Tytuł artykułu

OECD Recommendation’s Draft Concerning Access to Research Data from Public Funding: A Review

Tytuł czasopisma

Bulletin of the Polish Academy of Sciences Technical Sciences

Rocznik

2021

Wolumin

69

Numer

No. 1

Afiliacje

Madeyski, Lech : Faculty of Computer Science and Management, Wroclaw University of Science and Technology, ul. Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland ; Lewowski, Tomasz : Faculty of Computer Science and Management, Wroclaw University of Science and Technology, ul. Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland ; Kitchenham, Barbara : School of Computing and Mathematics, Keele University, Keele, Staffordshire, ST5 5BG, UK

Autorzy

Madeyski, Lech ; Lewowski, Tomasz ; Kitchenham, Barbara

Słowa kluczowe

open data ; open access ; empirical research ; data-driven research ; data science

Wydział PAN

Nauki Techniczne

Zakres

e135401

Bibliografia

1.  OECD, Recommendation of the council concerning access to research data from public funding. [Online]. https://legalinstruments.oecd. org/en/instruments/OECDLEGAL-0347
2.  D.N. Le, A. Shahbazian, and N. Medvidovic, “An Empirical Study of Architectural Decay in Open-Source Software”, IEEE International Conference on Software Architecture (ICSA), 2018.
3.  K.R. Sipido, “Irreproducible results in preclinical cardiovascular research: Opportunities in times of need”, Cardiovasc. Res. 115 (3), E34–E36 (2019).
4.  D.A. Eisner, “Reproducibility of science: Fraud, impact factors and carelessness”, J. Mol. Cell. Cardiol. 114, 364‒368 (2018).
5.  L. Madeyski and B. Kitchenham, “Would wider adoption of reproducible research be beneficial for Empir. Softw. Eng. research?”, J. Intell. Fuzzy Syst. 32(2), 1509–1521 (2017).
6.  T. Lewowski and L. Madeyski, “Creating Evolving Project Data Sets in Software Engineering”, Integr. Res. Pract. Softw. Eng. 851, 1–14 (2020), doi: 10.1007/978-3-030-26574-8_1.
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11.  B. Kitchenham, L. Madeyski, D. Budgen, J. Keung, P. Brereton, S. Charters, S. Gibbs, and A. Pohthong, “Robust Statistical Methods for Empir. Softw. Eng.”, Empir. Softw. Eng. 22(2), 579–630 (2017).
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13.  S. Greengard, “An inability to reproduce”, Commun. ACM 62(9), 13–15 (2019).
14.  F. Pasquale, “When machine learning is facially invalid”, Commun. ACM 61(8), 25–27 (2018).
15.  J.P.A. Ioannidis: “The mass production of redundant, misleading, and conflicted systematic reviews and meta-analyses”, Milbank Q. 94, 485–514 (2016).
16.  J.B. Carlisle, “Data fabrication and other reasons for nonrandom sampling in 5087 randomised, controlled trials in anaesthetic and general medical journals”, Anaesthesia 72(8), 944–952, (2017)
17.  Ch.H.J. Hartgerink, J.M. Wicherts, and M.A. van Assen, “The value of statistical tools to detect data fabrication”, Res. Ideas Outcomes 2, e8860 (2016).
18.  Ch.H.J. Hartgerink, J.G. Voelkel, J.M. Wicherts, and Marcel A.L.M. van Assen. “Detection of Data Fabrication Using Statistical Tools”, PsyArXiv, 2019, doi: 10.31234/osf.io/jkws4.
19.  N.J.L. Brown and J.A.J. Heathers, “The grim test: A simple technique detects numerous anomalies in the reporting of results in psychology”, Soc. Psychol. Personal Sci. 8(4), 363–369 (2017).
20.  J.A. Heathers, J. Anaya, T. van der Zee, and N. Brown “Recovering data from summary statistics: Sample Parameter Reconstruction via Iterative TEchniques (SPRITE)”, PeerJ Preprints, e26968v1 (2018). doi: 10.7287/peerj.preprints.26968v1.
21.  S. Al-Marzouki, S. Evans, T. Marshall, and I. Roberts, “Are these data real? Statistical methods for the detection of data fabrication in clinical trials”, BMJ, 331(7511), 267–270 (2005), doi: 10.1136/bmj.331.7511.267.

Data

11.02.2021

Typ

Article

Identyfikator

DOI: 10.24425/bpasts.2020.135401 ; ISSN 2300-1917

Źródło

Bulletin of the Polish Academy of Sciences: Technical Sciences; 2021; 69; No. 1; e135401