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19,493 results for “Populism”
Fig. 1 in Ectoparasites are unlikely to be a primary cause of population declines of bent-winged bats in south-eastern Australia
Fig. 1. Dorsal view of Penicillidia tectisentis. Note notopleural setae (Arrow).
Mesiodistal distance of portuguese population
<p>Mesiodistal distance of portuguese population</p>
Supporting data and script for "Productivity, biodiversity, and pathogens influence the global hunter-gatherer population density" (Tallavaara et al.)
<p>This submission contains data and R-script that enable to reproduce the data manipulations and analyses in the paper “Productivity, biodiversity, and pathogens influence the global hunter-gatherer population density” by Miikka Tallavaara, Jussi T. Eronen, and Miska Luoto (PNAS 2018 115 (6) 1232-1237, doi/10.1073/pnas.1715638115). Please, cite the above paper, if you use the files included in this Zenodo record in your work.</p> <p>Included in the submission are R-script as a pdf-file (Tallavaara_Data_analyses.pdf), global net primary productivity data (Tallavaara_Dataset_1.tif), global biodiversity data (Tallavaara_Dataset_2.tif), and global pathogen stress data (Tallavaara_Dataset_3.tif) as GeoTIFF-files. In addition, submission contain global hunter-gatherer data (Tallavaara_Dataset_4.xls) as xls-file. If these datasets are saved in the working directory they can be read in to the R using the included R-script (Tallavaara_Data_analyses).</p>
THE ORTHODOX CHURCH AND THE PRESERVATION OF THE ECOLOGICAL BALANCE OF THE POPULATION IN RUSSIA
<p> </p> <p>The article analyzes ecological problems associated with religion and faith, which is especially noticeable among the inhabitants of megacities. It is shown that in order to preserve nature, it is necessary to include environmental education in the priority tasks of universal education. Particular attention is drawn to different approaches to environmental problems.</p>
Data from a field plot experiment with the canola pathogen Leptosphaeria maculans including disease severity at the leaf spot and canker stages of the epidemic, and population composition as isolates infectivity pathotypes.
<p><strong>Data set</strong></p> <p>Data are from an experiment simulating how differences in <em>Brassica napus</em> resistance deployment strategies and landscape connectivity influence epidemic severity and pathogen population composition of the fungus <em>Leptosphaeria maculans</em> on field plots inoculated with combinations of stubble in 2016 at CSIRO Canberra, ACT, Australia. Disease severity was assessed on the 60 field plots [Data_severity.csv] and 1490 isolates were sampled and assessed for infectivity [Data_infectivity.csv]. This dataset is described and analyzed in Bousset et al. (2018).</p> <p>Treatments were factorial combination of Resistance, Genetic Connectivity and Spatial Connectivity, replicated in 4 blocks (B1 to B4). Resistance has 3 categories (Rlm4, Rlm6 LepR1) differing by the resistance genes in oilseed rape varieties. Genetic Connectivity has 2 levels (HighGC, LowGC) differing by the pre-adaptation of the stubble populations to the host variety. Spatial Connectivity has two levels (HighSC, LowSC) differing by the stubble load. Control plots had NoStubble.</p> <p><strong>Data files</strong></p> <p>[Data_severity.csv] Disease severity was assessed on the 60 field plots at leaf spot and canker stages of the epidemic. Leaf spots data are counts. Canker data are numbers of stems in 12 categories defined by the cankered area on cross section (0 = no canker to 100 = fully cankered).</p> <p>[Data_infectivity.csv] Two types of isolates (122 from 3 stubble sources with contrasting preadaptation and 1368 from leaves sampled on 50 field plots) were tested for infectivity response (V = infective; A = non-infective) on the three host varieties (Rlm4, Rlm6 LepR1), at the seedling stage in greenhouse.</p> <p><strong>Associated publication</strong></p> <p>Bousset L, Sprague S, Thrall PH, Barrett LG (2018). Spatio-temporal connectivity and host resistance influence evolutionary and epidemiological dynamics of the canola pathogen <em>Leptosphaeria maculans. Evolutionary Applications</em> [ DOI: 10.1111/eva.12630 ].</p> <p><strong>Funding information</strong></p> <p>This work benefited from the financial support of INRA – the French National Institute for Agronomical Research, a CSIRO Sir Frederick McMaster fellowship to L. Bousset (Impact of inoculum carry-over on landscape dynamics of the blackleg canola pathogen) and the Grains Research & Development Corporation (GRDC Grant CSP00192)</p>
Supplemental Tables Bordetella pertussis population dynamics and phylogeny in Japan after adoption of acellular pertussis vaccines
<p>Supplemental tables for paper titled "Bordetella pertussis population dynamics and phylogeny in Japan after adoption of acellular pertussis vaccines"</p>
Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria
<p>Code and data for Frenoy & Bonhoeffer 2018 (Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria, in PLoS Biology)</p> <p>See README for details and steps to reproduce the full analysis from the raw data</p>
DirtyGrid: 3D dust radiative transfer modeling of spectral energy distributions of dusty stellar populations
<p>Output global SEDs of a large grid of 3D stellar+dust radiative transfer models spanning the range of star formation and dust contents of regions of galaxies.</p> <p>Paper describing the DirtyGrid is Law, Gordo, & Misset (2018, ApJ, submitted)</p> <p>Code to make to access this data at: https://github.com/karllark/pydirtygrid</p>
Lifespan and fecundity data for: The evolutionary potential of diet-dependent effects on lifespan and fecundity in a multi-parental population of Drosophila melanogaster
<p>This repository contains 3 original data files for a study of heritability in a half-sibling design of outbred multi-parent population of Drosophila melanogaster treated with 3 nutritional conditions.</p> <p> </p> <p>1) lifespan_only.xlsx contains lifespan records.</p> <p>Columns:</p> <p>setDate, start date</p> <p>flipDate, observation date</p> <p>days, age</p> <p>fID, identity of line</p> <p>repl, replicate number</p> <p>treat, diet treatment (HS=high sugar, STD=standard, LY=low yeast)</p> <p>NstartF, starting number of females</p> <p>NstartM, starting number of males</p> <p>box, ccord, rcoord are position coordinates of a vial in a holding box</p> <p>deadF, number of female dead</p> <p>deadM, number of males dead</p> <p>cens, censored events</p> <p>carriedF, dead females that flip to a new food vial</p> <p>carriedM, dead males that flip to a new food vial</p> <p>flipper initials of observer</p> <p>time, time in which 108 vials were flipped.</p> <p> </p> <p>2) feclife_with-image-ids.xlsx - lifespan observation vials are matched with a specific image of eggs collected at a specific day (once a week).</p> <p>Columns:</p> <p>cameraid, image id assigned by the camera</p> <p>handcounted, images counted by hand</p> <p>handcount, number of eggs on an image counted by hand</p> <p>training_set, images that were used to develop and test a prediction model</p> <p>drop_from_lifespan & visually_recheck, quality control.</p> <p> </p> <p>3) egg_images.tgz - all original images. Each image represent fecundity from a single vial at a specific date (in feclife_with-image-ids.xlsx ). The images have been cropped to remove the excess area outside the egg disc. The area outside the circular disc has been converted to black to peripheral eliminate noise.</p>
Genotype data of 10 nuclear microsatellite loci for 30 Quercus acutissima populations in China
<p>This dataset includes genotype data of 10 nuclear microsatellite loci for 707 individuals of Quercus acutissima from 30 natural populations in China.</p>
nSSR genotype data for 30 Quercus acutissima populations and 18 Quercus chenii populations in China
<p>This dataset includes genotype data of seven nSSR loci for 696 individuals of Quercus acutissima from 30 natural populations, and 415 individuals of Quercus chenii from 18 natural populations in China.</p>
LD Estimated from 1k Genomes CEU Population
<p>These data contain estimated pairwise r^2 for variants with allele frequency greater than 0.05 in the 1000 Genomes CEU population. They were estimated using LDshrink (https://github.com/stephenslab/LDshrink). R^2 is only reported when the estimate is greater than 0.1.</p> <p> </p> <p>For each chromosome there are two files:</p> <p>chr<chr>_AF0.5_0.1.RDS is an R object containing a data frame with three columns: rowsnp, colsnp, and r2</p> <p>chr<chr>_AF0.5_snpdata.RDS is an R object containing a data frame with information for every SNP meeting the allele frequency cutoff.</p>
Dakar population estimates at 100x100m spatial resolution - grid layer - Dasymetric mapping
<p>This dataset contains the a raster layer with the population estimates obtained using a dasymetric mapping procedure (top-down approach). For a detailed description of the methodology, please refer to the following paper:</p> <p>Grippa, Taïs, Catherine Linard, Moritz Lennert, Stefanos Georganos, Nicholus Mboga, Sabine Vanhuysse, Assane Gadiaga, and Eléonore Wolff. 2019. “Improving Urban Population Distribution Models with Very-High Resolution Satellite Information.” <em>Data</em> 4 (1): 13. <a href="https://doi.org/10.3390/data4010013">https://doi.org/10.3390/data4010013</a>.</p> <p>Funding and aknowledgement: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be/">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be/">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p> <p>The authors gratefully thanks the \href{http://assess-sn.org/}{ASSESS project}, funded by the \href{https://www.ares-ac.be}{ARES-CDD}, that provided the access to the census data.</p>
Intermittent reproduction, mortality patterns and lifetime breeding frequency of females in a population of the adder (Vipera berus)
<p>Raw data used in paper by Dirk Bauwens & Katja Claus. 2019. Intermittent reproduction, mortality patterns and lifetime breeding frequency of females in a population of the adder (Vipera berus). PeerJ, DOI 10.7717/peerj.6912</p> <p>CH_data_AdultFemaleAdders.INP</p> <p>A text file with extension .INP for input in the program MARK (White & Burnham, 1999; Cooch & White, 2015)), containing the capture-recapture histories, including breeding status of individual adult female adders (n = 908). Each 18-digit string contains a 1-digit score (B/N/0 ; Breeding / Non-breeding / not captured) per year of study (2000 – 2017).</p> <p>SVL_BCI_RawData.xlsx</p> <p>Excell-file containing data for SVL, mass, BCI (body condition index) and breeding status (breeding/non-breeding) for individual females captured and measured during spring (March – half May; i.e., before ovulation) or late summer (August-September; i.e. after parturition or at end of activity season).</p> <p> </p>
3D cranial landmark coordinates from the Athens Collection. A modern Greek population reference dataset for testing 3D-ID software
<p>The present dataset comprises the landmark coordinates of 158 intact crania (80 males and 78 females) of adult individuals from the Athens Collection. The 3D coordinates of up to 34 landmarks have been extracted from high quality textured 3D models produced with photogrammetry. The dataset aims to evaluate the correct classification performance of 3D-ID software. Hence, the dataset contains the landmark 3D coordinates both in Meshlab's PickedPoints files (.pp) but also in 3D-ID's input text format (.3did). The dataset is accompanied by certain GNU Octave scripts and functions used for data conversion and integrity check. For more details see the Dataset Description pdf.</p>
MAPNET Population Genomics Workshop Content: Whole genome sequencing of Bactericera cockerelli
<p>Materials for the Workshop "Getting Started with Low Coverage Population Genomics" , Otago University, 22 March 2019. Alignment files for whole-genome sequence of N=143 Tomato and Potato Psyllids (<em>Bactericera cockerelli)</em> from 4 New Zealand , 1 Californian and 1 Honduran populations. Data files represent 250 kbp of one scaffold of a draft assembly.</p> <p>This data was generated by the Plant and Food Research in research programme funded by the Ministry of Business, Innovation and Employment (MBIE) . </p> <p> </p> <p><a href="https://github.com/MapNetNZ/Pop-Genomics-Workshop2019">Wokshop GitHub Repo</a></p> <p> </p> <p> </p>
Population estimation from mobile network traffic metadata
<p><em><strong>Please cite our paper if you publish material based on those datasets</strong></em></p> <blockquote> <p>G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata", in IEEE Trans. on Mobile Computing, 2018 (in Press). <a href="http://dx.doi.org/10.1109/TMC.2018.2871156">10.1109/TMC.2018.2871156</a></p> </blockquote> <p> </p> <p><strong>Abstract</strong></p> <p>Communication-enabled devices that are physically carried by individuals are today pervasive,<br> which opens unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. We show that our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. Specifically, it achieves higher accuracy than that granted by previous equivalent solutions. In addition, our approach enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. Our results build on significant real-world mobile network metadata and relevant ground-truth information in multiple urban scenarios.</p> <p><strong>Dataset Columns</strong></p> <p>This dataset cover one month of data taken during the month of April 2015 for three Italian cities: Rome, Milan, Turin. The raw data has been provided during the Telecom Italia Big Data Challenge (http://www.telecomitalia.com/tit/en/innovazione/archivio/big-data-challenge-2015.html)</p> <p>1. <strong>grid_id</strong>: the coordinate of the grid can be retrieved with the shapefile of a given city<br> 2. <strong>date</strong>: format Y-M-D H:M:S<br> 4. <strong>landuse_label</strong>: the land use label has been computed by through method described in [2]<br> 5. <strong>population</strong>: Census population of a given grid block as defined by the Istituto nazionale di statistica (ISTAT https://www.istat.it/en/censuses) in 2011<br> 6. <strong>estimation</strong>: Dynamics density population estimation (in person) as the result of the method described in [1]<br> 7. <strong>area</strong>: surface of the "grid id" considered in km^2<br> 8. <strong>geometry</strong>: the shape of the area considered with the EPSG:3003 coordinate system (only with quilt)</p> <p><strong>Note</strong></p> <p>Due to legal constraints, we cannot share directly the original data from the Telecom Italia Big Data Challenge we used to build this dataset.</p> <p><strong>Easy access to this dataset with quilt</strong></p> <p>Install the dataset repository:</p> <p>$ quilt install vgauthier/DynamicPopEstimate</p> <p>Use the dataset with a Panda Dataframe</p> <p>>>> from quilt.data.vgauthier import DynamicPopEstimate<br> >>> import pandas as pd<br> >>> df = pd.DataFrame(DynamicPopEstimate.rome())<br> <br> Use the dataset with a GeoPanda Dataframe<br> <br> >>> from quilt.data.vgauthier import DynamicPopEstimate<br> >>> import geopandas as gpd<br> >>> df = gpd.DataFrame(DynamicPopEstimate.rome())</p> <p><strong>References</strong></p> <p>[1] G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Population estimation from mobile network traffic metadata", in proc of the 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1 - 9, 2016. </p> <p>[2] A. Furno, M. Fiore, R. Stanica, C. Ziemlicki, and Z. Smoreda, "A tale of ten cities: Characterizing signatures of mobile traffic in urban areas," IEEE Transactions on Mobile Computing, Volume: 16, Issue: 10, 2017.<br> </p>
On the Observability of Individual Population III Stars and Their Stellar-mass Black Hole Accretion Disks through Cluster Caustic Transits
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018ApJS..234...41W/abstract">On the Observability of Individual Population III Stars and Their Stellar-mass Black Hole Accretion Disks through Cluster Caustic Transits</a></p>
Formation pathway of Population III coalescing binary black holes through stable mass transfer
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017MNRAS.468.5020I">Inayoshi et al. (2017)</a>. MESA version 8845.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stx757">10.1093/mnras/stx757</a></p>
VCF File containg replicate RAD-seq genotype calls for four Populs alba x Populus tremula hybrids.
<p>VCF file used to estimate RAD-seq genotyping errors in Bresadola et al. (2019).</p>
ScienceDex guides
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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.