Data Availability Statement


The Data Availability Statement is a section, typically towards the end of a research article, that describes how the reader can access the data and whether they are publicly available, available upon request, or otherwise restricted. The data referenced in this section is generally understood to be the data needed to support the conclusions in the article, but the interpretations of this can vary widely between researchers. Some consider that this includes the raw or underlying data used for the research, but others may only point to the summary data represented in the figures and tables in the article, a highly questionable practice. When the underlying data are available in a data repository, the permanent link can be found in the Data Availability Statement. If the article did not come about via data, the lack of applicability is mentioned in this section.


Open data example: Zhang, F., Shih, SF., Harapan, H. et al. Changes in COVID-19 risk perceptions: methods of an internet survey conducted in six countries. BMC Res Notes 14, 428 (2021). (direct link)

Restricted data example 1: Holden, T. M., Richardson, R., Arevalo, P., Duffus, W. A., Runge, M., Whitney, E., Wise, L., Ezike, N. O., Patrick, S., Cobey, S., & Gerardin, J. (2021). Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020. BMC Public Health, 21(1), 1105. 

Restricted data example 2: Randall, L. M., Dasgupta, S., Day, J., DeMaria, A., Jr, Musolino, J., John, B., Cranston, K., & Buchacz, K. (2022). An outbreak of HIV infection among people who inject drugs in northeastern Massachusetts: findings and lessons learned from a medical record review. BMC Public Health, 22(1), 257. 

Similar Terms

Data Access Statement
Data Accessibility Statement

Further Resources

Rule number 9 in this article details best practices in writing a data availability statement:

Contaxis N, Clark J, Dellureficio A, Gonzales S, Mannheimer S, et al. (2022) Ten simple rules for improving research data discovery. PLOS Computational Biology 18(2): e1009768.


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