cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc1mv16q
title: CCDC 1514573: Experimental Crystal Structure Determination

description: Related Article: Subramani Sathiyakumar, Pitchai Selvam, Faruck Lukmanul Hakkim, Krishnan Srinivasan, William T. A. Harrison |2018|J.Coord.Chem.|71|3521|doi:10.1080/00958972.2018.1519553
DOI: 10.5517/ccdc.csd.cc1zbnlk
title: CCDC 1827499: Experimental Crystal Structure Determination

description: Related Article: D. Elias Jesu Packiam, Kanamaluru Vidyasagar|2019|J.Solid State Chem.|270|35|doi:10.1016/j.jssc.2018.10.047
DOI: 10.5517/ccdc.csd.cc1zbnvt
title: CCDC 1827507: Experimental Crystal Structure Determination

description: Related Article: D. Elias Jesu Packiam, Kanamaluru Vidyasagar|2019|J.Solid State Chem.|270|35|doi:10.1016/j.jssc.2018.10.047
DOI: 10.5517/ccdc.csd.cc20cmst
title: CCDC 1858226: Experimental Crystal Structure Determination

description: Related Article: Ute Wild, Olaf Hübner, Lutz Greb, Markus Enders, Elisabeth Kaifer, and Hans-Jörg Himmel|2018|Eur.J.Org.Chem.|2018|5910|doi:10.1002/ejoc.201801378
description: Related Article: Ute Wild, Olaf Hübner, Lutz Greb, Markus Enders, Elisabeth Kaifer, Hans‐Jörg Himmel|2018|Eur.J.Org.Chem.|2018|5886|doi:10.1002/ejoc.201801632
DOI: 10.5517/ccdc.csd.cc20cjrp
title: CCDC 1858132: Experimental Crystal Structure Determination

description: Related Article: Jan T. Gozdalik, Paulina H. Marek, Izabela D. Madura, Błażej Gierczyk, Łukasz Popenda, Grzegorz Schroeder, Agnieszka Adamczyk-Woźniak, Andrzej Sporzyński|2019|J.Mol.Struct.|1180|237|doi:10.1016/j.molstruc.2018.11.090
DOI: 10.5517/ccdc.csd.cc20cmtv
title: CCDC 1858227: Experimental Crystal Structure Determination

description: Related Article: Ute Wild, Olaf Hübner, Lutz Greb, Markus Enders, Elisabeth Kaifer, and Hans-Jörg Himmel|2018|Eur.J.Org.Chem.|2018|5910|doi:10.1002/ejoc.201801378
description: Related Article: Ute Wild, Olaf Hübner, Lutz Greb, Markus Enders, Elisabeth Kaifer, Hans‐Jörg Himmel|2018|Eur.J.Org.Chem.|2018|5886|doi:10.1002/ejoc.201801632
DOI: 10.25592/dgs.corpus-type-49963
title: $NUM-NENNER1^ (MEINE DGS – annotiert. Öffentliches Korpus der Deutschen Gebärdensprache, mehrere Releases)
title: $NUM-DENOMINATOR1^ (MY DGS – annotated. Public Corpus of German Sign Language, several releases)

description: Ein Type aus der Annotation von MEINEDGS mit allen seinen Tokens aus MEINEDGS: The Public DGS Corpus consists of more than 50 hours of video data from the DGS-Korpus project made available together with annotations for research purposes. In this project, data were collected all across Germany in the timeframe 2010-2012. The public corpus shows 330 informants in 4 different age groups (from 18 years on) from 13 different regions. All parts are conversations between two informants in German Sign Language (DGS). The majority of transcripts included cover discussions and reports on Deaf life and personal experiences, although there are examples of other tasks such as story retellings as well.<br>Transcripts are made available in iLex and ELAN format as well as in SRT subtitles format that can be imported in MaxQDA and other analysis tools, together with the mp4 video files in 360p50. Über diese versionsunabhängige DOI ist die jeweils neueste Version des Datensatzes auffindbar.
description: A type from the annotation of MYDGS including all its tokens in MYDGS: Das Öffentliche DGS-Korpus besteht aus mehr als 50 Stunden Video aus dem Projekt DGS-Korpus , die zusammen mit Annotationen für Forschungszwecke zur Verfügung gestellt werden. Die Datenerhebung im Projekt fand im Zeitraum 2010-2012 in ganz Deutschland statt. Das öffentliche Korpus besteht aus Gesprächen jeweils zweier Informantinnen oder Informanten in Deutscher Gebärdensprache. Das Korpus umfasst 330 Informantinnen und Informanten in vier verschiedenen Altersgruppen (ab 18 Jahren) aus 13 verschiedenen Regionen. Der Schwerpunkt der Sammlung liegt auf Transkripten von Diskussionen und Berichten über das Leben Gehörloser sowie persönliche Erfahrungen, sie enthält aber auch Beispiele anderer Erhebungsformate wie Nacherzählungen.<br>Transkripte werden im iLex- und ELAN-Format sowie als SRT-Untertiteldateien (zum Import etwa in MaxQDA) zur Verfügung gestellt. Die zugehörigen MP4-Videodaten sind durchgängig im Format 360p50. This version-dependent DOI allows you to find the latest version of the dataset.
DOI: 10.5517/ccdc.csd.cc20cjsq
title: CCDC 1858133: Experimental Crystal Structure Determination

description: Related Article: Jan T. Gozdalik, Paulina H. Marek, Izabela D. Madura, Błażej Gierczyk, Łukasz Popenda, Grzegorz Schroeder, Agnieszka Adamczyk-Woźniak, Andrzej Sporzyński|2019|J.Mol.Struct.|1180|237|doi:10.1016/j.molstruc.2018.11.090
DOI: 10.5517/ccdc.csd.cc20cjtr
title: CCDC 1858134: Experimental Crystal Structure Determination

description: Related Article: Jan T. Gozdalik, Paulina H. Marek, Izabela D. Madura, Błażej Gierczyk, Łukasz Popenda, Grzegorz Schroeder, Agnieszka Adamczyk-Woźniak, Andrzej Sporzyński|2019|J.Mol.Struct.|1180|237|doi:10.1016/j.molstruc.2018.11.090
DOI: 10.5517/ccdc.csd.cc2166zg
title: CCDC 1882815: Experimental Crystal Structure Determination

description: Related Article: gang liao|2018|CSD Communication|||
description: Related Article: Gang Liao, Hao-Ming Chen, Yu-Nong Xia, Bing Li, Qi-Jun Yao, Bing-Feng Shi|2019|Angew.Chem.,Int.Ed.|58|11464|doi:10.1002/anie.201906700
DOI: 10.5517/ccdc.csd.cc21671k
title: CCDC 1882817: Experimental Crystal Structure Determination

description: Related Article: Gang Liao, Hao-Ming Chen, Yu-Nong Xia, Bing Li, Qi-Jun Yao, Bing-Feng Shi|2019|Angew.Chem.,Int.Ed.|58|11464|doi:10.1002/anie.201906700
description: Related Article: gang liao|2018|CSD Communication|||
DOI: 10.25592/dgs.corpus-type-49975
title: $NUM-MILLION1^ (MEINE DGS – annotiert. Öffentliches Korpus der Deutschen Gebärdensprache, mehrere Releases)
title: $NUM-MILLION1^ (MY DGS – annotated. Public Corpus of German Sign Language, several releases)

description: Ein Type aus der Annotation von MEINEDGS mit allen seinen Tokens aus MEINEDGS: The Public DGS Corpus consists of more than 50 hours of video data from the DGS-Korpus project made available together with annotations for research purposes. In this project, data were collected all across Germany in the timeframe 2010-2012. The public corpus shows 330 informants in 4 different age groups (from 18 years on) from 13 different regions. All parts are conversations between two informants in German Sign Language (DGS). The majority of transcripts included cover discussions and reports on Deaf life and personal experiences, although there are examples of other tasks such as story retellings as well.<br>Transcripts are made available in iLex and ELAN format as well as in SRT subtitles format that can be imported in MaxQDA and other analysis tools, together with the mp4 video files in 360p50. Über diese versionsunabhängige DOI ist die jeweils neueste Version des Datensatzes auffindbar.
description: A type from the annotation of MYDGS including all its tokens in MYDGS: Das Öffentliche DGS-Korpus besteht aus mehr als 50 Stunden Video aus dem Projekt DGS-Korpus , die zusammen mit Annotationen für Forschungszwecke zur Verfügung gestellt werden. Die Datenerhebung im Projekt fand im Zeitraum 2010-2012 in ganz Deutschland statt. Das öffentliche Korpus besteht aus Gesprächen jeweils zweier Informantinnen oder Informanten in Deutscher Gebärdensprache. Das Korpus umfasst 330 Informantinnen und Informanten in vier verschiedenen Altersgruppen (ab 18 Jahren) aus 13 verschiedenen Regionen. Der Schwerpunkt der Sammlung liegt auf Transkripten von Diskussionen und Berichten über das Leben Gehörloser sowie persönliche Erfahrungen, sie enthält aber auch Beispiele anderer Erhebungsformate wie Nacherzählungen.<br>Transkripte werden im iLex- und ELAN-Format sowie als SRT-Untertiteldateien (zum Import etwa in MaxQDA) zur Verfügung gestellt. Die zugehörigen MP4-Videodaten sind durchgängig im Format 360p50. This version-dependent DOI allows you to find the latest version of the dataset.
DOI: 10.23728/b2share.3cc2c884c84845218472d81609e991d1
title: Key Climate Change Communicators' Perceptions of Climate Migration in the UK

description: Between October 17th, 2018, and March 31st, 2019, an online survey was conducted among journalists, NGOs, and policy-makers in the UK. The purpose of the survey was to investigate their perspectives on climate migration and climate justice. The recruitment of participants was conducted through various channels, including e-mail, mailing lists, and Twitter. Aggravated data are provided to ensure transparency and reproducibility by other researchers.
DOI: 10.14469/hpc/13039
title: Endo_TS1_G16_PCM_chlorobenzene_b2plypd3_def2tzvpp_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/13040
title: Endo_TS1_G16_PCM_chlorobenzene_b2plypd3_def2tzvpp_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/13137
title: Exo_TS1_G16_PCM_carbon_tetrachloride_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/13142
title: Exo_TS1_G16_PCM_carbon_tetrachloride_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/13138
title: Exo_TS1_G16_PCM_chlorobenzene_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/13139
title: Exo_TS1_G16_PCM_chloroform_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/13143
title: Exo_TS1_G16_PCM_chlorobenzene_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/13144
title: Exo_TS1_G16_PCM_chloroform_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/13140
title: Exo_TS1_G16_PCM_DCM_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/13145
title: Exo_TS1_G16_PCM_DCM_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/13141
title: Exo_TS1_G16_PCM_dichloroethane_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/13146
title: Exo_TS1_G16_PCM_dichloroethane_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.25345/c5dn40600
title: MassIVE MSV000092751 - 20230825_PFBBrPanel_Odenwald_Liverdiseasestudy
title: Samples were derivatized and analyzed by GC-MS operating in positive mode. See "PFBBrPanel_Methods" file for full assay protocols. Identification of endogenous compounds was confirmed by comparison to authentic standards. Normalized peak areas were calculated by dividing raw peak areas of targeted analytes by averaged raw peak areas of spiked internal standards. Concentrations were determined by plotting against a 10-point calibration curve generated alongside samples from serial dilutions of a known concentration of SCFAs.
DOI: 10.5066/p9uxltg0
title: Capsaicin-treated bait trials for Argentine Black and White Tegu lizards in Georgia and Florida, USA 2020-2021

description: Invasive Argentine Black and White Tegu lizards (Salvator merianae) are trapped with chicken eggs when populations are discovered in the United States. Unfortunately, non-targeted mammals are trapped in tegu lizard traps which reduces trap efficacy. We evaluated if non-target mammal captures could be reduced by coating the chicken eggs with a known irritant to mammals (capsaicin) while maintaining a highly desired bait for tegu lizards. The data set quantifies trap disturbance by non-targets species and efficacy of capsaicin coated eggs as bait in live traps set for Argentine Black and White Tegu lizards (Salvatore merianae). The dataset consists of three live trapping experiments to test if 1) trap disturbance by mammals habituated to eggs without capsaicin from previous trapping efforts decreased when capsaicin-coated eggs were deployed (experiment 1), 2) mammals not habituated to eggs as bait (either treated with capsaicin or not treated with capsaicin) disturbed live traps at the same rate as those habituated to eggs in experiment 1 when presented with either capsiacin-treated or non-treated eggs (experiment 2), and 3) tegu capture rates were different when capsaicin-treated eggs were deployed in Florida (experiment 3).
DOI: 10.34740/kaggle/dsv/6356211
title: skills analysis dataset (internships)
DOI: 10.5281/zenodo.7649108
title: Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020

description: Leaf Area Index (LAI) with an explicit biophysical meaning is a critical variable to characterize terrestrial ecosystems. Long-term global datasets of LAI have served as fundamental data support for monitoring vegetation dynamics and exploring its interactions with other Earth components. However, current LAI products face several limitations associated with spatiotemporal consistency. In this study, we employed the Back Propagation Neural Network (BPNN) and a data consolidation method to generate a new version of the half-month 1/12° Global Inventory Modeling and Mapping Studies (GIMMS) LAI product, i.e., GIMMS LAI4g, for the period 1982−2020. The significance of the GIMMS LAI4g was the use of the latest PKU GIMMS NDVI product and 3.6 million high-quality global Landsat LAI samples to remove the effects of satellite orbital drift and sensor degradation and to develop spatiotemporally consistent BPNN models. The results showed that the GIMMS LAI4g exhibited higher accuracy than its predecessor (GIMMS LAI3g) and two mainstream LAI products (Global LAnd Surface Satellite [GLASS] LAI and Long-term Global Mapping [GLOBMAP] LAI), with an R<sup>2</sup> of 0.95, mean absolute error of 0.18 <em>m<sup>2</sup>/m<sup>2</sup></em>, and mean absolute percentage error of 15% which meet the accuracy target proposed by the Global Climate Observation System. It outperformed other LAI products for most vegetation biomes in a majority area of the land. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presented a better temporal consistency before and after the year 2000 and a more reasonable global vegetation trend. The GIMMS LAI4g product could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and could also benefit the model development in Earth and environmental sciences.
DOI: 10.5281/zenodo.8035760
title: Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020 (V1.1)

description: <strong>Brief Introduction:</strong> The fourth generation GIMMS Leaf Area Index product (GIMMS LAI4g, version 1.1) provides spatiotemporally consistent global LAI data in half-month and 1/12° from 1982 to 2020. It is created to address two major uncertainties presented in current global long-term LAI products, i.e., (1) the effects of NOAA satellite orbital drift and AVHRR sensor degradation and (2) insufficient LAI reference data to build robust LAI model particularly before the late 1990s. The GIMMS LAI4g was generated based on biome-specific BPNN models that employed the latest PKU GIMMS NDVI product and 3.6 million high-quality global Landsat LAI samples. It was then consolidated with the Reprocess MODIS LAI to extend the temporal coverage to 2020 via a pixel-wise Random Forests fusion method. The GIMMS LAI4g exhibits overall high accuracy and low underestimation evaluated by field LAI measurements and Landsat LAI samples. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency before and after the year 2000 and a more reasonable global vegetation trend. It could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and benefit the model development in Earth and environmental sciences. Here we provide two versions of GIMMS LAI4g for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the Reprocess MODIS LAI (1982−2020) <strong>Dataset Characteristics:</strong> Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN Projection: Geographic Spatial Resolution: 1/12 degree Temporal Resolution: Half month Temporal Coverage: January 1982 to December 2020 Image Dimension: Rows-2160; Columns-4320 Units: m<sup>2</sup>/m<sup>2</sup> Fill Value: 65535 Data Type: uint16 Valid Range: 0-7000 Scale Factor: 0.001 File Format: TIFF(.tif) File Size: ~8Mb each file <strong>References:</strong> Cao, S., Li, M., Zhu, Z., Zha, J., Zhao, W., Duanmu, Z., Chen, J., Zheng, Y., and Chen, Y.: Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-68, in review, 2023.
DOI: 10.5281/zenodo.8283791
title: K-means clustering of the DEMIX data set

description: The DEMIX database (Guth, 2023) contains statistics from 6 test 1 arc second DEMs (ALOS, ASTER, CopDEM, FABDEM, NASADEM, and SRTM) compared to high resolution reference DEMs. The database contains 236 DEMIX tiles (Guth and others, 2023) and has 7 tile characteristics for each tile. A K-means clustering of the database, and an additional set of 4 land cover and landform classifications for the 236 DEMIX tiles produced variable numbers of clusters. This data set includes five databases including the clustering results, a figure showing the scatterplots showing the relations among the tile characteristics in the database, and map showing the location of the clusters. References: Guth, P. L., 2023. DEMIX GIS Database Version 2 (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8062008 Guth, Peter L., Peter Strobl, Kevin Gross, & Serge Riazanoff. (2023). DEMIX 10k Tile Data Set (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7504791
DOI: 10.5281/zenodo.8283790
title: K-means clustering of the DEMIX data set

description: The DEMIX database (Guth, 2023) contains statistics from 6 test 1 arc second DEMs (ALOS, ASTER, CopDEM, FABDEM, NASADEM, and SRTM) compared to high resolution reference DEMs. The database contains 236 DEMIX tiles (Guth and others, 2023) and has 7 tile characteristics for each tile. A K-means clustering of the database, and an additional set of 4 land cover and landform classifications for the 236 DEMIX tiles produced variable numbers of clusters. This data set includes five databases including the clustering results, a figure showing the scatterplots showing the relations among the tile characteristics in the database, and map showing the location of the clusters. References: Guth, P. L., 2023. DEMIX GIS Database Version 2 (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8062008 Guth, Peter L., Peter Strobl, Kevin Gross, & Serge Riazanoff. (2023). DEMIX 10k Tile Data Set (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7504791
DOI: 10.15468/dl.f44zgx
title: Occurrence Download

description: A dataset containing 408316 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is University of Michigan Herbarium",
 "Issue is one of (Zero coordinate, Coordinate out of range, Coordinate invalid, Coordinate rounded, Geodetic datum invalid, Geodetic datum assumed WGS84, Coordinate reprojected, Coordinate uncertainty meters invalid, Country coordinate mismatch, Country invalid, Country derived from coordinates, Continent coordinate mismatch, Continent country mismatch, Continent invalid, Continent derived from coordinates, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Recorded date unlikely, Taxon match fuzzy, Taxon match higher rank, Elevation min max swapped, Identified date unlikely, Type status invalid)"
 ]
}

 The dataset includes 408316 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084227-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e3jutq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Anatolichthys transgrediens (Ermin, 1946))",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 100",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084262-240626123714530/datasets/export for details.
DOI: 10.15468/dl.mng7ms
title: Occurrence Download

description: A dataset containing 468 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Choristoneura diversana (Hübner, 1814-1817)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 468 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084264-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jkjddg
title: Occurrence Download

description: A dataset containing 160 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Etlingera alba (Blume) A.D.Poulsen"
 ]
}

 The dataset includes 160 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084265-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jtm7v9
title: Occurrence Download

description: A dataset containing 29824 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Leptinotarsa decemlineata (Say, 1824)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 29824 records from 222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084263-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jnsrg4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Aproteles bulmerae Menzies, 1977)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 100",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084266-240626123714530/datasets/export for details.