cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.er95g5
title: Occurrence Download

description: A dataset containing 236 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.30155 -17.2264,-70.0883 -17.2264,-70.0883 -15.36336,-74.30155 -15.36336,-74.30155 -17.2264))",
 "HasGeospatialIssue is false",
 "TaxonKey is Sicalis luteola (Sparrman, 1789)",
 "Year 2021-2025"
 ]
}

 The dataset includes 236 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010180-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.upkdfj
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Anathallis corticicola (Schltr. ex Hoehne) Pridgeon & M.W.Chase"
 ]
}

 The dataset includes 71 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010181-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.68wymh
title: Occurrence Download

description: A dataset containing 11507 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ixodes ricinus Linnaeus, 1758"
 ]
}

 The dataset includes 11507 records from 181 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010179-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w7xbxc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ixodes ricinus Linnaeus, 1758)",
 "Year is greater than or equal to 2010",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "DecimalLatitude is greater than or equal to 35",
 "DecimalLatitude is greater than or equal to 72",
 "DecimalLongitude is greater than or equal to 25",
 "DecimalLongitude is greater than or equal to 65"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010182-250127130748423/datasets/export for details.
DOI: 10.5281/zenodo.14794792
title: Inactivation-mimicking block of the epithelial calcium channel TRPV6
DOI: 10.5281/zenodo.14794793
title: Inactivation-mimicking block of the epithelial calcium channel TRPV6
DOI: 10.15468/dl.qjfezk
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Greece",
 "TaxonKey is Anthriscus cerefolium (L.) Hoffm."
 ]
}

 The dataset includes 30 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010183-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.3660880
title: Barcelona. "laCaixa" 2007. La escultura en los templos indios.

description: Barcelona. "laCaixa" 2007. La escultura en los templos indios.
description: Exhibition installation and condition photographs.
DOI: 10.5281/zenodo.3660881
title: Barcelona. "laCaixa" 2007. La escultura en los templos indios.

description: Barcelona. "laCaixa" 2007. La escultura en los templos indios.
description: Exhibition installation and condition photographs.
DOI: 10.15468/dl.wx8keb
title: Occurrence Download

description: A dataset containing 171746 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is eDNA from water column to characterise fish and invertebrate communities from 30 sites in the Belgian Part of the North Sea -COI"
 ]
}

 The dataset includes 171746 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010148-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cqzwc4
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.68655 -17.2443,-70.24615 -17.2443,-70.24615 -15.24147,-74.68655 -15.24147,-74.68655 -17.2443))",
 "HasGeospatialIssue is false",
 "TaxonKey is Sicalis raimondii Taczanowski, 1874",
 "Year 2021-2025"
 ]
}

 The dataset includes 66 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010184-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zk84eg
title: Occurrence Download

description: A dataset containing 797 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Syringa oblata Lindl."
 ]
}

 The dataset includes 797 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010188-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28057259
title: Model Selection for Multivalued-Treatment Policy Learning in Observational Studies

description: This study investigates the policy learning problem in observational studies, where the treatment variable can be multivalued and the propensity scores are unknown. We approximate the optimal policy in a global policy class with infinite complexity (VC/Natarajan) dimension, using a sequence of sieve policy classes with finite complexity dimension. The optimal policy within each sieve class is estimated by maximizing the empirical welfare, constructed through the doubly robust moment condition and cross-fitting method. To select the suitable sieve space, we maximize the penalized empirical welfare, with the penalty determined by either the Rademacher complexity or a holdout method. We establish oracle inequalities that demonstrate the bias and variance tradeoff achieved by the data-driven policy estimator. We also investigate two specific sieve selections: (a) a monotone single index model and (b) a systematic discretization method, which uses conventional sieve results for smooth functions such as linear sieves and deep neural networks. In the empirical study, we apply our method to examine the policy of assigning individuals to job training of different lengths.
DOI: 10.6084/m9.figshare.28057259.v2
title: Model Selection for Multivalued-Treatment Policy Learning in Observational Studies

description: This study investigates the policy learning problem in observational studies, where the treatment variable can be multivalued and the propensity scores are unknown. We approximate the optimal policy in a global policy class with infinite complexity (VC/Natarajan) dimension, using a sequence of sieve policy classes with finite complexity dimension. The optimal policy within each sieve class is estimated by maximizing the empirical welfare, constructed through the doubly robust moment condition and cross-fitting method. To select the suitable sieve space, we maximize the penalized empirical welfare, with the penalty determined by either the Rademacher complexity or a holdout method. We establish oracle inequalities that demonstrate the bias and variance tradeoff achieved by the data-driven policy estimator. We also investigate two specific sieve selections: (a) a monotone single index model and (b) a systematic discretization method, which uses conventional sieve results for smooth functions such as linear sieves and deep neural networks. In the empirical study, we apply our method to examine the policy of assigning individuals to job training of different lengths.
DOI: 10.6084/m9.figshare.28057259.v1
title: Model Selection for Multivalued-Treatment Policy Learning in Observational Studies ∗

description: This study investigates the policy learning problem in observational studies, where the treatment variable can be multivalued and the propensity scores are unknown. We approximate the optimal policy in a global policy class with infinite complexity (VC/Natarajan) dimension, using a sequence of sieve policy classes with finite complexity dimension. The optimal policy within each sieve class is estimated by maximizing the empirical welfare, constructed through the doubly robust moment condition and cross-fitting method. To select the suitable sieve space, we maximize the penalized empirical welfare, with the penalty determined by either the Rademacher complexity or a holdout method. We establish oracle inequalities that demonstrate the bias and variance trade-off achieved by the data-driven policy estimator. We also investigate two specific sieve selections: (1) a monotone single index model and (2) a systematic discretization method, which uses conventional sieve results for smooth functions such as linear sieves and deep neural networks. In the empirical study, we apply our method to examine the policy of assigning individuals to job training of different lengths.
DOI: 10.15468/dl.6qzxgu
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.19435 -17.49847,-69.49972 -17.49847,-69.49972 -14.79673,-75.19435 -14.79673,-75.19435 -17.49847))",
 "HasGeospatialIssue is false",
 "TaxonKey is Saltator aurantiirostris Vieillot, 1817",
 "Year 2021-2025"
 ]
}

 The dataset includes 68 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010187-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.99vthg
title: Occurrence Download

description: A dataset containing 1299 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Canavalia ensiformis (L.) DC."
 ]
}

 The dataset includes 1299 records from 207 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010186-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.20394/59qcvqp6
title: Adzuna panel dataset series: local authority and travel to work areas 1

description: This Adzuna data product is comprised of two series of panel datasets, one at the Local Authority level and the other at the Travel to Work Area level. Both series cover Great Britain and contain weekly counts of the number of Adzuna vacancies between April 2016 and December 2023. Disaggregated counts of the number of vacancies by pay frequency - hourly, daily, weekly and annual salaries – are also provided, as are breakdowns by sector, advert seniority and occupation. 

Since not all Adzuna vacancies can be assigned to a local authority or travel to work area, a supplementary dataset on panel sample sizes has been provided. This dataset can assist researchers in understanding the trade-off they face when choosing between panels with more granular breakdowns (with smaller vacancy coverages) and more aggregate panels (with larger vacancy coverages).
DOI: 10.15468/dl.pknb3r
title: Occurrence Download

description: A dataset containing 358580 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Canis Linnaeus, 1758"
 ]
}

 The dataset includes 358580 records from 869 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010152-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0175475.10fu
title: SH0175475.10FU

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/sh0175476.10fu
title: SH0175476.10FU

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/sh0175477.10fu
title: SH0175477.10FU

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/sh0175478.10fu
title: SH0175478.10FU

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/sh0175479.10fu
title: SH0175479.10FU

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: 10.25326/746
title: ALADIN/Aeolus - Wind-cloud interactions dataset

description: Orbit files of ALADIN/Aeolus containing : 
- Aeolus cloud mask built using Aeolus Level 1A data at 3 km along orbit track and re-sampled at 480 m vertical resolution (similar to CALIPSO-GOCCP). 
- Aeolus "u_allsky" built using Aeolus Level 2B Rayleigh-clear and Mie-cloudy winds winds re-sampled at the same resolution of the cloud mask (3 km x 480 m) and filtered using the cloud mask. 

Daily files :
3D cloud fraction profiles (2° latitude x 2° longitude x 480 m) 
3D u_allsky (2° latitude x 2° longitude x 480 m) 
2D low-, mid-, and high-level cloud covers (2° latitude x 2° longitude)
DOI: 10.15156/bio/sh0175480.10fu
title: SH0175480.10FU

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/sh0175481.10fu
title: SH0175481.10FU

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/sh0175482.10fu
title: SH0175482.10FU

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/sh0175483.10fu
title: SH0175483.10FU

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/sh0175484.10fu
title: SH0175484.10FU

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.