THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2014/2015
Archive for reference only
THIS PAGE IS OUT OF DATE

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 (PGSP10006)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 10 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe 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 ¿ 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.

Study Session 1 Social learning and social shaping of digital technologies
This session introduces builds on conceptual tools introduced in the Social Shaping of Digital Research, to help understand evolving ¿digital research¿ field, particular the role of different actors, in order to identify key players ad intermediaries in the research fields to be explored in this course.

Reflective Discussion Topic
Are you a user or a producer of novel digital research?
Who are the actors developing digital research? Who are the users? From your existing knowledge, and through discussion and web research, discuss how a market and an industry(ies) for digital research being created?

Study Session 2 ¿ Introduction to Science Metrics
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?


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.

Study Session 1 The case for open data
The Open Knowledge Foundation (https://okfn.org/opendata/) 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.

Reflective Discussion Topic:
Can open data really change the world?

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) http://www.nesta.org.uk/publications/make-it-local-scotland

Video intervention
Open data in the public sector ¿ case studies

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

Group Assessment Task ¿ 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 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.

Study Session 1 Big data and data science in practice
This session explores the promise of ¿big data¿ and the mergence of ¿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

Reading : Davenport Big Data at Work, Harvard Business Review
Short videos by two ¿data scientists¿ describing their work, the tools they use, and the types of applications they address.
Including Video intervention ¿ ¿big data¿ in technology policy Giuditta de Prato (European Commission), experiences, opportunities and challenges.

Reflective Discussion
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.

Includes video interventions by practitioners.

Reflective Exercise
What are the challenges of increasing the use of data science within organisations and in policy? How could this increased use affect relationships between professional groups, stakeholders and citizens?

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.

Reflective exercise


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 None
Information for Visiting Students
Pre-requisitesNone
Course Delivery Information
Academic year 2014/15, Available to all students (SV1) Quota:  None
Course Start Semester 2
Course Start Date 12/01/2015
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Knowledge and Understanding:
    ¿ The students will gain a critical and broad knowledge of how the digital age has created opportunities and challenges for social research that has been conducted in the past and is being developed today.
    ¿ Students will learn concepts in innovation studies, in particular the qualities and dynamics of ICTs and information systems, and of digital research is embedded in, and is shaped by broad socio-economic forces.
    ¿ understand the different types and dimensions of ¿digital¿ research, and how they are being shaped by the requirements of users, society and the embedding of ICTs in social and economic life.
    ¿ understand the conceptual ideas and approach behind ¿open data¿, ¿big data¿ and critically engage with the claims of those promoting and developing these approaches.
    ¿ Understand key elements of the use of models, simulations and games, both in policy modelling and in stakeholder engagement, including basic epistemological issues.
  2. Applied Knowledge:
    Students will understand how to choose relevant research approaches to tackle different research questions, and understand different challenges.
    Students will gain knowledge of the state of the art and practice in the digital research, and the limitations and challenges that face those conducting and commissioning digital research.
    The students will learn to plan a research project using digital research techniques, focusing on crowdsourcing.
    Students will learn to evaluate services offered by research organisations.
    Students will know how to initiate a game and simulation based research consultation.
  3. Skills
    Students will extend their critical skills and thinking through engagement with research texts
    Students will develop ability to communicate with their peers through the group assignments and discussions
    Students will gain skills in critical evaluation of research methods used to generate digital research.
    Students will gain practical Skills to access Digital Research
  4. Autonomy and Working with Others
    Students will develop skills in autonomously finding information, and critically engaging with both academic research and with sources from practice.
    Students will develop skills of online group work, and, especially how to bring together evidence and examples to develop common frameworks and understanding.

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 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 Won¿t Cure Us. Big Data, 1(3), 117¿123. 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), 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. 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

http://www.eludamos.org/index.php/eludamos/article/viewarticle/vol4no1-2/146

Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Contacts
Course organiserDr James Stewart
Tel: (0131 6)50 6392
Email: J.K.Stewart@ed.ac.uk
Course secretaryMiss Jade Birkin
Tel: (0131 6)51 1659
Email: Jade.Birkin@ed.ac.uk
Navigation
Help & Information
Home
Introduction
Glossary
Search DPTs and Courses
Regulations
Regulations
Degree Programmes
Introduction
Browse DPTs
Courses
Introduction
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Prospectuses
Important Information
 
© Copyright 2014 The University of Edinburgh - 12 January 2015 4:34 am