Smoothing the path to shared research data
We use AI and NLP to promote the sharing of research data
DataSeer scans scientific texts for sentences describing data collection, then gives best-practice advice for sharing that type of data.
Researchers can use DataSeer to ensure that their data sharing is complete and follows best practice.
Funders, journals, and institutions can use DataSeer to find all of the data associated with a corpus of articles, or use it to promote compliance with their data sharing policies.
How can DataSeer help?
DataSeer enables fast, efficient, and scalable
promotion of data sharing
- Which datasets should be shared?
- Where should the data go?
- What format should they be in?
- What about privacy, copyright, and licensing?
DataSeer provides best-practice sharing advice for each dataset in your article then helps you share it, so you can get back to your research.
Journal Editors find enforcement of data policies is either time-consuming and expensive, or weak and inconsistent. This is because journals and funders can’t work out what data the authors should share.
By highlighting each dataset in an article and leading authors through the sharing process, DataSeer delivers consistent, cost-effective, and highly scalable promotion of open research data.
Publishers want to promote open data, but lack the tools to help their journals establish workable data policies
DataSeer can promote data sharing across a wide range of disciplines, allowing your journals to adopt effective and efficient data policies. You can also document and report policy compliance across your portfolio.
Funding agencies want the data arising from their grants to be public and of maximum benefit, but can’t be sure that researchers have shared the right data in the right place.
DataSeer can rapidly assess a corpus of research articles and list the underlying datasets. We can help funded authors share data that need to be made public.
How does DataSeer work?
Here’s our workflow for an individual article