cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.bc3aud
title: Occurrence Download

description: A dataset containing 46684 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Nereididae, Sabellidae, Syllidae, Serpulidae, Capitellidae, Spionidae, Cirratulidae, Balanidae, Caprellidae, Carcinidae, Cheluridae, Cymothoidae, Varunidae, Euphausiidae, Ischyroceridae, Corophiidae, Limnoriidae, Aoridae, Talitridae, Palaemonidae, Sphaeromatidae, Paranthuridae, Hyalidae, Penaeidae, Mastigiidae, Pseudodiaptomidae, Panopeidae, Mysidae, Stenothoidae, Vesiculariidae, Electridae, Bugulidae, Celleporidae, Cryptosulidae, Bitectiporidae, Cribrilinidae, Nolellidae, Schizoporellidae, Sinoflustridae, Candidae, Watersiporidae, Sciaenidae, Ascidiidae, Styelidae, Cionidae, Clavelinidae, Cyprinidae, Moronidae, Didemnidae, Sparidae, Perophoridae, Engraulidae, Gobiidae, Latidae, Mugilidae, Pyuridae, Molgulidae, Muraenidae, Cichlidae, Poeciliidae, Polyclinidae, Blenniidae, Scorpaenidae, Soleidae, Uranoscopidae, Bougainvilliidae, Blackfordiidae, Cassiopeidae, Cladonematidae, Hydractiniidae, Cordylophoridae, Diadumenidae, Tubulariidae, Aiptasiidae, Campanulariidae, Pennariidae, Dendrophylliidae, Bolinopsidae, Mytilidae, Calyptraeidae, Teredinidae, Dotidae, Eubranchidae, Tethydidae, Ellobiidae, Ostreidae, Tateidae, Muricidae, Veneridae, Polyceridae, Semelidae, Trinchesiidae, Kareniaceae, Peridiniaceae, Amphoriscidae, Bonnemaisoniaceae, Caulacanthaceae, Caulerpaceae, Codiaceae, Dasyaceae, Gracilariaceae, Halymeniaceae, Cystocloniaceae, Solieriaceae, Rhodomelaceae, Sargassaceae, Ulvaceae, Alariaceae, Scombridae, Serranidae)",
 "Geometry POLYGON ((33.925781 30.297018, 30.849609 30.221102, 36.738281 17.978733, 42.539063 11.867351, 46.054688 9.362353000000001, 53.613281 12.297068, 64.6875 21.616579, 66.269531 25.403585, 65.302734 29.993002, 50.976563 32.398516, 33.925781 30.297018))"
 ]
}

 The dataset includes 46684 records from 261 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000918-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18306545
title: Salpichroa Miers. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.5281/zenodo.18306546
title: Salpichroa Miers. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.15468/dl.jzpn58
title: Occurrence Download

description: A dataset containing 940 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-72.84 4.944154, -72.910523 4.938584, -72.97930599999999 4.92201, -73.044653 4.894843, -73.104952 4.857751, -73.158717 4.81165, -73.204623 4.757677, -73.241541 4.697162, -73.268564 4.631597, -73.28502899999999 4.562596, -73.290532 4.491861, -73.284943 4.421132, -73.268401 4.352151, -73.241317 4.286616, -73.204359 4.226139, -73.158439 4.172207, -73.104688 4.126149, -73.04442899999999 4.089095, -72.97914299999999 4.061958, -72.910437 4.045404, -72.84 4.03984, -72.76956300000001 4.045404, -72.700857 4.061958, -72.635571 4.089095, -72.575312 4.126149, -72.52156100000001 4.172207, -72.475641 4.226139, -72.438683 4.286616, -72.411599 4.352151, -72.39505699999999 4.421132, -72.38946799999999 4.491861, -72.394971 4.562596, -72.41143599999999 4.631597, -72.43845899999999 4.697162, -72.47537699999999 4.757677, -72.521283 4.81165, -72.575048 4.857751, -72.635347 4.894843, -72.700694 4.92201, -72.76947699999999 4.938584, -72.84 4.944154))"},{"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 940 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000919-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18306558
title: Salpichroa origanifolia (Lam.) Thell. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.5281/zenodo.18306559
title: Salpichroa origanifolia (Lam.) Thell. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.5281/zenodo.18306532
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Alvan Wentworth Chapman, http://www.wikidata.org/entity/Q448279. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q448279
DOI: 10.15468/dl.eqv7vt
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hypochaeris glabra, Hypochaeris glabra, Hypochaeris glabra)",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "Year is greater than or equal to 2010",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 50",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 22 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000920-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nf2nfy
title: Occurrence Download

description: A dataset containing 14682 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Buprestidae"
 ]
}

 The dataset includes 14682 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000921-260119153935675/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.30998056
title: CROP: A feature-independent context-aware method for CRISPR-Cas9 frameshift prediction: Preprocessed Δlength datasets and test splits.

description: <br>All datasets and test sets utilized in the study <b>"</b><b>CROP: A feature-independent context-aware method for CRISPR-Cas9 frameshift prediction</b><b>."</b> These data include the 19 primary datasets used for CROP's training and testing, as well as the supplementary datasets referenced throughout the paper.The datasets come from the following studies:<br><br>Predicting the mutations generated by repair of Cas9-induced double-strand breaks (FORECasT)<br>Predictable and precise template-free CRISPR editing of pathogenic variants (inDelphi)Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells (SPROUT)Deep sampling of gRNA in the human genome and deep-learning-informed prediction of gRNA activities (Aldit)X-CRISP: domain-adaptable and interpretable CRISPR repair outcome prediction (X-CRISP preprocessing of inDelphi + FORECasT)<br>The provided datasets are structured with a <b>"</b>sequence<b>"</b> column, representing the target sequence, a <b>"</b>PAM position<b>"</b> column, and several columns representing unnormalized numerical data for each Δlength. Please note that any additional columns were utilized for preliminary testing and should be disregarded during primary analysis.Please observe the following guidelines regarding model selection and training:FORECasT: Do not utilize the original FORECasT dataset and the X-CRISP version of FORECasT simultaneously during training as they don't correlate well enough.SPROUT: We advise against using both versions of SPROUT. The standard version is recommended over the CROTON version, as the latter is easier to predict due to the removal of mixed events and long insertions prior to Δlength calculation.Furthermore, please be aware that datasets originating from the same study frequently contain a high proportion of shared target sequences. This overlap should be accounted for when partitioning data to prevent information leakage between training and evaluation sets.We provide two distinct test splits used to evaluate CROP's performance. The first, detailed in Section 3.1 (“CROP outperforms state-of-the-art models in frameshift prediction”), benchmarks CROP against existing methods. In this configuration, the SPROUT dataset (both variants) is excluded entirely from training to facilitate a direct comparison against all the other methods, which did not train on that dataset (But tested on the CROTON version of SPROUT). The second split supports the final model analysis presented in Figure 4A and the interpretability results. For this split, we trained on all datasets described in this study, with the exception of the CROTON version of SPROUT (while using the normal version of SPROUT), and evaluated the performance across all test sets.<br>
DOI: 10.6084/m9.figshare.30998056.v3
title: CROP: A feature-independent, context-aware method for CRISPR-Cas9 frameshift prediction: Preprocessed Δlength datasets and test splits.

description: <br>All datasets and test sets utilized in the study <b>"</b><b>CROP: A feature-independent, context-conditioned method for CRISPR-Cas9 frameshift prediction</b><b>."</b> These data include the 19 primary datasets used for model training and testing, as well as the supplementary datasets referenced throughout the paper.The original datasets come from the following studies:<br><br>Predicting the mutations generated by repair of Cas9-induced double-strand breaks (FORECasT)<br>Predictable and precise template-free CRISPR editing of pathogenic variants (inDelphi)Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells (SPROUT)Deep sampling of gRNA in the human genome and deep-learning-informed prediction of gRNA activities (Aldit)X-CRISP: domain-adaptable and interpretable CRISPR repair outcome prediction (X-CRISP preprocessing of inDelphi + FORECasT)<br>The provided datasets are structured with a <b>"</b>sequence<b>"</b> column, representing the target sequence, a <b>"</b>PAM position<b>"</b> column, and several columns representing unnormalized numerical data for each Δlength. Please note that any additional columns were utilized for preliminary testing and should be disregarded during primary analysis.Please observe the following guidelines regarding model selection and training:FORECasT: Do not utilize the original FORECasT dataset and the X-CRISP version of FORECasT simultaneously during training as they don't correlate well enough.SPROUT: We advise against using both versions of SPROUT. The standard version is recommended over the CROTON version, as the latter is easier to predict due to the removal of mixed indels and long insertions prior to Δlength calculation.Furthermore, please be aware that datasets originating from the same study frequently contain a high proportion of shared target sequences. This overlap should be accounted for when partitioning data to prevent information leakage between training and evaluation sets.We provide two distinct test splits to evaluate model performance. The first, detailed in Section 3.1 (“CROP outperforms state-of-the-art models in frameshift prediction”), benchmarks CROP against existing models. In this configuration, the SPROUT dataset is excluded entirely from training to facilitate a direct comparison against the CROTON version of SPROUT. The second split supports the “Final Model” analysis presented in Figure 4A and our interpretability results. For this split, we trained on all datasets described in this study, with the exception of the CROTON version of SPROUT (while using the normal version of SPROUT), and evaluated the performance across all test sets.<br>
DOI: 10.6084/m9.figshare.30998056.v4
title: CROP: A feature-independent context-aware method for CRISPR-Cas9 frameshift prediction: Preprocessed Δlength datasets and test splits.

description: <br>All datasets and test sets utilized in the study <b>"</b><b>CROP: A feature-independent context-aware method for CRISPR-Cas9 frameshift prediction</b><b>."</b> These data include the 19 primary datasets used for CROP's training and testing, as well as the supplementary datasets referenced throughout the paper.The datasets come from the following studies:<br><br>Predicting the mutations generated by repair of Cas9-induced double-strand breaks (FORECasT)<br>Predictable and precise template-free CRISPR editing of pathogenic variants (inDelphi)Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells (SPROUT)Deep sampling of gRNA in the human genome and deep-learning-informed prediction of gRNA activities (Aldit)X-CRISP: domain-adaptable and interpretable CRISPR repair outcome prediction (X-CRISP preprocessing of inDelphi + FORECasT)<br>The provided datasets are structured with a <b>"</b>sequence<b>"</b> column, representing the target sequence, a <b>"</b>PAM position<b>"</b> column, and several columns representing unnormalized numerical data for each Δlength. Please note that any additional columns were utilized for preliminary testing and should be disregarded during primary analysis.Please observe the following guidelines regarding model selection and training:FORECasT: Do not utilize the original FORECasT dataset and the X-CRISP version of FORECasT simultaneously during training as they don't correlate well enough.SPROUT: We advise against using both versions of SPROUT. The standard version is recommended over the CROTON version, as the latter is easier to predict due to the removal of mixed events and long insertions prior to Δlength calculation.Furthermore, please be aware that datasets originating from the same study frequently contain a high proportion of shared target sequences. This overlap should be accounted for when partitioning data to prevent information leakage between training and evaluation sets.We provide two distinct test splits used to evaluate CROP's performance. The first, detailed in Section 3.1 (“CROP outperforms state-of-the-art models in frameshift prediction”), benchmarks CROP against existing methods. In this configuration, the SPROUT dataset (both variants) is excluded entirely from training to facilitate a direct comparison against all the other methods, which did not train on that dataset (But tested on the CROTON version of SPROUT). The second split supports the final model analysis presented in Figure 4A and the interpretability results. For this split, we trained on all datasets described in this study, with the exception of the CROTON version of SPROUT (while using the normal version of SPROUT), and evaluated the performance across all test sets.<br>
DOI: 10.15468/dl.mrpy5c
title: Occurrence Download

description: A dataset containing 843 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-72.908 5.016153, -72.978531 5.010583, -73.047321 4.994009, -73.112675 4.966842, -73.17298 4.92975, -73.22675 4.883649, -73.272661 4.829675, -73.309583 4.76916, -73.336608 4.703594, -73.35307299999999 4.634594, -73.358577 4.563859, -73.352986 4.49313, -73.33644200000001 4.424149, -73.309355 4.358614, -73.27239299999999 4.298138, -73.226468 4.244207, -73.172712 4.198149, -73.112447 4.161096, -73.047156 4.133959, -72.978444 4.117405, -72.908 4.111841, -72.83755600000001 4.117405, -72.768844 4.133959, -72.703553 4.161096, -72.643288 4.198149, -72.58953200000001 4.244207, -72.54360699999999 4.298138, -72.50664500000001 4.358614, -72.479558 4.424149, -72.463014 4.49313, -72.45742300000001 4.563859, -72.46292699999999 4.634594, -72.479392 4.703594, -72.506417 4.76916, -72.543339 4.829675, -72.58925000000001 4.883649, -72.64302000000001 4.92975, -72.70332500000001 4.966842, -72.76867900000001 4.994009, -72.837469 5.010583, -72.908 5.016153))"},{"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 843 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000923-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8xtugw
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hypochaeris maculata)",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "Year is greater than or equal to 2010",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 50",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 34 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000924-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18306605
title: Salsola L. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.5281/zenodo.18306606
title: Salsola L. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.5281/zenodo.18306615
title: Salsola kali L. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.5281/zenodo.18306614
title: Salsola kali L. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.34740/kaggle/dsv/14552651
title: Impact_Digital_Yellow_Diamond
DOI: 10.5281/zenodo.18306623
title: Salvia L. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.5281/zenodo.18306622
title: Salvia L. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.48667/2kzs-ep56
title: Report of the Science Community on the Proposed First Sample Depot for the Mars Sample Return Campaign

description: Paper published in Meteoritics & Planetary Science reporting on the Mars 2020/Mars Sample Return (MSR) Sample Depot Science Community Workshop, which was held on September 28 and 30, 2022. The Workshop assessed the Scientifically-Return Worthiness (SRW) value of the full collection of samples acquired by the rover Perseverance at Jezero Crater, and of a proposed subset of samples to be left as a First Depot at a location within Jezero Crater called Three Forks
DOI: 10.5281/zenodo.18306632
title: Salvia cardiophylla Benth. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.5281/zenodo.18306631
title: Salvia cardiophylla Benth. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.15468/dl.kztea3
title: Occurrence Download

description: A dataset containing 909 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-72.827 5.047153, -72.89753399999999 5.041582, -72.966328 5.025009, -73.031685 4.997841, -73.091992 4.960749, -73.145765 4.914648, -73.191678 4.860674, -73.228601 4.800159, -73.255627 4.734593, -73.272093 4.665593, -73.277596 4.594858, -73.27200499999999 4.524129, -73.25546 4.455148, -73.228371 4.389614, -73.191408 4.329137, -73.145481 4.275207, -73.091722 4.229149, -73.03145499999999 4.192096, -72.966161 4.164959, -72.897446 4.148405, -72.827 4.142842, -72.75655399999999 4.148405, -72.687839 4.164959, -72.622545 4.192096, -72.56227800000001 4.229149, -72.50851900000001 4.275207, -72.462592 4.329137, -72.425629 4.389614, -72.39854 4.455148, -72.381995 4.524129, -72.37640399999999 4.594858, -72.381907 4.665593, -72.39837300000001 4.734593, -72.425399 4.800159, -72.462322 4.860674, -72.508235 4.914648, -72.56200800000001 4.960749, -72.622315 4.997841, -72.68767200000001 5.025009, -72.756466 5.041582, -72.827 5.047153))"},{"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 909 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000925-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.chnskk
title: Occurrence Download

description: A dataset containing 1054 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hypochaeris radicata)",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "Year is greater than or equal to 2010",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 50",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 1054 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000927-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.