Data Management for Undergraduate Researchers
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This document summarizes a seminar on data management for undergraduate researchers. It discusses what data is, why it needs to be managed, and key aspects of the data management process such as data organization, metadata, storage, and archiving. Topics covered include file naming best practices, version control, documentation, metadata standards, storage options, and long-term archiving. The goal is to help researchers organize and document their data so it can be understood, preserved, and reused.
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Data Management for Undergraduate Researchers
- 1. Data Management for Undergraduate Researchers Office of Undergraduate Research Seminar and Workshop Series Rebekah Cummings, Research Data Management Librarian J. Willard Marriott Library, University of Utah September 21, 2015
- 2. • Introductions • What are data? • Why manage data? • Data Management Plans • Data Organization • Metadata • Storage and Archiving • Questions
- 4. What is data management? The process of controlling the information (read: data) generated during a research project. https://www.libraries.psu.edu/psul/pubcur/what_is_dm.html
- 5. What are data? “The recorded factual material commonly accepted in the research community as necessary to validate research findings.” - U.S. OMB Circular A-110
- 8. Why manage data? • Save time and efficiency • Meet grant requirements • Promote reproducible research • Enable new discoveries from your data • Make the results of publicly funded research publicly available
- 9. We are trying to avoid this scenario…
- 10. Two bears data management problems 1. Didn’t know where he stored the data 2. Saved one copy of the data on a USB drive 3. Data was in a format that could only be read by outdated, proprietary software 4. No codebook to explain the variable names 5. Variable names were not descriptive 6. No contact information for the co-author Sam Lee
- 11. Scenario You develop a research project during your undergraduate experience.You write up the results, which are accepted by a reputable journal. People start citing your work! Three years later someone accuses you of falsifying your work. Scenario adapted from MANTRA training module
- 12. • Would you be able to prove you did the work as you described in the article? • What would you need to prove you hadn’t falsified the data? • What should you have done throughout your research study to be able to prove you did the work as described?
- 13. Data Management Plans • What data are generated by your research? • What is your plan for managing the data? • How will your data be shared?
- 14. Research Data Lifecycle Courtesy of the UK Data Archive http://www.data- archive.ac.uk/create-manage/life- cycle • Types of data • Data description • Data storage • Data sharing • Data archiving and responsibility • Data management costs
- 16. File naming
- 18. File naming best practices 1. Be descriptive 2. Don’t be generic 3. Appropriate length 4. Be consistent 5. Think critically about your file names
- 19. File naming best practices • Files should include only letters, numbers, and underscores/dashes. • No special characters • No spaces; Use dashes, underscores, or camel case (like-this or likeThis) • Not all systems are case sensitive. Assume this,THIS, and tHiS are the same.
- 20. Version Control - Numbering 001 002 003 009 010 099 Use leading zeros for scalability Bonus Tip: Use ordinal numbers (v1,v2,v3) for major version changes and decimals for minor changes (v1.1, v2.6) 1 10 2 3 9 99
- 21. Version Control - Dates If using dates useYYYYMMDD June2015 = BAD! 06-18-2015 = BAD! 20150618 = GREAT! 2015-06-18 = This is fine too
- 22. From a DMP… “Each file name, for all types of data, will contain the project acronym PUCCUK; a reference to the file content (survey, interview, media) and the date of an event (such as the date of an interview).
- 23. • PLPP_EvaluationData_Workshop2_2014.xlsx • MyData.xlsx • publiclibrarypartnershipsprojectevaluationdataw orkshop22014CummingsHelenaMontana.xlsx Who filed better?
- 24. Who filed better? • July 24 2014_SoilSamples%_v6 • 20140724_NSF_SoilSamples_Cummings • SoilSamples_FINAL
- 25. File organization best practices • Top level folder should include project title and date. • Sub-structure should have a clear and consistent naming convention. • Document your structure in a README text file.
- 27. Describing data
- 28. Research Documentation • Grant proposals and related reports • Applications and approvals (e.g. IRB) • Codebooks, data dictionaries • Consent forms • Surveys, questionnaires, interview protocols • Transcripts, hard copies of audio and video files • Any software or code you used (no matter how insignificant or buggy)
- 29. Three levels of documentation • Project level – what the study set out to do, research questions, methods, sampling frames, instruments, protocols, members of the research team • File or database level – How all the files relate to one another.A README file is a classic way of capturing this information. • Variable or item level – Full label explaining the meaning of each variable. http://datalib.edina.ac.uk/mantra/documentation_metadata_citation/
- 30. IJ? XVAR? FNAME?
- 32. Metadata Unstructured Data Structured Data There was a study put out by Dr. Gary Bradshaw from the University of Nebraska Medical Center in 1982 called “ Growth of Rodent Kidney Cells in Serum Media and the Effect of Viral Transformation On Growth”. It concerns the cytology of kidney cells. Title Growth of rodent kidney cells in serum media and the effect of viral transformations on growth. Author Gary Bradshaw Date 1982 Publisher University of Nebraska Medical Center Subject Kidney -- Cytology
- 33. Dublin Core
- 34. Disciplinary Metadata Digital Curation Centre’s list of subject-specific metadata schemas - http://www.dcc.ac.uk/resources/metadata-standards
- 35. Data Storage
- 36. LOCKSS (Lots of Copies Keeps Stuff Safe)
- 37. Options for data storage • Personal computers or laptops • Networked drives • External storage devices
- 39. Language from a DMP “All data files will be stored on the University server that is backed up nightly.The University's computing network is protected from viruses by a firewall and anti-virus software. Digital recordings will be copied to the server each day after interviews. Signed consent forms will be stored in a locked cabinet in the office. Interview recordings and transcripts, which may contain personal information, will be password protected at file-level and stored on the server. Original versions of the files will always be kept on the server. If copies of files are held on a laptop and edits made, their file names will be changed.”
- 41. Archiving options • Domain-specific repository • General Purpose Data Repository • Institutional repository
- 42. Major takeaways • Data management starts at the beginning of a project • Document your data so that someone else could understand it • Have more than one copy of your data • Consider archiving options when you are done with your project
- 43. Questions? rebekah.cummings@utah.edu (801) 581-7701 Marriott Library, 1705Y …or ask now!
Notas do Editor
- Specifically we are going to be be talking about data management of your research data, but some of the principles will help you when thinking about the organization of any digital materials, your notes, your PowerPoints, your grocery lists…. Most of these concepts are pretty straightforward, they almost seem like common sense, but the reality is that very few people manage their data well and if you do, you will be at a big advantage.
- Overview of what we will be covering in this session. Each of these could be a one hour course, but we are going to hit the highlights so to speak.
- Introductions Name Major Are you working on a research project?
- Let’s start at the very beginning… This definition is from the Penn State Library website. Controlling often means planning, organizing, and sharing that data effectively. Being thoughtful and deliberate in your data practices Really about being informed, deliberate, and in control of the lifecycle of your data.
- This is the most commonly cited definition when someone wants to pin a definition on data, which is surprisingly difficult to do. What data really is is evidence. Or as Michael Buckland puts it “alleged evidence”. It’s what you are putting forth as evidence for your research findings. “We’ve looked at all this stuff” using these methods and here are our conclusions. Research papers often give methods and conclusions but what they don’t usually contain is the underlying data or evidence. So what is data – EVIDENCE FOR YOUR RESEARCH
- One of the characteristics of data is that it tends to be incredibly diverse. Scientific data – observations, computational models, lab notebooks Social sciences – results of surveys, video recordings, field notes Humanities – text mining, newspapers, records of human history
- Another attribute of data is that it tends to get messy Most of us just don’t realize this because our messy, disorganized files are locked up in a neat little box called your computer. Don’t believe me? How long would it take you to find a photo from five years ago on your computer? Here is a hint. If your image files start with DSC_ or IMG_ and some number following it, it will probably take you a very long time. If most people’s digital files were analog, this is exactly what they would look like.
- The main reason you should manage your data is for yourself and for your own research team. Data management is one of those essential skills you need to get just like learning how manage citations or understand research methods. But it can feel a bit boring like filing. But six months later when you want to locate a file, or even understand your file, your future self will thank you. Most important reason to have good data management is for your own good and the good of your research team. If you want to be able to locate your files or understand your files in the future, good data management is crucial. Plus, unlike research methods and managing citations, this is something that even seasoned scientists are not very good at. So you will have something to offer your research team in the future even as a young scientists.
- https://www.youtube.com/watch?v=N2zK3sAtr-4
- Hopefully by now you can all see why data management is important. Now we’re going to think a little more deeply about how we can avoid the “Two bears” situation. Let’s look at this scenario…
- Get in teams of 2-3 and discuss… Retain the data for a period of at least five years Put your data in a repository Keep multiple copies Keep excellent documentation of your data practices, methods, and workflows, sources, plus any code you may have generated.
- The most important thing you can do is to have and follow and data management plan. Next we are going to move on and talk a little bit about these data management plans that funding agencies are requiring (and I am promoting as a good idea in general!!) Your DMP should answer three main questions…
- For all the reasons we have talked about, many agencies are now requiring data management plans at the start of a research project. This means when you apply for funding for a project, you will have to have a two-page data management plan as part of your proposal. That plan is going to talk about the “lifecycle” of your data throughout the course of the project. How many of you plan on applying for a grant at some point in your careers? Introduce data lifecycle. Funders know that the earlier you start thinking about your data, the better. It’s much more likely that the results of your research will be reproducible, it helps avoid data loss, and increases the value of your research.
- We’ve talked in broad strokes about data management but now we are going to focus in one some of the more specific aspects of managing data well. One of the simplest things that you can do is to be more consistent with file naming, version control, and folder structures. This section has a lot to do with organizing and naming your research materials so that you can find them later and so they will open in any environment.
- We’ve talked about data management at kind of a high level. What is data? Why should you manage it well? Now we are going to talk about some of the nuts and bolts of data management. Starting with file naming. How do you currently name files? Do you have a system? To some extent we are all guilty of bad file naming but when it comes to your research it is important to create a system that makes sense not just to you, but other people as well. are all guilty of bad file naming but when it comes to your research it is important to create a system that makes sense not just to you, but other people as well.
- Here are some examples of bad file names because they aren’t descriptive and don’t help us find the file later, and also because there is a possibility that these files will be overwritten the next time you name a file the same thing.
- File names should reflect the contents of a file and enough information to uniquely identify the data file without getting way too long. Don’t be generic in your file names Be consistent!!!! Your file name may include project acronym, location, investigator, date of data collection, data type, and version number. Whatever will help you or someone else uniquely identify that file in the future. Think about what can be added and what can be omitted in your file names. If you are the only person on a project, you probably don’t need your name. If there are going to be multiple versions of a file, make sure you add a version number or a date to differentiate.
- Here are some file naming best practices that will make sure your file will open in any environment. Special characters can have special meaning in certain programming languages and operating systems and can be misinterpreted in file names. Uppercase lettering can affect numbering. Ex: $ = beginning of a variable names in php. A backslash designates file path locations in the Windows operating system. Spaces make things easier for humans to read but some browsers and software don’t know how to interpret spaces. Sometimes it only reads a file up to the space, which can cause problems.
- There are also best practices around version control and numbering. Version control is often achieved by using dates or a standard numbering system
- #1 is the best one. Descriptive Not too long, not too short
- #2 is the best choice here. First example here has spaces, irregular dates that won’t line up in order, special characters Third example may not be descriptive enough for for a secondary user. Also, beware of the “FINAL” as opposed to using a standardized numbering system.
- That is how to name an individual file. What about your whole file structure? All your research materials need to be in one folder. The top level folder should include the project title and year. If it is multiple year, include the first and last year in the title. The substructures should have a clear and consistent naming convention that is documented in a README file.
- Exercise!! Possible solutions: Organize by type of file (all transcripts in one folder all audio recordings in another) Organize by person (Have a Cliff Barrett folder and a Robert Bennett folder) Problems with file names: Dates are not standardized Special characters/spaces File type in the file name which is unnecessary Unnecessary information in file name – “found on Internet, think okay, better than mine” picture NO consistency to file naming
- Next we are going to talk about data description. A third characteristic of data is that it often needs context in order to be understandable If you have a spreadsheet of survey responses, you need to have the survey to understand the responses. You also need the codebook that explains your variable names and the values that you used, how you cleaned your data. Once again, try to think how a secondary user would interpret your data. When we say metadata we are really talking about two things: human readable documentation and machine-readable metadata The importance of documenting your data throughout your research project cannot be overestimated. Document your data with a certain level of reuse in mind. Replication? Verification? inspection?
- First and foremost, metadata includes any surrounding documentation you may need to make sense of your data. An excel spreadsheet of survey responses is fairly useless if you haven’t kept the survey that generated those responses.
- If you are working in spreadsheets, there are three levels of documentation. http://datalib.edina.ac.uk/mantra/documentation_metadata_citation/ FRLP – Free reduced lunch program
- You must make a codebook and include it in your documentation. This is documenting at a variable level. It’s just as important that you document at a Project and file level as well.
- If you want to learn more about codebooks and how to create good ones, once again I highly recommend going to the ICPSR website and looking at their Guide to Codebooks
- Metadats is very, very important for other people looking to use your project. Often called data about data. Structured information about an object. Mention that there are standards for creating metadata (Dublin Core) including subject specific data.
- Simple standard, low barrier to entry
- Through the course of your research your data needs to be stored securely, backed up, and maintained regularly. Once again this sounds like common sense, but you will be happy when you pay some attention to it. (e.g. when your laptop crashes or is stolen.). I’m going to play a short video clip that has nothing to do with research data, but I think it perfectly captures the way we approach the storage aspect of data management. https://www.youtube.com/watch?v=QyMgNZHtdk8
- #1 rule of data storage – never just keep your data on one device. You are one dropped computer, one spilled glass of water, one unscrupulous thief away from losing all of your data. Every single day I go to Mom’s Café and see people leave their computers at their table while they go to the bathroom or grab a cup of coffee. LOCKSS - There should never just be one copy of your data. Do you backup your data? Most important data management task. NO less than two, preferably three copies of research data. How well are you covered against unexpected loss? Make sure that when disaster strikes, it isn’t a disaster
- There are three options for Personal computers and laptops – Convenient for storing your data while in use. Should not be used for storing master copies of your data. Networked drives – Highly recommended. You can share data. Your data is stored in a single place and backed up regularly. Available to you from any place at any time. If using a department drive or Box stored securing thereby minimizing the risk of loss, theft, or authorized access. BEST!!! External storage devices – thumb drives, flash drives, external hard drive. Cheap, easy to store and pass around. Feel better knowing it’s in your hands where you can see it. Not recommended for the long-term storage of your data.
- 50 GB of free storage and an additional 50 GB if you are on a sponsored project. Free! Secure! When you leave you can take a copy with you or create a new account
- This is an example of social science research where the data are interview recording and transcripts.
- Another area of data management that you will have to consider is data archiving. Archiving is not the same thing as storage Archiving adds additional value to your data. Long-term preservation Metadata Sharable, usually through a persistent identifier Makes data citable
- There are lots of archiving options for your data. Some people choose to put their data on their website which is an option, but not a best practice.