Find research datasets worth reusing

opencc-by-4.0Nov 2016View details →

Data from: Spatial and host-related variation in prevalence and population density of wheat curl mite (Aceria tosichella) cryptic genotypes in agricultural landscapes

Filename: coord.csv Names of the sampling locations and their geographic coordinates. <ol> <li>Name - sampling locality identifier</li> <li>Lat - latitude</li> <li>Long - longitude</li> </ol> Filename: lineages.csv <ol> <li>id.sample - sample identifier</li> <li>host - host species (Arrela=Arrhenantherum elatius, Avesat=Avena sativa, Broine=Bromus inermis, Elyres=Elymus repens, Horvul=Hordeum vulgaris, Seccer=Secale cereale, Triaes=Triticum aestivum, Tririm=Triticale rimpaui</li> <li>x, y - geodetic coordinates</li> <li>stems - no. of stems in a sample</li> <li>leaves - no. of leaves in a sample</li> <li>MT.01 to MT.27 - no. of mites belonging to each genetic lineage</li> </ol>

opencc-by-4.0Oct 2022View details →

diFUME Population Density V0.1

Description: Annual statistics per city block on residential population (by age group) and workplace employees (<a href="https://www.basleratlas.ch/">https://www.basleratlas.ch/</a> ) are used to derive maps of annual night-time and daytime building-scale population density (inhabitants per m2) for weekdays and weekends. The spatial resampling of the population is based on the assumption of proportionality between building inhabitants and building volume (estimated as mean building height×building plan area, derived by land cover and DSM products). Considering the building type, building volume is separated to residential volume and workplace volume, so that population is redistributed between night-time, daytime, workdays and weekends.   Data specifications: CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected Spatial Extent: 392120.0,5266860.0 : 395160.0,5269840.0 Temporal Extent: 2018 - 2020 Units: meters Width: 608 Height: 596 Bands: 1 Pixel Size: 5,-5 Data type: Float32 - Thirty two bit floating point GDAL Driver Description: GTiff GDAL Driver Metadata: GeoTIFF

opencc-by-4.0May 2024View details →

GLOBAL SNAPSHOT Physician Distribution and Density of Physicians per 1000 population - Worldwide 2021

The chart presents the most up-to-date data (2021) available for 49 of the worldâ€™s 195 countries, focusing on the total number of physicians and the number of physicians per 1000 population(1). The countries are categorized into four income groups based on World Bank classifications, which are updated annually on July 1st each year(2). Only 25% of the countries present current data. This information is critical for decision-making for healthcare planning and policy development. Equally crucial, is for researchers to have comparable data to propose initiatives, to establish benchmarks and  for crafting holistic strategies to gauge and advance progress in healthcare systems globally. Data sources: UnData <a href="https://data.un.org/">https://data.un.org/</a> Visualization tools used: RAWGraphs <a href="https://www.rawgraphs.io/">https://www.rawgraphs.io/</a>, MS PowerPoint and Microsoft Excel Intended Audience: Academics and Researchers; Students and Educators; Healthcare Administrators and Policy Makers; Non-Governmental Organizations The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The NNLM Data Visualization Challenge happens through work funded by the National Institutes of Health's National Library of Medicine, grant number U24LM013751   References: 1. United Nations, Department of Economic and Social Affairs. 10 Health Personnel. In: Statistical Yearbook. 66th issue (2023). New York: United Nations; 2023. (ST/ESA/STAT/SER.S/42). [Dataset available at UnData] <a href="https://data.un.org/_Docs/SYB/CSV/SYB66_154_202310_Health%20Personnel.csv">https://data.un.org/_Docs/SYB/CSV/SYB66_154_202310_Health%20Personnel.csv</a> 2 World Bank. World Bank Country and Lending Groups. World Bank Data Help Desk [Internet]. [cited 2024 Apr 5]. Available from:<a href="https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups"> https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</a>

opencc-by-4.0Jan 2023View details →

Relative density variations of common vole population based on index transect, Septfontaines - Le Souillot, France (1990-2000)

Transects were walked from village to village along a transect line. Common vole (Microtus arvalis) activity indices were recorded in every ten pace interval from October 1990 to April 2000. In 2014, the geographical coordinates of each interval has been computed by spatial interpolation based on georeferenced maps. Therefore, users must be aware that individual locations of intervals are unprecise, but not the general bearing of the transect in the landscape and interval succession. See articles published for reference and more details. During the same time span, small mammmals (including common voles) were sampled using live-trapping, see <a href="https://doi.org/10.5281/zenodo.6997316">10.5281/zenodo.6997316</a> FILE DESCRIPTION: <a href="https://zenodo.org/record/7544358/files/db.txt?download=1">db.txt </a>index transect file <ul> <li>name: transect name</li> <li>date: on eight digits, '19921014' reads 14/10/1992</li> <li>ID: interval ID = number (within a given transect at a given date)</li> <li>Habitat: (indicative) the habitat category crossed. Just mentioned when passing from one category to the other; the following intervals are assumed to belong to this habitat</li> <li>ma1: number of Microtus holes; A, 1-5 holes; B, 6-10 holes; C > 10 holes</li> <li>ma2: answered only if A, B, or C are defined in ma1; NA, not answered (ma1 not defined), 0, zero faeces, 1 some faeces or fresh indices (runways with grass freshly cut, etc.); 2 many faeces in heaps</li> <li>long: longitude (WGS84)</li> <li>lat: latitude (WGS84)</li> </ul> <a href="https://zenodo.org/record/7544358/files/StudyAreaBoundingBox.kml?download=1">StudyAreaBoundingBox.kml</a> Bounding box of the study area.

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Data to support "Stochastic density effects on adult fish survival and implications for population fluctuations"

Data on stage-specific abundance of black surfperch (Embiotoca jacksoni), the amount of foraging habitat and the availability of surfperch prey (crustaceans) were collected at fixed sites on the north shore of Santa Cruz Island, California annually (autumn) from 1993-2009. Data are grouped into four regions. Counts of fish distinguished among young-of-year, juveniles (1 year old) and adults (>= 2 years old). These data have been presented in Okamoto, D. K., R. J. Schmitt and S. J. Holbrook. 2016. Sochastic density effects on adult fish survival and implications for population fluctuations. Ecology Letters, 19:153-162. doi: 10.1111/ele.12547.

openCC (other)Oct 2022View details →

zenodo44/100

Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps

The dataset The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations. Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (Bemisia tabaci) (Gennadius) and the greenhouse whitefly (Trialeurodes vaporariorum) (Westwood). The dataset comprises two subsets: - a subset we suggest using for the training/validation phases (contained in the `train/` folder) - a subset we suggest using for the test phase (contained in the `test/` folder) Annotations of the two subsets are contained in `train/annotations.csv` and `test/annotations.csv`, respectively. They have the following columns: - *imageName* - filename of the image containing the whiteflies, - *X,Y* - 2D coordinates of the whitefly in the image space, - *class* - class index of the insect (always 0 in this dataset).   Citing our work If you found this dataset useful, please cite the following paper <blockquote> <pre>@inproceedings{CIAMPI2023102384, title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps},    journal = {Ecological Informatics}, volume = {78}, pages = {102384}, year = {2023}, issn = {1574-9541},     doi = {10.1016/j.ecoinf.2023.102384},   url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132},   year = 2023,     author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, } </pre> </blockquote> and this Zenodo Dataset <blockquote> <pre>@dataset{ciampi_2023_7801239,     author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa},     title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}},   month = apr,   year = 2023,   publisher = {Zenodo},   version = {1.0.0},   doi = {10.5281/zenodo.7801239},   url = {<a href="https://doi.org/10.5281/zenodo.7801239">https://doi.org/10.5281/zenodo.6560823</a>} } </pre> </blockquote>   Contact Information If you would like further information about the dataset or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">luca.ciampi@isti.cnr.it</a>

opencc-by-4.0Apr 2023View details →

zenodo44/100

Genetic diversity, population structure, and linkage disequilibrium among tropical quality protein maize (QPM) lines assessed with high-density SNP markers

The study of genetic diversity (GD), population structure, and linkage disequilibrium (LD) provides a better understanding of the genetic relationships between individuals in a population which can be utilized in crop research and improvement. Genotyping-by-sequencing (GBS) was used to detect and genotype single nucleotide polymorphisms (SNPs) in a collection of 74 quality protein maize (QPM) lines and further to characterize their genetic diversity, population structure, and linkage disequilibrium. A total of 235,214 high-quality SNPs were used for different genetic analyses except for structure analysis where 11,950 SNPs were used. Analysis of molecular variance (AMOVA) based on these SNPs revealed high genetic heterozygosity among the five populations with 1% of the total genetic variation present among the subpopulations and 99% of the variation among individuals within the populations.  Population structure analysis using Bayesian-based clustering revealed that the 74 lines could be clustered into four groups. However, neighbor-joining trees indicate the lines are grouped into three major clusters.  Further analysis using principal component analyses (PCA) clustered the genotypes into five groups which are concordant with the groups based on pedigree information. Higher genetic diversity was detected in population 1 with a GD value of 0.484 and the lowest in population 5 (0.396) and overall, with a mean of 0.434. The LD pattern in the quality protein maize was investigated and we observed a relatively rapid LD decay of 3.53kb and 10.66kb at r2 =0.2 and r2= 0.1, respectively. Our findings provide important information for future Linkage mapping studies, genome-wide association analyses, and marker-assisted selective breeding of maize as well as genomic prediction-based selection in tropical germplasm.

opencc-by-4.0Sep 2023View details →

edi44/100

Soils Bulk Density: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem

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.

openCC0Oct 2021View details →

dryad40/100

Evolution under pH stress and high population densities leads to increased density-dependent fitness in the protist Tetrahymena thermophila

Abiotic stress is a major force of selection that organisms are constantly facing. While the evolutionary effects of various stressors have been broadly studied, it is only more recently that the relevance of interactions between evolution and underlying ecological conditions, that is, eco-evolutionary feedbacks, have been highlighted. Here, we experimentally investigated how populations adapt to pH-stress under high population densities. Using the protist species Tetrahymena thermophila, we studied how four different genotypes evolved in response to stressfully low pH conditions and high population densities. We found that genotypes underwent evolutionary changes, some shifting up and others shifting down their intrinsic rates of increase (r0). Overall, evolution at low pH led to the convergence of r0 and intraspecific competitive ability (α) across the four genotypes. Given the strong correlation between r0 and α, we argue that this convergence was a consequence of selection for increased density-dependent fitness at low pH under the experienced high density conditions. Increased density-dependent fitness was either attained through increase in r0 , or decrease of α, depending on the genetic background. In conclusion, we show that demography can influence the direction of evolution under abiotic stress.

opencc-zeroDec 2019View details →

dryad40/100

Varying genetic imprints of roads and human density in North American mammal populations

Road networks and human density are major factors contributing to habitat fragmentation and loss, isolation of wildlife populations and reduced genetic diversity. Terrestrial mammals are particularly sensitive to road networks and encroachment by human populations. However, there are limited assessments of the impacts of road networks and human density on population-specific nuclear genetic diversity, and it remains unclear how these impacts are modulated by life history traits. Using generalized linear mixed models and microsatellite data from 1444 North American terrestrial mammal populations we show that taxa with large home range sizes, dense populations, and large body sizes had reduced nuclear genetic diversity with increasing road impacts and human density, but the overall influence of life history traits was generally weak. Instead, we observed a high degree of genus-specific variation in genetic responses to road impacts and human density. Human density negatively affected allelic diversity or heterozygosity more than road networks (13 versus 5-7 of 25 assessed genera, respectively); increased road networks and human density also positively affected allelic diversity and heterozygosity in 15 and 6-9 genera, respectively. Large bodied, human-averse species were generally more negatively impacted than small, urban-adapted species. Genus-specific responses to habitat fragmentation by ongoing road development and human encroachment likely depend on the specific capability to (i) navigate roads as either barriers or movement corridors, and (ii) exploit resource-rich urban environments. The non-uniform genetic response to roads and human density highlights the need to implement efforts to mitigate the risk of vehicular collisions, while also facilitating gene flow between populations of particularly vulnerable taxa.

opencc-zeroJun 2021View details →

opencc-by-nc-4.0Jan 2017View details →

High density genotypes of French Sheep populations

Genotypes of 27 French sheep populations on the Illumina Ovine HD SNP chip. Dataset and results are presented in the preprint: High density genome scan for selection signatures in French sheep reveals allelic heterogeneity and introgression at adaptive loci. Christina Marie Rochus, Flavie Tortereau, Florence Plisson-Petit, Gwendal Restoux, Carole Moreno-Romieux, Gwenola Tosser-Klopp, Bertrand Servin. bioRxiv 103010; doi: https://doi.org/10.1101/103010

Fig. 13 in Diversity And Density Of Mollusca (Gastropoda And Bivalvia) Population In The Euphrates River At Al-Nasiriyah, Southern Iraq

Fig. 13. Dominance values of Mollusca species from July 2018 to June 2019 in the study stations at the Euphrates River.

Fig. 14 in Diversity And Density Of Mollusca (Gastropoda And Bivalvia) Population In The Euphrates River At Al-Nasiriyah, Southern Iraq

Fig. 14. Evenness values of Mollusca species from July 2018 to June 2019 in the study stations at the Euphrates River.

Fig. 1 in Diversity And Density Of Mollusca (Gastropoda And Bivalvia) Population In The Euphrates River At Al-Nasiriyah, Southern Iraq

Fig. 1. Shows the three study stations on the Euphrates River within Al-Nasiriyahcity. Yelow circle: station 1; blue circle: station 2; red cicle: station 3.

Fig. 11 in Diversity And Density Of Mollusca (Gastropoda And Bivalvia) Population In The Euphrates River At Al-Nasiriyah, Southern Iraq

Fig. 11. Shannon index values of Mollusca species from July 2018 to June 2019 in the study stations at the Euphrates River.

Fig. 12 in Diversity And Density Of Mollusca (Gastropoda And Bivalvia) Population In The Euphrates River At Al-Nasiriyah, Southern Iraq

Fig. 12. Richness values of Mollusca species from July 2018 to June 2019 in the study stations at the Euphrates River.

FIGURE 5 in Population ecology and juvenile density hotspots of thornback ray (Raja clavata) around the Shetland Islands, Scotland

FIGURE 5 Spatial distribution of juvenile Raja clavata (<60 cm) catch per unit effort (CPUE) from annual Shetland inshore fish surveys (SIFS) conducted between 2017 and 2022. Blue crosses indicate inshore habitat surveys (20–50 m water depth), and red crosses indicate shallow water habitat surveys (50–150 m water depth). The size of circle indicates CPUE. The location of each R. clavata individual was assigned as the midpoint of the associated tow.

FIGURE 2 in Population ecology and juvenile density hotspots of thornback ray (Raja clavata) around the Shetland Islands, Scotland

FIGURE 2 Catch per unit effort (CPUE) of Raja clavata for the shallow (red) (2017–2022) and inshore (blue) (2011–2022) survey locations. The mean result is shown by solid lines, and the shaded area represents the variability between tows (standard error).

FIGURE 1 in Population ecology and juvenile density hotspots of thornback ray (Raja clavata) around the Shetland Islands, Scotland

FIGURE 1 Inshore (blue) and shallow (red) survey tow habitats during Shetland Inshore Fish Survey. Tows identified by their station code and corresponding fishing grounds, for example, HA01, Fitful Head.

FIGURE 4 in Population ecology and juvenile density hotspots of thornback ray (Raja clavata) around the Shetland Islands, Scotland

FIGURE 4 Non-metric multidimensional scaling (nMDS) plot showing ordinations generated from a Bray–Curtis similarity matrix on Raja clavata catch per unit effort (CPUE) Bray-Curtis similarities between shallow water and inshore habitat tow locations. Surveys are grouped into shallow (red) and inshore (blue) habitats. Labels represent survey habitat and year, for example, I22 = Inshore survey conducted in 2022. nMDS plot 2D stress is 0.06, indicating a clear distinction of the two clusters (dashed lines). Inset picture shows two Raja clavata sampled in a tow; basket diameter at base is 35 cm.