Dryad is making it easier to find and reuse data by collecting and sharing discipline-specific metadata. We’ve rolled out an optional metadata template for researchers to provide additional, enhanced information about their methods, variables, experimental design, and more, when they submit a dataset.

Our new template, embedded in our online submission form, prompts cognitive neuroscience researchers to provide richer information about the dataset, the experimental setup, and the analysis process that lead to its production, including key elements like data modality, data acquisition technique, experimental paradigm, state of preprocessing, and demographic/medical attributes.

Enriched metadata for cognitive neuroscience data

Our default metadata schema (maintained by DataCite) supports accurate and consistent identification of datasets that aids discovery and reuse across disciplines, and makes Dryad data visible in a range of data catalogs and discovery services. Complementing this fundamental information about a dataset with a set of metadata elements that are meaningful to a specific community can help translate discovery into reproducibility.

Cognitive neuroscience research often involves the integration of data from various sources and disciplines. Standardized metadata schemas and controlled vocabularies make it possible for humans and machines to successfully interpret and responsibly reuse complex neuroscience datasets in a range of ways, and for purposes well beyond those of the original authors. These granular metadata can also make it easier for researchers to search for and identify datasets relevant to the specific methods, populations, or variables of interest.

Bridging the gap between generalist and specialist data infrastructure

Collecting discipline-specific metadata also helps us build bridges between generalist and specialist data platforms. Dryad provides essential infrastructure for data generated through emerging methods or in nascent subdomains, which lack their own specialist data platforms. Providing the kind of rich, discipline-specific metadata that specialist repositories require readies Dryad data to be combined with datasets hosted elsewhere, interpreted by specialists, or even added to specialist repositories when appropriate.

Adding machine-actionable, discipline-specific metadata will help improve interoperability with disciplinary repositories and meet the information needs of neuroscience researchers. With just a few lines of code, researchers can parse these metadata files and create a reproducible pipeline to filter datasets of interest to a specific research question. When thoughtfully deployed, such a “template encapsulates in a single, machine-readable place everything that a third party—or a computer—needs in order to interpret what has been done and whether the data are reusable in a given context.”

How it works

When researchers start a Dryad data submission and enter keywords related to cognitive neuroscience, they will automatically be prompted to complete additional, optional metadata fields. This information will be captured as a downloadable JSON file included alongside the data (as in this example).

Metadata collection is powered by the CEDAR Embeddable Editor, a tool that helps communities create metadata templates that reflect best practices in their fields. We expect this to be the first of many CEDAR community templates that we support at Dryad.

Could your research community be next?

Enriched metadata holds the potential to advance interdisciplinary research and accelerate scientific discoveries. Does your researcher community have a broadly accepted metadata schema? Would researchers in your field benefit from access to more granular metadata for discovering, retrieving, and reusing data? Get in touch to start a conversation about implementing a metadata template for your community. 

About this project

This feature is powered by the CEDAR Embeddable Editor. Technical documentation can be found on GitHub. The metadata template was developed by the research team at the Max Planck Institute of Empirical Aesthetics in collaboration with DataCite, as part of the Implementing FAIR Workflows Project (TWCF0568), funded by the Templeton World Charity Foundation. Initial work for this project work was funded by the U.S. National Science Foundation, award 2134956.  Questions or comments about the metadata template can be directed to Zefan Zheng.

Funding
This work was, in part, funded by the U.S. National Institutes of Health, Office of Data Science Strategy and the Generalist Repository Ecosystem Initiative (GREI) OTA-21-00 [3OT2DB000005-01S3]. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the NIH.