cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18454958
title: CrackMNIST - Annotated Digital Image Correlation Displacement Fields from Fatigue Crack Growth Experiments

description: CrackMNIST is a curated, large-scale, and standardized dataset of experimentally acquired digital image correlation (DIC) displacement fields from fatigue crack growth experiments. The dataset is designed to support data-driven and physics-informed machine learning research in fracture mechanics, with a particular focus on crack tip detection and the prediction of fracture-mechanics descriptors from full-field measurements.

The dataset is derived from eight fatigue crack growth experiments conducted on aerospace-grade aluminium alloys (AA2024, AA7475, AA7010) covering multiple specimen geometries, material orientations, thicknesses, and load ratios. Full-field planar displacement fields were measured using a commercial Zeiss Aramis Stereo DIC system and consistently post-processed across all experiments.

Crack tip locations are provided as high-accuracy annotations, obtained via an iterative crack tip correction procedure based on Williams-series fitting, achieving sub-pixel crack tip accuracy.In addition to crack tip segmentation masks, CrackMNIST includes fracture-mechanics labels, namely the mode-I and mode-II stress intensity factors and the T-stress.

To ensure scalability and broad usability, the dataset is provided at three standardized spatial resolutions (28 x 28, 64 x 64, and 128 x 128 pixels), each representing the same physical region around the crack tip. For every resolution, three dataset sizes (S, M, L) are released with fixed and reproducible training, validation, and test splits, supporting applications ranging from lightweight educational use to high-capacity deep learning benchmarks.

Overall, CrackMNIST comprises 8,794 unique experimentally observed displacement fields and a total of 70,352 supervised samples generated through standardized interpolation and physically consistent data augmentation.

Each sample includes:





two-channel planar displacement fields,




crack tip segmentation masks




fracture-mechanics descriptors (K_I, K_II, T),




and rich metadata such as experiment ID, material, specimen type, orientation, load ratio, and applied force.



The dataset can be accessed either by direct download of the HDF5 files or via the accompanying open-source Python package crackmnist, which provides convenient data loading, filtering, and integration into reproducible machine learning workflows.

CrackMNIST aims to serve as a benchmark dataset for experimental fracture mechanics, enabling systematic comparison of learning-based crack tip detection methods, resolution studies, uncertainty quantification, and hybrid physics-ML approaches, while also providing a high-quality resource for teaching and education.

For further information, code, and usage examples, see: https://github.com/dlr-wf/crackmnist
DOI: 10.5281/zenodo.15013127
title: CrackMNIST - Annotated Digital Image Correlation Displacement Fields from Fatigue Crack Growth Experiments

description: CrackMNIST is a curated, large-scale, and standardized dataset of experimentally acquired digital image correlation (DIC) displacement fields from fatigue crack growth experiments. The dataset is designed to support data-driven and physics-informed machine learning research in fracture mechanics, with a particular focus on crack tip detection and the prediction of fracture-mechanics descriptors from full-field measurements.

The dataset is derived from eight fatigue crack growth experiments conducted on aerospace-grade aluminium alloys (AA2024, AA7475, AA7010) covering multiple specimen geometries, material orientations, thicknesses, and load ratios. Full-field planar displacement fields were measured using a commercial Zeiss Aramis Stereo DIC system and consistently post-processed across all experiments.

Crack tip locations are provided as high-accuracy annotations, obtained via an iterative crack tip correction procedure based on Williams-series fitting, achieving sub-pixel crack tip accuracy.In addition to crack tip segmentation masks, CrackMNIST includes fracture-mechanics labels, namely the mode-I and mode-II stress intensity factors and the T-stress.

To ensure scalability and broad usability, the dataset is provided at three standardized spatial resolutions (28 x 28, 64 x 64, and 128 x 128 pixels), each representing the same physical region around the crack tip. For every resolution, three dataset sizes (S, M, L) are released with fixed and reproducible training, validation, and test splits, supporting applications ranging from lightweight educational use to high-capacity deep learning benchmarks.

Overall, CrackMNIST comprises 8,794 unique experimentally observed displacement fields and a total of 70,352 supervised samples generated through standardized interpolation and physically consistent data augmentation.

Each sample includes:





two-channel planar displacement fields,




crack tip segmentation masks




fracture-mechanics descriptors (K_I, K_II, T),




and rich metadata such as experiment ID, material, specimen type, orientation, load ratio, and applied force.



The dataset can be accessed either by direct download of the HDF5 files or via the accompanying open-source Python package crackmnist, which provides convenient data loading, filtering, and integration into reproducible machine learning workflows.

CrackMNIST aims to serve as a benchmark dataset for experimental fracture mechanics, enabling systematic comparison of learning-based crack tip detection methods, resolution studies, uncertainty quantification, and hybrid physics-ML approaches, while also providing a high-quality resource for teaching and education.

For further information, code, and usage examples, see: https://github.com/dlr-wf/crackmnist
DOI: 10.82384/neccton-001
title: Mediterranean Sea PRISMA Satellite Surface Reflectance (Acqua Alta Oceanographic Tower)
DOI: 10.15468/dl.55qy4d
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "DatasetKey is one of (Taxon occurrence data for the FungalRoot database, Herbarium of the French Institute of Pondicherry, iNaturalist Research-grade Observations, SeasonWatch - Tree Phenology Dataset (India), Pl@ntNet observations, India Biodiversity Portal publication grade dataset, Plant Occurrence Data from: Plant Community Structure in Tropical Rain Forest Fragments of the Western Ghats, India, Pl@ntNet automatically identified occurrences, BARO Herbarium, The M.S University of Baroda, Floral Survey of Ahmedabad Metro Phase II)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "ScientificName is Lantana camara"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011416-260129131611470/datasets/export for details.
DOI: 10.15468/dl.xj8rn9
title: Occurrence Download

description: A dataset containing 1517 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is PAMP-tracheo: Vascular plants in the research project on dark diversity of plant communities in the centre-east of Navarra (Spain)"
 ]
}

 The dataset includes 1517 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011419-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pvwqyj
title: Occurrence Download

description: A dataset containing 1719 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sargassum vulgare",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1719 records from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011420-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n7czzs
title: Occurrence Download

description: A dataset containing 2609 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Allium suaveolens, Allium suaveolens, Allium suaveolens)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 2609 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011421-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u2m9sd
title: Occurrence Download

description: A dataset containing 9421 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-70.098169 -33.735920, -70.066705 -33.674026, -70.146494 -33.674026, -70.177993 -33.735920, -70.098169 -33.735920))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2026"
 ]
}

 The dataset includes 9421 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011414-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.guxc8u
title: Occurrence Download

description: A dataset containing 205780 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Quercus rubra",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 205780 records from 1070 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011385-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/6wr-2b4-bgx
title: Spirontocaris arcuata M. J. Rathbun, 1902

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/z0m-ht0-d1e
title: Pseudograpsus elongatus (A. Milne-Edwards, 1873)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/rgx-3zt-ccb
title: Spirontocaris arcuata M. J. Rathbun, 1902

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/c6v-t71-r3h
title: Pandalus danae Stimpson, 1857

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/j13-7ls-lwa
title: Paracrangon echinata Dana, 1852

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/r1p-bek-zpp
title: Minyocerus angustus (Dana, 1852)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/t96-1rd-9gq
title: Cancer pagurus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/6t6-gym-nst
title: Paracrangon echinata Dana, 1852

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/cc1-bja-cy2
title: Carcinus maenas (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.4funhr
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Potentilla indica (Andrews) Th.Wolf",
 "Country is Brazil",
 "HasCoordinate is true"
 ]
}

 The dataset includes 123 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0105713-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/rt0-bj3-7pj
title: Eurytium limosum (Say, 1818)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/gv7-j6j-qr8
title: Galathea intermedia Lilljeborg, 1851

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/yzj-4zt-pct
title: Neopontonides brucei Fransen & de Almeida, 2009

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0ls-92b-jzf
title: Clibanarius erythropus (Latreille, 1818)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/msn-3jy-y6r
title: Eusergestes arcticus (Krøyer, 1855)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/2yq-5ef-rbl
title: Anapagurus laevis (Bell, 1846)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zpz-ahl-ynt
title: Ovalipes trimaculatus (De Haan, 1833)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/tst-gch-fvq
title: Pisa nodipes Leach, 1815

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/2qh-ygd-crh
title: Heterocrypta lapidea Rathbun, 1901

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/d95-6qr-yra
title: Galathea intermedia Lilljeborg, 1851

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/clk-wb1-v2d
title: Heterocrypta lapidea Rathbun, 1901

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/xhj-3ng-j7p
title: Clibanarius erythropus (Latreille, 1818)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ef7-e19-fve
title: Clibanarius brasiliensis Dana, 1852

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/b1l-z8x-qxw
title: Pandalus eous Makarov, 1935

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/gsz-snf-nfn
title: Pisidia longicornis (Linnaeus, 1767)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3mf-1dz-320
title: Polyonyx Stimpson, 1858

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/d6k-r9t-rnt
title: Pisa nodipes Leach, 1815

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/e0d-wbq-d7k
title: Karstarma cerberus (Holthuis, 1964)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zsr-jeg-8xl
title: Macropodia rostrata (Linnaeus, 1761)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/rpr-hrg-zvz
title: Eualus cranchii (Leach, 1817)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/4cf-k04-fy0
title: Pagurus bernhardus (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/d5q-0fp-j4v
title: Paguristes Dana, 1851

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/4jb-tff-6nl
title: Pisidia longicornis (Linnaeus, 1767)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/cpy-j9x-1ls
title: Pagurus prideaux Leach, 1815

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qmd-3n5-a49
title: Eualus cranchii (Leach, 1817)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/m7w-nds-lj7
title: Synalpheus gambarelloides (Nardo, 1847)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/yrm-xyc-gl0
title: Rathbunixa sayana (Stimpson, 1860)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3a3-x35-jac
title: Eriphia verrucosa (Forskål, 1775)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/76t-41n-n85
title: Heptacarpus tridens (M. J. Rathbun, 1902)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ew9-qwg-zfj
title: Coenobita brevimanus Dana, 1852

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/9qx-7t7-1kt
title: Caridina xiphias Bouvier, 1925

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/kbh-df8-j49
title: Migmarma modestum (De Man, 1902)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3d9-5kc-0lb
title: Carcinus aestuarii Nardo, 1847

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/f9v-a1d-3ca
title: Pirimela denticulata (Montagu, 1808)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.9zkdda
title: Occurrence Download

description: A dataset containing 607 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pochota fendleri (Seem.) W.S.Alverson & M.C.Duarte"
 ]
}

 The dataset includes 607 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0105714-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/9qv-y2e-2bf
title: Cyclodius nitidus (Dana, 1852)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.17632/ffh4ydkk56
title: Workers Gaze dynamics sample dataset in precision machining work process

description: (data updated, 8th May ~ )

Data Description
Participants and Skill Levels

The dataset includes performance data from three operators with distinct skill levels:
jss (Level 1) – Less than 1 year of experience
lhj (Level 2) – Approximately 2 years of experience
smc (Level 3) – More than 10 years of experience
Task Description

All operators performed a standardized precision machining task involving handwork processes.
The task was conducted under controlled conditions to minimize external variables and ensure consistency across trials.
Data Collection Method

Data was collected using Tobii Glasses 3 (eye-tracking device).
The device provided detailed gaze and head movement data, which were used to assess visual attention, focus, and cognitive workload during the task.
Data was preprocessed to remove noise and irrelevant artifacts, ensuring high-quality input for analysis.

Notable Findings
✅ Performance Differences by Skill Level
More experienced operators (smc) demonstrated higher accuracy, better consistency, and shorter completion times compared to less experienced operators.
Novice operators (jss) showed more variability in gaze patterns and hand movements, suggesting higher cognitive load and less automation in task execution.

✅ Visual Attention Patterns
Expert operators (smc) displayed more focused and predictable gaze patterns, indicating better situational awareness and motor control.
Novice operators exhibited more scattered gaze patterns, reflecting increased difficulty in maintaining focus and task execution efficiency.

✅ Learning Curve and Task Adaptation
How to Interpret and Use the Data
This dataset can be used to develop predictive models for operator performance based on eye-tracking and task execution patterns.
The data provides insights into how skill acquisition influences task efficiency, which can be applied to training program design and workflow optimization.
Comparative analysis of gaze patterns and hand movement consistency can help identify key behavioral markers of expertise in precision machining.
The findings can also support ergonomic improvements and process standardization to reduce errors and improve operator performance.
DOI: 10.3535/tfs-hw0-rt6
title: Armases elegans (Herklots, 1851)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/yv2-jnw-7e3
title: Heptacarpus tridens (M. J. Rathbun, 1902)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.17632/ffh4ydkk56.6
title: Workers Gaze dynamics sample dataset in precision machining work process

description: (data updated, 8th May ~ )

Data Description
Participants and Skill Levels

The dataset includes performance data from three operators with distinct skill levels:
jss (Level 1) – Less than 1 year of experience
lhj (Level 2) – Approximately 2 years of experience
smc (Level 3) – More than 10 years of experience
Task Description

All operators performed a standardized precision machining task involving handwork processes.
The task was conducted under controlled conditions to minimize external variables and ensure consistency across trials.
Data Collection Method

Data was collected using Tobii Glasses 3 (eye-tracking device).
The device provided detailed gaze and head movement data, which were used to assess visual attention, focus, and cognitive workload during the task.
Data was preprocessed to remove noise and irrelevant artifacts, ensuring high-quality input for analysis.

Notable Findings
✅ Performance Differences by Skill Level
More experienced operators (smc) demonstrated higher accuracy, better consistency, and shorter completion times compared to less experienced operators.
Novice operators (jss) showed more variability in gaze patterns and hand movements, suggesting higher cognitive load and less automation in task execution.

✅ Visual Attention Patterns
Expert operators (smc) displayed more focused and predictable gaze patterns, indicating better situational awareness and motor control.
Novice operators exhibited more scattered gaze patterns, reflecting increased difficulty in maintaining focus and task execution efficiency.

✅ Learning Curve and Task Adaptation
How to Interpret and Use the Data
This dataset can be used to develop predictive models for operator performance based on eye-tracking and task execution patterns.
The data provides insights into how skill acquisition influences task efficiency, which can be applied to training program design and workflow optimization.
Comparative analysis of gaze patterns and hand movement consistency can help identify key behavioral markers of expertise in precision machining.
The findings can also support ergonomic improvements and process standardization to reduce errors and improve operator performance.
DOI: 10.3535/4kx-qqm-ht5
title: Caridina xiphias Bouvier, 1925

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/v0q-xtv-6wm
title: Penaeus kerathurus (Forskål, 1775)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/2bq-dr7-v5z
title: Penaeus semisulcatus De Haan, 1844

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/g6b-e2t-s3a
title: Inachoides laevis Stimpson, 1860

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/c1h-129-nsp
title: Eduarctus martensii (Pfeffer, 1881)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ep3-p6e-2dl
title: Pagurus prideaux Leach, 1815

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.