NISO Training Thursday Crafting a Scientific Data Management Plan
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Feb 26 NISO Training Thursday Crafting a Scientific Data Management Plan About the Training Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors. This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth. About the Instructors Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries
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NISO Training Thursday Crafting a Scientific Data Management Plan
- 1. NISO Training Thursday Crafting a Scientific Data Management Plan Thursday, February 26, 2015 Instructors: Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries http://www.niso.org/news/events/2015/training_Thursdays/TT_crafting/
- 2. Crafting a Scientific Data Management Plan Thursday, February 26, 2015
- 3. Instructors Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska- Lincoln Libraries, kdeards2@unl.edu Jenny Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries, jthoegersen2@unl.edu
- 4. Training overview Introduction to data management plan requirements Data Management Plan Checklist Review good and bad data management plan excerpts
- 5. Introduction to data management plans Follow guidelines provided by granting agency, directorate, and solicitation Keep the plan clear, complete, and concise Refer back to the project proposal, if necessary Recheck requirements for changes
- 6. Data Management Checklist 1. What type of data are being produced and what are the file formats? 2. How much data are being produced, and at what growth rate? Will the data change? 3. How long should the data be retained? 4. What directory and file naming conventions will be used? 5. Do you need data identifiers? 6. Are there tools and software needed to render the data? 7. Who will be responsible for data management? 8. Are there privacy, legal, ethical, or security requirements? 9. Does the funder require a data sharing policy, data management plan, or other information? 10. Are the data properly described (metadata) and the overall project documented? 11. How will you store and backup the data? 12. Do you need to publish the data in a repository?
- 7. Data types & file formats What types of data file formats have you encountered?
- 8. Data types & file formats Match data types to file formats Favor open source and widely used formats Consider data repository requirements
- 9. Quantity of data A LOT? A little data Or…
- 11. Directory and file naming conventions Avoid special characters ("/ : * ? " < > [ ] & $) Use underscores, not spaces Avoid names longer than 25 characters Use consistent versioning identification (DM_Guide_v03) Use the ISO 6801 standards for date formats (YYYY-MM-DD) Use names that describe the content
- 12. Directory and file naming conventions “…the PIs, senior personnel, technician and students on the project will convene a dedicated data management meeting. At this time, the PIs will set out naming, processing and storage conventions for all data collected at the experimental and observational sites…training will be reiterated at a yearly data management and analysis meeting to remind participants of the conventions and train any new participants.” From Elsa Cleland's proposal The influence of plant functional types on ecosystem responses to altered rainfall. Available at http://idi.ucsd.edu/data- curation/examples.html
- 13. Data identifiers “Message error 404” by Roberto Zingales, https://www.flickr.com/photos/filicudi/2891898817 (CC BY 2.0)
- 14. Rendering data By Images courtesy of http://abstrusegoose.com/ under a Creative Commons license via Wikimedia Commons, http://www.ccc.uga.edu/summer/programs/comic2.png (CC BY-SA 3.0) In 30 years, how will you access your data?
- 15. Who is responsible? From “Lease”, by Randall Monroe http://xkcd.com/616/ (CC BY-NC 2.5)
- 16. Privacy, legal, ethical, or security requirements “Speak no evil, See no evil, Hear no evil” by Rose Davies, https://www.flickr.com/photos/rosedavies/110850792/ (CC BY 2.0 )
- 17. Publishing, Preserving, & Rights Determine where data will be preserved and shared after the conclusion of a project Outline the rights associated with the data “Cat #24 - Mummy Cat” by Marty Omnitarian, https://www.flickr.com/photos/omnitarian/4300610111/ (CC BY-NC-ND 2.0)
- 18. Funder requirements Funder guidelines can be very simple or very complex
- 19. NSF Basic DMP Requirements 1. the types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of the project; 2. 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); 3. policies for access and sharing including provisions for appropriate protection of privacy, confidentiality, security, intellectual property, or other rights or requirements; 4. policies and provisions for re-use, re-distribution, and the production of derivatives; and 5. plans for archiving data, samples, and other research products, and for preservation of access to them. From the Grant Proposal Guide (http://www.nsf.gov/pubs/policydocs/pappguide/nsf13001/gpg_2.jsp)
- 20. Description & Documentation U.S. National Archives and Records Administration [Public domain], via Wikimedia Commons, http://commons.wikimedia.org/ wiki/File%3ADon't_kill_your_reputation%2C_organize_your_information_-_NARA_-_518156.jpg
- 21. Storage & backup Maintain 3 copies of data-- one remotely
- 22. Storage & backup Where do you store and back up your data?
- 23. Storage & backup Storage Option The Good The Bad Personal computer/laptop Convenient for active data Lost/stolen; fail; responsible for backups Network/departmen t drives Automatic backup & security Access/capacity limitations External devices Low cost; portable; easy use Lost/stolen; fail Holland Computing Center Automatic backup & security Cost for storage Box Global access; collaboration Security/privacy limitations Physical (e.g. notebook) Convenient; tangible Manual backup
- 24. Data management plan excerpts All sample data will be collected and organized using [Specialty Software Name]. The files will contain information about sample characteristics and the conditions under which these characteristics were measured. Approximately 1-2 Gb of data will be generated. What’s wrong with this example?
- 25. Data management plan excerpts All files will be stored on the PI’s secure computer. All laboratory notebooks will be stored in the PI’s office. What’s wrong with this example?
- 26. Data management plan excerpts Data will be available to anyone who desires access to our data. When possible, data will be made available online. What’s wrong with this example?
- 27. Data management plan excerpts This DMP covers the data which will be This study will only collect non-sensitive data. No personal identifiers will be recorded or retained by the researchers in any form. What’s right with this example?
- 28. Data management plan excerpts The project will leverage existing metadata standards currently stored in Ecological Metadata Language (EML) format. We chose EML format for our metadata since it allows integration with existing NutNet data housed in the Knowledge Network for Biocomplexity (KNB) data repository. What’s right with this example?
- 29. Questions?
- 30. Resources & References Basics of Data Management: http://unl.libguides.com/datamanagement UNL Libraries Data Management Services: http://libraries.unl.edu/data-management Example NSF DMPs from UC San Diego: http://idi.ucsd.edu/data- curation/examples.html
- 31. NISO Training Thursday • February 26, 2015 Questions? All questions will be posted with presenter answers on the NISO website following the webinar: http://www.niso.org/news/events/2015/training_Thursdays/TT_crafting/ NISO Training Thursday Crafting a Scientific Data Management Plan
- 32. Thank you for joining us today. Please take a moment to fill out the brief online survey. We look forward to hearing from you! THANK YOU
Editor's Notes
- Jenny
- Kiyomi has over 8 years of experience working as a chemist in industry, once upon a time she also spent over 3 years doing a research as a chemist. Jenny is an Assistant Professor and Data Curation Librarian at the University of Nebraska-Lincoln. She received her B.S.E. with a Library Science concentration from the University of Nebraska at Omaha. As an Erasmus Mundus Scholar, she earned her M.L.I.S. in Digital Library Learning through a joint, international program between Høgskolen i Oslo og Akershus (Oslo, Norway), Tallinna Ülikool (Tallinn, Estonia), and Università degli Studi di Parma (Parma, Italy). In 2013, Thoegersen completed a Fulbright fellowship at the University of Waikato assisting the developers of the open source digital library software Greenstone. As Data Curation Librarian, Thoegersen instructs and consults on data management planning and contributes to the preservation of digital assets at UNL Libraries.
- Kiyomi The purpose of today’s training is to introduce you to the tools and resources you need to evaluate and craft data management plans.
- Kiyomi - Keep in mind that there can be multiple layers of instructions. Always start with any general guidelines for the granter, then move to the program or directorate it is under (if applicable), they look at the specific award guidelines. Watch out for changes in guidelines, they can happen at any time. Do not assume that the grant proposal written last month had the same guidelines as the proposal you are currently working on. Granting agencies are looking for excuses not to consider an application. If your plan is not clear, complete and concise they may decide that the plan is incomplete and lower the ranking of the proposal, or remove it from consideration. It is ok to refer back to other sections of the proposal unless there are instructions not to do so. This can help you get around word and page limits if they exist. When you have finished writing the plan recheck the requirements, recheck them again before the final submission.
- See the checklist above, it is in our handout and you can copy and paste if from the slides as well. It is a guidelines of things to consider when reviewing or writing a plan, the weight and impact of each item may vary in importance depending on what type of data you are managing but all the elements should be addressed. If something does not apply, state why it does not apply.
- Jenny
- Jenny Ask yourself, will someone else who does not have my equipment/programs be able to read my files? If the answer is no you may want to convert your files to an open format. If your files are not readable by others what is the value of saving them? File Formats Select formats that ensure the best change for long-term access to data Favor commonly used and non-proprietary formats Consider longevity, popularity, and potential for migration Consider requirements of selected data repository UK National Archives’ PRONOM: http://apps.nationalarchives.gov.uk/PRONOM/Default.aspx Provides detailed technical information about data file formats File Format Recommendations/Preferences from: UK Data Archive: http://data-archive.ac.uk/create-manage/format/formats-table Library of Congress: http://www.digitalpreservation.gov/formats/content/content_categories.shtml Purdue University Research Repository: https://purr.purdue.edu/legal/file-format-recommendations
- Jenny The size of the data you are trying to save will determine how and where you save it. Is you data little like a kitty? (Think an Excel spreadsheet). or Big like a lion? (Think a three hour HD video file). Data quantity is also very relative. University of Waikato WAND research group (http://wand.net.nz/) collection, monitoring, and analysis of network packets: 10 GB binary files
- Jenny If you want to save data long term it will occasionally need to be moved and converted in order to maintain its integrity and ensure that it is readable as file formats and standards change.
- Kiyomi – The file naming conventions mentioned above will prevent you from having to spend hours renaming your files and folders when you move your files from one system to another. Spaces and special characters in file names can cause formatting changes and other problems when migrating files which render files useless. Different systems have issues with different characters. Some systems can handle spaces, others can’t. Rather than trying to remember what works and what doesn’t it is easier to not use special characters and spaces. Versioning, standardized dates, and descriptive names help tell the people who come after you what your files is about and how to read the data in the file. You should always have a readme.txt file that explains what file naming conventions you used. It is good practice to place copies of the naming conventions in any notebook, and by any instrument/device whose files the conventions will be used on.
- Kiyomi It is always the PIs responsibility to make sure that the practices laid out in a proposal are being followed. It is beneficial for the PI to spell out in a proposal, or the related grant, how they will ensure that the data management practices they outline will be supported and enforced.
- Jenny Data Reuse When considering whether to reuse other researchers’ data, determine whether the data is suitable for your purposes and, if so, determine the terms for reuse of the data. Properly cite the dataset in order to: Provide credit to data creators Enable others to access the data Assist in measuring impact of data Help researchers know how their data is being used A data citation should include: Authors/Creators Title of dataset Version information Publication data Publisher/Archive Identifier/Locator (DOI/URL) For more information on citing datasets, visit the Digital Curation Centre website: http://www.dcc.ac.uk/resources/how-guides/cite-datasets
- Jenny For data to be shared/preserved, is it in a format that is open/widely available? If specific software is necessary, will/can it be available? Can data be converted into a more open/widely-available format for preservation and sharing? What tools will be required to read the data?
- Jenny & Kiyomi Who makes decisions regarding the overall and day-to-day data management? Who and what is responsible for preserving the data?
- Jenny & Kiyomi Researchers should consider the legal and ethical issues involved in sharing (e.g. do they have consent to share participant data?). They should also consider the potential for reusability of their data, as well as whether outsiders will be able to understand the data. There are some potential drawbacks to sharing. Ensuring data is fit to share may be time-intensive. Others could misuse or misrepresent a dataset. Data released in the middle of a project may not have undergone sufficient quality assurance. There may be an overlap of publications if data are released during or immediately following a research project.
- Kiyomi & Jenny What happens if the PI passess away? Who owns the rights to the data? For long-term preservation, datasets should be deposited in a data repository or archive. There are a wide array of domain repositories available, which accept data from specific subjects or domains. The following websites provide directories of repositories and are a great starting point for considering a domain repository: DataBibRepository List (http://www.databib.org) Re3data (http://www.re3data.org) DataCite Repository List (www.datacite.org/repolist) Open Access Directory (OAD) Data Repositories (http://oad.simmons.edu/oadwiki/Data_repositories) If no suitable domain repository can be located, UNL Libraries hosts the UNL Data Repository (UNLDR), which provides researchers with a secure site for storage and long-term preservation of datasets that are no longer actively in use. UNL researchers can preserve up to 50 GB of data in UNLDR for free. Above that, there is a one-time fee (see https://dataregistry.unl.edu/ for details). DataONE (https://www.dataone.org/best-practices/preserve) provides best practice guides on things like deciding what data to preserve, identifying data sensitivity and what data has long-term value. To locate an appropriate repository, you can ask your faculty advisor, contact your subject librarian, or search through repositories in a data repository registry. Two registries: re3data and Open Access Directory (OAD) Data Repositories (figshare.com) When selecting a data repository, you should consider: The sustainability of the repository The security of the data (especially for sensitive information) How visible your data will be to your intended audience The availability of usage information (how many views/downloads) The repository’s backup policy The cost of depositing, and whether it is a one-time or on-going cost
- Jenny One book/stack of books
- Jenny Even though other funders may ask for this information in a different order or format, these elements should always be considered regardless of who the funder is.
- Jenny Organization & enlightment Metadata Standards Select standards based on discipline Researcher might know standards If not, a place to start: http://www.dcc.ac.uk/resources/metadata-standards Consider standards used by selected data repository Controlled Vocabulary Select based on discipline; Researcher might know standards If not, a place to start: http://www.jiscdigitalmedia.ac.uk/guide/controlling-your-language-links-to-metadata-vocabularies/ Consider standards used by selected metadata standard and data repository Data Dictionary Provides a detailed description for each element or variable in a dataset and data model Ensures consistent data entry and allows for future interpretation of data Example: For a column in a spreadsheet, document meaning of column, allowable values, format of values, etc. README.txt Provides introductory documentation for a dataset
- Jenny _Biggest area people miss Backup Allows for the restoration of data in the event that it is lost or compromised due to disaster, theft, hardware/software malfunctions, or unauthorized access. Vital for data that are unique or difficult/expensive to reproduce. Remember to create digital surrogates to backup analog materials What? Everything that would be required to restore data in event of loss (data/software/scripts/documentation) How many? Follow the Rule of 3: Original copy, second local copy, remote copy How often? Backup frequency is dependent on the project and the data. Consider how much data you would be willing to lose. What type? Full: Backup all files Incremental: Backup only files that have changed since last backup (either full or incremental) Differential: Backup only files that have changed since last full backup For more details: http://support.microsoft.com/kb/136621 Test your system: Go through the exercise of accessing backup to see that procedure works & you can fully restore your data Security Access security ensures only authorized users can access data. Utilize unique, role-based user IDs & passwords Password tips: Consider length, complexity, variation, and uniqueness Include no personal information, sequences, or repetition Don’t reuse passwords Balance difficulty to guess with difficulty to remember Systems security is the protection of hardware and software. Update anti-virus software, applications, and operating system and utilize firewall & intrusion detection Control access to hardware (e.g. keep doors to office/server room locked) Data Integrity ensures data has not been manipulated in an unauthorized way. Encryption: Coding information that cannot be read/deciphered unless someone has the decoding key Electronic signature: Coded message that is unique to both the document and the signer Watermarking: Embeds digital marker for authorship verification & can alert someone of alterations
- Jenny
- Jenny Other options include Dropbox, Github, etc.
- Jenny No file formats are mentioned, nor are preservation issues of the file formats. Sample characteristics are not defined. Refer back to grant if space is limited. Is the data being generated text, numeric, graphs, etc.? Unless a commonly-known format is identified (e.g. Word, Excel, TIFF), data type should be made explicit.
- No mention of backups (Rule of 3) No mention of back procedure during and after grant Notebook storage should be explicit, including who can potential access.
- Kiyomi how data will be accessible? how will online access be provided? Is any of the data restricted? How long will data be made available? How will access be provided if researcher leaves or passes away?
- Jenny Privacy & licensing are explicitly addressed
- Kiyomi Details metadata standard and interoperability of metadata
- Have more question? Contact Jennifer Thoegersen, see slide 2 for contact info