Research Data Policy
Our Commitment to Research Data Integrity
Journal of Creative Research in English Literature and Culture (JCRELC) recognizes research data as the backbone that ensures transparency, reproducibility, and thereby the credibility of the research being published. Our research data policy provides comprehensive guidelines related to data management, sharing, and preservation.
The Journal of Creative Research in English Literature and Culture (JCRELC) encourages authors to share the research data underlying the publication, as this enables verification, replication, and further analysis. This policy is in line with the principles of open science while fully considering ethical, legal, and practical issues regarding data sharing.
Data Sharing Encouraged
JCRELC strongly encourages authors to make research data openly available whenever possible. Data sharing enhances research transparency, enables verification, and maximizes the impact and utility of published research.
Why Share Data?
- Enables verification and replication of findings
- Facilitates meta-analysis and secondary research
- Increases citation rates and research impact
- Supports open science and transparency
- Meets funder and institutional requirements
- Builds research collaborations
What to Share
- Raw and processed research data
- Analysis scripts and code
- Questionnaires and interview protocols
- Data dictionaries and codebooks
- Methodological documentation
- Supplementary materials
FAIR Data Principles
Authors are encouraged to follow the FAIR data principles to maximize the utility of data:
Findable
- Assign persistent identifiers (DOIs)
- Provide rich metadata
- Store data in queryable repositories
- Data availability statements
- Use descriptive file names
- Clearly cite data sources
Accessible
- Use standard communication protocols
- Long-term accessibility
- Provide open access when possible
- Use trusted repositories
- Maintain authentication when necessary
- Ensure metadata remains accessible
Interoperable
- Use formal and accessible languages
- Apply standard vocabularies
- Include qualified references
- Use common data formats
- Document the data
- Ensure cross-platform compatibility
Reusable
- Provide clear usage licenses
- Include detailed provenance
- Meet domain-relevant standards
- Provide full documentation
- Ensure data quality measures
- Include methodological information
Data Sharing Benefits
Research Advancement
- Enables verification and replication
- Facilitates meta-analysis
- Supports new research questions
- Reduces duplication of effort
- Speeds up scientific advancements
- Improves research impact
Author Benefits
- Increases citation rates
- Provides greater visibility for research
- Builds research collaborations
- Supports career growth
- Demonstrates research integrity
- Meets funder requirements
Data Availability Requirements
Data Availability Statement
All manuscripts must include a Data Availability Statement describing data accessibility:
Statement Requirements:
- Location of supporting data
- Access conditions and restrictions
- Repository names and identifiers
- Embargo periods if applicable
- Contact details for dataset access
- Licensing information
Statement Examples:
- Open Data: "Data available in [Repository] at [DOI]"
- Restricted Data: "Data available on request due to [reason]"
- Third-party Data: "Data from [source] used under license"
- No Data: "No new data generated"
- Simulated Data: "Code to generate the data included"
Data Types and Formats
Common Data Types
- Survey and questionnaire data
- Experimental results
- Interview transcripts
- Statistical datasets
- Textual analysis data
- Cultural research data
- Simulation data
Recommended Formats
- Tabular Data: CSV, TSV, XLSX
- Statistical Data: SAV, DTA, RDA
- Qualitative Data: TXT, PDF, DOCX
- Code/Scripts: R, Python, SQL
- Documentation: PDF, README, CODEBOOK
- Metadata: XML, JSON
Data Repository Recommendations
Recommended Data Repositories
General Repositories:
- Zenodo: CERN-based multidisciplinary repository
- Figshare: General research data repository
- Dryad: Curated general-purpose repository
- Mendeley Data: Elsevier's research data platform
- Harvard Dataverse: Open data repository
- OSF: Open Science Framework
Discipline-Specific:
- Institutional repositories
- Subject-specific archives
Repository Selection Criteria
- Persistence: Long-term preservation commitment
- Stability: Reliable infrastructure and funding
- Access: Appropriate access controls
- Metadata: Rich metadata support
- Identifiers: Persistent identifier assignment
- Licensing: Clear options for usage licenses
- Cost: Free or reasonable costs
- Integration: Compatibility with research workflows
Data Documentation and Metadata
Required Documentation
- Data collection methods
- Variable definitions and codes
- Measurement instruments
- Sampling procedures
- Data processing steps
- Quality control measures
- Usage restrictions
- Citation directions
Metadata Standards
- Dublin Core: Basic bibliographic metadata
- DDI: Data Documentation Initiative
- Schema.org: Web markup for datasets
- DataCite: Repository metadata schema
- Discipline-specific standards
- Custom metadata as needed
Documentation Templates: We provide data documentation templates for the most common research types: surveys, experiments, and observational studies. To access templates and get documentation guidance, contact us at info@jcrelc.com
Ethical and Legal Considerations
Privacy Protection
- Anonymize personal identifiers
- Remove direct and indirect identifiers
- Aggregate sensitive information
- Use statistical disclosure control
- Obtain informed consent to share
- Follow institutional review board guidelines
Legal Compliance
- GDPR for European data
- National data protection laws
- Institutional data policies
- Funder data sharing requirements
- Copyright and database rights
- Contractual obligations
Sensitive Data Handling
For data that cannot be openly shared, consider these alternatives:
Access Controls:
- Embargo periods for delayed release
- Restricted access archives
- Data use agreements
- Application processes for access
- Secure data enclaves
- Virtual data access systems
Alternative Sharing:
- Share synthetic or simulated data
- Share data analysis code
- Share aggregated results
- Provide detailed methodology
- Provide information on request
- Use secure remote access systems
Data Licensing and Reuse
Recommended Licenses
- CC0: Public domain dedication
- CC BY: Attribution must be provided
- ODC BY: Open Data Commons Attribution
- ODbL: Open Database License
- Custom Licenses: For specific needs
- Repository Defaults: Repository-specific licenses
License Compatibility
Ensure data licenses are compatible with the CC BY 4.0 license used for articles. Choose licenses that maximize reuse while providing appropriate attribution and protection for sensitive data.
Note: Data licenses should not restrict the reuse of data for research purposes while protecting privacy and intellectual property rights.
Frequently Asked Questions
Related Policies
Data Policy Support
For inquiries on data sharing, selection of a repository, documentation, or policy compliance, please reach out to our data management team.
+91-7665235235
Subject Line: Please use "Research Data Policy" in the subject line and include information about your specific data management requirements.
If you have complicated data sharing scenarios or sensitive data to manage, we will provide you with personalized guidance.
Need Help with Data Management?
Contact our data management team for personalized guidance on data sharing, repository selection, and policy compliance.