Zoom RecordingNotes DocScientific data repositories are increasingly facing requirements to ensure their digital data holdings are findable, accessible, interoperable, and reusable (aka, FAIR), following the FAIR Guiding Principles defined by Wilkinson et al. (2016; DOI: 10.1038/sdata.2016.18), and that they are deemed to be trustworthy in managing and preserving these data holdings for the long term, i.e., demonstrating that they are a Trustworthy Data Repository (TDR). However, there are many existing FAIR implementations.
Research communities including the Earth sciences and many federal agencies such as NASA are promoting open-source science for improved transparency of and access to data and information. Improvements to data quality practices can increase the value of data and contribute to the future practices for fostering the use of data. Similar to data, quality information and other artifacts should also be FAIR. To this end, ESIP Information Quality Cluster led an international collaboration and developed FAIR dataset quality information community guidelines (Peng et al. 2022; DOI: 10.5334/dsj-2022-008).
The session calls for presentations that describe practices, technical implementations, and lessons learnt for improving the FAIRness of Earth Science data and quality information, identify ways in which their FAIRness can be improved to lower the barriers to access, and identify how the community can contribute to a guide with synthesized FAIR practices for federally funded data and quality information.
Recommended Ways to Prepare:
- https://www.go-fair.org/fair-principles;
- Peng et al. (2022; DOI: http://doi.org/10.5334/dsj-2022-008)
Agenda:- Welcome and Introduction – Ge Peng, UAHuntsville/NASA MSFC IMPACT
- Invited Presentations:
- Comparing the FAIR-DQI guidelines to Related Principles – Robert Downs, CIESIN/NASA SEDAC
- Overview of IOOS and Discovering Synergistic Implementations of QA/QC of Real-Time Ocean Data with FAIR DQI Principles – Mark Bushnell, NOAA
- Assessment of FAIRNESS of NASA Data Systems – Hampapuram Ramapriyan, SSAI/NASA GSFC
- USGS FAIR data assessment project – Tamar Norkin, USGS
- From Conceptualization to Implementation: FAIR Assessment of Research Data Objects – Robert Huber, PANGAEA, DE
- PyQuARC: Development of a Service to Enable FAIR-er Metadata – Aaron Kaulfus, NASA MSFC
