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1,751 results for “Future”
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for nuclear power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>nuclear power generation</span></span><span> <span>from</span><span> the open literature</span><span>. </span></span><span><span>Nuclear energy is the second-largest source of low-carbon generation, supplying 9% of global electricity</span><span>.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>604</span></span><span><span> datapoints from </span></span><span><span>19</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is </span><span>designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span> Technoeconomic data on nuclear power was collected from websites, reports, academic articles and databases of national and international organisations.</p>
Figure 3 in Cystic echinococcosis (Echinococcus granulosus sensu lato infection) in Tunisia, a One Health perspective for a future control programme
Figure 3. Seized offal in a Tunisian slaughterhouse that is often thrown on the ground.
Feeling the future: A meta-analysis of 90 experiments on the anomalous anticipation of random future events
<p>The research that created this data is described in:</p> <ul> <li>Bem D, Tressoldi P, Rabeyron T and Duggan M. Feeling the future: A meta-analysis of 90 experiments on the anomalous anticipation of random future events. F1000Research 2015, 4:1188 (<a href="https://f1000research.com/articles/4-1188">doi: 10.12688/f1000research.7177.1</a>)</li> </ul>
Financial Literacy among Young College Students: Advancements and Future Directions
Open the record for dataset details and reuse information.
Data set for the study "Interplay between climate and carbon cycle feedbacks could substantially enhance future warming"
<p>This repository contains the data necessary to reproduce the results of the paper: <br>"Interplay between climate and carbon cycle feedbacks could substantially enhance future warming" <br><a href="https://iopscience.iop.org/article/10.1088/1748-9326/adb6be" target="_blank" rel="noopener">https://iopscience.iop.org/article/10.1088/1748-9326/adb6be</a></p> <h3><strong>Data organization:</strong></h3> <p>The Zenodo repository is organized as follows inside of <code>results.zip</code>:</p> <ul> <li>Figure generation are given by "*.pynb" and "*.m" files<br><br></li> <li>Data files as NetCDF output are organized with the following structure inside of <code>data</code>:<br><br> <ul> <li><strong>Experiment/emission scenario</strong>: <code>hist-aer</code>, <code>ssp126</code>, <code>ssp434</code>, and <code>ssp245</code><br><br> <ul> <li><strong>Equilibrium climate sensitivity</strong>: <code>ecs_2.0K</code>, <code>ecs_2.5K</code>, <code>ecs_3.0K</code>, <code>ecs_3.5K</code>, <code>ecs_4.0K</code>, <code>ecs_4.5K</code>, and <code>ecs_5.0K</code><br><br> <ul> <li><strong>Experiment: </strong><code>comp</code>, <code>comp_fix_ch4</code>, <code>comp_fix_co2_ch4</code>, <code>comp_ssp_co2_ch4</code><br><br> <ul> <li><strong>Component</strong>: atmosphere (<code>atm</code>), ocean (<code>ocn</code>), land (<code>lnd</code>), sea ice (<code>sic</code>), carbon dioxide (<code>co2</code>), methane (<code>ch4</code>)<br><br></li> <li><strong>File type</strong>: for some experiments, files are divided into timeseries (<code>*_ts.nc</code>) or 2D data (<code>*.nc</code>)<br><br></li> <li>Note: <code>comp_ssp_co2_ch4</code> are the CLIMBER-X runs which used prescribed concentrations (rather than emissions) and is only available for ECS 3°C<br><br></li> <li>Note: <code>comp_fix_ch4</code> and <code>comp_fix_co2_ch4</code> is only available for ECS 2°C, 3°C, and 5°C (as shown in Fig. 4 in the manuscript)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Massaciuccoli Lake basin in Tuscany, Italy. Datasets of 75 environmental, geomorphologic, and socio-economic variables associated from remote past to remote future
<p>Collection of 148 datasets representing 75 environmental, geomorphologic, and socio-economic variables associated with the Massaciuccoli Lake basin in Tuscany, Italy. <br>The data cover five temporal snapshots: remote past (1950-1980), recent past (1981-2015), present (2016.2024), near future (2050 under RCP4.5 and 8.5), and remote future (2100 under RCP4.5 and 8.5)<br>Raster data have been harmonised and resampled at 0.0005° (~50 m) resolution.<br>Vector data have been aligned and cut over the basin boundaries.<br>A QGIS project using the WGS84 EPSG:4326 projection is included in the ZIP file. </p> <p>A metadata file (in MS Excel format) reports data content descriptions, the primary sources, their FAIRness levels, and the direct links to the primary sources when available. <br>The dataset numbers are aligned to the table Id-column entries.</p>
HERCA Suggestions for ICRP Future Work Areas
<p>The presentation describes first view of HERCA (Heads of European Radiation Protection Competent Autorities) on the refinement of System of Radiation Protection started by ICRP.</p> <p>Based on the consultation amongst the HERCA members it was concluded that currently the most popular topics for suggested future ICRP-work are:</p> <ul> <li>Simplification of the system of RP</li> <li>Justification and optimisation, use of reference levels</li> <li>Radon</li> <li>Communication</li> </ul> <p>It must be stressed also that these results cannot be understood as HERCA-priorities of these topics in general, but they reflect the (distribution of) votes within HERCA with respect to suggestions for further work of ICRP on these topics.</p> <p>It should be discussed and explored further what type of future work by ICRP on these topics is needed, according to the national radiation protection regulators collaborating within HERCA.</p> <p>Members of HERCA are prepared to contribute to the refinement of the System of Radiological Protection. Appreciable effort will be dedicated to this work. HERCA working groups and special task force will collect relevant feedback from European regulatory practices in specific areas and will formulate a common views and needs for modifications or changes of current system. This could be done by elaboration of specific HERCA documents or publications based on internal discussions or discussions with relevant stakeholders, organization of topical workshops or active participation in different events organized by others.</p>
Personal Online Dosimetry Using Computational Methods: The PODIUM Project and the Future of Active Dosimetry
<p>Individual monitoring of workers exposed to external ionizing radiation is essential to allow application of the ALARA principle and follow up of the official dose limits. However, large uncertainties still exist in personal dosimetry. Also, many practical problems exist for personal dosimetry, with many dosemeters getting lost and the reluctance of many workers to wear one or more dosemeters.</p> <p>Most legal dosimetry is done with passive dosemeters, which are analyzed after the wearing period in an accredited lab. Active dosemeters are also widely used, although mostly only for ALARA purposes or for specific exposure situations. Although the active dosemeters are dosimetrically and technically at least equivalent to passive dosemeters, their higher cost limits their use as only legal dosemeters.</p> <p>In an attempt of reinventing dosimetry by using the modern evolutions in simulations, artificial intelligence and computer vision, the PODIUM project was set up. PODIUM was a short feasibility project, funded by the EC CONCERT programme.</p> <p>The objective of the PODIUM project was to improve personal dosimetry by an innovative approach: the development of an online dosimetry application based on computer simulations without the use of physical dosemeters. Operational quantities, protection quantities and radiosensitive organ doses (e.g. eye lens, brain, heart, extremities) can be calculated based on the use of modern technology such as personal tracking devices, flexible individualized phantoms and scanning of geometry set-up. When combined with fast simulation codes, the aim was to perform personal dosimetry in real-time.</p> <p>We applied and validated the methodology for two situations where improvements in dosimetry are urgently needed: neutron workplaces and interventional radiology. Several validation and test measurements were done in different hospitals, and in 2 workplace fields with significant neutron exposure. Personal doses could be calculated within acceptable simulation times, just based on captured movements of the workers and information of the radiation fields. These doses agreed with the results from physical dosemeters within the standard uncertainties that are accepted in personal dosimetry.</p> <p>This PODIUM dosimetry method can also be used to visualize the radiation in near real time. This will increase awareness of radiation protection among workers and will improve the application of the ALARA principle, and it can also be used in training modules. The use of neural networks and big data will help in further reducing simulation time, making real time simulations and dosimetry without physical dosemeters possible in the near future.</p>
Dataset for "Winter inverse lake stratification under historic and future climate change"
<p>Summary results for Woolway et al., Winter inverse lake stratification under historic and future climate change. See README file for specific information on the variables provided.</p>
Teaser | The past, present and future, of African language dictionaries - from Eurocentric to Afrocentric compilations
<p>Professor D.J. Prinsloo (University of Pretoria, South Africa) gives us a glimpse of the course he will lecture in <em>Lisbon Summer School in Linguistics 2021</em>: "The past, present and future, of dictionaries for African Languages - from Eurocentric to Afrocentric compilations".</p> <p>Further information: https://clunl.fcsh.unl.pt/en/lisbon-summer-school-in-linguistics-2021/</p>
Current and future sources of open citations
<p>Video recording of the presentation done in the context of the Austrian DataCite Consortium on 19 November 2021. The video finishes after the last question.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Uccle KMI, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N, 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven City centre, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Leuven City Centre (50°52'48"N 4°42'0" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven Casa Blanca, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Casa Blanca neighbourhood Leuven (50°52'48"N, 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations
<p>Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"</p>
Past wet-dry climate cycles inform Central Asia's future
<p>Here we present the original dat of high-resolution pollen, sediment grain size and stable isotopes for cyclic moisture changes in Lop Nur, northwestern China, Central Asia since the Last Glacial Maximum. The pollen data includes the pollen abundance, concentration and flux of dominant types. The grain size data includes sand, silt, and clay. The stable isotope data comprises C and N. </p>
Data from: Foliar herbivory creates subtle soil legacy effects that alter future herbivores via changes in plant community biomass allocation
<p>Plants leave legacy effects in the soil they grow in, which can drive important vegetation processes, including productivity, community dynamics and species turnover. Plants at the same time also face continuous pressure posed by insect herbivores. Given the intimate interactions between plants and herbivores in ecosystems, plant identity and herbivory are likely to interactively shape soil legacies. However, the mechanisms that drive such legacy effects on future generations of plants and associated herbivores are little known.<br> In a greenhouse study, we exposed ten common grasses and non-leguminous forbs individually to insect herbivory by two closely related noctuid caterpillars, <i>Mamestra brassicae</i> and <i>Trichoplusia ni</i> (Lepidoptera: Noctuidae) or kept them free of herbivores. We then used the soil legacies created by these plant individuals to grow a plant community composed of all ten plant species in each soil, and exposed these plant communities to <i>M. brassicae</i>. We measured conditioning plant biomass, soil respiration and chemistry of the conditioned soils, as well as individual plant, plant community and herbivore biomass responses.</p> <p>At the end of the conditioning phase, soils with herbivore legacies had higher soil respiration, but only significantly so for <i>M. brassicae</i>. Herbivore legacies had minimal impacts on community productivity. However, path models reveal that herbivore-induced soil legacies affected responding herbivores through changes in plant community shoot: root ratios. Soil legacy effect patterns differed between functional groups. We found strong plant species and functional group-specific effects on soil respiration parameters, which in turn led to plant community shifts in grass: forb biomass ratios. Soil legacies were negative for the growth of plants of the same functional group. </p> <p><strong>Synthesis:</strong><i> </i>We show that insect herbivory, plant species and their functional groups, all incur soil microbial responses that lead to subtle (herbivory) or strong (plants and their functional group) effects in response plant communities and associated polyphagous herbivores. Hence, even though typically ignored, our study emphasizes that legacies of previous insect herbivory in the soil can influence current soil-plant-insect community interactions.</p>
A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps
<p>Deployment of wind energy is proposed as a mechanism to reduce greenhouse gas emissions. Yet, wind energy and large birds, notably soaring raptors, both depend on suitable wind conditions. Conflicts in airspace use may thus arise between wind energy development and wildlife protection due to the risks of collisions of birds with the blades of wind turbines. Using locations of GPS-tagged bearded vultures, a rare scavenging raptor reintroduced into the Alps, we built a spatially-explicit model to predict potential areas of conflict with future wind turbines deployments in the Swiss Alps. We modelled the probability of bearded vultures flying within or below the rotor-swept zone of wind turbines as a function of wind and environmental conditions, including food supply (presence of wild ungulates). Flight activity at potential risk of collision was generally high, concentrating on south-exposed mountainsides, especially in areas where ibex carcasses have a high occurrence probability, with critical areas covering vast expanses throughout the Swiss Alps. Our model provides a spatially-explicit decision tool that will guide authorities and energy companies for planning the deployment of wind farms in a proactive manner to reduce risk to emblematic Alpine wildlife.</p>
Resources for the Future Socioeconomic Projections (RFF-SPs)
<p>RFF-SPs Monte Carlo output data:</p> <p>This dataset includes the socioeconomic and emissions data generated by the Resources for the Future Socioeconomic Projections (RFF-SPs) model as discussed in Rennert et al. (forthcoming) (<a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/</a>). The data take the form of a Monte Carlo simulation with n = 10,000 draws. File structure and column metadata are described here:</p> <p>---</p> <p>death_rates/</p> <p>---</p> <ul> <li>rffsp_death_rates_run_1.feather</li> <li>rffsp_death_rates_run_2.feather</li> <li>rffsp_death_rates_run_2.feather</li> </ul> <p> ...</p> <ul> <li>rffsp_death_rates_run_999.feather</li> <li>rffsp_death_rates_run_1000.feather</li> </ul> <p>This folder contains 1,000 files in the .feather file format (<a href="https://arrow.apache.org/docs/python/feather.html">https://arrow.apache.org/docs/python/feather.html</a>), which is optimized for I/O speed and compressed to minimize storage requirements. Each file in this folder contains 3 columns: ISO3, Year, and DeathRate. For each row:</p> <p>- ISO3 contains the ISO numeric-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>) of the country whose GDP and population are projected. - Year contains the calendar year of the predictions.</p> <p>- year contains the calendar year of the prediction.</p> <p>- DeathRate contains a death rate in average deaths per 1000 people.</p> <p>Baseline mortality data found in data/mortality was derived from the death_rates.csv provided to the RFF team on October 7th from Hana Sevcikova. In that source file, the column DeathRate is the annual deaths per 1000 people. PopAvg is the denominator (average between two time periods) and PopStart is population at the start of the time interval. Values are average deaths per 1000 people.</p> <p>There is one mortality death rate trajectory for each population trajectory, and each RFF-SP is matched to one of these 1000 trajectories. The file sampled_pop_trajectory_numbers.csv described below maps each of the 10,000 RFF SP scenarios to the baseline mortality scenario (out of 1000) matched to its population draw.</p> <p>---</p> <p>emissions/</p> <p>---</p> <ul> <li>rffsp_co2_emissions.csv</li> <li>rffsp_ch4_emissions.csv</li> <li>rffsp_n2o_emissions.csv</li> </ul> <p>Each file in this folder contains 3 columns: sample, year, and value. For each row:</p> <p>- sample contains the number identifying which draw a prediction belongs to (from 1 to 10,000).</p> <p>- year contains the calendar year of the prediction.</p> <p>- value contains the projected annual global emissions of the gas specified in the filename.</p> <p>The units are as follows:</p> <ul> <li>rffsp_co2_emissions.csv is in gigatons C</li> <li>rffsp_ch4_emissions.csv is in megatons CH4</li> <li>rffsp_n2o_emissions.csv is in megatons N2</li> </ul> <p>---</p> <p>pop_income/</p> <p>---</p> <ul> <li>rffsp_pop_income_run_1.feather</li> <li>rffsp_pop_income_run_2.feather</li> <li>rffsp_pop_income_run_3.feather</li> </ul> <p> ...</p> <ul> <li>rffsp_pop_income_run_9999.feather</li> <li>rffsp_pop_income_run_10000.feather</li> </ul> <p> </p> <p>This folder contains 10,000 files in the .feather file format (<a href="https://arrow.apache.org/docs/python/feather.html">https://arrow.apache.org/docs/python/feather.html</a>), which is optimized for I/O speed and compressed to minimize storage requirements. Each file corresponds to one draw of our socioeconomic data, and contains 4 columns: Country, Year, Pop, and GDP. The number in each filename corresponds to the "sample" column in the emissions data. For each row:</p> <p>- Country contains the ISO Alpha-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>) of the country whose GDP and population are projected. - Year contains the calendar year of the predictions.</p> <p>- Pop contains the projected population for a given country and year, in units of thousands of people.</p> <p>- GDP contains the projected GDP for a given country and year, in units of millions of 2011 USD.</p> <p>---</p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936.</p> <p>The probabilistic economic and emissions projections are from Rennert et al. (forthcoming), which in turn are based in part on Müller, Stock, and Watson (forthcoming).</p> <p>---</p> <p>sample_numbers/</p> <p>---</p> <ul> <li>sampled_gdp_trajectory_numbers.csv</li> <li>sampled_pop_trajectory_numbers.csv</li> </ul> <p>These two files hold the sample IDs corresponding to the 10,000 draws from the dataset described above in the pop_income/ section. For reproducibility, these sample sets are available as inputs to the scripts which created them and used when the script is run in deterministic mode. More information on the weighting and specifications of such sampling is available in the code.</p> <p>---</p> <p>ypc1990/</p> <p>---</p> <ul> <li>rffsp_ypc1990.csv</li> </ul> <p>This CSV file is a matrix of 10,000 rows, pertaining to the 10,000 draws, and 184 columns, pertaining to the 184 countries in this analysis. The columns are labeled with the ISO Alpha-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>). The values are the GDP for a given country and sample, in units of millions of 2011 USD.</p> <p><strong>References</strong></p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: <a href="https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth">https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth</a></p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. <em>International Journal of Forecasting</em>, available online 7 October 2021. URL: <a href="https://www.sciencedirect.com/science/article/pii/S0169207021001394">https://www.sciencedirect.com/science/article/pii/S0169207021001394</a></p> <p>Rennert, K., Prest, B.C., Pizer, W., Newell, R.G., Anthoff, D., Kingdon, C., Rennels, L., Cooke, R., Raftery, A.E., Ševčíková, H, and Errickson, F. (forthcoming). The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. <em>Brookings Papers on Economic Activity.</em> Available online 27 October 2021. URL: <a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/</a></p> <p> </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.