cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17290249
title: QUADICA in RDF: Water Quality, Discharge and Catchment Attributes for Germany as a Knowledge Graph

description: This dataset presents an RDF (Turtle syntax) re-publication of the QUADICA collection (water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany), converted from the original tabular resources using the open-source HydroTurtle tool. The goal is to enhance the interoperability, reusability, and FAIRness of German river water-quality data in the spirit of the Semantic Web and Linked Open Data.

About QUADICA (source data). QUADICA is the first large-sample water-quality dataset assembled for 1,386 German catchments, combining water quality and quantity time series with meteorological drivers, diffuse nitrogen inputs, and >100 catchment attributes spanning topography, climate, land cover, lithology, and soils. 

This RDF version. We convert the public QUADICA tables into a Linked Data graph and mint stable URIs for stations, catchments, variables, and individual observations. Where appropriate, we expose monthly and annual WRTDS (Weighted Regressions on Time, Discharge, and Season) outputs with flow-normalized concentrations and mean fluxes, together with medians of observed discharge, and the long-term monthly climatologies.

Ontologies and vocabularies used





SOSA/SSN (observations, features, sensors)




QUDT (quantity values and units)




GeoSPARQL (spatial features/geometries, where available)




Dublin Core, Schema.org, SKOS (metadata and classification)




EnvThes (environmental terminology; e.g., catchment concept)




HM-Ontology (a custom ontology developed as part of this project, to cover hydrological and meteorological concepts not addressed by existing vocabularies.)



URIs are assigned to individual catchments, variables, and observations, enabling integration with other Linked Data sources and facilitating semantic queries. The RDF output is organized per catchment as multiple Turtle (.ttl) files. When loaded into a triple store, these can be unified into a single graph for querying and reasoning.

This conversion was developed as part of the NFDI4Earth inititative. The dataset is licensed under CC-BY 4.0.

This dataset is intended to support researchers and developers working on hydrology, environmental data integration, knowledge graphs, and natural language question answering over Linked Data.
DOI: 10.5281/zenodo.17290248
title: QUADICA in RDF: Water Quality, Discharge and Catchment Attributes for Germany as a Knowledge Graph

description: This dataset presents an RDF (Turtle syntax) re-publication of the QUADICA collection (water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany), converted from the original tabular resources using the open-source HydroTurtle tool. The goal is to enhance the interoperability, reusability, and FAIRness of German river water-quality data in the spirit of the Semantic Web and Linked Open Data.

About QUADICA (source data). QUADICA is the first large-sample water-quality dataset assembled for 1,386 German catchments, combining water quality and quantity time series with meteorological drivers, diffuse nitrogen inputs, and >100 catchment attributes spanning topography, climate, land cover, lithology, and soils. 

This RDF version. We convert the public QUADICA tables into a Linked Data graph and mint stable URIs for stations, catchments, variables, and individual observations. Where appropriate, we expose monthly and annual WRTDS (Weighted Regressions on Time, Discharge, and Season) outputs with flow-normalized concentrations and mean fluxes, together with medians of observed discharge, and the long-term monthly climatologies.

Ontologies and vocabularies used





SOSA/SSN (observations, features, sensors)




QUDT (quantity values and units)




GeoSPARQL (spatial features/geometries, where available)




Dublin Core, Schema.org, SKOS (metadata and classification)




EnvThes (environmental terminology; e.g., catchment concept)




HM-Ontology (a custom ontology developed as part of this project, to cover hydrological and meteorological concepts not addressed by existing vocabularies.)



URIs are assigned to individual catchments, variables, and observations, enabling integration with other Linked Data sources and facilitating semantic queries. The RDF output is organized per catchment as multiple Turtle (.ttl) files. When loaded into a triple store, these can be unified into a single graph for querying and reasoning.

This conversion was developed as part of the NFDI4Earth inititative. The dataset is licensed under CC-BY 4.0.

This dataset is intended to support researchers and developers working on hydrology, environmental data integration, knowledge graphs, and natural language question answering over Linked Data.
DOI: 10.15468/dl.j5hqgh
title: Occurrence Download

description: A dataset containing 41289 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is USA.44_1",
 "TaxonKey is Cyanocitta cristata",
 "Year 2016-2016"
 ]
}

 The dataset includes 41289 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021227-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.me5dtj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "HasCoordinate is true",
 "Geometry POLYGON ((-115.7268 35.13628, -115.7264 35.13632, -115.7259 35.13638, -115.7255 35.13646, -115.725 35.13656, -115.7246 35.13668, -115.7241 35.13681, -115.7237 35.13697, -115.7233 35.13714, -115.7229 35.13733, -115.7225 35.13754, -115.7221 35.13776, -115.7218 35.138, -115.7214 35.13825, -115.7211 35.13852, -115.7208 35.1388, -115.7205 35.13909, -115.7202 35.1394, -115.7199 35.13972, -115.7197 35.14004, -115.7195 35.14038, -115.7193 35.14073, -115.7191 35.14108, -115.719 35.14144, -115.7188 35.14181, -115.7187 35.14218, -115.7185 35.1434, -115.7184 35.14378, -115.7183 35.14417, -115.7183 35.14455, -115.7183 35.14494, -115.7183 35.14532, -115.7183 35.14571, -115.7184 35.14609, -115.7185 35.14647, -115.7186 35.14684, -115.7188 35.14721, -115.7189 35.14757, -115.7191 35.14793, -115.7193 35.14827, -115.7196 35.14861, -115.7198 35.14894, -115.7201 35.14925, -115.7204 35.14955, -115.7207 35.14984, -115.721 35.15012, -115.7214 35.15038, -115.7217 35.15062, -115.7221 35.15085, -115.7225 35.15106, -115.7229 35.15126, -115.7234 35.15144, -115.7238 35.1516, -115.7242 35.15174, -115.7247 35.15186, -115.7251 35.15196, -115.7256 35.15204, -115.7261 35.1521, -115.7265 35.15215, -115.7291 35.15232, -115.7296 35.15234, -115.7301 35.15234, -115.7306 35.15233, -115.731 35.15229, -115.7315 35.15223, -115.732 35.15215, -115.7324 35.15205, -115.7329 35.15193, -115.7333 35.15179, -115.7337 35.15163, -115.7342 35.15146, -115.7346 35.15126, -115.735 35.15105, -115.7354 35.15083, -115.7357 35.15058, -115.7361 35.15032, -115.7364 35.15005, -115.7367 35.14976, -115.737 35.14946, -115.7373 35.14915, -115.7375 35.14882, -115.7378 35.14849, -115.738 35.14814, -115.7382 35.14779, -115.7384 35.14742, -115.7385 35.14706, -115.7386 35.14668, -115.7387 35.1463, -115.7388 35.14592, -115.739 35.14446, -115.7391 35.14408, -115.7391 35.1437, -115.7391 35.14331, -115.7391 35.14293, -115.739 35.14255, -115.7389 35.14217, -115.7388 35.14179, -115.7387 35.14142, -115.7386 35.14106, -115.7384 35.1407, -115.7382 35.14035, -115.738 35.14001, -115.7377 35.13968, -115.7375 35.13936, -115.7372 35.13905, -115.7369 35.13875, -115.7366 35.13846, -115.7363 35.13819, -115.7359 35.13794, -115.7355 35.1377, -115.7352 35.13747, -115.7348 35.13727, -115.7344 35.13707, -115.7339 35.1369, -115.7335 35.13675, -115.7331 35.13661, -115.7326 35.13649, -115.7322 35.13639, -115.7317 35.13632, -115.7312 35.13626, -115.7308 35.13622, -115.7303 35.1362, -115.7298 35.1362, -115.7273 35.13626, -115.7268 35.13628))",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021229-251120083545085/datasets/export for details.
DOI: 10.5281/zenodo.17706582
title: Data for Global variability in the detectability of power plant NO$_2$ plumes from space

description: This repository contains the data for the paper "Global variability in the detectability of power plant NO2 plumes from space".

The paper presents the first global, data-driven analysis of power plant NO2 plume visibility from space. Using TROPOMI observations over 6,000 of the world's highest-emitting power plants and hourly CEMS data for 500 U.S. plants, we develop an automated algorithm that labels plumes and attributes them to their sources with 98% accuracy. We then train a machine learning model to predict plume detectability from environmental, meteorological, and observational variables (F1 score > 0.65, AUC > 0.8). Out of 25 variables, we find that NOx emission rate, surface albedo, wind speed, and sensor zenith angle jointly explain much of the detection variability. 

All of the data used in the paper is available.



processed U.S. power plants: us-power-plant-list.csv

processed global power plants: global-power-plant-list.csv

world cities: world-cities.csv

processed U.S. TROPOMI observations with corresponding variables: global-tropomi-observation-with-variables.csv

processed global TROPOMI observations with corresponding variables: us-tropomi-observation-with-variables.csv

ERA5 data with the following variables used in the paper could be downloaded at (U.S. 2019-2024, Global 2018): ERA5 hourly data on single levels from 1940 to present.



Temperature

TOA incident solar radiation

Total column water vapour



TROPOMI data with the following variables used in the paper could be downloaded at (U.S. 2019-2024, Global 2018): US, Global. A sample file is: S5P_OFFL_L2__NO2____20220726T064118_20220726T082247_24784_03_020400_20220727T224455.nc.



surface altitude

surface altitude precision

surface classification 

surface pressure

surface albedo



surface albedo nitrogendioxide window




cloud pressure crb




cloud fraction crb


cloud albedo crb

scene albedo



apparent scene pressure


snow ice flag

aerosol index 354 388

eastward wind

northward wind

scaled small pixel variance

sensor altitude

sensor azimuth angle

solar azimuth angle

sensor zenith angle

solar zenith angle
DOI: 10.5281/zenodo.17706581
title: Data for Global variability in the detectability of power plant NO$_2$ plumes from space

description: This repository contains the data for the paper "Global variability in the detectability of power plant NO2 plumes from space".

The paper presents the first global, data-driven analysis of power plant NO2 plume visibility from space. Using TROPOMI observations over 6,000 of the world's highest-emitting power plants and hourly CEMS data for 500 U.S. plants, we develop an automated algorithm that labels plumes and attributes them to their sources with 98% accuracy. We then train a machine learning model to predict plume detectability from environmental, meteorological, and observational variables (F1 score > 0.65, AUC > 0.8). Out of 25 variables, we find that NOx emission rate, surface albedo, wind speed, and sensor zenith angle jointly explain much of the detection variability. 

All of the data used in the paper is available.



processed U.S. power plants: us-power-plant-list.csv

processed global power plants: global-power-plant-list.csv

world cities: world-cities.csv

processed U.S. TROPOMI observations with corresponding variables: global-tropomi-observation-with-variables.csv

processed global TROPOMI observations with corresponding variables: us-tropomi-observation-with-variables.csv

ERA5 data with the following variables used in the paper could be downloaded at (U.S. 2019-2024, Global 2018): ERA5 hourly data on single levels from 1940 to present.



Temperature

TOA incident solar radiation

Total column water vapour



TROPOMI data with the following variables used in the paper could be downloaded at (U.S. 2019-2024, Global 2018): US, Global. A sample file is: S5P_OFFL_L2__NO2____20220726T064118_20220726T082247_24784_03_020400_20220727T224455.nc.



surface altitude

surface altitude precision

surface classification 

surface pressure

surface albedo



surface albedo nitrogendioxide window




cloud pressure crb




cloud fraction crb


cloud albedo crb

scene albedo



apparent scene pressure


snow ice flag

aerosol index 354 388

eastward wind

northward wind

scaled small pixel variance

sensor altitude

sensor azimuth angle

solar azimuth angle

sensor zenith angle

solar zenith angle
DOI: 10.6084/m9.figshare.29109695.v1
title: Transcriptional activity generates chromatin motion that drives nuclear blebbing

description: Abnormal nuclear morphology is a hallmark of human diseases, including cancers and age-related disorders. Previously, maintenance of nuclear morphology and integrity was thought to be solely dependent on a force balance between nuclear mechanical resistance and actin antagonism. However, our recent work revealed that inhibiting RNA polymerase II suppresses nuclear blebbing independent of altering force balance, but the mechanism remains unknown. Through removing cell culture media serum and then adding it back, we can decrease and then restore transcriptional activity. Decreasing transcriptional activity decreases nuclear bleb formation, stability, and rupture while returning transcriptional activity restores nuclear blebbing. These modulations of transcriptional activity did not alter nuclear or actin mechanics. The mean square displacement (MSD) of chromatin domains labeled via transfected Cy3-dNTPs revealed that transcription activity regulates chromatin motion. To determine if increasing chromatin motion is a mechanism to increase nuclear blebbing, we used an established RAD51 inhibitor BO2. We verified BO2 increases chromatin domain motion which resulted in increased nuclear blebbing. We reveal the mechanism by which transcriptional activity drives nuclear blebbing is through chromatin motion. Thus, two hallmarks of human disease are directly linked via transcriptional activity and abnormal nuclear shape.
DOI: 10.34894/mtk5aa
title: Linked Civil Registry, Zeeland, Release 2017_01

description: LINKS-gen_Zeeland_2017_01 contains life course and family reconstructions for all individuals who were born, married, or died within the Zeeland province. The dataset is based on the indexed Zeeland civil certificates as provided by WieWasWie (WWW) in December 2016.
&lt;br&gt;&lt;br&gt;
&lt;b&gt;Before using the data it is imperative to study the documentation. &lt;/b&gt;
DOI: 10.15468/dl.k58ch4
title: Occurrence Download

description: A dataset containing 31522 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Bucanetes githagineus"
 ]
}

 The dataset includes 31522 records from 96 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021231-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17672568
title: WP2 – Lindenberg campaign - PTB

description: Muon data measured with the PTB-DECOS2 detector at the Lindenberg site during the BIOSPHERE project
description: Acknowledgement

Project: 21GRD02 BIOSPHERE The project (21GRD02 BIOSPHERE) has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funded by the European Union.
DOI: 10.5281/zenodo.17672567
title: WP2 – Lindenberg campaign - PTB

description: Muon data measured with the PTB-DECOS2 detector at the Lindenberg site during the BIOSPHERE project
description: Acknowledgement

Project: 21GRD02 BIOSPHERE The project (21GRD02 BIOSPHERE) has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funded by the European Union.
DOI: 10.15468/dl.6szzeu
title: Occurrence Download

description: A dataset containing 48294 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is USA.44_1",
 "TaxonKey is Cyanocitta cristata",
 "Year 2017-2017"
 ]
}

 The dataset includes 48294 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021233-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sk3qru
title: Occurrence Download

description: A dataset containing 1588 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "TaxonKey is Liparis kumokiri"
 ]
}

 The dataset includes 1588 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021234-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.udk7yj
title: Occurrence Download

description: A dataset containing 30248 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-73.73411 46.36029,-71.60914 46.36029,-71.60914 48.54308,-73.73411 48.54308,-73.73411 46.36029))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 30248 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021235-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34894/aq486m
title: Linked Civil Registry, Drenthe-Groningen, Release 2018_01

description: LINKS-gen_Drenthe-Groningen_2018_01 contains life course and family reconstructions for all individuals who were born, married, or died within the provinces of Drenthe and Groningen. The dataset is based on the indexed Drenthe and Groninger civil certificates as provided by WieWasWie (WWW) in December 2017. 

&lt;b&gt;Before using the data it is imperative to study the documentation &lt;/b&gt;
DOI: 10.15468/dl.8z9f7k
title: Occurrence Download

description: A dataset containing 2253395 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Contopus virens, Regulus calendula)",
 "OccurrenceStatus is Present",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON ((-84.76571 41.22334, -58.93394 41.22334, -58.93394 49.85477, -84.76571 49.85477, -84.76571 41.22334))",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 2253395 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021208-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7qev7c
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "HasCoordinate is true",
 "Geometry POLYGON ((-120.822 36.70838, -120.8222 36.70803, -120.8223 36.70767, -120.8224 36.7073, -120.8225 36.70693, -120.8226 36.70655, -120.8227 36.70618, -120.8227 36.7058, -120.8227 36.70542, -120.8227 36.70504, -120.8226 36.70466, -120.8225 36.70429, -120.8224 36.70392, -120.8223 36.70355, -120.8221 36.70319, -120.822 36.70284, -120.8218 36.70249, -120.8216 36.70216, -120.8213 36.70183, -120.8211 36.70151, -120.8208 36.70121, -120.8205 36.70092, -120.8202 36.70064, -120.8122 36.69404, -120.8118 36.69377, -120.8115 36.69352, -120.8111 36.69329, -120.8107 36.69307, -120.8103 36.69287, -120.8099 36.69268, -120.8095 36.69251, -120.8091 36.69236, -120.8086 36.69223, -120.8082 36.69212, -120.8077 36.69203, -120.8073 36.69195, -120.8068 36.6919, -120.8063 36.69186, -120.8058 36.69185, -120.8054 36.69185, -120.8049 36.69188, -120.8044 36.69192, -120.804 36.69199, -120.8035 36.69207, -120.8031 36.69217, -120.8026 36.69229, -120.8022 36.69243, -120.8017 36.69259, -120.8013 36.69277, -120.8009 36.69296, -120.8005 36.69317, -120.8002 36.6934, -120.7998 36.69364, -120.7994 36.6939, -120.7971 36.69574, -120.7968 36.69601, -120.7965 36.69629, -120.7962 36.69658, -120.7959 36.69689, -120.7957 36.6972, -120.7954 36.69753, -120.7952 36.69786, -120.795 36.6982, -120.7949 36.69855, -120.7947 36.6989, -120.7946 36.69927, -120.7945 36.69963, -120.7945 36.7, -120.7944 36.70037, -120.7944 36.70074, -120.7944 36.70111, -120.7944 36.70149, -120.7945 36.70186, -120.7945 36.70222, -120.7946 36.70259, -120.7948 36.70295, -120.7949 36.7033, -120.7951 36.70365, -120.7953 36.70399, -120.7955 36.70432, -120.7957 36.70464, -120.7959 36.70496, -120.7962 36.70526, -120.7965 36.70555, -120.7968 36.70583, -120.7971 36.70609, -120.7975 36.70634, -120.8056 36.71197, -120.806 36.7122, -120.8063 36.71243, -120.8067 36.71263, -120.8071 36.71282, -120.8075 36.71299, -120.808 36.71315, -120.8084 36.71328, -120.8088 36.7134, -120.8093 36.7135, -120.8097 36.71359, -120.8102 36.71365, -120.8107 36.71369, -120.8111 36.71372, -120.8116 36.71372, -120.812 36.71371, -120.8138 36.71362, -120.8143 36.71358, -120.8148 36.71353, -120.8152 36.71345, -120.8157 36.71336, -120.8161 36.71325, -120.8166 36.71311, -120.817 36.71296, -120.8175 36.71279, -120.8179 36.7126, -120.8183 36.71239, -120.8187 36.71217, -120.819 36.71193, -120.8194 36.71167, -120.8197 36.7114, -120.8201 36.71112, -120.8204 36.71082, -120.8206 36.71051, -120.8209 36.71019, -120.8211 36.70985, -120.8214 36.70951, -120.8218 36.70873, -120.822 36.70838))",
 "Year is greater than or equal to 2001",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021237-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rghy6j
title: Occurrence Download

description: A dataset containing 3726 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Canada",
 "DatasetKey is iNaturalist Research-grade Observations",
 "MediaType is Image",
 "Month is May",
 "StateProvince is New Brunswick",
 "Year 2025-2025"
 ]
}

 The dataset includes 3726 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021239-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vwy84x
title: Occurrence Download

description: A dataset containing 204 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "TaxonKey is Epidendrum avicula"
 ]
}

 The dataset includes 204 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021240-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.