cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.3df467
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ciona intestinalis",
 "Country is Chile",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010609-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.77pvbm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Caulerpa taxifolia",
 "Country is Tunisia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010611-250711103210423/datasets/export for details.
DOI: 10.15468/dl.ynh9jy
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Impatiens longiloba"
 ]
}

 The dataset includes 44 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010608-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15131/shef.data.12328226.v2
title: CoVid Plots and Analysis

description: # COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.<br>Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmaths<br>This work is described in more detail on the UK Data Service Impact and Innovation Lab blog: http://lab.ukdataservice.ac.uk/2020/05/21/visualising-high-risk-areas-for-covid-19-mortality/<br>Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/<br>
DOI: 10.15468/dl.xbp75b
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Caulerpa taxifolia",
 "Country is Monaco",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010610-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2xgwwg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Andorra, Albania, Armenia, Austria, Azerbaijan, Bosnia and Herzegovina, Belgium, Bulgaria, Belarus, Switzerland, Cyprus, Czechia, Germany, Denmark, Algeria, Estonia, Egypt, Greece, Spain, Finland, Faroe Islands, France, Georgia, Guernsey, Gibraltar, Croatia, Hungary, Ireland, Israel, Isle of Man, Iceland, Italy, Jersey, Lebanon, Liechtenstein, Lithuania, Luxembourg, Latvia, Libya, Morocco, Monaco, Moldova, Republic of, Montenegro, North Macedonia, Malta, Netherlands, Norway, Poland, Palestine, State of, Portugal, Romania, Serbia, Russian Federation, Sweden, Slovenia, Svalbard and Jan Mayen, Slovakia, San Marino, Syrian Arab Republic, Tunisia, Türkiye, Ukraine, United Kingdom of Great Britain and Northern Ireland, French Guiana, Guadeloupe, Martinique, Mayotte, Réunion, Saint Martin (French part), Holy See)",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Machine Observation, Occurrence evidence, Observation)",
 "HasGeospatialIssue is false",
 "ScientificName is one of (Abies fraseri, Abies fraseri (Pursh) Poir., Olearia traversii, Olearia traversii (F.Muell.) Hook.f.)",
 "Year is greater than or equal to 2025"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010590-250711103210423/datasets/export for details.
DOI: 10.15468/dl.d5dj4v
title: Occurrence Download

description: A dataset containing 4709 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is one of (CHN.30_1, CHN.8_1, CHN.7_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Cypriniformes, Perciformes, Beloniformes, Siluriformes, Synbranchiformes, Anguilliformes)"
 ]
}

 The dataset includes 4709 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010612-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15131/shef.data.12328226.v3
title: CoVid Plots and Analysis

description: # COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.<br>Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmaths<br>This work is described in more detail on the UK Data Service Impact and Innovation Lab blog: http://lab.ukdataservice.ac.uk/2020/05/21/visualising-high-risk-areas-for-covid-19-mortality/<br>Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/<br>
DOI: 10.15468/dl.9f356d
title: Occurrence Download

description: A dataset containing 88140006 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 PRINTF('%04d-%02d-%02d', "year", "month", "day") yearmonthday,
 countrycode countrycode,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, countrycode, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, countrycode, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, countrycode, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, countrycode, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, countrycode, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, countrycode, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.datasetkey = '4fa7b334-ce0d-4e88-aaae-2e0c138d049e'
 AND occurrence.hasgeospatialissues = FALSE
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL AND occurrence."month" IS NOT NULL AND occurrence."day" IS NOT NULL AND occurrence.hascoordinate = TRUE)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 PRINTF('%04d-%02d-%02d', "year", "month", "day"),
 occurrence.countrycode

 The dataset's 88140006 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010528-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15131/shef.data.12328226.v4
title: CoVid Plots and Analysis

description: # COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.<br>Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmaths<br>This work is described in more detail on the UK Data Service Impact and Innovation Lab blog: http://lab.ukdataservice.ac.uk/2020/05/21/visualising-high-risk-areas-for-covid-19-mortality/<br>Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/<br>
DOI: 10.15468/dl.uddk4z
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:ABW2190"
 ]
}

 The dataset includes 31 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010613-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ryjkpd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ctenopharyngodon idella",
 "Country is Malawi",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010615-250711103210423/datasets/export for details.
DOI: 10.15468/dl.y6agtg
title: Occurrence Download

description: A dataset containing 66477 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Clematis smilacifolia, Clematis tangutica tangutica, Clematis tibetana, Clematis tibetana lineariloba, Clematis tongluensis, Clematis tortuosa, Clematis tsaii, Clematis venusta, Clematis yui, Clematis yunnanensis, Clematis zandaensis, Clematis zemuensis, Clerodendrum bracteatum, Clerodendrum canescens, Clerodendrum colebrookianum, Clerodendrum cordatum, Clerodendrum glandulosum, Clerodendrum griffithianum, Clerodendrum hastatum, Clerodendrum indicum, Clerodendrum inerme, Clerodendrum infortunatum, Clerodendrum japonicum, Clerodendrum laevifolium, Clerodendrum panigrahianum, Clerodendrum phlomidis, Clerodendrum serratum, Clerodendrum serratum wallichii, Clerodendrum tibetanum, Clerodendrum villosum, Clerodendrum wallichii, Clethra delavayi, Clethra delavayi delavayi, Cleyera japonica, Cleyera japonica wallichiana, Clinopodium capitellatum, Clinopodium discolor, Clinopodium gracile, Clinopodium nepalense, Clinopodium piperitum, Clinopodium polycephalum, Clinopodium repens, Clintonia udensis, Clitoria annua, Cnidium bhutanicum, Cnidium monnieri, Coccinia grandis, Cocculus hirsutus, Cocculus laurifolius, Cocculus pendulus, Cochlianthus gracilis, Cochlianthus gracilis gracilis, Cochlospermum religiosum, Codariocalyx gyroides, Codariocalyx motorius, Codonacanthus pauciflorus, Codonanthe devosiana, Codonopsis affinis, Codonopsis alpina, Codonopsis benthamii, Codonopsis bhutanica, Codonopsis bragaensis, Codonopsis bulleyana, Codonopsis clematidea, Codonopsis convolvulacea, Codonopsis convolvulacea vinciflora, Codonopsis dicentrifolia, Codonopsis foetens foetens, Codonopsis foetens nervosa, Codonopsis gracilis, Codonopsis inflata, Codonopsis javanica, Codonopsis levicalyx, Codonopsis ovata, Codonopsis purpurea, Codonopsis rotundifolia, Codonopsis subsimplex, Codonopsis thalictrifolia, Codonopsis vinciflora, Codonopsis viridis, Coelachne simpliciuscula, Coelogyne barbata, Coelogyne corymbosa, Coelogyne cristata, Coelogyne fimbriata, Coelogyne flaccida, Coelogyne fuscescens, Coelogyne griffithii, Coelogyne hajrae, Coelogyne hitendrae, Coelogyne holochila, Coelogyne longipes, Coelogyne micrantha, Coelogyne nitida, Coelogyne occultata, Coelogyne ovalis, Coelogyne pantlingii, Coelogyne pempahisheyana)"
 ]
}

 The dataset includes 66477 records from 662 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010593-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/srex-mw78
title: "DATA - Student Performance Prediction and Knowledge Discovery  in Small-Data Scenarios"

description: "The dataset contains 649 samples with a total sizes of 93200 bytes "
DOI: 10.15468/dl.4hbz9x
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ctenopharyngodon idella",
 "Country is Malaysia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010616-250711103210423/datasets/export for details.
DOI: 10.15131/shef.data.12328226.v6
title: CoVid Plots and Analysis

description: # COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.<br>Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmaths<br>This work is described in more detail on the UK Data Service Impact and Innovation Lab blog: http://lab.ukdataservice.ac.uk/2020/05/21/visualising-high-risk-areas-for-covid-19-mortality/<br>Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/<br>
DOI: 10.15468/dl.kd2u3d
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Adrianichthys oophorus"
 ]
}

 The dataset includes 26 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010617-250711103210423/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.29558102
title: Structural Insights into AVR-Rmg8 Recognition Mechanisms by the Wheat Blast Resistance Gene <i>Rmg8</i>

description: Wheat blast disease, caused by the <i>Triticum</i> pathotype of <i>Magnaporthe oryzae</i> (MoT), poses a significant threat to global food security. The blast resistance gene <i>Rmg8</i>, recently isolated from a hexaploidy wheat cultivar, strongly confers resistance to all Bangladeshi and Zambian MoT isolates carrying the eI type of <i>AVR-Rmg8</i>. However, the molecular interactions underlying this recognition at proteins level remain poorly understood. In this study, we elucidated the structural and biological characteristics of RMG8 proteins and their recognition of the AVR-Rmg8 effector proteins using computational biology approaches. Amino acid sequence comparison of four <i>AVR-Rmg8</i> types revealed that only three amino acid residues distinguish the eI type of AVR-Rmg8, which induces a higher level of resistance conferred by RMG8. We identified that the Protein Kinase C (PKC) domain of RMG8, where proline dependency mediates the phosphorylation of a serine residue, involves in the strong recognition of the eI type of AVR-Rmg8. Phylogenetic analyses indicated that <i>Rmg8</i> might be evolved from proteins intimately linked to plant signaling pathways. Moreover, our data propose that RMG8 is a calcium-dependent multiple C2 domain proteins with transmembrane regions (MCTP) kinase that specifically recognizes the eI type of the AVR-Rmg8. Taken together, our <i>in-silico</i> predictions provide the first detailed insights into the molecular recognition mechanism between AVR-Rmg8 and RMG8, which is expected to aid in wheat blast resistance breeding.<i> </i>Future studies involving the purification and structural characterization of MoT effector proteins and<i> Rmg8</i> gene products are necessary to validate these findings.
DOI: 10.6084/m9.figshare.29558102.v1
title: Structural Insights into AVR-Rmg8 Recognition Mechanisms by the Wheat Blast Resistance Gene <i>Rmg8</i>

description: Wheat blast disease, caused by the <i>Triticum</i> pathotype of <i>Magnaporthe oryzae</i> (MoT), poses a significant threat to global food security. The blast resistance gene <i>Rmg8</i>, recently isolated from a hexaploidy wheat cultivar, strongly confers resistance to all Bangladeshi and Zambian MoT isolates carrying the eI type of <i>AVR-Rmg8</i>. However, the molecular interactions underlying this recognition at proteins level remain poorly understood. In this study, we elucidated the structural and biological characteristics of RMG8 proteins and their recognition of the AVR-Rmg8 effector proteins using computational biology approaches. Amino acid sequence comparison of four <i>AVR-Rmg8</i> types revealed that only three amino acid residues distinguish the eI type of AVR-Rmg8, which induces a higher level of resistance conferred by RMG8. We identified that the Protein Kinase C (PKC) domain of RMG8, where proline dependency mediates the phosphorylation of a serine residue, involves in the strong recognition of the eI type of AVR-Rmg8. Phylogenetic analyses indicated that <i>Rmg8</i> might be evolved from proteins intimately linked to plant signaling pathways. Moreover, our data propose that RMG8 is a calcium-dependent multiple C2 domain proteins with transmembrane regions (MCTP) kinase that specifically recognizes the eI type of the AVR-Rmg8. Taken together, our <i>in-silico</i> predictions provide the first detailed insights into the molecular recognition mechanism between AVR-Rmg8 and RMG8, which is expected to aid in wheat blast resistance breeding.<i> </i>Future studies involving the purification and structural characterization of MoT effector proteins and<i> Rmg8</i> gene products are necessary to validate these findings.
DOI: 10.5281/zenodo.15828193
title: Computational cross-aggregation study of Dengue virus NS1, capsid anchor and 2k peptides with human amylin, Aβ42 and α-synuclein peptides

description: This dataset contains input files, topology files and parameter files related to the publication

"Computational cross-aggregation study of Dengue virus NS1, capsid anchor and 2k peptides with human amylin, Aβ42 and α-synuclein peptides".
DOI: 10.5281/zenodo.15878033
title: Computational cross-aggregation study of Dengue virus NS1, capsid anchor and 2k peptides with human amylin, Aβ42 and α-synuclein peptides

description: This dataset contains input files, topology files and parameter files related to the publication

"Computational cross-aggregation study of Dengue virus NS1, capsid anchor and 2k peptides with human amylin, Aβ42 and α-synuclein peptides".