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2,166 results for “benefit”
Data from Bieri Thesis: Evaluating Coastal Protection Benefits of Restored Oyster Reef Designs - 2022
This dataset consists of spreadsheets used in the creation of: "Elizabeth Bieri, Evaluating Coastal Protection Benefits of Restored Oyster Reef Designs. MS Thesis, University of Virginia, Charlottesville, VA. Advisors: Matthew Reidenbach & Patricia Wiberg, 2022" (https://doi.org/10.18130/2b04-wz72). Documentation on methods and types of data are given in the thesis and are not repeated here. There are two .zip files. One contains the original Excel workbooks. The other contains the data from individual sheets within the workbooks as comma-separated-value (.csv) text files. The file listing for the CSV files are: Archive: Bieri_Comma_separated_Value_Files.zip Length Date Time Name --------- ---------- ----- ---- 0 2025-12-15 12:47 Bieri_Comma_separated_Value_Files/ 1787 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_crests.csv 239 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_S4.csv 273 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_S7.csv 652 2025-12-15 12:45 Bieri_Comma_separated_Value_Files/erosionpins_exposed.csv 476 2025-12-15 12:45 Bieri_Comma_separated_Value_Files/erosionpins_exposed_no_reef.csv 102 2025-12-15 12:46 Bieri_Comma_separated_Value_Files/erosionpins_S4.csv 97 2025-12-15 12:46 Bieri_Comma_separated_Value_Files/erosionpins_S7.csv 297 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/grainsize_july.csv 290 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/grainsize_oct.csv 399 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_All.csv 509 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_July2021.csv 507 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Oct2021.csv 1242 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Sed_OM.csv 516 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Sept2020.csv 913981 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/jul2021waveS4.csv 715881 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/jul2021waveS7.csv 5251
Assessing the environmental benefit of palladium-based single-atom heterogeneous catalysts for Sonogashira coupling
<p>Dataset supporting the article "Assessing the environmental benefit of palladium-based single-atom heterogeneous catalysts for Sonogashira coupling" by D. Faust Akl, D. Poier, S. C. D’Angelo, T. P. Araújo, V. Tulus, O. V. Safonova, S. Mitchell, R. Marti, G. Guillén-Gosálbez, and J. Pérez-Ramírez<em>.</em></p>
Artifact for "Does Task Complexity Moderate the Benefits of Liveness? - A Controlled Experiment"
<p>This artifact includes the setup, data, and analysis for the experiment described in the article "Does Task Complexity Moderate the Benefits of Liveness? - A Controlled Experiment" published in the journal "The Art, Science, and Engineering of Programming<em>" </em>Volume 9.</p> <p>The artifact has the following structure:</p> <ul> <li> <p>experiment-setup</p> <ul> <li>experiment-environment.zip: Archive including the fully configured Squeak/Smalltalk environment</li> <li>experiment-protocol.pdf: Full protocol for conducting the experiment</li> <li>system: Includes the base system used for the experiment without any seeded faults</li> <li>tasks <ul> <li>task-descriptions.txt: The task descriptions of steps to reproduce and symptoms for the tasks used in the experiment</li> <li>patches: Patch files for all generated tasks</li> </ul> </li> <li>infrastructure: Contains source code of the tools used for controlling tasks in the development environment</li> <li>questionnaire <ul> <li>survey.dfglive.2023-01-06.xml: Questionnaire configuration for a SoSci survey server, includes demographics, experience, and skill questionnaire</li> </ul> </li> </ul> </li> <li> <p>results</p> <ul> <li>data <ul> <li>export-NN.zip: Each archive contains the raw data from one run <ul> <li>starTrackData-NN: Contains the detailed event log</li> <li>experimentState.json: Contains the measurements and the final state for each task (completed, notStarted, etc.)</li> <li>skillTestResult-NN.json: Contains the score reached in the skill test</li> <li>task-X-NN.cs: The submitted patch for task X</li> </ul> </li> </ul> </li> <li>questionnaire <ul> <li>data_dfglive-analysis.ods: Demographics and experience questionnaire results (gender column is redacted due to potential deanonymization)</li> <li>results of skill test are only available in graded form in the participant export files</li> </ul> </li> </ul> </li> <li> <p>analysis</p> <ul> <li>analysis-r: Main analysis scripts <ul> <li>main.R, main-contrast-based.R: Two versions of the main and moderation effect analysis using two different processes</li> <li>live-tools-usage-time.R: Main and moderation effect analysis on tool usage</li> <li>demographics.R: Analysis of demographics and rendering of charts</li> </ul> </li> <li>analysis-tool-usage-correlation: Includes scripts to extract tool usage frequencies from the event logs. Assumes that export-NN.zip files are in the parent folder.</li> </ul> </li> </ul>
Costs and Benefits of Energy Communities - Collection of Literature
<p>The files contain references to studies of different impacts of energy communities, based on the collection reviewed in Berka & Creamer (2018) and with some additions. The typology of impacts differs from that used by Berka and Creamer.</p>
Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> These data represent estimated mean value, standard error, and units for a range of metrics by land cover class in the Sacramento-San Joaquin Delta. Metrics are grouped into three major categories: Agricultural Livelihoods (including metrics for gross production value, number of agricultural jobs, and annual wages per employee), Water Quality (in terms of the application rates for pesticides identified as critical pesticides, groundwater contaminants, and those posing a high or moderate risk to aquatic organisms), and Climate Change Resilience (qualitative scores representing relative tolerance for heat, drought, and flood).</p> <p><strong>DESCRIPTION</strong><br> These data were developed to facilitate projecting the net impacts of land cover change scenarios on multiple metrics of interest to the Sacramento-San Joaquin Delta, including potential benefits and trade-offs. They were used in initial analyses of scenarios representing habitat restoration and perennial crop expansion, and they are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (In review) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta.</em> R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits </li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7504874.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7504874)</p> <p><strong>PROGRESS</strong><br> Complete, but note that the accompanying manuscript has not yet undergone peer-review, and thus these data may require future revision.</p> <p><strong>UPDATE FREQUENCY</strong><br> As Needed</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from the Quarterly Census of Employment and Wages 2014-2020 (EDD 2022), annual County Agricultural Commissioners Reports 2014-2020 (CDFA 2022), Pesticide Use Report Data 2014-2018 (CDPR 2022), and qualitative assessments of climate change resilience (Peterson et al. 2020, DSC 2021).</p> <p><strong>Literature Cited:</strong></p> <ul> <li>CDFA. 2022. County Ag Commissioners’ Data Listing. California Department of Food & Agriculture. Available from: https://www.nass.usda.gov/Statistics_by_State/California/Publications/AgComm/index.php</li> <li>CDPR. 2022. Pesticide Use Report Data. California Department of Pesticide Regulation. Available from: https://www.cdpr.ca.gov/docs/pur/purmain.htm</li> <li>DSC. 2021. Delta Adapts: Creating a Climate Resilient Future. Public Review Draft. Delta Stewardship Council. Available from https://deltacouncil.ca.gov/delta-plan/climate-change</li> <li>EDD. 2022. Quarterly Census of Employment and Wages (QCEW). California Employment Development Department. Available from: https://data.edd.ca.gov/Industry-Information-/Quarterly-Census-of-Employment-and-Wages-QCEW-/fisq-v939</li> <li>Peterson C, Marvinney E, Dybala K. 2020. Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA. Migratory Bird Conservation Partnership, Sacramento, CA. Dryad Dataset doi:10.25338/B8061X</li> </ul> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>METRIC_CATEGORY: </strong>Broad grouping assigned to each METRIC; one of Agricultural Livelihoods, Water Quality, or Climate Change Resilience</li> <li><strong>METRIC: </strong>Specific metric being estimated; one of Agricultural Jobs, Annual Wages, Gross Production Value, Drought, Flood, Heat, Critical Pesticides, Groundwater Contaminant, or Risk to Aquatic Organisms</li> <li><strong>UNIT: </strong>The units in which the <strong>METRIC </strong>is estimated</li> <li><strong>CODE_NAME:</strong> The land cover class or subclass for which the <strong>METRIC </strong>is estimated</li> <li><strong>LABEL: </strong>A more user-friendly version of <strong>CODE_NAME</strong>, useful for creating figures and tables</li> <li><strong>SCORE_MEAN:</strong> The mean value of each METRIC estimated for each land cover class or subclass</li> <li><strong>SCORE_SE: </strong>The standard error of the mean</li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong></p> <ul> <li><strong>FTE: </strong>full-time equivalents; refers to converting monthly agricultural jobs data to annual estimates by dividing by 12</li> <li><strong>ha:</strong> hectares</li> <li><strong>kg: </strong>kilograms</li> <li><strong>USD: </strong>U.S. dollars</li> <li><strong>yr: </strong>year</li> </ul> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> agriculture, livelihoods, economy, water quality, pesticides, climate change, resilience, multiple-benefit conservation</li> <li><strong>Place:</strong> Sacramento-San Joaquin River Delta, Central Valley, California<br> </li> </ul>
Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> Spatial data representing climate, proximity to streams, and probability of flooding in the Sacramento-San Joaquin Delta.</p> <p><strong>DESCRIPTION</strong><br> These data were compiled as predictors of the distribution of riparian landbird species and groups of waterbird species, to facilitate projecting the probability of species or group presence across a given landscape. They were used to identify Priority Bird Conservation Areas and in analyses of the impacts of scenarios representing habitat restoration and perennial crop expansion on suitable habitat. These data are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (<em>In review</em>) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta</li> <li>Dybala KE, Sesser K, Reiter M, Shuford WD, Golet GH, Hickey C, Gardali T (<em>In review</em>) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta. </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta</em>. R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits.</li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7672193.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7672193)</p> <p><strong>PROGRESS</strong><br> Complete</p> <p><strong>UPDATE FREQUENCY</strong><br> None planned</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from WorldClim (representing 1970-2000), National Hydrography Dataset (published 2020), and Point Blue's Water Tracker (representing 2013-2019).</p> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>bio_1: </strong>annual mean temperature (C), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>bio_12:</strong> total annual precipitation (mm), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>streamdist: </strong>square root of the distance to the nearest stream (m) (National Hydrography Dataset; USGS 2020)</li> <li><strong>pwater_fall:</strong> mean probability of open surface water during the fall, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> <li><strong>pwater_win:</strong> mean probability of open surface water during the winter, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> </ul> <p><strong>Literature Cited</strong></p> <ul> <li>Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 37:4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a> </li> <li>Reiter ME, Elliott NK, Barbaree B, Moody D. 2018. An automated open surface water tracking system for California’s Central Valley. Report to the U.S. Fish and Wildlife Service. Petaluma, California: Point Blue Conservation Science. Available from: <a href="https://data.pointblue.org/apps/autowater/ ">https://data.pointblue.org/apps/autowater/ </a></li> <li>[USGS] United States Geological Survey. 2020. National Hydrography Dataset Best Resolution (NHD) for Hydrologic Units (HU) 4 - 1802, 1803, 1804, 1805. Reston (VA): U.S. Geological Survey. Available from: <a href="https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products ">https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products </a></li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong><br> N/A</p> <p><strong>COORDINATE REFERENCE SYSTEM</strong><br> WGS 84 / UTM zone 10N (EPSG:32610)</p> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> climate, temperature, precipitation, hydrology, streams, water, flood, remote sensing </li> <li><strong>Place: </strong>Sacramento-San Joaquin River Delta, Central Valley, California</li> </ul>
Organic and inorganic data for soil cores from Brazil and Florida Bay seagrasses to support Howard et al 2018, CO2 released by carbonate sediment production in some coastal areas may offset the benefits of seagrass “Blue Carbon” storage, Limnology and Oceanography, DOI: 10.1002/lno.10621
Using piston corers, soils from Florida Bay and Brazilian seagrass meadows were collected to complete organic and inorganic carbon inventories for the top 1 m of soil. Instrumental analyses and loss on ignition at 500C were used to measure C content of downcore slices.
Data and Code Accompanying the Study on "Benefits of reflex prediction: A case study of Western Kho-Bwa"
<p>Cite the source of the dataset as:</p> <blockquote> <p>Timotheus A. Bodt and Johann-Mattis List (to appear): Benefits of reflex prediction: A case study of Western Kho-Bwa. Diachronica.</p> </blockquote>
The ecological and economic benefits of sustainable agricultural practices
<p><strong>Code and data for Mata et al.'s <em>The ecological and economic benefits of sustainable agricultural practices: Evidence from on-farm trials in broad-acre crops. </em></strong></p> <p><strong>Abstract</strong></p> <p>1. A transition to more sustainable agricultural practices is essential to mitigate the negative environmental impacts of conventional farming and to ensure long-term food security. However, widespread adoption requires robust evidence demonstrating their efficacy and economic viability.</p> <p>2. We co-designed a two-year field trial with farmers and agronomy advisors in Australia to evaluate the ecological and economic outcomes of sustainable agricultural practices for managing the redlegged earth mite, a major pest of Australian crops and pastures. We compared 'Novel' treatments – representing long-term farmer-implemented sustainable practices based on biological control – with 'Conventional' treatments, and 'Plus' treatments designed as counterfactuals to disentangle the effects of specific pest control and plant nutrient components.</p> <p>3. Redlegged earth mite densities remained below economic thresholds across all treatments and years, demonstrating effective pest control in both conventional and sustainable systems. Notably, the Novel treatment supported higher densities of beneficial arthropods, suggesting increased biological control potential.</p> <p>4. Yield and gross profit margins were generally similar across treatments, indicating that sustainable agriculture practices can maintain profitability while fostering biodiversity.</p> <p>Practical implication. Our study provides evidence that biological control and biofertiliser supplementation can be effectively used to manage agricultural pests. It also demonstrates the value of close collaboration with farmers and agronomy advisors in conducting ecological field research with real-world applications.</p>
Results from the OnStove Nepal model "Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes all result datasets and figures from the <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">OnStove Nepal</a> model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All model input data can be downloaded from the permanent repository at<em> </em><a href="https://doi.org/10.5281/zenodo.10641858">10.5281/zenodo.10641858</a>.</p> <h2>Folder structure</h2> <p>The folder structure consists of a <strong>Procedded GIS Data </strong>folder containing all GIS processed data. These are the outputs from the <strong>DataProcessor.ipynb </strong>script and the raw GIS input data files found in the input data repository.</p> <p>A folder for <strong>each scenario</strong> results. Within each scenario folder, there are:</p> <ul> <li>A <strong>model.pkl </strong>and a <strong>results.pkl </strong>files. These are a calibrated OnStove model with the scenario inputs and a complete results model file of the scenario respectively. Both of these files can be read and explored using the OnStove tool. </li> <li>A <strong>summary.csv </strong>file with the summary results of the scenario for each technology.</li> <li>A <strong>Subsidies_scenario_name.csv </strong>file showing the required total subsidies per technology of the scenario.</li> <li>Image files in pdf format for: <ul> <li>The baseline technologies used in the country (<strong>current_shares.pdf</strong>),</li> <li>The spatial mix of technologies providing the maximum net-benefits throughout the country (<strong>max_benefit_tech.pdf</strong>), </li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The bar plot of max benefit technology shares (<strong>tech_split.pdf</strong>),</li> <li>The max benefit technologies distribution over relative wealth in the country (<strong>tech_histogram.pdf</strong>),</li> </ul> </li> <li>A <strong>Rasters </strong>folder with raster files of different result maps in .tif format.</li> </ul> <p>Inside the <strong>MCA </strong>folder, all results from the prioritization analysis are found, including:</p> <ul> <li>The prioritized spatial technology mix to achieve the goals of the country (<strong>Prioritized_hh.pdf</strong>),</li> <li>The biogas cookstoves relative wealth distribution index (<strong>Biogas_index.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves relative wealth distribution index (<strong>Biomass_ICS_T3_index.pdf</strong>),</li> <li>The electrical cookstoves relative wealth distribution index (<strong>Electricity_index.pdf</strong>),</li> <li>The biogas cookstoves priority map (<strong>Biogas_priority_areas.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves priority map (<strong>Biomass_ICS_T3_priority_areas.pdf</strong>),</li> <li>The electrical cookstoves priority map (<strong>Electricity_priority_areas.pdf</strong>),</li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The prioritized technology shares distribution over relative wealth in the country (<strong>tech_histogram_prioritized.pdf</strong>),</li> <li>A <strong>Subsidies_prioritized.csv </strong>file showing the required total subsidies per technology,</li> <li>A <strong>mca.pkl </strong>file with the MCA model that can be manipulated using the OnStove tool,</li> <li>A <strong>access_results.txt </strong>file with the current and after prioritization clean cooking access shares in the country.</li> </ul> <p>A <strong>main_plot.pdf </strong>and a <strong>prioritized_plot.pdf </strong>files showing the compiled results for all scenarios and prioritized scenario respectively.</p> <h2>License</h2> <p>All datasets are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0).</p>
A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal
<p>This repository is linked to the following article:</p> <ul> <li>Prütz, R., Fuss, S., Lück, S. Stephan, L. & Rogelj, J., A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal. <em>Commun Earth Environ</em> <strong>5</strong>, 197 (2024). <a href="https://doi.org/10.1038/s43247-024-01365-z">https://doi.org/10.1038/s43247-024-01365-z</a></li> </ul> <p>This repository includes: </p> <ul> <li>The literature-based data set that was compiled and used to develop the taxonomy of carbon dioxide removal side effects</li> <li>Code to run the machine learning classifier and to process and visualise the data</li> <li>Training data and an abbreviation list which is required to run the provided code</li> </ul>
Data for publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020"
<p>Data to reproduce figures for the publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020" (DOI: 10.1177/01655515241245952). Each file contains the data underlying the figure corresponding to the file name.</p>
Deep neural networks and humans both benefit from compositional language structure
<p>This dataset holds the results generated in the paper:</p> <p>Deep neural networks and humans both benefit from compositional language structure</p> <p>by L. Galke, Y. Ram, and L. Raviv.</p>
Dataset for the paper "Combining near-term benefits of climate adaptation with long-term benefits of emissions abatement"
<p>This repo archives all data used in Duan et al. (2024), including model codes, raw model outputs, and post-processing scripts. </p> <p>A Readme file describes the data and structure included here. If you have any questions, please contact the lead author (Lei Duan: leiduan@carnegiescience.edu). </p> <p>We have updated the post-process codes to reflect changes in the revised manuscript</p> <p>==</p> <p>Paper associated with this dataset can be found at: https://www.nature.com/articles/s43247-024-01976-6#:~:text=Adaptation%20deployed%20in%20conjunction%20with,adaptation%20reducing%20near%2Dterm%20damage. </p>
Supplementary Datafile for "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe"
<p>The dataset contains the results of the study "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe" by Marco Springmann, Eugenia Dinivitzer, Florian Freund, Jørgen Dejgård Jensen, and Clara G Bouyssou. </p> <p>It contains VAT rates on foods across Europe, as well as the results of reforming VAT rates for foods, including increasing rates for meat and dairy and reducing rates for fruits and vegetables. The set of results include changes in food demand, changes in environmental impacts (greenhouse gas emissions, land use, water use, and eutrophication potential), changes in diet-related mortality, changes in costs (revenues, climate change costs, costs of illness). </p>
Are marriage-related taxes and Social Security benefits holding back female labor supply?
<p>Margherita Borella, Mariacristina De Nardi, and Fang Yang, "Are marriage-related taxes and Social Security benefits holding back female labor supply?". The Review of Economic Studies, Forthcoming. This package contains all the code and detailed instructions on how to replicate the data analysis and numerical results in the paper.</p>
Survey data on assumed benefits of visualizations in libraries
<p>Results of a survey with 242 participants on connections between assumed benefits of visualizations in the library context and research behavior aspects (frequency of digital material usage, material focus, research subjects etc.). Additionally, participants were subjected to the visualization "An Ocean of Books" by Gaël Hugo and instructed to choose up to five adjectives out of the reaction cards subset proposed by Merčun[1].</p> <p>The head of the file contains the question ID, the question itself, and short information on the data type.</p> <p>Feel free to contact me if you have any questions!</p> <p> </p> <p>[1] T. Merčun. Evaluation of information visualization techniques: analysing user experience with reaction cards. In Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization, BELIV ’14, pp. 103–109. Association for Computing Machinery, New York, NY, USA, Nov. 2014. doi: 10.1145/2669557.2669565</p>
Supplemental data for "Inequitable spatial and temporal patterns in the distribution of multiple environmental risks and benefits in Metro Vancouver"
<p><strong>DemoEnPoC2016.csv/DemoEnPoC2006.csv:</strong></p> <p>This is a table including environmental and demographic (Census variables) data at postal code level for Metro Vancouver in the year 2006 and 2016. The environmental data (SO2 metrics, PM2.5 metrics, Calculated ozone metrics, NO2 data, NDVI metrics, and Canadian Active Living Environments Index (Can-ALE) indexed to DMTI Spatial Inc. postal codes) were extracted from CANUE (Canadian Urban Environmental Health Research Consortium). The demographic data is extracted from Canadian Census analyzer (https://datacentre.chass.utoronto.ca/), the deprivation index is downloaded from from the Institut national de santé publique du Québec (INSPQ). </p> <p><strong>DGRwithLable:</strong></p> <p>This is the Dissemination Geographies Relationship File for the 2021 census year (Statistics Canada, 2021) with the lable of urban or rural, indicating which dissemination area (DA) is identified as urban and included in this study. The urban area is named as population certer. </p> <p><strong>Aggregation and SS Determination:</strong></p> <p>This script contains code for:</p> <ul> <li>Aggregating postal code level data to the Dissemination Area (DA) level.</li> <li>Eliminating rural DAs.</li> <li>Converting environmental data into ordinal categories using quartile and even break methods.</li> <li>Identifying sweet and sour spots for each DA based on these methods.</li> </ul> <p><strong>SSEJ Analysis:</strong></p> <p>This script includes code for:</p> <ul> <li>Creating violin and box plots to illustrate descriptive statistics of demographic groups across different environmental categories (sweet, sour, risky, and medium).</li> <li>Performing linear regression analyses between environmental categories and demographic variables.</li> </ul> <p><strong>SS Heatmap:</strong></p> <p>This script comprises code for:</p> <ul> <li>Summarizing the results of the linear regression analyses.</li> <li>Assessing changes in inequities among demographic groups between 2006 and 2016.</li> <li>Visualizing regression coefficients through heatmaps.</li> </ul> <p> </p>
Optimization of abdominal CT based on a model of total risk minimization by putting radiation risk in perspective with imaging benefit
<p><span>Population of one million cases simulating a liver cancer scenario. The demographic information was taken from the USA 2019 Census Population Estimates by Age, Sex, Race, and Hispanic Origin</span><span>. The patient population was simulated in eight different cohorts with genders of male and female, and races/ethnicities of white, Black, Hispanic, and Asian. The total population sample size allowed the inclusion of a significant number of cases in each group. For each simulated patient, the age was randomly sampled from a uniform distribution, which spanned the age range of the individual demographic groups. Radiation risk, clinical risk, and total imaging procedure risk were also calculated.<br></span></p>
Dataset for publication "Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares"
<p>This dataset contains all the results and scenarios for this paper. Each region has two files in which you can find all the scenarios for the market design with and without the local energy market. The folders within these files contain the results for one scenario for a given share of PV, EVs, and heat pumps (HPs). The results folders can also be used to rerun the scenarios. To do so, place the respective scenario in the tool's scenario folder.</p> <p>USE CASE:<br>If you do not want to check all the files of the results or want to rerun the scenarios, use this version. If you want to download the relevant files for the analysis and figure creation of the paper, use the <a href="https://zenodo.org/records/13907329" target="_blank" rel="noopener">compact version</a>.</p> <p>TOOL:<br><a href="https://github.com/TUM-Doepfert/lemlab/tree/doepfert2024_lem">lemlab</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.