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.
5,538
datasets available to search
ShareScore release 0.9.0
Dataset results
5,538 results for “Population data”
Data for paper: The making of a genetic cline: introgression of oceanic genes into coastal cod populations in the North East Atlantic
Open the record for dataset details and reuse information.
Data for: Mechanisms that can cause population decline under heavily skewed male-biased adult sex ratios
Open the record for dataset details and reuse information.
Data from: Genetic incompatibilities in reciprocal hybrids between populations of Tigriopus californicus with low to moderate mitochondrial sequence divergence
Open the record for dataset details and reuse information.
Protea repens whole transcriptome count data for control and drought treatment for 8 populations, climatic data for the 8 populations and phenotypic data collected, and data used for linear mixed models for climate gene expression/trait correlation testing
Open the record for dataset details and reuse information.
Data from: Mild temperatures differentiate while extreme temperatures unify gene expression profiles among populations of Dicosmoecus gilvipes in California
Open the record for dataset details and reuse information.
Post-processed data for: Diverse operant control of different motor cortex populations during learning
Open the record for dataset details and reuse information.
Data from: Geographic structuring of Antarctic penguin populations
Open the record for dataset details and reuse information.
Plant aboveground biomass data: The Effect of Nitrogen Addition and Different pH Levels on Microorganism Populations
The purpose of this experiment was to measure the effect of NH4NO3 addition and different levels of pH on microorganism populations. The experiment was located in field B. This experiment was laid out as a full factorial design with 3 nitrogen levels and 4 pH levels. The pH levels strived for are 4.0, 5.5, 6.5, and controls. The nitrogen levels are E, G, and I are defined in fertilization details. The experiment had 4 replicates. The treatments were randomly assigned to the 48 plots. The plots were 4 by 4 meters and were laid out in a 6 by 8 grid. On May 5, 1995, a wildfire burned all of the plots in experiment 24 in field B.
Root carbon/nitrogen data: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Plant aboveground biomass data: Effect of Herbivores on Vegetation Treated with Different N Levels and the Effect of N Addition on Herbivore Populations.
The purpose of this experiment is to study the effect of NH4NO3 addition on vegetation and herbivore populations and the effects of various herbivores on vegetation treated with different nitrogen levels. This experiment is being conducted in fields A, B, and C and is nested within the macroplots of E004. There are 4 fence exclosures each designed to exclude a specific group of herbivores, a fence exclosure to exclude all 4 groups, a fence that should exclude no herbivores to measure fence effects, and a plot with no fence as a control. Exclosures are 2 by 4 meters and are located in macroplot subsections 3 and 8. Herbivore treatments are randomly placed in the grid. For a description of fertilizer added to E005, see file fertilization details. For a list of treatments, see the treatment layouts in file trmte05.
Supporting R-code and data for "Why are population growth rate estimates of past and present hunter-gatherers so different?" (Tallavaara and Jørgensen, 2020)
<p>This submission contains data and R-code that enable to reproduce the data manipulations and analyses in the paper “Why are population growth rate estimates of past and present hunter-gatherers so different?” by Miikka Tallavaara and Erlend Kirkeng Jørgensen (Philosophical transactions of the Royal Society B). Please, cite the paper and this Zenodo repository if you use the files included in this Zenodo record in your work.</p> <p>The submission includes a html-file titled “Why are population growth rate estimates of past and present hunter-gatherers so different? - Data analyses” (TJ2020.html) that contains R-code and instructions and comments for running the code (open this file in your browser). In addition, the submission includes Rdata-file (dataTJ2020.Rdata) containing all the data that are not created within the code and pure R-code (TJ2020.R).</p>
Data Supplement: U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways.
<p>These data are to supplement the following in-press publication: </p> <p>Zoraghein, H., and O'Neill B. (2020). U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways. Sustainability.</p> <p>The data herein were generated using the `population_gravity` model which can be found here: <a href="https://github.com/IMMM-SFA/population_gravity">https://github.com/IMMM-SFA/population_gravity</a></p> <p>CONTENTS:</p> <p><strong>zoraghein-oneill_population_gravity_inputs_outputs.zip</strong></p> <ul> <li>contains a directory for each U.S. state for inputs and outputs</li> <li><strong>inputs</strong> contain the following: <ul> <li><strong><state-name>_<urban or rural>_<yr>_1km.tif: </strong> Urban and Rural population GeoTIF rasters at a 1km resolution <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_mask_short_term.tif: </strong> Mask GeoTIF rasters at a 1km resolution that contain values from 0.0 to 1.0 for each 1 km grid cell to help calculate suitability depending on topographic and land use and land cover characteristics <ul> <li>value per grid cell: values from 0.0 to 1.0 (float) that are generated from topographic and land use and land cover characteristics to inform suitability as outlined in the companion publication</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_<ssp>_popproj.csv: </strong> Population projection CSV files for urban, rural, and total population (number of humans; float) for SSPs 2, 3, and 5 for years 2010-2100</li> <li><strong><state-name>_coordinates.csv: </strong>CSV file containing the coordinates for each 1 km grid cell within the target state. File includes a header with the fields XCoord, YCoord, FID.,Where data types and field descriptions are as follows: (XCoord, float, X coordinate in meters),(YCoord, float, Y coordinate in meters),(FID, int, Unique feature id)</li> <li><strong><state-name>_within_indices.txt: </strong>text file containing a file structured as a Python list (e.g. [0, 1]) that contains the index of each grid cell when flattened from a 2D array to a 1D array for the target state.</li> <li><strong><state-name>_<ssp>_params.csv: </strong>CSV file containing the calibration parameters (alpha_rural, beta_rural, alpha_urban, beta_urban; float) for the `population_gravity` model for each year from 2010-2100 in 10-year time-steps as described in the companion publication</li> </ul> </li> <li><strong>outputs</strong> contain the following: <ul> <li><strong>jones_oneill</strong> directory; these are the comparison datasets used to build Figures 7 and 8 in the companion publication <ul> <li>contains three directories: SSP2, SSP3, and SSP5 that each contain a GeoTIF representing total population (number of humans; float) at 1km resolution for years 2050 and 2100. <ul> <li><strong><state-name>_1km_<ssp>_total_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>model</strong> directory; these are the model outputs from `population_gravity` for SSP2, SSP3, and SSP5 that each contain a GeoTIF representing urban, rural, and total population (number of humans; float) at 1km resolution for years 2020-2100 in 10-year time-steps. <ul> <li><strong><state-name>_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>zoraghein-oneill_population_gravity_national-ssp-maps.zip</strong></p> <ul> <li>Results of the `population_gravity` model mosaicked to the National scale at a 1km resolution and the comparison Jones and O'Neill research. These are used to generate Figure 6 of the companion paper <ul> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> <p> </p>
RAW data: Knockdown of UTX/KDM6A Enriches Precursor Cell Populations in Urothelial Cell Cultures and Cell Lines - single cell RNAseq - Fastq format and UMI counts
<p>This data set of the single cell sequencing experiment of the urothelial cell line HBLAK belongs to the publication: "Knockdown of UTX/KDM6A Enriches Precursor Cell Populations in Urothelial Cell Cultures and Cell Lines" Cancers 2020, 12(4), 1023; https://doi.org/10.3390/cancers12041023.</p> <p> </p>
Data from: Mortality limits used in wind energy impact assessment underestimate impacts of wind farms on bird populations
<p>In this archive we share the data and R code used for the construction of population models for seven bird species (Common Starling, Black-tailed Godwit<strong>,</strong> Marsh Harrier, Eurasian Spoonbill, White Stork, Common Tern and White-tailed Eagle) for our assessment of the effects of wind farms (Schippers et al. 2020). In most cases we parameterized our population models based on species-specific survival and reproduction rates from scientific articles and reports, but in the case of the Western Marsh Harrier we analyzed previously unpublished nest success and capture-mark-resighting data. Below we first describe per species which data we used for model parameterization, and then describe per data file what each variable represents.</p> <p>We selected populations of seven species based on the availability of data, considerable likelihood to collide with wind turbines and contrasting ages of first reproduction. For species for which long time series of demographic data were available with population trends clearly changing over time, we separately assessed periods with contrasting population trends, as detailed in the species descriptions below. Mean survival and reproduction rates, standard deviations and additional information like the age of first reproduction can be found in the accompanying paper by Schippers et al. (2020). </p> <p> </p> <p><strong>Common Starling</strong></p> <p>On the fast-slow continuum of reproductive capacity, the common starling is the fastest of the seven species we selected: it starts reproducing at an age of one year. We used the mean survival and reproductive rates for the whole Dutch breeding population (Versluijs et al. 2016), distinguishing three separate periods: 1960-1978, 1978-1990 and 1990-2012. In the first period (1960-1978) the population grew at 10% per year. This was followed by a period where the population was relatively stable (1978-1990). During the last period (1990-2012) the population declined strongly.</p> <p> </p> <p><strong>Black tailed Godwit</strong></p> <p>Kentie et al. (2017) studied two Dutch populations of the Black-tailed Godwit in southwestern Fryslân (Skriezekrite and Kuststrook) over four to five annual transitions (Kentie et al. 2017). Godwits started reproducing at age two, but only had 0.5-0.6 fledglings per breeding pair per year. The adults are rather long-lived with an 86% annual survival rate. We construct separate matrix models for the two populations.</p> <p> </p> <p><strong>Marsh Harrier</strong></p> <p>Mean vital rates of the Dutch breeding population of Marsh Harriers were estimated for 1997-2015 using respectively ring recoveries available at the Dutch Centre for Avian Migration and Demography NIOO-KNAW and reproduction data from the Dutch Raptor Working Group. Annual survival of Marsh Harriers was analyzed using live re-sightings and dead recoveries of 12,059 birds ringed as nestling between 1991 and 2016 and 74 birds ringed as ‘adult’ in the same period (due to low sample sizes, birds ringed in their first and second calendar year were lumped with older birds in the ‘adult’ category; see ‘marshHarrierSurvival.csv’ below). Nest success was estimated using data of 1914 nests, which were followed from the beginning to the end of the nest cycle, in the Netherlands between 1997 and 2015 (see ‘marshHarrierReproduction.csv’ below; we thank Rob G. Bijlsma for making the data available). </p> <p> </p> <p><strong>Spoonbill</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates were derived for the Dutch Spoonbill population from van der Jeugd et al. (2014). Participation in the breeding population was 0% in the first three years and went up from 63% at age four to 95% at age 6 and older.</p> <p> </p> <p><strong>White Stork</strong></p> <p>Schaub et al. (2004) analyzed demographic data on White Storks in Switzerland from 1977 till 2000. Here we extracted annual survival and reproduction rates from the COMADRE Animal Matrix Database (version 2.0.1; Salguero-Gómez et al., 2016). Storks start reproducing at age 3, with breeding participation increasing with age from 48% to 100%. </p> <p> </p> <p><strong>Common Tern</strong></p> <p>For the Common Tern we used mean vital rate estimates published by van der Jeugd et al. (2014) for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010 (van der Jeugd et al. 2014). The total Waddenzee and IJsselmeer population is estimated at 7,630 pairs (average population 2010-2014), constituting approximately 40% of the Dutch breeding population of about 20,000 pairs (Sovon 2016). </p> <p> </p> <p><strong>White-tailed Eagle</strong></p> <p>Krüger et al. (2010) published demographic data on White-tailed Eagles in Schleswig-Holstein, Germany, over the period 1947 till 2008. Following these authors, and based on the two matrices in COMADRE v.2.0.1 (Salguero-Gómez et al., 2016), we used separate matrix models for the early period (stable population dynamics) and from 1975 onwards (population growth). These eagles start reproducing at age five. </p> <p> </p> <p>Here we describe the archived files:</p> <p> </p> <p><strong>matrices.R</strong></p> <p>This annotated R file details how the vital rate estimates are used to construct age-structured, post-breeding-census, one-year-timestep population matrix models. In these so-called post-breeding census models the birds in the first class were 0 years old (Caswell 2001).</p> <p> </p> <p><strong>commonstarling19602012.csv</strong></p> <p>Mean survival and reproductive rates for the whole Dutch breeding population of Common Starlings for the time period 1960-2012. </p> <p>year = start year</p> <p>juvSurv = first-year survival of fledgelings</p> <p>adultSurv = annual survival of older birds</p> <p>fec = number of fledgelings per pair (which have a 1:1 sex ratio)</p> <p> </p> <p><strong>blacktailedgodwit20112016.csv</strong></p> <p>Mean survival and reproduction rates of the Black-tailed Godwit in southwestern Fryslân (populations Skriezekrite and Kuststrook) over four to five annual transitions in the period 2011-2016. </p> <p>pop = population</p> <p>startYear = start year</p> <p>adultSurv = annual survival of older birds</p> <p>chickSurv = first-year survival of chicks</p> <p>nestSuc = probability that a nest is successful</p> <p> </p> <p><strong>marshharrier19972015.csv</strong></p> <p>Mean vital rates of the Dutch breeding population of Western Marsh Harriers for 1997-2015.</p> <p>year = start year</p> <p>r = number of fledgelings per pair</p> <p>s1 = first-year survival of fledgelings</p> <p>s2 = annual survival of older birds</p> <p> </p> <p><strong>marshharrierreproduction.csv</strong></p> <p>Western Marsh Harrier nest record data of in the Netherlands.</p> <p>year = year</p> <p>clutchSize = number of eggs</p> <p>young = number of chicks (if known)</p> <p>fledgelings = number of fledgelings</p> <p> </p> <p> </p> <p> </p> <p><strong>marshharriersurvival.csv</strong></p> <p>Ringing and resighting data (using EURING coding) on Western Marsh Harriers in the Netherlands. </p> <p>ringID = ring identifier</p> <p>date = observation date</p> <p>metalRingInformation</p> <p>1 = Metal ring added (where no metal ring was present), position (on tarsus or above) unknown or unrecorded.</p> <p>2 = Metal ring added (where no metal ring was present), definitely on tarsus.</p> <p>3 = Metal ring added (where no metal ring was present), definitely above tarsus.</p> <p>4 = Metal ring is already present.</p> <p>condition </p> <p>0 = Condition completely unknown.</p> <p>1 = Dead but no information on how recently the bird had died (or been killed).</p> <p>2 = Freshly dead – within about a week.</p> <p>3 = Not freshly dead – information available that it had been dead for more than about a week.</p> <p>4 = Found sick, wounded, unhealthy etc. and known to have been released (including ring or other mark identified on a bird in poor condition without the bird having being caught).</p> <p>5 = Found sick, wounded, unhealthy etc. and not released or not known if released.</p> <p>6 = Alive and probably healthy but taken into captivity.</p> <p>7 = Alive and probably healthy and certainly released (including ring or other mark identified on a healthy bird without the bird having being caught).</p> <p>8 = Alive and probably healthy and released by a ringer (including ring or other mark identified on the bird by a ringer without the bird having being caught). </p> <p>ageReported </p> <p>0 = Age unknown, i.e. not recorded.</p> <p>1 = Pullus: nestling or chick, unable to fly freely, still able to be caught by hand.</p> <p>2 = Full-grown: able to fly freely but age otherwise unknown.</p> <p>3 = First-year: full-grown bird hatched in the breeding season of this calendar year.</p> <p>4 = Afer first-year: full-grown bird hatched before this calendar year; year of hatching otherwise unknown.</p> <p>5 = 2<sup>nd</sup> year: a bird hatched last calendar year and now in its second calendar year.</p> <p>6 = Afer 2<sup>nd</sup> year: full-grown bird hatched before last calendar year; year of hatching otherwise unknown.</p> <p>7 = 3<sup>rd</sup> year: a bird hatched two calendar years before, and now in its third calendar year.</p> <p>8 = Afer 3<sup>rd</sup> year: a full-grown bird hatched more than three calendar years ago (including present year as one); year if bird otherwise unknown.</p> <p>9 = 4<sup>th</sup> year: a bird hatched three calendar years before, and now in its fourth calendar year.</p> <p>A = Afer 4<sup>th</sup> year: a bird older than category 9 – age otherwise unknown.</p> <p>sexReported</p> <p>U = Unknown</p> <p>M = Male</p> <p>F = Female</p> <p> </p> <p><strong>eurasianspoonbill19942008.csv</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates are given for the Dutch Spoonbill population.</p> <p>year = start year</p> <p>fled = number of fledgelings per breeding pair</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>s3 = third-year survival rate</p> <p>s4 = older birds' annual survival rate</p> <p> </p> <p><strong>whitestork19772000.csv</strong></p> <p>Demographic data on White Storks in Switzerland from 1977 till 2000.</p> <p>year = start year</p> <p>fled = number of fledgelings per pair</p> <p>sj = first-year survival of fledgelings</p> <p>sa = annual survival of older birds</p> <p> </p> <p><strong>commontern19942009.csv</strong></p> <p>Mean vital rate estimates for the Common Tern for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010.</p> <p>year = start year</p> <p>r = number of daughter fledgelings per adult female</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>sA = older birds' annual survival rate</p> <p> </p> <p><strong>whitetailedeaglepmat1.csv</strong></p> <p><strong>whitetailedeaglepmat2.csv</strong></p> <p><strong>whitetailedeaglefmat1.csv</strong></p> <p><strong>whitetailedeaglefmat2.csv</strong></p> <p>White-Tailed Eagle age-specific survival (Pmat) and reproduction (Fmat) matrices as found in COMADRE v.2.0.1, for Schleswig-Holstein, Germany, studied over the period 1947-2008. Period 1 lasts upto 1975, period 2 from 1975. </p>
Data from: Demographic history and genomic diversity and divergence in blue tit populations across heterogeneous environments
<p>Understanding the genomic processes underlying local adaptation is a central aim of modern evolutionary biology. This task requires identifying footprints of local selection but also estimating spatio-temporal variation in population demography and variation in recombination rate and diversity along the genome. Here, we investigated these parameters in blue tit populations inhabiting deciduous <i>versus</i> evergreen forests and insular <i>versus</i> mainland areas, in the context of a previously described strong phenotypic differentiation. Neighboring population pairs of deciduous and evergreen habitats were weakly genetically differentiated (<i>F</i><sub>ST</sub> = 0.004 on average), nevertheless with a statistically significant effect of habitat type on the overall genetic structure. This low differentiation was consistent with the strong and long-lasting gene flow between populations, inferred by demographic modeling. In turn, insular and mainland populations were moderately differentiated (<i>F</i><sub>ST</sub> = 0.08 on average), in line with the inference of moderate ancestral migrations, followed by isolation since the end of the last glaciation. Effective population sizes were overall large, yet smaller on the island than on the mainland. Weak and non-parallel footprints of divergent selection between deciduous and evergreen populations were consistent with their high connectivity and the probable polygenic nature of local adaptation in these habitats. In turn, stronger footprints of divergent selection were identified between long isolated insular <i>versus</i> mainland birds, and were more often found in regions of low recombination as expected from theory. Lastly, we identified a genomic inversion on the mainland, spanning 2.8Mb. These results provide insights into the demographic history and genetic architecture of local adaptation in blue tit populations at multiple geographic scales.</p>
Data from: Disease swamps molecular signatures of genetic-environmental associations to abiotic factors in Tasmanian devil (Sarcophilus harrisii) populations
Landscape genomics studies focus on identifying candidate genes under selection via spatial variation in abiotic environmental variables, but rarely by biotic factors such as disease. The Tasmanian devil (Sarcophilus harrisii) is found only on the environmentally heterogeneous island of Tasmania and is threatened with extinction by a nearly 100% fatal, transmissible cancer, devil facial tumor disease (DFTD). Devils persist in regions of long-term infection despite epidemiological model predictions of species' extinction, suggesting possible adaptation to DFTD. Here, we test the extent to which spatial variation and genetic diversity are associated with the abiotic environment and/or DFTD. We employ genetic-environment association analyses using a RAD-capture panel including 6,886 SNPs from 3,286 individuals sampled pre- and post-disease arrival. Pre-disease, we find significant correlations of allele frequencies with environmental variables, including 365 unique loci linked to 71 genes, suggesting local adaptation to abiotic environment. The majority of candidate loci detected pre-DFTD were not detected post disease arrival. Several post-DFTD candidate loci were associated with disease prevalence and were in linkage disequilibrium with genes involved in tumor suppression and immune response. Loss of apparent signal of abiotic local adaptation post-disease suggests swamping by the strong selection resulting from the rapid onset of DFTD.
Data from: Assessing the effectiveness of a national protected area network in maintaining carnivore populations
<p>Protected areas (PAs) are essential to prevent further biodiversity loss yet their effectiveness varies largely with governance and external threats. Although methodological advances have permitted assessments of PA effectiveness in mitigating deforestation, we still lack similar studies for the impact of PAs on wildlife populations. Here we demonstrate the application ofuse an innovative combination of matching methods and hurdle-mixed models with a large-scale and long-term dataset of unprecedented coverage for Finland's large carnivore species. We show that the national PA network , at the national level, PAs does not support higher densities than non-protected habitat for 3 of the 4 species investigated. For the brown bear, PAs appear to have lower densities than non-protected areas. For some species, PA effects interact with region or time, i.e. wolverine densities decreased inside PAs over the study period and lynx densities increased inside eastern PAs. Although we show that matching approaches could and should be applied to wildlife population data, Wwe support their application of matching methods in combination of additional analytical frameworks for deeper understanding of conservation impacts on wildlife populations. These methodological advances are crucial for improving PA targets and extremely timely for preparing ambitious PA targets a post-2020 global framework for biodiversity.</p>
Data from: Examination of the efficacy of small genetic panels in genomic conservation of companion animal populations
<p>In many ways dogs are an ideal model for the study of genetic erosion and population recovery, problems of major concern in the field of conservation genetics. Genetic diversity in many dog breeds has been declining systematically since the beginning of the 1800's, when modern breeding practices came into fashion. As such, inbreeding in domestic dog breeds is substantial and widespread and has led to an increase in recessive deleterious mutations of high effect as well as general inbreeding depression. Pedigrees can in theory be used to guide breeding decisions, though are often incomplete and do not reflect the full history of inbreeding. Small microsatellite panels are also used in some cases to choose mating pairs to produce litters with low levels of inbreeding. However, the long-term impact of such practices have not been thoroughly evaluated. Here, we use forward simulation on a model of the dog genome to examine the impact of using limited markers panels to guide pairwise mating decisions on genome-wide population level genetic diversity. Our results suggest that in unmanaged populations, where breeding decisions are made at the pairwise- rather than population-level, such panels can lead to accelerated loss of genetic diversity at genome regions unlinked to panel markers, compared to random mating. These results demonstrate the importance of genome-wide genetic panels for managing and conserving genetic diversity in dogs and other companion animals.</p>
Data from: Increasing dependence of lowland populations on mountain water resources
<p>Mountain areas provide disproportionally high runoff in many parts of the world, and here we quantify for the first time their importance for water resources and food production from the viewpoint of the lowland areas downstream. The dataset maps the degree to which lowland areas potentially depend on runoff contributions from mountain areas (39% of land mass) between the 1960s and the 2040s.</p>
Data and code for: Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells
<p>This dataset contains experimental data and custom R code for likelihood-based model-fitting associated with the manuscript "Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells". In particular, we estimate the per-cell establishment probability (i.e. probability that a single cell gives rise to a large population) of a resistant strain, in the presence of antibiotics at concentrations below its standard minimum inhibitory concentration. The experiments are conducted here with <em>Pseudomonas aeruginosa</em>, while the model and methods can be applied more generally.</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.