cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15237730
title: Computational Modeling of Low-Abundance Proteins in the Venom Gland Transcriptomes: Bothrops asper and Bothrops jararaca
title: Supplementary Materials

description: Supplementary Material 1: File S1_AG5: sequence, model, and Pfam annotation of Allergen 5 in Bothrops asper and Bothrops jararaca; File S2_Apolipophorin: sequence, model, and Pfam annotation of Apolipophorin and von Willebrand Factor, in Bothrops asper and Bothrops jararaca; File S3_ARSB: sequence, model, and Pfam annotation of Arylsulfatase in Bothrops asper and Bothrops jararaca; File S4_Botrocetin: sequence, model, and Pfam annotation of Botrocetin in Bothrops asper and Bothrops jararaca; File S5_DHODH: sequence, model, and Pfam annotation of Dihydroorotate dehydrogenase in Bothrops asper and Bothrops jararaca; File S6_basparin: sequence, model, and Pfam annotation of Basparin and Bothropasin in Bothrops asper. 

Supplementary Material 2: Model Validation. File S7_Ag5: ERRAT, RAMACHANDRAN, and Verify3D of Allergen 5 in Bothrops asper and Bothrops jararaca; File S8_Apolipophorin: ERRAT, RAMACHANDRAN, and Verify3D of Apolipophorin and von Willebrand Factor in Bothrops asper and Bothrops jararaca; File S9_ARSB: ERRAT, RAMACHANDRAN, and Verify3D of Arylsulfatase in Bothrops asper and Bothrops jararaca; File S10_Botrocetin: ERRAT, RAMACHANDRAN, and Verify3D of Botrocetin in Bothrops asper and Bothrops jararaca; File S11_DHODH: ERRAT, RAMACHANDRAN, and Verify3D of Dihydroorotate dehydrogenase in Bothrops asper and Bothrops jararaca; File S12_Basparin: ERRAT, RAMACHANDRAN, and Verify3D of Basparin and Bothropasin in Bothrops asper.

Supplementary Material 3: Abundance analysis. Table S1: Bothrops asper TPM abundance analysis., Table S2: Bothrops jararaca TPM abundance analysis. The TPM_BA (Bothrops asper) and TPM_BJ (Bothrops jararaca) files contain information on the TPM values per contig for the proteins: arylsulfatase, CRISP, DHODH, lysophospholipase D and vWFD.
DOI: 10.5281/zenodo.15243249
title: Computational Modeling of Low-Abundance Proteins in the Venom Gland Transcriptomes: Bothrops asper and Bothrops jararaca
title: Supplementary Materials

description: Supplementary Material 1: File S1_AG5: sequence, model, and Pfam annotation of Allergen 5 in Bothrops asper and Bothrops jararaca; File S2_Apolipophorin: sequence, model, and Pfam annotation of Apolipophorin and von Willebrand Factor, in Bothrops asper and Bothrops jararaca; File S3_ARSB: sequence, model, and Pfam annotation of Arylsulfatase in Bothrops asper and Bothrops jararaca; File S4_Botrocetin: sequence, model, and Pfam annotation of Botrocetin in Bothrops asper and Bothrops jararaca; File S5_DHODH: sequence, model, and Pfam annotation of Dihydroorotate dehydrogenase in Bothrops asper and Bothrops jararaca; File S6_basparin: sequence, model, and Pfam annotation of Basparin and Bothropasin in Bothrops asper. 

Supplementary Material 2: Model Validation. File S7_Ag5: ERRAT, RAMACHANDRAN, and Verify3D of Allergen 5 in Bothrops asper and Bothrops jararaca; File S8_Apolipophorin: ERRAT, RAMACHANDRAN, and Verify3D of Apolipophorin and von Willebrand Factor in Bothrops asper and Bothrops jararaca; File S9_ARSB: ERRAT, RAMACHANDRAN, and Verify3D of Arylsulfatase in Bothrops asper and Bothrops jararaca; File S10_Botrocetin: ERRAT, RAMACHANDRAN, and Verify3D of Botrocetin in Bothrops asper and Bothrops jararaca; File S11_DHODH: ERRAT, RAMACHANDRAN, and Verify3D of Dihydroorotate dehydrogenase in Bothrops asper and Bothrops jararaca; File S12_Basparin: ERRAT, RAMACHANDRAN, and Verify3D of Basparin and Bothropasin in Bothrops asper.

Supplementary Material 3: Abundance analysis. Table S1: Bothrops asper TPM abundance analysis., Table S2: Bothrops jararaca TPM abundance analysis. The TPM_BA (Bothrops asper) and TPM_BJ (Bothrops jararaca) files contain information on the TPM values per contig for the proteins: arylsulfatase, CRISP, DHODH, lysophospholipase D and vWFD.
DOI: 10.3535/yg2-d6f-y8w
title: Cyathaspis alexanderi

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.15468/dl.fkrb64
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Maulisia Parr, 1960",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((42.8012 -16.5547,42.4342 -16.6376,42.2188 -17.038,42.391 -17.4288,42.6657 -17.5533,43.0375 -17.4752,43.2579 -17.0968,43.0959 -16.69,42.8012 -16.5547))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009439-250525065834625/datasets/export for details.
DOI: 10.15472/rym5rx
title: Flora asociada a los hábitos alimenticios de las aves del bosque seco tropical en la finca El Jordán ecolodge, Palermo (Huila)

description: El presente estudio se enmarca en la pasantía "HÁBITOS ALIMENTICIOS DE BOSQUE SECO TROPICAL BASADOS EN INTERACCIONES ECOLÓGICAS AVE-PLANTA EN LA FINCA EL JORDÁN ECOLODGE, PALERMO (HUILA)". Para esta publicación, se relacionan las especies vegetales reportadas en el marco de las observaciones, con 15 ejemplares botánicos, de los cuales reportan 7 especies. Este estudio permite reconocer la flora visitada por las aves presentes en la ecofinca El Jordán Ecolodge, en cada visita mensual (julio-noviembre de 2024).
DOI: 10.15468/dl.azcqpw
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "OccurrenceStatus is Present",
 "TaxonKey is Brachyrhaphis roswithae Meyer & Etzel, 1998"
 ]
}

 The dataset includes 44 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003538-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc110814439
title: Copper Scroll

description: [Text Description:] ❧ list of buried treasure ❧ [Text or Publication Number Note:] 3Q15 Copper Scroll ❧ [Excavation Description:] Found during survey of cliffs at Qumran in March 1952 by Roland de Vaux and William Reed. ❧ [Medium:] Metal ❧ [Script Note:] Jewish ❧ [Physical Object Description:] 23 oxydized copper half-cylinder segments with incised letters
DOI: 10.25549/inscriptifact-ouc110813191
title: Middle Corinthian Body Sherd, BMC P.2962

description: [Text Description:] Archaic ❧ [Text or Publication Number Note:] BMC P.2962 ❧ [Medium:] Clay ❧ [Script Note:] Non-Text ❧ [Physical Object Description:] Clay, Body Sherd ❧ [Physical Object Note:] 11/16 x 9/16 x 1/16 in. (1.7 x 1.4 x 0.2 cm)
DOI: 10.25549/inscriptifact-ouc110813299
title: Apulian Red-Figure Krater (Mixing Bowl), BMC P.3138

description: [Text Description:] Associate of the Lycurgus Painter, Classical ❧ [Text or Publication Number Note:] BMC P.3138 ❧ [Medium:] Clay ❧ [Script Note:] Non-Text ❧ [Physical Object Description:] Clay, Red-Figure Krater (Mixing Bowl) ❧ [Physical Object Note:] 20 1/2 x 13 1/2 x 17 1/2 in. (52 x 34.3 x 44.5 cm) ❧ [Physical Object Note:] Kraters
DOI: 10.25549/inscriptifact-ouc110813359
title: Apulian Red-Figure Krater (Mixing Bowl), BMC P.3138

description: [Text Description:] Associate of the Lycurgus Painter, Classical ❧ [Text or Publication Number Note:] BMC P.3138 ❧ [Medium:] Clay ❧ [Script Note:] Non-Text ❧ [Physical Object Description:] Clay, Red-Figure Krater (Mixing Bowl) ❧ [Physical Object Note:] 20 1/2 x 13 1/2 x 17 1/2 in. (52 x 34.3 x 44.5 cm) ❧ [Physical Object Note:] Kraters
DOI: 10.25549/inscriptifact-ouc110813201
title: Middle Corinthian Body Sherd, BMC P.2962

description: [Text Description:] Archaic ❧ [Text or Publication Number Note:] BMC P.2962 ❧ [Medium:] Clay ❧ [Script Note:] Non-Text ❧ [Physical Object Description:] Clay, Body Sherd ❧ [Physical Object Note:] 11/16 x 9/16 x 1/16 in. (1.7 x 1.4 x 0.2 cm)
DOI: 10.25549/inscriptifact-ouc110813298
title: Apulian Red-Figure Krater (Mixing Bowl), BMC P.3138

description: [Text Description:] Associate of the Lycurgus Painter, Classical ❧ [Text or Publication Number Note:] BMC P.3138 ❧ [Medium:] Clay ❧ [Script Note:] Non-Text ❧ [Physical Object Description:] Clay, Red-Figure Krater (Mixing Bowl) ❧ [Physical Object Note:] 20 1/2 x 13 1/2 x 17 1/2 in. (52 x 34.3 x 44.5 cm) ❧ [Physical Object Note:] Kraters
DOI: 10.25549/inscriptifact-ouc110815740
title: Proto-Sinaitic Inscription 365b

description: [Text Description:] Proto-Sinaitic Inscription ❧ [Text or Publication Number Note:] Sinai 365 ❧ [Excavation Description:] Discovered in 1930 in the tumuli field known as "Camp of the Egyptians" near Serabit el-Khadem ❧ [Medium:] Stone ❧ [Script Note:] Early Alphabetic ❧ [Physical Object Description:] Fragment of a stone plaque, inscribed on both sides, 15 x 12 cm
DOI: 10.25549/inscriptifact-ouc110815705
title: Proto-Sinaitic Inscription 365b

description: [Text Description:] Proto-Sinaitic Inscription ❧ [Text or Publication Number Note:] Sinai 365 ❧ [Excavation Description:] Discovered in 1930 in the tumuli field known as "Camp of the Egyptians" near Serabit el-Khadem ❧ [Medium:] Stone ❧ [Script Note:] Early Alphabetic ❧ [Physical Object Description:] Fragment of a stone plaque, inscribed on both sides, 15 x 12 cm
DOI: 10.15468/dl.2t3kxa
title: Occurrence Download

description: A dataset containing 37960 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Pipistrellus kuhlii (Kuhl, 1817)"
 ]
}

 The dataset includes 37960 records from 274 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012631-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.j00240.00100
title: Code Compilation for AIGC-based Image Detection Algorithm Papers
title: AIGIs图象检测算法论文代码汇总

description: The explosive boom of Artificial Intelligence Generated Content (AIGC) has led to a grave trust crisis, making AI-generated image detection a central issue in multimedia forensics. To tackle the problems of fragmented research perspectives and disjointed theoretical frameworks in existing detection technologies, this paper proposes a unified analytical framework based on "learning paradigms" for a systematic review of relevant techniques. It constructs a dual-paradigm system of "falsification" and "verification", elaborates on their technical principles, reveals the evolution trend from passive trace detection to active distribution modeling, and analyzes their respective limitations and challenges. Additionally, a multi-dimensional evaluation system is established to compare representative algorithms, with related open-source resources and datasets shared on the ScienceDB Community for future research.
description: The explosive boom of Artificial Intelligence Generated Content (AIGC) has led to a grave trust crisis, making AI-generated image detection a central issue in multimedia forensics. To tackle the problems of fragmented research perspectives and disjointed theoretical frameworks in existing detection technologies, this paper proposes a unified analytical framework based on "learning paradigms" for a systematic review of relevant techniques. It constructs a dual-paradigm system of "falsification" and "verification", elaborates on their technical principles, reveals the evolution trend from passive trace detection to active distribution modeling, and analyzes their respective limitations and challenges. Additionally, a multi-dimensional evaluation system is established to compare representative algorithms, with related open-source resources and datasets shared on the ScienceDB Community for future research.
DOI: 10.57760/sciencedb.j00240.00099
title: MSANet
title: 多阶段空间感知道路损伤检测网络

description: This paper proposes a multi-stage spatial perception and detail enhancement road damage detection model. A hybrid attention module for spatial perception and detail enhancement is designed. Through the collaborative mechanism of global direction perception and detail enhancement, long-range spatial dependencies are constructed, and the ability to represent the details of weak textures and blurred edges is significantly improved. A cross-scale feature cross-fusion module is constructed to optimize the network neck architecture to achieve heterogeneous cascaded fusion of cross-scale features, effectively balancing the collaborative expression of local spatial details and global semantic information. In addition, the improved C3K2 module embeds coordinate-aware convolution, effectively optimizing the spatial coupling modeling efficiency of high-dimensional features through spatial information enhancement. System experiments on the RDD2022 benchmark dataset show that the model in this paper effectively identifies various road damages, maintaining a real-time inference speed of 142 FPS while achieving an improvement of 1.9% in mAP@0.5, 4.9% in mAP@0.5:0.95, and 1.8% in F1-Score compared to the existing optimal methods. The mAP@0.5 reaches 87.7%. Ablation experiments verify the contribution of each module. Cross-dataset testing and generalization testing further confirm the excellent detection robustness and engineering applicability of this model.
description: This paper proposes a multi-stage spatial perception and detail enhancement road damage detection model. A hybrid attention module for spatial perception and detail enhancement is designed. Through the collaborative mechanism of global direction perception and detail enhancement, long-range spatial dependencies are constructed, and the ability to represent the details of weak textures and blurred edges is significantly improved. A cross-scale feature cross-fusion module is constructed to optimize the network neck architecture to achieve heterogeneous cascaded fusion of cross-scale features, effectively balancing the collaborative expression of local spatial details and global semantic information. In addition, the improved C3K2 module embeds coordinate-aware convolution, effectively optimizing the spatial coupling modeling efficiency of high-dimensional features through spatial information enhancement. System experiments on the RDD2022 benchmark dataset show that the model in this paper effectively identifies various road damages, maintaining a real-time inference speed of 142 FPS while achieving an improvement of 1.9% in mAP@0.5, 4.9% in mAP@0.5:0.95, and 1.8% in F1-Score compared to the existing optimal methods. The mAP@0.5 reaches 87.7%. Ablation experiments verify the contribution of each module. Cross-dataset testing and generalization testing further confirm the excellent detection robustness and engineering applicability of this model.
DOI: 10.5281/zenodo.18307332
title: Schizachyrium condensatum (Kunth) Nees. 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.18307333
title: Schizachyrium condensatum (Kunth) Nees. 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: 10.57760/sciencedb.j00240.00096
title: Self-Cropped FaceForensics++ Dataset for Face Fraud Detection
title: 基于FaceForensics++的自裁切人脸欺诈检测数据集

description: The facial image data used in this study is based on the FaceForensics++ (FF++) public dataset (Rössler et al., 2019), which contains 1000 real videos and 4000 forged videos, covering five typical deep forgery methods (DeepFakes, Face2Face, FaceSwap, NeuralTextures, FaceShifter). We use the MTCNN (Multi-task Convolutional Neural Network) face detection algorithm to extract frames (with a sampling rate of 2 frames per second) from the FF++ videos and automatically crop the face region images. All cropped face images are uniformly adjusted to 256×256 pixels.
description: The facial image data used in this study is based on the FaceForensics++ (FF++) public dataset (Rössler et al., 2019), which contains 1000 real videos and 4000 forged videos, covering five typical deep forgery methods (DeepFakes, Face2Face, FaceSwap, NeuralTextures, FaceShifter). We use the MTCNN (Multi-task Convolutional Neural Network) face detection algorithm to extract frames (with a sampling rate of 2 frames per second) from the FF++ videos and automatically crop the face region images. All cropped face images are uniformly adjusted to 256×256 pixels.
DOI: 10.15468/dl.bdreag
title: Occurrence Download

description: A dataset containing 1025 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lolium perenne, Lolium perenne)",
 "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 1025 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001150-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qb2xjx
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((105.328 -4.972853, 105.257473 -4.978415, 105.188678 -4.994967, 105.123308 -5.0221, 105.062968 -5.059149, 105.009143 -5.105202, 104.963157 -5.159127, 104.926143 -5.219598, 104.899014 -5.285129, 104.882441 -5.354106, 104.876835 -5.424832, 104.882337 -5.495566, 104.898817 -5.564566, 104.925871 -5.630133, 104.962837 -5.69065, 105.008807 -5.744627, 105.062648 -5.790731, 105.123036 -5.827825, 105.188481 -5.854995, 105.257369 -5.87157, 105.328 -5.877141, 105.398631 -5.87157, 105.467519 -5.854995, 105.532964 -5.827825, 105.593352 -5.790731, 105.647193 -5.744627, 105.693163 -5.69065, 105.730129 -5.630133, 105.757183 -5.564566, 105.773663 -5.495566, 105.779165 -5.424832, 105.773559 -5.354106, 105.756986 -5.285129, 105.729857 -5.219598, 105.692843 -5.159127, 105.646857 -5.105202, 105.593032 -5.059149, 105.532692 -5.0221, 105.467322 -4.994967, 105.398527 -4.978415, 105.328 -4.972853))"},{"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 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001152-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18298568
title: Network-Driven Prioritization of High-Impact Sectors for the Low-Carbon Transportation System

description: This data includes original carbon emission flow edge data between different national sectors in the transportation system (including those without and with self-loops), as well as retained cumulative carbon emission edge data of 70%, 80% (without and with self-loops), and 90%.
DOI: 10.5281/zenodo.18298569
title: Network-Driven Prioritization of High-Impact Sectors for the Low-Carbon Transportation System

description: This data includes original carbon emission flow edge data between different national sectors in the transportation system (including those without and with self-loops), as well as retained cumulative carbon emission edge data of 70%, 80% (without and with self-loops), and 90%.
DOI: 10.5281/zenodo.18307351
title: Schizachyrium hirtiflorum Nees. 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.18307350
title: Schizachyrium hirtiflorum Nees. 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.sas5gu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lolium multiflorum × perenne, Lolium hybridum)",
 "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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001154-260119153935675/datasets/export for details.
DOI: 10.15468/dl.m9bcj3
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((105.157 -4.842851, 105.086486 -4.848414, 105.017705 -4.864965, 104.952348 -4.892099, 104.89202 -4.929149, 104.838206 -4.975202, 104.79223 -5.029128, 104.755225 -5.089601, 104.728103 -5.155132, 104.711534 -5.224109, 104.70593 -5.294836, 104.711433 -5.36557, 104.72791 -5.434571, 104.75496 -5.500137, 104.791919 -5.560654, 104.837878 -5.61463, 104.891708 -5.660734, 104.952083 -5.697828, 105.017513 -5.724997, 105.086385 -5.741572, 105.157 -5.747143, 105.227615 -5.741572, 105.296487 -5.724997, 105.361917 -5.697828, 105.422292 -5.660734, 105.476122 -5.61463, 105.522081 -5.560654, 105.55904 -5.500137, 105.58609 -5.434571, 105.602567 -5.36557, 105.60807 -5.294836, 105.602466 -5.224109, 105.585897 -5.155132, 105.558775 -5.089601, 105.52177 -5.029128, 105.475794 -4.975202, 105.42198 -4.929149, 105.361652 -4.892099, 105.296295 -4.864965, 105.227514 -4.848414, 105.157 -4.842851))"},{"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 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001155-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.