Formal Acknowledgement of Citizen Scientists’ Contributions via Dynamic Data Citations

Research Data Management Challenges in Citizen Science Projects and Recommendations for Library Support Services. A Scoping Review and Case Study

“…The Research Data Alliance (RDA) Data Citation WG has developed a Recommendation based on two principles (Rauber et al, 2015): first, one must ensure that data are stored in a versioned and timestamped manner; second, the PID to the citable data should comprise a query to the dataset and a timestamp. Hunter and Hsu (2015) found the principles highly applicable to a test CS dataset.…”

“…According to the FAIR principles, data must be assigned a persistent identifier (PID), such as a DOI, for permanent findability. A general challenge for evolving datasets, such as many CS data, is how to cite and retrieve a subset of a dataset as it existed at a specific date and time (August et al, 2015;Hunter and Hsu, 2015). The Research Data Alliance (RDA) Data Citation WG has developed a Recommendation based on two principles (Rauber et al, 2015): first, one must ensure that data are stored in a versioned and timestamped manner; second, the PID to the citable data should comprise a query to the dataset and a timestamp.…”

“…Groom, Weatherdon and Geijzendorffer (2017) argue that recognition of contribution from citizen scientists should be supported by the data users, if citizen scientists for example may wish for a recognition of the work performed in their community. Another solution was explored by Hunter and Hsu (2015), who were able to credit individual citizen scientists contributing to a specific data subset. They based their initiative on RDA's Dynamic Data Citation approach (Rauber et al, 2015).…”

“…Something to be considered early in the project is the possibility of crediting the citizen scientists for their contributed data and if certain groups of citizen scientists should be involved as co-authors on scholarly publications. As demonstrated by Hunter and Hsu (2015), applying RDA's Dynamic Data Citation Recommendation (Rauber et al, 2015) was feasible for CS project data, however, there are currently no guidelines on how to recognize citizen scientists for their contributions. A related focus area, where the library may support, is to include clearly in the descriptive metadata that data are of CS origin.…”

From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research

“…In neuroimaging, Ganz et al (2017) showed it was feasible to crowdsource the detection of Freesurfer (Fischl, 2012) cortical surface delineation errors on mTurk. We expect to see more utilization of citizen science, gamification, and paid crowdsourcing platforms in neuroimaging research, and there are still many open questions about which strategies (citizen science vs. paid crowdsourcing) and task designs are better suited for various analyses, as well as how to properly acknowledge the contributions of citizen scientists [see (Hunter and Hsu, 2015) for a proposed method].…”