Open Science Badges in the Journal of Neurochemistry

Evaluation of Transparency and Openness Guidelines in Physical Therapist Journals

OBJECTIVE

The goals of this study were to evaluate the extent that physical therapist journals support open science research practices by adhering to the Transparency and Openness Promotion (TOP) guidelines and to assess the relationship between journal scores and their respective journal impact factor (JIF).

METHODS

Scimago, mapping studies, the National Library of Medicine, and journal author guidelines were searched to identify physical therapist journals for inclusion. Journals were graded on 10 standards (29 available total points) related to transparency with data, code, research materials, study design and analysis, preregistration of studies and statistical analyses, replication, and open science badges. The relationship between journal transparency and openness scores and their JIF was determined.

RESULTS

Thirty-five journals' author guidelines were assigned transparency and openness factor scores. The median score (interquartile range) across journals was 3.00 out of 29 (3.00) points (for all journals the scores ranged from 0 to 8). The 2 standards with the highest degree of implementation were design and analysis transparency (reporting guidelines) and study preregistration. No journals reported on code transparency, materials transparency, replication, and open science badges. TOP factor scores were a significant predictor of JIF scores.

CONCLUSION

There is low implementation of the TOP standards by physical therapist journals. TOP factor scores demonstrated predictive abilities for JIF scores. Policies from journals must improve to make open science practices the standard in research. Journals are in an influential position to guide practices that can improve the rigor of publication which, ultimately, enhances the evidence-based information used by physical therapists.

IMPACT

Transparent, open, and reproducible research will move the profession forward by improving the quality of research and increasing the confidence in results for implementation in clinical care.

Measuring the Impact of Data Sharing: From Author-Level Metrics to Quantification of Economic and Non-tangible Benefits

In early 2023, the National Institutes of Health (NIH) implemented its Data Management and Sharing (DMS) Policy, requiring researchers to share scientific data produced with NIH funding. The policy's objective is to amplify the benefits of public investment in research by promoting the dissemination and reusability of primary data. Given this backdrop, identifying a robust methodology to assess the impact of data sharing across diverse research domains is essential. In this review, we adopted two methodological paradigms, the bottom-up and top-down strategies, and employed content analysis to pinpoint established methodologies and innovative practices within this intricate field. Although numerous author-level metrics are available to gauge the impact of data sharing, their application is still limited. Non-traditional metrics, encompassing economic (e.g., cost savings) and intangible benefits, presently appear to hold more potential for evaluating the impact of primary data sharing. Finally, we address the primary obstacles encountered by open data policies and introduce an innovative "Shared model for shared data" framework to bolster data sharing practices and refine evaluation metrics.