Inroads into Data: Getting Involved in Data at Your Institution

Inroads into Data:Getting Involved in Data

at Your Institution

Margaret HendersonDirector, Research Data Management

mehenderson@vcu.edu@mehlibrarian

Beyond the SEA Webinar, November 18, 2015

“I believe that knowledge rather than the format or container should drive our work.”

~ Lucretia McClure, 1997

http://www.mlanet.org/blog/mcclure,-lucretia-w.-(ahip,-fmla)

Case 1

Case 2

http://retractionwatch.com/2015/08/17/trouble-with-data-prove-toxic-for-a-pair-of-toxicology-papers/

Case 3

Case 4

http://blogs.nature.com/ofschemesandmemes/2014/04/08/imagine-not-getting-the-phd-youd-been-working-towards-datadramas/

What is Data?

• Research results• Admission records• Student course marks• Patient health records• Financial statement• Supply order information• Inventories• Surgery counts • Surgery records• Genetic sequences

• Computer software• Study protocols• Clinical case histories• Samples• Physical collections• Cell lines• Spectroscopic data• Oral history interviews• Surveys• Laboratory Notebooks

“If it gives you pain, it is Big Data.”

~ Donald Brown, Director of Virginia Integrative Data Institute, speaking at Research Data and Technology Fair presented by Claude Moore Health Sciences Library, University of Virginia Health System

Presentation link at http://guides.hsl.virginia.edu/research-fair

The Value of Reference Skills

https://commons.wikimedia.org/wiki/File:1930%27s_-_ca._-_Alma_Custead,_Librarian,_and_Staff.jpg

Environmental Scan

• PEST - political, economic, social, and technological factors

• PESTEL – add environmental and legal factors• SWOT – strengths, weaknesses, opportunities,

and threats• Six Forces Model – competition, new entrants,

end users, suppliers, substitutes, and complementary products

Potential Departments• Information Technology/Technology Services –

backups and security• Office of Research – grants, research output

for assessment, patents• Administration – people, financial, facilities

data• Records – patient health records• Statistics or Biostatistics department

The Noun Project - http://t.co/oGuXfP7NBq

Data Life Cycle

http://www.dcc.ac.uk/resources/curation-lifecycle-model

Simplified Data Lifecycle

Data Management Plan and Ownership

Organizing and folder and file name suggestions

Metadata or Readme files

Clean data and statistics help

IR, subject repository, or journal that includes supporting data.

Stable file formats, duration as per funder or other policy.

Plan

Data Management Plan and Ownership

Data Management PlansOutlines how a researcher will:• collect• organize • back up• storing • sharethe data for a project, and indicates who the data steward will be.

DMPTool

https://dmptool.org/

NIH Policies• Public Access: ...all investigators funded by the NIH submit or have

submitted for them to the National Library of Medicine’s PubMed Central an electronic version of their final peer-reviewed manuscripts upon acceptance for publication, to be made publicly available no later than 12 months after the official date of publication. https://publicaccess.nih.gov/

• Data Sharing: extension of NIH policy on sharing research resources, and reaffirms NIH support for the concept of data sharing. http://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-032.html

• Genomic Data Sharing: Applies to all NIH-funded research that generates large-scale human or non-human genomic data, as well as the use of those data for subsequent research. Requires “Genomic Data Sharing Plan”.Allows for expenses in project budget. http://grants.nih.gov/grants/guide/notice-files/NOT-OD-07-088.html

NSF PoliciesNSF Data Sharing PolicyInvestigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing. See Award & Administration Guide (AAG) Chapter VI.D.4. http://www.nsf.gov/bfa/dias/policy/dmp.jsp

NSF Data Management Plan RequirementsProposals submitted or due on or after January 18, 2011, must include a supplementary document of no more than two pages labeled “Data Management Plan”. This supplementary document should describe how the proposal will conform to NSF policy on the dissemination and sharing of research results. See Grant Proposal Guide (GPG) Chapter II.C.2.j for full policy implementation. https://www.nsf.gov/eng/general/dmp.jsp

NSF PoliciesNSF Data Sharing PolicyInvestigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing. See Award & Administration Guide (AAG) Chapter VI.D.4. http://www.nsf.gov/bfa/dias/policy/dmp.jsp

NSF Data Management Plan RequirementsProposals submitted or due on or after January 18, 2011, must include a supplementary document of no more than two pages labeled “Data Management Plan”. This supplementary document should describe how the proposal will conform to NSF policy on the dissemination and sharing of research results. See Grant Proposal Guide (GPG) Chapter II.C.2.j for full policy implementation. https://www.nsf.gov/eng/general/dmp.jsp

Slide courtesy of Amanda Whitmire

OSTP MemorandumIncreasing Access to the Results of Federally Funded Scientific Research -February 22, 2013

“ensuring that, … the direct results of federally funded scientific research are made available to and useful for the public, industry, and the scientific community. Such results include peer-reviewed publications and digital data.”

“develop plans to make the results of federally-funded research publically available free of charge within 12 months after original publication.”https://www.whitehouse.gov/blog/2013/02/22/expanding-public-access-results-federally-funded-research

Data Management Plans

• All agencies will require a data management plan.

• “Not all data need to be shared or preserved. The costs and benefits of doing so should be considered in data management planning.” DOE third principlehttp://science.energy.gov/funding-opportunities/digital-data-management/

• DOE and NSF have indicated they will review and evaluate DMPs

Data Sharing•Digitally formatted data arising from unclassified, publicly releasable research and programs.

•Decentralized approach to data storage.•Allow for inclusion of costs for data management and access.•Will establish a system to enable the identification, attribution, (federated) storage, and access of digital data.

From NASA FAQ•“First of all, be reassured that we are not going to force you to reveal your precious proprietary data prior to publication. No personal, proprietary or ITAR data is included.”

http://science.nasa.gov/researchers/sara/faqs/dmp-faq-roses/

https://commons.wikimedia.org/wiki/File:SMPTE_Color_Bars.svg

AND NOW BACK TO OUR REGULARLY SCHEDULED PROGRAM

Ownership

• Check institutional policy• Consult with legal counsel for your institution• Can’t copyright data so think about licensing• How to License Research Data

http://www.dcc.ac.uk/resources/how-guides/license-research-data

• Patient Record Ownership by Statehttp://www.healthinfolaw.org/comparative-analysis/who-owns-medical-records-50-state-comparison

Collect

Organizing and folder and file name suggestions

https://twitter.com/DGuarch/status/663049353007931392

OrganizingWhat makes sense for person or group:• File type• Date• Type of analysis• Project

MyDocuments\Research\Sample20.tiff vs.C:\\NSFGrant2020\CellDynamics\Images\RatCell_141020.tiff

Naming

Use file naming conventions for related files• Be consistent• Short yet descriptive• Avoid spaces and special characterse.g. File2020.xls vs. Project_experiment_celltype_YYYYMMDD.xls

Possible elements for file names• Project/grant name and/or number.• Date of creation: useful for version control, e.g. YYYYMMDD• Name of creator/investigator: last name first followed by

(initials of) first name.• Name of research team/department associated with the

data.• Description of content/subject descriptor.• Data collection method (instrument, site, etc.).• Version number.

Describe

Metadata or Readme files

https://www.flickr.com/photos/sarahseverson/6245395188 https://twitter.com/textfiles/status/119403173436850176

Metadata• Descriptive – describes object in question,

whole dataset and each element of the set• Administrative – preservation, IP rights• Structural – physical and logical structure of

digital object• Metadata Standards Directory

http://rd-alliance.github.io/metadata-directory/

Readme Files• Names + contact information for people associated with the

project • List of files, including a description of their relationship to one

another • Copyright + licensing information • Limitations of the data • Funding sources / institutional support• Any information necessary for someone with no knowledge of

your research to understand and / or replicate your work.

http://datadryad.org/resource/doi:10.5061/dryad.jg05d

All Points Alone Points

Data Dictionary

• Define terms used• If measurements are made, gives units and

explains exactly how measured or calculated• How item is recorded, especially when there

are multiple options, e.g. date

https://docs.google.com/spreadsheets/d/1PYOhBh6bglh6BkQFlpvNLOwlpzvQyguWAG8AkQMtU0s/edit#gid=0

http://creativecommons.org/licenses/by-nc-nd/3.0/ http://theupturnedmicroscope.com/

Process & Analyze

Clean data and statistics help

You Can’t Do It All

https://twitter.com/kdnuggets/status/663427070677118976

Tools for Data Cleaning

• Open Refine - to clean and transform data to different formats http://openrefine.org/

• Trifecta Wrangler – free version of the program, so some limitations https://www.trifacta.com/trifacta-wrangler

• NLM-Scrubber – clinical text de-identificationhttps://scrubber.nlm.nih.gov/

• Johns Hopkins Coursera on Data Science https://www.coursera.org/specializations/jhudatascience

Analysis and Visualization

• The R Project - language and environment for statistical computing and graphics https://www.r-project.org/

• Tableau Public – analytical tools and visualizations without learning a programming language https://public.tableau.com/s/

• Flowing Data - Nathan Yau has written a couple of books on statistics and visualization; his website has examples, tutorials and more http://flowingdata.com/

https://public.tableau.com/profile/ifpri.td7290#!/

Publish & Share

IR, subject repository, or journal that includes supporting data.

Sharing Data• Helps to avoid duplication, thereby reducing costs and wasted effort. • Promotes scientific integrity and debate. • Enables scrutiny of research findings and allows for validation of results. • Leads to new collaborations between data users and data creators. • Improves research and leads to better science. • Enables the exploration of topics not envisioned by the initial

investigators.• Permits the creation of new datasets by combining data from multiple

sources.• Increases citations.*

* A study by Piwowar, Day and Fridsma showed a 69% increase in citation, http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0000308

Ways to Share DataUpload to open repository; general, subject, or institutional.• figshare http://figshare.com/ • Zenodo https://zenodo.org/ • Open Science Framework https://osf.io/ • DataVerse http://dataverse.org/ • Search Registry of Research Data Repositories

http://www.re3data.org/

Supplemental file with journal article or link to the upload.

– Be sure to check the contract. – Will the data be available to the public as per

OSTP if grant funded? – Will the rights conflict with institutional ownership

of the data?Tried and true methods? Send files upon request. Upload to personal web site.

Sharing Sensitive Data

http://iom.nationalacademies.org/Reports/2015/Sharing-Clinical-Trial-Data.aspx

Controlled Access• Researchers must request access to database,

explaining research and providing IRB approval forms.

• Data must be anonymized in some way before being made publicly available.

http://transparency.efpia.eu/responsible-data-sharing/efpia-clinical-trial-data-portal-gateway

Preserve

Stable file formats, duration as per funder or other policy.

Storage vs Backup

storage = working files The files you access regularly and change frequently. In general, losing your storage means losing current versions of the data. backup = regular process of copying data separate from storage. You don’t really need it until you lose data, but when you need to restore a file it will be the most important process you have in place.

Rule of 3

Keep THREE copies of your data – TWO onsite – ONE offsite Example – One: Laptop – Two: External hard drive – Three: Cloud storage This ensures that your storage and backup is not all in the same place – that’s too risky!

http://dataabinitio.com/?p=320

Preservation

Considerations

• How long must the data be kept?• What is the long-term value of the data?

Appraisal of Data

1. Relevance to Mission2. Scientific, Social, Cultural, Historical Value3. Uniqueness4. Potential for Redistribution5. Non-Replicability6. Economic Case7. Full Documentationfrom NECDMC, Module 7 activity, http://library.umassmed.edu/necdmc/modules based on Whyte and Wilson http://www.dcc.ac.uk/resources/how-guides/appraise-select-data

Where to Preserve Data

• Dryad• Figshare• Subject Repository • Institutional Repository• Government Repository

Don’t Forget Print• Set a schedule to scan lab notebooks and other print

materials (makes for a good back up and easier to share data within group).

• Print original should have similar security to digital data (i.e. good, secure storage and labelling of files).

Reusing Data

Data Information Literacy

DIL http://www.datainfolit.org/

https://www.dataone.org/education-modules

The New England Collaborative Data Management Curriculum (NECDMC)http://library.umassmed.edu/necdmc/index

ARE YOU DONE YET?

Case 1

• Data Dictionary• Readme File

Case 2

• Rule of 3• Learn statistics

Case 3

• PI needs to check notebook and provide guidance

Case 4

• Rule of 3!

Librarians and Data• Subject headings = Organization• Cataloging = Metadata• Reference = Data Reference and Interviewing• Collections = Purchasing data sets, Deciding what

data to keep• Archives = Preservation, Deciding what to keep• Instruction = Instruction• Policy = Funder Policies• Scholarly Communication = Data Citation, Licensing

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https://www.flickr.com/photos/travelinlibrarian/223839049 by Michael Sauers

References• Bishop, D. 2015. Who’s Afraid of Open Data. Blog post on BishopBlog.

http://deevybee.blogspot.co.uk/2015/11/whos-afraid-of-open-data.html • Carlson, Jake R. 2011. "Demystifying the Data Interview: Developing a Foundation for Reference

Librarians to Talk with Researchers about their Data." Reference Services Review 40 (1): 7-23. • Choudhury, S. 2013. Open Access & Data Management Are Do-Able Through Partnerships. In: ASERL;

2013 Summertime Summit: "Liaison Roles in Open Access & Data Management: Equal Parts Inspiration & Perspiration," https://smartech.gatech.edu/handle/1853/48696

• Christensen-Dalsgaard, et.al. 2012.Ten Recommendations for Libraries to Get Started with Research Data Management: Final report of the LIBER working group on E-Science / Research Data Management . Ligue des Bibliothèques Européennes de Recherche (LIBER)http://libereurope.eu/wp-content/uploads/The%20research%20data%20group%202012%20v7%20final.pdf

• McClure, Lucretia W. 1997. "Knowledge and the Container." In Health Information Management. What Strategies? Proceedings of the 5th European Conference of Medical and Health Libraries, Coimbra, Portugal, September 18–21, 1996, edited by Suzanne Bakker, 258-260: Springer Netherlands. doi:10.1007/978-94-015-8786-0_86

• Rinehart, Amanda K. September 2015. "Getting Emotional about Data: The Soft Side of Data Management Services." C&RL News 76 (8): 437-440.

• Ross, Catherine Sheldrick, Kirsti Nilsen, and Marie L. Radford. 2009. Conducting the Reference Interview: A how-to-do-it Manual for Librarians. 2nd ed. New York: Neal-Schuman Publishers.

Resources• Educating Yourself on Research Data Management: Resources and

Opportunities (resource list) Greater Midwest Region webinar by Abigail Goben and Rebecca Raszewski, Nov. 16, 2015

• Midwest Data Librarians Symposium - presentations and other materials http://dc.uwm.edu/mdls/2015/

• Pinfield, Stephen, Andrew M. Cox, and Jen Smith. 2014. "Research Data Management and Libraries: Relationships, Activities, Drivers and Influences." PloS One 9, no. 12: e114734. doi:10.1371/journal.pone.0114734

• Sweeney L, Crosas M, Bar-Sinai M. Sharing Sensitive Data with Confidence: The Datatags System. Technology Science. 2015101601. October 16, 2015. http://techscience.org/a/2015101601

• Table of NIH Data Sharing Policies and Repositorieshttps://www.nlm.nih.gov/NIHbmic/nih_data_sharing_policies.html