cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e240568v1
title: HIFLD OPEN Peak Shaving Facilities

description: This feature class/shapefile represents Peak Shaving Plants. Peak Shaving Plants are used to provide vaporized Liquefied Natural Gas or Liquefied Propane Gas Air injections into natural gas pipelines to augment the pipeline for shortfalls in natural gas supplies. Data contains locational and other attribute information for Peak Shaving Plants. Geographical coverage includes the Continental United States.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025. (Downloaded 08/26)
DOI: 10.34740/kaggle/dsv/13849999
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.17699075
title: Steel Industry – Energy Consumption Dataset

description: This dataset contains 15-minute interval energy usage records from a steel manufacturing facility. It includes continuous electrical measurements, CO₂ emissions, time-based features, and categorical indicators related to operational load and weekday/weekend status. The dataset is suitable for energy forecasting, industrial analytics, and smart factory research.

Features Included





Usage_kWh – energy consumption




Reactive Power (Lagging/Leading)




CO₂ emissions




Power Factor (Lagging/Leading)




NSM (Number of Seconds from Midnight)




WeekStatus – Weekday or Weekend




Day_of_week




Load_Type – Light, Medium, or Maximum load




Timestamp (date)



Total instances: ~35040 records covering the full year 2018.

Intended Use

Ideal for machine learning tasks such as:





Energy consumption prediction




Load forecasting




Peak demand analysis




Industrial process optimization




Machine learning modeling



Source / Reference





Sathishkumar V. E., Shin C., Cho Y.,Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city,Building Research & Information, 2021.




Sathishkumar V. E. et al.,An Energy Consumption Prediction Model for Smart Factory using Data Mining Algorithms,KIPS Transactions on Software and Data Engineering, Vol. 9, No. 5, 2020.




Sathishkumar V. E. et al.,Industry Energy Consumption Prediction Using Data Mining Techniques,International Journal of Energy Information and Communications, Vol. 11, 2020.
DOI: 10.5281/zenodo.17699076
title: Steel Industry – Energy Consumption Dataset

description: This dataset contains 15-minute interval energy usage records from a steel manufacturing facility. It includes continuous electrical measurements, CO₂ emissions, time-based features, and categorical indicators related to operational load and weekday/weekend status. The dataset is suitable for energy forecasting, industrial analytics, and smart factory research.

Features Included





Usage_kWh – energy consumption




Reactive Power (Lagging/Leading)




CO₂ emissions




Power Factor (Lagging/Leading)




NSM (Number of Seconds from Midnight)




WeekStatus – Weekday or Weekend




Day_of_week




Load_Type – Light, Medium, or Maximum load




Timestamp (date)



Total instances: ~35040 records covering the full year 2018.

Intended Use

Ideal for machine learning tasks such as:





Energy consumption prediction




Load forecasting




Peak demand analysis




Industrial process optimization




Machine learning modeling



Source / Reference





Sathishkumar V. E., Shin C., Cho Y.,Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city,Building Research & Information, 2021.




Sathishkumar V. E. et al.,An Energy Consumption Prediction Model for Smart Factory using Data Mining Algorithms,KIPS Transactions on Software and Data Engineering, Vol. 9, No. 5, 2020.




Sathishkumar V. E. et al.,Industry Energy Consumption Prediction Using Data Mining Techniques,International Journal of Energy Information and Communications, Vol. 11, 2020.
DOI: 10.15468/dl.7an479
title: Occurrence Download

description: A dataset containing 2211 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-121.9428271535047 42.18493704343283, -121.95426907287992 42.1829573770277, -121.96497058613504 42.17935441397578, -121.97451973144331 42.17426695045229, -121.98254919514513 42.167890918596896, -121.98875047986287 42.16047179234064, -121.99288574884046 42.15229508592591, -121.99479689312652 42.1436753206378, -121.99441148436941 42.13494389305696, -121.99174540334828 42.12643631786532, -121.98690206686733 42.11847933874723, -121.98006830749617 42.11137840198163, -121.97150708663484 42.10540596948973, -121.96154733710249 42.10079111265976, -121.95057133336823 42.097710777060605, -121.93900007302672 42.09628304344063, -121.92727722031435 42.09656263474681, -121.91585221016737 42.0985388350365, -121.90516313882712 42.10213589694161, -121.89562007395223 42.10721592268383, -121.88758940351558 42.11358411245891, -121.88137980861438 42.1209961862213, -121.877230391158 42.129167703406175, -121.87530141407476 42.13778493274074, -121.87566802059399 42.14651686371978, -121.87831719240154 42.15502790502361, -121.88314808696454 42.162990785284514, -121.88997576588329 42.17009915982884, -121.89853819345564 42.176079434401565, -121.90850625315673 42.18070134372864, -121.91949640537288 42.18378686856172, -121.93108549853056 42.18521713813771, -121.9428271535047 42.18493704343283))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 2211 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005501-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fhq34v
title: Occurrence Download

description: A dataset containing 30146 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((17.762287 -34.668307, 30.355373 -34.668307, 30.355373 -23.342043, 17.762287 -23.342043, 17.762287 -34.668307))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Poaceae)"
 ]
}

 The dataset includes 30146 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005500-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/e1fj-6g53
title: "Bark Texture Recognition of Indian Trees: Augmented Dataset for Robust Classification"

description: "The bark of a tree provides stable and distinctive visual patterns that play a crucial role in automated tree species identification. Our previously released dataset, Bark Texture Recognition of Indian Trees: A Bangalore-Centric Dataset, included 558 RGB images representing 22 commonly found tree species in Karnataka, India. In this extended version, we present an augmented and expanded dataset designed to improve robustness, generalization, and model performance for texture-based classification tasks. The dataset incorporates multiple augmentation strategies, including rotation, brightness variation, contrast adjustments, scaling, and noise addition, effectively increasing intra-class variability while preserving the natural characteristics of bark textures. This enhanced version supports deep learning, few-shot learning, and transfer learning experiments by providing richer variations that simulate real-world environmental conditions. The extended dataset aims to address challenges such as limited sample size, illumination differences, viewpoint variations, and bark texture diversity, thereby offering a more comprehensive resource for researchers working in forestry, biodiversity conservation, and computer vision-based species identification."
DOI: 10.15468/dl.6fmy53
title: Occurrence Download

description: A dataset containing 2101 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-121.97270033317108 42.18844332701347, -121.9841419067489 42.18646022201886, -121.9948421485718 42.18285402965641, -122.0043891462338 42.17776367044585, -122.01241566938288 42.17138518793025, -122.01861333451637 42.16396415020295, -122.02274444481117 42.155786144811465, -122.02465105180195 42.14716574269468, -122.02426090190747 42.13843436456836, -122.02159005818363 42.129927522854565, -122.0167421202019 42.12197193272452, -122.00990409680598 42.11487298683606, -122.00133911248832 42.1089030704788, -121.99137624382831 42.10429115836242, -121.98039788433039 42.10121408304335, -121.96882512145125 42.09978980024342, -121.9571016767639 42.10007290063353, -121.94567700787368 42.10205253377765, -121.93498919816467 42.105652820710766, -121.92544826736693 42.11073573996211, -121.91742052221143 42.11710638065553, -121.91121453224548 42.124520368543855, -121.9070692616757 42.132693189353, -121.90514481474003 42.14131106144879, -121.90551616098568 42.1500429492878, -121.90817010003657 42.158553262848876, -121.91300560590129 42.16651475840422, -121.91983756241389 42.1736211442494, -121.92840376871413 42.1795989024294, -121.93837496219194 42.18421786438797, -121.94936748196798 42.187300124293415, -121.96095808481101 42.18872693711676, -121.97270033317108 42.18844332701347))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 2101 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005502-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wzttsa
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((0.0320455263207686 0.0018933270206493, 0.0310641336500298 0.008151402407364, 0.0288885780400578 0.0140969187488412, 0.0256022302401581 0.0195011918967939, 0.0213312424799886 0.0241562622737186, 0.0162397219293971 0.0278829287250075, 0.0105234279314519 0.0305376822549528, 0.0044022346279974 0.0320182660009872, -0.0018883491716244 0.0322676409115705, -0.0081062723441224 0.0312761992312484, -0.0140122902214045 0.0290821374445974, -0.0193792033487684 0.0257699737782644, -0.0240006243643332 0.0214672694673532, -0.0276989306430734 0.0163396844818562, -0.0303320938497764 0.0105845641551731, -0.0317991234081126 0.0044233103691547, -0.0320439169396878 -0.0019071626863073, -0.0310573743780294 -0.0081635543685005, -0.0288777009014062 -0.014105531110099, -0.0255888941669276 -0.0195049476345281, -0.0213174807550005 -0.0241545837410081, -0.0162276326375224 -0.0278760655932942, -0.0105148545451267 -0.0305266735040489, -0.0043984853549785 -0.0320047817438364, 0.0018867005513534 -0.0322537281347491, 0.008099473831324 -0.0312639701595981, 0.0140013738416447 -0.0290734479739702, 0.0193658280355946 -0.0257661409320645, 0.0239868234013635 -0.0214688708917829, 0.0276868021148234 -0.0163464705046133, 0.0303234812273877 -0.0105954957960213, 0.0317953348978636 -0.0044367175153174, 0.0320455263207686 0.0018933270206493))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005503-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11632270
title: modelforge curated dataset: SPICE 2

description: Curated SPICE 2 Dataset:

Full dataset, version full_dataset_v0:

This provides a curated hdf5 file for the SPICE 2 dataset (release v2.0.1) designed to be compatible with modelforge, an infrastructure to implement and train NNPs.  This datafile  includes 2,008,126 total conformers for 113,986 unique molecules.

When applicable, the units of properties are provided in the datafile,  encoded as strings compatible with the openff-units package.  For more information about the structure of the data file, please see the following:



https://github.com/choderalab/modelforge/wiki/Dataset-and-curation#curation-module


This curated dataset was generated using the modelforge software at commit c5c7153:



Link to the source code at this commit: https://github.com/choderalab/modelforge/tree/c5c7153e06172fe8e6f25015250ecb5db05655cc

Link to the script file used to generate the dataset: https://github.com/choderalab/modelforge/blob/c5c7153e06172fe8e6f25015250ecb5db05655cc/modelforge/curation/scripts/curate_spice2.py 


 

Source Dataset:

Small-molecule/Protein Interaction Chemical Energies (SPICE).

The SPICE 2 dataset contains roughly 2 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 17 elements, charged and  uncharged molecules, and a wide range of covalent and non-covalent interactions.  SPICE 2 is an update to spice 1, roughly double the total dataset size and including 2 additional elements.  It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, using Psi4 1.4.1 along with other useful quantities such as multipole moments and bond orders.

 

Citations:

Original SPICE 1 publication:



Eastman, P., Behara, P.K., Dotson, D.L. et al. SPICE,  A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. Sci Data 10, 11 (2023). https://doi.org/10.1038/s41597-022-01882-6


Source dataset, released with CCO 1.0 Universal license:













Eastman, P., Behara, P. K., Dotson, D., Galvelis, R., Herr, J., Horton, J., Mao, Y., Chodera, J., Pritchard, B., Wang, Y., De Fabritiis, G., & Markland, T. (2024). SPICE 2.0.1 (2.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10975225
DOI: 10.5281/zenodo.11650631
title: modelforge curated dataset: SPICE 2

description: Curated SPICE 2 Dataset:

1000 conformer test set, version nc_1000_v0:

This provides a curated hdf5 file for a subset of the SPICE 2 dataset (release v2.0.1) designed to be compatible with modelforge, an infrastructure to implement and train NNPs.  This datafile  includes 1000 total conformers for 403 unique molecules.

When applicable, the units of properties are provided in the datafile,  encoded as strings compatible with the openff-units package.  For more information about the structure of the data file, please see the following:



https://github.com/choderalab/modelforge/wiki/Dataset-and-curation#curation-module


This curated dataset was generated using the modelforge software at commit c5c7153:



Link to the source code at this commit: https://github.com/choderalab/modelforge/tree/c5c7153e06172fe8e6f25015250ecb5db05655cc

Link to the script file used to generate the dataset: https://github.com/choderalab/modelforge/blob/c5c7153e06172fe8e6f25015250ecb5db05655cc/modelforge/curation/scripts/curate_spice2.py 


 

Source Dataset:

Small-molecule/Protein Interaction Chemical Energies (SPICE).

The SPICE 2 dataset contains roughly 2 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 17 elements, charged and  uncharged molecules, and a wide range of covalent and non-covalent interactions.  SPICE 2 is an update to spice 1, roughly double the total dataset size and including 2 additional elements.  It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, using Psi4 1.4.1 along with other useful quantities such as multipole moments and bond orders.

 

Citations:

Original SPICE 1 publication:



Eastman, P., Behara, P.K., Dotson, D.L. et al. SPICE,  A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. Sci Data 10, 11 (2023). https://doi.org/10.1038/s41597-022-01882-6


Source dataset, released with CCO 1.0 Universal license:













Eastman, P., Behara, P. K., Dotson, D., Galvelis, R., Herr, J., Horton, J., Mao, Y., Chodera, J., Pritchard, B., Wang, Y., De Fabritiis, G., & Markland, T. (2024). SPICE 2.0.1 (2.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10975225
DOI: 10.5281/zenodo.11658662
title: modelforge curated dataset: SPICE 2

description: Curated SPICE 2 Dataset:

Full dataset restricted to elements H, C, N, O, F, Cl, S, version "full_dataset_v0_HCNOFClS":

This provides a curated hdf5 file for a subset of the SPICE 2 dataset (release v2.0.1) designed to be compatible with modelforge, an infrastructure to implement and train NNPs.  This subset is limited to molecules containing any of the following 7 elements: H, C, N, O, F, Cl, and S.  This  dataset contains 1,620,239 total conformers, for 97,280 unique molecules. 

When applicable, the units of properties are provided in the datafile,  encoded as strings compatible with the openff-units package.  For more information about the structure of the data file, please see the following:



https://github.com/choderalab/modelforge/wiki/Dataset-and-curation#curation-module


This curated dataset was generated using the modelforge software at commit c5c7153:



Link to the source code at this commit: https://github.com/choderalab/modelforge/tree/c5c7153e06172fe8e6f25015250ecb5db05655cc

Link to the script file used to generate the dataset: https://github.com/choderalab/modelforge/blob/c5c7153e06172fe8e6f25015250ecb5db05655cc/modelforge/curation/scripts/curate_spice2.py 


 

Source Dataset:

Small-molecule/Protein Interaction Chemical Energies (SPICE).

The SPICE 2 dataset contains roughly 2 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 17 elements, charged and  uncharged molecules, and a wide range of covalent and non-covalent interactions.  SPICE 2 is an update to spice 1, roughly double the total dataset size and including 2 additional elements.  It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, using Psi4 1.4.1 along with other useful quantities such as multipole moments and bond orders.

 

Citations:

Original SPICE 1 publication:



Eastman, P., Behara, P.K., Dotson, D.L. et al. SPICE,  A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. Sci Data 10, 11 (2023). https://doi.org/10.1038/s41597-022-01882-6


Source dataset, released with CCO 1.0 Universal license:













Eastman, P., Behara, P. K., Dotson, D., Galvelis, R., Herr, J., Horton, J., Mao, Y., Chodera, J., Pritchard, B., Wang, Y., De Fabritiis, G., & Markland, T. (2024). SPICE 2.0.1 (2.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10975225
DOI: 10.5281/zenodo.11658793
title: modelforge curated dataset: SPICE 2

description: Curated SPICE 2 Dataset:

1000 conformer test set restricted to elements H, C, N, O, F, Cl, S, version "nc_1000_v0_HCNOFClS":

This provides a curated hdf5 file for a subset of the SPICE 2 dataset (release v2.0.1) designed to be compatible with modelforge, an infrastructure to implement and train NNPs.  This subset is limited to molecules containing any of the following 7 elements: H, C, N, O, F, Cl, and S.  This dataset contains 1000 total conformers, for 374 unique molecules, useful for testing purposes. 

When applicable, the units of properties are provided in the datafile,  encoded as strings compatible with the openff-units package.  For more information about the structure of the data file, please see the following:



https://github.com/choderalab/modelforge/wiki/Dataset-and-curation#curation-module


This curated dataset was generated using the modelforge software at commit c5c7153:



Link to the source code at this commit: https://github.com/choderalab/modelforge/tree/c5c7153e06172fe8e6f25015250ecb5db05655cc

Link to the script file used to generate the dataset: https://github.com/choderalab/modelforge/blob/c5c7153e06172fe8e6f25015250ecb5db05655cc/modelforge/curation/scripts/curate_spice2.py 


 

Source Dataset:

Small-molecule/Protein Interaction Chemical Energies (SPICE).

The SPICE 2 dataset contains roughly 2 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 17 elements, charged and  uncharged molecules, and a wide range of covalent and non-covalent interactions.  SPICE 2 is an update to spice 1, roughly double the total dataset size and including 2 additional elements.  It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, using Psi4 1.4.1 along with other useful quantities such as multipole moments and bond orders.

 

Citations:

Original SPICE 1 publication:



Eastman, P., Behara, P.K., Dotson, D.L. et al. SPICE,  A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. Sci Data 10, 11 (2023). https://doi.org/10.1038/s41597-022-01882-6


Source dataset, released with CCO 1.0 Universal license:













Eastman, P., Behara, P. K., Dotson, D., Galvelis, R., Herr, J., Horton, J., Mao, Y., Chodera, J., Pritchard, B., Wang, Y., De Fabritiis, G., & Markland, T. (2024). SPICE 2.0.1 (2.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10975225
DOI: 10.5281/zenodo.13883771
title: modelforge curated dataset: SPICE 2

description: Curated SPICE 2 Dataset:

1000 conformer test set restricted to elements H, C, N, O, F, Cl, S, version "nc_1000_v1_HCNOFClS":

This provides a curated hdf5 file for a subset of the SPICE 2 dataset (release v2.0.1) designed to be compatible with modelforge, an infrastructure to implement and train NNPs.  This subset is limited to molecules containing any of the following 7 elements: H, C, N, O, F, Cl, and S.  This dataset contains 1000 total conformers, for 374 unique molecules, useful for testing purposes. 

Changes: In this version, for each record `total_charge` is stored as an array of shape (N_conformers, 1), i.e., a value for each conformer; previously this was just a single value for each record as the charge state doesn't change for each conformer.  

 

When applicable, the units of properties are provided in the datafile,  encoded as strings compatible with the openff-units package.  For more information about the structure of the data file, please see the following:



https://github.com/choderalab/modelforge/wiki/Dataset-and-curation#curation-module


This curated dataset was generated using the modelforge software at commit <add commit>:



Link to the source code at this commit: <add commit>

Link to the script file used to generate the dataset: <add commit>


 

Source Dataset:

Small-molecule/Protein Interaction Chemical Energies (SPICE).

The SPICE 2 dataset contains roughly 2 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 17 elements, charged and  uncharged molecules, and a wide range of covalent and non-covalent interactions.  SPICE 2 is an update to spice 1, roughly double the total dataset size and including 2 additional elements.  It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, using Psi4 1.4.1 along with other useful quantities such as multipole moments and bond orders.

 

Citations:

Original SPICE 1 publication:



Eastman, P., Behara, P.K., Dotson, D.L. et al. SPICE,  A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. Sci Data 10, 11 (2023). https://doi.org/10.1038/s41597-022-01882-6


Source dataset, released with CCO 1.0 Universal license:













Eastman, P., Behara, P. K., Dotson, D., Galvelis, R., Herr, J., Horton, J., Mao, Y., Chodera, J., Pritchard, B., Wang, Y., De Fabritiis, G., & Markland, T. (2024). SPICE 2.0.1 (2.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10975225