cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.d04meb
title: Occurrence Download

description: A dataset listing the 41 species recorded in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Specimen"
 ]
 },
 "Continent is Africa",
 "Country is South Africa",
 "Year 1998-2020",
 "TaxonKey is Ophiuroidea"
 ]
}

 The dataset's 41 records were derived from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000206-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y2ab8j
title: Occurrence Download

description: A dataset containing 4749 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhizopogon roseolus (Corda) Th.Fr."
 ]
}

 The dataset includes 4749 records from 189 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023273-230810091245214/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tsuw29
title: Occurrence Download

description: A dataset containing 73 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Machine Observation",
 "DatasetKey is Monitoreos trianuales de grupos hidrobiológicos en el Valle del Cerrejón, La Guajira",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 73 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0210823-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9szsxt
title: Occurrence Download

description: A dataset containing 197 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "StateProvince is one of (Chiapas, CHiapas, ChiapAs, Chiapas (State), CHIAPAS, TABASCO, CHIAPAS, OAXACA, VERACRUZ DE IGNACIO DE LA LLAVE)",
 "TaxonKey is Mastigodryas melanolomus (Cope, 1868)"
 ]
}

 The dataset includes 197 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002948-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ahyhzm
title: Occurrence Download

description: A dataset listing the 51 species recorded in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Specimen"
 ]
 },
 "Continent is Africa",
 "Country is South Africa",
 "Year 1998-2020",
 "TaxonKey is Bivalvia"
 ]
}

 The dataset's 51 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000208-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q6xx43
title: Occurrence Download

description: A dataset containing 321452 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pyrus L."
 ]
}

 The dataset includes 321452 records from 933 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0210829-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v6ttrj
title: Occurrence Download

description: A dataset containing 35312 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-128.20769 50.10131,-125.30534 48.68038,-123.67825 48.22382,-123.46103 48.23412,-123.25427 48.33459,-123.22506 48.35328,-123.18301 48.41168,-123.14329 48.46659,-123.2239 48.54135,-123.26968 48.70107,-123.18541 48.71313,-123.00517 48.78133,-123.01028 48.83942,-123.26604 48.99779,-122.76294 49.00425,-122.40387 49.00182,-121.74678 48.99829,-121.0967 48.99828,-120.93242 49.67887,-121.0263 49.9605,-121.42526 50.17171,-123.23235 50.42987,-125.34452 51.36861,-125.43258 51.5033,-125.6393 51.81946,-125.96311 52.16504,-128.67706 54.46647,-129.05256 54.72462,-129.05256 54.72462,-129.21684 55.47562,-129.475 55.94499,-129.89743 56.10927,-130.21872 56.42466,-130.4362 56.58,-131.46147 56.79748,-131.8688 57.14189,-132.02071 57.51206,-132.78407 58.0337,-133.20651 58.43266,-133.39425 58.66735,-133.57415 58.91016,-134.28606 59.20713,-134.84931 59.41835,-135.08399 59.69997,-135.57684 59.84078,-136.32783 59.74691,-136.61617 59.48362,-136.93802 59.27753,-137.36045 59.08979,-137.5482 58.87857,-137.06929 59.06175,-136.70333 59.13672,-136.42171 59.34794,-136.16355 59.6061,-135.5299 59.69997,-135.10746 59.48875,-134.9287 59.28049,-134.68503 59.18366,-134.12178 58.80816,-133.44119 58.43266,-132.28812 57.15553,-132.06445 57.06172,-132.05375 56.87428,-131.61625 56.53054,-130.64843 56.27355,-130.10865 55.92152,-130.01478 55.33481,-130.39027 54.84197,-131.89226 54.63075,-133.53507 54.30219,-132.99529 53.2461,-132.8356 52.87031,-131.72733 52.2072,-128.20769 50.10131))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Melittidae, Megachilidae, Halictidae, Colletidae, Apidae, Andrenidae)"
 ]
}

 The dataset includes 35312 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014018-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13744789
title: Dataset for simulation studies of fleet vehicle selection in terms of pollutant emissions

description: The purpose of this dataset is to enable the replication of the research results presented in the article: Szczepański E, Jachimowski R, Rudyk T. Simulation studies of fleet vehicle selection in terms of pollutant emissions. Combustion Engines. 2024;196(1):80-88. https://doi.org/10.19206/CE-169802 - published online: 2023-08-10, which discusses the application of simulation in solving the problem of vehicle selection and determining optimal approaches considering pollutant emissions.

Dataset contains:



Readme.txt: description of the dataset

InputData.csv: contains the input data used in the model, including data from the COPERT model.

OutputOptimization.csv: contains output data

OutputSummary.xlsx: contains output data

imulation_model_xml.fsx: contains the code of the model in XML format.


The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022. E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).- The Chinese part of the project is funded by National Natural Science Foundation of China.- The Swedish part of the project is funded by Swedish Energy Agency.- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).
DOI: 10.5281/zenodo.13744790
title: Dataset for simulation studies of fleet vehicle selection in terms of pollutant emissions

description: The purpose of this dataset is to enable the replication of the research results presented in the article: Szczepański E, Jachimowski R, Rudyk T. Simulation studies of fleet vehicle selection in terms of pollutant emissions. Combustion Engines. 2024;196(1):80-88. https://doi.org/10.19206/CE-169802 - published online: 2023-08-10, which discusses the application of simulation in solving the problem of vehicle selection and determining optimal approaches considering pollutant emissions.

Dataset contains:



Readme.txt: description of the dataset

InputData.csv: contains the input data used in the model, including data from the COPERT model.

OutputOptimization.csv: contains output data

OutputSummary.xlsx: contains output data

imulation_model_xml.fsx: contains the code of the model in XML format.


The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022. E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).- The Chinese part of the project is funded by National Natural Science Foundation of China.- The Swedish part of the project is funded by Swedish Energy Agency.- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).
DOI: 10.5281/zenodo.13754539
title: Data set for risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm

description: The purpose of this dataset is to enable the replication of the research results presented in the article: Izdebski, M. (2023). Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm. Archives of Transport, 67(3), 139-153. https://doi.org/10.5604/01.3001.0053.7463 - published online: 2023-09-30, which discusses the allocation problem of vehicles to tasks, taking into account risk issues.

Dataset contains:



Readme.txt: description of the dataset

InputData.xlsx: Contains the input data used in the model

DistributionFit.xlsx: Compliance testing and distribution parameters for road accidents of any type and collision-type

OutputAssignment.xlsx: Results of assignment and alghoritm tests


The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022. E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).- The Chinese part of the project is funded by National Natural Science Foundation of China.- The Swedish part of the project is funded by Swedish Energy Agency.- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).
DOI: 10.5281/zenodo.13754540
title: Data set for risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm

description: The purpose of this dataset is to enable the replication of the research results presented in the article: Izdebski, M. (2023). Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm. Archives of Transport, 67(3), 139-153. https://doi.org/10.5604/01.3001.0053.7463 - published online: 2023-09-30, which discusses the allocation problem of vehicles to tasks, taking into account risk issues.

Dataset contains:



Readme.txt: description of the dataset

InputData.xlsx: Contains the input data used in the model

DistributionFit.xlsx: Compliance testing and distribution parameters for road accidents of any type and collision-type

OutputAssignment.xlsx: Results of assignment and alghoritm tests


The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022. E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).- The Chinese part of the project is funded by National Natural Science Foundation of China.- The Swedish part of the project is funded by Swedish Energy Agency.- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).
DOI: 10.15468/dl.5h65jw
title: Occurrence Download

description: A dataset containing 121 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bittacus hageni Brauer, 1860"
 ]
}

 The dataset includes 121 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014021-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48577/jpl.4lukea
title: Analytical Methods Used in the Design of Thermal Toolbox Elements for Extreme Environments

description: Analytical Methods Used in the Design of Thermal Toolbox Elements for Extreme Environments
DOI: 10.5281/zenodo.12572745
title: Upper Tropospheric Humidity with respect to Ice

description: We use brightness temperature data from the High-Resolution Infrared Radiation Sounder (HIRS) onboard the NOAA series of satellites, to produce a 40-year data set of Upper Tropospheric Humidity with respect to Ice (UTHi). The UTHi values are derived from brightness temperatures in channels 6 and 12 (upper tropospheric temperature channel and upper tropospheric water vapor channel correspondingly) using a retrieval method developed by Gierens and Eleftheratos (2019) in conjunction with the application of intercalibration coefficients from Shi and Bates (2011). As channel 6 is in the CO2 absorption band and CO2 concentrations have increased since 1980, channel 6 brightness temperature records have been influenced negatively by approximately 2oK in the 40-year period (Shi et al., 2016).  We correct the T6 data, by applying a correction formula based on the global CO2-related brightness temperature false decrease in the 40-year period and the month-to-month change of CO2 with a reference CO2 value of 370 ppm. Due to the switch of the HIRS instrument in 2000 which was accompanied by a change in the central wavelength of channel 12 there was a break in the time series of the respective brightness temperature data and subsequently to the UTHi data. We perform a bias correction and eliminate this break during the application of the retrieval equation. After the application of the previous bias corrections, we calculate the UTHi in a 2.5° x 2.5° data grid for 70°S-70°N on a monthly temporal scale.

 

Gierens, K. & Eleftheratos, K. On the interpretation of upper-tropospheric humidity based on a second-order retrieval from infrared radiances. Atmos Chem Phys 19, 3733–3746 (2019).

Shi, L. & Bates, J. J. Three decades of intersatellite-calibrated high-resolution infrared radiation sounder upper tropospheric water vapor. Journal of Geophysical Research Atmospheres 116, (2011).

Shi, L., Matthews, J. L., Ho, S. P., Yang, Q. & Bates, J. J. Algorithm development of temperature and humidity profile retrievals for long-term HIRS observations. Remote Sens (Basel) 8, (2016).Users who are interested in using this UTHi dataset are strongly recommended to contact first Dr. Kostas Eleftheratos (kelef@geol.uoa.gr).
DOI: 10.5281/zenodo.12572744
title: Upper Tropospheric Humidity with respect to Ice

description: We use brightness temperature data from the High-Resolution Infrared Radiation Sounder (HIRS) onboard the NOAA series of satellites, to produce a 40-year data set of Upper Tropospheric Humidity with respect to Ice (UTHi). The UTHi values are derived from brightness temperatures in channels 6 and 12 (upper tropospheric temperature channel and upper tropospheric water vapor channel correspondingly) using a retrieval method developed by Gierens and Eleftheratos (2019) in conjunction with the application of intercalibration coefficients from Shi and Bates (2011). As channel 6 is in the CO2 absorption band and CO2 concentrations have increased since 1980, channel 6 brightness temperature records have been influenced negatively by approximately 2oK in the 40-year period (Shi et al., 2016).  We correct the T6 data, by applying a correction formula based on the global CO2-related brightness temperature false decrease in the 40-year period and the month-to-month change of CO2 with a reference CO2 value of 370 ppm. Due to the switch of the HIRS instrument in 2000 which was accompanied by a change in the central wavelength of channel 12 there was a break in the time series of the respective brightness temperature data and subsequently to the UTHi data. We perform a bias correction and eliminate this break during the application of the retrieval equation. After the application of the previous bias corrections, we calculate the UTHi in a 2.5° x 2.5° data grid for 70°S-70°N on a monthly temporal scale.

The file <UTHi_Exceedances_HIRS.nc> contains the fraction of UTHi threshold exceedances above 70, 80, 90 and 100%, together with their respective deseasonalized anomalies. The temporal and spatial resolution is the same as in the file <UTHi_HIRS.nc>, which contains the monthly mean UTHi data.

Gierens, K. & Eleftheratos, K. On the interpretation of upper-tropospheric humidity based on a second-order retrieval from infrared radiances. Atmos Chem Phys 19, 3733–3746 (2019).

Shi, L. & Bates, J. J. Three decades of intersatellite-calibrated high-resolution infrared radiation sounder upper tropospheric water vapor. Journal of Geophysical Research Atmospheres 116, (2011).

Shi, L., Matthews, J. L., Ho, S. P., Yang, Q. & Bates, J. J. Algorithm development of temperature and humidity profile retrievals for long-term HIRS observations. Remote Sens (Basel) 8, (2016).Users who are interested in using this UTHi dataset are strongly recommended to contact first Dr. Kostas Eleftheratos (kelef@geol.uoa.gr).
DOI: 10.5281/zenodo.13755230
title: Upper Tropospheric Humidity with respect to Ice

description: We use brightness temperature data from the High-Resolution Infrared Radiation Sounder (HIRS) onboard the NOAA series of satellites, to produce a 40-year data set of Upper Tropospheric Humidity with respect to Ice (UTHi). The UTHi values are derived from brightness temperatures in channels 6 and 12 (upper tropospheric temperature channel and upper tropospheric water vapor channel correspondingly) using a retrieval method developed by Gierens and Eleftheratos (2019) in conjunction with the application of intercalibration coefficients from Shi and Bates (2011). As channel 6 is in the CO2 absorption band and CO2 concentrations have increased since 1980, channel 6 brightness temperature records have been influenced negatively by approximately 2oK in the 40-year period (Shi et al., 2016).  We correct the T6 data, by applying a correction formula based on the global CO2-related brightness temperature false decrease in the 40-year period and the month-to-month change of CO2 with a reference CO2 value of 370 ppm. Due to the switch of the HIRS instrument in 2000 which was accompanied by a change in the central wavelength of channel 12 there was a break in the time series of the respective brightness temperature data and subsequently to the UTHi data. We perform a bias correction and eliminate this break during the application of the retrieval equation. After the application of the previous bias corrections, we calculate the UTHi in a 2.5° x 2.5° data grid for 70°S-70°N on a monthly temporal scale.

The file <UTHi_Exceedances_HIRS.nc> contains the fraction of UTHi threshold exceedances above 70, 80, 90 and 100%, together with their respective deseasonalized anomalies. The temporal and spatial resolution is the same as in the file <UTHi_HIRS.nc>, which contains the monthly mean UTHi data.

Gierens, K. & Eleftheratos, K. On the interpretation of upper-tropospheric humidity based on a second-order retrieval from infrared radiances. Atmos Chem Phys 19, 3733–3746 (2019).

Shi, L. & Bates, J. J. Three decades of intersatellite-calibrated high-resolution infrared radiation sounder upper tropospheric water vapor. Journal of Geophysical Research Atmospheres 116, (2011).

Shi, L., Matthews, J. L., Ho, S. P., Yang, Q. & Bates, J. J. Algorithm development of temperature and humidity profile retrievals for long-term HIRS observations. Remote Sens (Basel) 8, (2016).Users who are interested in using this UTHi dataset are strongly recommended to contact first Dr. Kostas Eleftheratos (kelef@geol.uoa.gr).
DOI: 10.5281/zenodo.13743678
title: Evaluating Human Health Risks with Microbial Source Tracking and Quantitative Microbial Risk Assessment in Little Bay, Texas

description: These results are the median human health risks associated with a range of recreational activities (i.e., swimming, kayaking, fishing, boating, jet skiing) in Little Bay, Texas. The health risks were estimated based on a quantiative microbial risk assessment (QMRA) that was performed using enterococci concentrations as well as host-associated molecular marker (i.e., human, canine, gull) concentrations measured previously in Little Bay. These human health risks represent the probability of a gastrointestinal illness for recreators.