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.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “demographic projections”
Demographic factors and the environmental Kuznets curve: global plastic pollution by 2050 could be 2 to 4 times worse than projected
<p>These data are made of two files. One file provides the observed data we collected and cleaned from the World Bank database. The second file provides the simulation results from the STIRPAT model we designed based on the observed data abovementioned. Our results can be summarised as follows:</p> <p>Since 2015, the detrimental effects of plastic pollution have attracted media, public, and governmental attention. Considering economic growth is inevitable and a key driver of plastic contamination, it is worthwhile to analyze the environmental Kuznets curve (EKC) relationship between economic development and plastic pollution. To this end, we contribute by being the first to (i) use the Stochastic Impacts by Regression on Population, Affluence, and technology model (STIRPAT model) to investigate this EKC relationship; (ii) provide a comprehensive analysis of how demographic factors affect plastic pollution; and (iii) use panel model techniques to examine the drivers of plastic pollution. Our empirical results support an inverted U-shaped relationship between plastic pollution and income. They show that at current trends, global plastic pollution (that is, annual discard of inadequately managed plastic waste) is expected to grow from 52 million tons per year in 2020 to 257 million tons per year in 2050.</p>
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>
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: The demographic effects of functional traits: an integral projection model approach reveals population-level consequences of reproduction-defense tradeoffs
Quantitatively linking individual variation in functional traits to demography is a necessary step to advance our understanding of trait-based ecological processes. We constructed a population model for Asclepias syriaca to identify how functional traits affect vital rates and population growth and whether tradeoffs in chemical defense and demography alter population growth. Plants with higher foliar cardenolides had lower fiber, cellulose, and lignin levels, as well as decreased sexual and clonal reproduction. Average cardenolide concentrations had the strongest effect on population growth. In both the sexual and clonal pathway, the tradeoff between reproduction and defense affected population growth. We found that both increasing the mean of the distribution of individual plant values for cardenolides and herbivory decreased population growth. However, increasing the variance in both defense and herbivory increased population growth. Functional traits can impact population growth and quantifying individual-level variation in traits should be included in assessments of population-level processes.
Data for: Using a demographic model to project the long-term effects of fire management on tree biomass in Australian savannas
<p>Tropical savannas are characterised by high primary productivity and high fire frequency, such that much of the carbon captured by vegetation is rapidly returned to the atmosphere. Hence, there have been suggestions that management-driven reductions in savanna fire frequency and/or severity could significantly reduce greenhouse gas emissions and sequester carbon in tree biomass. However, a key knowledge gap is the extent to which savanna tree biomass will respond to modest shifts in fire regimes due to plausible, large-scale management interventions. Here, we: (1) characterise relationships between the frequency and severity of fires and key demographic rates of savanna trees, based on long-term observations in vegetation monitoring plots across northern Australia; (2) use these relationships to develop a process-explicit demographic model describing the effects of fire on savanna tree populations; and (3) use the demographic model to address the question: to what extent is it feasible, through the strategic application of prescribed burning, to increase tree biomass in Australian tropical savannas? Our long-term tree monitoring dataset included observations of 12,344 tagged trees in 236 plots, monitored for between 3 and 24 years. Analysis of this dataset showed that frequent high-severity fires significantly reduced savanna tree recruitment, survival and growth. Our demographic model suggested that: (1) despite the negative effects of frequent high-severity fires on demographic rates, savanna tree biomass appears to be suppressed by only a relatively small amount by contemporary fire regimes, characterised by a mix of low- to high-severity fires; and (2) plausible, management-driven reductions in the frequency of high-severity fires are likely to lead to increases in tree biomass of about 11.0 t DM ha<sup>–1</sup> (95% confidence interval: -1.2–20.8) over a century. Accounting for this increase in carbon storage could generate significant carbon credits, worth on average three times those generated annually by current greenhouse gas (methane and nitrous oxide) abatement projects, and has the potential to significantly increase the economic viability of fire/carbon projects, thereby promoting ecologically sustainable management of tropical savannas in Australia and elsewhere. This growing industry has the potential to bring much-needed economic activity to savanna landscapes, without compromising important natural and cultural values.</p>
Data from: The demographic effects of functional traits: an integral projection model approach reveals population-level consequences of reproduction-defense tradeoffs
Open the record for dataset details and reuse information.
Data for: Using a demographic model to project the long-term effects of fire management on tree biomass in Australian savannas
Open the record for dataset details and reuse information.
Data from: Quantifying demographic uncertainty: Bayesian methods for integral projection models (IPMs)
Integral projection models (IPMs) are a powerful and popular approach to modeling population dynamics. Generalized linear models form the statistical backbone of an IPM. These models are typically fit using a frequentist approach. We suggest that hierarchical Bayesian statistical approaches offer important advantages over frequentist methods for building and interpreting IPMs, especially given the hierarchical nature of most demographic studies. Using a stochastic IPM for a desert cactus based on a 10-year study as a worked example, we highlight the application of a Bayesian approach for translating uncertainty in the vital rates (e.g., growth, survival, fertility) to uncertainty in population-level quantities derived from them (e.g., population growth rate). The best-fit demographic model, which would have been difficult to fit under a frequentist framework, allowed for spatial and temporal variation in vital rates and correlated responses to temporal variation across vital rates. The corresponding posterior probability distribution for the stochastic population growth rate (λS) indicated that, if current vital rates continue, the study population will decline with nearly 100% probability. Interestingly, less-supported candidate models that did not include spatial variance and vital rate correlations gave similar estimates of λS. This occurred because the best-fitting model did a much better job of fitting vital rates to which the population growth rate was weakly sensitive. The cactus case study highlights several advantages of Bayesian approaches to IPM modeling, including that they: (1) provide a natural fit to demographic data, which are often collected in a hierarchical fashion (e.g., with random variance corresponding to temporal and spatial heterogeneity); (2) seamlessly combine multiple data sets or experiments; (3) readily incorporate covariance between vital rates; and, (4) easily integrate prior information, which may be particularly important for species of conservation concern where data availability may be limited. However, constructing a Bayesian IPM will often require the custom development of a statistical model tailored to the peculiarities of the sampling design and species considered; there may be circumstances under which simpler methods are adequate. Overall, Bayesian approaches provide a statistically sound way to get more information out of hard-won data, the goal of most demographic research endeavors.
Data from: Quantifying demographic uncertainty: Bayesian methods for integral projection models (IPMs)
Open the record for dataset details and reuse information.
Report of demographic and employment description of each of the interviewees. Covidless Approach & Trust Project
<p>Report of demographic and employment description of each of the interviewees. Covidless Approach & Trust Project</p>
Data and results of opscr demographic and spatial projection models
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.