Research Data Management University of East London 1

  • Slides: 40
Download presentation
Research Data Management University of East London, 1 st May 2013 Sarah Jones Digital

Research Data Management University of East London, 1 st May 2013 Sarah Jones Digital Curation Centre sarah. [email protected] ac. uk Twitter: sj. DCC Funded by:

Why are you here? • You’re managing data (your own or your group's) •

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 Photo: by Orijinal http: //www. flickr. com/photos/orijinal/3539418133

Why manage your data?

Why manage your data?

What if this was your desk? • http: //www. computerweekly. com

What if this was your desk? • http: //www. computerweekly. com

What if this was your laptop? Why YOU need a Data Management Plan http:

What if this was your laptop? Why YOU need a Data Management Plan http: //blogs. ch. cam. ac. uk/ pmr/2011/08/01/why-you -need-a-datamanagement-plan

Good data management is about making informed decisions

Good data management is about making informed decisions

 • http: //xkcd. com/949

• http: //xkcd. com/949

Why manage research data? • To make your research easier! • To stop yourself

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 and learn from • To get credit for producing it • Because somebody else said to do so

Expectations of public access “Publicly funded research data are a public good, produced in

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 http: //www. rcuk. ac. uk/research/Pages/Data. Policy. aspx

…open data http: //www. bis. gov. uk/innovatingforgrowth 10

…open data http: //www. bis. gov. uk/innovatingforgrowth 10

. . . personal data

. . . personal data

Benefits of sharing data (1) “It was unbelievable. Its not science the way most

Benefits of sharing data (1) “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 www. nytimes. com/2010/08/13/health/research/ 13 alzheimer. html? pagewanted=all&_r=0 • . . . scientific breakthroughs

Benefits of sharing data (2) “It was a mistake in a spreadsheet that could

Benefits of sharing data (2) “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. ” . . . validation of results www. guardian. co. uk/politics/2013/apr/18/ uncovered-error-george-osborne-austerity

Benefits of sharing data (3) “There is evidence that studies that make their data

Benefits of sharing data (3) “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“ https: //peerj. com/preprints/1. pdf • . . . more citations 9% - 30% increase

Things to think about. . . Photo by @boetter http: //www. flickr. com/photos /jakecaptive/3205277810

Things to think about. . . Photo by @boetter http: //www. flickr. com/photos /jakecaptive/3205277810

What is data management? “the active management and appraisal of data over the lifecycle

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

What is involved in RDM? • Data Management Planning • Creating data • Documenting data • Accessing / using data • Create • Share • Document • Preserve • Use • Storage and backup • Preserving data • Sharing data • Store

If you plan to share your data. . • Have you got consent for

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

File formats for long-term access • • • Unencrypted Uncompressed Non-proprietary/patent-encumbered Open, documented standard

File formats for long-term access • • • 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, RTF PDF/A only if layout matters Word Media Container: MP 4, Ogg Codec: Theora, Dirac, FLAC Quicktime H 264 Images TIFF, JPEG 2000, PNG GIF, JPG Structured data XML, RDF RDBMS • Further examples: http: //www. data-archive. ac. uk/create-manage/formats-table

Documentation What would someone unfamiliar with your data need in order to find, evaluate,

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 •

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 • Reason #1 for not reusing someone else’s data: “I don’t know enough

Methods • Reason #1 for not reusing someone else’s data: “I don’t know enough about how it was gathered to trust it. ” • 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!

Standards • Why reinvent the wheel? If there’s a standard format for your data

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 http: //www. dcc. ac. uk/resources/metadata-standards

Where to store your data? • Your own drive (PC, server, flash drive, etc.

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 • Departmental drive • “Cloud” drive – Do they care as much about your data as you do?

How to backup? • 3… 2… 1… backup! – at least 3 copies of

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 central IT team for advice

What to keep? It’s not possible to keep everything. Select based on: – What

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

How to share/preserve data? • What is required? – By your funder – By

How to share/preserve data? • What is required? – By your funder – By your publisher – By your uni • What subject repositories, data centres and structured databases are available? http: //databib. org

Putting the pieces together. . . Photo by Dread Pirate Jeff http: //www. flickr.

Putting the pieces together. . . Photo by Dread Pirate Jeff http: //www. flickr. com/photos /justageek/2851643792

Data Management Plans DMPs are often submitted with grant applications, but are useful whenever

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!

Which funders require a DMP? • www. dcc. ac. uk/resources/policy-and-legal/%20 overview-funders-data-policies

Which funders require a DMP? • www. dcc. ac. uk/resources/policy-and-legal/%20 overview-funders-data-policies

What do research funders want? • A brief plan submitted in grant applications, and

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 A 4 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.

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

A useful framework to get started • Think about why the questions are being

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 a web-based tool to help you write DMPs according to

Help from the DCC a web-based tool to help you write DMPs according to different requirements • https: //dmponline. dcc. ac. uk • www. dcc. ac. uk/resources/howguides/develop-data-plan • how-guides/develop-data-plan

How DMP Online works Create a plan based on relevant funder / institutional templates.

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

Example plans • Technical plan submitted to AHRC by Bristol Uni http: //data. bris.

Example plans • Technical plan submitted to AHRC by Bristol Uni http: //data. bris. ac. uk/files/2013/02/data. bris-AHRC-Technical-Plan-v 21. pdf • Rural Economy & Land Use (RELU) programme examples http: //relu. data-archive. ac. uk/data-sharing/planning/examples • UCSD example DMPs (20+ scientific plans for NSF) http: //rci. ucsd. edu/dmp/examples. html • My DMP – a satire (what not to write!) http: //ivory. idyll. org/blog/data-management. html

Tips on writing DMPs • Keep it simple, short and specific • Seek advice

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 or restrictions needed http: //www. youtube. com/watch? v=7 OJti. A 53 -Fk

Acknowledgement Thanks in particular to Dorothea Salo, Ryan Schryver and colleagues for content from

Acknowledgement Thanks in particular to Dorothea Salo, Ryan Schryver and colleagues for content from the “Escaping Datageddon” presentation, available at: http: //www. slideshare. net/cavlec/escaping-datageddon And to the Research 360 project at the University of Bath for the “Managing your research data” presentation, available at: http: //opus. bath. ac. uk/32296

Thanks – any questions? DCC guidance, tools and case studies: www. dcc. ac. uk/resources

Thanks – any questions? DCC guidance, tools and case studies: www. dcc. ac. uk/resources Follow us on twitter: @digitalcuration and #ukdcc

Exercise • Writing a DMP • Overcoming barriers to data sharing Which suits best

Exercise • Writing a DMP • Overcoming barriers to data sharing Which suits best based on who has signed up?