cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc6yptm
title: CCDC 207291: Experimental Crystal Structure Determination

description: Related Article: H.Davis, L.Peterson Junior, A.M.Goforth, M.D.Smith, P.Zhang, H.-C.zur Loye|2004|J.Chem.Cryst.|34|299|doi:10.1023/B:JOCC.0000026274.31401.7f
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc7cjhl
title: CCDC 219619: Experimental Crystal Structure Determination

description: Related Article: Z.Caliskan, N.Goren, W.H.Watson|2004|J.Chem.Cryst.|34|307|doi:10.1023/B:JOCC.0000026275.39368.fc
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc6kpxb
title: CCDC 195762: Experimental Crystal Structure Determination

description: Related Article: J.Katakawa, T.Tetsumi, K.Sakaguchi, M.Katai, T.Terai|2004|J.Chem.Cryst.|34|311|doi:10.1023/B:JOCC.0000026276.42939.49
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc6vl6t
title: CCDC 204296: Experimental Crystal Structure Determination

description: Related Article: R.Bhattacharya, S.Chanda, A.Cantoni, G.Bocelli, A.Ghosh|2004|J.Chem.Cryst.|34|325|doi:10.1023/B:JOCC.0000026278.80782.53
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc6vl7v
title: CCDC 204297: Experimental Crystal Structure Determination

description: Related Article: R.Bhattacharya, S.Chanda, A.Cantoni, G.Bocelli, A.Ghosh|2004|J.Chem.Cryst.|34|325|doi:10.1023/B:JOCC.0000026278.80782.53
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc6kc14
title: CCDC 195425: Experimental Crystal Structure Determination

description: Related Article: Z.Elaoud, S.Kamoun, T.Mhiri, A.Daoud, A.Driss|2004|J.Chem.Cryst.|34|337|doi:10.1023/B:JOCC.0000026280.18695.bc
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc6fxxf
title: CCDC 192135: Experimental Crystal Structure Determination

description: Related Article: S.H.Dale, M.R.J.Elsegood, M.Hemmings, A.L.Wilkinson|2004|CrystEngComm|6|207|doi:10.1039/b404563g
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc6fxzh
title: CCDC 192137: Experimental Crystal Structure Determination

description: Related Article: S.H.Dale, M.R.J.Elsegood, M.Hemmings, A.L.Wilkinson|2004|CrystEngComm|6|207|doi:10.1039/b404563g
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.ka4pus
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dendrocalamus minor (McClure) L.C.Chia & H.L.Fung",
 "Year 1975-2025"
 ]
}

 The dataset includes 42 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007723-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pbf729
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Opisthocomiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((160 -30,170 -30,170 -20,160 -20,160 -30))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025835-251009101135966/datasets/export for details.
DOI: 10.15468/dl.emnhtz
title: Occurrence Download

description: A dataset containing 38172 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Diapterus rhombeus)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 38172 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078826-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.24435/materialscloud:8j-b8
title: A kinetic Monte Carlo dataset of monoatomic step roughening for deep generative models approximation of stochastic dynamics

description: Deep Generative models have shown impressive capabilities in several applications, e.g., image, video, and audio synthesis. Importantly, they can infer probability distributions from data implicitly. For this reason, they have promising applications in learning stochastic dynamics, with potential applications in computational condensed matter physics and materials science. Indeed, random thermal fluctuations have a key role in several phenomena, such as surface roughening, nucleation, crystal growth, and phase transitions. Here we provide a comprehensive dataset of trajectories of one of such systems, i.e., the progressive roughening of monoatomic steps on top of a simple cubic (100) surface. The system has been simulated with a Kinetic Monte Carlo approach, implementing the edge diffusion conditions. Among other characteristics, this system is convenient because its dynamics can be represented as a series of binary images, with white and black pixels corresponding to occupied and unoccupied surface sites, respectively. The repository contains 3 KMC datasets composed of 300 independent trajectories each. Every trajectory contains 1000 subsequent states. The datasets differ in the initial condition (one flat stripe and one undulated one on a 64x64 computational cell) and the domain size (one undulated stripe on a 72x64 computational cell). Evolutions predicted by a Generative Adversarial Network approach trained on the flat stripe dataset are also provided, serving as a benchmark for comparing alternative implementations on the same problem. These comprise the evolution of flat profiles on domains of different lengths (32, 48, 64, 72, 80, 96, and 128) to the equilibrium value and relaxation dynamics from undulated profiles conformal to those used in the KMC datasets.
DOI: 10.24435/materialscloud:w7-y5
title: A kinetic Monte Carlo dataset of monoatomic step roughening for deep generative models approximation of stochastic dynamics

description: Deep Generative models have shown impressive capabilities in several applications, e.g., image, video, and audio synthesis. Importantly, they can infer probability distributions from data implicitly. For this reason, they have promising applications in learning stochastic dynamics, with potential applications in computational condensed matter physics and materials science. Indeed, random thermal fluctuations have a key role in several phenomena, such as surface roughening, nucleation, crystal growth, and phase transitions. Here we provide a comprehensive dataset of trajectories of one of such systems, i.e., the progressive roughening of monoatomic steps on top of a simple cubic (100) surface. The system has been simulated with a Kinetic Monte Carlo approach, implementing the edge diffusion conditions. Among other characteristics, this system is convenient because its dynamics can be represented as a series of binary images, with white and black pixels corresponding to occupied and unoccupied surface sites, respectively. The repository contains 3 KMC datasets composed of 300 independent trajectories each. Every trajectory contains 1000 subsequent states. The datasets differ in the initial condition (one flat stripe and one undulated one on a 64x64 computational cell) and the domain size (one undulated stripe on a 72x64 computational cell). Evolutions predicted by a Generative Adversarial Network approach trained on the flat stripe dataset are also provided, serving as a benchmark for comparing alternative implementations on the same problem. These comprise the evolution of flat profiles on domains of different lengths (32, 48, 64, 72, 80, 96, and 128) to the equilibrium value and relaxation dynamics from undulated profiles conformal to those used in the KMC datasets.
DOI: 10.6084/m9.figshare.30336664
title: Velocity models for NEC

description: Velocity models for NEC
DOI: 10.6084/m9.figshare.30336664.v3
title: Velocity models for NEC

description: Velocity models for NEC
DOI: 10.6084/m9.figshare.30336664.v2
title: Velocity model for NEC

description: Velocity models for NEC
DOI: 10.15156/bio/sh1428176.10fu
title: SH1428176.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1428196.10fu
title: SH1428196.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1428197.10fu
title: SH1428197.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1428204.10fu
title: SH1428204.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1428212.10fu
title: SH1428212.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.fyz2xa
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"TUR.1.3_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006412-260108223611665/datasets/export for details.
DOI: 10.15156/bio/sh1428216.10fu
title: SH1428216.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1428253.10fu
title: SH1428253.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1428269.10fu
title: SH1428269.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1428330.10fu
title: SH1428330.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1428331.10fu
title: SH1428331.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.34740/kaggle/dsv/14486192
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.60712/si-id107132.1
title: StrainInfo SI-ID 107132.1

description: StrainInfo dataset 107132 about a strain of Rhinocladiella phaeophora. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id107133.1
title: StrainInfo SI-ID 107133.1

description: StrainInfo dataset 107133 about a strain of Exophiala lecanii-corni. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.