cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.u8z5y7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"TAXON_KEY","value":"10057266","matchCase":false},{"type":"equals","key":"OCCURRENCE_STATUS","value":"PRESENT","matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"equals","key":"HAS_GEOSPATIAL_ISSUE","value":"false","matchCase":false},{"type":"lessThan","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"in","key":"YEAR","values":["2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","2022","2023","2024"],"matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["LIVING_SPECIMEN","HUMAN OBSERVATION","OBSERVATION","OCCURRENCE","MACHINE_OBSERVATION"],"matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014679-241024112534372/datasets/export for details.
DOI: 10.15468/dl.q4aet3
title: Occurrence Download

description: A dataset containing 574238 species occurrences available in GBIF matching the query:
{
 "and" : [
 "SpeciesKey is one of (Cirsium palustre (L.) Scop.)",
 "BasisOfRecord is one of (Human Observation, Observation, Occurrence evidence)",
 "Year is greater than or equal to 1901",
 "Year is less than or equal to 2019",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 574238 records from 1308 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014657-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15714296
title: Supplementary information for "Synchronous End-Permian Mass Extinction Zenith in Marine and Low-Middle Latitude Land"

description: The supplementary information and source data of the "Synchronous End-Permian Mass Extinction Zenith in Marine and Low-Middle Latitude Land".
DOI: 10.6084/m9.figshare.29377268
title: umbrella review of vitiligo

description: umbrella review of vitiligo
DOI: 10.6084/m9.figshare.29377268.v2
title: umbrella review of vitiligo

description: umbrella review of vitiligo
DOI: 10.6084/m9.figshare.29377268.v1
title: 白癜风伞式评论

description: umbrella review of vitiligo
DOI: 10.15468/dl.xqtpm6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tabellaria flocculosa (Roth) Kützing, 1844",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((18.2339 -32.5266,18.1411 -32.657,18.1007 -32.7021,18.0224 -32.6366,17.8923 -32.6419,17.754 -32.8255,17.7723 -32.9007,20.509 -32.9007,20.509 -28.5786,16.2782 -28.5786,16.4358 -28.7202,16.726 -29.1235,17.1957 -30.3752,17.6655 -31.1743,18.1322 -31.7679,18.2495 -32.2638,18.2339 -32.5266))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062774-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15714415
title: MDMU-Net:3D Multi-dimensional Decoupled Multi-scale U-Net for Pancreatic Cancer Segmentation

description: 胰腺癌作为一种高度致死的恶性肿瘤,对早期诊断和治疗提出了重大挑战。胰腺和肿瘤的准确分割对于手术计划和治疗策略的制定至关重要。然而,由于 CT 图像中的形态可变、边界模糊以及与周围组织的低对比度,传统的手动分割方法效率低下,并且严重依赖专家经验。为了应对这一挑战,本研究提出了一种轻量级的自动 3D 分割算法——MDMU-Net(Multi-Dimensional Decoupled Multi-Scale U-Net)。首先,采用深度可分离卷积来降低模型复杂性。其次,设计了多维解耦多尺度模块作为主编码器模块,通过并行多尺度卷积核沿深度、高度和宽度维度独立提取特征,实现复杂解剖结构的细粒度建模。最后,引入跨维度通道和空间注意力机制,以增强对小肿瘤和模糊边界的识别能力。

本文档是论文的公共代码。
DOI: 10.5281/zenodo.15714416
title: MDMU-Net:3D Multi-dimensional Decoupled Multi-scale U-Net for Pancreatic Cancer Segmentation

description: 胰腺癌作为一种高度致死的恶性肿瘤,对早期诊断和治疗提出了重大挑战。胰腺和肿瘤的准确分割对于手术计划和治疗策略的制定至关重要。然而,由于 CT 图像中的形态可变、边界模糊以及与周围组织的低对比度,传统的手动分割方法效率低下,并且严重依赖专家经验。为了应对这一挑战,本研究提出了一种轻量级的自动 3D 分割算法——MDMU-Net(Multi-Dimensional Decoupled Multi-Scale U-Net)。首先,采用深度可分离卷积来降低模型复杂性。其次,设计了多维解耦多尺度模块作为主编码器模块,通过并行多尺度卷积核沿深度、高度和宽度维度独立提取特征,实现复杂解剖结构的细粒度建模。最后,引入跨维度通道和空间注意力机制,以增强对小肿瘤和模糊边界的识别能力。

本文档是论文的公共代码。
DOI: 10.15468/dl.vmpyqg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tabellaria flocculosa (Roth) Kützing, 1844",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.6372 -28.0032,16.2782 -28.5786,20.509 -28.5786,20.509 -24.0715,15.4933 -24.0715,15.4945 -24.1257,16.0509 -25.6631,15.6372 -28.0032))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062775-250525065834625/datasets/export for details.
DOI: 10.15468/dl.pc5vy5
title: Occurrence Download

description: A dataset listing the 43508 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mammalia"
 ]
}

 The dataset's 43508 records were derived from 15210 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062772-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rrtj34
title: Occurrence Download

description: A dataset containing 56838 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((175.7661 -38.07773,176.88421 -38.07773,176.88421 -37.40405,175.7661 -37.40405,175.7661 -38.07773))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 56838 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043749-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7d3sqd
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Guatemala",
 "Geometry POLYGON((-122.60234 4.06497,-55.39766 4.06497,-55.39766 30.93503,-122.60234 30.93503,-122.60234 4.06497))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Amazona auropalliata (Lesson, 1842)"
 ]
}

 The dataset includes 49 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043750-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g468fe
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Guatemala",
 "Geometry POLYGON((-122.60234 4.06497,-55.39766 4.06497,-55.39766 30.93503,-122.60234 30.93503,-122.60234 4.06497))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Amazona auropalliata (Lesson, 1842)"
 ]
}

 The dataset includes 49 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043751-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/5e1d3212c57f365869468ec6d4a19f69
title: Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Black Butte Meteorological Station (BLBT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

description: The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. 
 
 SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours.
 
 This data package contains high-frequency meteorological measurements from the Black Butte Meteorological Station (BLBT). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetblbt/. These data complement and extend meteorological data recorded by an adjacent station (Met54), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.
DOI: 10.6073/pasta/b324c113df490aafe4731e68dfcb24e1
title: Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Bronco Well Meteorological Station (BRWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

description: The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. 
 
 SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours.
 
 This data package contains high-frequency meteorological measurements from the Bronco Well Meteorological Station (BRWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetbrwl/. These data complement and extend meteorological data recorded by an adjacent station (Met45), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.
DOI: 10.15468/dl.rabf8y
title: Occurrence Download

description: A dataset containing 14252 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Actualización de la base de datos de hongos macroscópicos de Jalisco"
 ]
}

 The dataset includes 14252 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043753-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8ka6fd
title: Occurrence Download

description: A dataset containing 7491 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Universidad de San Carlos de Guatemala - Colección de Entomología"
 ]
}

 The dataset includes 7491 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043754-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/aa7c5b114a3acdbd1bd6737917a5b2d8
title: Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Burris Well Meteorological Station (BUWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

description: The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. 
 
 SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours.
 
 This data package contains high-frequency meteorological measurements from the Burris Well Meteorological Station (BUWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetbuwl/. These data complement and extend meteorological data recorded by an adjacent station (Met50), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.
DOI: 10.15468/dl.h4ge6q
title: Occurrence Download

description: A dataset containing 7491 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Universidad de San Carlos de Guatemala - Colección de Entomología"
 ]
}

 The dataset includes 7491 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043755-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jwu4cz
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Unomia stolonifera (Gohar, 1938)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 30 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043756-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/f89bfbdd97f4be9edb6493a1573608cf
title: Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Contreras Meteorological Station (CONT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

description: The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. 
 
 SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours.
 
 This data package contains high-frequency meteorological measurements from the Burris Well Meteorological Station (BUWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetcont/.
DOI: 10.15468/dl.jvgevm
title: Occurrence Download

description: A dataset containing 4955 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Porphyrio hochstetteri (A.B.Meyer, 1883)"
 ]
}

 The dataset includes 4955 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043757-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m4nyvb
title: Occurrence Download

description: A dataset containing 6711 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lutjanus gibbus (Forsskål, 1775)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6711 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043758-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/cb8cc9b3a79b85c995a50d4f7c4712ba
title: Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Cerro Montoso Meteorological Station (CRMT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

description: The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. 
 
 SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours.
 
 This data package contains high-frequency meteorological measurements from the Cerro Montoso Meteorological Station (CRMT). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetcrmt/. These data complement and extend meteorological data recorded by an adjacent station (Met42), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.
DOI: 10.5281/zenodo.13147028
title: Efficacy of Minocycline in Substance Use Disorder: A Systematic Review
DOI: 10.5281/zenodo.13147029
title: Efficacy of Minocycline in Substance Use Disorder: A Systematic Review
DOI: 10.15468/dl.tvfrzt
title: Occurrence Download

description: A dataset containing 1479 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heniochus diphreutes Jordan, 1903",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1479 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043759-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.