# SAINE: Scientific Annotation and Inference Engine of Scientific Research

## TL;DR

SAINE is a scientific annotation and inference engine utilizing open-source tools to improve classification accuracy and understanding of scholarly publication spaces, fostering transparency and supporting meta-science research.

## Contribution

It introduces SAINE, a novel annotation and inference system that enhances classification of scientific research and aids meta-science projects using open-source software.

## Key findings

- User input via SAINE improves classification understanding
- Demonstrated application in hierarchical discipline classification
- Supports transparency and meta-science research

## Abstract

We present SAINE, an Scientific Annotation and Inference ENgine based on a set of standard open-source software, such as Label Studio and MLflow. We show that our annotation engine can benefit the further development of a more accurate classification. Based on our previous work on hierarchical discipline classifications, we demonstrate its application using SAINE in understanding the space for scholarly publications. The user study of our annotation results shows that user input collected with the help of our system can help us better understand the classification process. We believe that our work will help to foster greater transparency and better understand scientific research. Our annotation and inference engine can further support the downstream meta-science projects. We welcome collaboration and feedback from the scientific community on these projects. The demonstration video can be accessed from https://youtu.be/yToO-G9YQK4. A live demo website is available at https://app.heartex.com/user/signup/?token=e2435a2f97449fa1 upon free registration.