Hugging Face's logo

Datasets:
SciKnowOrg
/
Sci-ImageMiner

Tasks:
Image Classification
Image-to-Text
Visual Question Answering
Modalities:
Image
Text
Formats:
imagefolder
Size:
1K - 10K
ArXiv:
Tags:
scientific-images
materials-science
scientific-figures
atomic-layer-deposition
atomic-layer-etching
multimodal
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
 Community
1
Sci-ImageMiner / README.md
jdsouza's picture
jdsouza
Add Sci-ImageMiner Hugging Face dataset artifacts
4821755 verified
preview code
|
Raw
History Blame Contribute Delete
9.08 kB
metadata 
pretty_name: Sci-ImageMiner
license: other
license_name: mixed-rights
license_link: LICENSE
task_categories:
  - image-classification
  - image-to-text
  - visual-question-answering
tags:
  - scientific-images
  - materials-science
  - scientific-figures
  - atomic-layer-deposition
  - atomic-layer-etching
  - multimodal
  - figure-understanding
size_categories:
  - 1K<n<10K

Sci-ImageMiner

Sci-ImageMiner is a scientific-image dataset for multimodal figure understanding. The initial release focuses on figures extracted from atomic layer deposition (ALD) and atomic layer etching (ALE) publications in materials science. The schema is designed to support future scientific domains, subdomains, image types, data sources, and annotation tasks.

This dataset contains mixed-rights content. Metadata and annotations are described as CC BY 4.0 in the upstream repository, while source images are publisher/author copyrighted and are provided for non-commercial research use only unless the original source license grants broader rights.

Dataset Purpose

The dataset supports research on scientific image understanding tasks:

  • panel-level figure type classification;
  • panel localization with bounding boxes;
  • panel-level summarization;
  • data extraction from plots and scientific visuals;
  • visual question answering grounded in scientific figures.

Initial Scope

The current broad domain is:

materials_science

The initial subdomains represented by records are:

atomic_layer_deposition
atomic_layer_etching

The current source data also distinguishes:

experimental
simulation

These values describe the current release only. Future domains such as chemistry, biology, physics, earth science, medicine, or engineering are not included unless records are actually added and curated.

Splits

The dataset exposes three Hugging Face splits:

Split Records
train 1,170
validation 201
test 580
Total 1,951

The public rows include one split field with the Hugging Face split value.

Dataset Structure

The dataset is packaged in an ImageFolder-compatible layout:

train/
  metadata.jsonl
  images/
validation/
  metadata.jsonl
  images/
test/
  metadata.jsonl
  images/

Each metadata row references its image with file_name. When loaded with Hugging Face Datasets, this becomes an image column that renders in the Dataset Viewer.

Field Definitions

Core fields:

Field Description
image Hugging Face image feature created from file_name.
caption Source figure caption when available.
id Stable dataset-level record id.
sample_id Original source annotation sample_id.
split Hugging Face split.
domain Broad scientific domain.
subdomain Controlled scientific subdomain.
topic Reserved for curated lower-level topics or processes.
study_type Experimental or simulation study type.
classification Source classification annotations as a list of {panel_id, label} entries.
summarization Source summarization annotations as a list of {panel_id, summary} entries.
data_extraction Source data extraction annotations as a list of {panel_id, data} entries.
vqa Source visual question-answering annotations as a list of {panel_id, question_type, question, answer_type, answer} entries.
bbox Source bounding boxes as a list of {panel_id, x, y, width, height} entries.
source Publication and source-file provenance.
provenance Extraction and annotation provenance.
schema_version Dataset schema version.

The source annotation fields use panel ids such as a, b, and c. They are stored as lists rather than arbitrary-key objects so that Hugging Face can infer a stable viewer schema. If an annotation type does not apply or was not provided for a figure, it is represented as an empty list.

Images and Provenance

Images were extracted from published scientific articles. Source PDFs and content.json extraction outputs are present in the upstream repository. The conversion preserves relative paths to:

  • original image files;
  • original annotation JSON files;
  • source content.json files;
  • source PDF filenames where available.

Publication metadata such as title, author line, DOI, and publication year is best-effort and should be reviewed before being treated as authoritative.

Loading

After upload, users should be able to load the dataset with:

from datasets import load_dataset

dataset = load_dataset("SciKnowOrg/Sci-ImageMiner")

For local generated artifacts:

from datasets import load_dataset

dataset = load_dataset("imagefolder", data_dir="hf/dataset")

Display an image:

example = dataset["train"][0]
image = example["image"]
image.show()
print(example["caption"])
print(example["classification"])
print(example["bbox"])
print(example["vqa"])

Known Limitations

  • The initial release focuses only on ALD and ALE materials-science figures.
  • Some records have classification and bounding boxes but no summarization, data extraction, or VQA annotations.
  • Some captions or DOI fields are missing or best-effort extracted.
  • Byte-identical image duplicates exist, including some across original splits. Existing split assignments are preserved, but users should consider this when using the data as a benchmark.
  • The source image redistribution status needs human review before public upload.

Licensing and Redistribution

Annotations and metadata are described in the upstream repository as CC BY 4.0.

Images were extracted from published scientific articles. Copyright remains with original authors and/or publishers. The dataset repository uses a mixed-rights license marker rather than a plain CC BY marker because the image files are not released under CC BY 4.0 by this dataset. Treat source images as non-commercial research-use-only unless the original publication license grants broader rights.

Maintainers should decide whether the public dataset must be gated or whether a metadata-only variant is needed for images whose redistribution rights are not clear.

Contributions

Community contributions should follow hf/CONTRIBUTING_DATASET.md in the upstream repository. Contributions must include:

  • image files;
  • metadata.jsonl;
  • source publication provenance;
  • source-style annotation fields;
  • image reuse rights or license information;
  • validation output.

New vocabulary values for domains, subdomains, or study types should be proposed through pull requests to the versioned vocabulary files. Contributors must not upload images they lack permission to redistribute.

Citation

The Sci-ImageMiner project vision is described in the following working paper, pre-released on Zenodo. Please cite this paper if you find the project useful:

@misc{d_souza_2025_17130928,
  author       = {D'Souza, Jennifer},
  title        = {A Pathway to General-Purpose Scientific AI:
                  Multimodal Comprehension of Scientific Images},
  month        = sep,
  year         = 2025,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17130928},
  url          = {https://doi.org/10.5281/zenodo.17130928},
}

Sci-ImageMiner was also featured as the ICDAR 2026 Competition on Information Extraction from Atomic Layer Deposition/Etching (ALD/E) Scientific Figures, organized as part of the ICDAR 2026 competitions. Please cite the competition report when referring to the benchmark, dataset, competition, or associated information-extraction tasks:

@article{ahmed2026icdar,
  title   = {ICDAR 2026 Competition on Information Extraction from
             Atomic Layer Deposition/Etching (ALD/E) Scientific Figures},
  author  = {Ahmed, Fahad and Auer, S{\"o}ren and D'Souza, Jennifer},
  journal = {arXiv preprint arXiv:2607.26848},
  year    = {2026},
  url     = {https://arxiv.org/abs/2607.26848}
}

Acknowledgements

Within Sci-ImageMiner, the development of the expert-annotated ALD/E-ImageMiner benchmark resource was funded by the NFDI4DataScience initiative, funded by the German Research Foundation (DFG, Grant ID: 460234259) under the Speedboat Annotation Project funding scheme.

This research endeavor is conducted in the context of the AI-Aware Pathways to Sustainable Semiconductor Process and Manufacturing Technologies (AWASES) initiative (Mackus et al., 2024), funded by Merck and Intel, with collaboration between Eindhoven University, Leibniz University Hannover's L3S Research Centre, and University of Warwick. AWASES hosts three fully funded PhD positions and supports advances in generative AI, multimodal models, and FAIR scientific knowledge graph construction.

Versioning

This draft uses schema version 1.0.0.

Schema changes should be documented in the dataset card and in release notes. Additive fields are preferred. Breaking changes should require a new major schema version or a new dataset configuration.