Displaying datasets 151 - 175 of 478 in total

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published: 2021-04-12
Conjugate photoelectron energy spectra derived from coincident FUV and radio measurements. These are outputs of simulations from the semi-empirical SAMI2-PE (Varney et al. 2012) for the night of January 4, 2020.
keywords: Conjugate photoelectrons, SAMI2-PE, ICON
published: 2021-04-11
This dataset contains RNASim1000, Cox1-Het datasets as well as analyses of RNASim1000, Cox1-Het, and 1000M1(HF).
keywords: phylogeny estimation; maximum likelihood; RAxML; IQ-TREE; FastTree; cox1; heterotachy; disjoint tree mergers; Tree of Life
published: 2021-05-14
- The aim of this research was to evaluate the novel dietary fiber source, miscanthus grass, in comparison to traditional fiber sources, and their effects on the microbiota of healthy adult cats. Four dietary treatments, cellulose (CO), miscanthus grass fiber (MF), a blend of miscanthus fiber and tomato pomace (MF+TP), or beet pulp (BP) were evaluated.<br /><br />- The study was conducted using a completely randomized design with twenty-eight neutered adult, domesticated shorthair cats (19 females and 9 males, mean age 2.2 ± 0.03 yr; mean body weight 4.6 ± 0.7 kg, mean body condition score 5.6 ± 0.6). Total DNA from fresh fecal samples was extracted using Mo-Bio PowerSoil kits (MO BIO Laboratories, Inc., Carlsbad, CA). Amplification of the 292 bp-fragment of V4 region from the 16S rRNA gene was completed using a Fluidigm Access Array (Fluidigm Corporation, South San Francisco, CA). Paired-end Illumina sequencing was performed on a MiSeq using v3 reagents (Illumina Inc., San Diego, CA) at the Roy J. Carver Biotechnology Center at the University of Illinois. <br />- Filenames are composed of animal name identifier, diet (BP= beet pulp; CO= cellulose; MF= miscanthus grass fiber; TP= blend of miscanthus fiber and tomato pomace).
keywords: cats; dietary fiber; fecal microbiota; miscanthus grass; nutrient digestibility; postbiotics
published: 2021-05-07
- The objective of this study was to evaluate macronutrient apparent total tract digestibility (ATTD), gastrointestinal tolerance, and fermentative end-products in extruded, canine diets. <br />- Five diets were formulated to be isocaloric and isonitrogenous with either garbanzo beans (GBD), green lentils (GLD), peanut flour (PFD), dried yeast (DYD), or poultry by-product meal (CON) as the primary protein sources. Ten adult, intact, female beagles (mean age: 4.2 ± 1.1 yr, mean 28 weight: 11.9 ± 1.3 kg) were used in a replicated, 5x5 Latin square design with 14 d periods. Total DNA from fresh fecal samples was extracted using Mo-Bio PowerSoil kits (MO BIO Laboratories, Inc., Carlsbad, CA). Amplification of the 292 bp-fragment of V4 region from the 16S rRNA gene was completed using a Fluidigm Access Array (Fluidigm Corporation, South San Francisco, CA). Paired-end Illumina sequencing was performed on a MiSeq using v3 reagents (Illumina Inc., San Diego, CA) at the Roy J. Carver Biotechnology Center at the University of Illinois. <br />- Filenames are composed of animal name identifier, diet (CON=control; DY= dried yeast; GB= garbanzo beans; GL= green lentils; PF= peanut flour) and period replicate number (P1, P2, P3, P4, and P5).
keywords: Dog; Digestibility; Legume; Microbiota; Pulse; Yeast
published: 2021-04-06
These datasets contain modeling files and GIS data associated with a risk assessment study for the Cambrian-Ordovician sandstone aquifer system in Illinois from predevelopment (1863) to the year 2070. Modeling work was completed using the Illinois Groundwater Flow Model, a regional MODFLOW model developed for water supply planning in Illinois, as a base model. The model is run using the graphical user interface Groundwater Vistas 7.0. The development and technical details of the base Illinois Groundwater Flow Model, including hydraulic property zonation, boundary conditions, hydrostratigraphy, solver settings, and discretization, are described in Abrams et al. (2018). Modifications to this base model (the version presented here) are described in Mannix et al. (2018), Hadley et al. (2020) and Abrams and Cullen (2020). Modifications include removal of particular multi-aquifer wells to improve calibration, changing Sandwich Fault Zone properties to achieve calibration at production wells within and near the fault zone, and the incorporation of demand scenarios based on a participatory modeling project with the Southwest Water Planning Group. The zipped folder of model files contains MODFLOW input (package) files, Groundwater Vistas files, and a head file for the entire model run. The zipped folder of GIS data contains rasters of: simulated drawdown in the St. Peter sandstone from predevelopment to 2018, simulated drawdown in the Ironton-Galesville sandstone from predevelopment to 2018, simulated head difference between the St. Peter and Ironton-Galesville sandstone units in 2018, simulated head above the top of the St. Peter sandstone for the years 2029, 2050, and 2070, and simulated head above the top of the Ironton-Galesville sandstone for the years 2029, 2050, and 2070. Raster outputs were derived directly from the simulated heads in the Illinois Groundwater Flow Model. Rasters are clipped to the 8 county northeastern Illinois region (Cook, DuPage, Grundy, Kane, Kendall, Lake, McHenry, and Will counties). Well names, historic and current head targets, and spatial offsets for the Illinois Groundwater Flow Model are available upon request via a data license agreement. Please contact authors to set this up if needed.
keywords: groundwater; aquifer; sandstone aquifer; risk assessment; depletion; Illinois; MODFLOW; modeling
published: 2021-04-05
West Nile virus data, aggregated by 55 1-km hexagons, within the NWMAD jurisdiction Cook County, IL. The data incorporates deidentified human illness, mosquito infection and abundance, socio-economic data, and other abiotic and biotic predictors by epi-weeks 18-38 for the years 2005-2016.
keywords: WNV; modeling
published: 2021-03-31
This archive contains the datasets used in the paper "Recursive MAGUS: scalable and accurate multiple sequence alignment". - 16S.3, 16S.T, 16S.B.ALL - HomFam - RNASim These can also be found at https://sites.google.com/eng.ucsd.edu/datasets/alignment/pastaupp
published: 2021-03-23
DNN weights used in the evaluation of the ApproxTuner system. Link to paper: https://dl.acm.org/doi/10.1145/3437801.3446108
published: 2021-03-17
This dataset was developed as part of a study that assessed data reuse. Through bibliometric analysis, corresponding authors of highly cited papers published in 2015 at the University of Illinois at Urbana-Champaign in nine STEM disciplines were identified and then surveyed to determine if data were generated for their article and their knowledge of reuse by other researchers. Second, the corresponding authors who cited those 2015 articles were identified and surveyed to ascertain whether they reused data from the original article and how that data was obtained. The project goal was to better understand data reuse in practice and to explore if research data from an initial publication was reused in subsequent publications.
keywords: data reuse; data sharing; data management; data services; Scopus API
published: 2021-03-14
This dataset contains all the code, notebooks, datasets used in the study conducted to measure the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Specifically, the dataset measures spatial access for people to hospitals and ICU beds in Illinois. The spatial accessibility is measured by the use of an enhanced two-step floating catchment area (E2FCA) method (Luo & Qi, 2009), which is an outcome of interactions between demands (i.e, # of potential patients; people) and supply (i.e., # of beds or physicians). The result is a map of spatial accessibility to hospital beds. It identifies which regions need more healthcare resources, such as the number of ICU beds and ventilators. This notebook serves as a guideline of which areas need more beds in the fight against COVID-19. ## What's Inside A quick explanation of the components of the zip file * `COVID-19Acc.ipynb` is a notebook for calculating spatial accessibility and `COVID-19Acc.html` is an export of the notebook as HTML. * `Data` contains all of the data necessary for calculations: &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `Chicago_Network.graphml`/`Illinois_Network.graphml` are GraphML files of the OSMNX street networks for Chicago and Illinois respectively. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `GridFile/` has hexagonal gridfiles for Chicago and Illinois &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `HospitalData/` has shapefiles for the hospitals in Chicago and Illinois &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `IL_zip_covid19/COVIDZip.json` has JSON file which contains COVID cases by zip code from IDPH &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `PopData/` contains population data for Chicago and Illinois by census tract and zip code. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `Result/` is where we write out the results of the spatial accessibility measures &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `SVI/`contains data about the Social Vulnerability Index (SVI) * `img/` contains some images and HTML maps of the hospitals (the notebook generates the maps) * `README.md` is the document you're currently reading! * `requirements.txt` is a list of Python packages necessary to use the notebook (besides Jupyter/IPython). You can install the packages with `python3 -m pip install -r requirements.txt`
keywords: COVID-19; spatial accessibility; CyberGISX
published: 2021-03-10
The PhytoplasmasRef_Trivellone_etal.fas fasta file contains the original final sequence alignment used in the phylogenetic analyses of Trivellone et al. (Ecology and Evolution, in review). The 27 sequences (21 phytoplasma reference strains and 6 phytoplasmas strains from the present study) were aligned using the Muscle algorithm as implemented in MEGA 7.0 with default settings. The final dataset contains 952 positions of the F2n/R2 fragment of the 16S rRNA gene. The data analyses are further described in the cited original paper.
keywords: Hemiptera; Cicadellidae; Mollicutes; Phytoplasma; biorepository
published: 2021-03-08
These are abundance dynamics data and simulations for the paper "Higher-order interaction between species inhibits bacterial invasion of a phototroph-predator microbial community". In this V2, data were converted in Python, in addition to MATLAB and more information on how to work with the data was included in the Readme.
keywords: Microbial community; Higher order interaction; Invasion; Algae; Bacteria; Ciliate
published: 2021-03-08
In a set of field studies across four years, the effect of self-shading on photosynthetic performance in lower canopy sorghum leaves was studied at sites in Champaign County, IL. Photosynthetic parameters in upper and lower canopy leaves, carbon assimilation, electron transport, stomatal conductance, and activity of three C4-specific photosynthetic enzymes, were compared within a genetically diverse range of accessions varying widely in canopy architecture and thereby in the degree of self-shading. Accessions with erect leaves and high light transmission through the canopy are henceforth referred to as ‘erectophile’ and those with low leaf erectness, ‘planophile’. In the final year of the study, bundle sheath leakiness in erectophile and planophile accessions was also compared.
keywords: Sorghum; Photosynethic Performance; Leaf Inclination
published: 2021-03-06
This dataset consists of raw ADC readings from a 3 transmitter 4 receiver 77GHz FMCW radar, together with synchronized RGB camera and depth (active stereo) measurements. The data is grouped into 4 distinct radar configurations: - "indoor" configuration with range <14m - "30m" with range <38m - "50m" with range <63m - "high_res" with doppler resolution of 0.043m/s # Related code https://github.com/moodoki/radical_sdk # Hardware Project Page https://publish.illinois.edu/radicaldata
keywords: radar; FMCW; sensor-fusion; autonomous driving; dataset; RGB-D; object detection; odometry
published: 2021-03-05
Datasets that accompany Beilke, Blakey, and O'Keefe 2021 publication (Title: Bats partition activity in space and time in a large, heterogeneous landscape; Journal: Ecology and Evolution).
keywords: spatiotemporal; chiroptera
published: 2021-03-05
Adey_Larson_Behavior.csv: Results of behavioral assays for rusty crayfish Faxonius rusticus collected from six lakes in Vilas County, Wisconsin in summer 2018. Crayfish_ID is an individual crayfish ID or identifier that matches to individuals in Adey_Larson_Isotope. Collection is how organisms were collected (trapped = baited trapping, snorkel = by hand). Lake is the study lake crayfish were collected from. Length is crayfish carapace length in mm. CPUE is crayfish catch-per-unit effort from baited trapping in that lake during summer 2018. Shelter_Occupancy, Exploration, Feeding_Snail, Feeding_Detritus, Feeding_Crayfish, and Aggressiveness are behavioral assay scores for individual crayfish. Shelter_Occupancy is frequency of observation intervals (12 maximum) in which crayfish were observed in shelter over a 12 hour period. Exploration is time for crayfish to explore a new area measured in seconds (maximum possible time 1200 seconds or 20 minutes). Feeding_Snail, Feeding_Detritus, and Feeding_Crayfish is the time for crayfish to take a food item (snail, detritus, or snail in the presence of another crayfish) measured in seconds (maximum possibe time 1200 seconds or 20 minutes). Aggressiveness is the response to an approach with a novel object scored as a fast retreat (-2), slow retreat (-1), no visible response (0), approach without threat display (1), approach with threat display (2), interaction with closed chelae (3), or interaction with open chelae (4). Three repeated aggressiveness measures were made per individual (Aggresiveness1, Aggresiveness2, Aggresiveness3), which were summed for inclusion in subsequent analyses (Aggresiveness_Sum). More detailed behavioral assay methods can be found in Adey 2019 Masters thesis. Adey_Larson_Isotope.csv: Stable isotope (13C, 15N) values for rusty crayfish Faxonius rusticus and snail or mussel primary consumers from six lakes in Vilas County, Wisconsin collected during summer 2018. Crayf is an individual crayfish ID or identifier that matches to the same individual crayfish in Adey_Larson_Behavior. Lake is the study lake. Collection is how organisms were collected (trapped = baited trapping, snorkel = by hand). Sample type indicates whether isotope values are for crayfish, snail, or mussel. d13C and d15N are stable isotope values.
keywords: individual specialization; intraspecific competition; behavior; diet; stable isotopes; crayfish; invasive species; limnology; Faxonius rusticus
published: 2021-02-26
These data were used in the survival and cause-specific mortality analyses of translocated nuisance American black bear in Wisconsin published in Animal Conservation (Bauder, J.M., N.M. Roberts, D. Ruid, B. Kohn, and M.L. Allen. Accepted. Lower survival of nuisance American black bears (Ursus americanus) is not due to translocation. Animal Conservation). Included are CSV files including each bear's capture history and associated covariates and meta-data for each CSV file. Also included is an example R script of how to conduct the analyses (this R script is also included as supporting information with the published paper).
keywords: black bear; survival; translocation; nuisance wildlife management
published: 2021-02-25
Total nitrogen leaching rates were calculated over the Mississippi Atchafalaya River Basin (MARB) using an integrated economic-biophysical modeling approach. Land allocation for corn production and total nitrogen application rates were calculated for crop reporting districts using the Biofuel and Environmental Policy Analysis Model (BEPAM) for 5 RFS2 policy scenarios. These were used as input in the Integrated BIosphere Simulator-Agricultural Version (Agro-IBIS) and the Terrestrial Hydrologic Model with Biogeochemistry (THMB) to calculate the nitrogen loss. Land allocation and total nitrogen application simulations were simulated for the period 2016-2030 for 303 crop reporting districts (https://www.nass.usda.gov/Data_and_Statistics/County_Data_Files/Frequently_Asked_Questions/county_list.txt). The final 2030 values are reported here. Both are stored in csv files. Units for land allocation are million ha and nitrogen application are million kg. The nitrogen leaching rates were modeled with a spatial resolution of 5' x 5' using the North American Datum of 1983 projection and stored in NetCDF files. The 30-year average is calculated over the last 30 years of the 45 years being simulated. Leaching rates are calculated in kg-N/ha.
keywords: nitrogen leaching, bioethanol, bioenergy crops
published: 2021-02-24
This dataset contains model output from the Community Earth System Model, Version 2 (CESM2; Danabasoglu et al. 2020). These data were used for analysis in Impacts of Large-Scale Soil Moisture Anomalies in Southeastern South America, published in the Journal of Hydrometeorology (DOI: 10.1175/JHM-D-20-0116.1). See this publication for details of the model simulations that created these data. Four NetCDF (.nc) files are included in this dataset. Two files correspond to the control simulation (FHIST_SP_control) and two files correspond to a simulation with a dry soil moisture anomaly imposed in southeastern South America (FHIST_SP_dry; see the publication mentioned in the preceding paragraph for details on the spatial extent of the imposed anomaly). For each simulation, one file corresponds to output from the atmospheric model (file names with "cam") of CESM2 and the other to the land model (file names with "clm2"). These files are raw CESM output concatenated into a single file for each simulation. All files include data from 1979-01-02 to 2003-12-31 at a daily resolution. The spatial resolution of all files is about 1 degree longitude x 1 degree latitude. Variables included in these files are listed or linked below. Variables in atmosphere model output: Vertical velocity (omega) Convective precipitation Large-scale precipitation Surface pressure Specific humidity Temperature (atmospheric profile) Reference temperature (temp. at reference height, 2 meters in this case) Zonal wind Meridional wind Geopotential height Variables in land model output: See https://www.cesm.ucar.edu/models/cesm1.2/clm/models/lnd/clm/doc/UsersGuide/history_fields_table_40.xhtml Note that not all of the variables listed at the above link are included in the land model output files in this dataset. This material is based upon work supported by the National Science Foundation under Grant No. 1454089. We acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The CESM project is supported primarily by the National Science Foundation. We thank all the scientists, software engineers, and administrators who contributed to the development of CESM2. References Danabasoglu, G., and Coauthors, 2020: The Community Earth System Model Version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12, e2019MS001916, https://doi.org/10.1029/2019MS001916.
keywords: Climate modeling; atmospheric science; hydrometeorology; hydroclimatology; soil moisture; land-atmosphere interactions
published: 2021-02-23
Coups d'état are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup D'état Project (CDP) as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e. realized or successful coups, unrealized coup attempts, or thwarted conspiracies) the type of actor(s) who initiated the coup (i.e. military, rebels, etc.), as well as the fate of the deposed leader. This is version 2.0.0 of this dataset. The first version, <a href="https://clinecenter.illinois.edu/project/research-themes/democracy-and-development/coup-detat-project-cdp ">v.1.0.0</a>, was released in 2013. Since then, the Cline Center has taken several steps to improve on the previously-released data. These changes include: <ol> <li>Filling in missing event data values</li> <li>Removing events with no identifiable dates</li> <li>Reconciling event dates from sources that have conflicting information</li> <li>Removing events with insufficient sourcing (each event now has at least two sources)</li> <li>Removing events that were inaccurately coded and did not meet our definition of a coup event</li> <li>Extending the time period covered from 1945-2005 to 1945-2019</li> <li>Removing certain variables that fell below the threshold of inter-coder reliability required by the project</li> <li>The spreadsheet ‘CoupInventory.xls’ was removed because of inadequate attribution and citation in the event summaries</li></ol> <b>Items in this Dataset</b> 1. <i>CDP v.2.0.2 Codebook.pdf</i> <ul><li>This 14-page document provides a description of the Cline Center Coup D’état Project Dataset. The first section of this codebook provides a succinct definition of a coup d’état used by the CDP and an overview of the categories used to differentiate the wide array of events that meet the CDP definition. It also defines coup outcomes. The second section describes the methodology used to produce the data. <i>Created November 2020. Revised February 2021 to add some additional information about how the Cline Center edited some values in the COW country codes."</i> </li></ul> 2. <i>Coup_Data_v2.0.0.csv</i> <ul><li>This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup D’etat Project. It contains 29 variables and 943 observations. <i>Created November 2020</i></li></ul> 3. <i>Source Document v2.0.0.pdf</i> <ul><li>This 305-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify each particular event. <i>Created November 2020</i> </li></ul> 4. <i>README.md</i> <ul><li>This file contains useful information for the user about the dataset. It is a text file written in mark down language. <i>Created November 2020</i> </li></ul> <br> <b> Citation Guidelines</b> 1) To cite this codebook please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, and Jonathan Bonaguro. 2021. “Cline Center Coup D’état Project Dataset Codebook”. Cline Center Coup D’état Project Dataset. Cline Center for Advanced Social Research. V.2.0.2. February 23. University of Illinois Urbana-Champaign. doi: <a href="https://doi.org/10.13012/B2IDB-9651987_V2">10.13012/B2IDB-9651987_V3</a> 2) To cite the data please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, and Jonathan Bonaguro. 2020. Cline Center Coup D’état Project Dataset. Cline Center for Advanced Social Research. V.2.0.0. November 16. University of Illinois Urbana-Champaign. doi: <a href="https://doi.org/10.13012/B2IDB-9651987_V2">10.13012/B2IDB-9651987_V3</a>
keywords: Coup d'état; event data; Cline Center; Cline Center for Advanced Social Research; political science
published: 2021-02-18
Increasingly pervasive location-aware sensors interconnected with rapidly advancing wireless network services are motivating the development of near-real-time urban analytics. This development has revealed both tremendous challenges and opportunities for scientific innovation and discovery. However, state-of-the-art urban discovery and innovation are not well equipped to resolve the challenges of such analytics, which in turn limits new research questions from being asked and answered. Specifically, commonly used urban analytics capabilities are typically designed to handle, process, and analyze static datasets that can be treated as map layers and are consequently ill-equipped in (a) resolving the volume and velocity of urban big data; (b) meeting the computing requirements for processing, analyzing, and visualizing these datasets; and (c) providing concurrent online access to such analytics. To tackle these challenges, we have developed a novel cyberGIS framework that includes computationally reproducible approaches to streaming urban analytics. This framework is based on CyberGIS-Jupyter, through integration of cyberGIS and real-time urban sensing, for achieving capabilities that have previously been unavailable toward helping cities solve challenging urban informatics problems. The files included in this dataset functions as follows: 1) Spatial_interpolation.ipynb is a python based Jupyter notebook that enables users to conduct spatial interpolation with AoT data; 2) Urban_Informatics.ipynb is a Jupyter notebook that helps to explore the AoT dataset; 3) chicago-complete.weekly.2019-09-30-to-2019-10-06.tar includes all the high-frequency urban sensing data from AoT sensors from 2019 September 30th to 2019 October 6th collected in Chicago, US; 4) sensors.csv is a processed dataset including information about the temperature in Chicago, and it is used in Spatial_interpolation.ipynb.
keywords: CyberGIS; Urban informatics; Array of Things
published: 2021-02-16
Data from census of peer-reviewed papers discussing nosZ and published from 2013 to 2019. These data were reported in the manuscript titled, "Beyond denitrification: the role of microbial diversity in controlling nitrous oxide reduction and soil nitrous oxide emissions" published in Global Change Biology as an Invited Report.
keywords: atypical nosZ; Clade II nosZ; denitrification; nitrous oxide; N2O reduction; non-denitrifier; nosZ; nosZ-II; nosZ Clade II; soil N2O emissions
published: 2021-02-15
The file contains biomass and count data of food items encountered in the digestive tract of collected green-winged teal from the Illinois River Valley during spring 2016-2018. The file also contains biomass of food items collected from core samples collected at sites where the green-winged teal were collected. Together, the consumed and availability food data are used to calculate diet selection. The data also contains information on the teal, collection, sites, and other covariates used in analysis. Lastly, the dataset contains biomass of food items collected in medium (#35) and small (#60) sieves for 2018 core samples.
keywords: Anas crecca; food selection; green-winged teal; Illinois River Valley; moist-soil plants; spring migration; stopover ecology
published: 2021-05-01
This is the first version of the dataset. This dataset contains anonymize data collected during the experiments mentioned in the publication: “I can show what I really like.”: Eliciting Preferences via Quadratic Voting that would appear in April 2021. Once the publication link is public, we would provide an update here. These data were collected through our open-source online systems that are available at (experiment1)[https://github.com/a2975667/QV-app] and (experiment 2)[https://github.com/a2975667/QV-buyback] There are two folders in this dataset. The first folder (exp1_data) contains data collected during experiment 1; the second folder (exp2_data) contains data collected during experiment 2.
keywords: Quadratic Voting; Likert scale; Empirical studies; Collective decision-making