cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.hm27aq
title: Occurrence Download

description: A dataset containing 52 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nematoda",
 "Country is United States of America",
 "Geometry POLYGON ((-149.755981 68.789881, -149.723102 68.52113, -148.989388 68.531424, -149.013425 68.800318, -149.755981 68.789881))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 52 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0114170-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qtyz97
title: Occurrence Download

description: A dataset containing 194 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Euphorbia schimperi C.Presl"
 ]
}

 The dataset includes 194 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0206039-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pzc9rk
title: Occurrence Download

description: A dataset containing 11197 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Antigonon Endl."
 ]
}

 The dataset includes 11197 records from 413 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0114135-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7edy62
title: Occurrence Download

description: A dataset containing 2729 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Israel",
 "OccurrenceStatus is Present",
 "TaxonKey is Francolinus francolinus (Linnaeus, 1766)"
 ]
}

 The dataset includes 2729 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0427772-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.agynua
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calanthe ×varians J.J.Sm."
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0114134-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3asb8f
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Guatemala",
 "DatasetKey is Herbarium - Instituto Nacional de Pesquisas da Amazônia (INPA)"
 ]
}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017919-181003121212138/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.58mfqt
title: Occurrence Download

description: A dataset containing 321 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sibon sanniolus (Cope, 1866)"
 ]
}

 The dataset includes 321 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0283668-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6kvpxe
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calanthe versteegii J.J.Sm."
 ]
}

 The dataset includes 32 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0114139-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gzdcot
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eremophila acrida Chinnock",
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Observation"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 56 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039780-191105090559680/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25949/25608300.v1
title: Multicultural Australian English: The New Voice of Sydney – Supplementary Materials

description: Multicultural Australian English: The New Voice of Sydney (MAE-VoiS) is a project funded under the<i> </i>Australian Research Council Future Fellowship scheme. The aim of the project is to help us understand the speech patterns of young people from complex culturally and linguistically diverse communities across Sydney. Understanding how adolescents from different ethnicities use speech patterns to symbolically express their diverse sociocultural identities offers a window into understanding a rapidly changing Australian society.The MAE-VoiS corpus comprises audio recordings of 186 teenagers from 38 language backgrounds who each engaged in a picture naming task and a conversation with a peer facilitated by a local research assistant. Participants also completed an extensive ethnic orientation questionnaire and their parents completed a demographic/language survey. Speakers were located in five separate areas in Sydney that varied according to the dominant language backgrounds of speakers in the communities (four non-English dominant areas – Bankstown, Cabramatta/Fairfield, Inner West, Parramatta; and one English dominant area – Northern Beaches).The material in this record is a supplement to the corpus. It contains details of the following:a picture response task in which 183 single words and 41 short phrases were elicited through a set of images presented on a computer monitor. These items sampled the following characteristics specifically designed to target a wide range of phonetic features known to vary across individuals and groups: all AusE vowels in a targeted consonantal contexts; lexical stress patterns and word internal phonetic/phonological processes through a set of select polysyllabic words; and potential hiatus/juncture contexts through a set of short phrases;an Ethnic Orientation Questionnaire (modelled on Hoffman & Walker, 2010 and Clothier, 2019) to measure participants' orientation to their ethnicity, connection to their local community, and use of languages other than English;a Demographic survey to determine the child and parent place(s) of birth, gender, and languages spoken (including usage and domains of each language), parents’ age of arrival in Australia (if relevant), parents’ level of education, residence history, siblings and birth order, and whether there was any history of speech, hearing, or language problems/intervention. Clothier, J. (2019). Ethnolectal variability in Australian Englishes. In L. Willoughby & H. Manns (Eds.), <i>Australian English reimagined: Structure, features and developments</i> (pp. 155–172). Routledge.Hoffman, M. F., & Walker, J. A. (2010). Ethnolects and the city: Ethnic Orientation and linguistic variation in Toronto English<i>. Language Variation and Change, 22</i>, 37–67.
DOI: 10.15468/dl.xjn9wy
title: Occurrence Download

description: A dataset containing 719917 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (194647131, 193334694, 147219672, 194639590, 193261765, 187309969, 193223567, 194601136, 164689268, Sphingidae, Sphingidae, Sphingidae, 192810123, Sphingidae, 193063951, 193200305, 194653626, 193250981, Sphingidae, 193046399, 167678047, Sphingidae, 137278871, 194150821, 193329260, Sphingidae, 194592751, Sphingidae, 183235775, Sphingidae, 194623107, Sphingidae, 167609905, 192021536, 162234568, Sphingidae, 194383286, 194670430, 177018959, Sphingidae, 192737911, Sphingidae, Sphingidae, 192921811, 148824131, 168219391, 194621034, Sphingidae, 194635370, Sphingidae, Sphingidae, 193011475, Sphingidae, 194632074, 194634500, Sphingidae, Sphingidae, Sphingidae, Sphingidae, 193161338, 167584834, Sphingidae, 193398331, Sphingidae, 194632217, Sphingidae, Sphingidae, Sphingidae, 157919316, 193075462, 193878322, 193398295, Sphingidae, Sphingidae, 167588889, 194137543, 194654701, Sphingidae, Sphingidae Latreille, 1802, 124368494, Sphingidae Latreille, 1802, Sphingidae Latreille, 1802, Sphingidae Latreille, 1802, 161481118, Sphingidae Latreille, 1802, Sphingidae Latreille, Sphingidae Latreille, 1802, Sphingidae Latreille, 1802, Trachypetidae Schulz, 1911, Cossidae Leach, 1815, Saturniidae, Marcgraviaceae Bercht. & J.Presl, Rubiaceae Juss., Bombyliidae Latreille, 1802, Cocytiidae, Cocytiidae)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1970"
 ]
}

 The dataset includes 719917 records from 991 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0230648-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13745296
title: A combined NMR and Deep Neural Network approach for enhancing the spectral resolution of aromatic side chains in proteins

description: Nuclear magnetic resonance (NMR) spectroscopy has become an important technique in structural biology for characterising the structure, dynamics and interactions of macromolecules. While a plethora of NMR methods are now available to inform on backbone and methyl-bearing side-chains of proteins, a characterisation of aromatic side chains is more challenging and often requires specific labelling or 13C-detection. Here we present a deep neural network (DNN) named FID-Net-2, which transforms NMR spectra recorded on simple uniformly 13C labelled samples to yield high-quality 1H-13C correlation spectra of the aromatic side chains. Key to the success of the DNN is the design of a complementary set of NMR experiments that produce spectra with unique features to aid the DNN produce high-resolution aromatic 1H-13C correlation spectra with accurate intensities. The reconstructed spectra can be used for quantitative purposes as FID-Net-2 predicts uncertainties in the resulting spectra. We have validated the new methodology experimentally on protein samples ranging from 7 to 40 kDa in size. We demonstrate that the method can accurately reconstruct high resolution two-dimensional aromatic 1H-13C correlation maps, high resolution three-dimensional aromatic-methyl NOESY spectra to facilitate aromatic 1H-13C assignments, and that the intensities of peaks from the reconstructed aromatic 1H-13C correlation maps can be used to quantitatively characterise the kinetics of protein folding.  More generally, we believe that this strategy of devising new NMR experiments specifically for analysis using customised DNNs represents a substantial advance that will have a major impact on the study of molecules using NMR in the years to come.
DOI: 10.5281/zenodo.13745297
title: A combined NMR and Deep Neural Network approach for enhancing the spectral resolution of aromatic side chains in proteins

description: Nuclear magnetic resonance (NMR) spectroscopy has become an important technique in structural biology for characterising the structure, dynamics and interactions of macromolecules. While a plethora of NMR methods are now available to inform on backbone and methyl-bearing side-chains of proteins, a characterisation of aromatic side chains is more challenging and often requires specific labelling or 13C-detection. Here we present a deep neural network (DNN) named FID-Net-2, which transforms NMR spectra recorded on simple uniformly 13C labelled samples to yield high-quality 1H-13C correlation spectra of the aromatic side chains. Key to the success of the DNN is the design of a complementary set of NMR experiments that produce spectra with unique features to aid the DNN produce high-resolution aromatic 1H-13C correlation spectra with accurate intensities. The reconstructed spectra can be used for quantitative purposes as FID-Net-2 predicts uncertainties in the resulting spectra. We have validated the new methodology experimentally on protein samples ranging from 7 to 40 kDa in size. We demonstrate that the method can accurately reconstruct high resolution two-dimensional aromatic 1H-13C correlation maps, high resolution three-dimensional aromatic-methyl NOESY spectra to facilitate aromatic 1H-13C assignments, and that the intensities of peaks from the reconstructed aromatic 1H-13C correlation maps can be used to quantitatively characterise the kinetics of protein folding.  More generally, we believe that this strategy of devising new NMR experiments specifically for analysis using customised DNNs represents a substantial advance that will have a major impact on the study of molecules using NMR in the years to come.
DOI: 10.15468/dl.4aspeh
title: Occurrence Download

description: A dataset containing 1746 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myrcia freyreissiana (O.Berg) Kiaersk."
 ]
}

 The dataset includes 1746 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045525-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00010547
title: AMR India

description: Antimicrobial resistance is a multiscale global threat to human health and healthcare, and urgent attention and countermeasures are required to reduce its impact on global health. The need for surveillance programs in LMIC countries like India, where the burden of AMR is immense, and with the scale of the issue unclear, analyzing these datasets collected by Venus Remedies Limited could provide invaluable insights into the breadths and depths of the problem at the country level. This statistical analysis could help inform public health practice and strengthen health systems by observing antibiotic resistance trends in pathogens over time.
DOI: 10.15468/dl.becr87
title: Occurrence Download

description: A dataset containing 13701 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Celtis australis L."
 ]
}

 The dataset includes 13701 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045526-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sjyebr
title: Occurrence Download

description: A dataset containing 7218 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is Ammotragus lervia (Pallas, 1777)"
 ]
}

 The dataset includes 7218 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045527-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f2nd9a
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "Geometry POLYGON((104.80606 1.22589,109.34113 1.22589,109.34113 5.03811,104.80606 5.03811,104.80606 1.22589))",
 "OccurrenceStatus is Present",
 "TaxonKey is Chelonia mydas (Linnaeus, 1758)"
 ]
}

 The dataset includes 55 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045528-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qh98hs
title: Occurrence Download

description: A dataset containing 88 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Terfezia arenaria (Moris) Trappe"
 ]
}

 The dataset includes 88 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045529-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fvrnwp
title: Occurrence Download

description: A dataset containing 234 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rattus rattus (Linnaeus, 1758)"
 ]
}

 The dataset includes 234 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045530-240906103802322/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.27178287
title: DATA.xlsx

description: Evaluation of the Vaginal Panel Realtime PCR Kit (Vircell, SL) for diagnosing vaginitis: A comparative study with routinely used diagnosticsThe study aims to assess the efficacy and accuracy of a molecular diagnostic test in comparison to routinely used diagnostic methods for detecting three common vaginal infections: bacterial vaginosis (BV), vulvovaginal candidiasis (VVC), and trichomoniasis.
DOI: 10.15468/dl.b4u7gs
title: Occurrence Download

description: A dataset containing 25132 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((28.79036 35.65336,33.58246 35.65336,33.58246 37.51883,28.79036 37.51883,28.79036 35.65336))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 25132 records from 123 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045533-240906103802322/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.25197437
title: Supplementary Material for the paper "<b>Machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe.</b>" by Estrada-Peña and de la Fuente (2024).

description: The files consist in:a compete GIS dataset (GeoPackage format) of hexagonal grids over Europe, with data on climate, landscape, vegetation features, expected distribution of four species of ticks and 162 species of vertebrates.A metadata for the GeoPackage, explaining the contents of each columnMaps of 11 species of vertebrates, as a proof of concept of the predictive abilities of Machine Learning Algorithms using the descriptive variables of the dataset.Scripts written in Orange data mining software (https://orangedatamining.com) to reproduce the demonstrative parts of the modeling.TO NOTE: THIS MATERIAL IS PROTECTED BY PUBLICATION COPYRIGHT. IT CAN BE USED AS FAR AS IT IS CITED.
DOI: 10.6084/m9.figshare.27178305
title: Investigating the contributors to hit-and-run crashes using gradient boosting decision trees

description: This paper uses the 2021 traffic crash data from the NHTSA CRSS as a sample for model training and validation. The CRSS data collects crash report data provided by police departments from all 50 states in the United States. It details various factors of each traffic crash, including crash information, driver information, vehicle information, road information, and environmental information.The crash accident data provided by CRSS include crash-related details such as the location, time, cause, type of crash, driver’s age, gender, attention level, injury status, risky driving behavior, vehicle type, usage, damage, and hit-and-run situations. However, due to the separate recording of the dataset and the presence of systematic errors and redundant information, the CRSS 2021 data undergo the following merging and filtering processes:1) Match and merge separately recorded data based on the unique case number "CASENUM" in the dataset.2) Records with missing values in critical variables (e.g., whether the crash involved a hit-and-run) were removed to avoid bias in the analysis. For non-critical variables, missing values were imputed using the mean or mode depending on the variable type. For continuous variables, such as speed limits, we used mean imputation. For categorical variables (e.g., weather, road surface conditions), mode imputation was applied.3) Noise in the dataset arises from both human error in crash reporting and random fluctuations in recorded variables. We used z-scores to detect and remove extreme outliers in numerical variables (e.g., speed limits, crash angle). Data points with a z-score beyond ±3 standard deviations were considered outliers and were excluded from the analysis. To handle noisy fluctuations in continuous variables (e.g., speed limits), we applied a symmetrical exponential moving average (EMA) filter.After processing, the CRSS 2021 data include a total of 54,187 crash accidents, among which there are 5,944 hit-and-run accidents, accounting for 10.97% of crash accidents. The hit-and-run and non-hit-and-run categories face a serious class imbalance issue, and data balancing processing is applied to the target variable during parameter calibration. Hit-and-run crashes constitute a relatively small proportion of total crashes in the dataset, leading to class imbalance in the binary classification target. To address this issue, we utilized the resampling techniques available in the data mining software. Specifically, random undersampling was applied to the majority class (non-hit-and-run crashes), while Synthetic Minority Over-sampling Technique (SMOTE) was used for the minority class. This ensured balanced class distribution in the training set, improving model performance and preventing the classifier from being biased toward the majority class.