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The Digital Science Field of Design Science Research

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The Next Wave of Sociotechnical Design (DESRIST 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12807))

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Abstract

With all aspects of sciences quickly becoming digital, this paper proposes digital science as a new area of inquiry for design science research. Scientists, in every field, design and develop digital systems as artifacts to support their research, resulting in all of science now becoming what Herbert Simon called the Sciences of the Artificial. There are many significant software engineering challenges of digital science, including poor or unreliable artifacts, errors in coding, and unclear requirements. Software engineering solutions are not enough, but many digital science challenges can be addressed by the methodologies created by research in design science over the past two decades.

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References

  1. Simon, A.: The Science of the Artificial/Herbert. Cambridge (1969)

    Google Scholar 

  2. Linebaugh, K., Knutson, R.: The creator of the record-setting Covid vaccine. J. Wall Street J. Podcast (2020)

    Google Scholar 

  3. Larsen, H.: The crisis of public service broadcasting reconsidered: commercialization and digitalization in Scandinavia. Digit. Future 43–58 (2016)

    Google Scholar 

  4. Kirkpatrick, K.: 3D-printing human body parts. Commun. ACM 60(10), 15–17 (2017)

    Article  Google Scholar 

  5. Johanson, A., Hasselbring, W.: Software engineering for computational science: past, present, future. Comput. Sci. Eng. 20(2), 90–109 (2018)

    Article  Google Scholar 

  6. Apweiler, R., et al.: Whither systems medicine? Exp. Mol. Med. 50(3), e453 (2018)

    Google Scholar 

  7. Commins, J., Toft, C., Fares, M.A.: Computational biology methods and their application to the comparative genomics of endocellular symbiotic bacteria of insects. Biol. Proced. Online 11(1), 52 (2009)

    Article  Google Scholar 

  8. Lee, S., et al.: A transparent bending-insensitive pressure sensor. Nat. Nanotechnol. 11(5), 472 (2016)

    Article  Google Scholar 

  9. Ruiz, P., et al.: Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors. Chemosphere 178, 99–109 (2017)

    Article  Google Scholar 

  10. Szkuta, K., Osimo, D.: Rebooting science? Implications of science 2.0 main trends for scientific method and research institutions. Foresight 18(3), 204–223 (2016)

    Google Scholar 

  11. Lukyanenko, R., Wiggins, A., Rosser, H.K.: Citizen science: an information quality research frontier. Inf. Syst. Front. 22(4), 961–983 (2019). https://doi.org/10.1007/s10796-019-09915-z

    Article  Google Scholar 

  12. Shneiderman, B.: Science 2.0. Science 319(5868), 1349–1350 (2008)

    Google Scholar 

  13. Lutz, R.R.: Analyzing software requirements errors in safety-critical, embedded systems. In: Proceedings of the IEEE International Symposium on Requirements Engineering (1993)

    Google Scholar 

  14. Merali, Z.: Computational science: error, why scientific programming does not compute. Nature 467(7317), 775–777 (2010)

    Article  Google Scholar 

  15. Storer, T.: Bridging the chasm: a survey of software engineering practice in scientific programming. ACM Comput. Surv. 50(4), 32 (2017)

    Google Scholar 

  16. Robiou-du-Pont, S., et al.: Should we have blind faith in bioinformatics software? Illustrations from the SNAP web-based tool. PLoS ONE 10(3), 8 (2015)

    Article  Google Scholar 

  17. Fienen, M.N., Bakker, M.: Repeatable research: what hydrologists can learn from the Duke cancer research scandal. Hydrol. Earth Syst. Sci. 20(9), 3739–3743 (2016)

    Article  Google Scholar 

  18. Califf, R.M., Kornbluth, S.: Establishing a framework for improving the quality of clinical and translational research. J. Clin. Oncol. 30(14), 1725–1726 (2012)

    Article  Google Scholar 

  19. Hinsen, K.: Technical debt in computational science. Comput. Sci. Eng. 17(6), 103–107 (2015)

    Article  Google Scholar 

  20. Kelly, D.: Scientific software development viewed as knowledge acquisition: towards understanding the development of risk-averse scientific software. J. Syst. Softw. 109, 50–61 (2015)

    Article  Google Scholar 

  21. Shackley, S., et al.: Uncertainty, complexity and concepts of good science in climate change modelling: are GCMs the best tools? Clim. Change 38(2), 159–205 (1998)

    Article  Google Scholar 

  22. Oberkampf, W.L., Roy, C.J.: Verification and validation in scientific computing (2010)

    Google Scholar 

  23. Kaul, M., Storey, V.C., Woo, C.: A framework for managing complexity in information systems. J. Database Manag. (JDM) 28(1), 31–42 (2017)

    Article  Google Scholar 

  24. Reinhart, C.M., Rogoff, K.S.: Growth in a time of debt. Am. Econ. Rev. 100(2), 573–578 (2010)

    Article  Google Scholar 

  25. Britain, G.: House of Commons Science and Technology Committee. Forensic Science on Trial (2005)

    Google Scholar 

  26. Matthews, D., et al.: Configuration management for large-scale scientific computing at the UK Met office. Comput. Sci. Eng. 10(6), 56–64 (2008)

    Article  Google Scholar 

  27. Pries-Heje, J., et al.: Advances in information systems development: from discipline and predictability to agility and improvisation. In: IFIP World Computer Congress, TC 8 (2008)

    Google Scholar 

  28. Miller, G.: A scientist’s nightmare: software problem leads to five retractions. American Association for the Advancement of Science (2006)

    Google Scholar 

  29. Lynch, C.J., et al.: A content analysis-based approach to explore simulation verification and identify its current challenges. PloS One 15(5), e0232929 (2020)

    Google Scholar 

  30. Venable, J., Pries-Heje, J., Baskerville, R.: FEDS: a framework for evaluation in design science research. Eur. J. Inf. Syst. 25(1), 77–89 (2016)

    Article  Google Scholar 

  31. Moraila, G., et al.: Measuring reproducibility in computer systems research. Technical report, University of Arizona (2014)

    Google Scholar 

  32. Stodden, V., Guo, P., Ma, Z.: Toward reproducible computational research: an empirical analysis of data and code policy adoption by journals. PloS One 8(6), e67111 (2013)

    Google Scholar 

  33. Ramesh, B., et al.: Requirements traceability: theory and practice. Ann. Softw. Eng. 3(1), 397–415 (1997)

    Article  Google Scholar 

  34. Kelly, D., Hook, D., Sanders, R.: Five recommended practices for computational scientists who write software. Comput. Sci. Eng. 11(5), 48–53 (2009)

    Article  Google Scholar 

  35. Erickson, J., Lyytinen, K., Siau, K.: Agile modeling, agile software development, and extreme programming: the state of research. J. Database Manag. (JDM) 16(4), 88–100 (2005)

    Article  Google Scholar 

  36. Chilana, P.K., Palmer, C.L., Ko, A.J.: Comparing bioinformatics software development by computer scientists and biologists: an exploratory study. In: 2009 ICSE Workshop on Software Engineering for Computational Science and Engineering. IEEE (2009)

    Google Scholar 

  37. Hannay, J.E., et al.: How do scientists develop and use scientific software? In: 2009 ICSE Workshop on Software Engineering for Computational Science and Engineering. IEEE (2009)

    Google Scholar 

  38. Morris, C.: Some lessons learned reviewing scientific code. In: Proceedings of the 30th International Conference Software Engineering (iCSE08) (2008)

    Google Scholar 

  39. Segal, J.: Models of scientific software development (2008)

    Google Scholar 

  40. Jacobson, I.: Object-Oriented Software Engineering: A Use Case Driven Approach. Pearson (1993)

    Google Scholar 

  41. Baxter, A., et al.: Agile Scrum Development in an ad hoc Software Collaboration. arXiv preprint arXiv:2101.07779 (2021)

  42. Brat, G., et al.: Experimental evaluation of verification and validation tools on Martian rover software. Formal Methods Syst. Des. 25(2), 167–198 (2004)

    Article  Google Scholar 

  43. Sanders, R., Kelly, D.: Dealing with risk in scientific software development. IEEE Softw. 25(4), 21–28 (2008)

    Article  Google Scholar 

  44. Walls, J.G., Widmeyer, G.R., El Sawy, O.A.: Building an information system design theory for vigilant EIS. Inf. Syst. Res. 3(1), 36–59 (1992)

    Article  Google Scholar 

  45. Kim, W.C., Mauborgne, R.A.: Blue Ocean Strategy, Expanded Edition: How to Create Uncontested Market Space and Make the Competition Irrelevant. Harvard Business Review (2014)

    Google Scholar 

  46. Hevner, A.R., et al.: Design science in information systems research. MIS Q. 75–105 (2004)

    Google Scholar 

  47. van Aken, J.E.: Management research based on the paradigm of the design sciences: the quest for field-tested and grounded technological rules. J. Manag. Stud. 41(2), 219–246 (2004)

    Article  Google Scholar 

  48. vom Brocke, J., Lippe, S.: Taking a project management perspective on design science research. In: Winter, R., Zhao, J.L., Aier, S. (eds.) DESRIST 2010. LNCS, vol. 6105, pp. 31–44. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13335-0_3

    Chapter  Google Scholar 

  49. Conboy, K., Gleasure, R., Cullina, E.: Agile design science research. In: Donnellan, B., Helfert, M., Kenneally, J., VanderMeer, D., Rothenberger, M., Winter, R. (eds.) DESRIST 2015. LNCS, vol. 9073, pp. 168–180. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18714-3_11

    Chapter  Google Scholar 

  50. Sein, M.K., et al.: Action design research. MIS Q. 37–56 (2011)

    Google Scholar 

  51. Baskerville, R., Myers, M.D.: Special issue on action research in information systems: making IS research relevant to practice: foreword. MIS Q. 28(3), 329 (2004). https://doi.org/10.2307/25148642

    Article  Google Scholar 

  52. Baskerville, R.L., Kaul, M., Storey, V.C.: Genres of inquiry in design-science research. MIS Q. 39(3), 541–564 (2015)

    Article  Google Scholar 

  53. Peffers, K., et al.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24(3), 45–77 (2007)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Information Systems, J. Mack Robinson College of Business, Georgia State University, Atlanta, GA, USA

    Veda C. Storey & Richard L. Baskerville

Authors
  1. Veda C. Storey
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  2. Richard L. Baskerville
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Corresponding author

Correspondence to Veda C. Storey .

Editor information

Editors and Affiliations

  1. University of Liechtenstein, Vaduz, Liechtenstein

    Leona Chandra Kruse

  2. University of Liechtenstein, Vaduz, Liechtenstein

    Stefan Seidel

  3. University of Agder, Kristiansand, Norway

    Geir Inge Hausvik

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Storey, V.C., Baskerville, R.L. (2021). The Digital Science Field of Design Science Research. In: Chandra Kruse, L., Seidel, S., Hausvik, G.I. (eds) The Next Wave of Sociotechnical Design. DESRIST 2021. Lecture Notes in Computer Science(), vol 12807. Springer, Cham. https://doi.org/10.1007/978-3-030-82405-1_33

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  • DOI: https://doi.org/10.1007/978-3-030-82405-1_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82404-4

  • Online ISBN: 978-3-030-82405-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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