@ARTICLE{Madeyski_Lech_OECD_2021,
 author={Madeyski, Lech and Lewowski, Tomasz and Kitchenham, Barbara},
 volume={69},
 number={No. 1},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e135401},
 howpublished={online},
 year={2021},
 abstract={Sharing research data from public funding is an important topic, especially now, during times of global emergencies like the COVID-19 pandemic, when we need policies that enable rapid sharing of research data. Our aim is to discuss and review the revised Draft of the OECD Recommendation Concerning Access to Research Data from Public Funding. The Recommendation is based on ethical scientific practice, but in order to be able to apply it in real settings, we suggest several enhancements to make it more actionable. In particular, constant maintenance of provided software stipulated by the Recommendation is virtually impossible even for commercial software. Other major concerns are insufficient clarity regarding how to finance data repositories in joint private-public investments, inconsistencies between data security and user-friendliness of access, little focus on the reproducibility of submitted data, risks related to the mining of large data sets, and sensitive (particularly personal) data protection. In addition, we identify several risks and threats that need to be considered when designing and developing data platforms to implement the Recommendation (e.g., not only the descriptions of the data formats but also the data collection methods should be available). Furthermore, the non-even level of readiness of some countries for the practical implementation of the proposed Recommendation poses a risk of its delayed or incomplete implementation.},
 type={Article},
 title={OECD Recommendation’s Draft Concerning Access to Research Data from Public Funding: A Review},
 URL={http://www.czasopisma.pan.pl/Content/118379/PDF/15_01931_Bpast.No.69(1)_19.02.21_K1_A_TeX.pdf},
 doi={10.24425/bpasts.2020.135401},
 keywords={open data, open access, empirical research, data-driven research, data science},
}