Research Data Management for librarians

Michael Day and Marieke GuyDigital Curation Centre (DCC)

About this course

Short presentations with exercises and discussion

Five main sections― Research data and RDM (30 mins)― Data Management Planning (30 mins)― Data sharing (20 mins)― Skills (30 mins)― RDM at Cardiff (30 mins)

Coffee break halfway through, after DMP

Introductions

Introduce yourself and offer a reflection on the questions:

What is your understanding of research?

Do you know anything about data management?

What do you want to find out today?

Do you see a role for librarians in supporting RDM?

Digital Curation Centre (DCC) Consortium comprising units from the Universities of Bath

(UKOLN), Edinburgh (DCC Centre) and Glasgow (HATII) Launched 1st March 2004 as a national centre for solving

challenges in digital curation that could not be tackled by any single institution or discipline

Funded by JISC with additional HEFCE funding from 2011 for targeted institutional development

Support selection of tools: DAF, CARDIO, DMP Online, tools and metadata schema catalogues

Offer advice and support through ‘How to Guides’, ‘Briefing papers’ and Web site

Assess Needs

Make the case

Develop support and services

RDM policy development

DAF & CARDIO assessments Guidance and

training

Workflow assessment

DCC support team

Advocacy with senior management

Institutional data catalogues

Pilot RDM tools

Customised Data Management Plans

…and support policy implementation

Support from the DCC

Research data and RDM

Exercise: What are research data?

In pairs, list as many types of data as you can, focusing

(if appropriate) on the subject areas you support

You have 5 minutes

What are research data?

http://www.youtube.com/watch?v=2JBQS0qKOBU

Video from DCC – first 3.10 minutes

What are research data?

All manner of things produced in the course of research

Defining research data Research data are collected, observed or created, for

the purposes of analysis to produce and validate original research results

Both analogue and digital materials are 'data'

Lab notebooks and software may be classed as 'data'

Digital data can be: ― created in a digital form ('born digital')― converted to a digital form (digitised)

Types of research data Instrument measurements Experimental observations Still images, video and audio Text documents, spreadsheets, databases Quantitative data (e.g. household survey data) Survey results & interview transcripts Simulation data, models & software Slides, artefacts, specimens, samples Sketches, diaries, lab notebooks …

What is data management?

“the active management and appraisal of data over the lifecycle of scholarly and scientific interest”

Digital Curation Centre

What is involved in RDM? Data Management Planning Creating data Documenting data Accessing / using data Storage and backup Sharing data Preserving data

Create

Document

Use

Store

Share

Preserve

RDM principles and advice to share with researchers

See in particular:

UK Data Archive, Managing and sharing data: best practice for researchers http://data-archive.ac.uk/media/2894/managingsharing.pdf

n.b. Data Management Planning and Data Sharing are covered in separate sections

Data creation Decide what data will be created and how - this should

be communicated to the whole research team

Develop procedures for consistency and data quality

Choose appropriate software and formats - some are better for long-term preservation and reuse

Ensure consent forms, licences and partnership agreements don’t limit options to share data if desired

Documentation

Collect together all the information users would need to understand and reuse the data

Create metadata at the time - it’s hard to do later

Use standards where possible

Name, structure and version files clearly

Access and use

Restrict access to those who need to read/edit data

Consider the data security implications or where you store data and from which devices you access files

Choose appropriate methods to transfer / share data― filestores & encrypted media rather than email & Dropbox

Storage and backup

Use managed services where possible e.g. University filestores rather than local or external hard drives

Ask the local IT team for advice

3… 2… 1… backup!― at least 3 copies of a file― on at least 2 different media― with at least 1 offsite

Data selection It’s not possible to keep everything. Select based on:

― What has to be kept e.g. data underlying publications― What legally must be destroyed― What can’t be recreated e.g. environmental recordings ― What is potentially useful to others― The scientific or historical value― ...

How to select and appraise research data:www.dcc.ac.uk/resources/how-guides/appraise-select-research-data

Data preservation

Be aware of requirements to preserve data

Consult and work with experts in this field

Use available subject repositories, data centres and structured databases

― http://databib.org

Data Management Planning

Data Management Planning

DMPs are written at the start of a project to define:

What data will be collected or created?

How the data will be documented and described?

Where the data will be stored?

Who will be responsible for data security and backup?

Which data will be shared and/or preserved?

How the data will be shared and with whom?

Why develop a DMP?

DMPs are often submitted with grant applications, but are useful whenever researchers are creating data.

They can help researchers to: Make informed decisions to anticipate & avoid problems

Avoid duplication, data loss and security breaches

Develop procedures early on for consistency

Ensure data are accurate, complete, reliable and secure

Which funders require a DMP?

www.dcc.ac.uk/resources/policy-and-legal/ overview-funders-data-policies

What do research funders want?

A brief plan submitted in grant applications, and in the case of NERC, a more detailed plan once funded

1-3 sides of A4 as attachment or a section in Je-S form

Typically a prose statement covering suggested themes

Outline data management and sharing plans, justifying decisions and any limitations

Five common themes / questions Description of data to be collected / created

(i.e. content, type, format, volume...)

Standards / methodologies for data collection & management

Ethics and Intellectual Property(highlight any restrictions on data sharing e.g. embargoes, confidentiality)

Plans for data sharing and access (i.e. how, when, to whom)

Strategy for long-term preservation

Exercise: My DMP - a satire

Read through the satirical DMP

Highlight examples of bad practice

Suggest alternative methods / approaches

You have 15 minutes

My Data Management Plan – a satire, Dr C. Titus Brownhttp://ivory.idyll.org/blog/data-management.html

A useful framework to get started

Think about why the questions are

being asked

Look at examples to get an idea of what to include

www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/framework.html

Help from the DCC

https://dmponline.dcc.ac.uk

www.dcc.ac.uk/resources/how-guides/develop-data-plan

How DMPonline works

Create a plan based on relevant funder /

institutional templates...

...and then answer the questions using the guidance provided

Supporting researchers with DMPs

Various types of support could be provided by libraries:

Guidelines and templates on what to include in plans

Example answers, guidance and links to local support

A library of successful DMPs to reuse

Training courses and guidance websites

Tailored consultancy services

Online tools (e.g. customised DMPonline)

Tips to share: writing DMPs Keep it simple, short and specific

Seek advice - consult and collaborate

Base plans on available skills and support

Make sure implementation is feasible

Justify any resources or restrictions needed

Also see: http://www.youtube.com/watch?v=7OJtiA53-Fk

Data sharing

What is data sharing?

“… the practice of making data used for scholarly research available to others.” [Wikipedia]

Who’s involved? the data sharer the data repository the secondary data user support staff!

Reasons to share dataBENEFITS Avoid duplication Scientific integrity More collaboration Better research More reuse & value Increased citation

9-30% increase depending on e.g. discipline (Piwowar et al, 2007, 2013)

DRIVERS Public expectations Government agenda Content mining

― http://www.jisc.ac.uk/news/stories/2012/03/textmining.aspx

RCUK Data Policy― www.rcuk.ac.uk/research/Pages/Data

Policy.aspx

Institutional Policy

The expectation of public access

The RCUK Common Principles state that:

“Publicly funded research data are a public good, produced in the public interest, which should be

made openly available with as few restrictions as possible in a timely and responsible manner that

does not harm intellectual property.”

Exercise: barriers to data sharing

Constraints on data sharing Possible solutions / approaches

Briefly list some reasons why certain data can’t be shared and consider whether any actions could be taken to reduce or overcome these restrictions

You have 10 minutes

Managing restrictions on sharingEthicsBalance data protection with data sharing Informed consent – cover current and future use Confidentiality – is anonymisation appropriate? Access control – who, what, when?

IPR Clarify copyright before research starts Consider licensing options e.g. Creative Commons

Select formats for data sharingIt’s better to use formats that are: Unencrypted Uncompressed Non-proprietary/patent-encumbered Open, documented standard Standard representation (ASCII, Unicode)

Type Recommended Avoid for data sharing

Tabular data CSV, TSV, SPSS portable Excel

Text Plain text, HTML, RTFPDF/A only if layout matters

Word

Media Container: MP4, OggCodec: Theora, Dirac, FLAC

QuicktimeH264

Images TIFF, JPEG2000, PNG GIF, JPG

Structured data XML, RDF RDBMS

Further examples: http://www.data-archive.ac.uk/create-manage/format/formats-table

Research360

How to share research data Use appropriate repositories

― http://databib.org or http://www.re3data.org

License the data so it is clear how it can be reused― www.dcc.ac.uk/resources/how-guides/license-research-data

Make sure it’s clear how to cite the data― http://www.dcc.ac.uk/resources/how-guides/cite-datasets

Skills

How are libraries engaging in RDM?

Library

IT

ResearchOffice

The library is leading on most DCC institutional engagements.

They are involved in: defining the institutional strategy developing RDM policy delivering training courses helping researchers to write DMPs advising on data sharing and citation setting up data repositories ...

www.dcc.ac.uk/community/institutional-engagements

Why should libraries support RDM?

RDM requires the input of all support services, but libraries are taking the lead in the UK – why?

― existing data and open access leadership roles

― often run publication repositories

― have good relationships with researchers

― proven liaison and negotiation skills

― knowledge of information management, metadata etc

― highly relevant skill set

Exercise: skills to support RDM Based on the activities we discussed earlier, consider who

may have relevant skills or expertise to share.

You have 15 minutesActivity Library and LRC IT Services

(OBIS)Research Business Development Office

Copyright

Data citation

Information literacyData storage

Digital preservation

Metadata

...

Possible Library RDM roles Leading on local (institutional) data policy Bringing data into undergraduate research-based learning Teaching data literacy to postgraduate students Developing researcher data awareness Providing advice, e.g. on writing DMPs or advice on RDM within a project Explaining the impact of sharing data, and how to cite data Signposting who in the Uni to consult in relation to a particular question Auditing to identify data sets for archiving or RDM needs Developing and managing access to data collections Documenting what datasets an institution has Developing local data curation capacity Promoting data reuse by making known what is available

RDMRose Lite

An exciting opportunity

Leadership Providing tools and support Advocacy and training Developing data informatics capacity & capability

“Researchers need help to manage their data. This is a really exciting opportunity for libraries….”

Liz Lyon, VALA 2012

Potential challenges Librarians are already over-taxed!

― Other challenges in supporting research (Auckland, 2012)― Getting up-to-speed and keeping up-to-date

How deep is our understanding of research, especially scientific research and our level of subject knowledge?

Translating library practices to research data issues

Will researchers look to libraries for this support?

Still need to resource and develop infrastructure RDMRose Lite

RDM at Cardiff

Exercise: supporting RDM at Cardiff?

In small groups, discuss which activities you think should fall within your role and which shouldn’t.

Do you feel confident to support RDM?

How would you like to see things develop?

You have 15 minutes

Conclusion

Summary

In the light of external drivers, researchers at Cardiff need support for RDM

The library has a key role in shaping services for researchers in this area

Library staff have an opportunity to apply their skills in a new and exciting way

Feedback

Has the event met your expectations?― If not, what would you have liked to see more / less of?

Was the content useful?

Did you like the mix of exercises?

AcknowledgementIdeas and content have been taken from various courses:

― Skills matrix, ADMIRe project, University of Nottinghamhttp://admire.jiscinvolve.org/wp/2012/09/18/rdmnottingham-training-event

― DIY Training Kit for Librarians, University of Edinburghhttp://datalib.edina.ac.uk/mantra/libtraining.html

― Managing your research data, Research360, University of Bathhttp://opus.bath.ac.uk/32296

― RDMRose Lite, University of Sheffieldhttp://rdmrose.group.shef.ac.uk/?page_id=364

― RoaDMaP training materials, University of Leedshttp://library.leeds.ac.uk/roadmap-project-outputs

― SupportDM modules, University of East Londonhttp://www.uel.ac.uk/trad/outputs/resources