Finding the next important read or deciding which article to evaluate next can be a time consuming task for researchers.
That’s why our data team’s experimental arm Sciety Labs have designed a tool to make this process quicker and more simple by using Artificial Intelligence (AI) to recommend articles to you based on the things you are already reading. If you would like to explore this more, please read on and set up a call with the Sciety team.
If you are viewing an article page on Sciety, you can now see cards for three related articles at the bottom of the page.
Note that the Sciety Labs page does not include evaluations as part of the article page.
If you’ve logged in and are curating a List on Sciety, you can now view recommendations based on the articles saved in that List. It might inspire you to add more articles to your reading List, or to create another List on Sciety to share with your peers. Whatever your use case, we hope to remove barriers to preprint discovery so you can reprioritise your time.
How do recommendations work?
To recommend related articles, Sciety makes use of an AI powered research tool called Semantic Scholar. This tool searches across more than 200 million articles to recommend relevant results. Sciety uses this data to filter results for preprints and display them alongside articles and Lists, improving discovery of evaluated preprints. If you wish to explore the full corpus of research, including non-preprinted articles, each article card provides a link to Semantic Scholar.
If you would like to try out recommended articles in Sciety, please get in touch with the team.
Proud to be a curator on Sciety? Use this graphic to share the link to your List on social media, and remember to tag @scietyHQ so we can highlight your great work!