DataSeer.ai: Advancing Open Science in Academic Research

DataSeer.ai is an innovative AI and Natural Language Processing (NLP) platform designed to enhance research integrity by automating the assessment of open science practices in scholarly manuscripts. Tailored for publishers, funders, and academic institutions, it streamlines compliance monitoring with intelligent triage, real-time analysis, and actionable insights. The tool supports critical open science standards including data and code sharing, protocol reuse, preprint posting, and the use of open identifiers, empowering organizations to enforce policies efficiently and promote reproducibility. With three powerful tools—Compliance Checks, Open Science Metrics, and DataSeer SnapShot—DataSeer delivers both granular article-level evaluations and high-level aggregate reporting across journals, funders, countries, and institutions. Backed by the Sloan Foundation and recognized with awards such as the 2022 Society for Scholarly Publishing People’s Choice Award, DataSeer stands at the forefront of AI-driven research transparency. Its seamless integration into editorial workflows, combined with expert-developed machine learning algorithms, makes it a trusted ally in modern academic publishing.

Key Features:

• Compliance Checks: Article-specific reports verifying adherence to data sharing, code availability, and open science policies, with personalized revision guidance for authors.
• Open Science Metrics: Aggregate analytics on open science practices across large collections of articles, broken down by journal, publisher, funder, country, and institution.
• DataSeer SnapShot: Real-time manuscript triage that scans full-text articles in seconds, delivering a research integrity score and flagging critical issues for editorial review.
• Policy Integration: Supports a wide range of open science mandates, including data and code sharing, protocol reuse, preprint posting, and use of open identifiers.
• Workflow Automation: Integrates directly into journal editorial systems for submission and revision stages, reducing manual effort and speeding up review processes.
• Customized Reporting: Generates tailored reports aligned with organizational workflows and policy requirements.
• Research Integrity Scoring: Provides clear, actionable scores to identify high-quality, transparent research and support recognition through press releases and outreach.
• AI-Powered NLP Analysis: Leverages advanced machine learning and NLP to extract and validate datasets, code objects, and lab materials from research papers.
• Reproducibility Enhancement: Helps increase data-sharing rates and improve research quality, as demonstrated by a 12% to 69% improvement at ASAP.
• High-Level Insights: Enables institutions and publishers to track progress, set targets, and evaluate the impact of open science initiatives over time.

Pricing: DataSeer.ai offers a freemium model, allowing organizations to explore core functionalities with limited access, while advanced features and institutional-scale analytics are available through subscription plans.

Conclusion: DataSeer.ai is a transformative force in academic publishing, combining cutting-edge AI with open science principles to boost transparency, compliance, and research quality—making it an essential tool for forward-thinking publishers, funders, and research institutions.