1. Trends in Research Data Management
Trends in Research Data Management. Asim Qayyum & Mary Anne Kennan School of Information Studies.

2. Research? Images removed because of copyright restrictions
In its funding rules the Australian Research Council defines research as: ...the creation of new knowledge and/or the use of existing knowledge in a new and creative way so as to generate new concepts, methodologies and understandings. This could include synthesis and analysis of previous research to the extent that it is new and creative. This definition of research is consistent with a broad notion of research and experimental development (R&D) as comprising creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of humanity, culture and society, and the use of this stock of knowledge to devise [innovative] applications (Australian Research Council, 2011) .
Images removed because of copyright restrictions

3. Data use in research
In research, data are generated or collected as a part of any systematic investigation.
Data are then assembled in context, meticulously documented and systematically interpreted by experts to produce new knowledge.
All researchers use data, whether they are in the humanities, sciences or social sciences.
However, there are vast differences in the types of data generated and used in different disciplines. 
Simply research data are any forms of evidence used in research

4. Data trends
Recent theorising that we are in the fourth paradigm of research (Hay, Hansley and Tolley 2009). The first three paradigms are considered to be:
empirical science,
theoretical science, 
computational science, 
The data-intensive or fourth paradigm, arises from advances in computing which have opened up new ways of researching that increasingly rely on networked computing and the use of computer-generated data that vastly increases the potential size and locations of research groups and the size of data sets.

5. Move to sharing and/or open data
Data summarised in publications – “raw” data rarely seen
The analysis and summary of data in publications inevitably incorporates methodological and pragmatic choices made by the researchers at different stages of the research which may limit subsequent interrogation of the data.
Data that is abstracted and prepared for publishing in this way does not necessarily provide sufficient information for those who would like to:
examine  representation of the phenomenon from different epistemic or social perspectives (Markauskaite, 2010); 
re-use data to reproduce and validate original findings;
advance the original research or to open another line of enquiry (Witt, 2009); 
use it to contribute to answering different research questions which may require inter-disciplinary problem solving (Cragin, Palmer, Carlson, & Witt, 2010); or,
conduct research which requires large scale data collection, or data from a variety of sources, beyond the scope of one research team or location (Borgman, 2007).

6. Move to open and/or shared data enabled by:
Data increasingly born digital or digitised
Digitisation of data, computational research and data driven research increase the volume of data = big data
Changes data management, storage etc.

7. Data infrastructures
To use and manage digital data, many countries, including Australia, have made significant strategic investments in developing:
general and discipline-specific data repositories,
virtual laboratories and
other shared technology-enhanced research infrastructures often known under several broad umbrella terms, such as ‘eResearch’, ‘Cyberinfrastructure’, ‘eInfrastructure’, ‘eScience’, ‘eHumanities’, ‘eSocial Sciences’ and ‘The Grid’

8. Ways forward
To enable these other uses of already collected data and to leverage the potential of digital data, and advanced technological infrastructures, there is increasing interest in
Data management and data curation to enable data sharing and re-use.
Data sharing is a key element of collaboration (Borgman, 2006).

9. Data Lifecycle examples
Data Life University of QLD (
Data Life University of Virginia (

10. Data management in practice
Data gathering in a traditional sense: Deakin University: Research Monash University:

11. Role of Libraries in Data Management
The ISSUE:
Researchers deposit and retrieve information by themselves, so librarians are considered trainers of undergraduates students in finding information
The SOLUTION:
Libraries build data literacy within their own staff and thus lead development of data literacy among RHD students and researchers to manage their own data.
Practical support examples:
By increasing data awareness among researchers;
By providing data archiving and preservation services via the institutional repository particularly for small research groups or individual academics
By developing a new data oriented strand of professional practice.

12. Research into roles of data librarians and data managers
Participants worked in libraries, other information departments, and in specialist data units of universities and scientific organisations.
There was a huge range of job titles.
Data Librarian, Data Manager, Data Consultant, Data Support Officer, Data Archivist.
E-Research Librarian, E-Research Manager
Generic – e.g. Project Officer
But … most library and information staff NEED to understand data

13. Strengths of libraries
Experts at collection management;
Experts in classification and description of materials through cataloguing and metadata;
Experts in selecting, weeding and presenting information;
Experts in archiving (for example appraisal and preservation of primary source materials);
Experts in reference and reader services (identify researcher needs, provide access, advise on current standards, provide advise with plans etc.);
Experts in digital content and services delivery.

14. Knowledge and skills requirements – non data specific
Interpersonal skills and behavioural characteristics
Contextual knowledge
Training and advocacy
Team work
Management

15. Knowledge and skills requirements – data specific
Data types and contexts
Data processes
Legal and regulatory frameworks
Data analytics
Data curation
Metadata
Discovery

16. Information Technology
Important to have “just enough” of an understanding of IT
To bridge the perceived communication gap between IT departments and researchers
To understand IT options and make informed decisions
Be “programming savvy” rather than be a programmer

17. Education & Training Current Future Learned “on the job” Librarians
Information Technologists
Scientists
Learned “on the job”
Future
Librarians “with more”
Scientists “with more”
Short courses
MIS with specialisation
Graduate Certificate

18. Conclusions Obvious v. evidence
Difference – data curation only an emerging need
Scientific organisations require scientific knowledge
Curricular questions
New course

19. Data Management – specialisation and graduate certificate
Possible career options include:
research, faculty or liaison librarian
data librarian
data curator
data archivist
digital media curator
research management officer
e-research officer
roles in research infrastructure

20. About the GCDM/MIS DM specialisation
Requires completion of four (4) subjects including three (3) core subjects:
INF461 An Introduction to Data Management: Governance, Standards and Ethics
INF462 Data Curation
INF463 Introduction to Data Tools and Analytics
Plus one (1) of the following elective subjects:
INF449 Research Data Management
INF522 Project Management in Information Agencies

21. Admission requirements
For the Graduate Certificate in Data Management
An undergraduate degree in information or related disciplines
For the Masters in Information studies (Data Management)
An applicant for admission to the Master of Information Studies shall have a CSU recognised undergraduate degree in any discipline, or qualifications and/or work experience deemed equivalent by the University under the provisions of the Australian Qualifications Framework (AQF).

22. Thank you! Questions?