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Accountability in Research
Ethics, Integrity and Policy
Volume 24, 2017 - Issue 6
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Commentary

Data-Intensive Science and Research Integrity

, J.D., Ph.D., , Ph.D., , Ph.D. & , Ph.D.

References

  • American Statistical Association. 2016. Ethical guidelines for statistical practice. http://www.amstat.org/ASA/Your-Career/Ethical-Guidelines-for-Statistical-Practice.aspx. accessed November 8, 2016.
  • Australian Government. National Health and Medical Research Council. Australian Research Council. 2007. Australian Code for the Responsible. Conduct of Research. https://www.nhmrc.gov.au/_files_nhmrc/publications/attachments/r39.pdf. accessed October 28, 2016.
  • Babbage, C. 1830. Reflections on the Decline of Science in England, and on Some of its Causes. http://www.gutenberg.org/files/1216/1216-h/1216-h.htm. accessed October 27, 2016.
  • Bell, G., T. Hey, and A. Szalay. 2009. Beyond the data deluge. Science 323 (5919):1297–98. doi:10.1126/science.1170411.
  • Bosch, X., C. Hernández, J. M. Pericas, P. Doti, and A. Marušić. 2012. Misconduct policies in high-impact biomedical journals. Plos One 7 (12):e51928. doi:10.1371/journal.pone.0051928.
  • Broad, W., and N. Wade. 1993. Betrayers of Truth, 2nd ed. New York, NY: Simon and Schuster.
  • Center for Open Science. 2016. The transparency and openness promotion guidelines. https://cos.io/top/. accessed December 14, 2016.
  • Cheruvelil, K. S., P. A. Soranno, K. C. Weathers, P. C. Hanson, S. J. Goring, C. T. Filstrup, and E. K. Read. 2014. Creating and maintaining high-performing collaborative research teams: The importance of diversity and interpersonal skills. Frontiers in Ecology and Environment 12 (1):31–38. doi:10.1890/130001.
  • Compare Business Products. 2016. Top 10 largest databases in the world. http://www.comparebusinessproducts.com/fyi/10-largest-databases-in-the-world. accessed November 8, 2016.
  • Coombes, K. R., J. Wang, and K. A. Baggerly. 2007. Microarrays: Retracing steps. Nature Medicine 13 (11):1276–77. doi:10.1038/nm1107-1276b.
  • De Winter, J., and L. Kosolosky. 2013. The epistemic integrity of scientific research. Science and Engineering Ethics 19 (3):757–74. doi:10.1007/s11948-012-9394-3.
  • Devlin, B., S. E. Feinberg, D. P. Resnick, and K. Roeder, eds. 1997. Intelligence, Genes, and Success: Scientists Respond to The Bell Curve. New York, NY: Springer-Verlag.
  • Dooley, J. J., and H. M. Kerch. 2000. Evolving research misconduct policies and their significance for physical scientists. Science and Engineering Ethics 6 (1):109–21. doi:10.1007/s11948-000-0029-8.
  • Dubois, J. M., and J. M. Dueker. 2009. Teaching and assessing the responsible conduct of research: A Delphi consensus panel report. Journal of Research Administration 40 (1):49–70.
  • Duke University. 2007. Duke University policy and procedures governing research misconduct. http://provost.duke.edu/wp-content/uploads/FHB_App_P.pdf#page=33. accessed November 11, 2016.
  • Ekbia, H., M. Mattioli, I. Kouper, G. Arave, A. Ghazinejad, T. Bowman, V. R. Suri, A. Tsou, S. Weingart, and C. Sugimoto. 2015. Big data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology 66 (8):1523–45. doi:10.1002/asi.2015.66.issue-8.
  • Elliott, K. C. 2012. Epistemic and methodological iteration in scientific research. Studies in History and Philosophy of Science 43:376–82. doi:10.1016/j.shpsa.2011.12.034.
  • Elliott, K. C., K. S. Cheruvelil, G. M. Montgomery, and P. A. Soranno. 2016. Conceptions of good science in our data-rich world. Bioscience 66:880–89. [published online October 1, 2016] doi:10.1093/biosci/biw115.
  • Fan, J., F. Han, and H. Liu. 2014. Challenges of big data analysis. Natural Science Review 1:293–314.
  • Fischer, C. S., M. Hout, M. S. Jankowski, S. R. Lucas, A. Swidler, and K. Vos. 1996. Inequality by Design: Cracking the Bell Curve Myth. Princeton, NJ: Princeton University Press.
  • GenBank. 2016. GenBank and WGS statistics. https://www.ncbi.nlm.nih.gov/genbank/statistics/. accessed November 6, 2016.
  • Goldberg, P. 2015. Duke officials silenced med student who Reported trouble in Anil Potti’s lab. The Cancer Letter, January 9, 2015. http://cancerletter.com/articles/20150109_1/. accessed November 6, 2016.
  • Hand, D. J. 1998. Data mining: Statistics and more? The American Statistician 52 (2):112–18.
  • Herrnstein, R., and T. Murray. 1994. The bell curve: Intelligence and class structure in American life. New York, NY: Free Press.
  • Hey, T., S. Tansley, and K. Tolle. 2009. The fourth paradigm: Data-intensive scientific discovery. Redmond, WA: Microsoft Research.
  • Hughes, R. I. G. 1989. The structure and interpretation of quantum mechanics. Cambridge, MA: Harvard University Press.
  • Intergovernmental Panel on Climate Change. 2013. Climate change 2013: The physical basis. http://www.ipcc.ch/report/ar5/wg1/. accessed October 12, 2016.
  • Kitchin, R. 2014. The data revolution: Big data, Open data, Data infrastructures & their consequences. London, UK: Sage.
  • Kline, M. 1982. Mathematics: The loss of certainty. Oxford, UK: Oxford University Press.
  • Kuhn, T. S. 1970. The structure of scientific revolutions, 2nd ed. Chicago, IL: University of Chicago Press.
  • Laudan, L. 1981. Science and hypothesis: Historical essays on scientific methodology. Dordrecht, Netherlands: Reidel.
  • Leonelli, S. 2014. What difference does quantity make? On the epistemology of Big Data in biology. Big Data & Society, April-June, 2014: 1-11. 10.1177/2053951714534395
  • Longino, H. 1990. Science as social knowledge. Princeton, NJ: Princeton University Press.
  • Macrina, F., ed.. 2013. Scientific integrity, 4th ed. Washington, DC: American Society for Microbiology Press.
  • Master, Z., and D. B. Resnik. 2013. Hype and public trust in science. Science and Engineering Ethics 19 (2):321–35. doi:10.1007/s11948-011-9327-6.
  • Miller, H. J. 2010. The data avalanche is here: Should we be digging? Journal of Regional Science 50 (1):181–201. doi:10.1111/j.1467-9787.2009.00641.x.
  • Muller-Wille, S., and I. Charmantier. 2012. Natural history and information overload: The case of Linnaeus. Studies in the History and Philosophy of Biological and Biomedical Sciences 43:4–15. doi:10.1016/j.shpsc.2011.10.021.
  • National Academy of Sciences. 1992. Responsible science: Ensuring the integrity of the research process. Washington, DC: National Academy of Sciences.
  • National Genome Research Institute. (2015). Genome-wide association studies. https://www.genome.gov/20019523/ accessed October 27, 2016.
  • Nature Publishing Group. (2016). Image integrity and standards http://www.nature.com/authors/policies/image.html accessed November 8, 2016
  • O’Malley, M., K. C. Elliott, and R. Burian. 2010. From genetic to genomic regulation: Iterative methods in miRNA research. Studies in the History and Philosophy of Biological and Biomedical Sciences 41:407–17. doi:10.1016/j.shpsc.2010.10.011.
  • O’Malley, M., K. C. Elliott, C. Haufe, and R. Burian. 2009. Philosophies of funding. Cell 138:611–15. doi:10.1016/j.cell.2009.08.008.
  • Office of Research Integrity. (2015). Case Summary: Potti, Anil http://ori.hhs.gov/content/case-summary-potti-anil accessed November 6, 2016.
  • Office of Science and Technology Policy. 2000. Federal misconduct policy. Federal Register 65 (235):76260–64.
  • Parrish, D., and B. Noonan. 2009. Image manipulation as research misconduct. Science and Engineering Ethics 15 (2):161–67. doi:10.1007/s11948-008-9108-z.
  • Prensky, M. H. 2009. Sapiens digital: From digital immigrants and digital natives to digital wisdom. Innovate 2009; 5. http://www.innovateonline.info/index.php?view=article&id=705 accessed December 14, 2016.
  • PubMed. 2016. Home. https://www.ncbi.nlm.nih.gov/pubmed Accessed November 6, 2016.
  • Raman, H., and I. Ramos eds. 2013. Ethical data mining applications for socio-economic development. Association for computing machinery. Hershey, PA: IGI Global. http://www.igi-global.com/book/ethical-data-mining-applications-socio/73551?f=e-book accessed October 31, 2016.
  • Resnik, D. B. 1998. The ethics of science. New York, NY: Routledge.
  • Resnik, D. B. 2000. Statistics, ethics, and research: An agenda for education and reform. Accountability in Research 8 (1):163–88. doi:10.1080/08989620008573971.
  • Resnik, D. B. 2003. From baltimore to bell labs: Reflections on two decades of debate about scientific misconduct. Accountability in Research 10 (2):123–35. doi:10.1080/08989620300508.
  • Resnik, D. B., T. Neal, A. Raymond, and G. E. Kissling. 2015a. Research misconduct definitions adopted by U.S. research institutions. Accountability in Research 22 (1):14–21. doi:10.1080/08989621.2014.891943.
  • Resnik, D. B., L. M. Rasmussen, and G. E. Kissling. 2015b. An international study of research misconduct policies. Accountability in Research 22 (5):249–66. doi:10.1080/08989621.2014.958218.
  • Resnik, D. B., and C. N. Stewart. 2012. Misconduct versus honest error and scientific disagreement. Accountability in Research 19 (1):56–63.
  • Resnik, D. B., E. Wager, and G. E. Kissling. 2015c. Retraction policies of top scientific journals ranked by impact factor. Journal of Medical Librarian Association 103 (3):136–39. doi:10.3163/1536-5050.103.3.006.
  • Rossner, M., and K. Yamada. 2004. What’s in a picture? The temptation of image manipulation. The Journal of Cell Biology 166 (1):11–15. doi:10.1083/jcb.200406019.
  • Scott, J. 2013. Social Network Analysis. London, UK: Sage.
  • Shamoo, A. E., and D. B. Resnik. 2015. Responsible conduct of research, 3rd ed. New York, NY: Oxford University Press.
  • Soranno, P. A., K. S. Cheruvelil, K. C. Elliott, and G. Montgomery. 2015. It’s good to share: Why environmental scientists’ ethics are out of date. BioScience 65:69–73. doi:10.1093/biosci/biu169.
  • Steadman, I. (2013). Big data and the death of the theorist. Wired, January 25, 2013. http://www.wired.co.uk/news/archive/2013-01/25/big-data-end-of-theory accessed December 14, 2016.
  • Steneck, N. H. 1999. Confronting misconduct in science in the 1980s and 1990s: What has and has not been accomplished? Science and Engineering Ethics 5 (2):161–76. doi:10.1007/s11948-999-0005-x.
  • Steneck, N. H. 2006. ORI Introduction to Responsible Conduct of Research. Washington, DC: Office of Research Integrity.
  • Stodden, V., M. McNutt, D. H. Bailey, E. Deelman, Y. Gil, B. Hanson, M. A. Heroux, J. P. Ioannidis, and M. Taufer. 2016. Enhancing reproducibility for computational methods. Science 354 (6317):1240–41. doi:10.1126/science.aah6168.
  • Strasser, B. J. 2012. Data-driven sciences: From wonder cabinets to electronic databases. Studies in the History and Philosophy of Biological and Biomedical Sciences 43:85–87. doi:10.1016/j.shpsc.2011.10.009.
  • Sugimoto, C. R., H. R. Ekbia, and M. Mattioli, eds. 2016. Big data is not a monolith. Cambridge, MA: MIT Press.
  • United Kingdom Research Council. (2012). The research ethics guidebook. The RCUK (Research Council UK) Code of Conduct. http://www.ethicsguidebook.ac.uk/Research-Council-funding-122. accessed October 28, 2016.
  • van Wel, L., and L. Royakkers. 2004. Ethical issues in web data mining. Ethics and Information Technology 6 (2):129–40. doi:10.1023/B:ETIN.0000047476.05912.3d.

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