data management lab: session 1 slides
DESCRIPTION
Spring 2014 Data Management Lab: Session 1 Slides (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab) What you will learn: 1. Build awareness of research data management issues associated with digital data. 2. Introduce methods to address common data management issues and facilitate data integrity. 3. Introduce institutional resources supporting effective data management methods. 4. Build proficiency in applying these methods. 5. Build strategic skills that enable attendees to solve new data management problems.TRANSCRIPT
Research Data Management
Spring 2014: Session 1
Practical strategies for better results
University Library Center for Digital Scholarship
Acknowledgements
Department of Biostatistics – Data Management, Indiana University School of Medicine Colleagues at Johns Hopkins University, Purdue University, Oregon State University, University of Oregon, New York University, and others who shared their expertise.
ROAD MAP FOR THIS LAB
Overview
• Four sessions, 2 hours each • Some lecture, more discussion and activities • Major products
– Practical, detailed data management plan [DRAFT] – Map of data outcomes – Storage & backup plan – Documentation checklist – Data quality standards – Screening & cleaning checklist
Products & Resources
• Box folders – Session 1, 2, 3, 4: Materials for each session – Resources: Miscellaneous resources that span
sessions or are useful later – Upload HERE: Folder for uploading products
• Will be used to assess my teaching – content & delivery • Will NOT be used to assess you • Please delete your name from the file before you
upload them
1. Research data management plans & planning
2. Documentation & metadata
3. Data quality 4. Ethical & Legal issues
in data sharing & reuse
Session 1
1. Research data management plans & planning a) Planning for good data management from the
start b) Defining expected outcomes for your data c) Getting a storage and backup plan
Activities & Discussions
• Introductions (<1 minute each) –Name –Department or Program –What do you want to get out of these
workshops?
INTRODUCTION TO RESEARCH DATA MANAGEMENT
MODULE 1
LEARNING OUTCOMES • Describe key challenges
associated with managing digital research data
• Identify the potential consequences for irresponsible or inattentive data management
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Data is collected from sensors, sensor networks, remote sensing, observations, and more - this calls for increased attention to data management and stewardship
Data Deluge
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Source: John Gantz, IDC Corporation: The Expanding Digital Universe
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The World of Data Around Us)
Transient information or unfilled demand for storage
Information
Available Storage
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Why Data Management
• Natural disaster • Facilities infrastructure failure • Storage failure • Server hardware/software failure • Application software failure • External dependencies (e.g. PKI
failure) • Format obsolescence • Legal encumbrance • Human error • Malicious attack by human or
automated agents • Loss of staffing competencies • Loss of institutional commitment • Loss of financial stability • Changes in user expectations and
requirements
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Best Practices
Best Practices for Preparing Ecological Data Sets, ESA, August 2010
Poor data practice results in loss of information (data entropy)
Info
rmat
ion
Cont
ent
Time
Time of publication
Specific details
General details
Accident
Retirement or career change
Death
(Michener et al. 1997)
14
Data Loss
15
.33
Vines et al, 2014
“MEDICARE PAYMENT ERRORS NEAR $20B” (CNN) December 2004 Miscoding and Billing Errors from Doctors and Hospitals totaled $20,000,000,000 in FY 2003 (9.3% error rate) . The error rate measured claims that were paid despite being medically unnecessary, inadequately documented or improperly coded. In some instances, Medicare asked health care providers for medical records to back up their claims and got no response. The survey did not document instances of alleged fraud. This error rate actually was an improvement over the previous fiscal year (9.8% error rate).
“AUDIT: JUSTICE STATS ON ANTI-TERROR CASES FLAWED” (AP) February 2007 The Justice Department Inspector General found only two sets of data out of 26 concerning terrorism attacks were accurate. The Justice Department uses these statistics to argue for their budget. The Inspector General said the data “appear to be the result of decentralized and haphazard methods of collections … and do not appear to be intentional.”
“OOPS! TECH ERROR WIPES OUT Alaska Info” (AP) March 2007 A technician managed to delete the data and backup for the $38 billion Alaska oil revenue fund – money received by residents of the State. Correcting the errors cost the State an additional $220,700 (which of course was taken off the receipts to Alaska residents.)
Slide courtesy of BLM
Professional Stakes
Benefits of GOOD Data Management
• Efficiency • Safety • Quality • Reputation • Compliance
Minute paper
Why should we care about how research data is managed? [Subtext: Why should researchers spend time managing their data better?]
Don’t forget to upload your paper to Box.
References 1. DataONE Education Module: Data Management. DataONE. Retrieved
December 2013. From http://www.dataone.org/sites/all/documents/ L01_DataManagement.pptx
2. Cook, B. (2013). NACP All Investigator Meeting: Data Management Practices for Early Career Scientists. Presented February 3, 2013. From http://daac.ornl.gov/NACP_AIM_2013/NACP_AIM_Agenda.html
3. Vines et al, (2014), Current Biology, The availability of research data declines rapidly with article age. http://dx.doi.org/10.1016/j.cub.2013.11.014
DATA MANAGEMENT PLANS & PLANNING
MODULE 1
LEARNING OUTCOMES • Understand the life
cycle approach to managing research data
• Summarize the basic components of US federal funding agency requirements for data management and sharing.
• Outline planned project and data documentation in a data management plan.
• Define expected outcomes for data.
The Life Cycle Approach
• Helps define and explain complex processes (graphically). (Carlson, 2013)
• Help to identify important components, roles, responsibilities, milestones, etc. (Carlson, 2013)
• Demonstrates connections and relationships between parts and the whole. (Carlson, 2013)
• Emphasizes the role of data management as an active process embedded throughout the research and knowledge creation life cycles.
DataONE Data Life Cycle
Amanda Whitmire, 2013
Humphrey, Knowledge Creation Cycle
Progress Towards Openness
1985: National Research Council
1999: OMB
Circular A-110
revisions
2003: NIH Data Sharing Policy
2008: NIH
Public Access Policy
2011: NSF DMP Requirem
ent
2012: NEH,
Office of Digital
Humanities DMP
Requirement
2013: NSF Bio sketch change
2013: OSTP
Memo on Public
Access to the
Results of Federally-
Funded Research
OSTP Memo - February 2013
• Data – Maximize access by the general public and without charge…protecting
confidentiality and personal privacy – …recognizing proprietary interests, business confidential information,
and intellectual property rights – …preserving the balance between the relative value of long-term
preservation and access and administrative burden – …ensure all researchers develop data management plans – Ensure appropriate evaluation of the merits of submitted DMPs – Promote the deposit of data in publicly accessible databases – …support training, education, and workforce development related to
scientific data management, analysis, storage, preservation, and stewardship
Policy Drivers
• Funding agencies – Increased impact of funding dollars – Reduce redundant data collection – Further scientific research
• Research Communities – Enhance use and value of existing data – Address big challenges
Data Management Planning
Plan
Collect
Assure
Describe
Preserve
Discover
Integrate
Analyze
DMPs – What do they do?
• Outlines what you will do with your data during and after you complete your research
• Submitted to funders – formal document • Functional DMP – working document
– Start developing during design – Use to guide project start-up – Review and update throughout the project
DMPs – Why?
• Doing it right saves you time and makes your research more efficient – Document crucial information for your thesis or
dissertation
• Makes it easier to preserve and share your data • Increases visibility of research
Data management is an investment in your research to
make it easier and more efficient.
A dose of DMP realism
My data management plan – a satire
DMP
Introduction to the DMP • Workshop - emphasis on planning • BUT it is a working document
Sections to draft • Data description • Existing data (if applicable) • Format
Mapping Data Outcomes
• Clearly describe what you want your research project to accomplish
• Define what the data need to be in order for you to answer your research questions
• Review example
DMP
Data mapping exercise – map out research questions through data fields/points/variables
References
1. Carlson, J. (2013). ICPSR Curating and Managing Data for Reuse: Life Cycle Models and Principles.
2. DataONE Education Module: Data Management Planning. DataONE. From http://www.dataone.org/sites/all/ documents/L03_DataManagementPlanning.pptx
3. Humphrey, C. (2008). e-Science and the Life Cycle of Research. From http://datalib.library.ualberta.ca/ ~humphrey/lifecycle-science060308.doc
4. Whitmire, A. (2013). Research Life Cycle. From http://guides.library.oregonstate.edu/content.php?pid=502068&sid=4136875
ETHICAL & LEGAL OBLIGATIONS MODULE 1
LEARNING OUTCOMES • Identify your legal
obligations for sharing and long-term preservation.
• Identify your ethical obligations for ensuring data confidentiality, privacy, and security.
• Describe intellectual property issues for data that result in a patentable or commercial product.
Ethical vs. Legal • Ethical (Professional Society, Licensure, Community of Practice)
– Sharing (consent, IRB approval, de-identification, etc.) – Redistribution & Re-use – Citation
• Legal (Federal, State, Local, Funding Agency, Institution) – Intellectual Property (e.g., who owns it?) – Copyright – Patents – Trade secrets – Licensing – Monetary exchange – Open source vs. proprietary software – Data retention
Privacy
• Privacy: having control over the extent, timing, and circumstances of sharing oneself (physically, behaviorally, or intellectually) with others.
• Federal guidelines: FERPA, HIPAA • Most research involves asking subjects to provide or release
information voluntarily following an informed consent process.
• Privacy issues arise in regard to information obtained for research purposes without the consent of the subjects.
Confidentiality • Confidentiality: treatment of information that an individual has
disclosed in a relationship of trust and with the expectation that it will not be divulged to others in ways that are inconsistent with the understanding of the original disclosure without permission.
• Questions to consider: – Are identifiers really needed or could data be collected anonymously? – If identifiers are needed, can coded IDs be created to use for data collection,
merging, and analysis, with identifiers kept entirely separate and secure? – How will the data be protected from inadvertent disclosure or unauthorized
access during collection, storage, and analysis? – Should data be manipulated in specific ways to reduce specificity, by
collapsing data into categories with small numbers of individuals, reducing age or geographic specificity, etc.
Intellectual Property Rights
• Patent • Copyright • Trademark • Design • Circuit Layout Right • Plant Breeder’s Right • Trade Secret
DMP
Sections to work on: • Ethics and privacy • Legal obligations
References
1. Australian Research Council. (nd). National Principles of Intellectual Property Management for Publicly Funded Research. From http://www.arc.gov.au/pdf/01_01.pdf
STORAGE & BACKUP MODULE 1
LEARNING OUTCOMES • Prepare a
comprehensive storage and backup plan.
• Create protected copies of files at crucial points in your study.
Storage & Back-up Plan
• Storage – Keep primary copies in a secure, accessible location
• Backup – Additional copies to prevent data loss – Rule of 3 – Diversify hardware, software, and physical location
• Other considerations – Security, encryption, compression
Storage @ IU
• Box @ IU – http://kb.iu.edu/data/bdsv.html
• Research File System – http://kb.iu.edu/data/aroz.html
• Scholarly Data Archive – http://kb.iu.edu/data/aiyi.html
• REDCap – http://www.indianactsi.org/rct
• Slashtmp (sharing) – http://kb.iu.edu/data/angt.html
Backup Plan
• Rule of 3 – Local copy (ex: desktop or laptop) – Semi-local copy (ex: IU cloud storage) – Remote copy (ex: IU cloud storage)
• Backup frequency – How much data can you risk losing?
• Backup procedure – Manual or automatic? – Full or incremental? – Verification/testing? – Documentation
Security & Encryption
• Use IU systems – Strong authentication protocols
• Encryption – Useful for portable devices (e.g., laptops, external hard
drives, flash drives, smartphones, etc.) – Use for highly sensitive data – IU recommendations
• http://kb.iu.edu/data/ayzi.html • http://kb.iu.edu/data/bcnh.html
Master Files
• Provides snapshots of key phases in the data life cycle – Raw – Cleaned – Phases of processing
• In combination with detailed documentation, these files make write-up easier and supports reproducibility and reuse
EF-5 Horror Stories
• World’s Biggest Data Breaches: http://www.informationisbeautiful.net/visualizations/worlds-biggest-data-breaches-hacks/
• Excel error responsible for misinterpretation of data and resulting policy decisions: http://arstechnica.com/tech-policy/2013/04/ microsoft-excel-the-ruiner-of-global-economies/
• Sandy’s floodwaters damage 1500 volumes of digital art: http://www.theverge.com/2013/1/15/3876790/eyebeam-hurricane-sandy-digital-archive-rescue
EF-3 Horror Stories • UNC Researcher Demoted over data breach:
– http://www.insidehighered.com/news/2011/01/27/unc_case_highlights_debate_about_data_security_and_accountability_for_hacks
– http://www.databreaches.net/cancer-researcher-fights-unc-demotion-over-data-breach/
• UK Tamiflu Clinical Trial data: http://blogs.plos.org/speakingofmedicine/2014/01/03/follow-the-money-or-why-it-took-an-accounts-committee-to-decide-why-access-to-clinical-trial-data-matters/
• Data loss at Emory Healthcare exposes over 315,000 patients: http://www.bizjournals.com/atlanta/news/2012/04/18/data-loss-at-emory-healthcare-exposes.html?s=print
EF-1 Horror Stories
• PLoS Retraction: http://retractionwatch.com/2013/01/30/ study-links-failure-to-share-data-with-poor-quality-research-and-leads-to-a-plos-one-retraction/
• Stolen laptops, flash drives, etc: http://news.cnet.com/8301-17938_105-20028475-1.html http://gawker.com/5625139/grad-students-thesis-dreams-on-stolen-laptop http://www.techrepublic.com/forums/questions/help-i-am-afraid-ive-lost-my-dissertation/
• Data Management & Sharing Snafu in 3 acts: https://www.youtube.com/watch?v=N2zK3sAtr-4
Minute Paper
Describe how your storage and backup plan will address the key risks for your data.
Don’t forget to upload your paper to Box.
DMP
Sections to work on: • Data organization
– Storage & Backup Plan
Don’t forget to upload your DMP to Box.
Wrapping up
What’s next? Discussion • What worked? • What didn’t?