cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc7lsxh
title: CCDC 226607: Experimental Crystal Structure Determination

description: Related Article: K.Kandasamy, S.Kumar, H.B.Singh, G.Wolmershauser|2003|Organometallics|22|5069|doi:10.1021/om0340948
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/cc7lsyj
title: CCDC 226608: Experimental Crystal Structure Determination

description: Related Article: J.H.L.Ong, C.Nataro, J.A.Golen, A.L.Rheingold|2003|Organometallics|22|5027|doi:10.1021/om0340138
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/cc7lszk
title: CCDC 226609: Experimental Crystal Structure Determination

description: Related Article: J.H.L.Ong, C.Nataro, J.A.Golen, A.L.Rheingold|2003|Organometallics|22|5027|doi:10.1021/om0340138
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/cc7lt0m
title: CCDC 226610: Experimental Crystal Structure Determination

description: Related Article: Minghuey Shieh, Li-Fang Ho, Yu-Wen Guo, Shu-Fen Lin, Yi-Chun Lin, Shie-Ming Peng, Yi-Hung Liu|2003|Organometallics|22|5020|doi:10.1021/om034033+
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/cc7lt1n
title: CCDC 226611: Experimental Crystal Structure Determination

description: Related Article: Minghuey Shieh, Li-Fang Ho, Yu-Wen Guo, Shu-Fen Lin, Yi-Chun Lin, Shie-Ming Peng, Yi-Hung Liu|2003|Organometallics|22|5020|doi:10.1021/om034033+
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/cc7lt2p
title: CCDC 226612: Experimental Crystal Structure Determination

description: Related Article: Minghuey Shieh, Li-Fang Ho, Yu-Wen Guo, Shu-Fen Lin, Yi-Chun Lin, Shie-Ming Peng, Yi-Hung Liu|2003|Organometallics|22|5020|doi:10.1021/om034033+
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/cc7lt3q
title: CCDC 226613: Experimental Crystal Structure Determination

description: Related Article: Minghuey Shieh, Li-Fang Ho, Yu-Wen Guo, Shu-Fen Lin, Yi-Chun Lin, Shie-Ming Peng, Yi-Hung Liu|2003|Organometallics|22|5020|doi:10.1021/om034033+
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/cc7lt4r
title: CCDC 226614: Experimental Crystal Structure Determination

description: Related Article: J.-F.Carpentier, A.Martin, D.C.Swenson, R.F.Jordan|2003|Organometallics|22|4999|doi:10.1021/om0340443
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/cc7lt5s
title: CCDC 226615: Experimental Crystal Structure Determination

description: Related Article: J.-F.Carpentier, A.Martin, D.C.Swenson, R.F.Jordan|2003|Organometallics|22|4999|doi:10.1021/om0340443
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.4r6j5h
title: Occurrence Download

description: A dataset containing 16732 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Sporobolus anglicus, Spartina alterniflora)",
 "Year 2000-2025"
 ]
}

 The dataset includes 16732 records from 228 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017774-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/iw73uu/zekfzm
title: SWC_hour.nc

description: Uploaded with pyDataverse 2024-04-11 00:12
DOI: 10.22008/fk2/iw73uu/ysbwjj
title: SWC_day.nc

description: Uploaded with pyDataverse 2024-04-11 00:12
DOI: 10.22008/fk2/iw73uu/mfoul6
title: SWC_month.nc

description: Uploaded with pyDataverse 2024-04-11 00:12
DOI: 10.22008/fk2/iw73uu/cdfwwa
title: SWC_O_day.csv

description: Uploaded with pyDataverse 2024-04-11 00:12
DOI: 10.22008/fk2/iw73uu/ybmb7n
title: SWC_O_hour.csv

description: Uploaded with pyDataverse 2024-04-11 00:13
DOI: 10.6084/m9.figshare.30850404
title: Principal Component Analysis for Max-Stable Distributions

description: Principal component analysis (PCA) is one of the most popular dimension reduction techniques in statistics and is especially powerful when a multivariate distribution is concentrated near a lower-dimensional subspace. Multivariate extreme value distributions have turned out to provide challenges for the application of PCA since their constraint support impedes the detection of lower-dimensional structures and heavy-tails can imply that second moments do not exist, thereby preventing the application of classical variance-based techniques for PCA. We adapt PCA to max-stable distributions using a regression setting and employ max-linear maps to project the random vector to a lower-dimensional space while preserving max-stability. We also provide a characterization of those distributions which allow for a perfect reconstruction from the lower-dimensional representation. Finally, we demonstrate how an optimal projection matrix can be consistently estimated and show viability in practice with a simulation study and application to a benchmark dataset. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.30850404.v2
title: Principal Component Analysis for Max-Stable Distributions

description: Principal component analysis (PCA) is one of the most popular dimension reduction techniques in statistics and is especially powerful when a multivariate distribution is concentrated near a lower-dimensional subspace. Multivariate extreme value distributions have turned out to provide challenges for the application of PCA since their constraint support impedes the detection of lower-dimensional structures and heavy-tails can imply that second moments do not exist, thereby preventing the application of classical variance-based techniques for PCA. We adapt PCA to max-stable distributions using a regression setting and employ max-linear maps to project the random vector to a lower-dimensional space while preserving max-stability. We also provide a characterization of those distributions which allow for a perfect reconstruction from the lower-dimensional representation. Finally, we demonstrate how an optimal projection matrix can be consistently estimated and show viability in practice with a simulation study and application to a benchmark dataset. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.22008/fk2/iw73uu/bow7eh
title: SWC_O_month.csv

description: Uploaded with pyDataverse 2024-04-11 00:13
DOI: 10.6084/m9.figshare.30850404.v1
title: Principal Component Analysis for max-stable distributions

description: Principal component analysis (PCA) is one of the most popular dimension reduction techniques in statistics and is especially powerful when a multivariate distribution is concentrated near a lower-dimensional subspace. Multivariate extreme value distributions have turned out to provide challenges for the application of PCA since their constraint support impedes the detection of lower-dimensional structures and heavy-tails can imply that second moments do not exist, thereby preventing the application of classical variance-based techniques for PCA. We adapt PCA to max-stable distributions using a regression setting and employ max-linear maps to project the random vector to a lower-dimensional space while preserving max-stability. We also provide a characterization of those distributions which allow for a perfect reconstruction from the lower-dimensional representation. Finally, we demonstrate how an optimal projection matrix can be consistently estimated and show viability in practice with a simulation study and application to a benchmark dataset.
DOI: 10.22008/fk2/iw73uu/abfeg4
title: SWC_O_hour.nc

description: Uploaded with pyDataverse 2024-04-11 00:13
DOI: 10.22008/fk2/iw73uu/ysns5e
title: SWC_O_day.nc

description: Uploaded with pyDataverse 2024-04-11 00:13
DOI: 10.22008/fk2/iw73uu/bmresc
title: SWC_O_month.nc

description: Uploaded with pyDataverse 2024-04-11 00:13
DOI: 10.22008/fk2/iw73uu/pvt5z8
title: TAS_A_day.csv

description: Uploaded with pyDataverse 2024-04-11 00:13
DOI: 10.22008/fk2/iw73uu/cfyjcw
title: TAS_A_hour.csv

description: Uploaded with pyDataverse 2024-04-11 00:14
DOI: 10.22008/fk2/iw73uu/6varlq
title: TAS_A_month.csv

description: Uploaded with pyDataverse 2024-04-11 00:14
DOI: 10.22008/fk2/iw73uu/lkxypn
title: TAS_A_hour.nc

description: Uploaded with pyDataverse 2024-04-11 00:14
DOI: 10.22008/fk2/iw73uu/lvolo4
title: TAS_A_day.nc

description: Uploaded with pyDataverse 2024-04-11 00:14
DOI: 10.22008/fk2/iw73uu/zenpcx
title: TAS_A_month.nc

description: Uploaded with pyDataverse 2024-04-11 00:14
DOI: 10.22008/fk2/iw73uu/uwfjwz
title: TAS_L_day.csv

description: Uploaded with pyDataverse 2024-04-11 00:14
DOI: 10.6084/m9.figshare.31018250
title: Scalable Estimation of Multinomial Response Models with Random Consideration Sets

description: A common assumption in the fitting of unordered multinomial response models for <i>J</i> mutually exclusive categories is that the responses arise from the same set of <i>J</i> categories across subjects. However, when responses measure a choice made by the subject, it is more appropriate to condition the distribution of multinomial responses on a subject-specific consideration set, drawn from the power set of {1,2,…,J}. This leads to a mixture of multinomial response models governed by a probability distribution over the J∗=2J−1 consideration sets. We introduce a novel method for estimating such generalized multinomial response models based on the fundamental result that any mass distribution over J∗ consideration sets can be represented as a mixture of products of <i>J</i> component-specific inclusion–exclusion probabilities. Moreover, under time-invariant consideration sets, the conditional posterior distribution of consideration sets is sparse. These features enable a scalable MCMC algorithm for sampling the posterior distribution of parameters, random effects, and consideration sets. Under regularity conditions, the posterior distributions of the marginal response probabilities and the model parameters satisfy consistency. The methodology is demonstrated in a longitudinal dataset on weekly cereal purchases that cover J=101 brands, a dimension substantially beyond the reach of existing methods. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.31018250.v1
title: Scalable Estimation of Multinomial Response Models with Random Consideration Sets

description: A common assumption in the fitting of unordered multinomial response models for <i>J</i> mutually exclusive categories is that the responses arise from the same set of <i>J</i> categories across subjects. However, when responses measure a choice made by the subject, it is more appropriate to condition the distribution of multinomial responses on a subject-specific consideration set, drawn from the power set of {1,2,…,J}. This leads to a mixture of multinomial response models governed by a probability distribution over the J∗=2J−1 consideration sets. We introduce a novel method for estimating such generalized multinomial response models based on the fundamental result that any mass distribution over J∗ consideration sets can be represented as a mixture of products of <i>J</i> component-specific inclusion–exclusion probabilities. Moreover, under time-invariant consideration sets, the conditional posterior distribution of consideration sets is sparse. These features enable a scalable MCMC algorithm for sampling the posterior distribution of parameters, random effects, and consideration sets. Under regularity conditions, the posterior distributions of the marginal response probabilities and the model parameters satisfy consistency. The methodology is demonstrated in a longitudinal dataset on weekly cereal purchases that cover J=101 brands, a dimension substantially beyond the reach of existing methods. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.22008/fk2/iw73uu/bqtnkm
title: TAS_L_month.csv

description: Uploaded with pyDataverse 2024-04-11 00:15
DOI: 10.22008/fk2/iw73uu/ctufjp
title: TAS_L_hour.csv

description: Uploaded with pyDataverse 2024-04-11 00:15
DOI: 10.22008/fk2/iw73uu/ytwlv6
title: TAS_L_hour.nc

description: Uploaded with pyDataverse 2024-04-11 00:15
DOI: 10.22008/fk2/iw73uu/wbuojb
title: TAS_L_day.nc

description: Uploaded with pyDataverse 2024-04-11 00:15
DOI: 10.5281/zenodo.18673621
title: Dataset: Air Quality Dataset for Beijing (2022-08-01 to 2026-02-17)

description: This dataset contains historical air quality time series for Beijing covering 2022-08-01 to 2026-02-17. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Beijing, compare Beijing with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.22008/fk2/iw73uu/ngosbo
title: TAS_L_month.nc

description: Uploaded with pyDataverse 2024-04-11 00:15
DOI: 10.5281/zenodo.18673620
title: Dataset: Air Quality Dataset for Beijing (2022-08-01 to 2026-02-17)

description: This dataset contains historical air quality time series for Beijing covering 2022-08-01 to 2026-02-17. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Beijing, compare Beijing with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.15468/dl.m43tny
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mollusca",
 "DatasetKey is Biodiversity of the lower part of the Desna River basin",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-25.03991 31.75, 41.65954 31.75, 41.65954 71.53037, -25.03991 71.53037, -25.03991 31.75))"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0215367-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.26zt9y
title: Occurrence Download

description: A dataset containing 165 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Paspalum hirtum Kunth"
 ]
}

 The dataset includes 165 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061780-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.634rxw
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corvus cornix Linnaeus, 1758",
 "Year is greater than 2013",
 "Geometry MULTIPOLYGON (((18.331109 40.371052,18.331636 40.370611,18.331964 40.370218,18.332392 40.370019,18.332451 40.369757,18.333076 40.369084,18.334107 40.36823,18.334266 40.367998,18.334462 40.367689,18.335439 40.367133,18.336315 40.366549,18.337538 40.366046,18.338271 40.365803,18.338582 40.365631,18.339374 40.365127,18.340802 40.364717,18.34205 40.364396,18.342588 40.364132,18.343402 40.363443,18.344169 40.362758,18.345108 40.362278,18.345837 40.361669,18.346826 40.360856,18.348013 40.3601,18.349517 40.359208,18.351064 40.358275,18.351327 40.358107,18.35255 40.357605,18.353011 40.357127,18.353601 40.356896,18.354629 40.356372,18.354851 40.356244,18.354821 40.356027,18.355132 40.355855,18.355438 40.355647,18.355513 40.355494,18.355494 40.35502,18.355601 40.354754,18.356081 40.354422,18.356208 40.354302,18.356305 40.353964,18.356461 40.353365,18.357332 40.352745,18.359512 40.351211,18.360573 40.350575,18.360974 40.350359,18.361165 40.350014,18.36173 40.349601,18.363186 40.348713,18.364098 40.348382,18.364478 40.348021,18.364784 40.347813,18.365109 40.347054,18.365965 40.346325,18.366156 40.345979,18.36639 40.345594,18.369162 40.343499,18.369525 40.343359,18.370063 40.343095,18.370241 40.342312,18.370693 40.341432,18.371171 40.340404,18.371975 40.339642,18.372477 40.339125,18.373358 40.338576,18.373828 40.338172,18.374014 40.33779,18.374295 40.337401,18.374459 40.337204,18.367738 40.335702,18.36645 40.33676,18.365599 40.337526,18.360532 40.341017,18.360184 40.341265,18.359805 40.341296,18.356776 40.340518,18.356046 40.340431,18.350214 40.344277,18.349935 40.344336,18.348182 40.343783,18.346927 40.343373,18.346606 40.343472,18.342458 40.346009,18.342459 40.346016,18.337519 40.349049,18.337217 40.350319,18.336308 40.351016,18.335961 40.35196,18.332449 40.352539,18.331949 40.352726,18.331634 40.353025,18.331119 40.354333,18.330814 40.355933,18.330549 40.356431,18.327182 40.359086,18.326966 40.35925,18.326734 40.361027,18.326493 40.361707,18.325517 40.363984,18.323692 40.365671,18.32347 40.365799,18.321713 40.368323,18.320305 40.370599,18.318833 40.3731,18.318502 40.375545,18.318047 40.376059,18.317591 40.376572,18.319287 40.377057,18.319752 40.37592,18.320392 40.375355,18.322308 40.37432,18.324344 40.373458,18.326975 40.372401,18.328408 40.372028,18.329246 40.37185,18.329572 40.371787,18.33011 40.371523,18.331109 40.371052)))"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024226-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.63hhkf
title: Occurrence Download

description: A dataset containing 47382 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Neogobius melanostomus (Pallas, 1814)"
 ]
}

 The dataset includes 47382 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097854-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fw64cq
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((120.23431 -7.29531,136.76569 -7.29531,136.76569 4.29531,120.23431 4.29531,120.23431 -7.29531))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Timonius amboinicus (Miq.) Boerl."
 ]
}

 The dataset includes 10 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068493-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ezxp9n
title: Occurrence Download

description: A dataset containing 428064 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Year 2015-2015"
 ]
}

 The dataset includes 428064 records from 579 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024296-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fzztcq
title: Occurrence Download

description: A dataset containing 125018 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Anser rossii",
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "Year 2011-2021",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 125018 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061783-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f5frzd
title: Occurrence Download

description: A dataset containing 617 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pterostylis montana Hatch"
 ]
}

 The dataset includes 617 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097883-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.