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.
1,068
datasets available to search
ShareScore release 0.9.0
Dataset results
1,068 results for “demographic”
Demographic characteristics, site and phylogenetic distribution of dogs with appendicular osteosarcoma: 744 dogs (2000-2015)
Open the record for dataset details and reuse information.
Night warming alters mean warming effects on predator-prey interactions by modifying predator demographics and interaction strengths
Open the record for dataset details and reuse information.
Data in support of capturing functional strategies and compositional dynamics in vegetation demographic models
Open the record for dataset details and reuse information.
Demographic response to light environment of all species in the Barro Colorado plot: recruitment, growth, and mortality
Open the record for dataset details and reuse information.
Field courses narrow demographic achievement gaps in ecology and evolutionary biology
Open the record for dataset details and reuse information.
Gardener demographics, experience, and motivations drive differences in plant species richness and composition in urban gardens
Open the record for dataset details and reuse information.
Table S1: Demographic data and clinical conditions
<p>Table S1: Demographic data and clinical conditions. </p>
Census demographic data for the InMAP Source-Receptor Matrix (ISRM) dataset
<p>The InMAP Source-Receptor Matrix (ISRM) estimates the air quality impacts of emissions released from any source location in the contiguous United States to any receptor location. Specifically, the values in the ISRM dataset are the change in PM<sub>2.5</sub> concentration (µg m<sup>-3</sup>) in any receptor grid cell per unit of emissions (µg sec<sup>-1</sup>) in any source grid cell. ISRM was created from repeated runs of the Intervention Model for Air Pollution (<a href="http://spatialmodel.com/inmap/">InMAP</a>), isolating the impact of emissions from every grid cell in InMAP and from three emission heights representing ground-level, low-stack, and high-stack emissions. A file of the marginal impacts of emissions from each source location is also included ("marginal_values.csv"), which summarizes the estimates in ISRM by each source grid cell in terms of monetary damages ($ tonne<sup>-1</sup>), increased mortality (deaths tonne<sup>-1</sup>), and population exposure (population*µg m<sup>-3</sup> tonne<sup>-1</sup>).</p> <p>These files join US Census demographic information to the ISRM to facilitate future analyses. The original ISRM file can be found here: <a href="https://zenodo.org/record/3590127#.Xt0Z4GhKhPb">https://zenodo.org/record/3590127#.Xt0Z4GhKhPb</a></p>
Demographic history shaped geographical patterns of deleterious mutation load in a broadly distributed Pacific Salmon
<p class="17"><span>A thorough reconstruction of historical processes is essential for a comprehensive understanding the mechanisms shaping patterns of genetic diversity. Indeed, past and current conditions influencing effective population size have important evolutionary implications for the efficacy of selection, increased accumulation of deleterious mutations, and loss of adaptive potential. Here, we gather extensive genome-wide data that represent the extant diversity of the Coho salmon (<i><span>Oncorhynchus kisutch</span></i>) to address two objectives. We demonstrate that a single glacial refugium is the source of most of the present-day genetic diversity, with detectable inputs from a putative secondary micro-refugium. We found statistical support for a scenario whereby ancestral populations located south of the ice sheets expanded in postglacial time, swamping out most of the diversity from other putative micro-refugia. Demographic inferences revealed that genetic diversity was also affected by linked selection in large parts of the genome. Moreover, we demonstrate that the recent demographic history of this species generated regional differences in the load of deleterious mutations among populations, a finding that mirrors recent results from human populations and provides increased support for models of expansion load. We propose that insights from these historical inferences should be better integrated in conservation planning of wild organisms, which currently focuses largely on neutral genetic diversity and local adaptation, with the role of potentially maladaptive variation being generally ignored.</span></p>
Novel parasite invasion leads to rapid demographic compensation and recovery in an experimental population of guppies
<p><a name="_Hlk35864018">The global movement of pathogens is altering populations and communities through a variety of direct and indirect ecological pathways. The direct effect of a pathogen on a host is reduced survival, which can lead to decreased population densities. However, theory also suggests that increased mortality can lead to no change or even increases in the density of the host. </a>This paradoxical result can occur in a regulated population when the pathogen's negative effect on survival is countered by increased reproduction at the lower density. Here we analyze data from a long-term capture-mark-recapture experiment of Trinidadian guppies (<i>Poecilia reticulata</i>) that was recently infected with a nematode parasite (<i>Camallanus cotti</i>). By comparing the newly infected population with a control population that was not infected we show that decreases in the density of the infected guppy population were transient. The guppy population compensated for the decreased survival by a density-dependent increase in recruitment of new individuals into the population, without any change in the underlying recruitment function. Increased recruitment was related to an increase in the somatic growth of uninfected fish. Twenty months into the new invasion, the population had fully recovered to pre-invasion densities even though the prevalence of infection of fish in the population remained high (72%). These results show that density-mediated indirect effects of novel parasites can be positive, not negative, which makes it difficult to extrapolate to how pathogens will affect species interactions in communities. We discuss possible hypotheses for the rapid recovery.</p>
Figure 5 in Demographic characteristics, seasonal range and habitat topography of Balkan chamois population in its southernmost limit of its distribution (Giona mountain, Greece)
Figure 5. Used elevation, inclination (violin plots) and aspect (histogram) of the Balkan chamois in Giona Mt. Black lines in the violin plots indicate 95% probability of occurrence in terms of Fixed Kernel Density Estimator and white dots indicate median values.
Figure 4. Seasonal range generated from a in Demographic characteristics, seasonal range and habitat topography of Balkan chamois population in its southernmost limit of its distribution (Giona mountain, Greece)
Figure 4. Seasonal range generated from a Fixed Kernel Density Estimator (FKDE) (95% probability) and respective core areas of Balkan chamois in Giona Mt for (A) winter, (B) spring, (C) summer and (D) autumn. In the upper right corner the diagram presents the delineation of the probability of species occurrence within the core area.
Figure 3 in Demographic characteristics, seasonal range and habitat topography of Balkan chamois population in its southernmost limit of its distribution (Giona mountain, Greece)
Figure 3. Annual range and core area of Balkan chamois in the study area, and overlap with the Natura 2000 site in Giona Mt.
Figure 1 in Demographic characteristics, seasonal range and habitat topography of Balkan chamois population in its southernmost limit of its distribution (Giona mountain, Greece)
Figure 1. Balkan chamois distribution in Greece, modified from Papaioannou and Kati 2007 (see Appendix 1).
Data from: Ecological speciation in sympatric palms: 4. Demographic analyses support speciation of Howea in the face of high gene flow
The idea that populations must be geographically isolated (allopatric) to evolve into separate species has persisted for a long time. It is now clear that new species can also diverge despite ongoing genetic exchange, but few accepted cases of speciation in sympatry have held up when scrutinised using modern approaches. Here, we examined evidence for speciation of the Howea palms of Lord Howe Island, Australia, in light of new genomic data. We used coalescence-based demographic models combined with double digest restriction-site associated DNA sequencing of multiple individuals and provide support for previous claims by Savolainen et al. (Nature 441: 210–213, 2006) that speciation in Howea did occur in the face of gene flow.
Dataset for research article: Inluence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico
<p>R project folder, Spatial dataset and R code for research article <em>Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico: </em><a href="https://ij-healthgeographics.biomedcentral.com/">International Journal of Health Geographics</a></p>
The many population genetic and demographic routes to islands of genomic divergence
<ol> <li>The way that organisms diverge into reproductively isolated species is a major question in biology. The recent accumulation of genomic data provides promising opportunities to understand the genomic landscape of divergence, which describes the distribution of differences across genomes. Genomic areas of unusually high differentiation have been called genomic islands of divergence. Their formation has been attributed to a variety of mechanisms, but a prominent hypothesis is that they result from divergent selection over a small portion of the genome, with surrounding areas homogenised by gene flow. Such islands have often been interpreted as being associated with divergence with gene flow. However other mechanisms related to genomic structure and population history can also contribute to the formation of genomic islands of divergence.</li> <li>We currently lack a quantitative framework to examine the dynamics of genomic landscapes under the complex and nuanced conditions that are found in natural systems. Here, we develop an individual-based simulation to explore the dynamics of diverging genomes under various scenarios of gene flow, selection and genotype-phenotype maps.</li> <li>Our modelling results are consistent with empirical observations demonstrating the formation of genomic islands under genetic isolation. Importantly, we have quantified the range of conditions that produce genomic islands. We demonstrate that the initial level of genetic diversity, drift, time since divergence, linkage disequilibrium, strength of selection and gene flow are all important factors that can influence the formation of genomic islands. Because the accumulation of genomic differentiation over time tends to erode the signal of genomic islands, genomic islands are more likely to be observed in recently divergent taxa, although not all recently diverged taxa will necessarily exhibit islands of genomic divergence. Gene flow primarily slows the swamping of islands of divergence with time.</li> <li>By using this framework, further studies may explore the relative influence of particular suites of events that contribute to the emergence of genomic islands under sympatric, parapatric and allopatric conditions. This approach represents a novel tool to explore quantitative expectations of the speciation process, and should prove useful in elucidating past and projecting future genomic evolution of any taxa.</li> </ol>
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 for economic and demographic determinants of premium reserve in the Western Balkans
<p>All data are expressed as a percentage, except for GDP per capita, net wages, total population, life expectancy, expected years of education, average years of schooling, life and non-life premium, total premium, bank deposits, financial assets and deposits of insurance companies, which are expressed in absolute terms.</p> <p><strong>Source of data: </strong></p> <ol> <li>Data on Life and Non-life premium, Total (gross) premium, Premium reserve data, Financial assets and Deposits of insurance companies are collected from the official reports of insurance supervision agencies: Insurance Supervision Agency in Montenegro (<a href="http://www.ano.me/en/">http://www.ano.me/en/</a>), Croatian Financial Services Supervisory Agency (<a href="https://www.hanfa.hr/en/">https://www.hanfa.hr/en/</a>), National Bank of Serbia (<a href="https://www.nbs.rs/internet/english/">https://www.nbs.rs/internet/english/</a>, Insurance Supervision Agency of North Macedonia (<a href="http://aso.mk/en/?lang=en">http://aso.mk/en/?lang=en</a>) and Financial Supervisory Authority in Albania (<a href="https://amf.gov.al/">https://amf.gov.al/</a>).</li> <li>The economic indicators for the observed Western Balkan countries (GDP per capita, unemployment rate, inflation rate, net earnings and average effective deposit interest rate) are taken from the website Eurostat (<a href="https://ec.europa.eu/eurostat">https://ec.europa.eu/eurostat</a>) and Statista (<a href="https://www.statista.com/">https://www.statista.com/</a>) </li> <li>All demographic indicators, except for the expected and average years of schooling and education index, were collected from the Eurostat and UNDP database (<a href="https://ec.europa.eu/eurostat/data/database">https://ec.europa.eu/eurostat/data/database</a>; <a href="http://hdr.undp.org/en/countries/profiles/">http://hdr.undp.org/en/countries/profiles/</a> ).</li> <li>Data on expected and average school years were taken from the UNESCO Institute for Statistics (<a href="http://uis.unesco.org">http://uis.unesco.org</a>) , while the education index was obtained as a result of a calculation based on a formula published on the UNDP website (<a href="http://hdr.undp.org/en/content/education-index">http://hdr.undp.org/en/content/education-index</a>).</li> <li>Data on bond yield were collected from the website of European Commission (<a href="https://ec.europa.eu/">https://ec.europa.eu/</a>), i.e. from EC reports - EU Candidate Countries’ & Potential Candidates’ Economic Quarterly (CCEQ), except two data for Serbia (2006 and 2007) which were estimated by Makima extrapolation.</li> <li>Bank deposits data are taken from the official reports of banks' regulatory institutions: Central bank of Montenegro (<a href="https://www.cbcg.me/en">https://www.cbcg.me/en</a>), National bank of Serbia (<a href="https://www.nbs.rs/en/indeks/">https://www.nbs.rs/en/indeks/</a>), Croatian National bank (<a href="https://www.hnb.hr/en/home">https://www.hnb.hr/en/home</a>), National bank of the Republic of North Macedonia (<a href="https://www.nbrm.mk/pocetna-en.nspx">https://www.nbrm.mk/pocetna-en.nspx</a>); Bank of Albania (<a href="https://www.bankofalbania.org/home/">https://www.bankofalbania.org/home/</a>)</li> </ol> <p><strong>Description of columns:</strong></p> <p><strong>f1-</strong>GDPper capita;<strong> f2- </strong>Unemployment (%); <strong>f3-</strong>Inflation rate (%); <strong>f4-</strong> Net Wages €; <strong>f5-</strong> Deposit rate (%); <strong>f6-</strong> Population; <strong>f7-</strong> Female (%); <strong>f8-</strong> Population <15 (%); <strong>f9-</strong> Population 15-64 (%); <strong>f10-</strong> Dep old (%); <strong>f11-</strong> Dep young (%); <strong>f12-</strong> Urban population (%); <strong>f13-</strong>Life exp. (years); <strong>f14-</strong> Preschool enroll rate (%); <strong>f15-</strong> Elem school enroll rate (%); <strong>f16</strong>-High school enroll rate (%); <strong>f17-</strong> University enroll rate (%); <strong>f18-</strong> Expected years of schooling; <strong>f19-</strong> Avg. years of schooling; <strong>f20</strong>- Education Index (%); <strong>f21-</strong> Fertility rate (number of children to a woman); <strong>f22-</strong> Birth rate (per 1000 inhabitants); <strong>f23-</strong> Health costs (% GDP); <strong>f24-</strong>premium reserve per GDP,<strong> </strong></p> <p> <strong>i1-</strong> life premium €; <strong>i2</strong>- non-life premium €; <strong>i3</strong>- total premium €; <strong>i4</strong>- bond yield (%); <strong>i5a- </strong>bank deposits ( national currency);<strong> i5b- </strong>bank deposits €;<strong> i6a-</strong>financial assets in insurance (national currency)<strong>; i6b- </strong>financial assets in insurance €;<strong> i7a- </strong>deposits of insurers (national currency<strong>); i7b –</strong>deposit of insurers €</p>
A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data
<p>A common goal of population genomics and molecular ecology is to reconstruct the demographic history of a species of interest. A pair of powerful tools based on the sequentially Markovian coalescent have been developed to infer past population sizes using genome sequences. These methods are most useful when sequences are available for only a limited number of genomes and when the aim is to study ancient demographic events. The results of these analyses can be difficult to interpret accurately, because doing so requires some understanding of their theoretical basis and of their sensitivity to confounding factors. In this practical review, we explain some of the key concepts underpinning the pairwise and multiple sequentially Markovian coalescent methods (PSMC and MSMC, respectively). We relate these concepts to the use and interpretation of these methods, and we explain how the choice of different parameter values by the user can affect the accuracy and precision of the inferences. Based on our survey of 100 PSMC studies and 30 MSMC studies, we describe how the two methods are used in practice. Readers of this article will become familiar with the principles, practice, and interpretation of the sequentially Markovian coalescent for inferring demographic history.</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.