Data Management Best Practices: Training for Librarians
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This document provides an overview of research data management best practices. It discusses what constitutes research data, common data management issues like lack of planning and poor records management, and responsibilities around data management. It also covers best practices for data documentation, file organization, storage and backup, retention, and long-term preservation. The document emphasizes that libraries can help with writing data management plans, identifying appropriate tools and standards, and assisting with long-term data preservation and sharing.
![0 DataONE. 2013. “Best Practices for Data Management.”
http://www.dataone.org/best-practices.
0 DataONE Education Module: Data Entry and Manipulation. DataONE. Retrieved
Nov12, 2012.
http://www.dataone.org/sites/all/documents/L04_DataEntryManipulation.pp
tx
0 EDINA and Data Library, University of Edinburgh. 2014. Research Data
MANTRA [online course]. http://datalib.edina.ac.uk/mantra.
0 Lamar Soutter Library, University of Massachusetts Medical School. 2014. New
England Collaborative Data Management Curriculum.
http://library.umassmed.edu/necdmc.
0 MIT Libraries. 2013. “Data Management and Publishing.” MIT
http://libraries.mit.edu/guides/subjects/data-management/index.html.
0 Office of Research Integrity. 2013. “Data Management.” United States Department of
Health and Human Services. United States Federal Government.
http://ori.hhs.gov/education/products/rcradmin/topics/data/open.shtml.
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Data Management Best Practices: Training for Librarians
- 3. Objectives 0 Recognize what research data is & what data management entails 0 Identify common data management issues 0 Learn best practices & resources for managing these issues 0 Learn how the library can help identify data management resources, tools, & best practices
- 4. What is research data? “The recorded factual material commonly accepted in the research community as necessary to validate research findings” that is “collected, observed, or created, for purposes of analysis to produce original research results.” Situational
- 5. What is research data? 0 Data files 0 Documents (text, Word), spreadsheets 0 Laboratory notebooks, field notebooks, diaries 0 Questionnaires, transcripts, codebooks 0 Audiotapes, videotapes 0 Photographs, films 0 Test responses 0 Slides, artefacts, specimens, samples 0 Collection of digital objects acquired and generated during the process of research 0 Database contents (video, audio, text, images) 0 Models, algorithms, scripts 0 Contents of an application (input, output, log files for analysis software, simulation software, schemas) 0 Methodologies and workflows 0 Standard operating procedures and protocols
- 6. What else needs management? 0 Correspondence (electronic mail & paper-based correspondence) 0 Project files 0 Grant applications 0 Ethics applications 0 Technical reports 0 Technical Appendix 0 Research reports 0 Research publications 0 Master lists 0 Signed consent forms 0 Internal social media communications such as blogs, wikis etc. 0 Content stored via external social media/Web 2.0 /Cloud applications
- 7. Data Management Issues 0 Lack of responsibility 0 Lack of planning for data management 0 Poor records management 0 Lack of metadata and data dictionary 0 Data files are not backed up 0 Lack of security measures 0 Undetermined ownership and retention 0 Lack of long-term plan for the data
- 8. Responsibility: Whose job is this? 0 Define roles and assign responsibilities for data management 0 For each task identified in your data management plan, identify the skills needed to perform the task 0 Match skills needed to available staff and identify gaps 0 Develop training plans for continuity 0 Assign responsible parties and monitor results 0 Talk to your librarian about best practices for managing lab notebooks. We can help!
- 10. Lab Notebooks: Best Practices 0 Permanently bound book, pages numbered 0 In ink 0 Add things chronologically, date entries 0 Entries should be in first person with clear details of who did what 0 Abbreviations should be explained 0 Don’t remove pages or portions of pages 0 Put a line through blank space 0 Index completed notebooks & keep in a single location 0 Notebooks should be “checked out” 0 Originals stay with lab, copies go with researchers 0 Keep for at least 5 years after study is complete, longer under various conditions, i.e.: patents
- 11. Data Management Planning: What do you need to know? 0 What types of data will be created? 0 Who will own, have access to, and be responsible for managing these data? 0 What equipment and methods will be used to capture and process data? 0 Where will data be stored during and after?
- 12. Image credit: UK Data Service
- 13. 0 “the types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of the project; 0 the standards to be used for data and metadata format and content (where existing standards are absent or deemed inadequate, this should be documented along with any proposed solutions or remedies); 0 policies for access and sharing including provisions for appropriate protection of privacy, confidentiality, security, intellectual property, or other rights or requirements; 0 policies and provisions for re-use, re-distribution, and the production of derivatives; 0 plans for archiving data, samples, and other research products, and for preservation of access to them “ (NSF, 2011). Data Management Planning: National Science Foundation
- 14. Describe : 0 What types of data will be collected or generated 0 Methods of collection or generation 0 How you will prevent disclosure of personally identifying or proprietary information 0 What other documentation will be generated 0 Plans for preserving and archiving the data and related documentation 0 Where you will deposit the data after the study (name the repository) 0 Where your DMP will be located and how often it will be reviewed Data Management Planning: Institute of Museum and Library Services
- 15. Resources Contact the library for help with writing a data management and/or data sharing plan. Librarians can help you with: 0 Writing a data management plan for a funder (e.g. NSF or NIH grant) 0 Find and use online tools and resources to create your plan 0 Identify resources for annotating, storing, and sharing your research data
- 16. NOBODY PANIC!
- 18. DMP Tool A mostly helpful tool
- 19. Records Management: How do you organize data?
- 20. Records Management: How do you organize data?
- 21. Header
- 22. Records
- 23. All the columns are consistent • Standard names • Numbers vs Text • Same format for dates, weights
- 25. Records Management: How do you organize data? 0 There are a number of tools and different software available to assure quality in data entry 0 Contact your librarian for help identifying data entry best practices
- 26. NOBODY PANIC!
- 27. Records Management: File naming pitfalls 0 Inconsistently labeled files 0 Multiple versions 0 Inside poorly structured folders 0 Stored on multiple media 0 In multiple locations 0 In various formats
- 28. Records Management: File naming best practices 0 Avoid special characters in a file name. 0 Use capitals or underscores instead of periods or spaces. 0 Use 25 or fewer characters. 0 Use documented & standardized descriptive information about the project/experiment. 0 Use date format ISO 8601:YYYYMMDD. 0 Include a version number.
- 29. Librarians can help you with best practices, resources, and tools for: 0 Creating file naming conventions 0 Creating directory structure naming conventions 0 Versioning your files 0 Choosing appropriate file formats for preserving and sharing your data files Records Management: File naming best practices
- 30. NOBODY PANIC!
- 31. Documenting Data: How can others makes sense of this data? 0 How will someone make sense of your data e.g. the cells and values of your spreadsheet? 0 What universal or disciplinary standards could be used to label your data? 0 How can you describe a data set to make it discoverable? Shhh…this includes metadata
- 32. Documenting Data Code books and data dictionaries: 0 Describe the contents of data files 0 Define the parameters and the units on the parameter 0 Explain the formats for dates, time, geographic coordinates, and other parameters 0 Define any coded values 0 Describe quality flags or qualifying values 0 Define missing values 0 List and describe instruments used in data collection
- 33. Documenting Data MIT Libraries recommend noting: 0 Title 0 Creator 0 Identifier 0 Subject 0 Funders 0 Rights 0 Access information 0 Language 0 Dates 0 Location 0 Methodology 0 Data processing 0 Sources 0 List of file names 0 File Formats 0 File structure 0 Variable list 0 Code lists 0 Versions 0 Checksums
- 34. Close your eyes for a moment
- 36. Documenting Data Example from Dryad: This is a metadata standard called Dublin Core. Other repositories will use different standards, but the basic idea is the same. Some of it will be automatically created by the repository. For help with metadata standards, talk to your librarian.
- 38. NOBODY PANIC!
- 39. Storage & Backup: Where’re these data gonna go? Lost and found at TU’s Science & Engineering Library (which is rather a small library)
- 40. Storage & Backup 0 How often should data be backed up? 0 How many copies of data should you have? 0 Where can you store your data?
- 41. 0 Make 3 copies (original + external/local + external/remote) 0 In 2 locations 0 On more than 1 storage format 0 Uncompressed is preferred for storage, but if you must to conserve space, limit compression to your 3rd backup copy Storage & Backup Best Practices
- 42. Retention How Long? Intellectual Property Funder’s Policy Publisher’s Policy Federal & State Laws IRB Policy
- 43. Module 1: Overview of Research Data Management
- 44. 0 IRB OHRP Requirements: 45 CFR 46 requires research records to be retained for at least 3 years after the completion of the research. 0 HIPAA Requirements: Any research that involved collecting identifiable health information is subject to HIPAA requirements. As a result records must be retained for a minimum of 6 years after each subject signed an authorization. 0 FDA Requirements 21 CFR 312.62.c Any research that involved drugs, devices, or biologics being tested in humans must have records retained for a period of 2 years following the date a marketing application is approved for the drug for the indication for which it is being investigated; or, if no application is to be filed or if the application is not approved for such indication, until 2 years after the investigation is discontinued and FDA is notified. Retention
- 45. 0 VA Requirements: At present records for any research that involves the VA must be retained indefinitely per VA federal regulatory requirements. 0 Intellectual Property Requirements - Any research data used to support a patent through must be retained for the life of the patent in accordance with Intellectual Property Policy. 0 Check with your Funder and Publisher Requirements 0 Questions of data validity: If there are questions or allegations about the validity of the data or appropriate conduct of the research, you must retain all of the original research data until such questions or allegations have been completely resolved. Retention
- 46. Thinking Long-Term: What happens to data after the project? 0 What will happen to my data after my project ends? 0 How can I appraise the value of my data? 0 What are my options for archiving and preserving my data? 0 What are my options for publishing and sharing data?
- 47. We can help you: 0 Find and evaluate a suitable repository for your data 0 Upload your data sets to a repository 0 Interpret your funder or publisher’s repository requirements 0 Help make your data in a repository searchable and discoverable Thinking Long-Term: What happens to data after the project?
- 48. NOBODY PANIC!
- 49. Preservation: The Importance of File Formats Image from Padua, S. (2015). The thrilling adventures of Lovelace and Babbage. New York: Pantheon Books.
- 50. 0 Open or proprietary? 0 Software package needed to read & work with the data?* 0 Multiple files?* 0 Be consistent with your file formats & think long-term *Note in your metadata Preservation: The Importance of File Formats Image from Padua, S. (2015). The thrilling adventures of Lovelace and Babbage (Firstition. ed.). New York: Pantheon Books.
- 51. Last Activity
- 52. Librarians can help you: 0 Write data management plans 0 Employ data entry best practices 0 Organize and name files 0 Determine appropriate file formats 0 Document data 0 Determine how long and where to store data 0 Find a repository to deposit data in 0 Teach you, your lab, or your classes about data management best practices
- 53. Questions?
- 54. For More Information: Margaret Janz 215-204-4725 margaret.janz@temple.edu The best time to call me is email Science & Engineering Library (SEL) Engineering Building, rm. 202 guides.temple.edu/SEL Find your subject librarian: library.temple.edu/services/library-instruction/specialists
- 55. 0 DataONE. 2013. “Best Practices for Data Management.” http://www.dataone.org/best-practices. 0 DataONE Education Module: Data Entry and Manipulation. DataONE. Retrieved Nov12, 2012. http://www.dataone.org/sites/all/documents/L04_DataEntryManipulation.pp tx 0 EDINA and Data Library, University of Edinburgh. 2014. Research Data MANTRA [online course]. http://datalib.edina.ac.uk/mantra. 0 Lamar Soutter Library, University of Massachusetts Medical School. 2014. New England Collaborative Data Management Curriculum. http://library.umassmed.edu/necdmc. 0 MIT Libraries. 2013. “Data Management and Publishing.” MIT http://libraries.mit.edu/guides/subjects/data-management/index.html. 0 Office of Research Integrity. 2013. “Data Management.” United States Department of Health and Human Services. United States Federal Government. http://ori.hhs.gov/education/products/rcradmin/topics/data/open.shtml. Brought to you by:
Editor's Notes
- #2: This presentation was accompanied by hands-on activities found at the URL above. The main content slides were the same as the ones that would be used for RDM Best Practices presentation targeted at researchers. Every time a slide says “Ask a Librarian,” it was followed by a Don’t Panic slide, which was a cue that we were going to work on an activity. Data used in activities are Margaret Janz Real-Life Examples (CC-BY) unless otherwise cited.
- #3: NECDMC = New England Collaborative Data Management Curriculum
- #6: Research data objects
- #7: Research data reports that will also have to be managed.
- #8: (Outline for most of the presentation)
- #9: Challenges include: nature of team science; management of lab notebooks; rotating lab personnel “The take-away is that every person in the lab has a duty to maintain and manage data effectively, not just the project’s PI.“
- #10: There’s no activity for this first one. Just jump to the next slide and talk about lab notebook management.
- #13: Highlight that data has a life that extends beyond the project where it was created; and that researchers may not understand or anticipate this. These cycles help to visualize the activities in order to plan for the project’s data management needs and how data may be collected, stored, described, preserved, and/or shared. We’re going to talk about data management plan documents, but that being able to write a DMP isn’t our main objective. I want you all to leave here with a better sense of how you can employ a data management strategy for your research. Having that strategy is going to help you understand the DMP and also help you follow through with what you write in the DMP. While a 2-page plan for a grant application is very important, every research project will benefit from planning for managing a project’s data throughout the life of the project, including planning for how data will be produced, collected, analyzed, stored, archived or shared, etc.
- #14: The 2-page plan is required but its contents are recommendations that can differ by directorate.
- #15: The NSF has laid the foundation for requiring a data management and sharing plan. Here is an example of one the NSF Bio Directorates interpretations of the broader NSF recommendations. The library is a good place to start if you need assistance with writing your data management plan for a grant application. We can also help you identify local and distributed resources and policies to satisfy the criteria above: standards used to describe the data, plans for storage, back up and security, and plans for the data after the project has ended.
- #16: The library can also help you with data management questions that arise during your research.
- #17: Activity: tour of the Data Management LibGuide (next slide) and DMP Tool. Participants went into DMP Tool and played with it for a while.
- #18: Has links to funder policies and other resources
- #20: Don’t do this. This is a mess.
- #21: Slide from Sylvie Brouder’s presentation at Harvard Purdue Data Management Symposium, June 17, 2015
- #22: You can use coded columns, but be sure to have a code book nearby. More on that later.
- #23: You can use coded columns, but be sure to have a code book nearby. More on that later.
- #24: You can use coded columns, but be sure to have a code book nearby. More on that later.
- #25: Color also causes some problems. What does the purple mean? If this file is opened in something other than this version of this application, those colors might not be there and that information would be lost. Better to add a column to note whatever it is you’re trying to show.
- #27: Activity A: Data Entry
- #30: Many labs have to create systems to track thousands of samples and slides scattered across several freezers, their associated digital files such as images and experimental measures and observations, and then map these with the experiment documentation they are associated with in the paper of e-lab notebook.
- #31: Activity B: File Organization and Naming
- #32: (Shh…this is metadata)
- #33: Just a text document that should be kept in the same folder as your data files.
- #35: Ask audience: How many of you get nervous when I talk about metadata?
- #36: You probably won’t have to do this.
- #37: You’ll have to do this!
- #38: This is what Dryad’s system has you submit. Not so scary, eh?
- #39: Activity: Metadata (Under Documentation)
- #41: Here are some important questions regarding storing, backing up and securing your data that you should discuss with your lab members.
- #42: Here are some best practices for backing up your data. Electronic data should be saved on a device that has the appropriate security safeguards such as unique identification of authorized users, password protection, encryption, automated operating system patch (bug fix), anti-virus controls, firewall configuration, and scheduled and automatic backups to protect against data loss or theft.
- #43: When it comes to data ownership and data retention there are a lot of overlapping policies. IP policy can cover the ownership and retention of data related to patents, the IRB wants to ensure that documentation of human subjects’ data are retained and/or destroyed appropriately, and the funders and publishers want you to retain data to defend the integrity of your findings, and then there are federal guidelines like HIPAA.
- #44: “How long should I retain data?” is not a clear and cut data management question. For example, the JCI retracted a 7 year old published article because one of its data tables was duplicated. The publisher contacted the researchers to have them update the data, but they could not locate the original data files after six years, so the journal was forced to issue a retraction.
- #45: Here are some best practices for backing up your data. Electronic data should be saved on a device that has the appropriate security safeguards such as unique identification of authorized users, password protection, encryption, automated operating system patch (bug fix), anti-virus controls, firewall configuration, and scheduled and automatic backups to protect against data loss or theft.
- #46: Here are some best practices for backing up your data. Electronic data should be saved on a device that has the appropriate security safeguards such as unique identification of authorized users, password protection, encryption, automated operating system patch (bug fix), anti-virus controls, firewall configuration, and scheduled and automatic backups to protect against data loss or theft.
- #47: After a project you may want to consider appraising, and publishing or depositing your data in a repository. There are a variety of factors that impact your ability to share data with outside parties. According to the OHRP, you should contact the IRB prior to proceeding with a release of human subject data unless (a) your subjects signed an IRB approved consent document with HIPAA compliant authorization language that clearly details what information will be collected, used, and disclosed and (b) the outside party is specified in the document.
- #49: Activity: Depositing Data This activity is cribbed from the ICPSR Summer Program “Curating and Managing Research Data for Re-Use” 2014
- #50: One of the greatest challenges for preservation is thinking ahead about the formats of your data. This means that to be able to open and view this file, someone would need to know the software that created it, and be able to access that software. Thus converting your files to open source and sustainable formats and standards are essential for long-term sharing, preservation and access.
- #51: Here are some considerations for making your files available for the long-term. Do you need a certain software package to read & work with the data file? -> If so, the software package, version, and operating system platform should be cited in the metadata… Non-proprietary, open, documented standard, unencrypted, uncompressed, ASCII formatted files will be readable into the future.
- #52: Case Studies. Get into groups and look at the case studies and answer the questions.