Single file upload: The maximum file size limit for individual file upload is 3GB. The experience for each user may vary based on the stability of the internet connection, speed of connection, file complexity, file format, and type;

For tabular data files, additional processing by the Dataverse platform means that more time is required to upload. This may result in upload failures depending on the file size and the number of columns, cases, or rows in your file. We recommend you upload files less than 500MB in size that requires this feature (see Full Dataverse User Guide)

Multiple file upload: The repository accepts .zip or .tar file bundles. It automatically unpacks your single .zip package file and presents individual files contained within to users without any hierarchy or organization maintained. Alternatively, .tar or double-zipped files can be uploaded directly, preserving your original file organization. Given the additional processing required for .zip files, we recommend you upload zip files that contain less than 500 files.

If you have many separate data files and/or metadata that you would like to upload to create or populate a Dataset or collection, consider using the SWORD API (see Advanced Dataverse User Guide), which can support batch upload of files (same size limits as described above).

It is recommended that file size be factored in when considering data reuse. Larger files can be more difficult for researchers to handle and work with. Preparing and structuring your data for reuse requires careful consideration of potential use cases, the software required for analysis, the computer system, and file disk space. The Dataverse community is working to support more disciplines and data types. We anticipate that in the future, larger datasets will be better supported and managed in the platform.

If you have questions about file size limits, please get in touch with Researcher Support.

The repository is ideal for depositing and sharing research data and supports various research data types and formats. You are welcome to upload research data, supplementary tables, and documentation. Contact the KEEP repository service to submit publications and presentations that your data supports.

We support uploading any file type, including shapefiles, images, Flexible Image Transport System files, and compressed files. However, to use the Data Explorer feature, you need to upload tabular data files (e.g., Stata, SPSS, R, Excel (xlsx), and CSV).

Some files will generate an archival file format for viewing, but users can download either the original or archival version.

See the full Dataverse User Guide for more information.

Yes. The ASU Library can work with you to copy your Harvard Dataverse to the ASU Research Data Repository.

To maintain your original statistics, we will leave the original datasets in Harvard and create new copies at ASU. A metadata record will point back to the Harvard repository. We highly recommend that ASU researchers using the Harvard interface now use ASU's installation unless there are mitigating circumstances like a journal, funder, or collaboration requirements that require Harvard's platform.

To submit a migration or questions about the process, please contact ASU Library's Researcher Support and request a repository deposit consultation.

To assist users in interpreting the data for potential replication or reuse, you should add information that helps tell others exactly how you created and used your datasets. For example, tabular data can still be hard to read if someone doesn't know what your column headers or variable names mean. Projects are varied, and you should not assume someone can figure out what you did. A readme file, code sheet, or data dictionary can help mitigate confusion and help your data align with our charge to make research data findable, accessible, discoverable, and reusable (FAIR). These elements can be uploaded directly into your dataset or added to the metadata.

For more information, see:

• How to make a Data Dictionary (Center for Open Science)

• Guide to writing "readme" style metadata (Cornell University)

• ASU Library Data Management Tutorials

We recommend using CC0 and adding a suggested citation because, in most cases, data (collection of facts) might not be eligible for copyright protection and ultimately creates an unnecessary barrier to re-use. For further reading, see This post from the University of California’s Office of Scholarly Communication provides an overview of the situation and recommends the CC0 license for many kinds of data. The Open Database License (ODC-By) also has an attribution variant. See also our quick guide on licensing and data.

To directly contribute to the repository and manage your submission, including uploading files, you'll need an account. This is an additional step in requesting a submission and only needs to be done the first time you submit a dataset. Contributions to the repository are limited to ASU-affiliated projects and persons.

• See Login to the ASU Research Data Repository for instructions on creating your repository account.

We are using ASU single sign-on as the only method for signing in and users add their own accounts. Library admins cannot add them before they sign in for the first time, and users have read-only when they are initially added. Collection admins or the owner of the collection have to permit them. If you do not have permission to add people, please contact the library and notify us which user you need to be added to after they have signed into the repository for the first time.

Accounts are created at the time of first sign-in, and once users have done that, you can add them to the account. You can set various permission levels and even assign them by groups.

If you only want to list someone as an author but not grant permission to manage your datasets, you can add their name in the metadata author fields, and there is no need for them to sign in to the repository.

In a nutshell: You invite contributors by giving them the repository link, they sign in to create an account, and you then grant access. You nor the admins can create accounts for them before they sign in.

See Roles and Permissions in the full users guide

With our current authentication process, only asu.edu emails can create an account. Anyone from outside the university can view and access published collections, they cannot directly submit/deposit or manage the metadata. If you have members of your project outside of ASU, you can have them submit their datasets and documentation to a secure sharing space like Google Drive or Dropbox and then upload the files on their behalf.

If more direct interaction is desired, ASU sponsors can request collaborators get an “affiliate ID” (ASURITE) to access ASU systems. The ID expires after one year and must be renewed by the ASU sponsor then. To sponsor and request an ASU ID for your collaborators, fill out the ASU Courtesy Affiliate Request - Add or Renew form. 

Additional workarounds for research teams may include using the Dataverse API, research notebooks like LabArchives, or the OSF as a collaboration platform, and connecting the OSF to Dataverse (which uses an API token).

After leaving, users departing the university still have ASURITE identities and single sign-on capability. However, due to various policies and subscription contracts, they do not have access to all ASU-provided resources that login gave them access to. We limit access to library subscription resources when you graduate or leave. Still, our repositories present a unique case where a person might need continued access after separating from the institution. We are still working through this question, but if an owner of a collection recognizes a user with an ASURITE ID, they may be able to provide continued access for contributing to their respective dataset records, but this may not carry over to external contributors whose temporary IDs have expired and not been renewed.

The guestbook will show views and downloads. A user downloading one file counts as a single download. If a user selects multiple files, the repository zips them into a ZIP file but counts each compressed file as a separate download. If the same user downloads a previously zipped file individually, it will count as a download per file. 

The tree view sorts the folders alphabetically and defaults to the first folder being "open," In your case, users have to scroll for a long time because there are so many files in "audio" to see the remaining folders.  If you need one of the folders to come before another, you can make a folder name starting with 0 and alphabetically or numerically increment subsequent folders. See Preparing to share your data for guidance on setting up file and folder structure names.

You cannot add new or customized subjects to the Subject field. You choose from a list of options connected to predesignated options that will effect the metadata fields of your dataset.

If you have a specialized subject term to include with your dataset, use either the Keyword field or the Topic Classification. Both Keywords and Top Classification will be verified by an ASU data curator to ensure they are matched with an appropriate subject heading. See the Metadata Dictionary and Edit Dataset Metadata, Terms, and Thumbnails for adding and managing your metadata.