数据共享

In order to ensure the reproducibility of published results, improve the transparency of academic reporting, and promote open sharing of research data, this journal encourages authors to share supporting data for their papers.

1. Types and scope of shared data

This journal encourages authors to share data that has not been included in the main body of the paper due to technical, content, or presentation platform reasons, but is helpful in supporting repeated validation of the paper (Table 1).

Table 1 Shared Data Types and Scope
Data Policy Description
Encourage to sharing DNA/RNA/protein sequence data, deep sequencing data, biological macromolecule structure data, raw data of experimental results (duplicate data/validation data/opposition data), special experimental materials (mutant strains and cell lines, etc.), detailed data of research results such as images and audio and video information reflecting the studied properties of the research object
Experimental plan (including all experimental steps, specific operational procedures and methods), research hypotheses, videos of experimental operations, case analysis, etc.
Not suitable for sharing Data involving research ethics, sensitive information, confidential information, or shared data that may harm the legitimate rights and interests of third parties

 

2. Requirements for data storage and recommendations for data repositories
This journal encourages the storage of research data in public repositories and display a 'data availability statement' in the paper.

This journal recommends authors to use universal data repositories (Science Data Bank, Figshare, Dryad, Harvard Kubernetes, Open Science Framework, Zenodo) that are suitable for a wide range of data types. Among them, authors are encouraged to choose "Science Data Bank (ScienceDB)" as their first choice（ https://www.scidb.cn/ ）As a service platform for maintaining and sharing publicly supported data in academic papers. At the same time, the author may also store it in (but is not limited to) the following professional data repositories (Table 2).

Table 2 Professional Data Repository
Data Type Suitable Database
protein sequence UniPort
Nucleic acid sequencing&genomics Genbank
Genome Sequence Archive(GSA)
European Variation Archive (EVA)
ArrayExpress
Gene Expression Omnibus (GEO)
Molecular and macromolecular structures Biological Magnetic Resonance Data Bank (BMRB)
Electron Microscopy Data Bank (EMDB)
Worldwide Protein Data Bank (wwPDB)
Protein Circular Dichroism Data Bank (PCDDB)
Structural Biology Data Grid
Life Science Imaging Image Data Resource(IDR)
Neuroscience NeuroMorpho.org
G-Node
OpenNeuro
Cell analysis technology&immunology ImmPort
FlowRepository
Biological resources Eukaryotic Pathogen Database Resources (EuPathDB)

 

3. Data availability statement
When publishing a paper, this journal recommends displaying a "Data Availability Statement" at the end of the main text (before references), with the following declaration template:

The data supporting this study [dataset name] (DOI link, login code) can be publicly accessed in the [public repository name] database: [permanent access link]

4. Data Usage License Agreement

This journal recommends the author to use the CC-BY 4.0 license agreement.

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