DataSeer is a leading provider of AI solutions for scholarly communications, empowering funders, institutions and publishers to advance open science practice among affiliated researchers. Its products include Compliance Checks, Open Science Metrics, and SnapShot, which show researchers how to comply with policy, and deploy just-in-time interventions, bridging the gap between policy and application.
AAAS
The two pilot programs DataSeer is running with AAAS (American Association for the Advancement of Science) aim to support high-quality reporting across scientific disciplines. The first will generate pre-filled MDAR (Materials Design Analysis Reporting) reports for authors for AAAS’ flagship journal Science. The MDAR checklist is designed to support transparent reporting and capture key information necessary to reproduce and validate research. The pilot flips the script: instead of expecting authors to complete the checklist themselves, DataSeer’s Natural Language Processing technology pre-fills the MDAR checklist and invites the authors to confirm the entries and fill in any missing details.
The second will establish an Open Science Indicators baseline dataset, quantifying how and when authors at Science share their data and code. DataSeer will analyze a subset of AAAS articles and return top-level metrics including rates of data sharing, code sharing, protocol sharing, preprint posting, and more. The data will also show trends over time, and differences across regions, institutions, and journals. Armed with reliable business intelligence, the association can set meaningful open science targets, make informed policy decisions, and trace the impact of their interventions.
The full press release is here.
Taylor & Francis
The deal between DataSeer and Taylor & Francis also involves two pilot projects, which address the practical challenges of open research implementation. The first is a proof-of-concept project aimed at improving the accuracy and speed of editorial data-sharing checks across a broad range of journals with varying policies. It will assess a broad sample of articles against three different data sharing policies from the Taylor & Francis and F1000 portfolios, to determine how well the automation functions as compared to existing manual editorial checklists.
The second is an initial analysis of Open Science Metrics across a sample of 5,000 Taylor & Francis published articles. This will measure open science practices and quantify indicators of open science behavior including data sharing, code sharing, protocol sharing, preprint posting, repository use, and more. Together, the two pilots have the potential to increase data-sharing policy compliance, reduce the editorial workload and provide business intelligence to help drive openness forward.
The full press release is here.
KnowledgeWorks Global Ltd.
KnowledgeWorks Global Ltd. (KGL) and DataSeer agreed to embed automated checks for data sharing best practices into KGL’s Smart Review tool. This seamless integration expands Smart Review’s comprehensive checks and automation of manual tasks with top tools and platforms to further streamline the peer review process and boost efficiency. Publishers and researchers will see faster turnaround times, reduced manual work, and improved compliance with data-sharing best practices and funder requirements.
The full press release is here.