Methods to increase data availability
What is this about?
Research data availability is a growing burden due to the emerging number of studies, analytical improvements and unsatisfactory utilization of repository systems. One of the fast-growing initiatives that aim to increase data accessibility to the readers and other researchers is the open data movement. An increasing number of repositories allows routine and open publication of raw datasets along with the manuscript (eg. Open Science Framework – OSF), or alternatively upon reasonable request to the corresponding author.
Why is this important?
Data access is extremely important for transparent modern science. The rising number of research studies impedes the filtering of research findings, aggravates peer-review process and increases the possibility of false study reports. Having in mind the direct implications of scientific findings on everyday practice, data availability is further prioritized. The open data movement follows the principles of transparency, participation, and collaboration (1). Open data policy is important because it nurtures the virtues of transparency and honesty, which allows each respondent to check the authenticity of the published results at any time. Data sharing represents a significant part of research ethics and nowadays, many journals require researchers to publish resources to make them available to other investigators (2,3). However, deposited published data may be incomplete, in some cases intentionally because authors could feel like losing priority in future publishing, which may complicate new analyses on previously published data (2).
For whom is this important?
What are the best practices?
To support wide data availability, authors should publish their data so it could provide inputs to new hypotheses, and innovation (4,5). Journals could increase awareness on data availability and request mandatory data deposition. Modern manuscript checklists should routinely include data availability part which should additionally emphasize its importance to the authors. Finally, all of this could improve the verification of result, and reduce the misconduction related to data fabrication and falsification, and could serve as training tools for junior researchers (5).
In Detail
1. Kitchin R. The Data Revolution: Big Data, Open Data, Data Infrastructures, and Their Consequences. SAGE; 2014. 241 p.
2. Tedersoo L, Küngas R, Oras E, Köster K, Eenmaa H, Leijen Ä, et al. Data sharing practices and data availability upon request differ across scientific disciplines. Sci Data. 2021 Jul 27;8:192.
3. Fischer BA, Zigmond MJ. The essential nature of sharing in science. Sci Eng Ethics. 2010 Dec;16(4):783–99.
4. Sustainable Digital Data Preservation and Access Network Partners. NSF - National Science Foundation [Internet]. [cited 2022 Mar 2]. Available from: https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503141
5. Tenopir C, Allard S, Douglass K, Aydinoglu AU, Wu L, Read E, et al. Data Sharing by Scientists: Practices and Perceptions. PLOS ONE. 2011 Jun 29;6(6):e21101.
Josipa Domjanović, Ružica Bojčić contributed to this theme. Latest contribution was Feb 28, 2023
Themes
