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There are several steps to properly organizing your dataset. These include determining what data to include, how many identifiers to request, how to organize the data into folders, and creating the ReadMe file and data inventory.

Step 1. Determine what to include

A data collection may be composed of multiple files and different datasets. In preparing your data for publication:

  1. Identify the data and other materials that you consider useful for validation and reuse of your research:
    • Data associated to a research project may include multiple files with different roles.
    • If there are components of your dataset that belong in a public repository such as NCBI (e.g., fastq files), submit them to the repository, rather than to CyVerse Curated Data.
  2. Beyond data, you will include the ReadMe file (see Step 4), and you may include scripts or links to scripts to run your analysis.

Step 2. Determine how many permanent identifiers to request

To determine how many DOIs to request for a given data collection, consider the following:

  • Think about its size and components.
  • How many studies or publications does it represent?
  • Is your data collection formed by different datasets and are those likely to be used separately?
  • Do you want to create a data collection with one DOI for the entire project and additional related DOIs for distinct datasets so that they are cited individually?

If you are uncertain about how many DOIs to request, contact us at

Step 3. Organize your data into folder(s)

  1. Organize your data so that there is one folder for each DOI (see CyVerse Curated Data folder-naming guidelines for naming conventions).
  2. Within a folder, include all files in your data package plus the ReadMe file and the inventory.
    • You may have subfolders within a data package.
    • You may include compressed files in a package, as described on the Permanent Identifier FAQs, but do not compress the entire folder/package.

Step 4. Create a ReadMe file

Create a text file labeled "readMe" with the following information:

  • How you obtained, organized, and labeled your dataset.
  • How to reuse the data, such as which apps can analyze the data.

Step 5. Create an inventory


For examples of well organized datasets including thorough readme files with inventories see Duitama rice variation 2015 or Genomes To Fields Inbred Ear Imaging 2017.

Supporting documents on data management and organization

Here is a useful guide to data organization: Research Data Management: File Organization (PDF).





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