cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7648238
title: Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2020.1-2020.4)

description: This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W). This is the LST&E dataset in 2020.01-2020.04 <strong>Dataset Characteristics:</strong> Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E) Temporal Coverage: 2020.01-2020.04 Spatial Resolution: 0.02 ° Temporal Resolution: one hour Projection: GCS_WGS_1984 Data Format: NetCDF <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124 Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40 If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).
DOI: 10.5281/zenodo.7648237
title: Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2020.1-2020.4)

description: This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W). This is the LST&E dataset in 2020.01-2020.04 <strong>Dataset Characteristics:</strong> Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E) Temporal Coverage: 2020.01-2020.04 Spatial Resolution: 0.02 ° Temporal Resolution: one hour Projection: GCS_WGS_1984 Data Format: NetCDF <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124 Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40 If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).
DOI: 10.25582/data.2021-07.2269446/1950513
title: Data for EMSL Project 50686 from July 2021
DOI: 10.6084/m9.figshare.22047245
title: The second part of the dataset HKDD_AMC36 of paper "Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification".

description: <strong>https://github.com/yexijoe/HKDD</strong> <br> <strong>@ARTICLE{10042021,</strong> <strong> author={Zheng, Shilian and Zhou, Xiaoyu and Zhang, Luxin and Qi, Peihan and Qiu, Kunfeng and Zhu, Jiawei and Yang, Xiaoniu},</strong> <strong> journal={IEEE Transactions on Cognitive Communications and Networking}, </strong> <strong> title={Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification}, </strong> <strong> year={2023},</strong> <strong> volume={},</strong> <strong> number={},</strong> <strong> pages={1-1},</strong> <strong> doi={10.1109/TCCN.2023.3243899}}</strong> <br> Here we publish the second part of the dataset HKDD_AMC36 used in the paper "<strong>Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification</strong>". Automatic modulation classification (AMC) can generally be divided into knowledge-based methods and data-driven methods. In this paper, we explore combining the knowledgebased method and data-driven technology to take full advantage of both and propose a hybrid knowledge and data-driven deep learning framework (HKDD) for AMC. To make the handcrafted features more discriminative, various traditional features are adopted, including instantaneous features, statistical features, and spectral features. In the HKDD framework, a feature fusion mechanism is proposed to integrate the features learned from the original signal with those processed by a fully connected network from the handcrafted features. Besides, an attention mechanism is implemented on the fused features to neglect immature features and highlight important features. To evaluate the performance of the proposed method, we construct two modulation classification datasets containing both traditional features and raw IQ data. Simulation results show that our proposed method has significant performance gain in both adequate-sample classification scenario and few-shot classification scenario.
DOI: 10.25582/data.2021-07.2275718/1950514
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269135/1950515
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273814/1950516
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267086/1950517
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267233/1950518
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269921/1950519
title: Data for EMSL Project 50686 from July 2021
DOI: 10.5281/zenodo.7654324
title: Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2020.5-2020.8)

description: This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W). This is the LST&E dataset in 2020.05-2020.08 <strong>Dataset Characteristics:</strong> Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E) Temporal Coverage: 2020.05-2020.08 Spatial Resolution: 0.02 ° Temporal Resolution: one hour Projection: GCS_WGS_1984 Data Format: NetCDF <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124 Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40 If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).
DOI: 10.5281/zenodo.7654323
title: Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2020.5-2020.8)

description: This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W). This is the LST&E dataset in 2020.05-2020.08 <strong>Dataset Characteristics:</strong> Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E) Temporal Coverage: 2020.05-2020.08 Spatial Resolution: 0.02 ° Temporal Resolution: one hour Projection: GCS_WGS_1984 Data Format: NetCDF <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124 Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40 If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).
DOI: 10.25582/data.2021-07.2273248/1950520
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273682/1950521
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273736/1950522
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268997/1950523
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273650/1950524
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275079/1950525
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275699/1950526
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274113/1950527
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275507/1950528
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268873/1950529
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273137/1950530
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267417/1950531
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274390/1950532
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276045/1950533
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270097/1950534
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269894/1950535
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275444/1950536
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276340/1950537
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274737/1950538
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276456/1950539
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268489/1950540
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274580/1950541
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267072/1950542
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275460/1950543
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270697/1950544
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273269/1950545
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275843/1950546
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276054/1950547
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273653/1950548
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270682/1950549
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275247/1950550
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269621/1950551
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270235/1950552
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275962/1950553
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267814/1950554
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273327/1950555
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267013/1950556
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269752/1950557
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270661/1950558
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268354/1950559
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2266971/1950560
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268661/1950561
title: Data for EMSL Project 50686 from July 2021
DOI: 10.6084/m9.figshare.22047170
title: The dataset HKDD_AMC12 of paper "Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification".

description: <strong>https://github.com/yexijoe/HKDD</strong> <br> <strong>@ARTICLE{10042021,</strong> <strong> author={Zheng, Shilian and Zhou, Xiaoyu and Zhang, Luxin and Qi, Peihan and Qiu, Kunfeng and Zhu, Jiawei and Yang, Xiaoniu},</strong> <strong> journal={IEEE Transactions on Cognitive Communications and Networking}, </strong> <strong> title={Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification}, </strong> <strong> year={2023},</strong> <strong> volume={},</strong> <strong> number={},</strong> <strong> pages={1-1},</strong> <strong> doi={10.1109/TCCN.2023.3243899}}</strong> <br> Here we publish the dataset HKDD_AMC12 used in the paper "<strong>Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification</strong>". Automatic modulation classification (AMC) can generally be divided into knowledge-based methods and data-driven methods. In this paper, we explore combining the knowledgebased method and data-driven technology to take full advantage of both and propose a hybrid knowledge and data-driven deep learning framework (HKDD) for AMC. To make the handcrafted features more discriminative, various traditional features are adopted, including instantaneous features, statistical features, and spectral features. In the HKDD framework, a feature fusion mechanism is proposed to integrate the features learned from the original signal with those processed by a fully connected network from the handcrafted features. Besides, an attention mechanism is implemented on the fused features to neglect immature features and highlight important features. To evaluate the performance of the proposed method, we construct two modulation classification datasets containing both traditional features and raw IQ data. Simulation results show that our proposed method has significant performance gain in both adequate-sample classification scenario and few-shot classification scenario.
DOI: 10.6084/m9.figshare.22047170.v2
title: The dataset HKDD_AMC12 of paper "Towards Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification".

description: <strong>https://github.com/yexijoe/HKDD</strong> <br> <strong>@ARTICLE{10042021,</strong> <strong> author={Zheng, Shilian and Zhou, Xiaoyu and Zhang, Luxin and Qi, Peihan and Qiu, Kunfeng and Zhu, Jiawei and Yang, Xiaoniu},</strong> <strong> journal={IEEE Transactions on Cognitive Communications and Networking}, </strong> <strong> title={Towards Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification}, </strong> <strong> year={2023},</strong> <strong> volume={},</strong> <strong> number={},</strong> <strong> pages={1-1},</strong> <strong> doi={10.1109/TCCN.2023.3243899}}</strong> <br> Here we publish the dataset HKDD_AMC12 used in the paper "<strong>Towards Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification</strong>". Automatic modulation classification (AMC) can generally be divided into knowledge-based methods and data-driven methods. In this paper, we explore combining the knowledgebased method and data-driven technology to take full advantage of both and propose a hybrid knowledge and data-driven deep learning framework (HKDD) for AMC. To make the handcrafted features more discriminative, various traditional features are adopted, including instantaneous features, statistical features, and spectral features. In the HKDD framework, a feature fusion mechanism is proposed to integrate the features learned from the original signal with those processed by a fully connected network from the handcrafted features. Besides, an attention mechanism is implemented on the fused features to neglect immature features and highlight important features. To evaluate the performance of the proposed method, we construct two modulation classification datasets containing both traditional features and raw IQ data. Simulation results show that our proposed method has significant performance gain in both adequate-sample classification scenario and few-shot classification scenario.
DOI: 10.25582/data.2024-04.3088622/2555076
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3088624/2555077
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3088636/2555078
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3088637/2555079
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3088638/2555080
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3088639/2555081
title: Data for EMSL Project 51287 from April 2024
DOI: 10.25582/data.2024-04.3088640/2555082
title: Data for EMSL Project 51287 from April 2024
DOI: 10.15468/dl.73napb
title: Occurrence Download

description: A dataset containing 20786 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'MN'
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence."year" &gt;= 2000 AND occurrence."year" &lt;= 2024)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 20786 records were derived from 202 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021247-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w9f6va
title: Occurrence Download

description: A dataset containing 0 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 Cypriopachyiulus henroti Strasser, 1967",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079041-240626123714530/datasets/export for details.
DOI: 10.15468/dl.yrq8mz
title: Occurrence Download

description: A dataset containing 0 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 Apfelbeckiella bulgarica Verhoeff, 1926",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079043-240626123714530/datasets/export for details.
DOI: 10.15468/dl.wj3r3g
title: Occurrence Download

description: A dataset containing 580 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rana pyrenaica Serra-Cobo, 1993",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 580 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079042-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z7ejrd
title: Occurrence Download

description: A dataset containing 0 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 Apfelbeckiella byzantina Verhoeff, 1901",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079045-240626123714530/datasets/export for details.
DOI: 10.15468/dl.st4dg6
title: Occurrence Download

description: A dataset containing 1573 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Triturus pygmaeus (Wolterstorff, 1905)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 1573 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079044-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.