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 an article then helps authors to share it.
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 audit data sharing and other open science practices for both published articles and current content. Publishers can efficiently monitor the impact of their open science policies.
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
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