17 Association of Research Libraries Research Library Issues 302 — 2021 Guide to Accelerate Public Access to Research Data (Washington, DC: Association of American Universities and Association of Public and Land-grant Universities, 2021), https://doi.org/10.31219/osf.io/tjybn. 4. Chodacki et al., 5, emphasis added. 5. For a good example of the power of rich metadata see: Kristian Garza, “Are You There, Metadata? It’s Me, the Bibliometrician,” DataCite Blog, January 25, 2021, https://doi.org/10.5438/j4xv-y945. 6. Mark D. Wilkinson et al., “The FAIR Guiding Principles for Scientific Data Management and Stewardship,” Scientific Data 3 (March 2016): 160018, https://doi.org/10.1038/sdata.2016.18. 7. Daniella Lowenberg et al., Open Data Metrics: Lighting the Fire (version 1), computer software, Zenodo, 2019, http://doi.org/10.5281/ zenodo.3525349. 8. For an excellent overview of maDMPs see: Tomasz Miksa et al., “Ten Principles for Machine-Actionable Data Management Plans,” PLoS Computational Biology 15, no. 3 (2019): e1006750, https://doi. org/10.1371/journal.pcbi.1006750. 9. Joanne S. Tornow et al., “Dear Colleague Letter: Effective Practices for Data,” NSF 19-069, National Science Foundation, May 20, 2019, https://www.nsf.gov/pubs/2019/nsf19069/nsf19069.jsp. 10. Kristian Garza and Matt Buys, “A Brave New PID: DMP-IDs,” DataCite Blog, April 7, 2021, https://doi.org/10.5438/j22a-5d79. 11. For more, see: “Machine Actionable Data Management Plan Connections,” Jupyter nbviewer, accessed November 24, 2021, https://nbviewer.org/github/datacite/pidgraph-notebooks-python/ blob/master/dmp/user-story-single-dmp-connections.ipynb. 12. Helena Cousijn et al., “Connected Research: The Potential of the PID Graph,” Patterns 2, no. 1 (January 2021), https://doi. org/10.1016/j.patter.2020.100180.
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17 Association of Research Libraries Research Library Issues 302 — 2021 Guide to Accelerate Public Access to Research Data (Washington, DC: Association of American Universities and Association of Public and Land-grant Universities, 2021), https://doi.org/10.31219/osf.io/tjybn. 4. Chodacki et al., 5, emphasis added. 5. For a good example of the power of rich metadata see: Kristian Garza, “Are You There, Metadata? It’s Me, the Bibliometrician,” DataCite Blog, January 25, 2021, https://doi.org/10.5438/j4xv-y945. 6. Mark D. Wilkinson et al., “The FAIR Guiding Principles for Scientific Data Management and Stewardship,” Scientific Data 3 (March 2016): 160018, https://doi.org/10.1038/sdata.2016.18. 7. Daniella Lowenberg et al., Open Data Metrics: Lighting the Fire (version 1), computer software, Zenodo, 2019, http://doi.org/10.5281/ zenodo.3525349. 8. For an excellent overview of maDMPs see: Tomasz Miksa et al., “Ten Principles for Machine-Actionable Data Management Plans,” PLoS Computational Biology 15, no. 3 (2019): e1006750, https://doi. org/10.1371/journal.pcbi.1006750. 9. Joanne S. Tornow et al., “Dear Colleague Letter: Effective Practices for Data,” NSF 19-069, National Science Foundation, May 20, 2019, https://www.nsf.gov/pubs/2019/nsf19069/nsf19069.jsp. 10. Kristian Garza and Matt Buys, “A Brave New PID: DMP-IDs,” DataCite Blog, April 7, 2021, https://doi.org/10.5438/j22a-5d79. 11. For more, see: “Machine Actionable Data Management Plan Connections,” Jupyter nbviewer, accessed November 24, 2021, https://nbviewer.org/github/datacite/pidgraph-notebooks-python/ blob/master/dmp/user-story-single-dmp-connections.ipynb. 12. Helena Cousijn et al., “Connected Research: The Potential of the PID Graph,” Patterns 2, no. 1 (January 2021), https://doi. org/10.1016/j.patter.2020.100180.
