The best of both worlds: using semantic web with JSON-LD. An example with NIDM-Results & Datalad

International audienceNIDM-Results provides a harmonised representation for fMRI results reporting using Semantic Web technologies (Maumet et al. 2016). While those technologies are particularly well suited for aggregation across complex datasets, using them can be costly in terms of initial development time to generate and read the corresponding serialisations. While the technology is machine accessible, it can be difficult to comprehend by humans. This hinders adoption by scientific communities and by software developers used to more-lightweight data-exchange formats, such as JSON. JSON-LD: a JSON representation for semantic graphs (“JSON-LD 1.1” n.d.) was created to address this limitation and recent extensions to the specification allow creating JSON-LD documents that are structured more similar to simple JSON. This representation is simultaneously readable by a large number of JSON-based applications and by Semantic Web tools. Here we review our work on building a JSON-LD representation for NIDM-Results data and exposing it to Datalad (Halchenko et al. 2018), a data-management tool suitable for neuroimaging datasets with built-in support for metadata extraction and search