cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13883816
title: modelforge curated dataset: SPICE 2

description: Curated SPICE 2 Dataset:

1000 conformer test set, version "nc_1000_v1":

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.

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
DOI: 10.5281/zenodo.13883829
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_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 1,620,239 total conformers, for 97,279 unique molecules. 

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
DOI: 10.5281/zenodo.13883915
title: modelforge curated dataset: SPICE 2

description: Curated SPICE 2 Dataset:

Full dataset, version "full_dataset_v1":

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 113,985 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:

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
DOI: 10.5281/zenodo.15419983
title: modelforge curated dataset: SPICE 2

description: Modelforge Curated SPICE 2 Dataset:- Full dataset- Version: full_dataset_v1.1

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 corresponds to the full dataset and  includes 2,008,126 total configurations for 113,985 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


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.

Properties Included:



atomic_numbers 

positions      



"per_atom"

"nanometer"



dft_total_force



"per_atom"

"kilojoule_per_mole / nanometer"



mbis_charges   



"per_atom"

"elementary_charge"



mbis_dipoles   



"per_atom"

"elementary_charge * nanometer"



mbis_quadrupoles       



"per_atom"

"elementary_charge * nanometer ** 2"



mbis_octupoles 



"per_atom"

"elementary_charge * nanometer ** 3"



mayer_indices  



"per_atom"

"dimensionless"



wiberg_lowdin_indices  



"per_atom"

"dimensionless"



total_charge   



"per_system"

"elementary_charge"



dft_total_energy       



"per_system"

"kilojoule_per_mole"



formation_energy       



"per_system"

"kilojoule_per_mole"



scf_dipole     



"per_system"

"elementary_charge * nanometer"



scf_quadrupole 



"per_system""elementary_charge * nanometer ** 2"



smiles 



"meta_data"




 

Citations:



Spice 2 publication:





Eastman, P., Pritchard, B. P., Chodera, J. D., & Markland, T. E. Nutmeg and SPICE: models and data for biomolecular machine learning. Journal of chemical theory and computation, 20(19), 8583-8593 (2024). https://doi.org/10.1021/acs.jctc.4c00794




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.15429193
title: modelforge curated dataset: SPICE 2

description: Modelforge Curated SPICE 2 Dataset:- Full dataset - Version: full_dataset_v1.1b

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 dataset contains 113985 unique records for 2008126 total configurations. 

When applicable, the units of properties are provided in the datafile,  encoded as strings compatible with the openff-units package. 

This is compatible with modelforge hdf5 schema 2.

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


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.



 


Properties Included:



atomic_numbers 

positions      



"per_atom"

"nanometer"



dft_total_force



"per_atom"

"kilojoule_per_mole / nanometer"



mbis_charges   



"per_atom"

"elementary_charge"



mbis_dipoles   



"per_atom"

"elementary_charge * nanometer"



mbis_quadrupoles       



"per_atom"

"elementary_charge * nanometer ** 2"



mbis_octupoles 



"per_atom"

"elementary_charge * nanometer ** 3"



mayer_indices  



"per_atom"

"dimensionless"



wiberg_lowdin_indices  



"per_atom"

"dimensionless"



total_charge   



"per_system"

"elementary_charge"



dft_total_energy       



"per_system"

"kilojoule_per_mole"



formation_energy       



"per_system"

"kilojoule_per_mole"



scf_dipole     



"per_system"

"elementary_charge * nanometer"



scf_quadrupole 



"per_system""elementary_charge * nanometer ** 2"



smiles 



"meta_data"




 

Citations:



Spice 2 publication:





Eastman, P., Pritchard, B. P., Chodera, J. D., & Markland, T. E. Nutmeg and SPICE: models and data for biomolecular machine learning. Journal of chemical theory and computation, 20(19), 8583-8593 (2024). https://doi.org/10.1021/acs.jctc.4c00794




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: 10.3886/e231742v1
title: Revisiting the Long-Run Educational Effects of the Ancash Earthquake: A Comment of Caruso & Miller (2015)

description: Caruso & Miller (2015) estimated the long-run effects of
early-life exposure to the 1970 Ancash earthquake in Peru (7.9 Mw), the largest
and deadliest earthquake in the country's modern history. In this paper, we
replicate and extend Caruso & Miller's
(2015) analyses and find that the impact was around three times
larger, indicating that the original study significantly underestimated the size
of the quake's impact. The original study used a 10 percent sample of the 1993
and 2007 censuses and an aggregated measure of exposure. In contrast, we used
the complete census microdata and a more disaggregated measure of exposure,
which explains our most significant estimates in combination. These results
highlight the long-term educational consequences of natural disasters and the
importance of using good-quality data to estimate their impact better.
DOI: 10.15468/dl.ahvgud
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Prionodon linsang (Hardwicke, 1821))",
 "Country is one of (Anguilla, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Bouvet Island, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), South Georgia and the South Sandwich Islands, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguay, Venezuela (Bolivarian Republic of), Virgin Islands (British), Virgin Islands (U.S.))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016051-250525065834625/datasets/export for details.
DOI: 10.15468/dl.f9566d
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Prionodon linsang (Hardwicke, 1821))",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016052-250525065834625/datasets/export for details.
DOI: 10.15468/dl.e2hr5e
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Prionodon linsang (Hardwicke, 1821))",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 10 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016050-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tvwewe
title: Occurrence Download

description: A dataset containing 3183 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Material citation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Trapa L."
 ]
}

 The dataset includes 3183 records from 179 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001523-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.n8m9c
title: Data from: Distinguishing between convergent evolution and violation of the molecular clock for three taxa

description: We give a non-technical introduction to convergence-divergence models, a
 new modeling approach for phylogenetic data that allows for the usual
 divergence of lineages after lineage-splitting but also allows for taxa to
 converge, i.e. become more similar over time. By examining the 3-taxon
 case in some detail we illustrate that phylogeneticists have been
 ``spoiled'' in the sense of not having to think about the
 structural parameters in their models by virtue of the strong assumption
 that evolution is tree-like. We show that there are not always good
 statistical reasons to prefer the usual class of tree-like models over
 more general convergence-divergence models. Specifically we show many
 3-taxon data sets can be equally well explained by supposing violation of
 the molecular clock due to change in the rate of evolution along different
 edges, or by keeping the assumption of a constant rate of evolution but
 instead assuming that evolution is not a purely divergent process. Given
 the abundance of evidence that evolution is not strictly tree-like, our
 discussion is an illustration that as phylogeneticists we need to think
 clearly about the structural form of the models we use. For cases with
 four taxa we show that there will be far greater ability to distinguish
 models with convergence from non-clock-like tree models.
description: Python Script for
 Comparing Non-Clock-Like Triplets to Convergence-Divergence
 ModelsThe attached text file
 is intended to run in Python. Edge parameters are simulated from a 3-taxon
 non-clock-like tree and tested to see whether they are consistent with a
 3-taxon convergence-divergence model. Similarly, epoch parameters are
 simulated from a 3-taxon convergence-divergence model and tested to see
 whether they are consistent with a 3-taxon non-clock-like
 tree.SystematicBiologyScriptJonathanMitchell.txtOnline Appendix for Splitting OperatorOnline-only appendix describing the mathematical details of the splitting operator and how to generate rate matrices.OnlineAppendixSplittingOperator.pdf
DOI: 10.5061/dryad.4b95c
title: Data from: Determinants of litter decomposition rates in a tropical forest: functional traits, phylogeny and ecological succession

description: Plant litter decomposition is one of the most important processes in
 terrestrial ecosystems, as it is a key factor in nutrient cycling.
 Decomposition rates depend on environmental factors, but also plant
 traits, as these determine the character of detritus. We measured litter
 decomposition rate for 57 common tree species displaying a variety of
 functional traits within four sites in primary and four sites in secondary
 tropical forest in Madang Province, Papua New Guinea. The phylogenetic
 relationships between these trees were also estimated using molecular
 data. The leaves collected from different tree species were dried for two
 days, placed into detritus bags and exposed to ambient conditions for two
 months. Nitrogen, carbon and ash content were assessed as quantitative
 traits and used together with a phylogenetic variance-covariance matrix as
 predictors of decomposition rate. The analysis of the tree species
 composition from 96 quadrats located along a successional gradient of
 swidden agriculture enabled us to determine successional preferences for
 individual species. Nitrogen content was the only functional trait
 measured to be significantly positively correlated with decomposition
 rate. Controlling for plant phylogeny did not influence our conclusions,
 but including phylogeny demonstrated that the mainly early successional
 family Euphorbiaceae is characterized by a particularly high decomposition
 rate. The acquisitive traits (high nitrogen content and low wood density)
 correlated with rapid decomposition were characteristic for early
 successional species. Decomposition rate thus decreased from early
 successional to primary forest species. However, the decomposition of
 leaves from the same species was significantly faster in primary than in
 secondary forest stands, very probably because the high humidity of
 primary forest environments keeps the decomposing material wetter.
description: species traits and
 sitesDataset contains species
 with site characteristics and individual species
 traits.main_datasetS.xlsphylogenyPhylogeny tre file.
DOI: 10.15468/dl.hqqagz
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Guinea",
 "TaxonKey is Vernonia nimbaensis"
 ]
}

 The dataset includes 41 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001759-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.