cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7901615
title: Hourly wind and solar generation profiles for every EIA 2020 plant in the CONUS

description: Historical hourly time series of wind and solar generation profiles for every plant within the United States (US) that is part of the Energy Information Administration (EIA) 2020 dataset for the years 1980 through 2022. The data uses regional atmospheric climate model simulations and 2020 wind and solar power plant configurations across the entire contiguous US. This data is designed to be be aggregated to the Balancing Authority (BA) scale, or to other scales such as to the nodes of a production cost model which would allow the data to be used to perform reliability assessments and evaluations of technology innovation. There are ongoing efforts to extend this dataset for future climate projections, which additionally require the projection of future infrastructure under a wide range of uncertainties. This historical dataset is a benchmark for those projections and can be used to understand sensitivity to historical inter-annual variability, seasonality, and recent extreme events.

For more information please refer to the code repository at https://github.com/GODEEEP/tgw-gen.

The dataset consists of two components:



Plant configuration files - The plant configuration files (eia_solar_configs.csv and eia_wind_configs.csv) contain all the plant data that is relevant to a generation model, derived from EIA 860 2020 data. Each row corresponds to a single logical plant. In some cases actual plants were split into two logical plants for modeling purposes.

Generation data files - The generation data resides in the solar and wind directories and consists of one file per year. Each year contains an 8760 (hourly) profile of generation for every plant. The first column in each csv file is the datetime in Coordinated Universal Time (UTC), and the subsequent columns correspond to the plant_code_unique column in the configuration files. Data in these generation files is expressed as a capacity factor, which is generation divided by the plant capacity. To obtain the actual Megawatts (MW) generated, multiply the capacity factor for a plant by the value in the system_capacity column from the respective configuration file.


This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.
DOI: 10.5281/zenodo.8240163
title: Hourly wind and solar generation profiles for every EIA 2020 plant in the CONUS

description: Historical hourly time series of wind and solar generation profiles for every plant within the United States (US) that is part of the Energy Information Administration (EIA) 2020 dataset for the years 1980 through 2022. The data uses regional atmospheric climate model simulations and 2020 wind and solar power plant configurations across the entire contiguous US. This data is designed to be be aggregated to the Balancing Authority (BA) scale, or to other scales such as to the nodes of a production cost model which would allow the data to be used to perform reliability assessments and evaluations of technology innovation. There are ongoing efforts to extend this dataset for future climate projections, which additionally require the projection of future infrastructure under a wide range of uncertainties. This historical dataset is a benchmark for those projections and can be used to understand sensitivity to historical inter-annual variability, seasonality, and recent extreme events.

For more information please refer to the code repository at https://github.com/GODEEEP/tgw-gen.

The dataset consists of two components:



Plant configuration files - The plant configuration files (eia_solar_configs.csv and eia_wind_configs.csv) contain all the plant data that is relevant to a generation model, derived from EIA 860 2020 data. Each row corresponds to a single logical plant. In some cases actual plants were split into two logical plants for modeling purposes.

Generation data files - The generation data resides in the solar/ and wind/ directories and consists of one file per year. Each year contains an 8760 (hourly) profile of generation for every plant. The first column in each csv file is the datetime in Coordinated Universal Time (UTC), and the subsequent columns correspond to the plant_code_unique column in the configuration files. Data in these generation files is expressed as a capacity factor, which is generation divided by the plant capacity. To obtain the actual Megawatts (MW) generated, multiply the capacity factor for a plant by the value in the system_capacity column from the respective configuration file.


This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

Version v1.1.0 adds bias corrected solar and extends the data through 2022

Citation: 



Bracken, Cameron, Underwood, Scott, Campbell, Allison, Thurber, Travis B, & Voisin, Nathalie. (2023). Hourly wind and solar generation profiles for every EIA 2020 plant in the CONUS (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7901614


Corresponding Author:



Cameron Bracken, cameron.bracken@pnnl.gov
DOI: 10.5281/zenodo.8393319
title: Hourly wind and solar generation profiles for every EIA 2020 plant in the CONUS

description: Historical hourly time series of wind and solar generation profiles for every plant within the United States (US) that is part of the Energy Information Administration (EIA) 2020 dataset for the years 1980 through 2022. The data uses regional atmospheric climate model simulations and 2020 wind and solar power plant configurations across the entire contiguous US. This data is designed to be be aggregated to the Balancing Authority (BA) scale, or to other scales such as to the nodes of a production cost model which would allow the data to be used to perform reliability assessments and evaluations of technology innovation. There are ongoing efforts to extend this dataset for future climate projections, which additionally require the projection of future infrastructure under a wide range of uncertainties. This historical dataset is a benchmark for those projections and can be used to understand sensitivity to historical inter-annual variability, seasonality, and recent extreme events.

For more information please refer to the Scientific Data paper and the code repository.

The dataset consists of two components:



Plant configuration files - The plant configuration files (eia_solar_configs.csv and eia_wind_configs.csv) contain all the plant data that is relevant to a generation model, derived from EIA 860 2020 data. Each row corresponds to a single logical plant. In some cases actual plants were split into two logical plants for modeling purposes.

Generation data files - The generation data resides in the solar/ and wind/ directories and consists of one file per year. Each year contains an 8760 (hourly) profile of generation for every plant. The first column in each csv file is the datetime in Coordinated Universal Time (UTC), and the subsequent columns correspond to the plant_code_unique column in the configuration files. Data in these generation files is expressed as a capacity factor, which is generation divided by the plant capacity. To obtain the actual Megawatts (MW) generated, multiply the capacity factor for a plant by the value in the system_capacity column from the respective configuration file.


This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

Version v1.1.0 adds bias corrected solar and extends the data through 2022

Version 1.1.1 adds missing wind years 2021 and 2022

Citation: 



Bracken, Cameron, Underwood, Scott, Campbell, Allison, Thurber, Travis B, & Voisin, Nathalie. (2023). Hourly wind and solar generation profiles for every EIA 2020 plant in the CONUS (v1.1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7901614


Corresponding Author:



Cameron Bracken, cameron.bracken@pnnl.gov
DOI: 10.15468/dl.f2wffe
title: Occurrence Download

description: A dataset listing the 17956 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is University of Michigan Museum of Zoology, Division of Mollusks"
 ]
}

 The dataset's 17956 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021282-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8146228
title: Dataset of the manuscript "Assessing the influence of Eisenia andrei on the decomposition of Casuarina equisetifolia litter in vermicompost."

description: Data generated during an experiment of decomposition of Casuarina equisetifolia litter by the application of vermicompost (VC) or the combination vermicompost + the earthworm Eisenia andrei (E).


Six files are included:


"readme.csv" is a file where we explain the meaning of each column (and in which units is expressed) in each of the other five files.


"earthworm_N_biomass.csv" is a table with the number of Eisenia andrei individuals and the total earthworm fresh weight in each of the experimental units we sampled


"FTIR_spectra.csv" is a file with the raw spectral data we obtained from the litter by Fourier Transform Infrared spectroscopy combined with Attenuated Total Reflectance (FTIR-ATR). First column indicate the wavenumber (cm-1) and the other columns indicate the absorbance values of each litter sample for each wavenumber.


"litter_chemical_composition.csv" is a file with the raw data of the concentrations of different chemical elements measured in C. equisetifolia litter collected at different decomposition times.


"litter_mass_loss.csv" contains the dry weight data of the litter at time 0 and after each collection time, as well as the percentage of litter mass loss with time. .


"mesofaunal_com.csv" are the numbers of individuals of several groups of mesofaunal organisms (collembolans, mites, and others) we recovered in each of our experimental units.
DOI: 10.5281/zenodo.8146229
title: Dataset of the manuscript "Assessing the influence of Eisenia andrei on the decomposition of Casuarina equisetifolia litter in vermicompost."

description: Data generated during an experiment of decomposition of Casuarina equisetifolia litter by the application of vermicompost (VC) or the combination vermicompost + the earthworm Eisenia andrei (E).


Six files are included:


"readme.csv" is a file where we explain the meaning of each column (and in which units is expressed) in each of the other five files.


"earthworm_N_biomass.csv" is a table with the number of Eisenia andrei individuals and the total earthworm fresh weight in each of the experimental units we sampled


"FTIR_spectra.csv" is a file with the raw spectral data we obtained from the litter by Fourier Transform Infrared spectroscopy combined with Attenuated Total Reflectance (FTIR-ATR). First column indicate the wavenumber (cm-1) and the other columns indicate the absorbance values of each litter sample for each wavenumber.


"litter_chemical_composition.csv" is a file with the raw data of the concentrations of different chemical elements measured in C. equisetifolia litter collected at different decomposition times.


"litter_mass_loss.csv" contains the dry weight data of the litter at time 0 and after each collection time, as well as the percentage of litter mass loss with time. .


"mesofaunal_com.csv" are the numbers of individuals of several groups of mesofaunal organisms (collembolans, mites, and others) we recovered in each of our experimental units.
DOI: 10.48577/jpl.brkmt5
title: Endurance

description: No abstract available.
DOI: 10.15468/dl.xjhxmf
title: Occurrence Download

description: A dataset containing 131073 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "DatasetKey is Vascular plant records verified via iRecord",
 "License is CC-BY 4.0",
 "OccurrenceStatus is Present",
 "Year 2024-2024"
 ]
}

 The dataset includes 131073 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021270-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uxamxe
title: Occurrence Download

description: A dataset containing 20580 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Loxodonta africana (Blumenbach, 1797)"
 ]
}

 The dataset includes 20580 records from 153 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021296-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2hparg
title: Occurrence Download

description: A dataset containing 3915 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON Z((-99.12159 19.51599 0,-99.122 19.51578 0,-99.12218 19.51568 0,-99.12219 19.51568 0,-99.12257 19.51529 0,-99.12324 19.51465 0,-99.12329 19.5146 0,-99.12359 19.51428 0,-99.1236 19.51427 0,-99.12284 19.51124 0,-99.12229 19.51054 0,-99.12154 19.50972 0,-99.12153 19.50971 0,-99.12121 19.50929 0,-99.12099 19.50908 0,-99.12091 19.50895 0,-99.12066 19.50874 0,-99.12032 19.5083 0,-99.11973 19.50739 0,-99.1197 19.50675 0,-99.1194 19.50614 0,-99.11935 19.50596 0,-99.11929 19.50573 0,-99.11921 19.50553 0,-99.1192 19.50539 0,-99.11917 19.50488 0,-99.11915 19.50455 0,-99.11922 19.50429 0,-99.11926 19.50415 0,-99.11929 19.50335 0,-99.11935 19.5029 0,-99.11944 19.50291 0,-99.11952 19.50258 0,-99.11839 19.5024 0,-99.11746 19.50229 0,-99.11709 19.50243 0,-99.11706 19.50231 0,-99.11686 19.50243 0,-99.1161 19.50284 0,-99.11608 19.50284 0,-99.11211 19.50484 0,-99.11077 19.50576 0,-99.10944 19.50632 0,-99.10857 19.50711 0,-99.10852 19.50715 0,-99.1085 19.50718 0,-99.10798 19.50775 0,-99.10729 19.5087 0,-99.10648 19.50953 0,-99.10641 19.50962 0,-99.10594 19.51008 0,-99.10556 19.5103 0,-99.10536 19.51041 0,-99.10518 19.51048 0,-99.10485 19.51062 0,-99.10479 19.51065 0,-99.11253 19.51113 0,-99.11467 19.51129 0,-99.11468 19.51129 0,-99.11473 19.51133 0,-99.11785 19.51402 0,-99.11806 19.51401 0,-99.1191 19.51399 0,-99.1191 19.514 0,-99.11919 19.51409 0,-99.1202 19.51519 0,-99.12022 19.51521 0,-99.12111 19.51619 0,-99.12115 19.51622 0,-99.12159 19.51599 0))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 3915 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021300-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6fmuuz
title: Occurrence Download

description: A dataset containing 242662 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "GadmGid is MEX.10_1",
 "Year 2002-2024"
 ]
}

 The dataset includes 242662 records from 192 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021285-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yd6t53
title: Occurrence Download

description: A dataset containing 24184 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "CoordinateUncertaintyInMeters is less than or equal to 10.0",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-116.31133 45.68998,-116.31133 43.88894,-114.99519 42.91915,-113.64859 42.35944,-112.34457 41.70011,-111.89036 40.95286,-111.84641 39.59024,-112.24201 38.88695,-113.06251 38.22761,-113.20903 37.7441,-112.70926 37.51605,-110.76969 38.27803,-109.315 38.27803,-108.96161 37.75875,-108.24367 37.11407,-107.93598 36.76243,-107.4447 36.06137,-107.42317 35.4291,-106.47491 35.02231,-104.81242 35.50721,-104.3968 36.54626,-104.18898 38.20876,-104.46607 39.80198,-104.81242 41.94937,-105.43585 43.40405,-106.12856 44.51238,-106.8759 46.02231,-108.49049 47.06962,-110.49783 47.63691,-112.06878 48.29147,-113.02881 49.6006,-113.5234 51.10048,-113.94521 52.56797,-114.94887 53.65891,-117.1744 54.2262,-119.09446 55.62261,-120.70905 57.67358,-122.37617 59.41734,-123.01091 59.71669,-124.98737 59.74252,-126.31212 60.00227,-127.50699 59.30094,-127.90509 58.46454,-126.25447 57.83381,-125.58348 57.43122,-124.69778 57.02863,-123.90602 56.26371,-122.89094 54.5753,-121.36512 52.69787,-118.23438 49.73713,-117.69805 48.98967,-117.00404 47.90663,-117.07331 46.7983,-116.31133 45.68998))",
 "OccurrenceStatus is Present",
 "TaxonKey is Fabaceae",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 24184 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021302-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m736wf
title: Occurrence Download

description: A dataset containing 102490 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Field Museum of Natural History (Botany) Fungi Collection"
 ]
}

 The dataset includes 102490 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021261-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9ezchw
title: Occurrence Download

description: A dataset containing 24184 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters is less than or equal to 10.0",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-116.31133 45.68998,-116.31133 43.88894,-114.99519 42.91915,-113.64859 42.35944,-112.34457 41.70011,-111.89036 40.95286,-111.84641 39.59024,-112.24201 38.88695,-113.06251 38.22761,-113.20903 37.7441,-112.70926 37.51605,-110.76969 38.27803,-109.315 38.27803,-108.96161 37.75875,-108.24367 37.11407,-107.93598 36.76243,-107.4447 36.06137,-107.42317 35.4291,-106.47491 35.02231,-104.81242 35.50721,-104.3968 36.54626,-104.18898 38.20876,-104.46607 39.80198,-104.81242 41.94937,-105.43585 43.40405,-106.12856 44.51238,-106.8759 46.02231,-108.49049 47.06962,-110.49783 47.63691,-112.06878 48.29147,-113.02881 49.6006,-113.5234 51.10048,-113.94521 52.56797,-114.94887 53.65891,-117.1744 54.2262,-119.09446 55.62261,-120.70905 57.67358,-122.37617 59.41734,-123.01091 59.71669,-124.98737 59.74252,-126.31212 60.00227,-127.50699 59.30094,-127.90509 58.46454,-126.25447 57.83381,-125.58348 57.43122,-124.69778 57.02863,-123.90602 56.26371,-122.89094 54.5753,-121.36512 52.69787,-118.23438 49.73713,-117.69805 48.98967,-117.00404 47.90663,-117.07331 46.7983,-116.31133 45.68998))",
 "OccurrenceStatus is Present",
 "TaxonKey is Fabaceae",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 24184 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021304-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14036211
title: marisora_unimarginata_spades_01.fasta.gz
DOI: 10.5281/zenodo.14036212
title: marisora_unimarginata_spades_01.fasta.gz
DOI: 10.15468/dl.nxzcpd
title: Occurrence Download

description: A dataset containing 4548 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Potamotrygonidae"
 ]
}

 The dataset includes 4548 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021306-241024112534372/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.26528008
title: Data used in statistical analysis - Mayerl et al - turtle girdle

description: Data used in statistical analysis - sea turle vs river cooter girdle.
DOI: 10.6084/m9.figshare.26528008.v1
title: Data used in statistical analysis - Mayerl et al - turtle girdle

description: Data used in statistical analysis - sea turle vs river cooter girdle.
DOI: 10.25430/researchdata.cab.unipd.it.00001226
title: Ergonomic Design of Human-Robot Collaborative Workstation in the Era of Industry 5.0

description: The increasing adoption of collaborative robots to support job execution in manufacturing has catalyzed companies' attention to safety and well-being issues. Sharing the human-centric perspective and harmonious human-machine collaboration concepts emphasized by Industry 5.0, the design phase of a collaborative workstation must integrate both psychological and physical risk evaluations to provide a safe and inclusive work environment suitable for a diversified workforce. Accelerating the pre-deployment phase to quickly reconfigure workstation design and assess its impact on workload balancing and task sequencing during the deployment of assembly lines still represents a challenging task considering the available software tools. This research proposes a new mathematical model to accelerate the design of ergonomic human-robot collaborative workstations based on task alternatives and the combined consideration of postural assessment and fatigue analyses for each of them to design an ergo-friendly collaborative environment. Surface electromyography analysis is jointly adopted with postural risk assessment measured with inertial measurement units and developed by a digital ergonomic platform to determine the optimal workplace configuration for tools, equipment, and resources to promote physical well-being while considering station productivity. Experimental tests are performed to investigate arm muscles and postural risk assessment for different configurations of workstation design and collaborative human-robot job progression. Experimental results demonstrate the feasibility, and the advantages of the proposed approach compared to existing simulation software to quickly generate and assess alternative scenarios and find a trade-off between ergo-quality levels and system performance. The final discussion offers valuable information for decision-makers and practitioners to facilitate the integration of human factors throughout the early stages of ergo-friendly workspace design, while effectively managing the complexity generated by resource allocation and collaborative robots.
DOI: 10.15468/dl.y554g6
title: Occurrence Download

description: A dataset containing 261 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crocodylus novaeguineae Schmidt, 1928"
 ]
}

 The dataset includes 261 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021308-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.