Data Management Lab: Session 4 Slides
Upcoming SlideShare
Loading in...5
×
 

Data Management Lab: Session 4 Slides

on

  • 252 views

Data Management Lab: Session 4 Slides (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab) ...

Data Management Lab: Session 4 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.

Statistics

Views

Total Views
252
Views on SlideShare
216
Embed Views
36

Actions

Likes
0
Downloads
0
Comments
0

2 Embeds 36

http://coateshl.wordpress.com 34
http://feedly.com 2

Accessibility

Upload Details

Uploaded via as Adobe PDF

Usage Rights

CC Attribution License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Data Management Lab: Session 4 Slides Data Management Lab: Session 4 Slides Presentation Transcript

  • Research Data Management Spring 2014: Session 4 Practical strategies for better results University Library Center for Digital Scholarship
  • ETHICAL & LEGAL OBLIGATIONS DATA PROTECTION, RIGHTS, & ACCESS MODULE 4
  • LEARNING OUTCOMES • Identify your legal obligations for sharing and long-term preservation. • Identify how ethical and legal obligations affect data protection and sharing. • Identify core project documents for long-term access and preservation. • Select appropriate tools and platforms for storing, managing, and preserving data.
  • Ethical vs. Legal Obligations • 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
  • Federal Laws • HIPAA in research • FERPA • Centers for Medicare and Medicaid Services – Requires patient records be retained for a period of 5 years (see 42CFR482.24 (b) [PDF]). Medicaid requirements may vary by state. • AHIMA – Retention of Health Information – Federal Record Retention Requirements
  • Indiana State Laws • Data Retention – Health care records (Ind. Code § 16-39-7-1) • Health care providers must maintain the original health records or microfilms of the records for at least 7 years. • IU General Counsel Guidance - State Data Protection and Security Laws – Social Security Number law: Ind. Code § 4-1-10 – Security Breach law: Ind. Code § 4-1-11 – Data Destruction: Ind. Code § 24-4-14
  • Indiana University Policies • Human Subjects Standard Operating Procedures: http://researchadmin.iu.edu/HumanSubjects/hs_poli cies.html • Animal Care & Use (IACUC) Policies: http://researchadmin.iu.edu/IACUC/IUPUI/iacuc_pol icies.html • Research, Ethics, Education & Policy: http://researchadmin.iu.edu/REEP/reep_policies.ht ml – Code of Federal Regulations (CFR), Copyright, Patents, etc.
  • Handling Sensitive Data • IU Guidelines: http://protect.iu.edu/cybersecurity/data/handling • Privacy: having control over the extent, timing, and circumstances of sharing oneself (physically, behaviorally, or intellectually) with others. – Privacy issues arise in regard to information obtained for research purposes without the consent of the subjects. • 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.
  • Protect, Store, Preserve • Protection – Includes storage, backup, archiving, preservation AND physical security, encryption, and other topics • Backup v. archive – Backups (active files): a copy (or copies) of the original file; intended for rapid recovery – Archives (selected, static files): long-term preservation of the file, not intended for rapid recovery • Preservation is archiving PLUS data rescue, reformatting, conversion, metadata to ensure ACCESS
  • Deciding what to preserve Gutman et al, 2004, Data Science Journal • How significant are the records for research? • How significant is the source and context of the records? • Is the information unique? • How useable are the records? • Do the records document decisions that set precedents? • Are the records related to other permanent records? • What is the time frame covered by the information? • What are the cost considerations for permanent maintenance of the records?
  • IU Resources: Protect, Store, Preserve • Storage for active files – Research File System: http://kb.iu.edu/data/aroz.html • Collect and store sensitive data @ REDCap – http://www.indianactsi.org/rct • Encryption software @ IUWare • Preserve – open data @ IUPUI DataWorks – “dark data” @ Scholarly Data Archive: http://kb.iu.edu/data/aiyi.html
  • Documentation for Preservation • What will you need to reuse the data in 5 years? What will a colleague or student need to understand the data in 5-10 years? – Study: research questions/aims, IRB protocol, informed consents/authorizations, etc. – Data collection instruments or tools OR data sources – Data collection process or workflow – Data dictionary or model, codebook, readme.txt – Lab or research notebook – Processing or analytical scripts – Suggested citation
  • Metadata for Preservation • If your data is worth keeping and can be shared, put in in a repository that enables both preservation and sharing – Typically, the repository creates metadata to enable discovery and preservation – Standards depend on the community • If it can’t be shared openly, put it in a “dark” repository or storage system – IU Scholarly Data Archive – Metadata likely not necessary; submit documentation with the data
  • Discussion How do our ethical and legal obligations as researchers affect how we store and protect our data?
  • Backups, Archives, and Data Preservation 1. Backup, wikipedia.org, http://en.wikipedia.org/wiki/Backup , (accessed 3/16/2011) 2. Georgia Tech Library, NSF Data Management Plans – Research Data Management (Georgia Tech Library and Information Center), http://libguides.gatech.edu/content.php?pid=123776&sid=1514980 (accessed 3/16/2011) 3. Albanesius, Chloe, Google: Storage software update led to e-mail bug, http://www.pcmag.com/article2/0,2817,2381168,00.asp (accessed 11/18/2011) 4. Van den Eynden, Veerle, Corti, Louise, Woollard, Matthew, Bishop, Libby and Horton, Laurence, Managing and Sharing Data, http://www.data- archive.ac.uk/media/2894/managingsharing.pdf (accessed 4/25/12) For more information about physical security, encryption, and data disposal, visit: http://www.data-archive.ac.uk/media/2894/managingsharing.pdf
  • References 1. UK Data Service: How to share data. From http://ukdataservice.ac.uk/manage-data/plan/how-share.aspx 2. DataONE Education Module: Data Protection Backups. DataONE. Retrieved Nov12, 2012. From http://www.dataone.org/sites/all/documents/L06_DataProtectionBacku ps.pptx 3. Gutman, M. P., Schurer, K., Donakowski, D., Beedham, H. (2004). The Selection, Appraisal, and Retention of Social Science Data. Data Science Journal, 3, 209-221. doi: 10.2481/dsj.3.209
  • DATA SHARING & REUSE MODULE 4
  • LEARNING OUTCOMES • Evaluate resources for sharing data and openly or publicly available data.
  • Why should I care?
  • Why should I care? What matters is the totality of the evidence. Ioannidis JPA (2005) Why Most Published Research Findings Are False. PLoS Med 2(8): e124. doi:10.1371/journal.pmed.0020124
  • DISC-UK Data Sharing Continuum
  • Common Data Sharing Strategies • Depositing in a specialist data centre or archive • Submitting to a journal to support a publication • Depositing in a self-archiving system or an institutional repository • Disseminating via a project or institutional website • Informal peer-to-peer exchange http://ukdataservice.ac.uk/manage-data/plan/how-share.aspx
  • Strategies for Sensitive Data • Authorization or Informed Consent • De-identification – Statistical deletion or masking of 19 identifiers (HIPPA) – Can be reversed • Anonymization – Removing all links to subject so that data cannot be traced back – Cannot be reversed • Limited data set – A set of data in which most of the Protected Health Information has been removed. – Typically involves a Data Use Agreement • Restricted Use Data & Data Enclaves – ICPSR – UK Data Service Secure Lab
  • The Spectrum of Data Sharing ACCESS DARK OPEN SCOPEOFDATA RAW DATA CLEANED DATA LIMITED DATA RESTRICTED USE DATA Published results
  • Sharing Considerations What De-identified data Limited Data Set Publication- related All processed data With whom Upon request Colleagues Community Anyone Where/ How Secure system Community resource Subject repository Institutional repository When Embargoed With publication Immediately
  • IU Resources: Access, Sharing, Re-use • Slashtmp: http://kb.iu.edu/data/angt.html • Indiana CTSI Resources – Alfresco Share, REDCap @ http://www.indianactsi.org/rct • IU High Performance Storage: – Research File System: http://kb.iu.edu/data/aroz.html – Scholarly Data Archive: http://kb.iu.edu/data/aiyi.html • IUPUIDataWorks: http://dataworks.iupui.edu • Data Dryad: http://datadryad.org/ • Figshare: http://figshare.com/ (Data, tables, figures) • ICPSR: http://www.icpsr.umich.edu/icpsrweb/deposit/ • IU Github: https://github.iu.edu/repositories (Code)
  • Activity & Discussion Explore one of the following repositories. • ICPSR • Harvard Dataverse Network • National Database for Autism Research Be prepared to discuss the following: • Is it easy to browse or search for data? • Is it easy to view and download data and documentation? • Can you analyze or visualize the data in the repository?
  • References 1. DataONE Education Module: Data Management. DataONE. Retrieved December 2013. From http://www.dataone.org/sites/all/ documents/L04_DataEntryManipulation.pptx 2. Data Information Specialists Committee(DISC-UK). Data Sharing Continuum: http://www.disc-uk.org/docs/data_sharing_continuum.pdf
  • DATA ATTRIBUTION & CITATION MODULE 4
  • LEARNING OUTCOMES • Identify two technologies enabling data citation.
  • Unique Identifiers: Items • DOI (most common for data) – Provides an actionable, interoperable, persistent link – Actionable – through use of identifier syntax and network resolution mechanism (Handle System®) – Persistent – through combination of supporting improved handle infrastructure (registry database, proxy support, etc) and social infrastructure (obligations by Registration Agencies) – Interoperable – through use of a data model providing semantic interoperability and grouping mechanisms • Data Citation Index (Thomson Reuters) – Gathers citation data (often for free) to build a database of data citations – Takes in free data and charges institutions for the expensive tool • Handle – a unique URL – for example, an item record in a repository
  • Unique Identifiers: Authors • ORCID – 10 things – Can be used across multiple platforms • ResearcherID – Created by Thomson Reuters – limited to their products (e.g., Web of Science) • Scopus Author ID – Created by Elsevier – limited to the Scopus database
  • What do unique identifiers do? • Enable increased availability of impact measures – Citation metrics (e.g., Google Scholar) – Article-level metrics (e.g., PLoS) – Altmetrics (social media based: Twitter, ResearchGate, blog mentions, etc.) – Web analytics (views, downloads) • Make it easy to share, cite, and track • Enable you to be proactive in disseminating your work and gathering evidence of its impact
  • Open Data Repositories • These two will be merged soon: – DataBib: http://databib.org/ – Re3data: http://www.re3data.org/ • DOAR: http://www.opendoar.org/ • NCBI: http://www.ncbi.nlm.nih.gov/ • US Government: – https://www.data.gov/ – https://www.data.gov/open-gov/ – Healthdata.gov • Stats Indiana: http://www.stats.indiana.edu/
  • Licensed Statistics & Data @ IUPUI • Linked from http://ulib.iupui.edu/resources/abc/A – Business Insights: Essentials – Current Index to Statistics – Datastream (terminal in UL Reference Room) – ICPSR – National Archive of Criminal Justice Data – ProQuest Statistical Insight Tables • Statistical Data (research guide, many sources)
  • Practical Strategies • Sign up for an ORCID, especially if – You have a common name – You have publications in more than one name • Put your scholarship online where others can find and access it – Subject repository – Institutional repository – Personal portfolio
  • Activity & Discussion How would you cite the following dataset? http://www.icpsr.umich.edu/icpsrweb/NAHDAP/studies /34792/version/1 How can you track citations to your own data?
  • SYNTHESIS & WRAP UP MODULE 4
  • LEARNING OUTCOMES • ALL OF THEM!
  • Review of Strategies Covered: S1 • Research Data Management – Remember the stakes for poor data management • Data management plans & planning – Funding agency, publisher, & legal requirements – Plan to make your research more efficient – Map data outcomes • Ethical & legal obligations – Identify them before you begin a project – Ask for help • Storage & Backup – Choose reliable, secure tools available at IUPUI
  • Review of Strategies Covered: S2 • Organizing data & files – Create a good file organization plan & write it down – Create a good file naming scheme & write it down – Be consistent – Create master/locked copies of files • Project & data documentation – Identify key documentation & create it – Update documentation throughout the project – Use standards in your field or community of practice – Document for yourself in 5 years
  • Review of Strategies Covered: S3 • Quality assurance & control – Identify standards & write them down – Develop procedures to ensure data quality & write them down • Data collection • Data coding & entry – Use best practices • Data screening & cleaning – Develop a protocol or checklist based on the data outcomes map & identified quality standards • Automation – When possible, choose tools with automation features and detailed event logs
  • Review of Strategies Covered: S4 • Ethical & legal obligations – Know your obligations for data sharing, retention, & preservation – Be aware of the options (always changing) • Data protection, rights, & access – Know what resources are available • Data sharing & re-use – Data sharing exists on a spectrum – it is NOT all-or-none – Know the benefits of sharing your data – Know your options for sharing your data • Data attribution & citation – Know how to cite a dataset | Make it easy for others to cite yours
  • Activity Choose 2-3 strategies from all four sessions to improve your own data management (upload Word doc to Box: Upload HERE: Session 4) Share & Discuss: Which strategies did you choose? Why?
  • NACP Best Data Management Practices, February 3, 2013 Fundamental Data Practices 1. Define the contents of your data files 2. Use consistent data organization 3. Use stable file formats 4. Assign descriptive file names 5. Preserve information 6. Perform basic quality assurance 7. Provide documentation 8. Protect your data 49
  • Final Evaluation Please complete the evaluation before you leave. Your feedback will be used to improve the workshop for the next group.