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OSF Best Practices
OSF provides guides on some of the best practices for using OSF in your research workflow. Topics include:
- File Management- this includes file naming and organizing files.
- Version Control- good version control can lead to more efficient collaboration and increased accuracy of research results. OSF has a built-in version control for all files stored in your project.
- Handling Data- best data practices include how to make a data dictionary, sharing research outputs, and sharing data.
Research Data Management
The UW Libraries also has guides on research data management to help you with your research:
- Data Management Guide- guide of resources related to the many aspects of research data management. Data management encompasses the processes surrounding collecting, organizing, describing, sharing, and preserving data.
- Data Resources- Includes information & resources for finding & visualizing data.
- Useful Resources for Data Management Planning- includes a list of useful links for data management adapted from the Data Management Planning Workshop, an asynchronous online workshop for UW community members engaged in research with data.
Why should you organize your data?
The organizational structure of your data can help you easily locate files when revisiting a past project and can help secondary users find, identify, select, and obtain the data they require.
How do you organize your data?
For best results, data structure should be fully modeled top-to-bottom/beginning-to-end in the planning phase of a project.
You'll want to devise ways to express the following:
- The context of data collection: project history, aim, objectives, and hypothesis
- Data collection methods: sampling, data collection process, instruments used, hardware and software used, scale and resolution, temporal and geographic coverage, and secondary data sources used
- Dataset structure of data files, study cases, and relationships between files
- Data validation, checking, proofing, cleaning, and quality assurance procedure carried out
- Changes made to data over time since their original creation and identification of different versions of data files
- Information on access and use conditions or data confidentiality
File Naming & Structure
Why is file naming important?
Think of a file name as a unique identifier for each of your files. Following a naming convention allows you to simplify the organization of your files and locate your files with ease, as well as making it easier for others to understand and reuse your data. This is particularly important when you are working on a collaborative project.
How should you name your file?
Here are some recommended best practices for naming your files:
- Use names that are brief but descriptive
- Avoid spaces and special characters (ex: *, #, %, etc.)
- Come up with a naming convention adhered to by everyone using the files
- Identify versions of files using dates and version numbering in file name
- Use three letter file extensions to ensure backwards compatibility (ex: .doc, .tif, .txt)
- Do not use letter case to identify different files (ex: datasetA.txt vs. dataseta.txt)
How should files be structured?
Folder structure for your files can assist in the unique identification of the files contained within them. Consider the structure of the folders containing your data files before you begin to collect your data. Ideas for how to organize your folders include:
- Data type (text, images, models, etc.)
- Time (year, month, session, etc.)
- Subject characteristic (species, age grouping, etc.)
- Research activity (interview, survey, experiment, etc.)
Consider these examples:
- File naming: File001.txt vs. 201206blood_ID0234.txt
- Folder structure: MyDocuments\Research\Sample12.jpg vs. C:\\NEHGrant01234\WWI\Images\London_001.jpg
File Naming Resources