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Granting access to data requires the data to be well-documented, properly organized and stored, licensed (Creative Commons, for example), easily identifiable (DOI), and preserved for the long term.
Benefits of Sharing Data
There are many benefits to sharing data, including:
reinforcing open scientific inquiry
supporting verification and replication of original results
promoting new research and allows for testing of new methods
encouraging collaboration and varied perspectives
providing resources for education/teaching
reducing cost by avoiding duplicate data collection efforts
protecting against faulty or falsified data
enhancing visibility and impact of research projects
preserving data for future use
helping researchers in the broader community produce better research
Citing data properly is essential in order to:
Give the data producer appropriate credit
Allow access to the data for reference or reuse
Enable readers to verify your results
A dataset should be cited formally in an article's reference list, not just informally in the text. Many data repositories and publishers provide specific instructions for how to cite their data. If no citation information is provided, you can use generally agreed- upon guidelines. DataCite Metadata Schema is an example.
There are 5 core elements of a dataset citation, with additional elements added as needed.
Creator(s) – individuals or organizations
Publication year when the dataset was released (may be different from the Access date)
Publisher – the data center, archive, or repository
Although the core elements are sufficient in a simple citation (ie.: citation to the entirety of a static dataset), some additional elements may be needed if you are citing a dynamic/evolving dataset or a subset of a larger dataset. These include:
Version of the dataset analyzed in the citing paper
Access date when the data was accessed for analysis in the citing paper
Subset of the dataset analyzed (e.g., a range of dates or record numbers, a list of variables)
Verifier that the dataset or subset accessed by a reader is identical to the one analyzed by the author (e.g., a Checksum)
Location of the dataset on the internet, needed if the identifier is not "actionable" (convertible to a web address)
Morran LT, Parrish II RC, Gelarden IA, Lively CM (2012) Data from: Temporal dynamics of outcrossing and host mortality rates in host-pathogen experimental coevolution. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.c3gh6
Consult this list for links to: online communities, conference materials, tutorials and training materials, social media, and tools. Geared toward librarians, but very useful for anyone wanting to learn more about data management.
A final, important step is obtaining a persistent, unique identifier for the dataset (a digital object identifier, or DOI, for example). Unique identifiers facilitate easy citation of data and allows usage statistics to be tracked.
Public data identifiers should be:
actionable (you can click on them in a web browser)
Community resource for tracking, comparing, and understanding both current and future U.S. federal funder research data sharing policies. Use the icon menu to select up to three agencies to view or compare.