Data mgmtlab spr14-mod1-slides_201403245

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Information about Data mgmtlab spr14-mod1-slides_201403245

Published on March 28, 2014

Author: goldenphizzwizards



Data Management Lab: Session 1 Slides

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.


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?


LEARNING OUTCOMES • Describe key challenges associated with managing digital research data • Identify the potential consequences for irresponsible or inattentive data management Data is collected from sensors, sensor networks, remote sensing, observations, and more - this calls for increased attention to data management and stewardship Data Deluge Photocourtesyof Photocourtesyof CCimagebytajaionFlickr CCimagebyCIMMYTonFlickr ImagecollectedbyVivHutchinson

Source: John Gantz, IDC Corporation: The Expanding Digital Universe 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 2005 2006 2007 2008 2009 2010 The World of Data Around Us) Transient information or unfilled demand for storage Information Available Storage PetabytesWorldwide

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 CCimagebySharynMorrowonFlickr CCimagebymomboleumonFlickr

Best Practices Best Practices for Preparing Ecological Data Sets, ESA, August 2010 Poor data practice results in loss of information (data entropy) InformationContent 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 L01_DataManagement.pptx 2. Cook, B. (2013). NACP All Investigator Meeting: Data Management Practices for Early Career Scientists. Presented February 3, 2013. From 3. Vines et al, (2014), Current Biology, The availability of research data declines rapidly with article age.


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

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 Humaniti es DMP Requirem ent 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. DataONE Data Life Cycle 2. Humphrey, C. (2008). e-Science and the Life Cycle of Research. From ~humphrey/lifecycle-science060308.doc 3. Carlson, J. (2013). ICPSR Curating and Managing Data for Reuse: Life Cycle Models and Principles. 4. DataONE Education Module: Data Management Planning. DataONE. From documents/L03_DataManagementPlanning.pptx


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


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 – • Research File System – • Scholarly Data Archive – • REDCap – • Slashtmp (sharing) –

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 • •

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: biggest-data-breaches-hacks/ • Excel error responsible for misinterpretation of data and resulting policy decisions: policy/2013/04/ microsoft-excel-the-ruiner-of-global-economies/ • Sandy’s floodwaters damage 1500 volumes of digital art: hurricane-sandy-digital-archive-rescue

EF-3 Horror Stories • UNC Researcher Demoted over data breach: – ighlights_debate_about_data_security_and_accountability_for_ hacks – demotion-over-data-breach/ • UK Tamiflu Clinical Trial data: 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: at-emory-healthcare-exposes.html?s=print

EF-1 Horror Stories • PLoS Retraction: study-links-failure-to-share-data-with-poor-quality-research- and-leads-to-a-plos-one-retraction/ • Stolen laptops, flash drives, etc: stolen-laptop afraid-ive-lost-my-dissertation/ • Data Management & Sharing Snafu in 3 acts:

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?

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