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Kim, Hyunbin; Makhnenko, Roman (2022): Data on "Evaluation of CO2 sealing potential of heterogeneous Eau Claire shale". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5509498_V1
This dataset is provided to support the statements in Kim, H., and R.Y. Makhnenko. 2022. "Evaluation of CO2 sealing potential of heterogeneous Eau Claire shale". Journal of the Geological Society. In geologic carbon dioxide (CO2) storage in deep saline aquifers, buoyant CO2 tends to float upwards in the reservoirs overlaid by low permeable formations called caprocks. Caprocks should serve as barriers to potential CO2 leakage that can happen through a diffusion loss and permeation through faults, fractures, or pore spaces. The leakage through intact caprock would mainly depend on its permeability and CO2 breakthrough pressure, and is affected by the heterogeneities in the material. Here, we study the sealing potential of a caprock from Illinois Basin - Eau Claire shale, with sandy and shaly fractions distinguished via electron microscopy and grain/pore size and surface area characterization. The direct measurements of permeability of sandy shale provides the values ~ 10-15 m2, while clayey specimens are three orders of magnitude less permeable. The CO2 breakthrough pressure under in-situ stress conditions is 0.1 MPa for the sandy shale and 0.4 MPa for the clayey counterpart – these values are higher than those predicted by the porosimetry methods performed on the unconfined specimens. Sandy Eau Claire shale would allow penetration of large CO2 volumes at low overpressures, while the clayey formation can serve as a caprock in the absence of faults and fractures in it.
Geologic carbon storage; Caprock; Shale; CO2 breakthrough pressure; Porosimetry.
Vargas, Fabio (2021): Mesospheric gravity wave activity estimated via airglow imagery, multistatic meteor radar, and SABER data taken during the SIMONe–2018 campaign. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8585682_V1
Airglow images and Meteor radar data used in the paper "Mesospheric gravity wave activity estimated via airglow imagery, multistatic meteor radar, and SABER data taken during the SIMONe–2018 campaign".
airglow; meteor radar; gravity waves; momentum flux;
Lundstrom, Craig (2020): Experimental data from K-Na-Al-Si-H oxides systems. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7110302_V1
Phase equilibria; Granite; Quartz; Feldspar
has sharing link
Ruess, Paul ; Konar, Megan ; Wanders, Niko; Bierkens, Marc (2023): Data for Irrigation by crop in the Continental United States from 2008 to 2020. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4607538_V1
Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use by crop. The goal of this study is to estimate crop-specific irrigation water use from surface water withdrawals, total groundwater withdrawals, and nonrenewable groundwater depletion for the Continental United States. Water use by source is provided for 20 crops and crop groups from 2008 to 2020 at the county spatial resolution. These results present the first national-scale assessment of irrigation by crop, county, water source, and year. In total, there are nearly 2.5 million data points in this dataset (3,142 counties; 13 years; 3 water sources; and 20 crops). This dataset supports the paper by Ruess et al (2023) in Water Resources Research, https://doi.org/10.1029/2022WR032804. When using, please cite as: Ruess, P.J., Konar, M., Wanders, N. , & Bierkens, M. (2023). Irrigation by crop in the Continental United States from 2008 to 2020, Water Resources Research, 59, e2022WR032804. https://doi.org/10.1029/2022WR032804
Water use; irrigation; surface water; groundwater; groundwater depletion; counties; crops; time series
Wang, Junren; Konar, Megan; Dalin, Carole; Liu, Yu; Stillwell, Ashlynn S.; Xu, Ming; Zhu, Tingju (2022): Data for: Economic and Virtual Water Multilayer Networks in China. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5215221_V1
This dataset includes the blue water intensity by sector (41 industries and service sectors) for provinces in China, economic and virtual water network flow for China in 2017, and the corresponding network properties for these two networks.
Economic network; Virtual water; Supply chains; Network analysis; Multilayer; MRIO
Madhavan, Vidya; Aishwarya, Anuva (2022): Data for Evidence for a robust sign-changing s-wave order parameter in monolayer films of superconducting Fe(Se,Te)/Bi2Te3. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6972172_V1
This dataset consists of all the files that are part of the manuscript titled "Evidence for a robust sign-changing s-wave order parameter in monolayer films of superconducting Fe(Se,Te)/Bi2Te3". For detailed information on the individual files refer to the readme file.
thin film; mbe; topology; superconductivity; topological insulator; stm; spectroscopy; qpi
Winogradoff, David; Chou, Han-Yi; Maffeo, Christopher; Aksimentiev, Aleksei (2022): Simulation setup for "Percolation transition prescribes protein size-specific barrier to passive transport through the nuclear pore complex.". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3813848_V1
Example scripts and configuration files needed to perform select simulations described in the manuscript "Percolation transition prescribes protein size-specific barrier to passive transport through the nuclear pore complex."
Nuclear Pore Complex; simulation setup
Madhavan, Vidya; Aishwarya, Anuva (2022): Data for Spin-selective tunneling from nanowires of the candidate topological Kondo insulator SmB6. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9971603_V1
This dataset consists of all the files and codes that are part of the manuscript (main text and supplement) titled "Spin-selective tunneling from nanowires of the candidate topological Kondo insulator SmB6". For detailed information on the individual files refer to the specific readme files.
Topology; Kondo Inuslator; Spin; Scanning tunneling microscopy; antiferromagnetism
Kang, Jeon-Young; Farkhad, Bita Fayaz; Chan, Man-pui Sally; Michels, Alexander; Albarracin, Dolores; Wang, Shaowen (2022): Data for Spatial Accessibility to HIV (Human Immunodeficiency Virus) Testing, Treatment, and Prevention Services in Illinois and Chicago, USA. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9096476_V1
This dataset helps to investigate the Spatial Accessibility to HIV Testing, Treatment, and Prevention Services in Illinois and Chicago, USA. The main components are: population data, healthcare data, GTFS feeds, and road network data. The core components are: 1) `GTFS` which contains GTFS (<a href="https://gtfs.org/">General Transit Feed Specification</a>) data which is provided by Chicago Transit Authority (CTA) from <a href="https://developers.google.com/transit/gtfs">Google's GTFS feeds</a>. Documentation defines the format and structure of the files that comprise a GTFS dataset: <a href="https://developers.google.com/transit/gtfs/reference?csw=1">https://developers.google.com/transit/gtfs/reference?csw=1</a>. 2) `HealthCare` contains shapefiles describing HIV healthcare providers in Chicago and Illinois respectively. The services come from <a href="https://locator.hiv.gov/">Locator.HIV.gov</a>. 3) `PopData` contains population data for Chicago and Illinois respectively. Data come from The American Community Survey and <a href="https://map.aidsvu.org/map">AIDSVu</a>. AIDSVu (https://map.aidsvu.org/map) provides data on PLWH in Chicago at the census tract level for the year 2017 and in the State of Illinois at the county level for the year 2016. The American Community Survey (ACS) provided the number of people aged 15 to 64 at the census tract level for the year 2017 and at the county level for the year 2016. The ACS provides annually updated information on demographic and socio economic characteristics of people and housing in the U.S. 4) `RoadNetwork` contains the road networks for Chicago and Illinois respectively from <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> using the Python <a href="https://osmnx.readthedocs.io/en/stable/">osmnx</a> package. <b>The abstract for our paper is:</b> Accomplishing the goals outlined in “Ending the HIV (Human Immunodeficiency Virus) Epidemic: A Plan for America Initiative” will require properly estimating and increasing access to HIV testing, treatment, and prevention services. In this research, a computational spatial method for estimating access was applied to measure distance to services from all points of a city or state while considering the size of the population in need for services as well as both driving and public transportation. Specifically, this study employed the enhanced two-step floating catchment area (E2SFCA) method to measure spatial accessibility to HIV testing, treatment (i.e., Ryan White HIV/AIDS program), and prevention (i.e., Pre-Exposure Prophylaxis [PrEP]) services. The method considered the spatial location of MSM (Men Who have Sex with Men), PLWH (People Living with HIV), and the general adult population 15-64 depending on what HIV services the U.S. Centers for Disease Control (CDC) recommends for each group. The study delineated service- and population-specific accessibility maps, demonstrating the method’s utility by analyzing data corresponding to the city of Chicago and the state of Illinois. Findings indicated health disparities in the south and the northwest of Chicago and particular areas in Illinois, as well as unique health disparities for public transportation compared to driving. The methodology details and computer code are shared for use in research and public policy.
HIV;spatial accessibility;spatial analysis;public transportation;GIS
Winogradoff, David; Chou, Han-Yi; Maffeo, Christopher; Aksimentiev, Aleksei (2022): Trajectory files for "Percolation transition prescribes protein size-specific barrier to passive transport through the nuclear pore complex.". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5581194_V1
Nuclear pore complex; system files; trajectory files
Lee, Sangjun; Huang, Edwin W.; Johnson, Thomas A.; Guo, Xuefei; Husain, Ali A.; Mitrano, Matteo; Lu, Kannan; Zakrzewski, Alexander V.; de la Pena, Gilberto A.; Peng, Yingying; Huang, Hai; Lee, Sang-Jun; Jang, Hoyoung; Lee, Jun-Sik; Joe, Young Il; Doriese, William B.; Szypryt, Paul; Swetz, Daniel S.; Chi, Songxue; Aczel, Adam A.; MacDougall, Gregory J.; Kivelson, Steven A. ; Fradkin, Eduardo; Abbamonte, Peter (2022): Data for "Generic character of charge and spin density waves in superconducting cuprates". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1757317_V1
Data for "Generic character of charge and spin density waves in superconducting cuprates". - Neutron scattering data for SDW - RSXS scans of CDW of LESCO x=0.10, 0.125, 0.15, 0.17, 0.20 at various temperatures. - Temperature dependence of CDW peak intensity, correlation length, Qcdw (Lorentzian fit, S(q,T) fit, Landau-Ginzburg fit) - XAS data of LESCO x=0.10, 0.125, 0.15, 0.17, 0.20
has sharing link
Wong, Tony; Oudshoorn, Luuk; Sofovich, Eliyahu; Green, Alex; Shah, Charmi; Indebetouw, Remy; Meixner, Margaret; Hacar, Alvaro; Nayak, Omnarayani; Tokuda, Kazuki; Bolatto, Alberto D.; Chevance, Melanie; De Marchi, Guido; Fukui, Yasuo; Hirschauer, Alec S.; Jameson, K. E.; Kalari, Venu; Lebouteiller, Vianney; Looney, Leslie W.; Madden, Suzanne C.; Onishi, Toshikazu; Roman-Duval, Julia; Rubio, Monica; Tielens, A. G. G. M. (2022): Data for: The 30 Doradus Molecular Cloud at 0.4 pc Resolution with ALMA: Physical Properties and the Boundedness of CO-emitting Structures. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1671495_V1
12CO and 13CO emission maps of the 30 Doradus molecular cloud in the Large Magellanic Cloud, obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) during Cycle 7. See the associated article in the Astrophysical Journal, and README file, for details. Please cite the article if you use these data.
Madhavan, Vidya; Aishwarya, Anuva (2022): Data for Long-lifetime spin excitations near domain walls in 1T-TaS2. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0883774_V1
The data files are for the paper entitled: Long-lifetime spin excitations near domain walls in 1T-TaS2 to be published in PNAS. The data was obtained on a 300 mK custom designed Unisoku scanning tunneling microscope using the Nanonis module. All the data files have been named based on the Figure numbers that they represent.
Mott Insulator; Spins; Charge Density Wave; Domain walls; Long lifetime
Saleh, Ehsan; Ghaffari, Saba; Forsyth, David; Yu-Xiong, Wang (2022): Dataset for On the Importance of Firth Bias Reduction in Few-Shot Classification. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1016367_V1
This data repository includes the features and the trained backbone parameters used in the ICLR 2022 Paper "On the Importance of Firth Bias Reduction in Few-Shot Classification". The code accompanying this data is open-source and available at https://github.com/ehsansaleh/firth_bias_reduction The code and the data have three modules: 1. The "code_firth" module (10 files) relates to the basic ResNet backbones and logistic classifiers (e.g., Figures 2 and 3 in the main paper). 2. The "code_s2m2rf" module (2 files) relates to the S2M2R feature backbones and cosine classifiers (e.g., Figure 4 in the main paper). 3. The "code_dcf" module (3 files) relates to the few-shot Distribution Calibration (DC) method (e.g., Table 1 in the main paper). The relevant files for each module have the module name as a prefix in their name. 1. For instance, the "code_dcf_features.tar" file should be placed at the "features" directory of the "code_dcf" module. 2. As another example, "code_firth_features_cifarfs_novel.tar" should be placed in the "features" directory of the "code_firth" module, and it includes the features extracted from the novel split of mini-ImageNet dataset. Each tar-ball should be extracted in its relevant directory, and the md5 check-sums of the extracted files are also provided in the open-source code repository for verification. Please note that the actual datasets of images are not included here (since we do not own those datasets). However, helper scripts for automatically downloading the original datasets are also provided in the every module and sub-directory of the GitHub code repository.
Computer Vision; Few-Shot Classification; Few-Shot Learning; Firth Bias Reduction
Liu, Shanshan; Kontou, Eleftheria (2022): Data for Quantifying transportation energy vulnerability and its spatial patterns in the United States.. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9337369_V2
This data set contains all the map data used for "Quantifying transportation energy vulnerability and its spatial patterns in the United States". The multiple dimensions (i.e., exposure, sensitivity, adaptive capacity) of transportation energy vulnerability (TEV) at the census tract level in the United States, the changes in TEV with electric vehicles adoption, and the detailed data for Chicago, Los Angeles, and New York are in the dataset.
Transport energy; Vulnerability; Fuel costs; Electric vehicles
Kudeki, Erhan; Reyes, Pablo (2022): EVEX Campaign Ground Based Radar Data. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8835972_V1
Ground based radar data sets collected during the 2013 NASA EVEX Campaign conducted in Roi-Namur island of the Kwajalein Atoll in the Republic of Marshall Islands are deposited in this databank. Radar data were collected with IRIS VHF and ALTAIR VHF/UHF systems.
Wang, Junren; Karakoc, Deniz Berfin; Konar, Megan (2022): Data for: The Carbon Footprint of Cold Chain Food Flows in the United States. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8455093_V1
This dataset is a estimation of county-to-county commodity delivery through cold chain in 2017. For each county pair, the weight[kg] and value[$] of the cold chain flow between origin and destination for SCTG 5 and SCTG 7 commodities are estimated by our model. - SCTG 5 - Meat, poultry, fish, seafood, and their preparations - SCTG 7 - Other prepared foodstuffs, fats, and oils
food flows; cold chain; county-scale; United States; carbon footprint
Yao, Yu; Curtis, Jeffrey; Ching, Joseph; Zheng, Zhonghua; Riemer, Nicole (2022): Data for: Quantifying the effects of mixing state on aerosol optical properties. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8157303_V1
This dataset contains simulation results from numerical model PartMC-MOSAIC used in the article "Quantifying the effects of mixing state on aerosol optical properties". This article is submitted to the journal Atmospheric Physics and Chemistry. There are total 100 scenario directories in this dataset, denoted from 00-99. Each scenario contains 25 NetCDF files hourly output from PartMC-MOSAIC simulations containing the simulated gas and particle information. The data was produced using version 2.5.0 of PartMC-MOSAIC. Instructions to compile and run PartMC-MOSAIC are available at https://github.com/compdyn/partmc. The chemistry code MOSAIC is available by request from Rahul.Zaveri@pnl.gov. For more details of reproducing the cases, please contact firstname.lastname@example.org and email@example.com.
Aerosol mixing state; Aerosol optical properties; Mie calculation; Black Carbon
Karakoc, Deniz Berfin; Wang, Junren; Konar, Megan (2022): Data for: Food flows between counties in the Unites States from 2007 to 2017. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9585947_V1
This dataset provides estimates of agricultural and food commodity flows [kg] between all county pairs within the United States for the years 2007, 2012, and 2017. The database provides 206.3 million data points, since pairwise information is provided between 3134 counties, for 7 commodity categories, and 3 time periods. The commodity categories correspond to the Standardized Classification of Transported Goods and are: - SCTG 1: Iive animals and fish - SCTG 2: cereal grains - SCTG 3: agricultural products (except for animal feed, cereal grains, and forage products) - SCTG 4: animal feed, eggs, honey, and other products of animal origin - SCTG 5: meat, poultry, fish, seafood, and their preparations - SCTG 6: milled grain products and preparations, and bakery products - SCTG 7: other prepared foodstuffs, fats and oils For additional information, please see the related paper by Karakoc et al. (2022) in Environmental Research Letters.
food flows; high-resolution; county-scale; time-series; United States
has sharing link
Dominguez, Francina (2022): Data for The Orinoco Low-level Jet and the Cross-Equatorial Moisture Transport over tropical South America: Lessons from seasonal WRF simulations. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9924420_V1
This dataset contains results from WRF simulations over northern South America. The Orinoco Low-Level Jet (OLLJ) and the Cross-Equatorial Moisture Transport are important circulation structures of the climate of tropical South America. We explore the sensitivity of the OLLJ and cross-equatorial transport to the representation of surface fluxes and turbulence by using two different Land Surface Model (LSM) schemes (Noah and CLM) and three Planetary Boundary Layer (PBL) schemes (YSU, QNSE and MYNN).
WRF; Orinoco LLJ; preicpitation
Riemer, Nicole; Yao, Yu; Dawson, Matthew; Dabdub, Donald (2021): Data for: Evaluating the impacts of cloud processing on resuspended aerosol particles after cloud evaporation using a particle-resolved model. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8367769_V2
This dataset contains simulation results from PartMC-MOSAIC-CAPRAM used in the article ”Eval- uating the impacts of cloud processing on resuspended aerosol particles after cloud evaporation using a particle-resolved model”. In this V2, there are eight folders: one for urban plume simulation to provide the initial particle population for cloud processing, the other four folders are for the four cloud cycles simulated and the last two are for the coagulation cases. Within the urban plume simulation, there are 25 NetCDF files hourly output from PartMC-MOSAIC simulations containing the gas and particle information. Within the four cloud cycle folders, there are 25 subdirectories that contain the cloud processing results for aerosol population from urban plume environment. For each subdirectory, there are 31 NetCDF files out- put every minute from PartMC-MOSAIC-CAPRAM simulations containing aerosol and gas information after aqueous chemistry. Another two folders are for the cases considering Brownian coagulation and sedimentation coalescence. Each contained 93 NetCDF files, produced from repeating the 30-minutes simulations for three times to consider the coagulation randomness. The low polluted case folder includes the simulated cloud processing results for 25 urban plume cases with less aerosol number concentration. This dataset was used to investigate the effects of cloud processing on aerosol mixing state and CCN properties.
cloud process; coagulation; aqueous chemistry; aerosol mixing state; CCN
Dawson, Matthew; Guzman Ruiz, Christian; Curtis, Jeffrey H.; Acosta, Mario C.; Zhu, Shupeng; Dabdub, Donald; Conley, Andrew; West, Matthew; Riemer, Nicole; Jorba, Oriol (2021): Data from: Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry model. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8012140_V1
This dataset contains all the data for the results section in the study presented in the paper entitled "Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry mode" submitted to Geoscientific Model Development (GMD). In this paper, two sets of simulations were run to test CAMP with this results included here. This consists of (1) box model inputs and outputs presented in Section 4.2 for modal, binned and particle-resolved simulations to compare the application of identical chemical mechanisms to different aerosol representations and (2) the 3D Eulerian output presented in Section 4.3.
Atmospheric chemistry; Aerosols and particles; Numerical Modeling
Lyu, Fangzheng; Xu, Zewei; Ma, Xinlin; Wang, Shaohua; Li, Zhiyu; Wang, Shaowen (2021): A Vector-Based Method for Drainage Network Analysis Based on LiDAR Data . University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6359717_V1
Drainage network analysis is fundamental to understanding the characteristics of surface hydrology. Based on elevation data, drainage network analysis is often used to extract key hydrological features like drainage networks and streamlines. Limited by raster-based data models, conventional drainage network algorithms typically allow water to flow in 4 or 8 directions (surrounding grids) from a raster grid. To resolve this limitation, this paper describes a new vector-based method for drainage network analysis that allows water to flow in any direction around each location. The method is enabled by rapid advances in Light Detection and Ranging (LiDAR) remote sensing and high-performance computing. The drainage network analysis is conducted using a high-density point cloud instead of Digital Elevation Models (DEMs) at coarse resolutions. Our computational experiments show that the vector-based method can better capture water flows without limiting the number of directions due to imprecise DEMs. Our case study applies the method to Rowan County watershed, North Carolina in the US. After comparing the drainage networks and streamlines detected with corresponding reference data from US Geological Survey generated from the Geonet software, we find that the new method performs well in capturing the characteristics of water flows on landscape surfaces in order to form an accurate drainage network. This dataset contains all the code, notebooks, datasets used in the study conducted for the research publication titled " A Vector-Based Method for Drainage Network Analysis Based on LiDAR Data ". ## What's Inside A quick explanation of the components * `A Vector Approach to Drainage Network Analysis Based on LiDAR Data.ipynb` is a notebook for finding the drainage network based on LiDAR data *`Picture1.png` is a picture representing the pseudocode of our new algorithm * HPC` folder contains codes for running the algorithm with sbatch in HPC ** `execute.sh` is a bash script file that use sbatch to conduct large scale analysis for the algorithm ** `run.sh` is a bash script file that calls the script file `execute.sh` for large scale calculation for the algorithm ** `run.py` includes the codes implemented for the algorithm * `Rowan Creek Data` includes data that are used in the study ** `3_1.las` and `3_2.las ` are the LiDAR data files that is used in our analysis presented in the paper. Users may use this data file to reproduce our results and may replace it with their own LiDAR file to run this method over different areas ** `reference` folder includes reference data from USGS *** `reference_3_1.tif` and `reference_3_2.tif` are reference data for the drainage system analysis retrieved from USGS.
CyberGIS; Drainage System Analysis; LiDAR
Wang, Justin; Curtis, Jeffrey H; Riemer, Nicole; West, Matthew (2021): Data from: Learning coagulation processes with combinatorially-invariant neural networks. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3904737_V1
This dataset contains all the necessary information to recreate the study presented in the paper entitled "Learning coagulation processes with combinatorially-invariant neural networks". This consists of (1) the aggregated output files used for machine learning, (2) the machine learning codes used to learn the presented models, (3) the PartMC model source code that was used to generate the simulation data and (4) the Python scripts used construct the scenario library for training and testing simulations. This data was used to investigate a method (combinatorally-invariant neural network) for learning the aerosol process of coagulation. This data may be useful for application of other methods.
Machine learning; Atmospheric chemistry; Particle-resolved modeling; Coagulation; Atmospheric Science
Felix, Hanau; Hannes, Rost; Ochoa, Idoia (2021): mspack-data. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1396774_V2
This data set contains mass spectrometry data used for the publication "mspack: efficient lossless and lossy mass spectrometry data compression".
mass-spectrometry data; compression; proteomics