Data Management Planning and DMPonline Sarah Jones DCC, University of Glasgow VADS4R, UCA Epsom, 22 nd July 2014 Funded by:

What is a data management plan? A brief plan written at the start of your project to define: how your data will be created? how it will be documented? who will access it? where it will be stored? who will back it up? whether (and how) it will be shared & preserved? DMPs are often submitted as part of grant applications, but are useful whenever you’re creating data.

Why develop a DMP? to help you manage your data to make informed decisions so you don’t have to figure out things as you go to anticipate and avoid problems e.g. data loss to make your life easier!

Which UK funders require a DMP? overview-funders-data-policies

DCC Checklist for a DMP 13 questions on what’s asked across the board Prompts / pointers to help researchers get started Guidance on how to answer /resource/DMP_Checklist_2013.pdf

Common themes in DMPs 1.Description of data to be collected / created (i.e. content, type, format, volume...) 2.Standards / methodologies for data collection & management 3.Ethics and Intellectual Property (highlight any restrictions on data sharing e.g. embargoes, confidentiality) 4.Plans for data sharing and access (i.e. how, when, to whom) 5.Strategy for long-term preservation

1. Describing data to be collected What type of data will you produce? What file format(s) will your data be in? How much data will be produced? How will you create your data?

Preferred formats Some formats are better for long-term sharing and preservation. Formats that are: Uncompressed Non-proprietary Open, documented Standard representation (ASCII, Unicode) Data centres may have preferred formats for deposit e.g. TypeRecommendedNon-preferred Tabular dataCSV, TSV, SPSS portableExcel TextPlain text, HTML, RTF PDF/A only if layout matters Word MediaContainer: MP4, Ogg Codec: Theora, Dirac, FLAC Quicktime H264 ImagesTIFF, JPEG2000, PNGGIF, JPG Structured dataXML, RDFRDBMS Further examples:

Tools for managing data managing-active-research-data

2. Standards and methodologies What metadata and documentation will you record? What standards are used in your field? How will your data be organised? Where will it be stored and backed-up?

Documentation and standards Metadata: basic info e.g. title, author, dates, access rights... Documentation: methods, code, data dictionary, context... Use standards wherever possible for interoperability metadata-standards

3. Ethical and IPR implications Are you seeking consent from participants? Who owns your data or has rights in it? Are you re-using other people’s data?

Seek consent for data sharing & preservation If you don’t ask, data centres won’t be able to accept your data – regardless of any conditions on the original grant or your desire for it to be shared.

4. Data sharing and reuse Are you allowed to share your data? Who will you share with and how? Do you need to impose conditions on reuse? How will you license the data for clarity?

CREATIVE COMMONS LIMITATIONS NCNon-Commercial What counts as commercial? SAShare Alike Reduces interoperability NDNo Derivatives Severely restricts use how-guides/license-research-data License your data for reuse Outlines pros and cons of each approach and gives practical advice on how to implement your licence

Data citation Makes it easier for readers to locate the data and validate findings Data citations ensure that data contributors receive proper credit Can link to reuse to show impact Less danger of rival researchers ‘stealing’ results from those who publish their data openly /data-citation-and-linking

ImpactStory: Altmetrics

5. Preservation Which data do you need to keep? Will you deposit your data in a repository? Do you need to prepare it for deposit?

Archiving: lists of data repositories Zenodo OpenAIRE-CERN joint effort Multidisciplinary repository Multiple data types – Publications – Long tail of research data Citable data (DOI) Links to funding, pubs, data & software

Managing and sharing data: a best practice guide How to write a DMP Formatting your data Documentation Data sharing Ethics and consent Copyright …

Tips for writing DMPs Seek advice - consult and collaborate Consider good practice for your field Base plans on available skills & support Make sure implementation is feasible

A useful framework to get you started Think about why the questions are being asked – why is it useful to consider that topic? Look at examples to help you understand what to write

Example plans Technical plan submitted to AHRC by Bristol Uni Technical-Plan-v2.pdf Rural Economy & Land Use (RELU) programme examples UCSD example DMPs (20+ scientific plans for NSF) My DMP – a satire (what not to write!) More at:

Help from the DCC A web-based tool to help researchers write data management plans

DMPonline demo

Thanks – any questions? DCC guidance, tools and case studies: Follow us on and #ukdcc