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

description: A dataset containing 3260 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Atelopus ignescens (Cornalia, 1849)"
 ]
}

 The dataset includes 3260 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046814-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cyzun9
title: Occurrence Download

description: A dataset containing 442 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Ontario Breeding Bird Atlas 2001-2005",
 "Geometry POLYGON((-80.27801 43.33639,-80.00468 43.33639,-80.00468 43.43891,-80.27801 43.43891,-80.27801 43.33639))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 442 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046815-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c8py39
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Kyrgyzstan",
 "DatasetKey is one of (Illinois Natural History Survey Insect Collection, University of Tartu Natural History Museum and Botanical Garden Zooloogical Collections)",
 "GadmGid is KGZ",
 "TaxonKey is Chalcididae"
 ]
}

 The dataset's 1 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046809-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nmjbfm
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Faxonius theaphionensis (Simon, Timm & Morris, 2005)"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046816-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3vs7vt
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Türkiye",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lupinus L.",
 "Year 1970-2024"
 ]
}

 The dataset includes 118 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046817-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2xwndc
title: Occurrence Download

description: A dataset containing 409 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Odonata Central",
 "Geometry POLYGON((-80.27801 43.33639,-80.00468 43.33639,-80.00468 43.43891,-80.27801 43.43891,-80.27801 43.33639))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 409 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046818-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3k4rmk
title: Occurrence Download

description: A dataset containing 296 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is International Barcode of Life project (iBOL)",
 "Geometry POLYGON((-80.27801 43.33639,-80.00468 43.33639,-80.00468 43.43891,-80.27801 43.43891,-80.27801 43.33639))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 296 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046819-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p4tjhr
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Mongolia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ovis ammon (Linnaeus, 1758)"
 ]
}

 The dataset includes 112 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046820-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fvfzuq
title: Occurrence Download

description: A dataset containing 220 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is University of Guelph Insect Collection (DEBU)",
 "Geometry POLYGON((-80.27801 43.33639,-80.00468 43.33639,-80.00468 43.43891,-80.27801 43.43891,-80.27801 43.33639))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 220 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046821-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.krkx53
title: Occurrence Download

description: A dataset containing 137 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Pl@ntNet automatically identified occurrences",
 "Geometry POLYGON((-80.27801 43.33639,-80.00468 43.33639,-80.00468 43.43891,-80.27801 43.43891,-80.27801 43.33639))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 137 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046822-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7kw54x
title: Occurrence Download

description: A dataset containing 143678 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((-80.27801 43.33639,-80.00468 43.33639,-80.00468 43.43891,-80.27801 43.43891,-80.27801 43.33639))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 143678 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046810-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/6gwm-1447
title: Multi-complex-Seg

description: To develop effective and efficient multimodal image interpretation techniques, it is essential to comprehensively consider both phase and amplitude components of SAR data. Therefore, to facilitate advancements in multimodal segmentation in remote sensing, we propose and construct a multimodal terrain segmentation dataset, named Multi-Complex-Seg. Our dataset encompasses both urban and suburban areas and includes seven categories of land cover. These comprise six typical terrain types: water, road, grass, forest, farmland, and building, as well as an additional category labeled others.
DOI: 10.15468/dl.pentsx
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Marsh Monitoring Program - Birds",
 "Geometry POLYGON((-80.27801 43.33639,-80.00468 43.33639,-80.00468 43.43891,-80.27801 43.43891,-80.27801 43.33639))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 112 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046823-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mr638j
title: Occurrence Download

description: A dataset containing 111 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Mammalogy Collection - Royal Ontario Museum",
 "Geometry POLYGON((-80.27801 43.33639,-80.00468 43.33639,-80.00468 43.43891,-80.27801 43.43891,-80.27801 43.33639))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 111 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046824-241126133413365/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.28082710.v1
title: Identification of a potential anti-viral drug targeting allosteric site of papain-like protease against rubella using a molecular modeling approach

description: Rubella virus (RUBV) is responsible for causing rashes, lymphadenopathy, and fever which are the hallmarks of an acute viral illness called Rubella. For RUBV replication, the non-structural polyprotein p200 must be cleaved by the rubella papain-like protease (RubPro) into the multifunctional proteins p150 and p90. Hence, RubPro is an attractive target for anti-viral drug discovery. Moreover, the binding of host Calmodulin 1 (CaM) to RubPro modulates the protease activity and infectivity of RUBV. However, the binding mode of CaM and RubPro remain uncertain. Therefore, our investigation not only delves into understanding the interaction between CaM and the RubPro but also aims to recognize the allosteric site for the development of antiviral protease inhibitors. In this study, we interestingly identified the allosteric site in close vicinity with the CaM binding domain of RubPro. Considering the allosteric site of RubPro, we employed a computational modelling approach to identify the potential antiviral compounds. Leveraging ChemDiv protease inhibitors database, we employed structure-based virtual screening, ADME, pass prediction, and docking studies, unveiling three potent compounds: C073-2897, C073-3328, and C073-3368. Moreover, molecular dynamics simulation analysis revealed that these compounds affect the RubPro structure and dynamics and may also influence the binding of CaM with RubPro. Notably, binding energy calculation showed that the compound C073-3328 exhibits higher binding affinity, while C073-3368 displays a lower binding affinity with RubPro. These compounds signify potential for managing RUBV infections and pioneering effective antiviral treatments. This computational study could pave the way for improved methods of managing or controlling rubella infections.
DOI: 10.6084/m9.figshare.28082710
title: Identification of a potential anti-viral drug targeting allosteric site of papain-like protease against rubella using a molecular modeling approach

description: Rubella virus (RUBV) is responsible for causing rashes, lymphadenopathy, and fever which are the hallmarks of an acute viral illness called Rubella. For RUBV replication, the non-structural polyprotein p200 must be cleaved by the rubella papain-like protease (RubPro) into the multifunctional proteins p150 and p90. Hence, RubPro is an attractive target for anti-viral drug discovery. Moreover, the binding of host Calmodulin 1 (CaM) to RubPro modulates the protease activity and infectivity of RUBV. However, the binding mode of CaM and RubPro remain uncertain. Therefore, our investigation not only delves into understanding the interaction between CaM and the RubPro but also aims to recognize the allosteric site for the development of antiviral protease inhibitors. In this study, we interestingly identified the allosteric site in close vicinity with the CaM binding domain of RubPro. Considering the allosteric site of RubPro, we employed a computational modelling approach to identify the potential antiviral compounds. Leveraging ChemDiv protease inhibitors database, we employed structure-based virtual screening, ADME, pass prediction, and docking studies, unveiling three potent compounds: C073-2897, C073-3328, and C073-3368. Moreover, molecular dynamics simulation analysis revealed that these compounds affect the RubPro structure and dynamics and may also influence the binding of CaM with RubPro. Notably, binding energy calculation showed that the compound C073-3328 exhibits higher binding affinity, while C073-3368 displays a lower binding affinity with RubPro. These compounds signify potential for managing RUBV infections and pioneering effective antiviral treatments. This computational study could pave the way for improved methods of managing or controlling rubella infections.
DOI: 10.5281/zenodo.13832325
title: DC79_BLM8

description: https://dyncolldev.ht.lu.se/plus/artifact_view.php?item=79
DOI: 10.15468/dl.x9z5m7
title: Occurrence Download

description: A dataset containing 255 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (University of British Columbia Herbarium (UBC) - Bryophytes Collection, University of British Columbia Herbarium (UBC) - Vascular Plant Collection, University of British Columbia Herbarium (UBC) - Algae Collection, University of British Columbia - Spencer Entomological Collection (UBCZ), University of British Columbia Herbarium (UBC) - Lichen Collection, University of British Columbia Herbarium (UBC) - Fungi Collection, Cowan Tetrapod Collection - Mammals, Cowan Tetrapod Collection - Birds, Cowan Tetrapod Collection - Herpetology, Beaty Biodiversity Museum Fish Collection, Beaty Biodiversity Museum Fossil Collection, Beaty Biodiversity Museum Marine Invertebrate Collection)",
 "RecordedBy is one of (chris sears)"
 ]
}

 The dataset includes 255 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046825-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14548002
title: trimeresurus_trigonocephalus_spades_01.fasta.gz
DOI: 10.5281/zenodo.14548003
title: trimeresurus_trigonocephalus_spades_01.fasta.gz
DOI: 10.15468/dl.qy5qk5
title: Occurrence Download

description: A dataset containing 139813 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Scomber Linnaeus, 1758"
 ]
}

 The dataset includes 139813 records from 405 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046826-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14353088
title: A machine learning algorithm-based approach (MaxEnt) for predicting habitat suitability of Formica rufa

description: Machine learning techniques are quite effective for simulating species habitat appropriateness. Species distribution models are statistical algorithms founded on the ecological niche idea. These models estimate the association between existing species records and the environmental and spatial characteristics of the habitat. From 2022 to 2023, field survey was conducted in the Kastamonu Forest Enterprise, resulting in the identification of 267 active Formica rufa nests. The habitat preferences of F. rufa were assessed based on factors such as stand characteristics, topography, and climatic variables. MaxEnt, a prevalent machine learning technique for predicting species habitat suitability, was employed in the habitat suitability modeling of Formica rufa. 30 distinct variables were employed in the modeling process. Receiver Operating Characteristic (ROC) analysis examined model accuracy. AUC was 0.941 for training data and 0.946 for test data. With 39.5% of the model, the development stage is the most important variable for F. rufa habitat selection. The development stage, productivity, and temperature annual range (BIO7) variables make up 75.1% of the model. The habitat suitability map shows that 79% of F. rufa nests are in moderately and highly appropriate areas. The Formica rufa group, widely prevalent in northern hemisphere forests, significantly impacts forest ecosystems and is recognized as the foremost bioindicator species within these environments. Determining the elements that affect habitat selection by these species is essential for their conservation and management.
DOI: 10.5281/zenodo.14353087
title: A machine learning algorithm-based approach (MaxEnt) for predicting habitat suitability of Formica rufa

description: Machine learning techniques are quite effective for simulating species habitat appropriateness. Species distribution models are statistical algorithms founded on the ecological niche idea. These models estimate the association between existing species records and the environmental and spatial characteristics of the habitat. From 2022 to 2023, field survey was conducted in the Kastamonu Forest Enterprise, resulting in the identification of 267 active Formica rufa nests. The habitat preferences of F. rufa were assessed based on factors such as stand characteristics, topography, and climatic variables. MaxEnt, a prevalent machine learning technique for predicting species habitat suitability, was employed in the habitat suitability modeling of Formica rufa. 30 distinct variables were employed in the modeling process. Receiver Operating Characteristic (ROC) analysis examined model accuracy. AUC was 0.941 for training data and 0.946 for test data. With 39.5% of the model, the development stage is the most important variable for F. rufa habitat selection. The development stage, productivity, and temperature annual range (BIO7) variables make up 75.1% of the model. The habitat suitability map shows that 79% of F. rufa nests are in moderately and highly appropriate areas. The Formica rufa group, widely prevalent in northern hemisphere forests, significantly impacts forest ecosystems and is recognized as the foremost bioindicator species within these environments. Determining the elements that affect habitat selection by these species is essential for their conservation and management.
DOI: 10.5281/zenodo.14548023
title: A machine learning algorithm-based approach (MaxEnt) for predicting habitat suitability of Formica rufa

description: Machine learning techniques are quite effective for simulating species habitat appropriateness. Species distribution models are statistical algorithms founded on the ecological niche idea. These models estimate the association between existing species records and the environmental and spatial characteristics of the habitat. From 2022 to 2023, field survey was conducted in the Kastamonu Forest Enterprise, resulting in the identification of 267 active Formica rufa nests. The habitat preferences of F. rufa were assessed based on factors such as stand characteristics, topography, and climatic variables. MaxEnt, a prevalent machine learning technique for predicting species habitat suitability, was employed in the habitat suitability modeling of Formica rufa. 30 distinct variables were employed in the modeling process. Receiver Operating Characteristic (ROC) analysis examined model accuracy. AUC was 0.941 for training data and 0.946 for test data. With 39.5% of the model, the development stage is the most important variable for F. rufa habitat selection. The development stage, productivity, and temperature annual range (BIO7) variables make up 75.1% of the model. The habitat suitability map shows that 79% of F. rufa nests are in moderately and highly appropriate areas. The Formica rufa group, widely prevalent in northern hemisphere forests, significantly impacts forest ecosystems and is recognized as the foremost bioindicator species within these environments. Determining the elements that affect habitat selection by these species is essential for their conservation and management.
DOI: 10.6084/m9.figshare.28079978
title: appendix (2).docx

description: This study examines the factors affecting ADL and IADL disabilities of hypertensive patients of India.
DOI: 10.5281/zenodo.14548055
title: Improving hemodialysis patients' adherence and clinical outcomes using BCT
DOI: 10.5281/zenodo.14548056
title: Improving hemodialysis patients' adherence and clinical outcomes using BCT
DOI: 10.15468/dl.d4rhj8
title: Occurrence Download

description: A dataset containing 375 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Elachistocleis panamensis (Dunn, Trapido & Evans, 1948)"
 ]
}

 The dataset includes 375 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046832-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2s5u3n
title: Occurrence Download

description: A dataset containing 193 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Alsophila spinulosa (Wall. ex Hook.) R.M.Tryon"
 ]
}

 The dataset includes 193 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046833-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q5ny7n
title: Occurrence Download

description: A dataset containing 99657 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bromus tectorum"
 ]
}

 The dataset includes 99657 records from 1302 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005891-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13145/bacdive148256.20241212.9.2
title: Fusobacterium varium

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive148256.20251217.10
title: Fusobacterium varium

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0136268.10fu
title: SH0136268.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive148257.20241212.9.2
title: Finegoldia magna

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive148257.20251217.10
title: Finegoldia magna

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.