Image alterations and duplications
To prevent and avoid image alteration and duplication in research articles, the aim is to develop and deploy tools that help scholarly publishers detect this in submitted manuscripts.
Guidelines were made available on types of image alteration that are and that are not allowable, under which conditions, including a standard classification of types and severity of image-related issues. For more information and the current status, visit this page.
Duplicate submissions detection
Manuscripts that are submitted illicitly to multiple journals simultaneously are a big burden voor journal editors and reviewers, and frequently associated with paper mills (illegal commercial organizations that produce, sell and/or submit fraudulent scientific manuscripts on demand). A working group was established to investigate the size of the problem, and investigate possible solutions to detect duplicate submissions when they occur. A report with first findings is pending.
Artificial Intelligence plays an increasing role in society, and also in publishing. Tools to detect fraud are often based on AI. A working group was formed to bring together the current thinking on how STM publishers contribute to the ethical and trustworthy development, deployment, and application of artificial intelligence. This resulted in a White Paper.
Peer Review Taxonomy
STM recognises a need for identifying and standardising definitions and terminology in (open) peer review practices. A peer review taxonomy that is used across publishers will help make the peer review process for articles more transparent and trustworthy. STM’s Peer Review Taxonomy is currently in the process of being formalized as an ANSI/NISO standard
STM’s Research Data Program
FAIR (Findable, Accessible, Interoperable and Reusable) data is a crucial element in making research more robust, reproducible, and cost-efficient. The published article is an important hub for the sharing, linking and citing research data that is built upon (or refers to) the published content, making research data findable and accessible within the scholarly ecosystem. STM is running the Research Data Program with its members to stimulate the sharing, citing and linking of high-quality research data alongside publications.
STM is involved in several cross-industry projects on research integrity, including a COPE working group on paper mils, a NISO project on a recommended practice for retracted research and a NISO project on Quality Badging.
Ideas for other initiatives? Contact STM’s Director of Research Integrity, Joris van Rossum, at firstname.lastname@example.org.