The cornerstone of scientific research integrity is data disclosure. However, in practice, many researchers face confusion and challenges on how to share data in compliance with regulations.
The central role of data availability statements
When submitting a manuscript to an NRR journal, the data availability statement is an absolutely indispensable part of the manuscript. This statement should clearly indicate whether the author has followed the journal's data disclosure policy and the exact location of the data storage. It is not only a formal requirement, but also builds a bridge between the paper's conclusions and verifiable evidence.
If the paper is ultimately accepted, this statement will be published with the article and become part of its permanent record. This can provide very clear guidance to other scholars for subsequent verification or in-depth research, preventing problems such as duplication of research or unverifiable conclusions due to missing data.
The specific connotation of the minimum data set
NRR provides a clear definition of the "minimum data set". The data it must contain must be data that can be used to reproduce all the research results in the paper, and must be accompanied by necessary metadata and method descriptions. This shows that it is not enough to just provide raw values. It is also necessary to explain the background of the relevant data, the method of collection, and the steps of processing.
If the study uses only a portion of a larger data set, the authors only need to submit and analyze the directly relevant subset of the data, which reflects flexibility in the policy. Likewise, if the domain practice is to share processed data, then there is no need to submit the raw, unprocessed data.
Strict requirements for image data
NRR emphasizes the integrity of image data (such as microscopic images, brain scans, etc.). The author must be able to provide original, uncropped, and only minimally adjusted image files that support all illustrations in the article. These images are key evidence to determine the authenticity of the results.
Although it is not mandatory to submit all original image files at the time of initial submission, the editorial office has the right to require authors to provide them during subsequent peer review or before the article is accepted. If there are concerns about images after the article is published, the journal can still ask the author to submit basic image data for verification.
Data repository selection criteria
NRR requires that the data supporting the paper should be placed in an appropriate public repository, the corresponding code of the paper should also be placed in an appropriate public repository, and the relevant software must be stored in an appropriate public repository. This repository is either disciplinary-specific, as is the case with GEO for gene expression data, or it is a general platform across disciplines. Authors should give priority to repositories that are recognized and mature in their research fields.
The journal recommends the use of repositories that use open licenses such as Creative Commons Attribution (CC BY) to ensure data accessibility and reuse efficiency. If there are generally accepted data storage standards in your field, authors should operate in accordance with these standards.
Practical considerations behind the policy
NRR explicitly prohibits the use of vague expressions such as "data not shown" and requires that all data supporting conclusions must be presented in an accessible form. The purpose of this regulation is to prevent selective reporting of data and promote the transparency of research.
The flexibility of the policy is reflected in respect for disciplinary differences. It does not mandate a unique repository, but creates a selection framework that allows authors to make the most appropriate choice based on the nature of the data and field practices, balancing the feasibility of open sharing and practical operation.
Profound impact on scientific research ecology
Promoting the enforcement of data disclosure policies will, in the long run, be helpful in accumulating scientific research assets that can be reused and reduce the waste of resources caused by repeated experiments. When data can be independently verified, the reliability of the research itself and the public's trust in science are increased.
However, this instead imposes stricter requirements on researchers, covering the degree of standardization of data management, the length of initial investment required, and comprehensive considerations of data ethics (such as privacy issues). This is an inevitable step towards greater transparency in scientific research.
In the field where you are engaged in research, what are the most practical obstacles and difficulties you encounter when implementing data sharing strategies? Would you be happy to share your experiences and personal opinions in the comment area?
