Prepare your research data for publication

Compliance with legal standards and guidelines

• Only research data to which you hold the rights or have consent from the rights holders can be published in the Research Collection.
• Research data has to comply with the specifications set out in ETH Zurich’s Guidelines for Research Data Management and the Guidelines for Research Integrity at ETH Zurich.
• Programming code must be licensed under an open source license, and the license must be included in the upload. For more information, see Licensing software and programming code/scripts.

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Choose a license

• According to the Guidelines for Research Data Management, ETH Zurich advocates the use of Creative Commons licences or public domain dedications (CC0) when publishing research data. 
• These licences allow authors / data producers to define what types of reuse are permitted for their works. Data published without a licence can only be reused with explicit permission from the copyright holder or based on an exemption in national copyright law. Works published with a CC license can be reused as specified in the license.
• For help in choosing a Creative Commons license check the page Creative Commons Licenses.
• Creative Commons licences are inappropriate for software because they do not include provisions for source code or its further development. Open‑source licences are inappropriate for research data, as they were designed specifically for software. For more information on software licensing, see Licensing software and programming code/scripts.

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Document your data

• Include a README file with file extention to document your data. A guide can be found here: https://documentation.library.ethz.ch/x/bQBIB
• Use meaningful file and folder names
• Include metadata
• Link your item to an article or other publication (example)
  Example of linked item

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Edit your data for publication

• Remove temporary and backup files
• Remove duplicate files
• Remove personal information
• Rename files and folders where helpful (meaningful names)
  • Avoid overly long folder and file names. Total path lengths >200 characters (files and folders combined) can lead to problems for windows users 
• Remove third party files and software for which you don‘t have permission
• Check for hardcoded file paths, symbolic links, references
• Don‘t include your manuscript: Publishers PDFs, Preprints and AAMs (Author's Accepted Manuscripts) should be published as a separate entry. See Open Access articles (self-archiving)
• Spell check your text files
• File extensions should be consistent with file formats 
• Avoid special characters in names of files and folders. These characters hamper compatibility because they lead to undesired effects depending on the operating system
  • Avoid the following characters:
    • \ / ? : * " > < | : # % " { } | ^ [ ] ` ~ as well as blanks
    • Non ASCII characters such as ¢ ™ ® , umlauts (ä ö ü), diacritics such as à é ô etc.
  • The following ASCII characters are permitted:
    • ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789
  • We are currently not aware of problems with the folllowing characters:
    • ! $ & ' ( ) + , - . ; = @ _ 

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Choose appropriate file formats

• Choose open, well documented standards from your domain
• For long term preservation (> 15 years) only formats from our list of recommended file formats should be used. Other Formats should be converted. Consult our list of File formats for archiving research data
• When non-recommended formats are used, long term readability cannot be guaranteed

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Packagage your files for the upload

• Upload single files directly
• Pack file collections containing a large number of files or subfolders into a container
  • Use standard container formats: Zip or TAR-files (avoid .7z, tar.gz, .rar, and so on)
  • Use preferably uncompressed containers (compression level “store”)
  • Do not use archives within your ZIP or tar files.
  • Don't use encryption or password protection
• Large datasets can lead to problems with up- and downloads of your dataset. Limit single files to around 10 GB for a limit of 50 GB per entry.
  • Please split larger folder structures manually into meaningful subunits and package them separately.
  • Don't use the automatic split features of your software. 
  • Datasets larger than 50 GB can be published using our service libdrive.

Instructions for Windows

On Windows operating systems, you should create ZIP archives by using the software tool 7-Zip.

You start by selecting your files and folders in Windows. Then, click your right mouse button to open a menu. Select the software tool “7-Zip”, and “Add to archive …” A dialog box opens as shown below. In the white field on top, you write the name of your archive file. Use the option “zip” and the compression level “store”.

Intructions for macOS

On a Macintosh computer, you should create tar archives.

You may create tar container files either by using the command line (tar -cvf <archive_name.tar> <folder_to_tar>) or by using the software Keka. If you choose the second option, you start the program Keka and select “Compression” in “Preferences”. You then select the default format “TAR” as in the dialog box shown below. You finally drag your folder onto the Keka icon and fill in the name of your archival file. You should select the option "Exclude Mac resource forks (e.g. .DS_Store)" in the Keka settings. This prevents the inclusion of macOS specific hidden files. 

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