Generating new data, code, and models can be costly and resource intensive. Teams are encouraged to consider reusing existing resources when appropriate and possible.
Identify Opportunities to Reuse Data, Code, and Models
Identify opportunities to reuse data where appropriate. Existing data may enhance or replace the need to generate new data. Existing benchmark data can be used for model training or assessment. The Genesis Mission platform is developing a data catalog of catalogs and additional data resources that will be released at a future date.
Understand the Access and Reuse Rules
Understand the access and reuse rules for data and other artifacts reused by the project. Some data, including some publicly available data, are copyrighted, have licenses, or have other provisions that dictate and possibly limit how data can be reused and how derived products can be shared. Work with your technology transfer office to understand how governance rules and access restrictions may flow down to derived data products, particularly in cases where data from different sources are combined.
Understand your responsibilities for protecting data and other assets where access is controlled and/or other policies are in effect. Some Genesis Mission resources, for example, require specific credentials and authorizations.
Cite and Provide Appropriate Attribution
When existing data is used, whether public or non-public, it is critical to provide proper attribution. All derivative products must cite the original data sources. Follow the citation guidance provided by the data repository. Include any persistent identifiers, e.g. DOIs, where available.
Elements of a data citation should include: creator(s) or contributor(s); date of publication; title of dataset; data repository or archive; version, if applicable; identifier, e.g. DOI, or URL of source; version; date accessed.
Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11; 2014: https://doi.org/10.25490/a97f-egyk
Stall, S., Bilder, G., Cannon, M. et al. Journal Production Guidance for Software and Data Citations. Sci Data 10, 656 (2023). https://doi.org/10.1038/s41597-023-02491-7
Next Section: Prepare for Data Preservation and Sharing
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