# DeepShovel: An Online Collaborative Platform for Data Extraction in Geoscience Literature with AI Assistance

arXiv: https://arxiv.org/abs/2202.10163 · HTML: https://mlsanity.com/paper/2202.10163/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 62 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Machine Learning for the Geosciences: Challenges and Opportunities](https://mlsanity.com/paper/1711.04708/) (2017)
- [Increasing the Speed and Accuracy of Data LabelingThrough an AI Assisted Interface](https://mlsanity.com/paper/2104.04122/) (2021)
- [Fonduer: Knowledge Base Construction from Richly Formatted Data](https://mlsanity.com/paper/1703.05028/) (2017)
- [Goldilocks: Consistent Crowdsourced Scalar Annotations with Relative Uncertainty](https://mlsanity.com/paper/2108.01799/) (2021)
- [Information Extraction from Scientific Literature for Method Recommendation](https://mlsanity.com/paper/1901.00401/) (2018)
- [TableLab: An Interactive Table Extraction System with Adaptive Deep Learning](https://mlsanity.com/paper/2102.08445/) (2021)

## Built on by (most cited first, 3 of 3)

- [K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization](https://mlsanity.com/paper/2306.05064/) (2023)
- [GeoGalactica: A Scientific Large Language Model in Geoscience](https://mlsanity.com/paper/2401.00434/) (2023)
- [AceMap: Knowledge Discovery through Academic Graph](https://mlsanity.com/paper/2403.02576/) (2024)

## Abstract (arXiv)

Geoscientists, as well as researchers in many fields, need to read a huge amount of literature to locate, extract, and aggregate relevant results and data to enable future research or to build a scientific database, but there is no existing system to support this use case well. In this paper, based on the findings of a formative study about how geoscientists collaboratively annotate literature and extract and aggregate data, we proposed DeepShovel, a publicly-available AI-assisted data extraction system to support their needs. DeepShovel leverages the state-of-the-art neural network models to support researcher(s) easily and accurately annotate papers (in the PDF format) and extract data from tables, figures, maps, etc. in a human-AI collaboration manner. A follow-up user evaluation with 14 researchers suggested DeepShovel improved users' efficiency of data extraction for building scientific databases, and encouraged teams to form a larger scale but more tightly-coupled collaboration.