Tools, techniques, methods, and processes for the detection and mitigation of fraudulent or erroneous data in evidence synthesis: a scoping review protocol.

Objective: This scoping review aims to identify, catalogue, and characterize previously reported tools, techniques, methods, and processes that have been recommended or used by evidence synthesizers to detect fraudulent or erroneous data and mitigate its impact.

Introduction: Decision-making for policy and practice should always be underpinned by the best available evidence-typically peer-reviewed scientific literature. Evidence synthesis literature should be collated and organized using the appropriate evidence synthesis methodology, best exemplified by the role systematic reviews play in evidence-based health care. However, with the rise of "predatory journals," fraudulent or erroneous data may be invading this literature, which may negatively affect evidence syntheses that use this data. This, in turn, may compromise decision-making processes.

Inclusion criteria: This review will include peer-reviewed articles, commentaries, books, and editorials that describe at least 1 tool, technique, method, or process with the explicit purpose of identifying or mitigating the impact of fraudulent or erroneous data for any evidence synthesis, in any topic area. Manuals, handbooks, and guidance from major organizations, universities, and libraries will also be considered.

Methods: This review will be conducted using the JBI methodology for scoping reviews and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Databases and relevant organizational websites will be searched for eligible studies. Title and abstract, and subsequently full-text screening will be conducted in duplicate using Covidence. Data from identified full texts will be extracted using a pre-determined checklist, while the findings will be summarized descriptively and presented in tables.

This scoping review protocol was registered in open science framework: https://osf.io/u8yrn.