# EXSCLAIM! -- An automated pipeline for the construction of labeled materials imaging datasets from literature

arXiv: https://arxiv.org/abs/2103.10631 · HTML: https://mlsanity.com/paper/2103.10631/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2021.

## Built on by (most cited first, 7 of 7)

- [PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents](https://mlsanity.com/paper/2303.07240/) (2023)
- [Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery](https://mlsanity.com/paper/2111.01905/) (2021)
- [Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization](https://mlsanity.com/paper/2509.09307/) (2025)
- [Advancing Medical Representation Learning Through High-Quality Data](https://mlsanity.com/paper/2503.14377/) (2025)
- [Plot2Spectra: an Automatic Spectra Extraction Tool](https://mlsanity.com/paper/2107.02827/) (2021)
- [Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up](https://mlsanity.com/paper/2110.03923/) (2021)
- [Microscopy is All You Need](https://mlsanity.com/paper/2210.06526/) (2022)

## Abstract (arXiv)

Due to recent improvements in image resolution and acquisition speed, materials microscopy is experiencing an explosion of published imaging data. The standard publication format, while sufficient for traditional data ingestion scenarios where a select number of images can be critically examined and curated manually, is not conducive to large-scale data aggregation or analysis, hindering data sharing and reuse. Most images in publications are presented as components of a larger figure with their explicit context buried in the main body or caption text, so even if aggregated, collections of images with weak or no digitized contextual labels have limited value. To solve the problem of curating labeled microscopy data from literature, this work introduces the EXSCLAIM! Python toolkit for the automatic EXtraction, Separation, and Caption-based natural Language Annotation of IMages from scientific literature. We highlight the methodology behind the construction of EXSCLAIM! and demonstrate its ability to extract and label open-source scientific images at high volume.