Managing your research data Stephen Grace and David McElroy Managing your research data workshop, 02 February 2014

Why are you here? You’re managing data (your own or your group's) Or you think you maybe should be You’re not sure why it matters You’re not sure how best to do it You’d like to know whether you’re on the right track

Why manage research data? To make your research easier To stop yourself drowning in irrelevant stuff In case you need the data later To avoid accusations of fraud or bad science To share your data for others to use & learn from Potential collaborations To get credit for producing it (even if you aren’t the lead author) Get citations for datasets, independently of publications Because somebody else said to do so

What is data management? “the active management and appraisal of data over the lifecycle of scholarly and scientific interest” Digital Curation Centre Data management is just part of good research practice

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

Today’s Workshop 1.Defining your data 2.Looking after your data 3.Sharing your data 4.Archiving your data 5.Executing your plan

1. Defining your data Formatting Documentation –Metadata –Methods Standards

File formats for long-term access Unencrypted and uncompressed Non-proprietary/patent-encumbered Open, documented standard Standard representation (ASCII, Unicode) TypeRecommendedAvoid for data sharing 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: manage/format/formats-tablehttp:// manage/format/formats-table

Documentation What would someone unfamiliar with your data need in order to find, evaluate, understand, and reuse them? Consider the differences between someone inside your research group, someone outside your group but in your field, and someone outside your field Two parts: metadata and methods

Metadata About the project –Title, people, key dates, funders and grants About the data –Title, key dates, creator(s), subjects, rights, included files, format(s), versions, checksums Keep this with the data

Methods Document what you did (A published article may not be enough) Document any limitations of what you did If you ran code on the data, document the code and keep it with the data Need a codebook? Or a data dictionary? –If I can’t identify at sight what each bit of your dataset means, yes, you do need a codebook or data dictionary –DO NOT FORGET THE UNITS! Reason #1 for not reusing someone else’s data: “I don’t know enough about how it was gathered to trust it.” Reason #1 for not reusing someone else’s data: “I don’t know enough about how it was gathered to trust it.”

Standards Why reinvent the wheel? If there’s a standard format for your data or how to describe it, use that! The tricky part is finding the right standard –Standards are like toothbrushes... –But using standards is good hygiene! –Your librarian can often help you find relevant standards. –Also check out the DCC catalogue of disciplinary metadata

2. Looking after your data What if… Where to store your data How to backup your data Sensitive data What to keep

What if this was your desk?

For more on this story, see “Why YOU need a Data Management Plan” blog post: 11/08/01/why-you-need-a-data- management-plan What if this was your laptop?

Where to store your data? Your own drive (PC, server, flash drive, etc.) –And if you lose it? Or it breaks? Somebody else’s drive University drive Cloud services like Dropbox/OneDrive –Do they care as much about your data as you do?

How to backup? 3… 2… 1… backup! –at least 3 copies of a file –on at least 2 different media –with at least 1 offsite Use managed services where possible e.g. University filestores rather than local or external hard drives Ask IT Services or your supervisor for advice

Is your sensitive data secure? Access –Who should/shouldn’t have access to your live data? Encryption –Working data, Backups, Shares –TrueCrypt project terminated: VeraCrypt & CipherShed are new but not compatible with TrueCrypt containers. –Backup your password Deletion –Data is stored on drive even after deletion –Software is available to ‘shred’ files –And physical destruction is effective

What to keep? It’s not possible to keep everything. Select based on: –What has to be kept e.g. data underlying publications –What can’t be recreated e.g. environmental recordings –What is potentially useful to others –What has scientific, cultural or historical value –What legally must be destroyed –... How to select and appraise research data:

3. Sharing your data Requirements Plan to share Examples and benefits

How to share/preserve data? What is required? –By your funder –By your publisher –By your university –By your supervisor What subject repositories, data centres and structured databases are available?

Expectations of public access “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.” RCUK Common Principles on Data Policy

If you plan to share your data.... Have you got consent for sharing? Do any licences you’ve signed permit sharing? Is your data in suitable formats? Decisions made early on affect what you can do later

…open data

...personal data

Benefits of sharing data (1) h/13alzheimer.html?pagewanted=all&_r=0 “It was unbelievable. Its not science the way most of us have practiced in our careers. But we all realised that we would never get biomarkers unless all of us parked our egos and intellectual property noses outside the door and agreed that all of our data would be public immediately.” Dr John Trojanowski, University of Pennsylvania... scientific breakthroughs

Benefits of sharing data (2) 8/uncovered-error-george-osborne- austerity... validation of results “It was a mistake in a spreadsheet that could have been easily overlooked: a few rows left out of an equation to average the values in a column. The spreadsheet was used to draw the conclusion of an influential 2010 economics paper: that public debt of more than 90% of GDP slows down growth. This conclusion was later cited by the International Monetary Fund and the UK Treasury to justify programmes of austerity that have arguably led to riots, poverty and lost jobs.”

Benefits of sharing data (3) bites-the-dust-thanks-to-new-planck-data "We're still discussing the details but the idea is to exchange data between the two teams and eventually come out with a joint paper," Dr Jan Tauber, Planck satellite project scientist (July 2014) “Scientists on rival projects looking for evidence that the early Universe underwent a super-expansion are in discussion about working together.” Initial paper by BICEP2 team has now been shown to misinterpreted results due to cosmic dust. Data from both Planck Satellite and BICEP2 now being studies together, and will lead to more papers. important collaborations

Benefits of sharing data (4) “There is evidence that studies that make their data available do indeed receive more citations than similar studies that do not.” Piwowar H. and Vision T.J 2013 "Data reuse and the open data citation advantage“ 9% - 30% increase... more citations

Think about barriers to sharing... Photo jakecaptive/

4. Archiving your data Handing over long-term care and management of data to someone else –A national data centre –A subject repository –UEL’s own data repository –Online repository (e.g. Figshare)

data.uel Data repository to complement ROAR (which is for research publications) Submit data to data.uel and get citations if it is reused, and statistics on where in the world it is downloaded Students, submit your complete thesis to ROAR with data appendix/appendices DOI (digital object identifier) for open records

5. Executing your plan Data Management Plans (DMP) Funders may require a DMP DMPonline tool to help you write a plan

Data Management Plans DMPs are often submitted with grant applications, but are useful whenever you are creating data to: Make informed decisions to anticipate and avoid problems Avoid duplication, data loss and security breaches Develop procedures early on for consistency Ensure data are accurate, complete, reliable and secure Save time and effort – make your life easier!

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 An outline of data management and sharing plans, justifying decisions and any limitations

Five common themes 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

Help from the DCC how-guides/develop-data-plan a web-based tool to help you write DMPs according to different requirements, with UEL templates for staff and students

How DMPonline works Create a plan based on relevant funder / institutional templates......and then answer the questions using the guidance provided

DMPonline template for PGR use Short questions to take you through each step Designed with PGR students in mind Another one available for research staff Try it and share with us for feedback/review

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

Tips on 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 needed or sharing restrictions

–We can help when you write Data Management Plans for grants to increase your chances of getting funded –Put plans in place to help existing projects –Help you manage/describe/share (if appropriate) your data more effectively –Give advice and signposting with your own data needs and questions –Training for staff, students and support staff

Acknowledgements Based on Sarah Jones “Research Data Management” presentation at UEL 1 May 2013 © DCC 2013 CC-BY Thanks to Dorothea Salo, Ryan Schryver and colleagues for content from the “Escaping Datageddon” presentation, available at: datageddonhttp:// datageddon And to the Research360 project at the University of Bath for the “Managing your research data” presentation, available at:

Thank you Stephen Grace, David McElroy, Questions to Find us at Blog at datamanagementuel.wordpress.comdatamanagementuel.wordpress.com