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87 results for “future projections”
Input data for the Resources for the Future Socioeconomic Projections (RFF-SPs)
<p>This repository contains large input data files for generating the RFF-SPs, and small accompanying files with data necessary for interpreting the larger files.</p> <p>--------------------------------------------------------------------------------<br> /MSW_GDP/<br> --------------------------------------------------------------------------------</p> <p>-MSW.mat is a MATLAB data file based on Müller, Stock, and Watson (forthcoming) containing two series:<br> 1) Year-by-sample draws "F-paths" representing frontier values of ln(GDP/capita) for the OECD, and <br> 2) Year-by-country-by-sample deviations from those F-paths, denoted as the U-path.</p> <p>A country's ln(GDP/capita) is equal to the sum of the sample's F-path and the country-sample's U-path. There are 418 years representing 1900-2317, 113 countries with names in country_names.txt, and 2000 sample trajectories. GDP/capita is measured in 2011$.</p> <p>-country_names.txt contain iso codes for the 113 countries in the order in which they appear in MSW.mat's U-path object. OECD countries are indicated with an "x" for reference.</p> <p>This information was transmitted via email from Ulrich Mueller (Princeton) to Kevin Rennert (RFF) on January 28, 2020, with a link to the file on Mueller's website at http://www.princeton.edu/~umueller/matlabJan2020.mat.</p> <p>--------------------------------------------------------------------------------<br> /Raftery population/<br> --------------------------------------------------------------------------------</p> <p>-pop_trajectories_probmig.csv contains country-by-year-by-sample (here, a sample is labelled a "Trajectory") population estimates based on Raftery and Ševčíková (2021). Population is in units of thousands of people, with 1000 samples. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on August 11, 2021.</p> <p>-death_rates.csv contains country-by-year-by-sample death rates in units of annual deaths per one thousand people, with 1000 samples corresponding to the sample order of samples in pop_trajectories_probmig.csv. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on October 7, 2021.</p> <p>-iso_key.csv contains a key mapping ISO alpha-3 country codes ("ISO3") to ISO numeric-3 country codes (denoted "NumericCode" in this file and "LocID" in pop_trajectories_probmig.csv). This data was acquired from a search on https://www.iso.org/obp/ui#search in 2021. The "country codes" box was checked and no keywords were used. The list was trimmed to the 184 countries included in the current version of the GIVE model.</p> <p>--------------------------------------------------------------------------------<br> Information<br> --------------------------------------------------------------------------------</p> <p>The probabilistic economic and emissions projections are from Müller, Stock, and Watson (forthcoming). </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>--------------------------------------------------------------------------------<br> References <br> --------------------------------------------------------------------------------</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: https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth </p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: https://www.sciencedirect.com/science/article/pii/S0169207021001394 </p>
Data for Nicolas & Boos, "Sensitivity of tropical orographic precipitation to wind speed with implications for future projections"
<p><span>This directory contains all data used in producing the plots in Nicolas & Boos (2024), "Sensitivity of tropical orographic precipitation to wind speed with implications for future projections". It is divided in four subdirectories:</span></p> <p><span> - wrfData contains processed simulation output needed to reproduced figures 1, 2, and SI figure 1.</span></p> <p><span> - regionsData and globalData contain processed observational (APHRODITE and IMERG) and ERA5 data necessary to reproduce figure 3 and SI figures 2 and 3.</span></p> <p><span> - cmipData contains processed CMIP surface wind data necessary to reproduce figure 4.</span></p> <p><span>Code used in producing these figures will be made available and linked to this dataset once any needed revisions are complete.</span></p>
Future Projections and Life Cycle Assessment of End-of-life Tires to Energy Conversion in Hong Kong: Environmental, Climate and Energy Benefits for Regional Sustainability
<p>The dataset presents the findings of the study "Future Projections and Lifecycle Assessment of End-of-life Tires to Energy Conversion in Hong Kong: Environmental, Climate and Energy Benefits for Regional Sustainability". The data results are contained in the files "Results_data.xlsx" and "LCIs and LCA results.zip," while the "Figures data.xlsx" file includes the data needed for plotting. </p>
SAFRAN Present Forcing (Historical: 2004/2014) and Future projections (RCP4.5 and RCP8.5: 2041/2060).
<p>SAFRAN forcing refers to a set of meteorological data used to model the climate, covering both historical periods (2004-2014) and future projections under two greenhouse gas concentration scenarios (RCP4.5 and RCP8.5) for the period 2041-2060. Each dataset includes key parameters for climate analysis: cloud cover (NB), humidity (QA), direct radiation (RD), infrared radiation (RI), precipitation (RR), visible radiation (RV), snow cover (SS), temperature (TA), and wind strength (UA). These parameters are measured on a grid with variable resolution, ranging from 8 km in plains to 1 km in mountains and irrigated agricultural lands. This data is essential for understanding current and future atmospheric dynamics in the context of climate change.</p>
FIG. 2 in Clearing up the Crystal Ball: Understanding Uncertainty in Future Climate Suitability Projections for Amphibians
FIG. 2.—Mean predicted change in suitable climate for amphibian species by citation and model parameters from the meta-analysis. Points represent means and bars represent 95% confidence intervals. Mean predicted change in suitable climate was calculated for each amphibian order within each study for each of the model settings, including Representative Concentration Pathway, Year, and Dispersal Limitation. Triangles indicate estimates from the case study, and circles indicate all other studies. A color version of this figure is available online.
FIG. 1 in Clearing up the Crystal Ball: Understanding Uncertainty in Future Climate Suitability Projections for Amphibians
FIG. 1.—Map of countries for which suitable climate has been projected under future climate scenarios for at least one amphibian species. Colors represent the number of studies in which a given country was included. A color version of this figure is available online.
FIG. 3 in Clearing up the Crystal Ball: Understanding Uncertainty in Future Climate Suitability Projections for Amphibians
FIG. 3.—Percent change in climate suitability predicted for each species in the case study. For each species, predicted percent change in climate suitability is shown for the year 2050 under 13 general circulation models and two representative concentration pathways (2.6, 8.5) climate scenarios for both the default Maxent species distribution models (SDMs; regularization multiplier ¼ 1; open red boxes) and the best-fit SDM (Akaike information criterion; ΔAICc ¼ 0; shaded blue boxes) for three levels of dispersal: no dispersal, limited, or unlimited. Boxplots show median, interquartile range, and minimum and maximum values. A color version of this figure is available online.
Projected changes in forest biomass to 2100 by county and species for 20 future scenarios
<p>Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) are important drivers of forest demography. Here we apply previously-derived growth and survival responses for 94 tree species, representing >90% of the contiguous U.S. forest basal area, to project how changes in mean annual temperature, precipitation, and N and S deposition from 20 different future scenarios may affect forest composition to 2100. We find that under the low climate change scenario (RCP 4.5), reductions in aboveground tree biomass from higher temperatures are roughly offset by increases in aboveground tree biomass from reductions in N and S deposition. However, under the higher climate change scenario (RCP 8.5) the decreases from climate change overwhelm increases from reductions in N and S deposition. These broad trends underlie wide variation among species. We found that averaged across temperature scenarios, the relative abundance of 60 species was projected to decrease by more than 5%, 20 species were projected to increase by more than 5%, and reductions of N and S deposition led to a decrease for 13 species and an increase for 40 species. This suggests large shifts in the composition of U.S. forests in the future. Negative climate effects were mostly from elevated temperature and were not offset by scenarios with wetter conditions. We found that by 2100 an estimated 1 billion trees under the RCP 4.5 scenario and 20 billion trees under the RCP 8.5 scenario may be pushed outside the temperature record upon which these relationships were derived. These results may not fully capture future changes in forest composition as several other factors were not included. Overall efforts to reduce atmospheric deposition of N and S will likely be insufficient to overcome climate change impacts on forest demography across much of the United States unless we adhere to the low climate change scenario.</p>
The Healthy Start Project: Primary Prevention of Overweight in Preschool Children Susceptible to Future Overweight
ClinicalTrials.gov study NCT01583335. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Healthy Futures Project: A Community Based Obesity Prevention Program
ClinicalTrials.gov study NCT00685555. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Harmonised projections of future forest resources in Europe
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Data from: Current and projected future risks of freshwater fish invasions in China
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Projected changes in forest biomass to 2100 by county and species for 20 future scenarios
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Ecological niche modelling to project past, current and future distributional shift of black ebony tree (Diospyros melanoxylon Roxb.) in India
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FORMATION OF FUTURE ENGINEERS ON THE BASIS OF HIGHER MATHEMATICS THROUGH THE USE OF INTEGRATED PROJECTS IN EDUCATION
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"PROJECT METHOD" IN ORGANIZING INDEPENDENT EDUCATION OF FUTURE GEOGRAPHY TEACHERS UNDER CREDIT-MODULE SYSTEM.
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Biome classification influences the projected rate of future biome transitions
<p><strong>Aim. </strong>Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence biome patterns, and vegetation models can be used to assess the susceptibility of biomes to experience transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns.</p> <p><strong>Location.</strong> Africa, Australia, Tropical Asia</p> <p><strong>Time period.</strong> 2000-2099</p> <p><strong>Major taxa studied.</strong> Tropical vegetation</p> <p><strong>Methods.</strong> We used adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of functional types, (2) a cluster analysis based on the cover of functional types, and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation-based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes.</p> <p><strong>Results.</strong> As expected, biome patterns were strongly related to the scheme used for biome classification. The highest data-model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes.</p> <p><strong>Main conclusions. </strong> Studies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.</p>
Nuclease footprints in sperm project past and future chromatin regulatory events
GEO Series GSE78075. Homo sapiens; Mus musculus. 11 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Data From: The Future of OA: A large-scale analysis projecting Open Access publication and readership
<p>This is the raw data behind the publication on bioRxiv at https://doi.org/10.1101/795310: </p> <p><strong>Piwowar, Priem, Orr (2019) The Future of OA: A large-scale analysis projecting Open Access publication and readership. bioRxiv: <a href="https://doi.org/10.1101/795310">https://doi.org/10.1101/795310</a></strong></p> <p>The jupyter notebook that produces the manuscript using the data here is available at: <a href="https://github.com/Impactstory/future-oa">https://github.com/Impactstory/future-oa</a></p> <p> </p> <p>Summary:</p> <p>Understanding the growth of open access (OA) is important for deciding funder policy, subscription allocation, and infrastructure planning.</p> <p>This study analyses the number of papers available as OA over time. The models includes both OA embargo data and the relative growth rates of different OA types over time, based on the OA status of 70 million journal articles published between 1950 and 2019.</p> <p>The study also looks at article usage data, analyzing the proportion of views to OA articles vs views to articles which are closed access. Signal processing techniques are used to model how these viewership patterns change over time. Viewership data is based on 2.8 million uses of the Unpaywall browser extension in July 2019.</p> <p>We found that Green, Gold, and Hybrid papers receive more views than their Closed or Bronze counterparts, particularly Green papers made available within a year of publication. We also found that the proportion of Green, Gold, and Hybrid articles is growing most quickly.</p> <p>In 2019:</p> <ul> <li> <p>31% of all journal articles are available as OA</p> </li> <li> <p>52% of article views are to OA articles</p> </li> </ul> <p>Given existing trends, we estimate that by 2025:</p> <ul> <li> <p>44% of all journal articles will be available as OA</p> </li> <li> <p>70% of article views will be to OA articles</p> </li> </ul> <p>The declining relevance of closed access articles is likely to change the landscape of scholarly communication in the years to come.</p>
Future Mental Projections in Schizophrenia
ClinicalTrials.gov study NCT03040505. IPD Sharing: NO. Countries: 1. Publications: 0.
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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)
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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.