Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
113
datasets available to search
ShareScore release 0.9.0
Dataset results
113 results for “economic cost”
Data of paper "Global supply chains amplify economic costs of future extreme heat risk"
<p>This is the database of articles "Global supply chains amplify economic costs of future extreme heat risk". The database contains the number of deaths caused by future heat waves in regions around the world under different SSP scenarios (e.g. SSP119, SSP245, SSP585), as well as global health losses, labor losses, and indirect losses as a percentage of regional or sectoral value added under different SSP scenarios. The regions of the database are aggregated using the GTAP 141 aggregating schema.</p>
Climate change impact and mitigation cost data - The economically optimal warming limit of the planet
<p>This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper:</p> <p>Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. <a href="https://doi.org/10.5194/esd-10-741-2019">https://doi.org/10.5194/esd-10-741-2019</a></p> <p>Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).</p> <p> </p> <p><strong>Climate change impact data</strong></p> <p>File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv</p> <p>Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.</p> <p> </p> <p>File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv</p> <p>Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).</p> <p> </p> <p>File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv</p> <p>Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).</p> <p><br> In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, <a href="https://tntcat.iiasa.ac.at/SspDb/">https://tntcat.iiasa.ac.at/SspDb/</a>, last accessed 15Nov 2019).</p> <p> </p> <p><strong>Climate change mitigation cost data</strong></p> <p>The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].</p> <p>File 4: REMIND_scenario_results_economic_data.csv</p> <p>File 5: REMIND_scenarios_climate_data.csv</p> <p>Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature.</p> <p>In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.</p> <p>The first dimension specifies the climate policy regime (delayed action, baseline scenarios):</p> <p>1xx: climate action from 2010<br> 5xx: climate action from 2015<br> 2xx climate action from 2020 (used in this study)<br> 3xx climate action from 2030<br> 4x1 weak policy baseline (before Paris agreement)</p> <p>The second dimension specifies the technology portfolio and assumptions:</p> <p>x1x Full technology portfolio (used in this study)<br> x2x noCCS: unavailability of CCS<br> x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed<br> x4x NucPO: phase out of investments into nuclear energy<br> x5x Limited SW: penetration of solar and wind power limited<br> x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)<br> x6x noBECCS: unavailability of CCS in combination with bioenergy</p> <p>The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.).</p> <p>xx1 0$/tCO2 (baseline)<br> xx2 10$/tCO2<br> xx3 30$/tCO2<br> xx4 50$/tCO2 <br> xx5 100$/tCO2<br> xx6 200$/tCO2<br> xx7 500$/tCO2<br> xx8 40$/tCO2<br> xx9 20$/tCO2<br> xx0 5$/tCO2</p> <p>For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).</p> <p> </p> <p>[1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a.</p> <p>[2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.</p>
Replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits"
<p>This repository contains replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits". All non-confidential data inputs are included, as well as intermediate data outputs, final data outputs, and final tables and figures for all main text and supplementary tables and figures. Some input data are confidential (e.g., mortality records in some countries); therefore, intermediate regression results files are included in the upload to ensure all later stages of the analysis are fully replicable. The full data output files resulting from Monte Carlo simulations of future climate change impacts on mortality far exceed Zenodo file size limits; therefore, key aggregates of the raw output files are included here, which allow for replication of all tables and figures in the paper.</p> <ul> <li><strong>data.zip </strong>contains raw, intermediate, and final datasets</li> <li><strong>outputs.zip </strong>contains output tables and figures </li> </ul> <p>All replication code for the paper is available on a public Github repository, accessible <a href="https://github.com/ClimateImpactLab/carleton_mortality_2022">here</a>.<br><br>The manuscript and supplementary information are available at the QJE, <a href="https://doi.org/10.1093/qje/qjac020">here</a>.</p>
Fig. 1 in It is recreational but profitability also matters: A cost-effective economic approach to marine recreational fishing in Spain Abstract
Fig. 1: Map of the study area. The darker regions highlighted correspond to Spanish coastal Autonomous Communities.
Fig. 4 in It is recreational but profitability also matters: A cost-effective economic approach to marine recreational fishing in Spain Abstract
Fig. 4: Economic indicator by: A) the main fishing modalities: spearfishing, shore-fishing and boat-fishing and B) spearfishing diving approach.
Fig. 3 in It is recreational but profitability also matters: A cost-effective economic approach to marine recreational fishing in Spain Abstract
Fig. 3: Daily expenses. A) Spearfishing by diving approach and B) Boat fishing (angling) by type of vessel. The percentage of responses by modality in brackets.
The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK"
<p>The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK".</p> <p>Data collector: Runda Zheng</p>
Table S1 - Economic Costs of Protecting Islands from Invasive Alien Species
<p>Dataset analysed in relation to the paper 'Economic Costs of Protecting Islands from Invasive Alien Species' published in Conservation Biology in 2022</p>
Supplementary Data for the publication "High economic costs of reduced carbon sinks and declining biome stability in Central American forests"
<p>Supplementary data from the DGVM simulations underlying the main figures presented in the publication.</p> <p>Naming convention: {variable}_{aggregation period}-{comparison period [only relevant for bsprob]}_{climate model}-{climate scenario}.tif</p> <p>Variables are:</p> <ul> <li>bsprob-Snell2013ed = biome shift probability (biomization adjusted from Snell et al. 2013) [%]</li> <li>nee = net ecosystem exchange [kgC/m2/year]</li> </ul> <p>Global climate models include GFDL = GFDL-ESM4 and IPSL= IPSL-CM6A-LR. Climate scenarios refer to the shared socioeconomic pathways (SSP) SSP126= SSP1-2.6 and SSP370= SSP3-7.0.</p>
Data from: Factoring economic costs into conservation planning may not improve agreement over priorities for protection
Conservation organizations must redouble efforts to protect habitat given continuing biodiversity declines. Prioritization of future areas for protection is hampered by disagreements over what the ecological targets of conservation should be. Here we test the claim that such disagreements will become less important as conservation moves away from prioritizing areas for protection based only on ecological considerations and accounts for varying costs of protection using return-on-investment (ROI) methods. We combine a simulation approach with a case study of forests in the eastern United States, paying particular attention to how covariation between ecological benefits and economic costs influences agreement levels. For many conservation goals, agreement over spatial priorities improves with ROI methods. However, we also show that a reliance on ROI-based prioritization can sometimes exacerbate disagreements over priorities. As such, accounting for costs in conservation planning does not enable society to sidestep careful consideration of the ecological goals of conservation.
Environmental impacts and economic costs of perovskite light-emitting diodes
<p>Environmental impacts and economic costs of perovskite light-emitting diodes (PeLEDs), which are based on the life cycle assessments (LCA) and techno-economic assessments (TEA). They are also the dataset for the manuscript entitled "Towards sustainable perovskite light-emitting diodes".</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Cost and performance data for electricity generation and storage technologies
<p>Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92% of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV.</p> <p>The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5510 datapoints from 56 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability in future.</p>
Fig. 2 in It is recreational but profitability also matters: A cost-effective economic approach to marine recreational fishing in Spain Abstract
Fig. 2: Summary diagram of the cost-effectiveness indicator calculation process.
Bark-scratching of storm-felled trees preserves biodiversity at lower economic costs compared to debarking
<p>The abundance data presented here focus on saproxylic beetles collected over two years on trees in a mountain forest ecosystem (analysed and discussed in Thorn et al. <a href="https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecm.1343#ecm1343-bib-0043">2016</a>). The design consists of 12 plots each composed of three experimentally felled trees, resulting in a total of 36 experimental felled trees. In each plot, the bark of one tree was completely removed, the bark of a second tree was only partially removed (i.e., bark-scratched), and the third tree served as a control. The design is thus composed of 12 replications of three different treatments (i.e., control, bark-scratched, and debarked). A total of 120 species of saproxylic beetles were trapped with emergence traps on felled trees (Thorn et al. <a href="https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecm.1343#ecm1343-bib-0043">2016</a>).</p>
Limiting scaring activities reduces economic costs associated with foraging barnacle geese: results from an individual-based model
<ol> <li>With increasing numbers of large grazing birds on agricultural grassland, conflict with farmers is rising. One management approach to alleviate conflict allows foraging on dedicated agricultural land (accommodation areas) and nature reserves, combined with scaring on remaining agricultural land. Here, we examine the cost-effectiveness of these measures by studying the influence on barnacle goose distribution and associated economic damage.</li> <li>We present an individual/agent-based model of barnacle geese (<em>Branta</em> <em>leucopsis</em>) foraging on grasslands in Fryslân, the Netherlands. The model is parameterized using field observations and GPS-tracks and allows simulation of management scenarios, differing in scaring probability and accommodation area size, with different potential management costs. </li> <li>Our model shows that, while yield loss decreases with higher scaring probabilities, costs of damage appraisal increase because geese graze on more fields. With small accommodation areas, achieving high scaring probabilities takes more effort and could result in goose population decline. Total management costs are lowest without scaring activity. </li> <li> <em>Synthesis and applications</em>: Considering costs of active scaring and the need to maintain the barnacle goose population in a favourable conservation status, our model suggests that the most cost-effective scenario is to prevent disturbance of geese. A high scaring probability could be beneficial if applied in small areas, for example around sensitive crops or airfields. Scaring in large areas could result in costs outweighing benefits and a declining barnacle goose population.</li> </ol>
Data from: Factoring economic costs into conservation planning may not improve agreement over priorities for protection
Open the record for dataset details and reuse information.
Limiting scaring activities reduces economic costs associated with foraging barnacle geese: results from an individual-based model
Open the record for dataset details and reuse information.
Data from: Economic costs and health-related quality of life for hand, foot and mouth disease (HFMD) patients in China
Background: Hand, foot and mouth disease (HFMD) is a common illness in China that mainly affects infants and children. The objective of this study is to assess the economic cost and health-related quality of life associated with HFMD in China. Method: A telephone survey of caregivers were conducted in 31 provinces across China. Caregivers of laboratory-confirmed HFMD patients who were registered in the national HFMD enhanced surveillance database during 2012-2013 were invited to participate in the survey. Total costs included direct medical costs (outpatient care, inpatient care and self-medication), direct non-medical costs (transportation, nutrition, accommodation and nursery), and indirect costs for lost income associated with caregiving. Health utility weights elicited using EuroQol EQ-5D-3L and EQ-Visual Analogue Scale (VAS) were used to calculate associated loss in quality adjusted life years (QALYs). Results: The subjects comprised 1136 mild outpatients, 1124 mild inpatients, 1170 severe cases and 61 fatal cases. The mean total costs for mild outpatients, mild inpatients, severe cases and fatal cases were $201 (95%CI $187, $215), $1072 (95%CI $999, $1144), $3051 (95%CI $2905, $3197) and $2819 (95%CI $2068, $3571) respectively. The mean QALY losses per HFMD episode for mild outpatients, mild inpatients and severe cases were 3.6 (95%CI 3.4, 3,9), 6.9 (95%CI 6.4, 7.4) and 13.7 (95%CI 12.9, 14.5) per 1000 persons. Cases who were diagnosed with EV-A71 infection and had longer duration of illness were associated with higher total cost and QALY loss. Conclusion: HFMD poses a high economic and health burden in China. Our results provide economic and health utility data for cost-effectiveness analysis for HFMD vaccination in China.
Data from: Cost-effectiveness of a specialist geriatric medical intervention for frail older people discharged from acute medical units: economic evaluation in a two-centre randomised controlled trial (AMIGOS)
Background: Poor outcomes and high resource-use are observed for frail older people discharged from acute medical units. A specialist geriatric medical intervention, to facilitate Comprehensive Geriatric Assessment, was developed to reduce the incidence of adverse outcomes and associated high resource-use in this group in the post-discharge period. Objective: To examine the costs and cost-effectiveness of a specialist geriatric medical intervention for frail older people in the 90 days following discharge from an acute medical unit, compared with standard care. Methods: Economic evaluation was conducted alongside a two-centre randomised controlled trial (AMIGOS). 433 patients (aged 70 or over) at risk of future health problems, discharged from acute medical units within 72 hours of attending hospital, were recruited in two general hospitals in Nottingham and Leicester, UK. Participants were randomised to the intervention, comprising geriatrician assessment in acute units and further specialist management, or to control where patients received no additional intervention over and above standard care. Primary outcome was incremental cost per quality adjusted life year (QALY) gained. Results: We undertook cost-effectiveness analysis for 417 patients (intervention: 205). The difference in mean adjusted QALYs gained between groups at 3 months was -0.001 (95% confidence interval [CI]: -0.009, 0.007). Total adjusted secondary and social care costs, including direct costs of the intervention, at 3 months were £4412 (€5624, $6878) and £4110 (€5239, $6408) for the intervention and standard care groups, the incremental cost was £302 (95% CI: 193, 410) [€385, $471]. The intervention was dominated by standard care with probability of 62%, and with 0% probability of cost-effectiveness (at £20,000/QALY threshold). Conclusions: The specialist geriatric medical intervention for frail older people discharged from acute medical unit was not cost-effective. Further research on designing effective and cost-effective specialist service for frail older people discharged from acute medical units is needed.
Data Archival for Economic Cost Modeling of Chinook Habitat Restoration in the Stillaguamish River Basin
<p>We used geospatial data to model economic cost estimates of habitat restoration in the Stillaguamish River Basin in the Puget Sound. We utilized data pertaining to the streams/rivers, floodplain habitat, subbasins, elevation, distance to roads, demographics, and land use within the Stillaguamish River Basin to do so. Analysis included using the different attributes of the Stillaguamish River Basin to create low and high cost estimates for floodplain, engineered log jam, and riparian planting habitat restoration. We specifically looked at the slope and size of streams, area of habitat that needed to be restored, slopes of the riparian area, distance to nearest road, and canopy angles as our model inputs. We followed cost estimate guidance provided by the Puget Sound Shared Strategy to identify our cost ranges and updated them to todays prices using the producer price index. An additional land use analysis was performed to quantify the total area and cost of potential agricultural land in the basin. Lastly, we investigated the demographics of the region to identify areas of POC and low income in relation to proposed restoration actions.</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.