cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18175797
title: LLMs_Phishing

description: Este repositório contém os conjuntos de dados utilizados na pesquisa“Cibercrime e Inteligência Artificial: O Papel dos Modelos de Linguagem no Aperfeiçoamento de Ataques de Phishing”.

O dataset é composto por 2.000 e-mails de phishing, sendo:– Human_phishing.csv: 1.000 mensagens autênticas provenientes do Nazario Phishing Corpus e SpamAssassin Public Corpus;– LLM_phishing.csv: 1.000 mensagens sintéticas geradas por Grandes Modelos de Linguagem (ChatGPT e LLaMA-2-13B) em ambiente controlado, com domínios fictícios e anonimização integral.

Os dados possibilitam a reprodução completa das análises estatísticas de persuasão, personalização e realismo textual descritas no artigo científico associado.Licença: Creative Commons Attribution 4.0 International (CC BY 4.0).
description: Passoa para reproduzir: 

1. Baixe os arquivos Human_phishing.csv e LLM_phishing.csv.

2. Importe ambos os conjuntos de dados em um ambiente Python (Python ≥ 3.9 recomendado).

3. Instale as bibliotecas necessárias:   pandas, numpy, scipy, nltk, spacy, textstat, matplotlib e seaborn.

4. Realize o pré-processamento dos textos, normalizando a capitalização, removendo registros nulos e verificando a codificação UTF-8.

5. Extraia as características de persuasão por meio de correspondência léxico-semântica baseada nos princípios de influência de Cialdini (reciprocidade, autoridade, escassez, prova social, compromisso e consistência, afeição e unidade), utilizando expressões regulares e dicionários de palavras-chave.

6. Extraia as características de personalização utilizando Reconhecimento de Entidades Nomeadas (NER) e correspondência por padrões para identificar nomes próprios, instituições e referências contextuais. Atribua escores ordinais de personalização (0–3).

7. Calcule as métricas de realismo textual: Índice de Legibilidade de Flesch, proporção de caracteres em caixa alta, proporção de pontuações repetidas e comprimento médio das frases. Normalize os valores em escala de 1–5 e calcule um índice composto de realismo textual.

8. Execute a comparação estatística entre os conjuntos Humano e LLM utilizando o teste de Mann–Whitney U e calcule o tamanho de efeito Cliff’s δ.

9. Gere boxplots e histogramas comparativos para visualizar as distribuições de persuasão, personalização e realismo textual.

10. Valide a reprodutibilidade repetindo as análises com os mesmos parâmetros de pré-processamento e extração de características.
DOI: 10.5281/zenodo.18175798
title: LLMs_Phishing

description: Este repositório contém os conjuntos de dados utilizados na pesquisa“Cibercrime e Inteligência Artificial: O Papel dos Modelos de Linguagem no Aperfeiçoamento de Ataques de Phishing”.

O dataset é composto por 2.000 e-mails de phishing, sendo:– Human_phishing.csv: 1.000 mensagens autênticas provenientes do Nazario Phishing Corpus e SpamAssassin Public Corpus;– LLM_phishing.csv: 1.000 mensagens sintéticas geradas por Grandes Modelos de Linguagem (ChatGPT e LLaMA-2-13B) em ambiente controlado, com domínios fictícios e anonimização integral.

Os dados possibilitam a reprodução completa das análises estatísticas de persuasão, personalização e realismo textual descritas no artigo científico associado.Licença: Creative Commons Attribution 4.0 International (CC BY 4.0).
description: Passoa para reproduzir: 

1. Baixe os arquivos Human_phishing.csv e LLM_phishing.csv.

2. Importe ambos os conjuntos de dados em um ambiente Python (Python ≥ 3.9 recomendado).

3. Instale as bibliotecas necessárias:   pandas, numpy, scipy, nltk, spacy, textstat, matplotlib e seaborn.

4. Realize o pré-processamento dos textos, normalizando a capitalização, removendo registros nulos e verificando a codificação UTF-8.

5. Extraia as características de persuasão por meio de correspondência léxico-semântica baseada nos princípios de influência de Cialdini (reciprocidade, autoridade, escassez, prova social, compromisso e consistência, afeição e unidade), utilizando expressões regulares e dicionários de palavras-chave.

6. Extraia as características de personalização utilizando Reconhecimento de Entidades Nomeadas (NER) e correspondência por padrões para identificar nomes próprios, instituições e referências contextuais. Atribua escores ordinais de personalização (0–3).

7. Calcule as métricas de realismo textual: Índice de Legibilidade de Flesch, proporção de caracteres em caixa alta, proporção de pontuações repetidas e comprimento médio das frases. Normalize os valores em escala de 1–5 e calcule um índice composto de realismo textual.

8. Execute a comparação estatística entre os conjuntos Humano e LLM utilizando o teste de Mann–Whitney U e calcule o tamanho de efeito Cliff’s δ.

9. Gere boxplots e histogramas comparativos para visualizar as distribuições de persuasão, personalização e realismo textual.

10. Valide a reprodutibilidade repetindo as análises com os mesmos parâmetros de pré-processamento e extração de características.
DOI: 10.57702/x7ekr09v
title: Yelp Reviews

description: Yelp Reviews is a large dataset of customer reviews.
DOI: 10.57702/mionyki6
title: Energy Consumption Data

description: The dataset is not explicitly described in the paper, but it is mentioned as a real-world dataset for energy consumption from 02/2011-02/2013.
DOI: 10.57702/ciamq2lg
title: HierText

description: The HierText dataset for end-to-end unified scene text detection and layout analysis.
DOI: 10.5519/qd.xr5dp5v7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.57702/yu20qfxp
title: Movie Subtitles

description: The dataset is used to test the proposed methodologies for mining parallel data from comparable corpora.
DOI: 10.57702/6dbrg87a
title: COVID-19 Chest CT Dataset

description: A large-scale dataset of chest CT scans for COVID-19 diagnosis and triage
DOI: 10.57702/kb999l28
title: 2D advection-diffusion

description: Experimental data set of the 2D advection-diffusion process described by ut = −∇ · (−D∇u + (cid:126)v).
DOI: 10.57702/09l1pa8w
title: Multi-View Brain HyperConnectome

description: Graph embedding is a powerful method to represent graph neurological data (e.g., brain connectomes) in a low dimensional space for brain connectivity mapping, prediction and classiﬁcation. However, existing embedding algorithms have two major limitations. First, they primarily focus on preserving one-to-one topological relationships between nodes (i.e., regions of interest (ROIs) in a connectome), but they have mostly ignored many-to-many relationships (i.e., set to set), which can be captured using a hyperconnectome structure. Second, existing graph embedding techniques cannot be easily adapted to multi-view graph data with heterogeneous distributions. In this paper, while cross-pollinating adversarial deep learning with hypergraph theory, we aim to jointly learn deep latent embeddings of subject-speciﬁc multi-view brain graphs to eventually disentangle diﬀerent brain states such as Alzheimer’s disease (AD) versus mild cognitive impairment (MCI). First, we propose a new simple strategy to build a hyperconnectome for each brain view based on nearest neighbour algorithm to preserve the con-nectivities across pairs of ROIs. Second, we design a hyperconnectome autoencoder (HCAE) framework which operates directly on the multi-view hyperconnectomes based on hypergraph convolutional layers to better capture the many-to-many relationships between brain regions (i.e., graph nodes). For each subject, we further regularize the hyper-graph autoencoding by adversarial regularization to align the distribution of the learned hyperconnectome embeddings with the original hyperconnectome distribution. We formalize our hyperconnectome embedding within a geometric deep learning framework to optimize for a given subject, thereby designing an individual-based learning framework.
DOI: 10.57702/uuktzujr
title: PHOENIX-2014T

description: PHOENIX-2014T is a German sign language (DGS) dataset that mainly includes weather forecast content from TV programs. It consists of 7096, 519, and 642 video text pairs in training, validation, and testing sets.
DOI: 10.57702/lj8ecmn1
title: Level 5 Perception Dataset 2020

description: A benchmark for 3D object detection in urban environments.
DOI: 10.57702/maxhos8p
title: Eye-tracking data

description: The dataset used in the paper is a collection of eye-tracking data from various eye-tracking systems, including the EyeLink-1000, Tobii Pro TX300, and a custom-built system.
DOI: 10.25574/43197
title: Chandra X-ray Observatory ObsId 43197
DOI: 10.57702/uckj2llp
title: COIL-DEL

description: The COIL-DEL dataset contains 3900 graphs which are converted from images by the Harris corner detection algorithm and Delaunay triangulation.
DOI: 10.57702/8aclhv4e
title: YahooR3 and Coat datasets

description: The dataset used in the paper is a combination of two datasets: YahooR3 and Coat. The dataset is used to evaluate the performance of the proposed Epsilon Non-Greedy framework.
DOI: 10.57702/31jkbmvj
title: Structural carbon steel dataset

description: The dataset used in this paper for testing the proposed modular machine learning-based elastoplasticity framework.
DOI: 10.57702/m80m3hif
title: Reuters21578

description: The problem of similarity search is to find the most similar items in a large collection to a query item of interest. Fast similarity search is at the core of many information retrieval applications, such as collaborative filtering, content-based retrieval, and caching.
DOI: 10.15468/dl.px73s2
title: Occurrence Download

description: A dataset containing 12123 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Chenopodiastrum hybridum, Anserina stramoniifolia, Atriplex hybrida, Blitum hybridum, Botrys hybridus, Chenopodium angulatum, Chenopodium angulatum, Chenopodium angulosum, Chenopodium hybridum, Chenopodium hybridum aggregatum, Chenopodium hybridum crassiusculum, Chenopodium hybridum glandulosum, Chenopodium hybridum podolicum, Chenopodium hybridum hybridum, Chenopodium hybridum hybridum, Chenopodium hybridum minus, Chenopodium hybridum simplex, Chenopodium hybridum subintegrum, Chenopodium serotinum, Chenopodium stramonifolium, Chenopodium stramoniifolium, Polycnemum hybridum, Polycnemum hybridum, Vulvaria stramoniifolia)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1970",
 "Year is less than or equal to 2024",
 "CoordinateUncertaintyInMeters is greater than or equal to 0",
 "CoordinateUncertaintyInMeters is less than or equal to 4500",
 "BasisOfRecord is one of (Human Observation, Observation, Specimen, Material sample, Living Specimen)"
 ]
}

 The dataset includes 12123 records from 269 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080248-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8s4zp8
title: Occurrence Download

description: A dataset containing 210 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Platax boersii Bleeker, 1853"
 ]
}

 The dataset includes 210 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069791-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vnqbek
title: Occurrence Download

description: A dataset containing 380969 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bromus hordeaceus L.",
 "HasCoordinate is true"
 ]
}

 The dataset includes 380969 records from 1561 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029178-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hge5kn
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Thlaspi martiale Jord."
 ]
}

 The dataset includes 8 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042448-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b5286a
title: Occurrence Download

description: A dataset containing 207 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Brevitrygon imbricata (Bloch & Schneider, 1801)"
 ]
}

 The dataset includes 207 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029147-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xvwtxd
title: Occurrence Download

description: A dataset containing 3136 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lathyrus sativus L.",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3136 records from 148 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069792-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13821045
title: Table 1 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls

description: Table 1. Comparison of adult and neonate index of encephalization (EQ) for 15 cetacean (1 mysticete, 14 odontocete) species. EQs were derived from brain and body masses in Table S1.
FamilySpeciesAdult EQNeonate EQBalaenopteridaeB. physalus0.4952.297DelphinidaeC. commersonii5.1497.234D. delphis3.9627.801G. griseus4.0555.807L. acutus3.8056.632L. obliquidens4.6358.315O. orca2.4258.317S. bredanensis5.6338.065T. truncatus3.9728.328PhyseteridaeP. macrocephalus0.6814.402KogiidaeK. breviceps1.7034.767PontoporiidaeP. blainvillei1.9302.571MonodontidaeD. leucas2.6435.764PhocoenidaeP. phocoena2.8374.406P. dalli2.9093.275
description: Published as part of Ridgway, Sam H., Carlin, Kevin P. & Van Alstyne, Kaitlin R., 2018, Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls, pp. 424 in Marine Mammal Science 34 (2) on page 424, DOI: 10.1111/mms.12464, http://zenodo.org/record/12761743
DOI: 10.5281/zenodo.13821046
title: Table 1 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls

description: Table 1. Comparison of adult and neonate index of encephalization (EQ) for 15 cetacean (1 mysticete, 14 odontocete) species. EQs were derived from brain and body masses in Table S1.
FamilySpeciesAdult EQNeonate EQBalaenopteridaeB. physalus0.4952.297DelphinidaeC. commersonii5.1497.234D. delphis3.9627.801G. griseus4.0555.807L. acutus3.8056.632L. obliquidens4.6358.315O. orca2.4258.317S. bredanensis5.6338.065T. truncatus3.9728.328PhyseteridaeP. macrocephalus0.6814.402KogiidaeK. breviceps1.7034.767PontoporiidaeP. blainvillei1.9302.571MonodontidaeD. leucas2.6435.764PhocoenidaeP. phocoena2.8374.406P. dalli2.9093.275
description: Published as part of Ridgway, Sam H., Carlin, Kevin P. & Van Alstyne, Kaitlin R., 2018, Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls, pp. 424 in Marine Mammal Science 34 (2) on page 424, DOI: 10.1111/mms.12464, http://zenodo.org/record/12761743
DOI: 10.5281/zenodo.13821877
title: Table 2 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls

description: Table 2. Comparison of seven terrestrial cetartiodactyls (and the African elephant) with eight aquatic cetartiodactyls on brain and body mass for neonates and adults. ABoM = adult body mass; ABrM = adult brain mass; NBoM = neonatal body mass; NBrM = neonatal brain mass. All brain and body mass data for the aquatic species come from Table S1.
ABoMABrMNboMNBrMAquaticAboMABrMNboMNBrMTerrestrial speciesCommon name(kg)(g)(kg)(g)speciesCommon name(kg)(g)(kg)(g)D. dorcas phillipsi S. scrofaBlesbok antelope Wild boar60a 149b155a 133b— —— —D. delphis L. acutusCommon dolphin Atlantic white-sided68 156715 1,28511 28430 733T. strepsiceros G. camelopardalis C. bactrianusGreater kudu Giraffe Bactrian camel218a 470c 594d307a 537c 518d— 150c —— 428c —T. truncatus G. griseus G. macrorhynchusdolphin Bottlenose dolphin Risso’s dolphin Short-finned pilot190 301 6541,550 2,132 2,67918 85 —685 796 —B. taurus H. amphibiusCow Hippopotamus598e 1,351f492e 720f25g 40g199g 195gD. leucas G. melaswhale Beluga Long-finned pilot560 1,3692,087 3,49950 —938 —L. africanaAfrican elephant5,000a4,619a—1,724hO. orcawhale Killer whale3,7236,6421713,006
a Herculano-Houzel (2015).
b Minervini et al. (2016).
c Gra¨ıc et al. (2017).
d Xie et al. (2011).
e Ballarin et al. (2016).
f Silva and Downing (1995).
g Sacher and Staffeldt (1974).
h Shoshani et al. (2006).
description: Published as part of Ridgway, Sam H., Carlin, Kevin P. & Van Alstyne, Kaitlin R., 2018, Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls, pp. 430 in Marine Mammal Science 34 (2) on page 430, DOI: 10.1111/mms.12464, http://zenodo.org/record/12761743
DOI: 10.5281/zenodo.13821876
title: Table 2 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls

description: Table 2. Comparison of seven terrestrial cetartiodactyls (and the African elephant) with eight aquatic cetartiodactyls on brain and body mass for neonates and adults. ABoM = adult body mass; ABrM = adult brain mass; NBoM = neonatal body mass; NBrM = neonatal brain mass. All brain and body mass data for the aquatic species come from Table S1.
ABoMABrMNboMNBrMAquaticAboMABrMNboMNBrMTerrestrial speciesCommon name(kg)(g)(kg)(g)speciesCommon name(kg)(g)(kg)(g)D. dorcas phillipsi S. scrofaBlesbok antelope Wild boar60a 149b155a 133b— —— —D. delphis L. acutusCommon dolphin Atlantic white-sided68 156715 1,28511 28430 733T. strepsiceros G. camelopardalis C. bactrianusGreater kudu Giraffe Bactrian camel218a 470c 594d307a 537c 518d— 150c —— 428c —T. truncatus G. griseus G. macrorhynchusdolphin Bottlenose dolphin Risso’s dolphin Short-finned pilot190 301 6541,550 2,132 2,67918 85 —685 796 —B. taurus H. amphibiusCow Hippopotamus598e 1,351f492e 720f25g 40g199g 195gD. leucas G. melaswhale Beluga Long-finned pilot560 1,3692,087 3,49950 —938 —L. africanaAfrican elephant5,000a4,619a—1,724hO. orcawhale Killer whale3,7236,6421713,006
a Herculano-Houzel (2015).
b Minervini et al. (2016).
c Gra¨ıc et al. (2017).
d Xie et al. (2011).
e Ballarin et al. (2016).
f Silva and Downing (1995).
g Sacher and Staffeldt (1974).
h Shoshani et al. (2006).
description: Published as part of Ridgway, Sam H., Carlin, Kevin P. & Van Alstyne, Kaitlin R., 2018, Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls, pp. 430 in Marine Mammal Science 34 (2) on page 430, DOI: 10.1111/mms.12464, http://zenodo.org/record/12761743
DOI: 10.25346/s6eecgix/pizykn
title: plane_1.jpg
DOI: 10.25346/s6eecgix/yqup3h
title: plane_0.jpg
DOI: 10.25346/s6eecgix/ql4pbr
title: info.json
DOI: 10.25346/s6eecgix/mcoikv
title: plane_8.jpg
DOI: 10.25346/s6eecgix/zmjlqj
title: plane_7.jpg
DOI: 10.25346/s6eecgix/y0hlnm
title: plane_6.jpg
DOI: 10.25346/s6eecgix/kwfb6o
title: plane_5.jpg
DOI: 10.25346/s6eecgix/yeublw
title: plane_4.jpg
DOI: 10.25346/s6eecgix/0ejjhk
title: plane_3.jpg
DOI: 10.25346/s6eecgix/yk6bfn
title: plane_2.jpg
DOI: 10.25346/s6eecgix/9q3dzu
title: plane_1.jpg
DOI: 10.25346/s6eecgix/tc0uma
title: plane_0.jpg
DOI: 10.25346/s6eecgix/4jyzhz
title: materials.png
DOI: 10.15468/dl.w3sm5b
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nymphargus anomalus (Lynch & Duellman, 1973)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0238978-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dknkjv
title: Occurrence Download

description: A dataset containing 312 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phragmipedium longifolium (Warsz. & Rchb.f.) Rolfe"
 ]
}

 The dataset includes 312 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0237823-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pu3wj8
title: Occurrence Download

description: A dataset containing 35207 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-146.15237 11.59927,-33.60292 11.59927,-33.60292 56.59927,-146.15237 56.59927,-146.15237 11.59927))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Harmonia axyridis (Pallas, 1773)"
 ]
}

 The dataset includes 35207 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0231557-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vwzkiu
title: Occurrence Download

description: A dataset containing 7291 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is United States of America",
 "Country is Canada"
 ]
 },
 "HasCoordinate is true",
 "TaxonKey is Ochotona princeps (Richardson, 1828)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 7291 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012076-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.67sv7x
title: Occurrence Download

description: A dataset containing 111 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bellucia dichotoma Cogn."
 ]
}

 The dataset includes 111 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0214922-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.0pbwe7
title: Occurrence Download

description: A dataset containing 62 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Carangoides ruber (Bloch, 1793)"
 ]
}

 The dataset includes 62 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012077-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.66aew3
title: Occurrence Download

description: A dataset containing 106 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crinodendron brasiliense Reitz & L.B.Sm."
 ]
}

 The dataset includes 106 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009626-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.