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

Researchers find complying with data sharing policies difficult.

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

Client feedback

Read what other think about us

DataSeer is an excellent example of how AI can be used to overcome big challenges in preservation and sharing of research data – meeting the needs of funders, institutions, and publishers. The concept is simple and practical, and it promises to drive a system change.

There’s a lot of welcome movement towards open data from funders and journals, but the task of actually making data sharing happen falls on Editorial Offices. All too often there’s insufficient time and expertise for effective enforcement, which is frustrating for EO’s and confusing for authors. By efficiently automating the enforcement of data sharing policies, DataSeer promises to ease the burden on EO’s and drive a massive increase in the availability of research data.