Open science and research

Data availability statements

With data availability statements you are able tell about your data and its possible availability to the readers.

Scientific journals are becoming increasingly interested in data underlying the submitted articles. Given that fields of science have different challenges in moving towards open data, journals currently rarely require open data, but are increasingly requiring or suggesting that authors add a data availability statement to their manuscript. With data availability statements you are able tell about your data and its possible availability to the readers. For instance, Elsevier sees data statement as a way to make scientific data more transparent [1].

Data availability statements:

  • Mention all of the data types underlying the publication
  • If the data can be shared, specify how readers can access it (e.g., repository link, contact person). Use persistent identifiers, such as DOIs, when available.
  • Specify the access and use conditions.
  • If data cannot be shared, justify this in the data availability statement (DAS) (e.g., “The GDPR legislation requires us to protect the identity of participants, and the raw data cannot be publicly shared.”)
  • If the publication did not utilize any data, mention this in the data availability statement (DAS)

We suggest that you study our research data management pages and see if you can find ways to improve findability, accessibility and reusability of your research data. In general, studies show that articles that openly deposit data receive more citations [2]. However, If you work with human subjects, be extra careful with GDPR and personal data (see Aalto's instructions).

If you have any questions regarding data availability statements or research data management, please contact [email protected]

References

[1] Elsevier. 2021. Data statement. Online document [checked 24.2.2021]. 

[2] Colavizza G, Hrynaszkiewicz I, Staden I, Whitaker K, McGillivray B (2020) The citation advantage of linking publications to research data. PLOS ONE 15(4): e0230416. https://doi.org/10.1371/journal.pone.0230416

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