Data sharing guidance for researchers seeking permission ...
How to assist researchers in sharing their research data | October … · 2016-02-17 · Data...
Transcript of How to assist researchers in sharing their research data | October … · 2016-02-17 · Data...
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How to assist researchers in sharing their research data | October 22, 2015
Lisa R. Johnston Research Data Management/Curation Lead and Co-Director of the University Digital Conservancy University of Minnesota Alex Ball Research Data Librarian University of Bath Joe Shell Head of Research Data Management Mendeley
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Data Sharing
Five ways that YOUR Library can support researchers when sharing their data
Library Connect Webinar:
How to assist researchers in sharing their research data
Lisa Johnston University of Minnesota - Twin Cities
October 22, 2015
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5 ways that your Library might help researchers share their data:
1. Keep doing what you do….be an information resource 2. Educate on data management skills/best practices 3. Develop policy + institutional guidelines for data 4. Create a data sharing service: Lots of options! 5. Curate and archive institutional data for reuse
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1. Keep doing what you do….be an information resource
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Library website pulls campus resources together
http://lib.umn.edu/datamanagement
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Bring data service providers together in an informal way
Past RDM Discussion Topics:
● Data Storage Options on Campus
● Metadata Standards ● Spatial Data ● Best Practices for De-
identifying Research Data ● Data Repositories (Local,
National) ● Data Services at the
Supercomputing institute ● Practical Examples for
Managing data (Sciences)
https://sites.google.com/a/umn.edu/rdm-cop/home
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Keep up-to-date information for administration
http://lib.umn.edu/datamanagement/funding
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2. Educate on data management skills/best practices
Image: http://www.spellboundblog.com/wp-content/uploads/2008/09/floppy_photo.jpg
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Offer workshop on “How to write a Data Management Plan (DMP)” ● For researchers ● Discussion
Based ● RCR CE Credit ● Departments
request custom sessions
● Co-teach with Liaison
“Training Researchers on Data Management: A Scalable, Cross-Disciplinary Approach," (2012) Available in the Journal of eScience Librarianship (Vol. 1: Iss. 2)
https://www.lib.umn.edu/datamanagement/workshops
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Provide DMP Templates and offer in-person consultations
● One-on-One consults
● DMP Template
● Boilerplate text
● DMPOnline Tool (CDL)
● Examples
https://www.lib.umn.edu/datamanagement/DMP
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Graduate programs do not always include data information literacy (DIL) skills/competencies.
http://datainfolit.org
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U of MN Data Management Online Course
● Hybrid online and in person workshops
● Structured around DMP
● Hands on activities
● Direct application to their data
http://z.umn.edu/datamgmt15
Johnston & Jeffryes (2014). “Steal This Idea.” ACRL News Oct 2014. Slides, handouts, activities for 5-session Data Management Course
http://z.umn.edu/teachdatamgmt
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Scaffold DIL skills for undergrads using Personal Information management tools/stories
https://www.lib.umn.edu/pim/archiving
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3. Develop policy + institutional guidelines for data
Image: http://www.businesscomputingworld.co.uk/wp-content/uploads/2012/01/Computer-Connections.jpg
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Institution-wide data policies define roles and responsibilities for long-term data management issues
Also read: Erway, R. (2013). Starting the Conversation: University-wide Research Data Management Policy. EDUCAUSE Review Online.
Policy: http://policy.umn.edu/research/researchdata
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Ensures accessibility and preservation of research data through curation, metadata, repositories, and other access and retrieval mechanisms to meet
federal, state, sponsor, and University requirements.
Trains and supports researchers in the creation and implementation of data management plans.
Research Data Management Responsibilities University of Minnesota Libraries
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4. Create a data sharing service: Lots of options!
Image Http://cdn.slashgear.com/wp-content/uploads/2012/10/google-datacenter-tech-13.jpg
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Typical Data Sharing
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Data Sharing Techniques
Ways to Share your Data Pros? Cons? Post online to a personal or project website
Publish data in a journal as a “supplement” to your main research article.
Make your data “Available on request” via email or dropbox to those who ask.
Deposit in a disciplinary repository (e.g. Dryad, FlyBase, etc.)
Deposit in a general/commercial repository (e.g. FigShare, Mendeley, etc.)
Deposit in an institutional repository, such as the Data Repository for the University of Minnesota (DRUM)
Lisa Johnston, University of Minnesota Libraries ([email protected])
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DRUM http://z.umn.edu/drum Available to U of M researchers and provides: ○ Open access ○ Curation services ○ Permanent
identifiers (DOI) ○ Flexible Licenses ○ File download
analytics ○ Preservation
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Data Repository of the University of Minnesota (DRUM)
● Utilize existing repository technologies for cost savings/efficiencies (DSpace, open source software)
Custom upload form and metadata schema for research data
Apply Creative Commons licenses
Curation workflow allows for review of data before openly available
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5. Curate institutional research data for sharing and long-term preservation/reuse
Image: http://www.fujitsu.com/img/INTSTG/products/bpm/business-process-management-582x240.jpg
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What is data curation?
Data curation steps may include appraisal, ingest, arrangement and description, metadata creation, format transformation, dissemination and
access, archiving, and preservation of digital research data.
Twin Cities Housing GIS Data, UMN
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What was the process to curate the data?
Stage 0: Stage 1: Stage 2: Stage 3: Stage 4: Stage 5: Stage N:
Receive Data
Appraise / Inventory
Organize Treatment Actions /
Processing
Description/ Metadata
Access Reuse Data
Workflow Stages drafted by the “Digital Curation Sandbox” participants borrowed from DCC Curation Lifecycle.
http://hdl.handle.net/11299/162338
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What happens after submission to DRUM?
After submission, U of Minn researcher receives a confirmation email
Within two business days, we will review their data and contact them about proposed modifications to the submission
Missing files
Changes/additions to data documentation
Reshaping directory structure
Converting proprietary software to more archival-
friendly formats
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Document Extensive Information for Sharing
Methodological Information
Data collection
Processing
Analysis steps
Data-Specific Information
File abbreviations
Name glossary
http://z.umn.edu/readme
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Data Sharing = Services in Support of Research Data Lifecycle
Grant Prep
Storage
Data Collection
Analysis
Preservation
Project Begins
Published Results
Project Close
Sharing
Data Management Plan (DMP) Consultation
Metadata Consultation
Data Management Best Practices/Training
Data Management
Data Curation & Repository Services
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Thanks and questions!
Visit our website http://lib.umn.edu/datamanagement
Lisa Johnston, DMCI Lead University Libraries, [email protected]
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Making the case for sharing with indicators of research data impact
Alex Ball University of Bath
Library Connect Webinar: How to assist researchers in sharing their research data
22 October 2015
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Motivation
Mandates get things done, but grudgingly. To get high quality data sharing, we need to make it rewarding. We need to demonstrate that datasets have impact. CASRAI Dataset-Level Metrics: http://www.casrai.org/Dataset_Level_Metrics NISO Altmetrics Initiative: http://www.niso.org/topics/tl/altmetrics_initiative/
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Thompson Reuters Data Citation Index
http://wokinfo.com/products_tools/multidisciplinary/dci/
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PloS Article Level Metrics (ALM/Lagotto)
http://mdc.lagotto.io/
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PlumX
– Usage – Captures – Mentions – Social media – Citations
http://plu.mx/
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Altmetric
http://www.altmetric.com/
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ImpactStory
https://impactstory.org/
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Other notable services
ResearchGate – https://www.researchgate.net/
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Other notable services
Google Scholar – https://scholar.google.com/
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Other notable services
Microsoft Academic Search – http://academic.research.microsoft.com/
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Data papers and data journals
Murphy, F., Jefferies, N., & Ingraham, Thomas. (2015). Giving Researchers Credit for their Data: Jisc Research Data Spring, July 2015 (version 3). Available from Figshare: http://doi.org/10.6084/m9.figshare.1483297
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figshare
http://figshare.com/
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Final thoughts
Encourage data sharing at your institution: Make it as easy as possible to deposit data Provide stable bibliographic information and an identifier –
this makes tracking citations easier Show depositors numbers of views and downloads Make these statistics available to other services via an API
Researchers are more likely to take care over sharing if they can see it makes a difference!
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Further information
Ball, A. & Duke, M. (2015). How to track the impact of research data with metrics. Edinburgh, UK: Digital Curation Centre. Retrieved from http://www.dcc.ac.uk/resources/how-guides/ track-data-impact-metrics
FORCE11, Data Citation Synthesis Group. (2014). Joint declaration of data citation principles. Retrieved from https://www.force11.org/datacitation
Ball, A. & Duke, M. (2015). How to cite datasets and link to publications. Edinburgh, UK: Digital Curation Centre. Retrieved from http://www.dcc.ac.uk/resources/how-guides/ cite-datasets
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Tools for research data sharing: Mendeley Data and the Electronic Lab
Notebook
Joe Shell, 2015
Library Connect Webinar: How to assist researchers in sharing
their research data
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We agree that research data is important
• Funders are increasingly mandating that data be shared, open and accessible for all the research they fund. In the UK, EU and US, this has implications for 31-34% of all research carried out.
• Articles that have linked datasets have a 50-70% higher chance of being cited!*
*Piwowar, H.A., Day, R.S., Fridsma, D.B. “Sharing detailed research data is associated with increased citation rate” (2007) PLoS ONE http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000308 Dorch, B. (2012) “On the Citation Advantage of linking to data” http://findresearcher.sdu.dk/portal/files/82728348/Dorch_2012a.pdf
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Our current Data situation - Christine L. Borgman
“…issues are becoming clear: the need for coordination among stakeholders, economic challenges to the sustainability of archives, and misaligned public policies for open access to publications and data. The practice and policy issues on the ground are much less well understood, however. Norms for the acquisition, release, and reuse of data –and the very definition of data – vary widely between research domains, and motivations to share data vary accordingly.... Releasing data offers benefits, but so does controlling data. The workforces required for the stewardship of data are many and varied; they too must be nurtured and sustained. Wise investments must be made in knowledge infrastructures – and soon – if research data are to remain useful for generations to come.”
- Christine L. Borgman. "Keynote: Data, Data, Everywhere, Nor Any Drop to Drink (slides)" Research Data Alliance Fourth Plenary Meeting. Amsterdam. Sep. 2014.
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Librarians can be our Data Stewards
Librarians can be the stewards of helping make happy data. The gatekeepers of not just influencing best practice, but potentially enforcing quality of data collected at point of collection Benefits - Reporting - Meeting funding body requirements - Non-duplication - Value of research data at an institute - Good archiving
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Answer – We need to be there every step of the way
Enabling Research Doing Research Sharing Research Search,
discover, read, review
Synthesize and analyze
Experiment
Recruit/evaluate researchers
Secure funding
Manage facilities
Publish and disseminate
Manage Data
Promote Commer-cialize
Collaborate
Establish partnerships
?
Develop strategy
Have impact
! $
Research Data Management Space
Search, discover, read, review
Synthesize/ Analyze Experiment
Publish and disseminate
Manage Data
Collaborate & network
• Discover data- sets of third parties (world)
• Re-use third party datasets
• Search / query own datasets (overlaps with “manage data”)
• Share data with colleagues in the lab or broader (unstructured)
• Experimental workflow (structured collab)
• Plan and prepare experiments
• Protocol templates
• Capture data from experiments
• Process raw data to enhance value
• Analyse • Visualise • Predict/model
• Store and preserve data
• Integrate data from disparate sources
• Be able to effectively search within all data of your own institution/lab/group
• Publish research
dataset
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Questions and Thank You!
Lisa R. Johnston Research Data Management/Curation Lead and Co-Director of the University Digital Conservancy University of Minnesota [email protected] Alex Ball Research Data Librarian University of Bath [email protected] Joe Shell Head of Research Data Management Mendeley [email protected], @JosephShell