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Tips to prepare your data for AHED


Collect your data


Consider including all stages of data that would help others to understand and reuse your dataset.
  • Files
    • Unprocessed raw data.
    • Processed data.
    • Organized tables and spreadsheets.
    • Code for data processing and visualization.
    • Manuscripts.
    • Graphs, images, etc...
  • URL
    • Publicly available repositories where your data is already stored.
    • Project website.
    • DOI of publications related to your dataset.
    • GitHub.

Recommended formats


  • Choose open file formats when possible to facilitate the use and preservation of the data. If you include proprietary files, such as instrument vendor’s data files, consider also providing the same data in an open format.
  • Plain text formats are recommended.

Verify that your data can be shared publicly


  • Make sure that you have the rights to distribute any data uploaded to this site and that the data is appropriate to distribute. You should not upload any information considered:
    • Classified, Sensitive But Unclassified (SBU)/Official Use Only.
    • Copyrighted (without retaining proper copyright notices and without permission where not the owner)ITAR restricted.
    • Proprietary without permission.
  • For URL to other repositories, make sure that the data is publicly available and not password protected.

Organize data logically


  • Create a clear and logical file structure.
  • Name files descriptively and consistently.
  • For large collections of files (e.g. spectra, diffractograms, chemical information), bundle organized files into compressed file archives (such as .zip). Verify they can be opened and not corrupted.
  • Omit unneeded files.
  • Try to keep individual files or compressed files smaller than 10GB to facilitate data download.

README file


  • Describing your dataset with a README.txt file is highly recommended. It will facilitate the re-use and replication.
    • Write it clear for a broad audience.
    • Describe data collection methods, processing and analysis steps.
    • Define variables and allowable values.
    • Describe software used to process, visualize, analyze, and compress your data.