Data Services
What is Research Data?
Any piece of recorded information can be considered data. Images, video, text, audio recordings, code, and numerical measurements are types of data commonly produced by research. Because research often requires significant labor and funding, it is essential to organize and secure research data. This page provides an overview about how to create a data management plan, organize and store your data, and how to cite others’ data in your bibliographies.
Creating a Data Management Plan
A data management plan (DMP) documents how data will be collected, secured, stored, organized, and shared for a research project, a lab, a department, or an entire organization. DMPs can be as long or as short as needed. They are often required by funding agencies as part of a research proposal.
Data management prevent future headaches, like mistakenly deleting valuable research results, misplacing files, or having to redo the analysis of your data. When done properly, data management will keep your work organized and intelligible to others as well as to your future self. This will make your work reproducible and increase your data’s value after the completion of the research.
Types of Data
Contextual Details
Storage, Backup, and Security
Provisions for Protection & Privacy
Policies for Access & Reuse
Plan for Archiving & Preservation
Putting Your Plan into Action
Adding Metadata to Your Research Data
Metadata are data that describe data. They are everywhere; we just don’t always notice them. As an example, think about a book you might buy online. The book itself contains text, a kind of data. Information you might find about the book in an online store includes:
- the name of the author
- the name of the publisher
- the date of publication
The metadata above provide more information about the item you’re looking at. When metadata for many items are brought together and standardized, they become powerful tools for locating and discovering things – like a library catalog or an internet search engine.
Why Describe Your Data?
Metadata are important because they explain a data set to others. Data sets exist within a certain context, and this context must be communicated well so that others can reuse the data set.
For example, the City of Boston has open data on 311 Service Requests. If a researcher wanted to use these data and didn’t know the data was about Boston, what a 311 request is, or the year the data was created, it would be very difficult for them to understand or reuse this data set. Even with this information, without a data dictionary like the City of Boston’s CRM Value Codex, it would be hard to understand what some variables are, what blank values mean, or what values are possible.
Metadata provide necessary information for others (sometimes your future self) to understand the data set and properly reuse it. It often takes time to create metadata, but the effort is worthwhile.
Naming Conventions
Researchers often use structured guidelines known as file-naming conventions to describe the content and date of the file. Using naming conventions will also save you time by keeping your work organized and understandable. Consistently organizing your files in a logical way will also make it easier to analyze and process your files, and the data they contain, with scripts and other tools.
Best Practices for Naming Conventions
As you and your research partners establish file-naming conventions, we recommend you consider these guidelines:
- Define file conventions at the start of your project and follow them consistently during the course of your research.
- Include a text file, often called a readme, in your file directory that describes the naming conventions you are using. This information
will be helpful for individuals who are new to the project. - Use descriptive file names that are meaningful to you and your colleagues. This might include the project name, subject, or acronym.
- Keep file names relatively short.
- Include dates in your filename, which can help with sorting different versions of your file. Recommended format: yyyymmdd.
- Use a sequential numbering system to keep track of different versions or revisions to a file. For example, try with leading 0’s. (rehab01 instead of rehab1)
- Use hyphens, underscores, or camelCase instead of spaces.
Things to Avoid
- Spaces within your files; not all software recognizes spaces within file names.
- Special characters in your file names such as: “/ \ : * ? “ < > [ ] & $.
- These characters have specific meanings for various operating systems and could result in your files being deleted or misplaced.
- Long or wordy names that may not have meaning to you and other researchers on your team.
Examples
A Daily Journal
A researcher often keeps a daily electronic journal. In her research project folder she has a folder titled “journal.” Inside this folder are text files named with a simple date format – YYYYMMDD.
project-folder
journal
20170101.txt
20170102.txt
20170102.txt
other-sub-folder
Meeting Minutes
A lab has weekly meetings to discuss their work, issues, and upcoming events. Each week a different lab member takes the meeting minutes to help distribute the workload. A file naming convention for this group might be:
project_folder
minutes
20170206-project-weekly-minutes.docx
20170213-project-weekly-minutes.docx
20170220-project-weekly-minutes.docx
Biomedical Samples
A biomedical engineering lab is taking numerous samples from a heart and staining them to learn more about the tissue samples. The lab would like the samples to link the experiment and lab notebook in a systematic method so the entire lab understands which samples connect to which experiment.
The team develops a method that formats their naming convention using the experiment number, A or B, section number, and stain used. An example of this naming convention would look like 0231A_216_act where 0231 is the experiment number, A stands for apex, 216 is the section number, and act is the stain (actinin). A folder of these slides might be organized like:
0231A_216
0231A_216_act
0231B_100
0232A_215
0232B_215_act
In this way, the researchers can programmatically sort a large number of files by experiment number, section, or stain. While this naming convention is more complex than our other examples it fits the needs of a particular lab.
This example is provided by University of Massachusetts Medical School’s Lamar Soutter Library in the New England Collaborative Data Management Curriculum in Module 1.