cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6339553
title: Supplemental Data for Dyreson et al. 2021, Earth's Future

description: <pre>Supplemental Data for Dyreson et al. 2021, Earth's Future Publication Title: The Role of Regional Connections in Planning for Future Power System Operations under Climate Extremes Abstract: Extreme climate events can negatively impact power system operations. Identifying the sensitivity of a power system to climate extremes must consider future infrastructure changes. We investigate the sensitivity of a historic Western U.S. power system (5% variable renewable penetration of energy) and a future system (31%) to compound drought and heat wave events. We use an electricity operational model combined with a model of historically extreme drought (for hydropower and freshwater-reliant thermoelectric generators) over the Western U.S. and a synthetic, regionally extreme heat event focused on Southern California (for thermoelectric generators and electricity load). We find that drought has the highest impact on summertime production cost (+10 to +12%), while temperature-based deratings have minimal effect (at most +1%). The heat wave scenario impacting load increases the summertime regional net imports to that region by 10 to 14%, while the drought decreases them by 5 to 12%. Combined heat and drought conditions have a moderate effect on imports to Southern California (-2%) in the historic system and a stronger effect (+8%) in the future system. Southern California dependence on other regions decreases in the summertime with the moderate increase in variable renewable energy (-34% imports), but hourly peak regional imports are maintained under those infrastructure changes. Using a novel method that combines synthetic and historically-driven conditions to test multiple infrastructures, we consolidate the importance of considering compounded heat wave and drought in planning studies and show that region-to-region energy transfers during peak periods are key to optimal operations under climate extremes. Dataset Summary: There are four categories of input files: 1) Water-constrainted generation capacity and monthly energy targets for production cost modeling (PNNL) 2) Temperature and precipitation results from 100 simulations [Temerature_precip_simulations] 3) Derating inputs [Derating_inputs]: dry bulb and wet bulb temperature based on four selected simulations and county design point temperature for thermoelectric generation 4) Load inputs [Load_inputs]: hourly county electricity demand temperature sensitivites (slopes), hourly county electricity demand for ten scenarios (baseline year + four simulated years for low VG and moderate VG infrastructures; moderate VG infrastrcutures also incorporate load growth) As well as the output files for bulk electricity grid operations as simulated by production cost model in a historical and future infrastructure informed by a combination of drought and heat scenarios. Each data set described in detail in each read me file.</pre>
DOI: 10.5281/zenodo.6339552
title: Supplemental Data for Dyreson et al. 2021, Earth's Future

description: <pre>Supplemental Data for Dyreson et al. 2021, Earth's Future Publication Title: The Role of Regional Connections in Planning for Future Power System Operations under Climate Extremes Abstract: Extreme climate events can negatively impact power system operations. Identifying the sensitivity of a power system to climate extremes must consider future infrastructure changes. We investigate the sensitivity of a historic Western U.S. power system (5% variable renewable penetration of energy) and a future system (31%) to compound drought and heat wave events. We use an electricity operational model combined with a model of historically extreme drought (for hydropower and freshwater-reliant thermoelectric generators) over the Western U.S. and a synthetic, regionally extreme heat event focused on Southern California (for thermoelectric generators and electricity load). We find that drought has the highest impact on summertime production cost (+10 to +12%), while temperature-based deratings have minimal effect (at most +1%). The heat wave scenario impacting load increases the summertime regional net imports to that region by 10 to 14%, while the drought decreases them by 5 to 12%. Combined heat and drought conditions have a moderate effect on imports to Southern California (-2%) in the historic system and a stronger effect (+8%) in the future system. Southern California dependence on other regions decreases in the summertime with the moderate increase in variable renewable energy (-34% imports), but hourly peak regional imports are maintained under those infrastructure changes. Using a novel method that combines synthetic and historically-driven conditions to test multiple infrastructures, we consolidate the importance of considering compounded heat wave and drought in planning studies and show that region-to-region energy transfers during peak periods are key to optimal operations under climate extremes. Dataset Summary: There are four categories of input files: 1) Water-constrainted generation capacity and monthly energy targets for production cost modeling (PNNL) 2) Temperature and precipitation results from 100 simulations [Temerature_precip_simulations] 3) Derating inputs [Derating_inputs]: dry bulb and wet bulb temperature based on four selected simulations and county design point temperature for thermoelectric generation 4) Load inputs [Load_inputs]: hourly county electricity demand temperature sensitivites (slopes), hourly county electricity demand for ten scenarios (baseline year + four simulated years for low VG and moderate VG infrastructures; moderate VG infrastrcutures also incorporate load growth) As well as the output files for bulk electricity grid operations as simulated by production cost model in a historical and future infrastructure informed by a combination of drought and heat scenarios. Each data set described in detail in each read me file.</pre>
DOI: 10.5281/zenodo.6341957
title: Using snapshot measurements to identify high-emitting vehicles

description: This repo includes codes and sample data for Qiu and Borken-kleefeld, ERL, 2022. <strong>Material for reproducing figures in the paper</strong> R script: plot.r Data for plot 2: <em>RS_Zurich_data.csv</em>: the sample RS data from Zurich. <em>algorithm_eu5d_final_iteration.rds</em>: the estimated average emission factor for each city fleet (outputs from the iterative algorithm) Data for plot 3:<em> </em> <em>Zurich_clean_identification.xlsx</em>: summary of the fraction of clean vehicles being identified by each potential RS threshold. <em>Zurich_high_emitter_identification.xlsx</em>: summary of the fraction of high-emitters being identified by each potential RS threshold. Data for plot 4: <em>validation_test_dataset.csv</em>: the original validation dataset that includes the underlying average emission factor and the simulated instantaneous emissions. <em>validation_algorithm_results.rds</em>: algorithm outputs when applied to the validation dataset. <strong>The iterative algorithm and sample data that can be used for demonstration</strong> Algorithm script: <em>iterative_algorithm.r</em> Sample RS data: <em>RS_Zurich_data.csv</em> Sample PEMS/Chassis test cycles: <em>sample_pems_chassis_cycles.csv</em>
DOI: 10.5281/zenodo.6341956
title: Using snapshot measurements to identify high-emitting vehicles

description: This repo includes codes and sample data for Qiu and Borken-kleefeld, ERL, 2022. <strong>Material for reproducing figures in the paper</strong> R script: plot.r Data for plot 2: <em>RS_Zurich_data.csv</em>: the sample RS data from Zurich. <em>algorithm_eu5d_final_iteration.rds</em>: the estimated average emission factor for each city fleet (outputs from the iterative algorithm) Data for plot 3:<em> </em> <em>Zurich_clean_identification.xlsx</em>: summary of the fraction of clean vehicles being identified by each potential RS threshold. <em>Zurich_high_emitter_identification.xlsx</em>: summary of the fraction of high-emitters being identified by each potential RS threshold. Data for plot 4: <em>validation_test_dataset.csv</em>: the original validation dataset that includes the underlying average emission factor and the simulated instantaneous emissions. <em>validation_algorithm_results.rds</em>: algorithm outputs when applied to the validation dataset. <strong>The iterative algorithm and sample data that can be used for demonstration</strong> Algorithm script: <em>iterative_algorithm.r</em> Sample RS data: <em>RS_Zurich_data.csv</em> Sample PEMS/Chassis test cycles: <em>sample_pems_chassis_cycles.csv</em>
DOI: 10.14469/hpc/10195
title: Synthesis of 4-ethyl-2-methoxyphenyl-icosanoate

description: 4-ethylguaiacol (0.76 g, 5.0 mmol) and arachidic acid (1.72 g, 5.5 mmol) were allowed to react with presence of acetonitrile (25 mL, 0.5 mol), 4-(dimethylamino) pyridine (DMAP) (0.06 g, 0.5 mmol) and EDC.HCl (1.25 g, 6.5 mmol). The resulting mixture was stirred and heated at 92°C until the reaction completed as shown by the results from Thin-layer chromatography. The organic layer was separated by aqueous/organic workup, where hexane (20 mL), 1M aqueous HCl (2*20 mL), 10 wt% sodium bicarbonate (2*20 mL)were added separately to the mixture and the aqueous layer formed every time collected in a conical flask, washed by water (20 mL) and saturated sodium chloride (2×20 mL), dried (MgSO4), and evaporated to afford a colourless oil. This oil solidified under room temperature, turning into a white solid: 1.49 g (67%). M.P. 48.4 – 49.1 d = 6.94 (1H, d, J = 8.0 Hz), 6.80 (2H, m, 1J = 8.0 Hz, 2J=4.0 Hz), 3.82 (3H, s), 2.66 (2H, q, J = 8.0 Hz), 2.58 (2H, t, J = 6.0 Hz), 1.79 (2H, p, J = 8.0 Hz), 1.44 (4H, m), 1.34 (6H, J = 12.0 Hz), 1.30 (19H, s), 1.27 (6H, t), 0.92 (3H, t, J = 8.0 Hz). IR: v (cm-1) = 2955, 2915, 2870 and 2848 (s, CH), 1744 (s, C=O), 1510, 1469, 1450, 1411 (s, CO), 1335, 1304, 1270, 1241, 1227, 1200, 1146, 1118, 1104, 1035, 916, 896, 844, 718, 631, 541, 504, 466, 409.
DOI: 10.6073/pasta/b8d1a75dde20aad617622d50cf3df6f4
title: Jornada Basin LTER Cross-scale Interactions Study (CSIS) Block 10 meteorological station: 30-minute summary data: 2017 - ongoing

description: This data package contains 30-minute air temperature, relative humidity, and wind summary data from Jornada Basin LTER Cross Scale Interactions Study (CSIS) Block-1 met station. Average air temperature, relative humidity, wind speed, wind direction, and precipitation are measured and calculated at an automated meteorological station connected to a 900 MHz spread spectrum wireless network. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Measurements are aggregated at intervals of 5, 30, 60 minutes and daily, with precipitation additionally provided with 1 second aggregation during precipitation events. The met station variables measured include air temperature, precipitation, relative humidity, wind direction, wind speed. Precipitation is not measured at Block 1 and Block 12 because of proximity to Block 2 and Block 13, respectively. This is an ONGOING dataset.
DOI: 10.5519/qd.pzrts9vs
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (40 records)
DOI: 10.5281/zenodo.5920479
title: ModelNet10 - Watertight

description: Official ModelNet10 dataset from here, for which the models were made watertight using ManifoldPlus. The dataset is used in the paper Deep Surface Reconstruction from Point Clouds with Visibility Information. Refer to this GitHub repository for more information.
DOI: 10.5281/zenodo.5940164
title: ModelNet10 - Scans

description: Official ModelNet10 dataset from here for which the models were scanned using the procedure described in the paper Deep Surface Reconstruction from Point Clouds with Visibility Information. The dataset also includes training data for Convolutional Occupancy Networks, Shape As Points, Points2Surf and DGNN. Refer to this GitHub repository for more information.
DOI: 10.5281/zenodo.5920478
title: ModelNet10 - Watertight

description: Official ModelNet10 dataset from here, for which the models were made watertight using ManifoldPlus. The dataset is used in the paper Deep Surface Reconstruction from Point Clouds with Visibility Information. Refer to this GitHub repository for more information.
DOI: 10.5281/zenodo.5940163
title: ModelNet10 - Scans

description: Official ModelNet10 dataset from here for which the models were scanned using the procedure described in the paper Deep Surface Reconstruction from Point Clouds with Visibility Information. The dataset also includes training data for Convolutional Occupancy Networks, Shape As Points, Points2Surf and DGNN. Refer to this GitHub repository for more information.
DOI: 10.5281/zenodo.6341976
title: Potamarius usumacintae RB0600 GRAHAM decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6341975
title: Potamarius usumacintae RB0600 GRAHAM decontaminated FSCR

description: .
DOI: 10.18419/darus-2154/1
title: huq_350_11_1mus_7.5ns-p-s.mp4

description: MD Simulation trajectory of 1 mus simulated at 350 K of a Pore with 11 beta-CD molecules attached to the surface using the Huq linker with 30 p-nitrophenol molecules in water. Hereby one video second corresponds to 7.5 ns.
DOI: 10.18419/darus-2154/2
title: trofy_350_11_1mus_7.5ns-p-s.mp4

description: MD Simulation trajectory of 1 mus simulated at 350 K of a Pore with 11 beta-CD molecules attached to the surface using the Trofymchuk linker with 30 benzene molecules in water. Hereby one video second corresponds to 7.5 ns.
DOI: 10.18419/darus-2154/3
title: trofy_298_05_1mus_7.5ns-p-s.mp4

description: MD Simulation trajectory of 1 mus simulated at 298 K of a Pore with 5 beta-CD molecules attached to the surface using the Trofymchuk linker with 30 benzene molecules in water. Hereby one video second corresponds to 7.5 ns.
DOI: 10.18419/darus-2154/4
title: trofy_298_11_1mus_7.5ns-p-s.mp4

description: MD Simulation trajectory of 1 mus simulated at 298 K of a Pore with 11 beta-CD molecules attached to the surface using the Trofymchuk linker with 30 benzene molecules in water. Hereby one video second corresponds to 7.5 ns.
DOI: 10.18419/darus-2154/5
title: 298_trofy_05_ben_20.ndx

description: Index file of the simulation box of the pore with 5 beta-CD molecules attached to the surface using a Trofymchuk linker with 20 Benzene molecules in water simulated at 298 K in .ndx format.
DOI: 10.18419/darus-2154/6
title: 298_trofy_05_ben_20.gro

description: Cartesian coordinates of the simulation box of the pore with 5 beta-CD molecules attached to the surface using a Trofymchuk linker with 20 Benzene molecules in water simulated at 298 K in .gro format.
DOI: 10.18419/darus-2154/7
title: 298_trofy_05_ben_20.top

description: Main topology file of the simulation box of the pore with 5 beta-CD molecules attached to the surface using a Trofymchuk linker with 20 Benzene molecules in water simulated at 298 K in .top format.
DOI: 10.18419/darus-2154/8
title: 298_trofy_05_ben_40.ndx

description: Index file of the simulation box of the pore with 5 beta-CD molecules attached to the surface using a Trofymchuk linker with 40 Benzene molecules in water simulated at 298 K in .ndx format.
DOI: 10.18419/darus-2154/9
title: 298_trofy_05_ben_40.gro

description: Cartesian coordinates of the simulation box of the pore with 5 beta-CD molecules attached to the surface using a Trofymchuk linker with 40 Benzene molecules in water simulated at 298 K in .gro format.
DOI: 10.18419/darus-2154/10
title: 298_trofy_05_ben_40.top

description: Main topology file of the simulation box of the pore with 5 beta-CD molecules attached to the surface using a Trofymchuk linker with 40 Benzene molecules in water simulated at 298 K in .top format.
DOI: 10.18419/darus-2154/11
title: 298_trofy_05_ben_60.ndx

description: Index file of the simulation box of the pore with 5 beta-CD molecules attached to the surface using a Trofymchuk linker with 60 Benzene molecules in water simulated at 298 K in .ndx format.
DOI: 10.18419/darus-2154/12
title: 298_trofy_05_ben_60.gro

description: Cartesian coordinates of the simulation box of the pore with 5 beta-CD molecules attached to the surface using a Trofymchuk linker with 60 Benzene molecules in water simulated at 298 K in .gro format.
DOI: 10.5281/zenodo.6339600
title: SuperDARN data in netCDF format (2018-Jan)

description: 2018-Jan SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.18419/darus-2154/13
title: 298_trofy_05_ben_60.top

description: Main topology file of the simulation box of the pore with 5 beta-CD molecules attached to the surface using a Trofymchuk linker with 60 Benzene molecules in water simulated at 298 K in .top format.
DOI: 10.18419/darus-2154/14
title: 298_trofy_11_ben_05.ndx

description: Index file of the simulation box of the pore with 11 beta-CD molecules attached to the surface using a Trofymchuk linker with 5 Benzene molecules in water simulated at 298 K in .ndx format.
DOI: 10.5281/zenodo.6327070
title: SuperDARN data in netCDF format (2017-Jun)

description: 2017-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.18419/darus-2154/15
title: 298_trofy_11_ben_05.gro

description: Cartesian coordinates of the simulation box of the pore with 11 beta-CD molecules attached to the surface using a Trofymchuk linker with 5 Benzene molecules in water simulated at 298 K in .gro format.
DOI: 10.18419/darus-2154/16
title: 298_trofy_11_ben_05.top

description: Main topology file of the simulation box of the pore with 11 beta-CD molecules attached to the surface using a Trofymchuk linker with 5 Benzene molecules in water simulated at 298 K in .top format.
DOI: 10.18419/darus-2154/17
title: 298_trofy_11_ben_10.ndx

description: Index file of the simulation box of the pore with 11 beta-CD molecules attached to the surface using a Trofymchuk linker with 10 Benzene molecules in water simulated at 298 K in .ndx format.
DOI: 10.5281/zenodo.6331071
title: SuperDARN data in netCDF format (2018-Feb)

description: 2018-Feb SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.18419/darus-2154/18
title: 298_trofy_11_ben_10.gro

description: Cartesian coordinates of the simulation box of the pore with 11 beta-CD molecules attached to the surface using a Trofymchuk linker with 10 Benzene molecules in water simulated at 298 K in .gro format.
DOI: 10.5281/zenodo.6323083
title: SuperDARN data in netCDF format (2018-Mar)

description: 2018-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5519/qd.bui2n552
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (73 records)
DOI: 10.18419/darus-2154/19
title: 298_trofy_11_ben_10.top

description: Main topology file of the simulation box of the pore with 11 beta-CD molecules attached to the surface using a Trofymchuk linker with 10 Benzene molecules in water simulated at 298 K in .top format.
DOI: 10.18419/darus-2154/20
title: 298_trofy_11_ben_15.ndx

description: Index file of the simulation box of the pore with 11 beta-CD molecules attached to the surface using a Trofymchuk linker with 15 Benzene molecules in water simulated at 298 K in .ndx format.