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.

By providing authors with tools to enhance their reporting standards, we collectively advance the reliability and trustworthiness of scientific literature.

Surfacing reagents and resources

A chrome extension to import all your tweets to bluesky at a click of a button

YouTube video by martijnroelandse_dev

Reproducibility checklists are very useful things to remind us of the things that we should remember when publishing our results. They ar...

Can AI writing detectors be trusted? We dig into the theory behind them.

Ask a question and get answers backed by real research. Use Scite Assistant to explore new topics, get help with literature reviews, build reference lists, and more

This presentation was made (and given) for the “Scholarly Reading and Writing in the Age of AI and the Need for Visibility and Impact: Issues and Challenges" workshop at the Johannes Gutenberg University in Mainz on 23 and 24 March, 2023.    More information about the workshop: https://book-studies.uni-mainz.de/scholarly-reading-and-writing- in-the-age-of-ai-and-the-need-for-visibility-and-impact-issues-and-challenges-a-workshop- particularly-for-young-researchers/  My presentation showed examples for AI-support in the research cycle, particularly on the example of SciScore, explanation of the criteria like reproducibility, blinding, randomisation, persistent identifiers, etc.

BREAKING: Microsoft Is In Talks To ACQUIRE A 49% Stake Worth $10 billion In ChatGPT Owner OpenAI — This Is A Big Deal 🤯 Microsoft would receive 75% of… | 939 comments on LinkedIn

Earlier this year, Anita Bandrowski and I chatted with the lovely folks at Chan Zuckerberg Initiative - Dario Taraborelli, Jennifer Lin, and Carly Strasser -…

It was great working with the ]u[ Ubiquity team on this project and out of all the interested parties, glad they found a great home. Quoting Georg Nold…

Hugo Health is committed to helping people embrace the potential of their health information for themselves, their families, and society.

/PRNewswire-PRWeb/ -- The new AI-powered scientific search engine, Consensus, and scientific methods review tool, SciScore, have officially announced their...