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.
106
datasets available to search
ShareScore release 0.9.0
Dataset results
106 results for “population projections”
State Water Project, Genetic Determination of Population of Origin 2011-2024
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring
Central Valley Project, Genetic Determination of Population of Origin 2011-2024
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring
QuantMig microsimulation population projection model and migration scenarios for 31 European countries
<p>This open data deposit contains the data and model code of QuantMig-Mic microsimulation population projection model for 31 European countries and accompanies deliverables D8.3: Model outputs for dissemination and D8.1: Microsimulation projection model.</p> <p>This Zenodo deposit contains datasets of model input (baseline population and immigration database) and output data (demography and components output tables) and model code with parameters of the Baseline scenario (termed Default in the model code) deposited in QuantMig_mic.zip file. To view the code the users must first install MODGEN software (or can view code files in Visual Studio). All scenarios share the same parameters except the immigrant population - to change the immigration assumptions the users can import immigration assumptions for any other scenario from the ImmigDataBase.csv and change it in the immigration module using the MODGEN user interface or using Visual Studio.</p> <p><strong>The file structure and codebook for the data files is included in the cover note file "readme_quantmig_datasets.pdf"</strong></p> <p>Detailed <strong>information about the QuantMig-Mic microsimulation model, its modules and parameters</strong>:</p> <p>Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf</p> <p>Instructions how to install MODGEN can be found in:</p> <p>Marois, G. and Potančoková, M. (2022) QuantMig-mic microsimulation tool. QuantMig Project Deliverable D8.1. International Institute for Applied Systems Analysis (IIASA). http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.1%20v1.1.pdf </p> <p>Detailed <strong>information about the QuantMig migration scenarios</strong> can be found in:</p> <p>Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf</p> <p>A <strong>guide to the datasets</strong> and the codebook can be found in: <strong>readme_quantmig_datasets.pdf</strong></p> <p><strong>Countries included in the model: </strong></p> <p>Austria, Belgium, Bulgaria, Croatia, Czechia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom</p> <p> </p>
Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project
<p>Tree sequences inferred for the SGDP autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("sgdp_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("sgdp_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Central Valley Project, Genetic Determination of Population of Origin 2011-2021
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring
Hubbard Brook Wildlife Monitoring Project: Assessing wildlife population presence, activity and habitat use through continual camera trap monitoring, 2018
Monitoring of wildlife at Hubbard Brook is essential to understand how these species are responding to forest and environmental condition over time, while also placing those wildlife species in the context of ecosystem structural and functional attributes. The presence and persistence of wildlife species common to an area can indicate suitable habitat conditions as well as refugia for less common species. Changes in species presence and activity, such as fewer to no sightings, may point to shifting conditions not suitable to the species missing from the area. Camera trap monitoring allows for continuous, non-obtrusive observation of many different species of wildlife and can be used as part of our understanding of current suitability of habitat condition. To better understand integrated forest condition, we established a camera trap network located at the Hubbard Brook Experimental Forest in the White Mountains of central New Hampshire. The cameras have logged over 1,500 wildlife observations, confirming the presence of many species, including those not previously reported (pine marten and river otter). A total of 15 mammal species have been detected and have also been effective at detecting some bird species, including the Northern Harrier. Natural history observations have provided insight into the lives of the species detected, including reproduction (Bull moose following cow during rut, moose calves, deer fawns), predation (red fox with snow-shoe hare) and presence of parasites (winter ticks on moose with hairless shoulders). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Wittgenstein Center (WIC) Population and Human Capital Projections - 2023
<p>THIS IS VERSION WIC3.004 (Beta) (<strong>internal version V14,</strong> <strong>the last three versions were called V13, V12 and V11). </strong>This is a release version corresponding to the <a href="https://data.ece.iiasa.ac.at/ssp/#/login?redirect=%2Fworkspaces">SSP-Database 3.0</a> (will be updated) release and <a href="https://dataexplorer.wittgensteincentre.org/wcde-v3/">WIC-Data Explorer 2023</a></p> <p>Short Abstract</p> <p>We update the population and human capital components of the Shared Socio-Economic Pathways (SSPs) at the global level, considering the most recent baseline information. While the long-term assumptions based on extensive analysis and expert solicitations remain unchanged, we modify only the trend component. The first set of SSPs was based on demographic data through 2012. The population structures by age, sex, and education of the base year (2010) have since changed for most countries, mainly due to more recent data and reliable information. The mortality situation has improved in many countries affected by HIV/AIDS and among children in countries with higher mortality. The impact of COVID-19 on demographic trends must be addressed. In many countries, fertility rates have fallen faster than expected. International migration has been irregular and volatile as usual. These changes are reflected in the new update, with some improvements in operationalization.</p> <p>Changes in version "V14":</p> <p> We found a bug affecting the education distribution sex (interchanged) in 37 countries (with UN country code): 32 Argentina, 44 Bahamas, 48 Bahrain, 64 Bhutan, 76 Brazil, 108 Burundi, 132 Cabo Verde, 156 China, 158 China, Taiwan Province of China, 170 Colombia, 214 Dominican Republic, 218 Ecuador, 226 Equatorial Guinea, 288 Ghana, 296 Kiribati, 360 Indonesia, 392 Japan, 400 Jordan, 410 Republic of Korea, 414 Kuwait, 496 Mongolia, 528 Netherlands, 558 Nicaragua, 583 Micronesia (Fed. States of), 591 Panama, 626 Timor-Leste, 630 Puerto Rico, 634 Qatar, 643 Russian Federation, 662 Saint Lucia, 702 Singapore, 764 Thailand, 784 United Arab Emirates, 788 Tunisia, 792 Turkey, 840 United States of America, 894 Zambia. The impact on the total population is minimal.</p>
Van Dijk et al. (2021), A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050, data and scripts
<p>This repository contains all data and R scripts to reproduce the figures in Van Dijk et al. (2021), A meta-analysis of global food demand and population at risk of hunger projections for the period 2010-2050, Nature Food. More specifically, it includes two databases: (1) A database with standardized information to describe the characteristics of 57 studies that were identified by the systematic literature review and (2) The Global Food Security Projections Database v1.0.1 with harmonized projections for three global food security indicators: food consumption in kcal per capita and total kcal, and population at risk of hunger. The database also includes projections for total global population that are required to derive the global food security indicators.</p> <p>The two scripts (nf_figures.r and nf_meta_regression.r) can be used to reproduce the figures and tables in the main paper and the supplementary information. Please start with the first script, which sources the second script. </p> <p>This is the first version of the Global Food Projections Database. We expect to update the data, including additional studies and variables in the future. For issues and suggestions, please contact michiel.vandijk@wur.nl.</p> <p> </p>
FUME Local population projections in destination cities
<p>FUME data on projected distributions of migrants at local level between 2030 and 2050.</p> <p>The dataset contains a folder of data for each destination city as a gridded dataset at 100m resolution in GeoTIFF format. The examined destination cities are: Amsterdam, Copenhagen, Krakow and Rome. The dataset is provided as 100m grid cells based on the Eurostat GISCO grid of the 2021 NUTS version, using ETRS89 Lambert Azimuthal Equal-Area (EPSG: 3035) as coordinate system. The file names consist of the projected year, the corresponding scenario, and the reference migrant group. The projections have been performed for the years 2030, 2040 and 2050. The investigated scenarios are the following:<br> • benchmark (bs),<br> • baseline (bs),<br> • Rising East (re),<br> • EU Recovery (eur),<br> • Intensifying Global Competition (igc), and<br> • War (war).</p> <p>The migration background is derived from data about the Region of Origin (RoO) for migrants in Copenhagen and Amsterdam, and from Region of Citizenship (CoC) for migrants in Krakow and Rome.</p> <p>The case study of <strong>Copenhagen</strong> covers the two central NUTS3 areas (DK011, DK012) and the groups presented are the following:<br> • total population (totalpop),<br> • native population (DNK),<br> • Eastern EU European migrants (EU_East),<br> • Western EU Europeans migrants (EU_West),<br> • Non-EU European migrants (EurNonEU),<br> • migrants from Turkey (Turkey),<br> • the MENAP countries (MENAP; excluding Turkey),<br> • other non-Western (OthNonWest), and<br> • other Western countries (OthWestern).</p> <p>The case study of <strong>Amsterdam</strong> covers one NUTS3 area (NL329) and the presented groups are the following:<br> • total population (totalpop),<br> • native population (NLD),<br> • Eastern EU European migrants (EU East),<br> • Western EU European migrants (EU West),<br> • migrants from Turkey and Morocco (Turkey + Morocco),<br> • migrants from the Middle East and Africa (Middle East + Africa),<br> • migrants from the former colonies (Former Colonies), and<br> • migrants from the rest of the world (Other Europe etc).</p> <p>The case study of <strong>Krakow</strong> covers the Municipality of Krakow, and the presented groups are the following:<br> • total population (totalpop),<br> • native population (POL),<br> • EU/EFTA European migrants (EU),<br> • non-EU European migrants (Europe_nonEU), and<br> • migrants from the rest of the world (Other).</p> <p>The case of <strong>Rome</strong> covers the Municipality of Rome, and the presented groups are the following:<br> • total population (totalpop),<br> • native population (ITA),<br> • migrants from Romania (ROU),<br> • Philippines (PHL),<br> • Bangladesh (BGD),<br> • the EU (EU; excluding Romania),<br> • Africa (Africa),<br> • Asia (Asia; excluding Philippines and Bangladesh) and<br> • America (America).</p>
State Water Project, Genetic Determination of Population of Origin 2011-2021
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring
Virgin Islands National Park: Coral Reef: Populations Dynamics: Population Projections
In this study, size-based matrix models for the reef-building coral Orbicella annularis at 14-m depth on the Tektite reef in St. John, U.S. Virgin Islands, were used to: (1) explore the demography of changing coral cover over 25 yr, (2) test for spatial homogeneity in demographic properties through a contrast with a previous study (at Yawzi Point, Edmunds and Elahi 2007), and (3) evaluate the potential for future population stability. During three, five year intervals from 1988 to 2002, St. John was affected by hurricanes and bleaching, yet coral cover at Tektite increased from 33% to 49%; from 2002 to 2007, it declined to 27%; and from 2010 to 2013, it stabilized at ∼ 28%. Over a quarter-century, colonies > 50 cm2 became rare, the abundance of colonies ≤ 50 cm2 increased from 58% (1988) to 92% (2013), and population density doubled to 67 colonies m-2 by 2013. Population growth (λ) was greater at Tektite (1.152 ≥ λ ≥ 1.018) than Yawzi Point (0.679 ≥ λ ≥ 0.586), and while population size at Tektite declined due to bleaching and disease in 2005 (λ = 0.753 over 2003–2008), it recovered between 2008 and 2013 (λ = 0.966); the population at Yawzi Point declined from 1988 to 2003 without signs of recovery. Projections suggest a continuation of recent conditions could allow O. annularis at Tektite to retain ∼ 9% cover after 100 yr, but with a return to the rates of growth and survival of 1993–1998, it could attain coverage similar to that of 1988 (33%) in ∼ 15 yr.
The Helicobacter pylori Genome Project (HpGP) Phase1 dataset and 255 H. pylori population reference dataset
<p>This repository holds the HpGP Phase 1 genomic dataset for Hp26695 and 1011 study samples. All 1012 genomic sequences were annotated using the NCBI Prokaryotic Genome Annotation Pipeline(PGAP). Also, it has 255 curated public available H. pylori genomic sequences used for population structure analysis in Thorell et al. Nature Communications, 14:8184 (2023).</p> <p>You can check the NCBI BioProject website for the latest annotation and sequence updates.</p> <p>https://www.ncbi.nlm.nih.gov/bioproject/?term=HpGP</p> <p>Please cite the above-mentioned paper if you use the data.</p>
The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates
<p>This repository contains the socioeconomic and emissions projections generated by the Resources for the Future Socioeconomic Projections (RFF-SPs) model as discussed in Rennert et al., "<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/">The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates</a>" , forthcoming at the <em>Brookings Papers on Economic Activity</em>. The data take the form of a Monte Carlo simulation with n = 10,000 draws.</p> <p>File structure and column metadata are described here: </p> <p>--- <br> emissions/ <br> --- </p> <p>- rffsp_co2_emissions.csv <br> - rffsp_ch4_emissions.csv <br> - rffsp_n2o_emissions.csv </p> <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). <br> - year contains the calendar year of the prediction. <br> - value contains the projected annual global emissions of the gas specified in the filename. IMPORTANT: Units are as follows: "rffsp_co2_emissions.csv" is in gigatons C (not CO2), "rffsp_ch4_emissions.csv " is in megatons CH4, and "rffsp_n2o_emissions.csv " is in megatons N2 (not N2O). </p> <p>--- <br> pop_income/ <br> --- </p> <p>- rffsp_pop_income_run_1.feather <br> - rffsp_pop_income_run_2.feather <br> - rffsp_pop_income_run_3.feather <br> . <br> . <br> . <br> - rffsp_pop_income_run_9999.feather <br> - rffsp_pop_income_run_10000.feather </p> <p>This folder contains 10,000 files in the .feather file format (https://arrow.apache.org/docs/python/feather.html), 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 (https://www.iso.org/iso-3166-country-codes.html) of the country whose GDP and population are projected. <br> - Year contains the calendar year of the predictions. <br> - Pop contains the projected population for a given country and year, in units of thousands of people. <br> - 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), based in part on those from Müller, Stock, and Watson (forthcoming). </p> <p>---</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. International Journal of Forecasting, 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. Brookings Papers on Economic Activity. 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>
Data: Applying stochastic and Bayesian integral projection modeling to amphibian population viability analysis
<p>Integral projection models (IPMs) can estimate the population dynamics of species for which both discrete life stages and continuous variables influence demographic rates. Stochastic IPMs for imperiled species, in turn, can facilitate population viability analyses (PVAs) to guide conservation decision-making. Biphasic amphibians are globally distributed, often highly imperiled, and ecologically well-suited to the IPM approach. Herein, we present the first stochastic size- and stage-structured IPM for a biphasic amphibian, the U.S. federally threatened California tiger salamander (<em>Ambystoma</em> <em>californiense</em>; CTS). This Bayesian model reveals that CTS population dynamics show the greatest elasticity to changes in juvenile and metamorph growth and that populations are likely to experience rapid growth at low density. We integrated this IPM with climatic drivers of CTS demography to develop a PVA and examined CTS extinction risk under the primary threats of habitat loss and climate change. The PVA indicates that long-term viability is possible with surprisingly high (20–50%) terrestrial mortality, but simultaneously identified likely minimum terrestrial buffer requirements of 600–1000 m while accounting for numerous parameter uncertainties through the Bayesian framework. These analyses underscore the value of stochastic and Bayesian IPMs for understanding both climate-dependent taxa and those with cryptic life histories (e.g., biphasic amphibians) in service of ecological discovery and biodiversity conservation. In addition to providing guidance for CTS recovery, the contributed IPM and PVA supply a framework for applying these tools to investigations of ecologically-similar species.</p>
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters).
SSP1 and SSP3 population projections with demographic categories
<p>Demographic projections for the Shared Socioeconomic Pathways SSP1 and SSP3 scenarios with demographic breakdown for "young", "adult" and "old" populations, defined as <15 years old, 15-65 years old, and >65 years old, at 10 year intervals. Projections are calculated combining SSP population projections, UN WPP country wide demographic trends, and SEDAC demographic spatial distributions.</p> <p> </p> <p>The "interp" version of the files includes linear interpolationed values for each grid cell for every year.</p>
IuliaMargineanGitHub/Projecting-Heat-Stress-Vulnerable-Populations-at-Intra-Urban-Scales: Projecting heat stress vulnerable populations at intra-urban scales
<p>This repository provides the data and scripts necessary for full reproduction of results, as well as example datasets that were used for data generation and analysis in the manuscript Marginean et al., "High-resolution Modelling and Projecting Local Dynamics of Differential Vulnerability to Urban Heat Stress".</p> <p>The folders in this repository contain the following:</p> <ol> <li>The input datasets: <ul> <li>shares of educational attainment in 2012 and 2020, by sex, age group and census tract</li> <li>internal migration by sex, age group and census tracts</li> <li>decadal mortality and fertility, by sex, age group, scenario, and census tract</li> </ul> </li> <li>Projected decadal data (2012 to 2050) by sex, age group, scenario and census tract</li> <li>Scripts for generating high resolution projections for three Shared Socioeconomic Pathways (SSPs): one script for SSP 2 projections and one scripts for SSP 1 and SSP 3 projections</li> <li>Example input data to reconstruct the projections in SSP 1, 2 and 3 for females ages 25 to 64</li> </ol>
MANET: uncertainty in demographics – data on population projections
<p>This is a repository of global and regional human population data collected from: the databases of scenarios assessed by the Intergovernmental Panel on Climate Change (Sixth Assessment Report, Special Report on 1.5 C; Fifth Assessment Report), multi-national databases of population projections (World Bank, International Database, United Nation population projections), and other very long-term population projections (Resources for the Future).</p> <p>More specifically, it contains:</p> <p>- in `other_pop_data` folder files from <a href="https://databank.worldbank.org/source/population-estimates-and-projections">World Bank,</a> the <a href="https://www.census.gov/data-tools/demo/idb/#/dashboard?COUNTRY_YEAR=2023&COUNTRY_YR_ANIM=2023">International Database</a> from the US Census, and from <a href="https://ghdx.healthdata.org/record/ihme-data/global-population-forecasts-2017-2100">IHME</a></p> <p>- in the `SSP` folder, the Shared Socioeconomic Pathways, as in the version 2.0 downloaded from <a href="https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10">IIASA</a> and as in the version 3.0 downloaded from <a href="https://data.ece.iiasa.ac.at/ssp/#/workspaces">IIASA workspace</a></p> <p>- in the `UN` folder, the demographic projections from <a href="https://population.un.org/wpp/Download/Standard/Population/">UN</a></p> <p>- `IAMstat.xlsx`, an overview file of the metadata accompanying the scenarios present in the IPCC databases</p> <p>- `RFF.csv`, an overview file containing the population projections obtained by <a href="../record/6016583#.Y42iFuzP2rP">Resources For the Future</a> </p> <p>'- the remaining `.csv` files with names `AR6#`, `AR5#`, `IAMC15#` contain the IPCC scenarios assessed by the IPCC for preparing the IPCC assessment reports. They can be downloaded from <a href="https://tntcat.iiasa.ac.at/AR5DB">AR5</a>, <a href="https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/downloads">SR 1.5</a>, and <a href="https://data.ene.iiasa.ac.at/ar6/#/workspaces">AR6</a></p> <p>This data in intended to be downloaded for use together with the package downloadable <a href="https://github.com/sgiarols/Climate_Scenario_Data_Science">here</a>.</p> <p>The dataset was used as a supporting material for the paper "Underestimating demographic uncertainties in the synthesis process of the IPCC" accepted on npj Climate Action (DOI : 10.1038/s44168-024-00152-y).</p>
Data from: Evaluating the importance of individual heterogeneity in reproduction to Weddell seal population dynamics using integral projection models
<ol> <li>Identifying and accounting for unobserved individual heterogeneity in vital rates in demographic models is important for estimating population-level vital rates and identifying diverse life-history strategies, but much less is known about how this individual heterogeneity influences population dynamics.</li> <li>We aimed to understand how the distribution of individual heterogeneity in reproductive and survival rates influenced population dynamics using vital rates from a Weddell seal population by altering the distribution of individual heterogeneity in reproduction, which also altered the distribution of individual survival rates through the incorporation of our estimate of the correlation between the two rates and assessing resulting changes in population growth.</li> <li>We constructed an integral projection model (IPM) structured by age and reproductive state using estimates of vital rates for a long-lived mammal that has recently been shown to exhibit large individual heterogeneity in reproduction. Using output from the IPM, we evaluated how population dynamics changed with different underlying distributions of unobserved individual heterogeneity in reproduction.</li> <li>Results indicate that the changes to the underlying distribution of individual heterogeneity in reproduction cause very small changes in the population growth rate and other population metrics. The largest difference in the estimated population growth rate resulting from changes to the underlying distribution of individual heterogeneity was less than 1%.</li> <li>Our work highlights the differing importance of individual heterogeneity at the population level compared to the individual level. Although individual heterogeneity in reproduction may result in large differences in the lifetime fitness of individuals, changing the proportion of above- or below-average breeders in the population results in much smaller differences in annual population growth rate. For a long-lived mammal with stable and high adult-survival that gives birth to a single offspring, individual heterogeneity in reproduction has a limited effect on population dynamics. We posit that the limited effect of individual heterogeneity on population dynamics may be due to canalization of life-history traits.</li> </ol>
Data: Applying stochastic and Bayesian integral projection modeling to amphibian population viability analysis
Open the record for dataset details and reuse information.
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.