authors: Queder, Nazek, Tien, Vivian B, Abraham, Sanu Ann, Urchs, Sebastian Georg Wenzel, Helmer, Karl G, Chaplin, Derek, van Erp, Theo G M, Kennedy, David N, Poline, Jean-Baptiste, Grethe, Jeffrey S, Ghosh, Satrajit S, Keator, David B
Format: Journal Article
Language:English
Published: Switzerland Frontiers Research Foundation 18-07-2023
Frontiers Media S.A
Subjects:
annotation
Annotations
Data dictionaries
Data models
dataset
Datasets
Informatics
Jargon
Medical imaging
Medical research
Metadata
Names
Neuroimaging
Neuroscience
NIDM
Personal information
query
Resource Description Framework-RDF
Tool use
Variables
NIDM
annotation
dataset
query
neuroimaging
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Edited by: Maaike M. H. Van Swieten, Integral Cancer Center Netherlands (IKNL), Netherlands
Reviewed by: B. Nolan Nolan Nichols, Maze Therapeutics, United States; Alexandre Rosa Franco, Nathan Kline Institute for Psychiatric Research, United States
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2023.1174156