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Part of the book series: Collaborative Bioethics ((CB,volume 3))

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Abstract

Data alteration requires consideration of: what are data, when should they be available and what is their quality. Alteration may be intended or unintended: scientific misconduct, scientific error, use of questionable research practices, or community-based and cultural interpretations of data relevance. Situations in which data alteration are at risk include those in which conflict of interest (and thus potential for bias) is endemic, and those in which powerful incentives that do not support research integrity are present. Consequences of intentional and unintentional data alteration are morally important but are not uniformly addressed. Norm and governance changes from the current system are in initial stages of development and must address issues of the data revolution. Points of decision address ethical concerns and moral quandaries researchers will likely face in a system of research practice that does not comprehensively support research integrity.

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References

  • Accounting for sex and gender makes science better. (2020). Nature, 588, 196.

    Article  Google Scholar 

  • Amsterdam, J., et al. (2017). Industry-corrupted psychiatric trials. Psychiatria Polska, 51(6), 993–1008.

    Article  Google Scholar 

  • Anderson, L., & Wray, K. (2019). Detecting errors that result in retractions. Social Studies of Science, 49(6), 942–954.

    Article  Google Scholar 

  • Artino, A., Driessen, E., & Maggio, L. (2019). Ethical shades of gray: International frequency of scientific misconduct and questionable research practices in health professions education. Academic Medicine, 94(1), 76–84.

    Article  Google Scholar 

  • Asknes, D., Langfeldt, L., & Wouters, P. (2019). Citations, citation indicators, and research quality: An overview of basic concepts and theories. SAGE Open, 9, 1–17.

    Google Scholar 

  • Avenell, A., et al. (2019). An investigation into the impact and implications of published papers from retracted research: Systematic search of affected literature. BMJ Open, 9, e031909.

    Article  Google Scholar 

  • Barnett, A., Zardo, P., & Graves, N. (2018). Randomly auditing research labs could be an affordable way to improve research quality: A simulation study. PLoS One, 13(4), e0195613.

    Article  Google Scholar 

  • Berggren, C., & Karabag, S. (2019). Scientific misconduct at an elite medical institute: The role of competing institutional logics and fragmented control. Research Policy, 48, 428–443.

    Article  Google Scholar 

  • Boulbes, D., et al. (2018). A survey on data reproducibility and the effect of publication process on the ethical reporting of laboratory research. Clinical Cancer Research, 24(14), 3447–3455.

    Article  Google Scholar 

  • Brown, A., et al. (2018). Issues with data and analyses: Errors, underlying themes and potential solutions. PNAS, 115(11), 2563–2570.

    Article  Google Scholar 

  • Bruton, S., et al. (2020). Personal motivations and systemic incentives: Scientists on questionable research practices. Science and Engineering Ethics, 26, 1531–1547.

    Article  Google Scholar 

  • Buchanan, A. (2009). Philosophy and public policy: A role for social moral epistemology. Journal of Applied Philosophy, 26(3), 276–290.

    Article  Google Scholar 

  • Bunkle, P. (2015). Correcting error in academic publishing: An ethical responsibility. Bioethical Inquiry, 12, 665–673.

    Article  Google Scholar 

  • Byrne, J., et al. (2019). Possibility of systemic research fraud targeting under-studied human genes: Causes, consequences, and potential solutions. Biomarker Insights, 14, 1–12.

    Article  Google Scholar 

  • Carlisle, J. (2017). Data fabrication and other reasons for non-random sampling in 5087 randomised, controlled trials in anaesthetic and general medical journals. Anaesthesia, 72, 944–952.

    Article  Google Scholar 

  • Carlisle, J. (2021). False individual patient data and zombie randomized controlled trials submitted to Anaesthesia. Anaesthesia, 76(4), 472–479.

    Article  Google Scholar 

  • Catillon, M. (2019). Trends and predictors of biomedical research quality, 1990-2015: A meta-research study. BMJ Open, 9, e030342.

    Article  Google Scholar 

  • Chevrier, R., et al. (2019). Use and understanding of anonymization and de-identification in the biomedical literature: Scoping review. Journal of Medical Internet Research, 21(5), e13484.

    Article  Google Scholar 

  • Davies, S. (2019). An ethics of the system: Talking to scientists about research integrity. Science and Engineering Ethics, 25(4), 1235–1253.

    Article  Google Scholar 

  • de Vries, Y., et al. (2019). Hiding negative trials by pooling them: A secondary analysis of pooled-trials publication bias in FDA-registered antidepressant trials. Psychological Medicine, 49(12), 2020–2026.

    Article  Google Scholar 

  • Douglas, H. (2014). The moral terrain of science. Erkenntnis, 79, 961–979.

    Article  Google Scholar 

  • Edwards, A. (2016). Team up with industry. Nature, 531, 299–301.

    Article  Google Scholar 

  • Elliott, C. (2017). The anatomy of research scandals. The Hastings Center Report, 47(3) inside back cover.

    Google Scholar 

  • Enriquez, J. (2020). Right wrong: How technology transforms our ethics. MIT Press.

    Book  Google Scholar 

  • Fusenig, N., et al. (2017). The need for a worldwide consensus for cell line authentication: Experience implementing a mandatory requirement at the International Journal of Cancer. PLoS Biology, 15(4), e2001438.

    Article  Google Scholar 

  • Hand, D. (2020). Dark Data. Princeton University Press.

    Book  Google Scholar 

  • Hardwicke, T., et al. (2020). Calibrating the scientific ecosystem through meta-research. The Annual Review of Statistics and Its Application, 7, 11–37.

    Article  Google Scholar 

  • Holman, B., & Elliott, K. (2018). The promise and perils of industry-funded science. Philosophy Compass, 13, e12544.

    Article  Google Scholar 

  • Hosseini, M., et al. (2018). Doing the right thing: A qualitative investigation of retraction due to unintentional error. Science and Engineering Ethics, 24, 189–206.

    Article  Google Scholar 

  • Jones, D., Grady, C., & Lederer, S. (2016). “Ethics and clinical research” – The 50th anniversary of Beecher’s bombshell. The New England Journal of Medicine, 374, 2393–2398.

    Article  Google Scholar 

  • Kahan, B., et al. (2020). Public availability and adherence to prespecified statistical analysis approaches was low in published randomized trials. Journal of Clinical Epidemiology, 128, 29–34.

    Article  Google Scholar 

  • Kingori, P., & Gerrets, R. (2016). Morals, morale and motivations in data fabrication: Medical research fieldworkers’ views and practices in two Sub-Saharan African context. Social Science & Medicine, 166, 150–159.

    Article  Google Scholar 

  • Kingori, P., & Gerrets, R. (2019). The masking and making of fieldworkers and data in postcolonial global health research contexts. Critical Public Health, 29(4), 494–507.

    Article  Google Scholar 

  • Labbe, C., et al. (2020). Flagging incorrect nucleotide sequence reagents in biomedical papers: To what extent does the leading publication format impede automatic error detection? Scientometrics, 124, 1139–1156.

    Article  Google Scholar 

  • Leonelli, S. (2017). Global data quality assessment and the situated nature of “best” research practices in biology. Data Science Journal, 16(32), 1–11.

    Google Scholar 

  • Leonelli, S. (2019). The challenges of big data biology. eLife, 8, 47381.

    Article  Google Scholar 

  • Osipenko, L. (2019). Blockchain’s potential to improve clinical trials – An essay by Leeza Osipenko. BMJ, 367, l5561.

    Article  Google Scholar 

  • Sacco, D., et al. (2018). In defense of the questionable: Defining the basis of research scientists’ engagement in questionable research practices. Journal of Empirical Research on Human Research Ethics, 13(1), 101–110.

    Article  Google Scholar 

  • Sacco, D., et al. (2019). Grounds for ambiguity: Justifiable bases for engaging in questionable research practices. Science and Engineering Ethics, 25(5), 1321–1337.

    Article  Google Scholar 

  • Schickore, J., & Hangel, N. (2019). “It might be this, it should be that…” uncertainty and doubt in day-to-day research practice. The European Journal of Philosophy of Science, 9, 31.

    Article  Google Scholar 

  • Seife, C. (2015). Research misconduct identified by the US Food and Drug Administration; Out of sight, out of mind, out of the peer reviewed literature. JAMA Internal Medicine, 175(4), 567–577.

    Article  Google Scholar 

  • Smaldino, P., & McElreath, R. (2016). The natural selection of bad science. Royal Society Open Science, 3(9), 160384.

    Article  Google Scholar 

  • Tadros, V. (2020). Distributing responsibility. Philosophy & Public Affairs, 48(3), 223–261.

    Article  Google Scholar 

  • Wager, E. (2020). Why we could stop worrying about gaming metrics if we stopped using journal articles for publishing scientific research. In M. Biagioli & A. Lippman (Eds.), Gaming the metrics: Misconduct and manipulation in academic research. MIT Press.

    Google Scholar 

  • Wallach, J., et al. (2018). Research, regulatory and clinical decision-making: The importance of scientific integrity. Journal of Clinical Epidemiology, 93, 88–93.

    Article  Google Scholar 

  • Williams, C., et al. (2019). Figure errors, sloppy science and fraud: Keeping eyes on your data. The Journal of Clinical Investigation, 129(5), 1805–1807.

    Article  Google Scholar 

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Redman, B.K. (2023). Data Alteration. In: Valdés, E., Lecaros, J.A. (eds) Handbook of Bioethical Decisions. Volume II. Collaborative Bioethics, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-031-29455-6_2

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