NIDM-Experiment: An Ontology for Annotating Neuroscientific Data Karl G. Helmer1, Derek Chaplin1, Nazek Queder2, Satrajit Ghosh3, Camille Maumet4, Jean- Baptiste Poline6, Theo van Erp2, David Keator2 1 Massachusetts General Hospital, Boston, MA, USA 2 University of California, Irvine, Irvine, CA, USA 3 Massachusetts Institute of Technology, MA, USA 4 University of Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France 5 McGill University, Montreal, Canada Abstract NIDM-Experiment (NIDM-E) provides a collection of general and domain-specific terms that can be used to annotate data from neuroscientific experiments. NIDM-E reuses terms from existing ontologies and standards (such as DICOM and the Brain Imaging Data Standard) and adds new defined domain-specific terms. NIDM-E was created by annotating existing datasets and provides tools such as an online schema browser and term URI-resolution pages, and a GitHub-based workflow for users to propose new terms and edits existing ones. Keywords 1 neuroscience, ontology, data annotation, neuroimaging 1. Introduction used to describe neuroscience experiments and the resulting data artifacts. Efficacious reuse of data [1] relies on the The goal of NIDM-E is to provide semantic- capture and availability of information describing web and other tools, a collection of defined terms the acquisition and processing of that data. Crucial that can be used to annotate data to an arbitrary to data annotation are the use of defined terms, a level of detail. Other, often used standards, such practice that avoids ambiguities in interpretation, as the Brain Imaging Data Structure (BIDS) [4], and the reuse of terms from established, active embed metadata into fixed directory structures vocabularies and ontologies [2]. In addition, terms and metadata file formats, and these can be should be resolvable through a URL so that restrictive when dealing with complicated study automated methods and users are able to retrieve or experimental configurations, such as multiple information about each term from a single acquisition modalities, multi-site studies, and resource that is kept up to date. It is also critical cutting-edge acquisition methods. NIDM-E can that any ontology allow users to have input on its provide a framework to both annotate development with the goal that community complicated experiments and data, as well as adoption will be provide a feedback loop that accommodate terms for new modalities and drives further development. We report here on acquisition methods. It also provides tools to find recent developments in the Neuroimaging Data Model-Experiment (NIDM-E) [3], an ontology ICBO 2022, September 22-25, 2022, Ann Arbor, MI, USA. EMAIL: khelmer@mgh.harvard.edu (A. 1); dchaplin1@ mgh.harvard.edu (A. 2); naqueder@gmail.com (A. 3); satra@mit.edu (A. 4); camille@inria.fr (A. 5); jbpoline@gmail.com (A. 6); tvanerp@hs.uci.edu (A. 7); dbkeator@hs.uci.edu (A. 8) ORCID: 0000-0002-5113-6843 (A. 1); none (A. 2); 0000-0001- 6268-862X (A. 3); 0000-0002-5312-6729 (A. 4); 0000-0002- 6290-553X (A. 5); 0000-0002-9794-749X (A. 6); 0000-0002- 2465-2797 (A. 7); 0000-0001-5281-5576 (A. 8) 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org) ©️ CEUR ceur-ws.org Workshop ISSN 1613-0073 Proceedings terms, webpages for term URL resolution, and a We show in Fig. 1 a simple example of how framework for community involvement. NIDM-E can annotate an acquisition object, T1.nii, with an image contrast type of “T1- weighted” and an image usage type of 2. Methods “Anatomical”, and showing the scan session activity it was acquired at (“Session:Visit_2”), the protocol that was used (“MyProtocol.pdf”), and NIDM-E began by focusing on the processing the study participant (“ID:a8d4j3”) from which it and analysis of neuroimaging experiment data, was acquired and who had the role of “In-Vivo but has since expanded to encompass other Participant”. neuroscientific modalities as well as more general terms that describe experiments. NIDM-E was NIDM-E is currently used as a term source by built by annotating of several large real-world the PyNIDM [10] package, a set of python-based multi-modality neuroscientific data sets. open-source data annotation tools that is both customizable and extensible. Recently, PyNIDM NIDM-E reuses terms from other ontologies has been used to augment the data dictionary such as the Semanticscience Integrated Ontology terms for the OpenNeuro data repository [11], the (SIO) [5], Information Artifact Ontology (IAO) publicly-accessible repository for NIH’s BRAIN [6] and Prov-O [7]. These more general ontologies project MRI data and other related datasets. provide the scaffold onto which domain-specific terms can be added. Terms that are created for NIDM-E is accessible by cloning the NIDM-E include formal definitions in the “X is a repository from GitHub [3]. Web-accessible Y that Z” format [8]. NIDM-E also includes a infrastructure has been built so that the wide range of datatype, object, and annotation neuroscientific community can suggest terms to properties. NIDM-E vocabulary, terms have resolvable URI’s, and the ontology can be browsed to aid in Because NIDM-E began in support of a term discovery. We use GitHub issue templates to project to annotate neuroimaging data, it has allow users to suggest new terms or edits to particularly strong coverage in that domain. It existing ones. This allows us to have a record of contains two unique properties: discussions regarding a particular term and its “hadImageContrastType” and resolution. To discover terms, we have provided a “hadImageUsageType” that are used to “Schema Browser” webpage [12] that allows distinguish between the physical mechanism for users to view the entire graph of NIDM-E terms the contrast in an image volume (e.g., “T1- including all of the terms imported from other weighted”) and the eventual application for that ontologies. For semantic web applications, we image (e.g., “Anatomical”). These are particularly also have created a “Terms Resolution” page in important for the discovery of data in and across which each term has a unique URL [13] so that repositories, where datasets with different image terms by applications have a unique reference contrasts may be annotated by usage. For location. example, T1-weighted, T2-weighted, and diffusion-weighted images all may be stored as “Anatomical” data. To further support the 3. Summary annotation of neuroimaging data, NIDM-E also includes terms from two widely used standards: NIDM-E is a flexible collection of classes and DICOM [9] and the BIDS standards, which are properties that can be used to annotate a wide ubiquitous in the neuroimaging domain over range of neuroscientific data, with a strong current multiple imaging modalities. We have created a focus on neuroimaging. It provides tools to help set of datatype properties, each representing a users discover terms and provides a resolvable specific DICOM tag, which can be used to URL for each term. It also provides a workflow associate acquisition parameters with an for the community to suggest new terms and edits acquisition object. We have also included BIDS to existing ones. terms so that datasets that are organized according to the BIDS standard can annotated using BIDS- approved terms. [5] M. Dumontier, C. J.O. Baker, J. Baran, A. Callahan, L. Chepelev, J. Cruz-Toledo, N. R Del Rio, G. Duck, L. I. Furlong, N. Keath, D. Klassen, J. P. McCusker, N. Queralt- Rosinach, M. Samwald, N. Villanueva- Rosales, M. D. Wilkinson, .t Hoehndorf. The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery. J. Biomed. Semantics 5: 14 (2014). [6] W. Ceusters. An information artifact ontology perspective on data collections and associated representational artifacts. Stud Health Technol Inform. 180:68-72 (2012). [7] PROV-O: The PROV Ontology. URL: https://www.w3.org/TR/prov-o/ [8] S. Seppalla, A Ruttenberg, B. Smith. Guidelines for writing definitions in ontologies. Ciência da Informação 46 (1): Figure 1: Simple example showing how NIDM-E 73-88 (2017). can annotate an acquisition object, showing the [9] DICOM. URL: scan session it was acquired at and from what https://www.dicomstandard.org/ study participant. [10] incf-nidash/PyNIDM. URL: https://github.com/incf-nidash/PyNIDM. 4. Acknowledgements [11] OpenNeuro. URL: https://openneuro.org/ [12] NIDM-Experiment Schema Browser. URL: https://incf-nidash.github.io/nidm- We acknowledge support from NIH grant experiment/schema_menu.html 1RF1MH120021-01 and ongoing support from [13] NIDM Experiment Term Resolution Page. the International Neuroinformatics Coordinating URL: https://incf-nidash.github.io/nidm- Facility. experiment/ 5. References [1] J-B Poline, J.L. Breeze, S. Ghosh, K. Gorgolewski, Y. O. Halchenko, M. Hanke, C. Haselgrove, K. G. Helmer, D. B. Keator, D. S. Marcus, R. A. Poldrack, Y. Schwartz, J. Ashburner, D. N. Kennedy. " Data sharing in neuroimaging research." Front Neuroinform. (2012) 6: 9. doi:10.3389/fninf.2012.00009.. [2] R. Arp, B. Smith, A. D. Spear. Building Ontologies with Basic Formal Ontology, MIT Press, 2015. https://doi.org/10.7551/mitpress/978026252 7811.001.0001. [3] incf-nidash/nidm-experiment: This repository contains the terms used in nidm- experiment and the code to create the term- resolution and term-schema webpages. URL: https://github.com/incf-nidash/nidm- experiment. [4] Brain Imaging Data Structure. URL: https://bids.neuroimaging.io/