Postgraduate Course: Engaging with Digital Research (PGSP11401)
|School||School of Social and Political Science
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Course type||Online Distance Learning
||Availability||Available to all students
|Summary||The course will enable students to understand new emerging models of professional practice in business and policy making developing and deploying digital research methods and results. This will include collecting, curating, exchanging and analyzing of digitally-derived data, the use of research from digital environments, and the way this is leveraged turning this data into tools for active use and behaviour change. This module will equip students with a sufficient understanding appropriate to work in areas of professional practice where increasing use is being made of digital research tools, and where domain experts are expected to work with technical research experts. It will explore the methodological, ethical, legal, and practical issues of digital research, and the roles and interests of the actors shaping the practice and institutions.
This course will address the development of transferable insights in managing cross-institutional and citizen collaboration in digital data collection and analysis. This course is meant to provide students with basic skills and knowledge to (i) work in interdisciplinary digital research teams including different information professionals; (ii) organize open data projects using that both analyse and produce tools ; (iii) understand emerging use of digital resources to engage stakeholders in research, and move beyond conventional "expert" analysis to interactive use of data by stakeholders; (iv) address barriers and enablers to adoption of digital and open models of research; and (v) organize the procurement of services to match the need of their organization.
Week 1 Digital research as an innovation process
This week introduces students to conceptual tools that can be used to map and understand the evolving digital research field. Digital research practices and tools are emerging in multi-dimensional innovation process involving many different actors. How can we conceptalise the relationship between actors proposing new types of digital research, and those who may use it? The week continues by looking at a case of how data generated in the scientific research has been used to build services that shape the practices and funding of science.
Week 2 Science metrics and altmetrics
This week builds on the previous week, and uses the example of citation and metrics in research as an example of how a heterogeneous industry such as science attempts to exploit the possibilities of web2.0, focusing on case studies of the academic publishers and innovators building altmetrics. It illustrates how the emergence of new forms of metric based on online media data struggle to establish themselves in the face of the attitudes and practices of researchers and research funders.
Week 3 Open Data - How data can change the world
This week looks at the phenomena of open data. Making data available for free and open use, instead of being kept proprietary and private. The aim of the week is to understand the rational behind the promotion Open Data, identify how Open Knowledge is being promoted in government and industry, and learn how to start an open data project.
Week 4 Big Data and Data Science
Another key buzzword in digital research is Big Data, capturing the explosion in scale and diversity of data available in digital forms, that can be used for both research and operations. This week we will explore what is meant by big data, who is championing it, and where and how it is being used, and look at the role of the data scientist and how they can be integrated into a multi-disciplinary team.
Week 5 Models, modelling, simulation and serious games
Modelling is as a long history in social science and economics, but with the development of computing power, modelling and simulation are finding their way into mainstream decision making in government and firms, and into to communication with publics around complex issues such as climate change.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- Critically evaluate how novel data sources and analysis have been proposed and adopted in practice by organisations, professions and markets
- Understand how novel data sources and methods are developed and validated
- Understand the professional and institutional factors that shape the search for, validation adoption and acceptance of novel data sources and computational sources
- Explore academic and professional literature, and use this to critically evaluate contemporary use of novel data sources
- Plan the development of use of novel data sources, analysis approaches, and manage the co-shaping of validation and use of this evidence in their professional context
|Adler, N. J., & Harzing, A.-W. (2009). When Knowledge Wins: Transcending the Sense and Nonsense of Academic Rankings. Academy of Management Learning & Education, 8(1), 7295. doi:10.5465/AMLE.2009.37012181|
Darby (2014) Make It Local Scotland , 3.04.14 NESTA http://www.nesta.org.uk/publications/make-it-local-scotland
Hannay, T. (2009). From Web 2.0 to the Global Database. In Hey, T. Tansley, S. and Tolle, K. (Eds.) The Fourth Paradigm: Data-Intensive Scientific Research. Microsoft Research, Washington, USA.
Fenner, M. (2013). What can article-level metrics do for you? PLoS Biology, 11(10), e1001687. doi:10.1371/journal.pbio.1001687
Fiore-Silfvast, B & Neff, G (Forthcoming) Communication, Mediation, and the Expectations of Data: Data Valences across Health and Wellness Communities.
Harzing, A.W.; Wal, R. van der (2009) A Google Scholar h-index for journals: An alternative metric to measure journal impact in Economics & Business?, Journal of the American Society for Information Science and Technology, vol. 60, no. 1, pp 41-46. http://www.harzing.com/papers.htm#gshindex
Hargreaves et al (2014) Standardisation in the area of innovation and technological development, notably in the field of Text and Data Mining Report from the Expert Group, European Commission DG Research and Innovation http://ec.europa.eu/research/innovation-union/pdf/TDM-report_from_the_expert_group-042014.pdf
Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 1218772110¿. doi:10.1073/pnas.1218772110
Lyall, C., Williams, R., & Meagher, L. (2009). A Short Guide to Developing Interdisciplinary Strategies for Research Groups, ISSTI Briefing Note (Number 7) October 2009 (available online at http://www.issti.ed.ac.uk/resources/briefing_notes).
Morardet, S.; Milhau, F.; Murgue, C.; Ferrand, N.; Abrami, G.; Popova, A. (2012). Wet-WAG , a role-playing game to support stakeholder dialogue on wetland management. Retrieved from http://cemadoc.irstea.fr/oa/PUB00037191-wet-wag-role-playing-game-support-stakeholder-dial.html
Neff, G. (2013). Why Big Data Wot Cure Us. Big Data, 1(3), 117123. doi:10.1089/big.2013.0029 http://ginaneff.com/wp-content/uploads/2013/09/Neff_Why-big-data-wont-cure-us.pdf
Open Knowledge Foundation (2012) Open Data Handbook Documentation, Open Knowledge Foundation http://opendatahandbook.org/pdf/OpenDataHandbook.pdf
Priem, J and Hemminger, B M (2010) Scientometrics 2.0: Toward new metrics of scholarly impact on the social Web, First Monday, Volume 15, Number 7, 5
Stewart, J., & Hyysalo, S. (2008). Intermediaries, Users and Social Learning in Technological Innovation. International Journal of Innovation Management, 12(03), 295. doi:10.1142/S1363919608002035
Stewart, James, Rob Procter, Robin Williams and Meik Poschen (2012) The role of academic publishers in shaping the development of Web 2.0 services for scholarly communication, New Media and Society, DOI: 10.1177/1461444812465141.
Thelwall, M., Haustein, S., Larivière, V., & Sugimoto, C. R. (2013). Do altmetrics work? Twitter and ten other social web services. PloS One, 8(5), e64841. doi:10.1371/journal.pone.0064841
MILLER, S. (2014). COLLABORATIVE APPROACHES NEEDED TO CLOSE THE BIG DATA SKILLS GAP. Journal of Organization Design, 3(1), 2630. Retrieved from 10.7146/jod.3.1.9823
Morardet, S; Milhau, F; Murgue, C.; Ferrand, N.; Abrami, G.; Popova, A. (2012). Wet-WAG , a role-playing game to support stakeholder dialogue on wetland management. IRSTEA. http://cemadoc.irstea.fr/oa/PUB00037191-wet-wag-role-playing-game-support-stakeholder-dial.html
Johannes Breuer, Gary Bente (2010) Why so serious? On the Relation of Serious Games and Learning, Eludamos. Journal for Computer Game Culture. 2010; 4 (1), p. 7-24
Open Knowledge Foundation http://okfn.org/
Alex Howard Beware openwashing. Question secrecy. Acknowledge ideology.
Crooked Timber Open Data Seminar 2012 http://crookedtimber.org/wp-content/uploads/2012/07/open_data-latex1.pdf
Recommended Reading in Serious Games and Simulations, Igor Mayer (accessed Spring 2014) http://signaturegames.nl/gamelab/recommended-reading-in-serious-games-and-simulations-httpwww-mendeley-comgroups3597521recommended-readings-in-serious-games-simulation-gaming-open-version/
Climate simulations: http://www.climateinteractive.org/tools/en-roads/
Data and Society http://www.datasociety.net/
International Journal of Communication http://ijoc.org/index.php/ijoc?utm_content=buffer94c73
Journal of Policy Modeling: http://www.journals.elsevier.com/journal-of-policy-modeling/
Centre for Policy Modelling http://cfpm.org/
Journal of Artificial Societies and Social Simulation http://jasss.soc.surrey.ac.uk/JASSS.html
|Graduate Attributes and Skills
|Course organiser||Dr James Stewart
Tel: (0131 6)50 6392
|Course secretary||Ms Maria Brichs
Tel: (0131 6)51 3205