Timetable information in the Course Catalogue may be subject to change.

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Social and Political Science : Postgrad (School of Social and Political Studies)

Postgraduate Course: Engaging with Digital Research (PGSP11446)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryPlease note that this course is only available to students of the Data Science, Technology and Innovation (DSTI) online distance learning programme

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.
Course description Week 1 - Introduction to Science Metrics
The week looks 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.
When scientists publish research papers, those papers are judged on the quality and impact. The careers of scientists, and the fortunes of their employers have come to depend heavily on the ranking of the journals where they publish, and subsequent citation by others (Arms and Larsen, 2007; Harley et al., 2010). This ranking and rating system has been built over the recent years by the create of a system that increasingly relies of calculation of impact derived from publisher databases. While introducing an 'objective' measure based on data, the system is also highly criticized (Adler and Harzing 2009) as distorting the entire scientific research process. Harzing and van de Wal propose a more 'democratic' approach to citation analysis using the Google Scholar, suggesting that the open approach of Google is at least as good as the existing closed systems, and in many cases may be better. Can a search engine really be relied to generate metrics that can make or break a career?

Reflective Discussion Topic
What are the arguments for and against the use of the current data-driven system of research ranking? Can we identify who are the primary users and producers of the rankings? Who do they benefit? Reflect on where else we find this type of ranking?

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.

Study Session 1 - Web2.0 and academic publishers
The research community and scholarly publication industry experimented for a number of years to discover how the 'digital'in the form of the internet and web2.0 could be incorporated into, and potentially transform scientific publishing and research. Stewart et al (2012) documents how two firms, NPG, and PLOS tackled the problems, and attempted to use the models and power of the internet to provide new services to users and to transform their business.

Short Video talk by Euan Adie, (founder, Altmetric), and Timo Hanney (MD, Digital Science).

Study Session 2 - Altmetrics
One of the key developments in internet use that attracted the interest of those involved in measuring scientific impact has been the use of social media, including blogs and microblogs, social bookmarking. Do these provide an alternative to more conventional metrics of impact (Priem and Hemminger, 2010; Fenner, M. 2013; Thelwall, et al, 2013)? The videos of the previous session showed how two businesses attempted to use their new online publish platforms to transform the exisiting use of metrics, proposing 'alternative' metrics. What do these metrics offer, and is there evidence that they are valuable and have an impact on the practice of scientists or funders?

Reflective Discussion Topic:
What challenges were faced by the firms in the two cases of digital publishing innovation? What can we learn from the stories of Hanney and Adie, as they try to build a business based on digital science and altmetrics?


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.

Study Session 1 The case for open data
The Open Knowledge Foundation ( sets out the case for open data, and the public and private value that opening data can being to society, including increasing accountability of government and business, increasing Participation of citizens in government, community and research, Tackling Global challenges that cross disciplinary, organisation and national boundaries, to open up research processes ,and by leveraging the 'wisdom' of the crowd to work on data.
This session will direct students to a number of cases of open data use in journalism and in the public sector, and include a specially made video from the Open Knowledge Foundation.

Study Session 2 - The challenges of open data
Opening data is not however without its problems: Those wishing to make data available and promote its use need to overcome technical and legal hurdles, and be sensitive to privacy concerns, and to management fears.
This class will include video interventions by a public sector organisation releasing data, and a developer making use of open data discussing how they approached the task, and how it was accomplished. The report NESTA report Make It Local Scotland (Darby 2013)

Video intervention
Open data in the public sector - case studies

Virtual Classroom
Does open data really make open government? A Class debate.

Reflective Discussion Topic - Describe an example of open data use for either public accountability, or for the design of improved services. What was the rationale, how was it executed?

Week 4 - Big Data
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.

Study Session 1 Big data and data science in practice
This session explores the promise of ¿big data¿ and data science. Davenport (2013) suggests that 'big data' for industry has gone beyond the hype, although there is still a great deal service push by IT-based consultancy.
Big Data and data science may have roots in physical sciences, but is finding value in a range of industries, and in government. Readings and video interventions will help students understand the techniques being brought together as 'Data science', and some of the ways these are being put to work
Learning materials will include:
- short videos by two 'data scientists' describing their work, the tools they use, and the types of applications they address.
- Video intervention - 'big data' in technology policy Giuditta de Prato (European Commission), experiences, opportunities and challenges.

Reflective Discussion Topic
How is big data being sold to your industry? How is it being handled? Find out what are the particular challenges and opportunities in your field (technical, ethical, political etc), and compare with the other students.

Study Session 2 Integration of data science into professional practice
Data Science and the Data scientist are being promoted as important new competences to incorporate in to modern organisations, but there are issues in both recruiting staff, and building understanding within an organisation of the value of big data and data science (Miller, S. 2014).. This session will address the challenges of this process - both the generic challenges of interdisciplinary teams, and the particular challenges interdisciplinary working in around data. Neff (2013) and Neff & Fiore-Silfvast, (Forthcoming) provide examples of how data becomes boundary objects between different professional groups in the field of healthcare.

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.

Study Session 1 Modelling and Simulation
This session gives students a basic grasp of modelling and simulation use in research including agent based modelling, through case studies of practice.

Study Session 2 Simulations and serious gaming in research
Policy and business researchers often have to engage with stakeholder groups, including policy makers on complex issues such as city planning, or the environment. This can be as part of stakeholder and public consultations, or helping decision makers understand the results of research. While complex simulations and models can be used by specialists, simplified models or 'serious games' are starting to be used as useful tools that allow more in depth engagement with the complex problems, and a provide richer feedback rather than conducting surveys, or running seminars. The example of Wet-WAG game is used to understand how a serious game can be used to support stakeholder dialogue.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements Only available to students of Data Science, Technology and Innovation online distance learning programme
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. understand the work-practices of information professionals in digital research;
  2. appreciate the practical benefits and limitations of digital data for organizational decision-making and policy-making;
  3. manage and coordinate an interdisciplinary digital research team where social scientists, computer scientists and domain experts work together;
  4. identify, access and commission on-line data analytics tools and services appropriate to their needs;
  5. understand when and how to procure social media data analytics services and how to combine them with existing knowledge practice.
Reading List
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), 72-95. doi:10.5465/AMLE.2009.37012181

Darby (2014) Make It Local Scotland , 3.04.14 NESTA

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.

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

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

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

Neff, G. (2013). Why Big Data Won¿t Cure Us. Big Data, 1(3), 117-123. doi:10.1089/big.2013.0029

Open Knowledge Foundation (2012) Open Data Handbook Documentation, Open Knowledge Foundation

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), 26¿30. 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.

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

Alex Howard 'Beware openwashing. Question secrecy. Acknowledge ideology.'

Crooked Timber Open Data Seminar 2012

Recommended Reading in Serious Games and Simulations, Igor Mayer (accessed Spring 2014)

Climate simulations:
Additional Information
Graduate Attributes and Skills Not entered
Special Arrangements Enrolment is restricted to studnets on the Online Distance Learning Data Science Programme.
KeywordsNot entered
Course organiserDr James Stewart
Tel: (0131 6)50 6392
Course secretaryMr Jason Andreas
Tel: (0131 6)51 3969
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information