If a dataset draft is to be included in a double-blind peer review, i.e. both author and reviewers are anonymous, then the researcher cannot share the dataset as with an editor, because the name of the depositor appears in the version control (see Version tab). In such cases, the curator creates an anonymized version of the dataset draft according to the guidance below.
- The curator curates the dataset as usual, but without publishing it.
- Once the dataset is ready for sharing, the curator must create a new, identical, but anonymized dataset in a collection which is dedicated to datasets that are part of double-blind review.
- To get access to this collection, send an email to support@dataverse.noand ask for the username and password for the DataverseNO Review Curator.
- Log into dataverse.no as Review Curator. You have to be logged out as “normal” curator, or open a new window in your web browser in incognito mode. Select the log-in alternative Other Options – Username/Email, and log in.
- Navigate to this collection: https://dataverse.no/dataverse/review(Note! The collection is/must not be published.)
- Create the anonymized dataset (see more information below), don’t publish it, but create a Preview URL (see Reading access to unpublished dataset). When finished, log out as Review Curator.
- How to anonymize the dataset:
- Make sure that all information about the author is removed by either removing identifying information or adding “Anon.” into the metadata fields that need to be anonymized: Author(Name), Contact (Name etc.), Producer (Name etc.) and possible other fields containing author-identifying information. In the default metadata template in the Review collection, “Anon.” is already added to these fields.
- Also make sure that the author name and other identifying information is replaced with “Anon.” in the ReadMe file.
- In addition, identifying file metadata must be removed from all files. See this guidanceon how to remove this kind of information from Microsoft Office documents. For advice on how to remove personal information from other types of files, see the help pages of the software or information on the web. For any questions, contact DataverseNO.
- It is this new, anonymized dataset that the researcher can sharewith the editor, usually via a Preview URL (see Reading access to unpublished dataset).
- But note!Once the article is accepted, it will be the original dataset created by the author which will be published, and the curator will then delete the anonymized version of the dataset. Therefore, it is crucial that the author right from the beginning uses an anonymized reference (incl. DOI) to the original dataset in the publication (manuscript).
