Advertisement

Bridging sustainability science, earth science, and data science through interdisciplinary education

  • Deana PenningtonEmail author
  • Imme Ebert-Uphoff
  • Natalie Freed
  • Jo Martin
  • Suzanne A. Pierce
Overview Article
Part of the following topical collections:
  1. Sustainability Science Innovation and Capacity Development

Abstract

Given the rapid emergence of data science techniques in the sustainability sciences and the societal importance of many of these applications, there is an urgent need to prepare future scientists to be knowledgeable in both their chosen science domain and in data science. This article provides an overview of required competencies, educational programs and courses that are beginning to emerge, the challenges these pioneering programs face, and lessons learned by participating instructors, in the broader context of sustainability science competencies. In addition to data science competencies, competencies collaborating across disciplines are essential to enable sustainability scientists to work with data scientists. Programs and courses that target both sets of competencies—data science and interdisciplinary collaboration—will improve our workforce capacity to apply innovative new approaches to yield solutions to complex sustainability problems. Yet developing these competencies is difficult and most instructors are choosing instructional approaches through intuition or trial and error. Research is needed to develop effective pedagogies for these specific competencies.

Keywords

Education Interdisciplinary studies Competencies Data science applications 

Notes

Acknowledgements

This work was supported in part through NSF grant #1632211 EarthCube RCN IS-GEO: Intelligent Systems Research to Support Geosciences (S. Pierce). Support was also provided by NSF awards DGE-1545404 and DBI-1356707 (Pennington), and AGS-3891445978 (Ebert-Uphoff).

Supplementary material

11625_2019_735_MOESM1_ESM.docx (18 kb)
Supplementary material 1 (DOCX 18 kb)

References

  1. Barile S, Orecchini F, Saviano M, Farioli F (2018) People, technology, and governance for sustainability: the contribution of systems and cyber-systemic thinking. Sustain Sci 2018:1–12Google Scholar
  2. Belmont Forum (2017) Data skills curricula framework. Technical report. http://www.bfe-inf.org/resource/data-skills-curricula-framework-full-recommendations-report
  3. Berman F, Stodden V, Szalay AS, Rutenbar R, Hailpern B, Christensen H, Davidson S, Estrin D, Franklin M, Martonosi M, Raghavan P (2018) Realizing the potential of data science. Commun ACM 61(4):67–72Google Scholar
  4. Blei DM, Smyth P (2017) Science and data science. Proc Natl Acad Sci 114(33):8689–8692Google Scholar
  5. Bosque-Prez NA, Klos PZ, Force JE, Waits LP, Cleary K, Rhoades P, Galbraith SM, Brymer ALB, ORourke M, Eigenbrode SD et al (2016) A pedagogical model for team-based, problem-focused interdisciplinary doctoral education. BioScience 2016:biw042Google Scholar
  6. Bozeman B, Fay D, Slade CP (2013) Research collaboration in universities and academic entrepreneurship: the-state-of-the-art. J Technol Transfer 38(1):1–67Google Scholar
  7. Caputo F, Buhnova B, Walletzk L (2018) Investigating the role of smartness for sustain- ability: insights from the smart grid domain. Sustain Sci 13:1299–1309Google Scholar
  8. Ceri S (2018) On the role of statistics in the era of big data: A computer science perspective. Stat Prob Lett 136:68–72Google Scholar
  9. Choi BC, Pak AW (2006) Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness. Clin Investig Med 29(6):351Google Scholar
  10. Corbin A, Strauss A (1990) Grounded theory research: procedures, canons, and evaluative criteria. Qual Sociol 13(1):3–21Google Scholar
  11. Creutzig F, Lohrey S, Bai X, Baklanov A, Dawson R, Dhakal S, Walsh B et al (2019) Upscaling urban data science for global climate solutions. Glob Sustain 2:e2.  https://doi.org/10.1017/sus.2018.16 CrossRefGoogle Scholar
  12. Derrick EG, Falk-Krzesinski HJ, Roberts MR (2013) Facilitating interdisciplinary research and education: A practical guide. https://www.aaas.org/report/facilitating-interdisciplinary-research-and-education-practical-guide
  13. Ebert-Uphoff I, Deng Y (2017) Three steps to successful collaboration with data scientists. EOS, Trans Am Geophys UnionGoogle Scholar
  14. Ebert-Uphoff I, Thompson D, Demir I, Gel Y, Hill M, Karpatne A, Guereque M, Kumar VEC-C, Smyth P (2017) A vision for the development of benchmarks to bridge geoscience and data science. In: Proceedings of the Seventh International Workshop on Climate Informatics (CI 2017)Google Scholar
  15. Eigenbrode SD, O’Rourke M, Wulfhorst JD, Althoff DM, Goldberg CS, Merrill K, Morse W, Nielsen-Pincus M, Stephens J, Winowiecki L, Bosque-Perez NA (2007) Employing philosophical dialogue in collaborative science. Bioscience 57(1):55–64Google Scholar
  16. Faghmous JH, Kumar V (2014) A big data guide to understanding climate change: the case for theory-guided data science. Big Data 2(3):155–163Google Scholar
  17. Faghmous JH, Banerjee A, Shekhar S, Steinbach M, Kumar V, Ganguly AR, Samatova N (2014) Theory-guided data science for climate change. Computer 47(11):74–78Google Scholar
  18. Falk-Krzesinski HJ, Contractor N, Fiore SM, Hall KL, Kane C, Keyton J, Klein JT, Spring B, Stokols D, Trochim W (2011) Mapping a research agenda for the science of team science. Res Eval 20(2):145–158Google Scholar
  19. Fiore S (2008) Interdisciplinarity as teamwork—how the science of teams can inform team science. Small Group Res 39(3):251–277Google Scholar
  20. Fisher DH, Bian Z, Chen S (2016) Incorporating sustainability into computing education. IEEE Intell Syst 5:93–96Google Scholar
  21. Fox P, Hendler J (2014) The science of data science. Big Data 2(2):68–70Google Scholar
  22. Gibert K, Horsburgh JS, Athanasiadis IN, Holmes G (2018) Environmental data science. Env Model Softw 106:4–12Google Scholar
  23. Gil Y et al (2015) Final workshop report. In: Workshop on intelligent and information systems for geosciencesGoogle Scholar
  24. Gomes CP (2009) Computational sustainability: computational methods for a sustainable environment, economy, and society. The Bridge 39(4):5–13Google Scholar
  25. Haider LJ, Hentati-Sundberg J, Giusti M, Goodness J, Hamann M, Masterson VA, Meacham M, Merrie A, Ospina D, Schill C et al (2018) The undisciplinary journey: early-career perspectives in sustainability science. Sustain Sci 13(1):191–204Google Scholar
  26. Hall KL, Feng AX, Moser RP, Stokols D, Taylor BK (2008) Moving the science of team science forward. Am J Prev Med 35(2):S243–S249Google Scholar
  27. Hall KL, Vogel AL, Huang GC, Serrano KJ, Rice EL, Tsakraklides SP, Fiore SM (2018) The science of team science: a review of the empirical evidence and research gaps on collaboration in science. Am Psychol 73(4):532–548.  https://doi.org/10.1037/amp0000319 CrossRefGoogle Scholar
  28. Hampton SE, Jones MB, Wasser LA, Schildhauer MP, Supp SR, Brun J, Hernandez R, Boettiger C, Collins SL, Gross LJ et al (2017) Skills and knowledge for data-intensive environmental research. Bioscience 67(6):546–557Google Scholar
  29. Hey T, Tansley S, Tolle KM et al (2009) The fourth paradigm: data-intensive scientific discovery, volume 1. Microsoft research Redmond, WAGoogle Scholar
  30. Hou C-Y (2015) Meeting the needs of data management training: the federation of earth science information partners (esip) data management for scientists short course. In: Issues in Science and Technology LibrarianshipGoogle Scholar
  31. Huppenkothen D, Arendt A, Hogg DW, Ram K, VanderPlas JT, Rokem A (2018) Hack weeks as a model for data science education and collaboration. Proc Natl Acad Sci 115(36):8872–8877.  https://doi.org/10.1073/pnas.1717196115 CrossRefGoogle Scholar
  32. Hutchins E (1995) Cognition in the wild. MIT Press, CambridgeGoogle Scholar
  33. Jensenius AR (2012) Disciplinarities: intra, cross, multi, inter, trans. http://www.arj.no/2012/03/12/disciplinarities-2/.Blog
  34. Karpatne A, Atluri G, Faghmous JH, Steinbach M, Banerjee A, Ganguly A, Shekhar S, Samatova N, Kumar V (2017) Theory-guided data science: a new paradigm for scientific discovery from data. IEEE Trans Knowl Data Eng 29(10):2318–2331Google Scholar
  35. Karpatne A, Ebert-Uphoff I, Ravela S, Babaie HA, Kumar V (2018) Machine learning for the geosciences: Challenges and opportunities. In: IEEE transactions on knowledge and data engineering (TKDE)Google Scholar
  36. Kastens K, Dere A, Pennington D, Ricchezza V (2018) Research on the cognitive domain in Geoscience learning: Quantitative reasoning, problem solving, and use of models. A Community Framework for Geoscience Education Research. In: National Association of Geoscience Teachers. https://eos.org/opinions/an-evolutionary-leap-in-how-we-teach-geosciences
  37. Kempler S, Mathews T (2017) Earth science data analytics: definitions, techniques and skills. Data Sci J 2017:16Google Scholar
  38. Kitchin R (2014) The real-time city? Big data and smart urbanism. GeoJournal 79(1):1–14Google Scholar
  39. Klein JT (2010) A taxonomy of interdisciplinarity. In: Frodeman R, Klein JT, Mitcham C (eds) The oxford handbook of interdisciplinarity, pp 15–30. Oxford University PressGoogle Scholar
  40. Klein J, Newell W (1998) Advancing Interdisciplinary Studies. In: Newell W (ed) Interdisciplinarity: essays from the literature. College Board, New York, pp 3–22Google Scholar
  41. Kliskey A, Alessa L, Wandersee S, Williams P, Trammell J, Powell J, Grunblatt J, Wipfli M (2017) A science of integration: frameworks, processes, and products in a place-based, integrative study. Sustain Sci 12(2):293–303Google Scholar
  42. Kolb DA (1984) Experiential learning: experience as the source of learning and development. Prentice-Hall, Englewood CliffsGoogle Scholar
  43. Lee C (2007) Boundary negotiating artifacts: unbinding the routine of boundary objects and embracing chaos in collaborative work. Comput Support Coop Work 16:307–339Google Scholar
  44. Mâsse LC, Moser RP, Stokols D, Taylor BK, Marcus SE, Morgan GD, Hall KL, Croyle RT, Trochim WM (2008) Measuring collaboration and transdisciplinary integration in team science. Am J Prev Med 35(2):S151–S160Google Scholar
  45. Mann S (2016) A rethink for computing education for sustainability. In: International Association for Development of the Information SocietyGoogle Scholar
  46. Mann S, Smith L, Muller L (2008) Computing education for sustainability. ACM SIGCSE Bull 40(4):183–193Google Scholar
  47. Mann S, Muller L, Davis J, Roda C, Young A (2009) Computing and sustainability: evaluating resources for educators. ACM SIGCSE Bull 41(4):144–155Google Scholar
  48. Mezirow J (1997) Transformative learning: theory to practice. New Direct Adult Continuing Educ 1997(74):5–12Google Scholar
  49. Monteleoni C, Schmidt GA, McQuade S (2013) Climate informatics: accelerating discovering in climate science with machine learning. Comput Sci Eng 15(5):32–40Google Scholar
  50. National Academies of Sciences, Engineering, and Medicine (2018) Envisioning the data science discipline: the undergraduate perspective: interim report. The National Academies Press, Washington, DC. https://www.nap.edu/catalog/24886/envisioning-the-data-science-discipline-the-undergraduate-perspective-interim-report
  51. National Research Council and others (2015) Enhancing the effectiveness of team science. National Academies Press. https://www.nap.edu/catalog/19007/ enhancing-the-effectiveness-of-team-science
  52. Nersessian NJ (1999) Model-based reasoning in conceptual change. In: Magnani L, Nersessian NJ, Thagard P (eds) Model-based reasoning in scientific discovery (pp 5–22). http://link.springer.com/chapter/10.1007/978-1-4615-4813-3_1
  53. Newell WH, Luckie DB (2013) Pedagogy for interdisciplinary habits of the mind. In: McCright AM, Eaton W (eds) The May 2012 Conference on Interdisciplinary Teaching and Learning, White paper. East Lansing, MI: Michigan State University. http://lbc.msu.edu/faculty_staff/CITL%20White%20Paper.pdf
  54. O’Rourke M, Crowley S, Eigenbrode SD, Wulfhorst J (2013) Enhancing communication & collaboration in interdisciplinary research. Sage Publications, Thousand OaksGoogle Scholar
  55. O’Rourke M, Crowley S, Gonnerman C (2016) On the nature of cross-disciplinary integration: a philosophical framework. Stud Hist Philos Sci Part C Stud Hist Philos Biol Biomed Sci 56:62–70Google Scholar
  56. Oskam I (2009) T-shaped engineers for interdisciplinary innovation: an attractive perspective for young people as well as a must for innovative organisations. In: 37th Annual Conference–Attracting students in Engineering, Rotterdam, The Netherlands, vol 14Google Scholar
  57. Pankratius V, Li J, Gowanlock M, Blair DM, Rude C, Herring T, Lind F, Erickson PJ, Lonsdale C (2016) Computer-aided discovery: toward scientific insight generation with machine support. IEEE Intell Syst 31(4):3–10Google Scholar
  58. Pennington D (2008) Cross-disciplinary collaboration and learning. Ecol Soc 13(2):8Google Scholar
  59. Pennington D (2010) The dynamics of material artifacts in collaborative research teams. Comput Support Coop Work 19(2):175–199Google Scholar
  60. Pennington D (2011a) Bridging the disciplinary divide: co-creating research ideas in eScience teams. Comput Support Cooper Work Spec Issue Embedd eRes Appl Proj Manag Usabil 20(3):165–196Google Scholar
  61. Pennington D (2011b) Collaborative, cross-disciplinary learning and co-emergent innovation in informatics teams. Int J Earth Syst Inf 4(2):55–68Google Scholar
  62. Pennington D (2016) A conceptual model for knowledge integration in interdisciplinary teams: orchestrating individual learning and group processes. J Env Stud Sci 6(2):300–312Google Scholar
  63. Pennington D, Simpson G, McConnell M, Fair J, Baker R (2013) Transdisciplinary science, transformative learning, and transformative science. Bioscience 63(7):564–573Google Scholar
  64. Pennington D, Bammer G, Danielson A, Gosselin D, Gouvea J, Habron G, Hawthorne D, Parnell R, Thompson K, Vincent S, Wei C (2016) The EMBeRS project: employing model-based reasoning in socio-environmental synthesis. J Env Stud Sci 6(2):278–286Google Scholar
  65. Plale B, McDonald RH, Chandrasekar K, Kouper I, Konkiel S, Hedstrom ML, Myers J, Kumar P (2013) Sead virtual archive: building a federation of institutional repositories for long-term data preservation in sustainability science. Int J Dig Curat 8:172–180Google Scholar
  66. Repko AF (2011) Interdisciplinary research: process and theory. Sage, Thousand Oaks, 2nd editionGoogle Scholar
  67. Seele P (2016) Envisioning the digital sustainability panopticon: a thought experiment of how big data may help advancing sustainability in the digital age. Sustain Sci 11(5):845–854Google Scholar
  68. Sellars SL et al (2017) Big data and the earth sciences: grand challenges workshop. In: Technical report. http://pacificresearchplatform.org/images/reports/BigDataWorkshop2017_Report_FINAL_082417.pdf
  69. Sellars S, Nguyen P, Chu W, Gao X, Hsu K-L, Sorooshian S (2013) Computational earth science: big data transformed into insight. Eos Trans Am Geophys Union 94(32):277–278Google Scholar
  70. Spelt EJ, Biemans HJ, Tobi H, Luning PA, Mulder M (2009) Teaching and learning in interdisciplinary higher education: a systematic review. Educ Psychol Rev 21(4):365Google Scholar
  71. Star S, Griesemer L (1989) Institutional ecology, translations and boundary objectsâ”Amateurs and professionals in Berkeleys Museum of Vertebrate Zoology, 1907-39. Soc Stud Sci 19(3):387–420Google Scholar
  72. Stokols D, Misra S, Moser R, Hall K, Taylor B (2008) The ecology of team science: under-standing contextual influences on transdisciplinary collaboration. Am J Prevent Med 35(2):S96–S115Google Scholar
  73. Stone DA (2013) The experience of the tacit in multi- and interdisciplinary collaboration. Phenomenol Cognit Sci 12(2):289–308Google Scholar
  74. Szalay A, Gray J (2006) 2020 computing: science in an exponential world. Nature 440(7083):413–414Google Scholar
  75. The World Economic Forum (2018) Harnessing artificial intelligence for the earth. Technical report. http://www3.weforum.org/docs/Harnessing_Artificial_Intelligence_for_the_Earth_report_2018.pdf
  76. Thompson JL (2009) Building collective communication competence in interdisciplinary research teams. J Appl Commun Res 37(3):278–297Google Scholar
  77. Thompson K, Ashe D, Carvalho L, Goodyear P, Kelly N, Parisio M (2013) Processing and visualizing data in complex learning environments. Am Behav Sci 57(10):1401–1420.  https://doi.org/10.1177/0002764213479368 CrossRefGoogle Scholar
  78. Thompson K, Gouvea J, Habron G (2016) A design approach to understanding the activity of learners undertaking a model based reasoning course: environment and diversity. In: Presented at the International Conference of the Learning Sciences, SingaporeGoogle Scholar
  79. Thompson K, Danielson A, Gosselin D, Knight S, Martinez-Maldonado R, Parnell R, Pennington D (2017) Designing the EMBeRS Summer School: Connecting Stakeholders in Learning, Teaching and Research. In: Proceedings of the 25th International Conference on Computers in Education, 6. New Zealand: Asia-Pacific Society for Computers in EducationGoogle Scholar
  80. Tibaut A, Zazula D (2018) Sustainable management of construction site big visual data. Sustain Sci 13:1311–1322Google Scholar
  81. Tullock G (2001) A comment on daniel klein’s” a plea to economists who favor liberty”. Eastern Econ J 27(2):203–207Google Scholar
  82. Virapongse A, Duerr R, Metcalf EC (2018) Knowledge mobilization for community re- silience: perspectives from data, informatics, and information science. Sustain Sci (online first)Google Scholar
  83. Wiek A, Withycombe L, Redman CL (2011) Key competencies in sustainability: a reference framework for academic program development. Sustain Sci 6(2):203–218Google Scholar
  84. Wiek A, Bernstein MJ, Foley RW, Cohen M, Forrest N, Kuzdas C, Keeler LW et al (2015) Operationalising competencies in higher education for sustainable development. In: Barth M, Michelsen G, Rieckmann M, Thomas I (eds) Handbook of higher education for sustainable development. Routledge, London, pp 241–260Google Scholar
  85. Wilkinson MD, Dumontier M, Aalbersberg Ij, Appleton J, Axton GM, Baak A, Mons B (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3(1):160018.  https://doi.org/10.1038/sdata.2016.18 CrossRefGoogle Scholar
  86. Xie Y, Eftelioglu E, Ali RY, Tang X, Li Y, Doshi R, Shekhar S (2017) Transdisciplinary Foundations of geospatial data science. ISPRS Int J Geo-Inf 6(12):395Google Scholar
  87. Yarime M (2017) Facilitating data-intensive approaches to innovation for sustainability: opportunities and challenges in building smart cities. Sustain Sci 12(6):881–885Google Scholar

Copyright information

© Springer Japan KK, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Geological SciencesUniversity of Texas at El PasoEl PasoUSA
  2. 2.Cooperative Institute for Research in the Atmosphere, Colorado State UniversityFort CollinsUSA
  3. 3.Department of Curriculum and InstructionUniversity of Texas at AustinAustinUSA
  4. 4.Mathematics and Statistics, University of VermontBurlingtonUSA
  5. 5.Texas Advanced Computing Center, University of Texas at AustinAustinUSA
  6. 6.Electrical and Computer EngineeringColorado State UniversityFort CollinsUSA

Personalised recommendations