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Best Practices for Research Data Management (RDM) : Using Dataverse for Research Data Barsky, Eugene Nov 16, 2016

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494-Barsky_Eugene_WestGrid_Nov16_2016.mp4 [ 1.94GB ]
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Best Practices for Research Data Management (RDM) - Using Dataverse for Research DataNovember 2016Eugene Barsky, UBCeugene.barsky@ubc.ca Image by Outline● General overview of RDM● Tri-Agency approach to RDM● Best Practices for RDM● Focus on DataverseYou will also need UBC Library DataGuide (version 4.1, July 2016) –  a quick guide we have developed to get you started on Research Data Management.Image - - Data richRace teams at the U.S. Grand Prix collected more than 243 TB of data in 2015, a few terabytes more data than there are in the Library of Congress.Image - Data rich- Feed of about 150 key parameters, which is transmitted in real time with a 4G connection- 5 GB of raw data per boat per dayImage - Data poor...- APGAR testImage - Image -  What issues are raised in this video?Data Sharing and Management Snafu in 3 Short Acts Data ManagementProper data management can make it easier for you to:● Find your files● Keep track of different versions of your data● Organize and compile information at the end of a project● Reproduce your work (if required for a journal or patent)● Pass on your work to another researcher● Share your work● Satisfy grant requirements● Satisfy research ethics board and institutional requirements● Satisfy journal requirementsImage - Data Lifecycle● Let’s take a look - Image - Federal Mandates in the U.S.From Developing data services: a tale from two Oregon universities - In Canada...● Tri-Agency Statement of Principles on Digital Data Management - June 2016And you know about the Tri-Agency Open Access policy, right? - Feb 2015 Image - expectationsfor RDMResearchers:- Incorporate RDM best practices (in their discipline)- Develop Data Management Plans (DMPs)- Follow institutional policies and standardsImage - Tri-Agency expectationsfor RDMFunders● Develop policy and requirements that facilitate responsible data management ● Provide clear guidance for fulfill RDM requirements● Promote the importance of excellent RDM● Provide peer-reviewers with guidance for applications assessment Image - Tri-Agency expectationsfor RDMInstitutions● Provide researchers access to repositories that securely preserve, curate and provide access to research data● Provide researchers with guidance to properly manage their data, including DMPs● Promote the importance of research data management to researchers, students and staffImage - Typical Data Management PlanA typical plan includes information about:● Types of data produced● Metadata or documentation standards● Data security and encryption● Data storage● Intellectual property rights● Data sharing● Data archivingImage - Tools● In Canada - DMP Assistant: (CAF authentication coming soon)● In US (for NSF, NIH, NOAA, etc) - Data Management Planning Tool:  Image -  Metadata and Documentation● Metadata is often described as “data about data” ● It helps answer the questions: who?what?where? when? why?Image - Familiar Metadata ExampleImage: Naming● Downloadable instructions –● DataGuide, page 9Image - MetadataDescriptive: content and context of your data at both the dataset and item level.Examples: title, author, keywordsAdministrative: information needed to use the data.Examples: software requirements, copyrightStructural: how different data sets relate to one anotherExamples: Information about the relationship between data sets in a database, file formatsImage - Standards● Many disciplines have metadata standards: e.g. DDI, Dublin Core, Darwin Core ● List of standards in your field - Image - Data Documentation● At the very least you should document your data in a readme.txt file stored alongside your data● DataGuide, page 8Image -   Data Storage and Security● Where do you store your data?● Do you secure it?Image -  Data Storage 3-2-1 backup rule:● Have at least three copies of your data● Store the copies on two different media● Keep one backup copy offsite● DataGuide, page 11Image: UBC provides● WestGrid Data Storage network● UBC’s Storage Grid ● UBC’s Workspace 2.0● Talk to your IT admins!Image - File Formats● Downloadable instructions – to stay with:➢ Non-proprietary➢ Open, with documented standards➢ Used by your community➢ Encoded using standard character encoding➢ Uncompressed● DataGuide, page 11-12Image -  Data Security● Network security  Who has access to the network? Are there firewalls?● Physical security  Who has access to the computers? Who can access physical files? How is data transported?● Computer security Is anti-virus software up to date? Are you protected against power surges? Do you use passwords and firewalls? Is data encrypted? Is data storage secure?● UBC IT encryption service Image - Data Sharing and Re-use Why share data?● Transparency and integrity● Promote innovation and collaboration● Required by funding agencies or journals:○ PLoS - ○ JDAP - - Challenges to Sharing Data● Privacy or ethical issues○ Compliance with institutional (e.g. BREB) or other regulations○ De-identify data○ checklist from UBC ARC● Intellectual property issues (e.g. third-party data, co-authors)Image - How to Share Data● UBC Abacus Dataverse● License ● Data Citation● DOIs ● Version control● Integrity checks● Preservation ● Granular access for sensitive data● Other repositories - Image -  Dataverse - Live presentation20-30 min of live presentation, driving UBC Abacus Dataverse:● Create and change records● Metadata, types of metadata, standards● Uploading files, large files, zipping● Version control● Tabular analysis in your browser● Granular access to datasets: public, institutional, groups● UNFs for data analysis● OAI for discoverabilityHow can we help?● Host and let you manage your data in UBC Abacus Dataverse ● Help you with DMPs - Image - Questions? Image - 


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