Promoting Fair Principles And Research Integrity Through Manual Curation Of Manuscripts at the Point of Submission.
Creators: name: Tuli, Mary Ann
name: Fan, Yannan
name: Hunter, Christopher
name: Molcrette, Bastien
Publisher: name: Zenodo
Year: 2025
Subjects: Sustainability and strategy: Work on curation processes, techniques, costs, and workflows
Working with challenging data: Data on a large scale: big data or large collections of long-tail data
Working with challenging data: Sensitive data, retrieval and secure curation
Working with challenging data: Universal standards and expectations and that of context and discipline specific adaptations
Sharing data: Demonstrating research integrity
Legal and ethical issues: Privacy
Legal and ethical issues: Artificial Intelligence (AI) and Machine Learning
Legal and ethical issues: Trusting individuals to have power over their own data and its (re)use
Legal and ethical issues: Intellectual Property Rights, legal and policy issues around AI training data collection and use
Open Science journal
FAIR
Big data
Data sharing
IDCC25
Lightning Talks

description: GigaScience Press publishes award winning Open Science journals, which have the goal of making scientific communication reach researchers and communities around the globe. GigaScience focuses on ‘big data’ research from the life and biomedical sciences, and GigaByte focuses on short, data-driven articles, promoting a rapid exchange of scientific information. Both journals have implemented rigorous procedures to promote FAIR (Findable, Accessible, Interoperable, Reusable) data principles and research integrity starting at the point of manuscript submission.

Once it has been established that a new submission meets the scope of the journal, a thorough manual review is conducted to ensure compliance with FAIR guidelines and to verify research integrity.

Data curators confirm that all data underlying the study’s findings are made available in subject-specific public repositories (e.g. EMBL, ArrayExpress) or are provided as data files to be hosted in GigaDB, the database that accompanies GigaScience and GigaByte. Furthermore, they will verify that stable accession numbers and DOIs are assigned for deposited datasets and that the appropriate license has been obtained. Additionally, the metadata accompanying any datasets are assessed for completeness and quality to enable future reuse and reproducibility. Where a public repository is not available, authors will submit their data or tools to GigaDB. These files are manually inspected to verify they are in non-proprietary, machine-readable formats that align with community standards. Figures and images are also examined using specialised software tools which can identify signs of manipulation or duplication.

Data curators also inspect custom code and software developed for the study to ensure it is shared in a suitable public repository (e.g. GitHub) with the appropriate documentation and licensing.

A recent development has seen us work with the DOME consortium. The DOME-ML (Data, Optimization, Model and Evaluation in Machine Learning) recommendations establish standards of supervised machine learning validation in biology. For studies that use machine or deep learning methods, authors are encouraged to submit supporting annotations to the DOME-ML registry.

For studies involving human subjects or animals, data curators take great care to confirm that all ethical approvals are documented, and that data sharing complies with consent and privacy regulations.

In addition to the above assessment of the scientific data, the editorial team will verify the roles of all authors and assess any potential conflicts of interest and will screen all manuscripts for plagiarism or text recycling using similarity detection software.

This rigorous and comprehensive manual inspection of manuscripts ensures that issues related to data sharing, reproducibility, and research integrity can be identified and addressed prior to peer review which not only ensures higher quality submissions but also educates authors on best practices for open and reproducible science. Furthermore, reviewers can focus on evaluating the manuscript rather than spend valuable time ensuring data integrity.

The Press’ commitment to reproducibility through open access data sharing and digital curation even before the FAIR guidelines were introduced has put us at the forefront of Big Data publishing in the life sciences, a position we are committed to maintaining over the next 20 years and beyond.