cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.58145/akf/zpkilp
title: Exploring Associations Between Energetic and Anthropometric Characteristics and Front Crawl Technique in Young Male Swimmers Using IMU Sensors: raw data
title: Antropometryczno-Energetyczne Uwarunkowania Techniki Pływania Młodych Chłopców z wykorzystaniem sensorów IMU - dane badawcze

description: The study explored the relationships between hand movement patterns and overall kinematic indicators of front-crawl sprint technique in boys aged 12–14, while considering the role of the morpho-functional profile. The sample consisted of 41 participants. Data were collected from anthropometric measurements (8 variables), a 20-s arm-cranking test (6 variables), and a 25-m front crawl trial. Swimming motion was characterized using inertial measurement unit sensors (BlueTrident) and Artificial Intelligence (8 variables). Video analysis was used to determine swimming velocity (v), stroke rate (SR), stroke length (SL), and stroke index (SI). Principal Component analysis identified five components: Large Body Size (LBS), Low Fat (LF), Enormous Power (EP), Dynamism (D), and High Angular Velocity (HAV). Pearson correlation analysis revealed strong associations between LBS and EP (r=0.85), as well as between D and EP (r=0.51) and D and LBS (r=0.34). Velocity was related to LBS, EP, D, and LF (r=0.38–0.56). SR showed strong associations with D (r=0.76) and EP (r=0.35), whereas SL was negatively associated with D (r=−0.44). SI was related to LBS, LF, and EP (r=0.38–0.49). Overall kinematic indicators are influenced by hand acceleration and angular velocity, and inertial measurement unit based technique assessment supports evaluation of how effectively young swimmers utilize their anthropometric–energetic potential.
DOI: 10.13140/rg.2.2.13814.87365
title: Supplement to ``Modeling Zero-Inflated Longitudinal Circular Data Using Bayesian Methods: Application to Ophthalmology"
DOI: 10.15468/dl.zjqv5n
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((5.26553 45.92763,5.26969 45.92926,5.27444 45.93196,5.28171 45.93491,5.28656 45.93672,5.28474 45.93834,5.27964 45.93714,5.27358 45.93389,5.26216 45.92944,5.26553 45.92763))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2016-2026"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001982-260120142942310/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.31109203
title: From Data to Decision: An Intelligent Framework for Identifying Ponzi Schemes in Decentralized Finance (DeFi) Platforms

description: The booming development of decentralized finance, while driving financial innovation, has also spawned new forms of fraud such as smart Ponzi schemes. Existing detection methods exhibit significant deficiencies in feature representation, imbalanced sample handling, and early warning capabilities. This paper proposes MM-GAT, a graph attention network detection framework that fuses multimodal features and performs joint modeling from two dimensions: static code semantics and dynamic transaction behavior of smart contracts. The code feature extraction module employs multi-channel convolutional neural networks to capture local patterns in opcode sequences, combined with a Transformer encoder to model long-range dependencies. The transaction feature extraction module constructs an account transaction graph and utilizes graph attention networks for structural encoding. A gated fusion mechanism adaptively adjusts the contribution weights of different modal features according to sample characteristics. The contrastive learning strategy enhances minority class feature representation by maximizing positive sample pair similarity. Experimental results on the Ethereum smart contract dataset show that MM-GAT achieves an F1 score of 94.67% and an AUC value of 97.23%, representing improvements of approximately 11.2 and 8.5 percentage points respectively compared to the best baseline method. Ablation experiments validate the effectiveness of each module, and the model demonstrates robust identification capabilities under extremely imbalanced conditions and early detection scenarios. This research provides a new technical pathway for blockchain financial fraud detection.
DOI: 10.6084/m9.figshare.31109203.v1
title: From Data to Decision: An Intelligent Framework for Identifying Ponzi Schemes in Decentralized Finance (DeFi) Platforms

description: The booming development of decentralized finance, while driving financial innovation, has also spawned new forms of fraud such as smart Ponzi schemes. Existing detection methods exhibit significant deficiencies in feature representation, imbalanced sample handling, and early warning capabilities. This paper proposes MM-GAT, a graph attention network detection framework that fuses multimodal features and performs joint modeling from two dimensions: static code semantics and dynamic transaction behavior of smart contracts. The code feature extraction module employs multi-channel convolutional neural networks to capture local patterns in opcode sequences, combined with a Transformer encoder to model long-range dependencies. The transaction feature extraction module constructs an account transaction graph and utilizes graph attention networks for structural encoding. A gated fusion mechanism adaptively adjusts the contribution weights of different modal features according to sample characteristics. The contrastive learning strategy enhances minority class feature representation by maximizing positive sample pair similarity. Experimental results on the Ethereum smart contract dataset show that MM-GAT achieves an F1 score of 94.67% and an AUC value of 97.23%, representing improvements of approximately 11.2 and 8.5 percentage points respectively compared to the best baseline method. Ablation experiments validate the effectiveness of each module, and the model demonstrates robust identification capabilities under extremely imbalanced conditions and early detection scenarios. This research provides a new technical pathway for blockchain financial fraud detection.
DOI: 10.15468/dl.y5esqy
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((100.613 1.336183, 100.542718 1.330615, 100.474168 1.314049, 100.409037 1.286892, 100.348931 1.249813, 100.29533 1.203726, 100.249552 1.149767, 100.212727 1.089264, 100.185758 1.023706, 100.169311 0.9547099999999999, 100.163789 0.883973, 100.169328 0.813237, 100.18579 0.744244, 100.212771 0.678693, 100.249604 0.618197, 100.295384 0.564246, 100.348983 0.518167, 100.409081 0.481096, 100.4742 0.453945, 100.542735 0.437382, 100.613 0.431816, 100.683265 0.437382, 100.7518 0.453945, 100.816919 0.481096, 100.877017 0.518167, 100.930616 0.564246, 100.976396 0.618197, 101.013229 0.678693, 101.04021 0.744244, 101.056672 0.813237, 101.062211 0.883973, 101.056689 0.9547099999999999, 101.040242 1.023706, 101.013273 1.089264, 100.976448 1.149767, 100.93067 1.203726, 100.877069 1.249813, 100.816963 1.286892, 100.751832 1.314049, 100.683282 1.330615, 100.613 1.336183))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001983-260120142942310/datasets/export for details.
DOI: 10.15468/dl.xa436j
title: Occurrence Download

description: A dataset containing 540 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((118.135 5.655144, 118.064396 5.649573, 117.995535 5.632999, 117.930115 5.60583, 117.86975 5.568736, 117.815928 5.522632, 117.769975 5.468657, 117.733021 5.40814, 117.705975 5.342574, 117.689499 5.273573, 117.683997 5.202839, 117.689599 5.132112, 117.706164 5.063134, 117.733281 4.997602, 117.770281 4.93713, 117.81625 4.883203, 117.870056 4.837149, 117.930375 4.800099, 117.995724 4.772964, 118.064496 4.756412, 118.135 4.750849, 118.205504 4.756412, 118.274276 4.772964, 118.339625 4.800099, 118.399944 4.837149, 118.45375 4.883203, 118.499719 4.93713, 118.536719 4.997602, 118.563836 5.063134, 118.580401 5.132112, 118.586003 5.202839, 118.580501 5.273573, 118.564025 5.342574, 118.536979 5.40814, 118.500025 5.468657, 118.454072 5.522632, 118.40025 5.568736, 118.339885 5.60583, 118.274465 5.632999, 118.205604 5.649573, 118.135 5.655144))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 540 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001984-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17032/shu-0000000315
title: Mean stability and between-session reliability of cycling biomechanics variables in elite pursuit cyclists

description: The purpose of this study was to determine the number of crank revolutions required to obtain stable mean values of sagittal plane biomechanics variables, and the between-session reliability of these variables, whilst cyclists used an aerodynamic position. Eighteen elite cyclists completed a 3-min maximal bout on a cycling ergometer. Lower-limb kinematic and kinetic data were captured using 2D motion capture and force pedals. Raw data were filtered using a 4th order Butterworth low-pass filter (6 hz) and interpolated to 100 points per revolution. The middle 60 revolutions of each trial were extracted and 37 discrete and 15 time-series variables were calculated. Mean stability was assessed in all participants, and between-session reliability was analysed in a subset of 11 participants. Sequential averaging indicated more revolutions to stability than iterative intra-class correlation coefficients. Crank kinetics were more stable than joint kinematics and kinetics. For stable discrete and time-series variables, 30 and 38 revolutions are recommended, respectively. Between-day reliability for all variables was moderate to excellent, and good to excellent for crank kinetics and joint kinematics variables. Hip flexion-extension and ankle dorsiflexion kinetics were least reliable. Researchers and applied practitioners should consider these findings when planning, and interpreting results from, cycling biomechanics interventions.
description: Basic demographic and anthropometric information. Lower-limb kinematic and kinetic data were captured using 2D motion capture and force pedals
DOI: 10.15468/98g4tm
title: 2023_snc_observation_citizensciencemonitoring_17999

description: 2023 Seongnam City Citizen Science monitorin Data (Via NATURING Citizen Science Platform (www.naturing.net)).
Seongnam City has been operating a citizen science monitoring since 2004, the first local government in Korea. This is to collect wide-ranging opinions on local environmental issues and the progress of environmental policies, to identify the current status of natural resources (monitoring), and to prepare improvement measures together with citizens. About 50 citizen scientists are monitoring 11 areas, including local wetlands, birds, fish, plants, mammals, and amphibians and reptiles.
DOI: 10.15468/dl.a2av75
title: Occurrence Download

description: A dataset containing 11347 records in GBIF matching the query:
SELECT
 '1' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= -51.21395264452012
 AND decimalLatitude &lt;= -46.479780028101516
 AND decimalLongitude &gt;= -80.17725619669127
 AND decimalLongitude &lt;= -69.23837900975829
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 11347 records were derived from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001977-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15045569
title: Machine Learning Approaches for the Development of Early Warning Systems: Predictive Mapping of Harmful Algal Blooms using Sentinel-2

description: This work was supported by the CHIST-ERA grant CHIST-ERA23-MultiGIS-06, project PCI2025-163208 funded by MICIU/AEI/10.13039/501100011033 and co-financed by the European Union.
DOI: 10.5281/zenodo.15045568
title: Machine Learning Approaches for the Development of Early Warning Systems: Predictive Mapping of Harmful Algal Blooms using Sentinel-2

description: This work was supported by the CHIST-ERA grant CHIST-ERA23-MultiGIS-06, project PCI2025-163208 funded by MICIU/AEI/10.13039/501100011033 and co-financed by the European Union.
DOI: 10.15468/dl.apd34u
title: Occurrence Download

description: A dataset containing 546 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((118.077 5.657144, 118.006396 5.651573, 117.937534 5.634999, 117.872114 5.607829, 117.811749 5.570736, 117.757927 5.524632, 117.711974 5.470657, 117.67502 5.41014, 117.647973 5.344574, 117.631498 5.275573, 117.625995 5.204839, 117.631597 5.134112, 117.648163 5.065134, 117.67528 4.999602, 117.71228 4.93913, 117.758249 4.885203, 117.812055 4.839149, 117.872375 4.802099, 117.937724 4.774964, 118.006496 4.758412, 118.077 4.752849, 118.147504 4.758412, 118.216276 4.774964, 118.281625 4.802099, 118.341945 4.839149, 118.395751 4.885203, 118.44172 4.93913, 118.47872 4.999602, 118.505837 5.065134, 118.522403 5.134112, 118.528005 5.204839, 118.522502 5.275573, 118.506027 5.344574, 118.47898 5.41014, 118.442026 5.470657, 118.396073 5.524632, 118.342251 5.570736, 118.281886 5.607829, 118.216466 5.634999, 118.147604 5.651573, 118.077 5.657144))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 546 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001986-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7g56xw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((118.403 1.336183, 118.332718 1.330615, 118.264168 1.314049, 118.199037 1.286892, 118.138931 1.249813, 118.08533 1.203726, 118.039552 1.149767, 118.002727 1.089264, 117.975758 1.023706, 117.959311 0.9547099999999999, 117.953789 0.883973, 117.959328 0.813237, 117.97579 0.744244, 118.002771 0.678693, 118.039604 0.618197, 118.085384 0.564246, 118.138983 0.518167, 118.199081 0.481096, 118.2642 0.453945, 118.332735 0.437382, 118.403 0.431816, 118.473265 0.437382, 118.5418 0.453945, 118.606919 0.481096, 118.667017 0.518167, 118.720616 0.564246, 118.766396 0.618197, 118.803229 0.678693, 118.83021 0.744244, 118.846672 0.813237, 118.852211 0.883973, 118.846689 0.9547099999999999, 118.830242 1.023706, 118.803273 1.089264, 118.766448 1.149767, 118.72067 1.203726, 118.667069 1.249813, 118.606963 1.286892, 118.541832 1.314049, 118.473282 1.330615, 118.403 1.336183))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001985-260120142942310/datasets/export for details.
DOI: 10.15468/dl.gynw9e
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((119.014 5.657144, 118.943396 5.651573, 118.874534 5.634999, 118.809114 5.607829, 118.748749 5.570736, 118.694927 5.524632, 118.648974 5.470657, 118.61202 5.41014, 118.584973 5.344574, 118.568498 5.275573, 118.562995 5.204839, 118.568597 5.134112, 118.585163 5.065134, 118.61228 4.999602, 118.64928 4.93913, 118.695249 4.885203, 118.749055 4.839149, 118.809375 4.802099, 118.874724 4.774964, 118.943496 4.758412, 119.014 4.752849, 119.084504 4.758412, 119.153276 4.774964, 119.218625 4.802099, 119.278945 4.839149, 119.332751 4.885203, 119.37872 4.93913, 119.41572 4.999602, 119.442837 5.065134, 119.459403 5.134112, 119.465005 5.204839, 119.459502 5.275573, 119.443027 5.344574, 119.41598 5.41014, 119.379026 5.470657, 119.333073 5.524632, 119.279251 5.570736, 119.218886 5.607829, 119.153466 5.634999, 119.084604 5.651573, 119.014 5.657144))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 36 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001990-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wcyv2d
title: Occurrence Download

description: A dataset containing 4216 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-5000.0",
 "TaxonKey is Carissa spinarum"
 ]
}

 The dataset includes 4216 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001989-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23708/ojjngm
title: Replication data for: Resource partitioning among small pelagic fish remains stable regardless of food availability in a seasonally pulsed upwelling system (2020-2021)

description: This dataset includes biological information, the isotopic (carbon and nitrogen) and fatty acid compositions of three species of small pelagic fish (sardine, anchovy and round herring) caught between November 2020 and June 2021 on the coast of South Africa during surveys conducted by the Department of Environment, Forestry and Fisheries (Cape Town, South Africa) on board of the Africana RV.
DOI: 10.6084/m9.figshare.31109377.v1
title: Adaptive Multi-Objective Mixed-Integer Bayesian Optimization for Collaborative Planning of Offshore Transmission Networks: Supplemental Materials

description: Some details concerning the case study in the paper "Adaptive Multi-Objective Mixed-Integer Bayesian Optimization for Collaborative Planning of Offshore Transmission Networks" are listed in the file.
DOI: 10.6084/m9.figshare.31109377
title: Adaptive Multi-Objective Mixed-Integer Bayesian Optimization for Collaborative Planning of Offshore Transmission Networks: Supplemental Materials

description: Some details concerning the case study in the paper "Adaptive Multi-Objective Mixed-Integer Bayesian Optimization for Collaborative Planning of Offshore Transmission Networks" are listed in the file.
DOI: 10.15468/dl.tua4fk
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Piper longum",
 "Year 1970-2000"
 ]
}

 The dataset includes 47 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001991-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cgeg74
title: Occurrence Download

description: A dataset containing 669 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-5000.0",
 "TaxonKey is Caryota mitis"
 ]
}

 The dataset includes 669 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001993-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.crezmb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((100.697 1.343183, 100.626718 1.337615, 100.558167 1.321049, 100.493037 1.293892, 100.43293 1.256813, 100.379329 1.210726, 100.333552 1.156767, 100.296726 1.096263, 100.269757 1.030706, 100.25331 0.961709, 100.247788 0.890972, 100.253327 0.820237, 100.26979 0.751244, 100.29677 0.685693, 100.333604 0.625197, 100.379384 0.571246, 100.432983 0.5251670000000001, 100.493081 0.488096, 100.558199 0.460945, 100.626735 0.444382, 100.697 0.438816, 100.767265 0.444382, 100.835801 0.460945, 100.900919 0.488096, 100.961017 0.5251670000000001, 101.014616 0.571246, 101.060396 0.625197, 101.09723 0.685693, 101.12421 0.751244, 101.140673 0.820237, 101.146212 0.890972, 101.14069 0.961709, 101.124243 1.030706, 101.097274 1.096263, 101.060448 1.156767, 101.014671 1.210726, 100.96107 1.256813, 100.900963 1.293892, 100.835833 1.321049, 100.767282 1.337615, 100.697 1.343183))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001992-260120142942310/datasets/export for details.