cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.43q2f6
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pyrrhosoma elisabethae Schmidt, 1948)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 100",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 23 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089178-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jjkv5j
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Zoropsis oertzeni Dahl, 1901",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089180-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fvzg6a
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron tianmenshanense C.L.Peng & L.H.Yan"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089179-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gp8f3m
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Xystrosoma tectosagum Ribaut, 1927",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089184-240626123714530/datasets/export for details.
DOI: 10.15468/dl.th2q3s
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Eudrilus eugeniae (Kinberg, 1866)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 12 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089182-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7wjw3b
title: Occurrence Download

description: A dataset containing 734 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Physatocheila dumetorum (Herrich-Schaeffer, 1838)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 734 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089183-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w8yve6
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Latheticus oryzae C.O.Waterhouse, 1880",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 71 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089185-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qn4urv
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Iberochondrostoma oretanum (Doadrio & Carmona, 2003))",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 100",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089186-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vr5k7n
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Zoropsis lutea (Thorell, 1875)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 39 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089187-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.28nda5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Xystrosoma cassagnaui Mauriès, 1965",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089188-240626123714530/datasets/export for details.
DOI: 10.15468/dl.8wa6yf
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Doru luteipes (Scudder, 1876)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 10 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089190-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.33uf96
title: Occurrence Download

description: A dataset containing 517 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Physatocheila smreczynskii China, 1952",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 517 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089189-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.88geqb
title: Occurrence Download

description: A dataset containing 2847 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Tenebrio molitor Linnaeus, 1758",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 2847 records from 162 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0089191-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.6327217
title: Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers"

description: Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers".
description: {"references": ["A. Cumberworth and P. R. ten Wolde, Constriction of actin rings by passive crosslinkers, arXiv:2203.04260 [physics.bio-ph]."]}
DOI: 10.5281/zenodo.6327218
title: Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers"

description: Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers".
DOI: 10.5281/zenodo.7781775
title: Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers"

description: Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers".
description: {"references": ["A. Cumberworth and P. R. ten Wolde, Constriction of actin rings by passive crosslinkers, arXiv:2203.04260 [physics.bio-ph]."]}
DOI: 10.6084/m9.figshare.19829908
title: An evaluation of inter and intra population structure of Uttar Pradesh, inferred from 24 autosomal STRs

description: The present study was designed to explore the STR diversity and genomic history of the inhabitants of the most populous subdivision of the country. A set of 24 hypervariable autosomal STRs was used to estimate the genetic diversity within the studied population. A panel of 15 autosomal STRs, which is most common in the previously reported data sets, was used to estimate the genetic diversity between the studied population, and obtained unique relations were reported here. The genetic diversity and polymorphism among 636 individuals of different ethnic groups, residing in Bareilly, Pilibhit, Shahjahanpur, Gorakhpur, Jhansi, and Varanasi regions of Uttar Pradesh, India, was investigated. This investigation was carried out via 24 autosomal STRs. The 24 loci studied showed the highest value of combined power of discrimination (CPD = 1), combined power of exclusion (CPE = 0.99999999985), combined paternity index (CPI = 6.10 × 10<sup>9</sup>) and lowest combined matching probability (CPM = 7.90 × 10<sup>−31</sup>). The studied population showed genetic closeness with the population of Uttarakhand, the Jats of Delhi,the Jat Sikh (Punjab), and the population of Rajasthan. Among the tested loci, SE33 and Penta E were found to be most useful in terms of the highest discrimination power, lowest matching probability, the highest power of exclusion, and highest polymorphism information content for the Uttar Pradesh population .
DOI: 10.6084/m9.figshare.19829908.v1
title: An evaluation of inter and intra population structure of Uttar Pradesh, inferred from 24 autosomal STRs

description: The present study was designed to explore the STR diversity and genomic history of the inhabitants of the most populous subdivision of the country. A set of 24 hypervariable autosomal STRs was used to estimate the genetic diversity within the studied population. A panel of 15 autosomal STRs, which is most common in the previously reported data sets, was used to estimate the genetic diversity between the studied population, and obtained unique relations were reported here. The genetic diversity and polymorphism among 636 individuals of different ethnic groups, residing in Bareilly, Pilibhit, Shahjahanpur, Gorakhpur, Jhansi, and Varanasi regions of Uttar Pradesh, India, was investigated. This investigation was carried out via 24 autosomal STRs. The 24 loci studied showed the highest value of combined power of discrimination (CPD = 1), combined power of exclusion (CPE = 0.99999999985), combined paternity index (CPI = 6.10 × 10<sup>9</sup>) and lowest combined matching probability (CPM = 7.90 × 10<sup>−31</sup>). The studied population showed genetic closeness with the population of Uttarakhand, the Jats of Delhi,the Jat Sikh (Punjab), and the population of Rajasthan. Among the tested loci, SE33 and Penta E were found to be most useful in terms of the highest discrimination power, lowest matching probability, the highest power of exclusion, and highest polymorphism information content for the Uttar Pradesh population .
DOI: 10.6084/m9.figshare.22353985
title: Vitamin K2 Supplementation Improve Impaired Glycemic Homeostasis and Insulin Sensitivity for Type 2 Diabetes through Gut Microbiome and Fecal Metabolites

description: There is insufficient evidence for the ability of vitamin K2 to improve type 2 diabetes mellitus symptoms by regulating gut microbial composition. Here, we demonstrated that improved glycemic homeostasis and insulin sensitivity were closely correlated with changes in the gut microbiota and its metabolites following intervention with MK-7 (a natural form of vitamin K2 with a longer half-life). After MK-7 intervention, the microbiota exhibited increased biosynthesis of gut short-chain fatty acids, secondary bile acids, and circulating glucagon-like peptide-1 and ameliorated inflammatory immune responses in liver, colon and pancreas tissues. After fecal microbial transplantation with the MK-7-regulated gut microbiota, the improvement in impaired glucose metabolism in obese mice was comparable to that after transplantation of the gut microbiota from lean donors. These findings highlight the benefits of vitamin K2 supplementation in diabetic patients, revealing novel gut-dependent effects of MK-7 on glycemic homeostasis and benefits for both the host and the microbiota.
DOI: 10.35078/uufjru
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Varginha| Município: Coronel Xaiver Chaves

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.5281/zenodo.7058087
title: Aerial Power Infrastructure Detection Dataset

description: <strong>Aerial Power Infrastructure Detection Dataset:</strong> Autonomous inspection of power networks with Unmanned Aerial Vehicles (UAVs) has recently gained significant scientific attention mainly due to rapid advances in UAV technology. In this context, the UAV must autonomously navigate across the network acquiring high resolution data in a safe and fast manner, which poses challenges especially in cases where the location of infrastructure components, i.e. poles, is not known. The Aerial Power Infrastructure Detection Dataset is constructed aiming to create a repository available to the research community, which can be used for training online detection models, which in turn facilitate UAV localization across the network. Specifically, this dataset is used for implementing the “Pole Detection” process of ICARUS toolkit, which is a vision-based UAV monitoring platform for autonomous inspection of Medium Voltage (MV) power distribution network. Specifically, the UAV is supplied with the best-known coordinates of poles and navigates to the designated location searching for the pole. As soon as the pole is detected, using an one-class detection model, the UAV applies a control procedure to correct its position by aligning directly above the pole. For the training, we used samples containing the T-shaped bar of the pole with the insulators and the top of pole [1]. The dataset consists of top-view images of MV poles from various locations across Cyprus. Images were captured across different seasons to account for a variety of background conditions, such as grass or ground, as well as at different heights to account for variations in the UAV’s height during inspection. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (t-bars): Subset Images T-Bars Training 2730 2814 Validation 983 983 Testing 243 243 Reference: [1] A. Savva et al., "ICARUS: Automatic Autonomous Power Infrastructure Inspection with UAVs," 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021, pp. 918-926, doi: 10.1109/ICUAS51884.2021.9476742.
DOI: 10.5281/zenodo.7148922
title: Aerial Power Infrastructure Detection Dataset

description: <strong>Aerial Power Infrastructure Detection Dataset:</strong> Autonomous inspection of power networks with Unmanned Aerial Vehicles (UAVs) has recently gained significant scientific attention mainly due to rapid advances in UAV technology. In this context, the UAV must autonomously navigate across the network acquiring high resolution data in a safe and fast manner, which poses challenges especially in cases where the location of infrastructure components, i.e. poles, is not known. The Aerial Power Infrastructure Detection Dataset is constructed aiming to create a repository available to the research community, which can be used for training online detection models, which in turn facilitate UAV localization across the network. Specifically, this dataset is used for implementing the “Pole Detection” process of ICARUS toolkit, which is a vision-based UAV monitoring platform for autonomous inspection of Medium Voltage (MV) power distribution network. Specifically, the UAV is supplied with the best-known coordinates of poles and navigates to the designated location searching for the pole. As soon as the pole is detected, using an one-class detection model, the UAV applies a control procedure to correct its position by aligning directly above the pole. For the training, we used samples containing the T-shaped bar of the pole with the insulators and the top of pole [1]. The dataset consists of top-view images of MV poles from various locations across Cyprus. Images were captured across different seasons to account for a variety of background conditions, such as grass or ground, as well as at different heights to account for variations in the UAV’s height during inspection. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (t-bars): Subset Images T-Bars Training 2730 2814 Validation 983 983 Testing 243 243 Reference: [1] A. Savva et al., "ICARUS: Automatic Autonomous Power Infrastructure Inspection with UAVs," 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021, pp. 918-926, doi: 10.1109/ICUAS51884.2021.9476742.
DOI: 10.5281/zenodo.7181315
title: Aerial Power Infrastructure Detection Dataset

description: <strong>Aerial Power Infrastructure Detection Dataset:</strong> Autonomous inspection of power networks with Unmanned Aerial Vehicles (UAVs) has recently gained significant scientific attention mainly due to rapid advances in UAV technology. In this context, the UAV must autonomously navigate across the network acquiring high resolution data in a safe and fast manner, which poses challenges especially in cases where the location of infrastructure components, i.e. poles, is not known. The Aerial Power Infrastructure Detection Dataset is constructed aiming to create a repository available to the research community, which can be used for training online detection models, which in turn facilitate UAV localization across the network. Specifically, this dataset is used for implementing the “Pole Detection” process of ICARUS toolkit, which is a vision-based UAV monitoring platform for autonomous inspection of Medium Voltage (MV) power distribution network. Specifically, the UAV is supplied with the best-known coordinates of poles and navigates to the designated location searching for the pole. As soon as the pole is detected, using an one-class detection model, the UAV applies a control procedure to correct its position by aligning directly above the pole. For the training, we used samples containing the T-shaped bar of the pole with the insulators and the top of pole [1]. The dataset consists of top-view images of MV poles from various locations across Cyprus. Images were captured across different seasons to account for a variety of background conditions, such as grass or ground, as well as at different heights to account for variations in the UAV’s height during inspection. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (t-bars): Subset Images T-Bars Training 8580 9296 Validation 2131 2343 Testing 1232 1336 Reference: [1] A. Savva et al., "ICARUS: Automatic Autonomous Power Infrastructure Inspection with UAVs," 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021, pp. 918-926, doi: 10.1109/ICUAS51884.2021.9476742. <strong>**NOTE** If you use this dataset in your research/publication please cite us using the following :</strong> Antonis Savva, Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Power Infrastructure Detection Dataset (2.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.718131