Aka, describing your datasets
The following fields are required when uploading data files to figshare. These descriptive elements are necessary to create a complete citation to your published dataset.
- Title Figshare automatically uses the first uploaded file's filename as the title. Please change it to something descriptive!
- If the data accompanies a paper, use Dataset: Title of Paper
- Authors The person uploading the data is automatically set as the author. If you're uploading for someone else, simply add the correct authors, then delete your name.
- When adding co-authors, it's best to use their ORCiD in order to guarantee the correct researcher is credited!
- Categories Choose at least one category from this Figshare supplied subject term taxonomy - just start typing in the term you want and the field will auto-populate.
- Keywords Use as many or few as you want, these can be anything you would find useful.
- Description/Abstract - re-use the abstract created as part of the Read Me file that describes the data.
- License - please use the least restrictive license you are comfortable with.
- Default is CC-BY-NC (Creative Commons Attribution Non-Commercial) but CC-0 (Creative Commons Zero, similar to Public Domain) is even better.
- If you are uploading software or code, please choose the Apache 2.0 license.
In addition to the descriptive fields above....
The deposited dataset must include sufficient description to understand and reuse it.
If the data consists of one or two fields/variables, it may be enough to provide the description of those fields in the abstract, but generally each dataset should have at least one Read Me file (aka data key or data dictionary). The Read Me can be in a separate plain text or delimited tabular file, or can be part of a tabular data file, e.g., a new tab in an .xslx document.
More info on creating ReadMe files can be found on the Libraries' website https://library.si.edu/research/describing-your-data-data-dictionaries
The Read Me should include (where applicable):
- Who collected or aggregated that data, or in the case of many contributors, who is the principal investigator or contact.
- When the data was collected.
- What the data elements are measuring or describing.
- Why the data was collected, e.g., the name of the larger project or program.
- Methodologies used or assumptions made while collecting the data.
- Description of any transformations or calculations applied to the raw data (if the data being described is not the raw data) or to specific data elements, including references to any scripts used.
- Any validation or quality control process that has been applied.
- A description of each component or element of your data. If your data is tabular, describe each column (field) and what it should contain.
- If your data includes images, describe how they are organized, and where detailed metadata can be found.
Any datasets which do not have either an abstract or a separate ReadMe file that includes sufficient information to interpret the deposited dataset are not guaranteed to be permanently managed by central SI services after the owner of the data has retired or moved on.