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Neuroscience Bulletin Data Paper Guidelines


 Overview 

A Data Paper is a peer-reviewed publication that focuses on describing datasets of high scientific value to the neuroscience community, rather than reporting traditional research findings. It provides detailed documentation of data collection, processing, and validation methods, along with comprehensive metadata to facilitate data reuse and sharing.


Key Features of Data Papers

  • Citable Research Output: Your dataset receives an independent DOI, enabling formal citation by other researchers
  • Enhanced Data Visibility: Peer-reviewed publication increases the recognition and impact of your data
  • Promotes Data Reuse: Detailed descriptions help other researchers understand and utilize your data effectively
  • Additional Publication: Gain an extra, high-impact academic publication beyond your original research papers


Current Submission Policy

Data Papers are currently published on a solicitation basis. Authors interested in submitting a Data Paper are encouraged to contact the editorial office in advance to discuss their dataset. Please send a pre-submission enquiry to nsb@ion.ac.cn with a brief description of your dataset, including:

  • Type and size of the dataset
  • Scientific significance and potential reuse value
  • Current status of data deposition
  • Any previous publications using this data


 Scope of Accepted Data Types 

Neuroscience Bulletin welcomes Data Papers describing the following types of neuroscience data:

1. Neuroimaging Data

Includes: Structural MRI, functional MRI (fMRI), diffusion tensor imaging (DTI), PET, SPECT, and other molecular imaging data, as well as large-scale brain imaging cohort datasets.

Why Important:

  • Neuroimaging data collection is costly and equipment-dependent
  • Large-scale imaging datasets provide significant value for understanding brain patterns and brain health research
  • Supports collaborative brain imaging analysis across studies

Recommended Formats: DICOM, NIfTI (.nii, .nii.gz), CIFTI

________________________________________

2. Electrophysiological Data

Includes: Electroencephalography (EEG), magnetoencephalography (MEG), intracellular and extracellular recordings, local field potentials (LFP), optogenetics combined with electrophysiology, and neural activity imaging data (calcium imaging, neurotransmitter imaging, intracellular signaling).

Why Important:

  • Electrophysiological data represent a core data type in neuroscience research
  • Raw waveform data are essential for validating research conclusions
  • Supports data comparison and method validation across laboratories

Recommended Formats: Neurodata Without Borders (.nwb), FieldTrip (.mat)

________________________________________

3. Single-Cell Omics Data

Includes: Single-cell RNA sequencing (scRNA-seq), single-cell ATAC-seq, spatial transcriptomics, and related genomic, transcriptomic, and proteomic data.

Why Important:

  • Single-cell technologies represent the frontier of neuroscience
  • Cell type atlases are crucial for understanding brain function
  • Data integration enables the discovery of new cell subtypes and marker genes

Recommended Formats: FASTQ (.fastq), HDF5 (.h5, .h5ad), MTX (.mtx)

________________________________________

4. Behavioral and Cognitive Data

Includes: Large-scale behavioral experimental data, cognitive task testing data, animal behavior tracking data, human psychometric data, and behavioral audio/video recordings.

Why Important:

  • Behavioral data serve as the bridge between neural mechanisms and function
  • Long-term behavioral tracking data have unique value
  • Supports cross-study comparison of behavioral phenotypes

Recommended Formats: MP4, MOV, CSV (.csv), JSON (.json)

________________________________________

5. Neural Circuit Connectivity Data

Includes: Neuronal morphology data, synaptic connection data, viral tracing data, functional connectivity data, and connectomics datasets.

Why Important:

  • Neural circuits are central to understanding brain function
  • Connectomics data are essential for constructing brain atlases
  • Supports cross-species and cross-region comparison of connectivity patterns

Recommended Formats: HDF5 (.h5), CSV (.csv), Neurodata Without Borders (.nwb)

________________________________________

6. Other High-Value Neuroscience Data

  • Cross-modal integrated datasets
  • Long-term longitudinal cohort data
  • Rare disease or special case data
  • Innovative experimental method validation data
  • Computational neuroscience models and simulations
  • Clinical neuroscience datasets (with appropriate ethical approvals)


 Manuscript Structure and Format 

Data Papers should follow the structure outlined below. Please download our Data Paper Template(Word) for detailed formatting guidance.

Required Sections

1. Title

  • Must accurately describe the dataset contents
  • Avoid acronyms (except very common ones such as MRI, EEG), abbreviations, and unnecessary punctuation
  • Do not include subjective claims (e.g., “novel,” “first,” “comprehensive”)
  • Maximum 110 characters, including spaces


2. Authors and Affiliations

  • Provide full author information, including name, institution, department, and country
  • Include email addresses for corresponding authors
  • Use ORCID identifiers where available


3. Abstract (Maximum 150 words)

  • The Abstract should succinctly describe:
  • What the dataset contains
  • How the data were collected and processed
  • Potential applications and reuse value

Do not include: URLs for data access, sub-headings, or claims regarding new scientific findings.


4. Background & Summary

This section should provide:

  • Overview of the dataset and its scientific context
  • Motivation for creating the dataset
  • Potential reuse value and applications
  • Summary of any previous publications that used these data (in whole or in part)

Note: Data Papers do not present results or analyses, so there is no formal requirement to cite prior art for comparison. However, citing relevant datasets or outputs in the field is recommended for readers’ reference.


5. Methods

This section should describe:

  • Data Collection: Experimental procedures, equipment used, sampling strategies
  • Data Processing: Processing methods, software tools, quality control steps
  • Technical Specifications: Resolution, temporal/spatial coverage, data format details

Important Notes:

  • Focus on documenting practical tasks rather than general results or analyses
  • If data have been analyzed or published elsewhere, experimental methods can be cited rather than restated
  • Include an Ethics Statement subsection if the data involve human or animal studies


6. Data Records

This section should explain:

  • Repository where the dataset is hosted (must be ScienceDB Neuroscience Bulletin Community)
  • Overview of data files and their formats
  • Folder structure and organization
  • Definition of column headings, variable names, or other fields/metadata that may not be self-explanatory
  • Each external dataset should be cited using the data citation format.
  • Technical Validation that describes:
  • Experiments, analyses, or checks performed to support the technical quality of the dataset
  • Quality control procedures and validation methods
  • Any supporting figures and tables
  • Usage Notes (Optional) that provide:
  • Additional technical notes on how to access or process the data
  • Software requirements or dependencies
  • Known limitations or considerations for data reuse
  • Do not use this section for: conclusions, general selling points, worked case studies, or promotional content.


7. Data Availability

Authors should include a Data Availability Statement in their manuscript.

Template:

The datasets generated and/or analyzed during the current study are available in the Neuroscience Bulletin community of Science Data Bank (ScienceDB) at https://doi.org/10.57760/sciencedb.xxxxx (DOI) and https://cstr.cn/31253.11.sciencedb.xxxxx (CSTR).

Why we recommend ScienceDB:

  • Free DOI and CSTR registration
  • Long-term preservation by the Chinese Academy of Sciences
  • Compliance with FAIR principles
  • Dedicated Neuroscience Bulletin community：https://www.scidb.cn/c/j00221


8. Code Availability

Include a statement indicating:

  • Whether custom code was used
  • How and where custom code can be accessed
  • Any restrictions on access
  • Software versions and specific parameters used

If no custom code was used, state this explicitly.


9. Ethics statement, Author Contributions, Competing Interests, Funding, and Acknowledgements

Follow standard Neuroscience Bulletin requirements as outlined in the Instructions for Authors.


10. References

Cite all resources used for data production, including datasets, articles, preprints, and online resources. Reference style could be found in the Instructions for Authors.


 Requirements for Data Quality 

General Requirements

All datasets submitted as Data Papers must meet the following quality standards:

Requirement

Description

Completeness

Datasets should be   complete with no missing essential components. Any missing data must be   clearly described and explained.

Accessibility

Data must be   stored in a public repository (ScienceDB) with stable access links provided.

Reusability

Data should use   open standard formats that facilitate reuse by other researchers.

Validation

Data must undergo   quality control to ensure accuracy and reliability.

Documentation

Comprehensive   metadata must accompany the dataset, describing context, experimental design,   and sample characteristics.


Ethical and Regulatory Compliance

  • Human Data: Studies involving human subjects must include appropriate ethical approval statements and informed consent documentation. Data must be anonymized before deposition.
  • Animal Data: Studies involving animals must include statements confirming compliance with institutional and national guidelines for animal care and use.
  • Sensitive Data: Special categories of data may require additional documentation and access restrictions.



Scope of Neuroscience Data Types

1. Neuroimaging Data

Includes: Structural MRI, functional MRI (fMRI), diffusion tensor imaging (DTI), PET, SPECT, and other molecular imaging data, as well as large-scale brain imaging cohort datasets.

Why Important:

  • Neuroimaging data collection is costly and equipment-dependent
  • Large-scale imaging datasets provide significant value for understanding brain patterns and brain health research
  • Supports collaborative brain imaging analysis across studies

Recommended Formats: DICOM, NIfTI (.nii, .nii.gz), CIFTI

________________________________________

2. Electrophysiological Data

Includes: Electroencephalography (EEG), magnetoencephalography (MEG), intracellular and extracellular recordings, local field potentials (LFP), optogenetics combined with electrophysiology, and neural activity imaging data (calcium imaging, neurotransmitter imaging, intracellular signaling).

Why Important:

  • Electrophysiological data represent a core data type in neuroscience research
  • Raw waveform data are essential for validating research conclusions
  • Supports data comparison and method validation across laboratories

Recommended Formats: Neurodata Without Borders (.nwb), FieldTrip (.mat)

________________________________________

3. Single-Cell Omics Data

Includes: Single-cell RNA sequencing (scRNA-seq), single-cell ATAC-seq, spatial transcriptomics, and related genomic, transcriptomic, and proteomic data.

Why Important:

  • Single-cell technologies represent the frontier of neuroscience
  • Cell type atlases are crucial for understanding brain function
  • Data integration enables the discovery of new cell subtypes and marker genes

Recommended Formats: FASTQ (.fastq), HDF5 (.h5, .h5ad), MTX (.mtx)

________________________________________

4. Behavioral and Cognitive Data

Includes: Large-scale behavioral experimental data, cognitive task testing data, animal behavior tracking data, human psychometric data, and behavioral audio/video recordings.

Why Important:

  • Behavioral data serve as the bridge between neural mechanisms and function
  • Long-term behavioral tracking data have unique value
  • Supports cross-study comparison of behavioral phenotypes

Recommended Formats: MP4, MOV, CSV (.csv), JSON (.json)

________________________________________

5. Neural Circuit Connectivity Data

Includes: Neuronal morphology data, synaptic connection data, viral tracing data, functional connectivity data, and connectomics datasets.

Why Important:

  • Neural circuits are central to understanding brain function
  • Connectomics data are essential for constructing brain atlases
  • Supports cross-species and cross-region comparison of connectivity patterns

Recommended Formats: HDF5 (.h5), CSV (.csv), Neurodata Without Borders (.nwb)

________________________________________

6. Other High-Value Neuroscience Data

  • Cross-modal integrated datasets
  • Long-term longitudinal cohort data
  • Rare disease or special case data
  • Innovative experimental method validation data
  • Computational neuroscience models and simulations
  • Clinical neuroscience datasets (with appropriate ethical approvals)



 Data Deposition Requirements 

Required Repository: ScienceDB

All Data Papers must deposit their raw data in the Neuroscience Bulletin Community on Science Data Bank (ScienceDB) prior to manuscript submission.

Why ScienceDB?

  • Leading scientific data repository established and maintained by the Computer Network Information Center, Chinese Academy of Sciences
  • Free DOI registration for persistent identification - CSTR (China Science and Technology Resource) identifier compliant with national standards (GB/T 32843-2016)
  • Long-term preservation and secure storage
  • Flexible access control supporting open access, embargo periods, and restricted access modes


ScienceDB Community URL

Neuroscience Bulletin Community: https://www.scidb.cn/c/j00221


For Details on Science DB Data Submission Guidelines, please refer to:

https://www.scidb.cn/en/help?p=publishing_process



 Submission Workflow 

Step 1: Pre-submission Enquiry

    ↓ Contact nsb@sibs.ac.cn with the dataset description

    

Step 2: Editorial Assessment

    ↓ Editorial office evaluates data quality and scientific value

    

Step 3: Invitation to Submit

    ↓ If approved, the editorial office sends a formal invitation

    

Step 4: Data Deposition

    ↓ Upload data to the ScienceDB Neuroscience Bulletin Community

    ↓ Obtain DOI and CSTR identifiers

    

Step 5: Manuscript Preparation

    ↓ Write a Data Paper using the provided Template

    ↓ Include ScienceDB DOI in manuscript

    

Step 6: Manuscript Submission

    ↓ Submit via Neuroscience Bulletin manuscript system: https://mc03.manuscriptcentral.com/nsb

    ↓ Specify "Data Paper" as the article type during submission

    

Step 7: Peer Review

    ↓ Reviewers evaluate data quality and documentation

    

Step 8: Revision (if needed)

    ↓ Address reviewer comments

    

Step 9: Acceptance and Publication

    ↓ Data Paper published with a linked dataset



Additional Resources

Neuroscience Bulletin Resources


ScienceDB Resources


Contact Information

Neuroscience Bulletin Editorial Office

Email: nsb@sibs.ac.cn

Website: https://www.neurosci.cn


ScienceDB Support

Email: sciencedb@cnic.cn

Website: https://www.scidb.cn