# Use as directed? A comparison of software tools intended to check rigor and transparency of published work

## TL;DR

This paper compares 11 automated tools for checking scientific rigor and transparency, finding that combining tools improves performance in some areas.

## Contribution

The study provides a systematic comparison of 11 tools across 9 rigor criteria and identifies optimal strategies for their use.

## Key findings

- Some tools outperformed others in detecting open data.
- Combining tools improved detection of inclusion/exclusion criteria.
- Recommendations were made for improving tool development.

## Abstract

The causes of the reproducibility crisis include lack of standardization and transparency in scientific reporting. Checklists such as ARRIVE and CONSORT seek to improve transparency, but they are not always followed by authors and peer review often fails to identify missing items. To address these issues, there are several automated tools that have been designed to check different rigor criteria. We have conducted a broad comparison of 11 automated tools across 9 different rigor criteria from the ScreenIT group. We found some criteria, including detecting open data, where the combination of tools showed a clear winner, a tool which performed much better than other tools. In other cases, including detection of inclusion and exclusion criteria, the combination of tools exceeded the performance of any one tool. We also identified key areas where tool developers should focus their effort to make their tool maximally useful. We conclude with a set of insights and recommendations for stakeholders in the development of rigor and transparency detection tools. The code and data for the study is available at https://github.com/PeterEckmann1/tool-comparison.