Introduction to research data management

… because good research needs good data

Introduction to Research Data Management: activities, roles and

requirements

Demystifying Research Data, JIBS/RLUK event, SOAS, London, 17 July 2012

Funded by:

Michael DayDigital Curation Centre

UKOLN, University of [email protected]

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 UK: Scotland License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/2.5/scotland/ ; or, (b) send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA.


Outline• Introduction • The researcher perspective

• Codes of Practice• Research funding bodies


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• Research funding bodies• The institutional perspective• Research lifecycles

• Some lifecycle models• The role of the library

• Activities, roles and requirements


Why manage research data?

• Enable reuse• Research integrity• Research impact


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• Linking data and publication• Making data citable

• Regulatory requirements• Controlling costs• Maximising value


Who are the main actors?

• Researchers - as creators and users• Other Data creators• Other Data (re)users


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• Funding bodies• Data Centres• Computer science research• Libraries• Research support/grant offices• Archivists/records managers


What is required?• Technical infrastructure

• Storage (many options)• Tools• Discovery


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• Discovery• Research Intelligence (RIM)

• Policy & commitment• Human infrastructure

• Researcher skills• Support services• Training


Potential national-level actions

• Building dataset discovery• Collecting data policies• Liaise with other national & international actors


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• Support uptake of cloud-based tools• Exploit pool of data plans• Collecting stories on data re-use• Supporting effective citation, referencing, etc• Sharing good practice


The researcher perspective

• Managing and sharing data is simply part of good research:• Adhering to disciplinary and/or institutional codes of practice

and policies


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and policies• Has been practiced since the advent of modern science, but

not always consistently; data intensive research makes it even more critical

• Meeting the specific requirements of funding bodies

• Reputational risks if data management is not handled properly


Research codes of practice (1)

• UK Research Integrity Office Code of Practice for Research (2009)

Data management planning is an essential part of research design


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designOrganisations should have in place procedures, resources (including physical space) and administrative support to assist researchers in the accurate and efficient collection of data and its storage in a secure and accessible form [3.12.5]


Research codes of practice (2)

• RCUK Code of Conduct on the Governance of Good Research Conduct (2011)

Primary data and research evidence [should be made] accessible to others for reasonable periods after the


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accessible to others for reasonable periods after the completion of the research: data should normally be preserved and accessible for 10 yrs (in some cases 20 yrs or longer)Responsibility for proper management and preservation of data and primary materials is shared between the researcher and the research organisation [although deposit within national collections is endorsed]


Research funding bodies

• UK Research Councils• Help fund some data archives, e.g.:

• Archaeology Data Service, European Bioinformatics Institute, the NERC data centres, UK Data Archive


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Institute, the NERC data centres, UK Data Archive• Support for JISC (and DCC)• RCUK Common Principles on Data Policy

• Recognises that data are a critical output of the research process

http://www.rcuk.ac.uk/research/Pages/DataPolicy.aspx


RCUK Principles (in a nutshell)• Publicly funded research data should be made openly available• Data with acknowledged long-term value should be preserved and

remain accessible and usable for future research • Sufficient metadata should be recorded to enable other researchers to

find and understand the research to enable re-use; published results


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find and understand the research to enable re-use; published results should always include information on how to access the supporting data

• Recognition that there may be legal, ethical and commercial constraints• Recognition that researchers may need privileged use of data for a

limited period• All users of research data should acknowledge their sources• Appropriate to use public funds to support MRD


EPSRC expectations

• Roadmap approved May 2012; compliance by May 2015

Appropriate metadata (including unique IDs) to be made freely available on the Internet within 12 months of data generation


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available on the Internet within 12 months of data generationData not generated in digital format should be stored in a manner to facilitate it being sharedData should be securely preserved for a minimum of 10 years after privileged access expires or the last date access was requested by a third partyAdequate resources from existing funding streamsEPSRC will monitor progress and compliance, and reserves the right to impose appropriate sanctions


Implications for researchers• Increasing number of research councils and funding bodies with data

management and sharing requirements

• Potential loss of research income if these mandates are not met

• Need to determine the costs associated with short and longer-term


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• Need to determine the costs associated with short and longer-term management and curation and to request funds as part of grant

• Responsibility for infrastructure shifting more to HEIs and less to centralised data archives, but institutional infrastructures and services are still emerging

• Need guidance - some good external support

• But also need more local support; often fragmented (need to draw upon existing channels within your institution wherever possible)


Institutional drivers• Safeguarding research integrity• Increasing number of FOI requests for data• Adhering to existing codes of research practice and ethics • Developing new institution-wide strategies, policies and services


Funded by:

• Developing new institution-wide strategies, policies and services for data storage and management

• Increased institutional focus on research management (e.g., in response to REF)

• Benchmarking – self-assessing infrastructure and planning for improvement

• More demands but less resources to work with


Institutional actors• Researchers

• Both as creators and users of data• PIs (e.g., have specific roles WRT grants)• Computer scientists (informaticians, data scientists)


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• Computer scientists (informaticians, data scientists)

• Administration• Research support office (e.g., grants support, research

information management)• Records managers, archivists, FOI office

• Central services• Computing services• Libraries (e.g., institutional repository)


Research data lifecycles


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(e)-Research Life Cycle view of Data Curation?Formulate hypothesis / ideas, test, experiment, observe: data creation,

collection & capture

Adding value: Data

(New) knowledge extraction: data mining, modelling, analysis, synthesis

e-InfrastructureData management

storage & validation:

Data processing

Data processing

Data processing


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Adding value: Data linking, annotation,

visualisation, simulation

e-Infrastructure

Open access

Collaboration

Scholarly communications: data disclosure, publication, citation, discovery, re-use

storage & validation: description, deposit,

self-archiving, preservation,

certificationData processing

Data processing

This work is licensed under a Creative Commons LicenseAttribution-ShareAlike 2.0 Liz Lyon December 2005


E-Science Curation Report - 2003• E-science

discipline• Appropriate

for current focus


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focus• Takes

integrated look at higher education data curation problems

• Granularity on curationactivities?


Open Archival Information System


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RDM at Oxford


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Research360@Bath

• New institutional data scientist role

• Addresses EPSRC expectations (published)


Funded by:http://blogs.bath.ac.uk/research360/

• Doctoral Training Centre hubs

• Faculty-Industry focus• Faculty cascade model• Multi-team approach



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Some library roles (in the lifecycle)

• Leadership – coordinate action• Audit – who has what, where does it go?• Advice on access – data, wherever it is


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• Preservation (long-term access requirements)• Citability• Data/publication linking• Promoting data in teaching• Identifying skill gaps / CPD requirements


Re-skilling for research (RLUK, 2012)• Mary Auckland identified 9 key areas with skill gaps for

subject librarians:• Ability to advise on preserving research outputs• Knowledge to advise on data management and

curation, including ingest, discovery, access, dissemination, preservation, and portability

• Knowledge to support researchers in complying with the


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• Knowledge to support researchers in complying with the various mandates of funders, including open access requirements

• Knowledge to advise on potential data manipulation tools used in the discipline/ subject

• Knowledge to advise on data mining• Knowledge to advocate, and advise on, the use of

metadata• Ability to advise on the preservation of project records

e.g. correspondence• Knowledge of sources of research funding to assist

researchers to identify potential funders• Skills to develop metadata schema, and advise on

discipline/subject standards and practices, for individual research projects


Understanding data requirements


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http://www.dcc.ac.uk/


Data management planning


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Data registries• Findable, citable data has value

• Important to link publications to data (and vice versa)• Increases citations – of data & publication• Increases reuse (hence value)


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• Increases reuse (hence value)• But effects exist even without publication• All benefit – researcher; institution; publisher


Tools to track impact

http://total-impact.org/


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Activities, roles, requirements (1)

• Requirements gathering• Identifying researchers’ data requirements• Developing a shared understanding of what needs to be

done (e.g., identifying where data exist, its form and scale,


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done (e.g., identifying where data exist, its form and scale, any existing retention requirements)

• Identifying good practice within the institution (and the opposite)

• Methods: surveys, focus groups, case studies, joint R&D projects, assessment tools (e.g. DAF)



• Identifying motivations and benefits• For researchers, support services, the institution

• Identifying risks• Data loss (institution, research group, individual)


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• Data loss (institution, research group, individual)• Increased costs (lack of planning, service inefficiency, data

loss)• Legal compliance (research funder, H&S, ethics, FoI)• Reputation (institution, unit, individual)

• Identifying costs• Keeping Research Data Safe (KRDS) toolkit


Activities, roles, requirements (3)• Assessing institutional preparedness

• Identifying institutional stakeholders, existing data support services, gaps

• Benchmarking and planning for the future


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• Skills audit• CARDIO tool

• Policy development• Policies – approval by senior management is just the start; policies

need to be embedded in research practice and responsive to changing requirements

• Data management planning• DMP online, DCC How-to Develop a Data Management Plan guide



• Implementation and service development• Integrating where possible with existing services, e.g. IR,

CRIS, VRE, HPC, cloud services, social media, etc.• Appraisal, deciding what needs to be kept and for how long


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• Appraisal, deciding what needs to be kept and for how long• Storage choices – no one-size-fits-all solution, e.g. Bristol’s

BluePeta petascale storage facility, Bath’s X-Drive approach, cloud approaches

• Data documentation and metadata – layered approaches: top-level discovery (core metadata, collection/experiment-level?), role of standards like DCMI, CERIF, DDI, etc.



• Data issues:• Appraisal: selection criteria, retention periods (who decides?)

• DCC How to appraise and select research data for curation guide


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curation guide• Documentation: metadata, schema, semantics• Formats: proprietary formats, community standards, etc.• Provenance and authenticity• Citation (assignment of persistent IDs?) • Access (embargo policies?)• Licensing

• DCC How to license research data guide


Things to do …

• Create policy – collaborate with others• Growing number of policies being published (EPSRC,

Wellcome Trust)

• Build on existing digital services


Funded by:

• Build on existing digital services• Examples: storage, data registry

• Learn about audit tools (DCC & others)• Learn about data & sources• Re-skill subject librarians• Bridge between publishers & researchers

… because good research needs good dataWhat data to keep

DCC resources


Funded by:http://www.dcc.ac.uk/resources


Thank-you. Any questions?


Funded by:

Michael DayDigital Curation Centre

UKOLN, University of [email protected]

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 UK: Scotland License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/2.5/scotland/ ; or, (b) send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA.

Technology

Introduction to research data management