cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.7b952z
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Limosella inflata Hilliard & B.L.Burtt",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.2855 -34.7533,20.509 -34.5676,20.509 -32.9007,17.7723 -32.9007,17.8156 -33.0786,18.3471 -33.7176,18.3748 -33.8287,18.2181 -34.0492,18.3197 -34.3408,18.4452 -34.429,18.5435 -34.4213,18.5872 -34.3667,18.5363 -34.167,18.7345 -34.1508,18.7216 -34.3637,18.7808 -34.4521,19.0289 -34.4206,19.0831 -34.4835,19.2655 -34.5017,19.2032 -34.6157,19.2394 -34.6926,19.375 -34.6963,19.6091 -34.8524,19.8251 -34.8396,19.9798 -34.9064,20.1081 -34.8731,20.1517 -34.7847,20.2855 -34.7533))"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043217-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57783/tajc-1364
title: LibreChat-DEV
DOI: 10.57783/vstx-bw32
title: LibreChat
DOI: 10.57783/3xhh-nn44
title: LibreChat-TEST
DOI: 10.2908/sdg_11_31
title: Settlement area per capita
title: Zone de peuplement par tête
title: Siedlungsfläche pro Kopf

description: Dieser Indikator erfasst die Siedlungsfläche durch Wohnbebauung, Industrie- und Gewerbegebiete, Infrastruktur, Sportplätze usw. und umfasst sowohl versiegelte als auch unversiegelte Flächen.
description: This indicator captures the amount of settlement area used for buildings, industrial and commercial areas, infrastructure and sports grounds etc. and includes both sealed and non-sealed surfaces.
description: Cet indicateur rend compte de la surface de peuplement, par exemple pour les bâtiments, les zones industrielles et commerciales, les infrastructures, les terrains de sport etc. et comprend à la fois les surfaces scellées et les surfaces non scellées.
DOI: 10.2908/sdg_13_60
title: Durch die Unterzeichner des Konvent der Bürgermeister für Klima und Energie gedeckte Bevölkerung
title: Population covered by the Covenant of Mayors for Climate & Energy signatories
title: Population couvert par les signataires de La Convention des Maires pour le Climat et l'Energie

description: Der Indikator bemisst pro Mitgliedsstaat den Anteil der EU-Bevölkerung, der unter das neue Konvent der Bürgermeister fällt, als Verhältnis zwischen der Bevölkerung der Städte eines Landes, die das Konvent unterzeichnet haben, und der Gesamtbevölkerung dieses Landes. Unterzeichner "in Wartestellung" sind ausgeschlossen.
description: The indicator measures the share of EU population covered by the new Covenant of Mayors per Member State, as the ratio between the population of the cities that have signed in a country and the total population of that country. Signatories to the Covenant of Mayors with an “On-hold/suspended” status are excluded.
description: Cet indicateur donne le pourcentage de la population de l’UE couverte par la nouvelle Convention des Maires par État membre, comme le ratio entre la population des villes d'un pays qui ont signé la convention et la population totale de ce pays. Les signataires de la Convention des maires ayant le statut de «suspension» sont exclus.
DOI: 10.2908/sdg_15_41
title: Flächenversiegelungsindex
title: Soil sealing index
title: Indice d’imperméabilisation des sols

description: Der Indikator schätzt die Zunahme versiegelter Bodenoberflächen mit undurchlässigen Materialien aufgrund von Stadtentwicklung und Bauwesen (z. B. Gebäuden, Konstruktionen und dem Verlegen von vollständig oder teilweise undurchlässigem künstlichem Material, wie Asphalt, Metall, Glas, Kunststoff oder Beton). Dies gibt einen Hinweis auf die Geschwindigkeit der Bodenversiegelung, wenn Flächen die Landnutzung in Richtung künstliche und städtische Landnutzung ändern. Der Indikator basiert auf den High Resolution Layers (ein Produkt des Copernicus Land Monitoring Service). Der Indikator wird in den folgenden Einheiten dargestellt: Index 2006 = 100 % der gesamten Oberfläche gesamte versiegelte Fläche in km2.
description: The indicator estimates the increase in sealed soil surfaces with impervious materials due to urban development and construction (e.g. buildings, constructions and laying of completely or partially impermeable artificial material, such as asphalt, metal, glass, plastic or concrete). This provides an indication of the rate of soil sealing, when areas change land use towards artificial and urban land use. The indicator builds on data from the imperviousness High Resolution Layer (a product of the Copernicus Land Monitoring Service). The indicator is presented in the following units: Index 2006=100 % of total surface total sealed surface in km2.
description: L’indicateur estime l’augmentation des surfaces de sol étanches avec des matériaux imperméables en raison du développement urbain et de la construction (par exemple, des bâtiments, des constructions et la pose de matériaux artificiels totalement ou partiellement imperméables, tels que l’asphalte, le métal, le verre, le plastique ou le béton). Cela donne une indication du taux d'imperméabilisation des sols, lorsque les zones changent d'utilisation des terres en terres artificielles ou urbaines. L'indicateur s'appuie sur les données de la couche haute résolution imperméable (un produit du service de surveillance des terres Copernicus). L'indicateur est présenté dans les unités suivantes : Indice 2006 = 100 % de la surface totale surface totale scellée en km2.
DOI: 10.2908/sdg_16_60
title: Population avec confiance dans les institutions de l'UE par institution
title: Population with confidence in EU institutions by institution
title: Bürger mit Vertrauen in die EU-Institutionen nach Institution

description: Der Indikator misst das Vertrauen der EU-Bürger in eine Auswahl von EU-Institutionen: Das Europäische Parlament, die Europäische Kommission und die Europäische Zentralbank. Es wird ausgedrückt als Anteil der positiven Stellungnahmen (Personen, die erklären, dass sie den Institutionen eher vertrauen). Die Bürgerinnen und Bürger werden gebeten, ihr Vertrauen zu bekunden, indem sie die folgenden Alternativen wählen: „tendiere zu Vertrauen“, „eher kein Vertrauen“ und „nicht weiß“ oder „keine Antwort“. Der Indikator basiert auf dem Eurobarometer, einer seit 1973 zweimal jährlich durchgeführten Erhebung zur Beobachtung der Entwicklung der öffentlichen Meinung in den Mitgliedstaaten.
description: The indicator measures confidence among EU citizens in a selection of EU institutions: the European Parliament, the European Commission, and the European Central Bank. It is expressed as the share of positive opinions (people who declare that they tend to trust) about the institutions. Citizens are asked to express their confidence levels by choosing the following alternatives: ‘tend to trust’, ‘tend not to trust’ and ‘don’t know’ or ‘no answer’. The indicator is based on the Eurobarometer, a survey which has been conducted twice a year since 1973 to monitor the evolution of public opinion in the Member States.
description: Cet indicateur mesure la confiance des citoyens de l’UE dans une sélection d’institutions de l’UE: Le Parlement européen, la Commission européenne et la Banque centrale européenne. Il est exprimé comme la part des avis positifs (les personnes qui déclarent plutôt confiance) sur les institutions. Les citoyens sont invités à exprimer leurs niveaux de confiance en choisissant les options suivantes: «tend à faire confiance», «tend à ne pas confiance» et de «ne sait pas» ou «pas de réponse». L’indicateur est fondé sur l’Eurobaromètre, une étude bisannuelle effectuée depuis 1973 afin de suivre l’évolution de l’opinion publique dans les États membres.
DOI: 10.15468/dl.c88ywc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Craticula halophila (Grunow) D.G.Mann, 1990",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.0251 -31.3959,30.5701 -30.7329,30.5701 -28.5786,25.5395 -28.5786,25.5395 -32.9007,28.2048 -32.9007,28.5753 -32.6569,29.4902 -31.7517,30.0251 -31.3959))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043218-250525065834625/datasets/export for details.
DOI: 10.15468/dl.j2ngh2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Limosella inflata Hilliard & B.L.Burtt",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.628 -23.023,35.6007 -22.9702,35.6007 -23.165,35.628 -23.023))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043219-250525065834625/datasets/export for details.
DOI: 10.15468/dl.agzqnu
title: Occurrence Download

description: A dataset containing 3374 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Panthera tigris (Linnaeus, 1758)",
 "Year 2015-2025"
 ]
}

 The dataset includes 3374 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043220-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.29314721
title: Supplementary information files for "When are fact-checks effective? An experimental study on the inclusion of the misinformation source and the source of fact-checks in 16 European countries"

description: Supplementary files for article "When are fact-checks effective? An experimental study on the inclusion of the misinformation source and the source of fact-checks in 16 European countries"<br><br>Despite increasing academic attention, several questions about fact-checking remain unanswered. First, it remains unclear to what extent fact-checks are effective across different political and media contexts. Second, we know little on whether features of the fact-check itself influence its success. Conducting an experiment in 16 European countries, this study aims to fill these gaps by examining two features of fact-checks that may affect their success: whether fact-checks include the political source of the misinformation, and the source of the fact-check itself. We find that fact-checks are successful in debunking misperceptions. Moreover, this debunking effect is consistent across countries. Looking at features of fact-checks, we find no indication that it matters whether fact-checks include the political source of the misinformation claim. Comparing fact-checks from independent organizations with those from public broadcasters, we do find, however, that who the fact-checker is matters, especially in combination with trust in this source.<br><br>© The Author(s), CC BY-NC-ND 4.0
DOI: 10.17028/rd.lboro.29314721.v1
title: Supplementary information files for "When are fact-checks effective? An experimental study on the inclusion of the misinformation source and the source of fact-checks in 16 European countries"

description: Supplementary files for article "When are fact-checks effective? An experimental study on the inclusion of the misinformation source and the source of fact-checks in 16 European countries"<br><br>Despite increasing academic attention, several questions about fact-checking remain unanswered. First, it remains unclear to what extent fact-checks are effective across different political and media contexts. Second, we know little on whether features of the fact-check itself influence its success. Conducting an experiment in 16 European countries, this study aims to fill these gaps by examining two features of fact-checks that may affect their success: whether fact-checks include the political source of the misinformation, and the source of the fact-check itself. We find that fact-checks are successful in debunking misperceptions. Moreover, this debunking effect is consistent across countries. Looking at features of fact-checks, we find no indication that it matters whether fact-checks include the political source of the misinformation claim. Comparing fact-checks from independent organizations with those from public broadcasters, we do find, however, that who the fact-checker is matters, especially in combination with trust in this source.<br><br>© The Author(s), CC BY-NC-ND 4.0
DOI: 10.17045/sthlmuni.27952080
title: Fig. 2b-c | Quantitative immunofluorescence microscopy for intracellular FUS localization in ALS motor neurons

description: This item is part of the <b>Figshare Project</b>:<br><b>Early mitochondrial dysfunction revealed across FUS- and TARDBP-ALS at single cell resolution</b><br>From <i>Data Availability Statement</i> for the paper in <i>Nature Communications</i> entitled:<b>Single-cell RNA sequencing reveals early mitochondrial dysfunction unique to motor neurons shared across FUS- and TARDBP-ALS</b>"We have deposited all raw and processed RNA sequencing data generated in this study on the NCBI Gene Expression Omnibus (GEO) under the accession number GSE226482. The <i>C9orf72</i>-ALS bulk RNA sequencing data was retrieved directly from the authors of the study.[Items under this Figshare Project contain:] "Scans of fluorescent western blots, raw imaging files from confocal microscopy, the analysis files from Opera Phenix, qPCR data sets, and Seahorse assay result files."-------------------------<br>[Item specific description:]<br>Quantititative fluorescene microscopy for FUS protein in ALS motor neuronsThe dataset are Harmony archives for the high content imager Opera Phenix (Perkim Elmer). The proprietary harmony software creates a database with file organization stored in xml and oar files and folders with raw tif files. More information here on our imaging platform: https://www.kclwcic.co.uk/operaphenixThese archives contain thousands of tif files. Therefore, they are compressed into tar.gz files with matching md5 checksums for verification.The prism file is just a graphpad prism file with the summary data. More info here: https://www.graphpad.com/features
DOI: 10.17045/sthlmuni.27952080.v1
title: Fig. 2b-c | Quantitative immunofluorescence microscopy for intracellular FUS localization in ALS motor neurons

description: This item is part of the <b>Figshare Project</b>:<br><b>Early mitochondrial dysfunction revealed across FUS- and TARDBP-ALS at single cell resolution</b><br>From <i>Data Availability Statement</i> for the paper in <i>Nature Communications</i> entitled:<b>Single-cell RNA sequencing reveals early mitochondrial dysfunction unique to motor neurons shared across FUS- and TARDBP-ALS</b>"We have deposited all raw and processed RNA sequencing data generated in this study on the NCBI Gene Expression Omnibus (GEO) under the accession number GSE226482. The <i>C9orf72</i>-ALS bulk RNA sequencing data was retrieved directly from the authors of the study.[Items under this Figshare Project contain:] "Scans of fluorescent western blots, raw imaging files from confocal microscopy, the analysis files from Opera Phenix, qPCR data sets, and Seahorse assay result files."-------------------------<br>[Item specific description:]<br>Quantititative fluorescene microscopy for FUS protein in ALS motor neuronsThe dataset are Harmony archives for the high content imager Opera Phenix (Perkim Elmer). The proprietary harmony software creates a database with file organization stored in xml and oar files and folders with raw tif files. More information here on our imaging platform: https://www.kclwcic.co.uk/operaphenixThese archives contain thousands of tif files. Therefore, they are compressed into tar.gz files with matching md5 checksums for verification.The prism file is just a graphpad prism file with the summary data. More info here: https://www.graphpad.com/features
DOI: 10.3886/e232841
title: Ocean Heat Content - Yearly Difference from Average

description: Dataset consists of annual images for 2000-2024, available in small, large, broadcast media, full size zip, and KML archive formats. These images were derived from NCEI Global Ocean Heat and Salt Content Yearly and 3-Month 0-700m Heat Content.<br><br>Description from Climate.gov<br><br>Q:How much heat energy is being stored in Earth’s ocean?A:Colors on the map show where instruments detected more or less heat energy in the top 2,300 feet of water than the long-term average annual heat content. Orange areas show where heat content was higher than the long-term average. Blue areas show where heat content was lower than the long-term average. The darker the shade of blue or orange, the larger the heat-content difference from the long-term average.<br><br>Q:Where do these measurements come from?A:Oceanographers use a range of temperature-sensing instruments to measure heat energy in the ocean. Among them is a fleet of more than 3,000 robotic “floats” that measure ocean temperature in the top 2,300 feet of water. Scientists constantly compare data from floats, probes, and Conductivity-Temperature-Depth (CTD) sensors to verify that the values they produce make sense.<br><br>Q:What do the colors mean?A:Colors show the relative differences in average heat energy in the top 2,300 feet of the ocean, computed for each year, compared to long-term average annual heat content. The long-term temperature climatology is the average of five “decadal” climatologies calculated for 1955-1964, 1965-1974, 1975-1984, 1985-1994, and 1995-2006. Orange and blue areas show where the upper ocean’s heat storage rose or fell by up to 3 gigajoules (109 joules) per square meter at the surface. Areas where heat content was close to the long-term average are white.<br><br>Q:Why do these data matter?A:The ocean’s ability to store and release heat over long periods of time gives it a central role in stabilizing Earth’s climate system. Short-term variations in heat content due to natural climate patterns like El Niño influence regional climates, tropical cyclones, and marine life, including coral reefs and fisheries. As the ocean takes up more heat due to global warming, water temperatures rise, causing it to increase in volume, raising global sea level around the world. Warming ocean waters also have major implications for marine ecosystems, melting ice caps, and precipitation patterns.<br><br>Q:How did you produce these snapshots?A:Data Snapshots are derivatives of existing data products. To meet the needs of a broad audience, we present the same data as current products in a simplified visual style. This set of snapshots is based on data from NOAA’s Ocean Climate Laboratory.To produce the maps for Data Snapshots, we run a script that accesses the source data, applies a custom color palette, and assembles them on a prepared base map.<br><br>ReferencesLevitus, S., et al. (2012), World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010, Geophys. Res. Lett., 39, L10603.
description: Downloaded June 12, 2025.
DOI: 10.3886/e232841v1
title: Ocean Heat Content - Yearly Difference from Average

description: Dataset consists of annual images for 2000-2024, available in small, large, broadcast media, full size zip, and KML archive formats. These images were derived from NCEI Global Ocean Heat and Salt Content Yearly and 3-Month 0-700m Heat Content.<br><br>Description from Climate.gov<br><br>Q:How much heat energy is being stored in Earth’s ocean?A:Colors on the map show where instruments detected more or less heat energy in the top 2,300 feet of water than the long-term average annual heat content. Orange areas show where heat content was higher than the long-term average. Blue areas show where heat content was lower than the long-term average. The darker the shade of blue or orange, the larger the heat-content difference from the long-term average.<br><br>Q:Where do these measurements come from?A:Oceanographers use a range of temperature-sensing instruments to measure heat energy in the ocean. Among them is a fleet of more than 3,000 robotic “floats” that measure ocean temperature in the top 2,300 feet of water. Scientists constantly compare data from floats, probes, and Conductivity-Temperature-Depth (CTD) sensors to verify that the values they produce make sense.<br><br>Q:What do the colors mean?A:Colors show the relative differences in average heat energy in the top 2,300 feet of the ocean, computed for each year, compared to long-term average annual heat content. The long-term temperature climatology is the average of five “decadal” climatologies calculated for 1955-1964, 1965-1974, 1975-1984, 1985-1994, and 1995-2006. Orange and blue areas show where the upper ocean’s heat storage rose or fell by up to 3 gigajoules (109 joules) per square meter at the surface. Areas where heat content was close to the long-term average are white.<br><br>Q:Why do these data matter?A:The ocean’s ability to store and release heat over long periods of time gives it a central role in stabilizing Earth’s climate system. Short-term variations in heat content due to natural climate patterns like El Niño influence regional climates, tropical cyclones, and marine life, including coral reefs and fisheries. As the ocean takes up more heat due to global warming, water temperatures rise, causing it to increase in volume, raising global sea level around the world. Warming ocean waters also have major implications for marine ecosystems, melting ice caps, and precipitation patterns.<br><br>Q:How did you produce these snapshots?A:Data Snapshots are derivatives of existing data products. To meet the needs of a broad audience, we present the same data as current products in a simplified visual style. This set of snapshots is based on data from NOAA’s Ocean Climate Laboratory.To produce the maps for Data Snapshots, we run a script that accesses the source data, applies a custom color palette, and assembles them on a prepared base map.<br><br>ReferencesLevitus, S., et al. (2012), World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010, Geophys. Res. Lett., 39, L10603.
description: Downloaded June 12, 2025.
DOI: 10.6084/m9.figshare.29314772
title: Ambrella_combined_dataset

description: DNA combined matrix (ITS:1-850; matK:853-2211)
DOI: 10.6084/m9.figshare.29314772.v1
title: Ambrella_combined_dataset

description: DNA combined matrix (ITS:1-850; matK:853-2211)
DOI: 10.15468/dl.ket3w7
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Craticula halophila (Grunow) D.G.Mann, 1990",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.5701 -24.0715,25.5395 -24.0715,25.5395 -28.5786,30.5701 -28.5786,30.5701 -24.0715))"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043221-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.