LIS 653, Session 11: Data Management & Curation
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This document discusses data management practices for researchers. It defines what constitutes data, such as observations, experiments, simulations, and documents. It outlines the roles of librarians in advising on data management plans, metadata practices, and archiving data. It also discusses why data management is important for validation, replication of research, and compliance with funder requirements. The document provides examples of file structures, naming conventions, metadata, codebooks, and archiving data in institutional repositories to facilitate long-term access and reuse of research data.
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LIS 653, Session 11: Data Management & Curation
- 1. Data Management LIS 653 Starr Hoffman
- 2. Data
- 3. What is (are) Data? Observations Sensor data, telemetry, survey data, sample data Experiments Gene sequences, chromatograms Simulations Economic models Derivations/Compilations Text mining, data from public documents Documents & texts themselves = data Research Process Observational conditions, experimental procedure, instrumentation, label descriptions, units, metadata
- 4. Librarian Roles & Data Advisory Original data: Consult on creating DMP Consult on data organization, methodology, etc. Consult on metadata practices Consult on archiving Help disseminate research Journal publication, OA resources, blogs, etc. Deposit into repository (IR, 3rd party, etc.) Secondary data: Consult on methodology / analysis Discovery… Curatorial Manage IR (institutional repository) Create metadata for datasets Purchase / catalog / discovery for secondary data
- 5. What is Data Management? Planning for the short-term and long-term: care of and access to …your data. Or: What are you going to do with that data? How will you describe it? How are you organizing it? After you’re done, where will you put it? How will you/others be able to access it? For how long?
- 6. Data Management: Why Does it Matter? Grant requirements Public access to funded research Validation Replication Re-use, continue research Teaching Natural disasters Computer failure/stolen USB/hard drive failure/lost Files corrupted
- 7. Funding Requirements NSF: Proposals must include a supplementary document of no more than two pages labeled “Data Management Plan” …describe how the proposal will conform to NSF policy on the dissemination and sharing of research results. NIH: The NIH expects and supports the timely release and sharing of final research data… for use by other researchers. …expected to include a plan for data sharing or state why data sharing is not possible. NEH Office of Digital Humanities NOAA IMLS NIJ
- 8. DMP Considerations What data types, from what sources, in what formats will this project produce? How much of it will there be? How will you describe or document your data? Are there standards you will be using for this? Will you be sharing your data? Do you have the rights to share the data? What did you tell the IRB? How often do you need to backup your files? How do you need to be able to access your files? How many backups will you have? How much storage space do you need? What is your budget for your storage? Where are you going to archive or store the data? and how will it be accessed? What are the roles and responsibilities around all of these things? i.e., Who's going to be doing all this?
- 9. DMP Examples
- 10. Planning the Data Life-Cycle Consider… Files: Size, format, organization Security Storage/Backup system Retention Access/Transparency
- 11. Data Lifecycle: Create / Analyze / Edit File Management Consistency, brevity, description Versioning (v01, v02, FINAL) Avoid spaces Directory structure /[Project]/[Grant Number]/[Event]/[Date] File naming [description]_[instrument]_[location]_[YYYYMMDD].[ext] Transparency/Sharing Document data: codebook, metadata
- 12. File Structure & Naming Examples Directory Structure /[Project]/[Grant Number]/[Event]/[Date] /NYCPhysicalActivity/NOT-MH-14-033/Interview/20141109 /Dissertation/LitReview/LibraryLeadership/ File Naming [description]_[instrument]_[location]_[YYYYMMDD].[ext] PhysicalActivity_InterviewQs_PS193_20141109.doc PhysicalActivity_InterviewResponses_20141022.xls LibraryLeadershipHenson_Article_2011.pdf Leadership_Survey_20130917.doc
- 13. Metadata & Description Variables: labels, meaning, how they were measured, units, codes Survey questions Experimental procedures Research methodology Statistical analyses performed Preferred data citation Pew Hispanic Center. (2008). 2007 Hispanic Healthcare Survey [Data file and code book]. Retrieved from http://pewhispanic.org/datasets/
- 16. Data Lifecycle: Publish, Store, Access, Reuse File size & format Open vs. proprietary Security Anonymize or encrypt? Levels may vary by access (org. vs. 3rd party) Data Citation Sharing Upload data & metadata Institutional repository, data center, etc. Persistent identifier
- 21. Dataset Record in IR
- 22. Other places data can live… Figshare ICPSR Github DataUp Dropbox (or other cloud storage) IF you use proper encryption Lists of data repositories: DataCite DataBib
- 23. Data Discovery Data Depositories (previous slide) ICPSR Figshare Institutional Repositories OpenDOAR (directory) Specific institutions Data Catalogs Numeric Data Catalog (Columbia) GeoData (Columbia, others) Gov & Public Sources (data producers) NYC OpenData Data.gov Census Bureau Bureau of Labor Statistics IMLS (Institute of Museum & Library Services)
- 24. Replicated Data And Finally… Geeky puns.
Editor's Notes
- Data isn’t just for science! Lots of fields collect lots of kinds of data… Quantitative data—usually numbers, but anything that’s quantifiable—survey data maybe Qualitative data—interviews, some surveys, Ethnographic-- observations of space use Maps Photographs Sound recordings, video
- Observations, e.g.: Sensor data, telemetry, survey data, sample data, neuroimages. Experiments, e.g.: gene sequences, chromatograms, toroid magnetic field data. Simulations, e.g.: climate models, economic models Derivations / Compilations, e.g.: text and data mining, compiled database, 3D models, data gathered from public documents. Research process, e.g.: observational conditions, experimental procedure, instrumentation, label descriptions, units *http://www.nytimes.com/2011/10/06/science/06nobel.html?_r=0 “when you get to see everyone’s mistakes you can tell the mistake from a pattern” Micah Altman at RDS2013 Observational Data/Media Real time captureUsually irreplaceable Derivation/Compilation Data ReproducibleExpensive Research Process Data Data documentation & description (aka ‘metadata’)Analysis algorithms & codes Simulation Data Model & Inputs are usually the important elements
- So what are librarian roles, related to data? -- advisory -- curatorial Crossover of public services & technical services… -- metadata (TS) -- subject expertise (either; often PS) -- methodology/analysis (may be PS) -- data mgmt (TS, PS, or Scholarly Communication dept.) -- IRs… (digital services, schol comm—could be outside the libraries!) -- secondary data (usually PS)
- Some question prompts – librarian might work through this checklist with a researcher -- help shape their DMP
- DMPs include things like: -- kinds of data -- data collection methods -- hardware, software -- archival plans…
- These are things that the librarian may consult with a researcher on… Consider: Files: size, format, organization -- open format vs. proprietary Security -- Be vigilant and protective of data in your custody. Observe precautions when transporting paper files with patient or individual identifiable information. Do not leave documents or USB drives in unsecured locations (cars, lockers). Encrypt information (email, cloud storage) -- who needs to access this data—institutional, 3rd party access? -- before sharing data, anonymize individually identifying information, be aware of geo-location tags Storage/backup system -- Checksum validation, test to be sure backups are working properly Retention -- how long does this data need to be accessible/stored? Grant requirements, your own plans, others -- is it reproducible or not? Access/Transparency -- do you (or others) need to access it frequently, or only need periodic access? -- document the data (processes, labels, etc.) -- deposit in repository (for public access)
- Consistency: Pick a system, write it down, & stick with it Identify necessary elements Create brief, understandable names Date: YYYY-MM-DD Version: v01, v02,…FINAL In general, try to stay away from spaces in filenames as well as the following characters: .\ / : * ? “ < > | [ ] & $
- Metadata is important: -- to help the researcher understand and remember their data (variables!) -- to help others find that data (for validation, replication, reuse) -- replication in particular = GOOD metadata!
- Codebook is an essential part of metadata for data… Tells what each variable is, what it’s called, how it was measured, what units it’s in, how it’s coded…
- Codebooks can also include survey questions asked to obtain the variables. This is also important in Data Reference work w/ secondary data…
- Both funders and some journals require data sharing at time of publication / w/in 12 months So, when it’s all done… where do we PUT this stuff?
- More places to store original data….
- So from the other end —as a researcher wanting to use secondary data (already collected) --or a student wanting to do data analysis… Where do we FIND data?