STEM Imaging · foundry-ml
training_locating_atoms_stem_images_v1-2
A training dataset of scanning transmission electron microscopy images for localising atomic positions.
What training_locating_atoms_stem_images_v1-2 is
training_locating_atoms_stem_images_v1-2 is a specialised dataset curated by foundry-ml for physics and materials science applications. It consists of scanning transmission electron microscopy (STEM) images designed for identifying and localising individual atomic coordinates.
Manual identification of atoms in electron microscopy micrographs is labour-intensive and difficult to scale across large experimental datasets. By providing reference STEM imagery, this dataset enables researchers to build and train machine learning models for automated microscopy workflows and structural analysis.
Key facts
- Publisher
- foundry-ml
- Domain
- STEM imaging
- Scientific fields
- Materials science, physics
- Primary application
- Atomic-position localisation
What you can use it for
- Atomic-Position Localisation
- Training computer vision and machine learning models to detect, segment, and pinpoint exact atomic coordinates in STEM micrographs.
- Automated Microscopy Pipelines
- Integrating trained localisation models into automated microscopy workflows to rapidly process incoming experimental imagery.
- Materials Science Characterisation
- Supporting quantitative physics and materials science studies that depend on accurate atomic coordinates extracted from electron microscopy data.
Explore the data
No licence declared
The authors have not published a licence, which means no permission to redistribute the data has been granted to anyone. The rows are not served here; read them on the original dataset page.Strengths and limits
Good at
- Targets the specific task of atomic-position localisation directly from STEM imagery.
- Designed specifically to integrate into machine learning pipelines for automated microscopy analysis.
Watch out for
- Focuses strictly on scanning transmission electron microscopy imagery and atomic localisation tasks.
- Additional image preprocessing and domain-specific fine-tuning may be necessary when adapting to different microscope configurations or materials.
Questions
Can I look inside training_locating_atoms_stem_images_v1-2 without downloading it?
Not here: it declares no licence, so no permission to redistribute its rows has been granted. Its description and statistics are still shown.
What licence is training_locating_atoms_stem_images_v1-2 under?
Its authors have not declared a licence, which means no permission to reuse or redistribute it has been granted.
What is the primary purpose of this dataset?
It serves as a training set for machine learning models designed to locate atomic positions within scanning transmission electron microscopy (STEM) images.
Which research domains does this dataset support?
The dataset is tailored for researchers working within materials science, physics, and electron microscopy.
Can this dataset be integrated into automated analysis workflows?
Yes, it is specifically configured to support machine learning pipelines for automated microscopy analysis.
Written from training_locating_atoms_stem_images_v1-2's own dataset card. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.