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19,493 results for “Populism”
Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"
<p>City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics.</p> <p>To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: <strong><em><span>Escherichia coli</span></em></strong>, the emerging pathogen <strong><em><span>Streptococcus suis</span></em></strong>, and the sewer-inhabiting <strong><em><span>Arcobacter cryaerophilus</span></em></strong>. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin).<span> Using</span> these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.</p> <p> </p> <p><strong>Usage</strong></p> <p>Users can perform the analysis by running the R script (TC3D.R) after the installation of all</p> <p>package mentioned in the preamble,<span> </span></p> <p>This folder contains:</p> <p>- 3 datasets with OD measures for the 3 species:</p> <p><span> </span>* data_acrya.xlsx</p> <p><span> </span>* data_ecoli.xlsx</p> <p><span> </span>* data_ssuis.xlsx</p> <p>- 1 excel files with metadata (plate, well, species, environmental conditions)</p> <p><span> </span>* map_plate_all.xlsx</p> <p>- 4 datasets giving time series of the flow and several measures including <span> </span>conductivity and <span> </span>temperaturefor 2 sewers of Barcelona obtained from sample cabines <span> </span>set during the implementation of SCOREWATER (ID:820751)</p> <p><span> </span>* carmel_flow.csv</p> <p><span> </span>* carmel_quality.csv</p> <p><span> </span>* poblenou_flow.csv</p> <p><span> </span>* poblenou_quality.csv</p> <p>- 1 C++ script compiled and run with the R TMB package:</p> <p><span> </span>* fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.</p> <p><br><br></p>
Activity based synthetic population of residents for Gothenburg, Sweden
<p>A synthetic population is a distribution of synthetic agents that replicates the demographic distribution of a real-world population according to census records.<br>This dataset contains a synthetic population of residents in the city of Gothenburg in Sweden, along with activity schedules and mobility patterns for 2019. The synthetic population model is designed for applications in neighbourhood planning and includes detailed replicas of people in different neighbourhoods of Gothenburg organized as persons, households, houses, buildings, and daily activity chains. While the persons, households, and houses are synthetic replicas, they are connected to existing buildings.<br>The model considers the allocation of primary and secondary locations based on a gravity model, realistic routing for active, public and private motorised modes of transportation and allows users to introduce new buildings and amenities if needed. The population data is provided as an SQLite3 database file for each neighbourhood of Gothenburg.</p>
First Study of the Supernova Remnant Population in the Large Magellanic Cloud with eROSITA
<p>Aims. The all-sky survey carried out by the extended Roentgen Survey with an Imaging Telescope Array (eROSITA) on board Spektrum-Roentgen-Gamma (Spektr-RG, SRG) has provided us with spatially and spectrally resolved X-ray data of the entire Large Magellanic Cloud (LMC) and its immediate surroundings in the soft X-ray band down to 0.2 keV with an average angular resolution</p> <p>of 26′′ in the field of view. In this work, we have studied the supernova remnants (SNRs) and candidates in the LMC using data from the first four all-sky surveys (eRASS:4). From the X-ray data in combination with results at other wavelengths, we obtain information about the SNRs, their progenitors, and the surrounding interstellar medium (ISM). The study of the entire population of SNRs in a galaxy helps us to understand the underlying stellar populations, the environments, in which the SNRs are evolving, and the stellar feedback on the ISM.</p> <p>Methods. The eROSITA telescopes are the best instruments currently available for the study of extended soft sources like SNRs in an entire galaxy due to their large field of view and high sensitivity in the softer part of the X-ray band. We applied the Gaussian gradient magnitude (GGM) filter to the eROSITA images of the LMC to highlight the edges of the shocked gas in order to find new SNRs. We visually compared the X-ray images with those of their optical and radio counterparts to investigate the true nature of the extended emission. The X-ray emission is evaluated using the contours with respect to the background, while for the optical we used line ratio</p> <p>diagnostics, and non-thermal emission in the radio images. We used the Magellanic Cloud Emission Line Survey (MCELS) for the optical data. For the radio comparison, we used data from the Australian Square Kilometre Array Pathfinder (ASKAP) survey of the LMC. Using the star formation history (SFH) derived from the near-IR photometry of the VISTA survey of the Magellanic Clouds (VMC) we have investigated the possible progenitor type of the new SNRs and SNR candidates in our sample. Results. We present the most updated catalogue of SNRs in the LMC. Previously known SNRs and candidates were detected with 1σ significance down to a surface brightness of Σ [0.2–5.0 keV] = 3.0 × 10−15 erg s−1 cm−2 arcmin−2 and were examined. The eROSITA data have allowed us to confirm one of the previous candidates as an SNR. We confirm three newly detected extended sources as new SNRs, while we propose 13 extended sources as new X-ray SNR candidates. We also present the analysis of the follow-up</p> <p>XMM-Newton observation of MCSNR J0456–6533 discovered with eROSITA. Among the new candidates, we propose J0614–7251 (4eRASSU J061438.1−725112) as the first X-ray SNR candidate in the outskirts of the LMC.</p>
Genome data and resources on the recombination landscape and population history of the harlequin fly
<p>This dataset contains phased vcf files of <em>Chironomus riparius, </em>ouput files of RepeatMasker, MELT, RepeatOBserver, MSMC2, iSMC and bedtools, such as supporting files. </p> <p>For further details also check the GitHub page: <a href="https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024" target="_blank" rel="noopener">https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024</a></p> <ul> <li><strong>phased-vcfs: </strong>Artificially phased vcf-files of five populations with four individuals each. Needed to generate multihetsep files. Input files for iSMC.<br> <ul> <li>Hesse in Germany = MG</li> <li>Rhône-Alpes in France = MF</li> <li>Lorraine in France = NMF</li> <li>Piemont in Italy = SI</li> <li>Andalusia in Spain = SS</li> </ul> </li> <li><strong>multihetsep-files: </strong>Created with msmc-tools. Input files for MSMC2. </li> <li><strong>RepeatMasker: </strong>Raw output of RepeatMasker run. Summary file and file with filtered <em>Cla</em>-element (a transposable element) included.<strong><br></strong></li> <li><strong>MELT: </strong>MELT ouput with added info on population and numbered insertions reflecting all 441 detected <em>Cla </em>insertions.<strong><br></strong></li> <li><strong>RepeatOBserver: </strong>Summary files on centromere predictions based on histograms and Shannon Diversity from RepeatOBserver. Genome-wise Shannon Diversity per chromosome included. <strong><br></strong></li> <li><strong>MSMC2: </strong>Raw ouput of combined cross-coalescence and mean values if MSMC2 per populations. <strong><br></strong></li> <li><strong>iSMC: </strong>Recombination rate rho in 10 kb windows and 100 kb windows along the genome. <strong><br></strong></li> <li><strong>bedtools closest ismc 10 kb: </strong>Bedtools closest analysis of the distance of the next <em>Cla</em>-element to the recombination rate rho in 10 kb windows.<strong><br></strong></li> <li><strong>bedtools closest ismc 100 kb: </strong>Bedtools closest analysis of the distance of the next <em>Cla</em>-element to the recombination rate rho in 100 kb windows.</li> <li><strong>input-files figures: </strong>Supporting files needed to create figures.<strong><br></strong></li> </ul>
Van Dijk et al. (2021), A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050, data and scripts
<p>This repository contains all data and R scripts to reproduce the figures in Van Dijk et al. (2021), A meta-analysis of global food demand and population at risk of hunger projections for the period 2010-2050, Nature Food. More specifically, it includes two databases: (1) A database with standardized information to describe the characteristics of 57 studies that were identified by the systematic literature review and (2) The Global Food Security Projections Database v1.0.1 with harmonized projections for three global food security indicators: food consumption in kcal per capita and total kcal, and population at risk of hunger. The database also includes projections for total global population that are required to derive the global food security indicators.</p> <p>The two scripts (nf_figures.r and nf_meta_regression.r) can be used to reproduce the figures and tables in the main paper and the supplementary information. Please start with the first script, which sources the second script. </p> <p>This is the first version of the Global Food Projections Database. We expect to update the data, including additional studies and variables in the future. For issues and suggestions, please contact michiel.vandijk@wur.nl.</p> <p> </p>
GWAS to single cell: Intersecting single-cell transcriptomics and genome wide association studies identifies crucial cell-populations and candidate genes for atherosclerosis.
<p><strong>Background</strong></p> <p>Genome-wide association studies (GWAS) have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation of susceptibility loci into biological mechanisms and targets for drug discovery remains challenging. Intersecting genetic and gene expression data has led to identification of candidate genes. However, the assayed tissues are often non-diseased and heterogeneous in cell composition confounding the candidate prioritization. We collected single-cell transcriptomics (scRNA-seq) from atherosclerotic plaques and aimed to identify cell-type-specific expression of disease-associated genes. </p> <p> </p> <p><strong>Methods and Results</strong></p> <p>To identify disease-associated candidate genes, we applied gene-based analyses using GWAS summary statistics from 46 atherosclerotic, cardiometabolic, and other traits. Next we intersected these candidates with single-cell transcriptomics (scRNA-seq) to identify those genes that are specifically expressed in individual cell (sub)populations of atherosclerotic plaques. We derive an enrichment score and show that loci that associated with coronary artery disease demonstrated a prominent substrate in plaque smooth muscle cells (<em>SKI</em>, <em>KANK2</em>, <em>SORT1</em>), endothelial cells (<em>SLC44A1</em>, <em>ATP2B1</em>), and macrophages (<em>APOE</em>, <em>HNRNPUL1</em>). Further sub clustering of SMC-subtypes revealed genes in risk loci for coronary calcification specifically enriched in a synthetic cluster of SMCs. To verify the robustness of our approach, we used liver-derived scRNAseq-data and showed enrichment of circulating lipids-associated loci in hepatocytes.</p> <p><br> <strong>Conclusion</strong></p> <p>We confirm known gene-cell pairs relevant for atherosclerotic disease, and discovered novel pairs pointing to new biological mechanisms amenable for therapy. We present an intuitive single-cell transcriptomics driven workflow rooted in human large-scale genetic studies to identify putative candidate genes and affected cells associated with cardiovascular traits.</p> <p> </p>
REPIN population analysis in 42 Pseudomonas chlororaphis genomes
<p>This dataset is the output of RAREFAN (http://rarefan.evolbio.mpg.de/) a webserver to identify REPIN populations across an entire bacterial species. The data was created using the following command "java -jar -Xmx10g rarefan.jar chlororaphis/in/ chlororaphis/out/ chlTAMOak81.fas 55 21 chlororaphis/in/yafM_SBW25.faa chlororaphis.nwk 1e-30 true 1"</p> <p>All input files are located in the folder chlororaphis/in/, all output data is located in chlororaphis/out/.</p> <p>The input files include the 42 fasta formatted <em>P. chlororaphis</em> genome files (*.fas) and a RAYT protein sequence called yafM_SBW25.faa.</p> <p>The output files include the following:</p> <p>A phylogenetic tree "chlororaphis.nwk" of all genomes generated with andi (<a href="http://github.com/evolbioinf/andi/">http://github.com/evolbioinf/andi/</a>) and clustDist (http://guanine.evolbio.mpg.de/problemsBook/node1.html).</p> <p>A file containing the frequencies of all 21bp long sequences found in the TAMOak81 genome: chlTAMOak81.wfr</p> <p>A file containing all 21bp long sequences that occur more frequently than 55 times in the TAMOak81 genome: chlTAMOak81.overrep</p> <p>A file containing information on the RAYTs and their cooccurrence with different REPIN populations: prox.stats</p> <p>A file containing the nucleotide sequences of all yafM_SBW25.faa relatives identified with BLAST+ in the <em>P. chlororaphis</em> species: yafM_relatives.fna</p> <p>maxREPIN_[0-5] Contains the most frequent REPIN identified for each sequence type in each <em>P. chlororaphis</em> strain.</p> <p> presAbs_[0-5].txt Contains for each strain information on the number of RAYTs, the number of REPINs, the master sequence, the number of master sequences, the entire REP/REPIN population size, the number of REPIN clusters that contain more than 10 sequences, all REPINs in the population as well as all all REPINs that differ to the master sequences in at most three nucleotides.</p> <p>rayt_[strain name].tab contains location information for each identified RAYT relative for each strain. The files can be viewed with artemis.</p> <p>results.txt contains for each strain the frequency of the six identified 21bp long seeds.</p> <p>There is one folder called groupSeedSequences, which includes the data for identifying the most common 21 bp long sequences in <em>P. chlororaphis</em> TAMOak81. All 21bp long sequences in the genome that occur more frequently than 55 times are sorted into 6 sequence groups. These sequence groups are stored in the files Group_chlTAMOak81_*.out and .out.fas. There is also a chlTAMOak81_words.tab file, which contains the locations of all overrepresented 21bp long sequences in the TAMOak81 genome. This file can be viewed in artemis (https://www.sanger.ac.uk/tool/artemis/) together with the TAMOak81 genome file. The most common sequence in each group is used as a seed sequence to determine REPIN populations across all 42 genomes.</p> <p> </p> <p>For each genome there are six output folders (ending in _0 to _5), for each sequence group one.</p> <p>Each folder contains the following files:</p> <p>*.dd: Degree distribution of the REPIN network, where each REPIN is a node. A REPIN is connected to another REPIN if they differ in exactly one position. The degree distribution is a histogram of the number of connections of all the nodes. </p> <p>*.hist For the largest sequence cluster determined by mcl that consists of REPINs (two REPs in inverted orientation) this file contains the number of REPINs in each sequence class. Sequence class 0 is the master sequence. By definition the most common REPIN in the sequence population. Sequence class 1 contains all REPINs differing in exactly one position to the master sequence. Sequence class 2 contains REPINs differing in 2 positions etc.</p> <p>*.mcl Contains the clustering output by mcl. Each line contains the member of a cluster. Lines are sorted by cluster size.</p> <p>*.mw Contains the most common 21bp long sequence and its frequency in the genome, which is the basis for identifying first all related REP sequences and from those the REPINs formed by these REP sequences.</p> <p>*.nodes The identity and frequency of all REPINs and REP sequences for either all sequences or only for the largest sequence cluster.</p> <p>*.ss Contains REPINs and REP sequences as well as their positions in fasta format. Position information starts with the location in genome fasta file (first sequence is 0...) followed by the start and end position of the entire REPIN/REP sequence. </p> <p>*.ss.REP REP sequence information in fasta format.</p> <p>*.tab Location in tab format. Can be used to display locations of REPs and REPINs in the genome via artemis.</p> <p>*_[0-9].ss Contains REPIN/REP sequence information for each subcluster separately.</p> <p>*_[0-9].tab Contains the location of REP/REPINs for each subcluster separately for viewing in artemis.</p> <p>*allSeed.nw Contains network connections between nodes of all sequences. Can be used to view network in for example R or cytoscape together with the nodes file.</p> <p>*largestCluster.nodes Information on nodes only from the largest REPIN cluster.</p> <p>*largestCluster.ss *.ss file for the largest REPIN cluster.</p> <p>*largestCluster.tab *.tab file for the largest REPIN cluster.</p> <p>*_rayt_repin_prox.txt shows which REPIN/REP cluster is in proximity to any of the RAYT genes identified in the genome (within 200bp).</p> <p>And a subfolder that contains the complete sequences (including the variable region) for all identified REPs and REPINs.</p> <p><strong>The dataset was generated using the following external tools:</strong></p> <p>andi for tree building:</p> <p>B Haubold, F Klötzl, and P Pfaffelhuber. <strong>andi: fast and accurate estimation of evolutionary distances between closely related genomes.</strong> Bioinformatics, 2015 vol. 31 (8) pp. 1169-1175.</p> <p>MCL for REPIN population clustering:</p> <p>A J Enright, S Van Dongen, and C A Ouzounis. <strong>An efficient algorithm for large-scale detection of protein families.</strong> Nucleic Acids Research, 2002 vol. 30 (7) pp. 1575-1584.</p> <p>BLAST+ for identifying RAYT relatives in the different genomes:</p> <p>C Camacho, G Coulouris, V Avagyan, N Ma, J Papadopoulos, K Bealer, and T L Madden. <strong>BLAST+: architecture and applications.</strong> BMC Bioinformatics, 2009 vol. 10 (1) pp. 421-9.</p>
REPIN population analysis in 130 Neisseria meningitidis and N. gonorrhoeae genomes
<p>This dataset is the output of RAREFAN (http://rarefan.evolbio.mpg.de/) a webserver to identify REPIN populations across an entire bacterial species. The data was created using the following command "java -jar -Xmx10g rarefan.jar neisseria/in/ neisseria/out/ Nmen_2594.fas 55 21 neisseria/in/NMAA_0235.faa neisseria.nwk 1e-80 true 1"</p> <p>All input files are located in the folder neisseria/in/, all output data is located in neisseria/out/.</p> <p>The input files include the 130 fasta formatted <em>Neisseria</em> genome files (*.fas) and a RAYT protein sequence called NMAA_0235.faa.</p> <p>The output files include the following:</p> <p>A phylogenetic tree "neisseria.nwk" of all genomes generated with andi (<a href="http://github.com/evolbioinf/andi/">http://github.com/evolbioinf/andi/</a>) and clustDist (http://guanine.evolbio.mpg.de/problemsBook/node1.html).</p> <p>A file containing the frequencies of all 21bp long sequences found in the Nmen_2594 genome: Nmen_2594.wfr</p> <p>A file containing all 21bp long sequences that occur more frequently than 55 times in the Nmen_2594 genome: Nmen_2594.overrep</p> <p>A file containing information on the RAYTs and their cooccurrence with different REPIN populations: prox.stats</p> <p>A file containing the nucleotide sequences of all NMAA_0235.faa relatives identified with BLAST+ in the <em>Neisseria </em>species: yafM_relatives.fna</p> <p>maxREPIN_[0-5] Contains the most frequent REPIN identified for each sequence type in each <em>Neisseria</em> strain.</p> <p> presAbs_[0-5].txt Contains for each strain information on the number of RAYTs, the number of REPINs, the master sequence, the number of master sequences, the entire REP/REPIN population size, the number of REPIN clusters that contain more than 10 sequences, all REPINs in the population as well as all REPINs that differ to the master sequences in at most three nucleotides.</p> <p>rayt_[strain name].tab contains location information for each identified RAYT relative for each strain. The files can be viewed with artemis.</p> <p>results.txt contains for each strain the frequency of the six identified 21bp long seeds.</p> <p>There is one folder called groupSeedSequences, which includes the data for identifying the most common 21 bp long sequences in <em>Neisseria meningitidis</em> WUE 2594. All 21bp long sequences in the genome that occur more frequently than 55 times are sorted into 6 sequence groups. These sequence groups are stored in the files Group_Nmen_2594_*.out and .out.fas. There is also a Nmen_2594_words.tab file, which contains the locations of all overrepresented 21bp long sequences in the Nmen_2594 genome. This file can be viewed in artemis (https://www.sanger.ac.uk/tool/artemis/) together with the Nmen_2594 genome file. The most common sequence in each group is used as a seed sequence to determine REPIN populations across all 130 genomes.</p> <p> </p> <p>For each genome there are six output folders (ending in _0 to _5), for each sequence group one.</p> <p>Each folder contains the following files:</p> <p>*.dd: Degree distribution of the REPIN network, where each REPIN is a node. A REPIN is connected to another REPIN if they differ in exactly one position. The degree distribution is a histogram of the number of connections of all the nodes. </p> <p>*.hist For the largest sequence cluster determined by mcl that consists of REPINs (two REPs in inverted orientation) this file contains the number of REPINs in each sequence class. Sequence class 0 is the master sequence. By definition the most common REPIN in the sequence population. Sequence class 1 contains all REPINs differing in exactly one position to the master sequence. Sequence class 2 contains REPINs differing in 2 positions etc.</p> <p>*.mcl Contains the clustering output by mcl. Each line contains the member of a cluster. Lines are sorted by cluster size.</p> <p>*.mw Contains the most common 21bp long sequence and its frequency in the genome, which is the basis for identifying first all related REP sequences and from those the REPINs formed by these REP sequences.</p> <p>*.nodes The identity and frequency of all REPINs and REP sequences for either all sequences or only for the largest sequence cluster.</p> <p>*.ss Contains REPINs and REP sequences as well as their positions in fasta format. Position information starts with the location in genome fasta file (first sequence is 0...) followed by the start and end position of the entire REPIN/REP sequence. </p> <p>*.ss.REP REP sequence information in fasta format.</p> <p>*.tab Location in tab format. Can be used to display locations of REPs and REPINs in the genome via artemis.</p> <p>*_[0-9].ss Contains REPIN/REP sequence information for each subcluster separately.</p> <p>*_[0-9].tab Contains the location of REP/REPINs for each subcluster separately for viewing in artemis.</p> <p>*allSeed.nw Contains network connections between nodes of all sequences. Can be used to view network in for example R or cytoscape together with the nodes file.</p> <p>*largestCluster.nodes Information on nodes only from the largest REPIN cluster.</p> <p>*largestCluster.ss *.ss file for the largest REPIN cluster.</p> <p>*largestCluster.tab *.tab file for the largest REPIN cluster.</p> <p>*_rayt_repin_prox.txt shows which REPIN/REP cluster is in proximity to any of the RAYT genes identified in the genome (within 200bp).</p> <p>And a subfolder that contains the complete sequences (including the variable region) for all identified REPs and REPINs.</p> <p>The dataset was generated using the following external tools:</p> <p>andi for tree building:</p> <p>B Haubold, F Klötzl, and P Pfaffelhuber. <strong>andi: fast and accurate estimation of evolutionary distances between closely related genomes.</strong> Bioinformatics, 2015 vol. 31 (8) pp. 1169-1175.</p> <p>MCL for REPIN population clustering:</p> <p>A J Enright, S Van Dongen, and C A Ouzounis. <strong>An efficient algorithm for large-scale detection of protein families.</strong> Nucleic Acids Research, 2002 vol. 30 (7) pp. 1575-1584.</p> <p>BLAST+ for identifying RAYT relatives in the different genomes:</p> <p>C Camacho, G Coulouris, V Avagyan, N Ma, J Papadopoulos, K Bealer, and T L Madden. <strong>BLAST+: architecture and applications.</strong> BMC Bioinformatics, 2009 vol. 10 (1) pp. 421-9.</p>
1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate
<p>1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate.</p> <p> </p> <p>How to use</p> <pre>from pathlib import Path import requests import pandas as pd url = ("https://raw.githubusercontent.com/" "epogrebnyak/ru-cities/main/assets/towns.csv") # save file locally p = Path("towns.csv") if not p.exists(): content = requests.get(url).text p.write_text(content, encoding="utf-8") # read as dataframe df = pd.read_csv("towns.csv") print(df.sample(5))</pre> <p> </p> <p>Files:</p> <ul> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/towns.csv">towns.csv</a> - city information</li> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/regions.csv">regions.csv</a> - list of Russian Federation regions</li> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/alt_city_names.json">alt_city_names.json</a> - alternative city names</li> </ul> <p> </p> <p>Сolumns (towns.csv):</p> <p>Basic info:</p> <ul> <li><code>city</code> - city name (several cities have alternative names marked in <code>alt_city_names.json</code>)</li> <li><code>population</code> - city population, thousand people, Rosstat estimate as of 1.1.2020</li> <li><code>lat,lon</code> - city geographic coordinates</li> </ul> <p>Region:</p> <ul> <li><code>region_name</code> - subnational region (oblast, republic, krai or AO)</li> <li><code>region_iso_code</code> - <a href="https://en.wikipedia.org/wiki/ISO_3166-2:RU">ISO 3166 code</a>, eg <code>RU-VLD</code></li> <li><code>federal_district</code>, eg <code>Центральный</code></li> </ul> <p>City codes:</p> <ul> <li><code>okato</code></li> <li><code>oktmo</code></li> <li><code>fias_id</code></li> <li><code>kladr_id</code></li> </ul> <p> </p> <p>Data sources</p> <ul> <li>City list and city population collected from Rosstat publication <a href="https://rosstat.gov.ru/folder/210/document/13206">Регионы России. Основные социально-экономические показатели городов</a> and parsed from publication Microsoft Word files.</li> <li>City list corresponds to <a href="https://ru.wikipedia.org/wiki/%D0%A1%D0%BF%D0%B8%D1%81%D0%BE%D0%BA_%D0%B3%D0%BE%D1%80%D0%BE%D0%B4%D0%BE%D0%B2_%D0%A0%D0%BE%D1%81%D1%81%D0%B8%D0%B8">this Wikipedia article</a>.</li> <li>Alternative dataset is <a href="https://github.com/hflabs/city">wiki-based Dadata city dataset</a> (no population data).</li> </ul> <p> </p> <p>Comments</p> <p> </p> <p>City groups</p> <ul> <li> <p><code>Ханты-Мансийский</code> and <code>Ямало-Ненецкий</code> autonomous regions excluded to avoid duplication as parts of <code>Тюменская область</code>.</p> </li> <li> <p>Several notable towns are classified as administrative part of larger cities (<code>Сестрорецк</code> is a municpality at Saint-Petersburg, <code>Щербинка</code> part of Moscow). They are not and not reported in this dataset.</p> </li> </ul> <p> </p> <p>By individual city</p> <ul> <li><code>Белоозерский</code> not found in Rosstat publication, but <a href="https://github.com/epogrebnyak/ru-cities/issues/5#issuecomment-886179980">should be considered a city as of 1.1.2020</a></li> </ul> <p> </p> <p>Alternative city names</p> <ul> <li> <p>We suppressed letter "ё" <code>city</code> columns in towns.csv - we have <code>Орел</code>, but not <code>Орёл</code>. This affected:</p> <ul> <li><code>Белоозёрский</code></li> <li><code>Королёв</code></li> <li><code>Ликино-Дулёво</code></li> <li><code>Озёры</code></li> <li><code>Щёлково</code></li> <li><code>Орёл</code></li> </ul> </li> <li> <p><code>Дмитриев</code> and <code>Дмитриев-Льговский</code> are the same city.</p> </li> </ul> <p><code>assets/alt_city_names.json</code> contains these names.</p> <p> </p> <p>Tests</p> <pre><code>poetry install poetry run python -m pytest </code></pre> <p> </p> <p>How to replicate dataset</p> <p> </p> <p>1. Base dataset</p> <p>Run:</p> <ul> <li>download data stro rar/get.sh</li> <li>convert <code>Саратовская область.doc</code> to docx</li> <li>run make.py</li> </ul> <p>Creates:</p> <ul> <li><code>_towns.csv</code></li> <li><code>assets/regions.csv</code></li> </ul> <p> </p> <p>2. API calls</p> <p>Note: do not attempt if you do not have to - this runs a while and loads third-party API access.</p> <p>You have the resulting files in repo, so probably does not need to these scripts.</p> <p>Run:</p> <ul> <li><code>cd geocoding</code></li> <li>run coord_dadata.py (needs token)</li> <li>run coord_osm.py</li> </ul> <p>Creates:</p> <ul> <li>coord_dadata.csv</li> <li>coord_osm.csv</li> </ul> <p> </p> <p>3. Merge data</p> <p>Run:</p> <ul> <li>run merge.py</li> </ul> <p>Creates:</p> <ul> <li>assets/towns.csv</li> </ul> <p> </p>
Proactive COVID-19 testing in a partially vaccinated population.
<p>Complete simulation-generated datasets analyzed in McGee et al. (2021) Proactive COVID-19 testing in a partially vaccinated population. medRxiv 2021.08.15.21262095.</p> <p>Data is uploaded in comma-separated .csv files which have been compressed using gzip. Descriptions of data columns can be found in the column_descriptions.csv file.</p>
Setting files from: Spatially explicit paleogenomic simulations support cohabitation with limited admixture between Bronze Age Central European populations.
<p><strong>Simulated Data and Custom Scripts</strong></p> <p>This dataset release permits to simulate the expansion of populations from the Pontic Steppes to Central Europe with the version of SPLATCHE3 which is included. There are 2 main zipped folders: i) the one called "SPLATCHE3executableAndSettings" contains a "ReadMe.txt" file that contains all required information to make the simulations: the resulting ".prop" file contains proportions of genomic ancestry of the P2 layer, ancestry from P1 layer is equal to 1-(proportion from P2) ; ii) the other one called "HowToMakeFigure2" contains the R script and the tables necessary to reproduce Figure 2. See Rio J, Quilodrán CS & Currat M., Communications Biology (2021), for background.</p> <p><strong>Acknowledgments</strong></p> <p>This project was financially supported by the Swiss National Research Foundation grants n° 31003A_182577 to MC and n° P400PB_183930 to CQ, as well as the IGE3 Student Salary Award to JR.</p>
Gerational issues in linking family farming production, traditional food in diet, physical activity and obesity in Pacific Islands countries and territories: the case of the Melanesian population on Lifou Island
<p>In the Melanesian culture, traditional activities are organized around family farming, although the lifestyle transition taking place over the last several decades has led to imbalances in diet and physical activity, with both leading to obesity. The aim of this interdisciplinary study was to understand the links between family farming (produced, exchanged, sold, and consumed food), diet (focused on produced, hunted, and caught food), physical activity (sedentary, light, and moderate-to-vigorous physical activity) and obesity in Melanesian Lifou Island families (parents and children). Forty families, including 142 adults and children, completed individual food frequency questionnaires, wore tri-axial accelerometers for seven continuous days, and had weight and height measured with a bio-impedance device. Qualitative and quantitative interviews were conducted at the household level concerning family farming practices and sociodemographic variables. Multinomial regression analyses and logistic regression models were used to analyze the data. Results showed that family farming production brings a modest contribution to diet and active lifestyles for the family farmers of Lifou Island. The drivers for obesity in these tribal communities were linked to diet in the adults, whereas parental socioeconomic status and moderate-to-vigorous physical activity were the main factors associated to overweight and obesity in children. These differences in lifestyle behaviors within families suggest a transition in cultural practices at the intergenerational level. Future directions should consider seasonality and a more in-depth analysis of diet including macro- and micro- nutrients to acquire more accurate information on the intergenerational transition in cultural practices and its consequences on health outcomes in the Pacific region.</p>
speaker populations of the languages targeted by translations of COVID 19 preventive measures
<p>The data are based on the list of the languages listed on the repository of the Endangered Languages Project (https://endangeredlanguagesproject.github.io/COVID-19) and on the figures found on ethnologue.org</p> <p> </p> <p> </p>
Datasets for 'Mandrake: visualising microbial population structure by embedding millions of genomes into a low-dimensional representation'
<p>Datasets for the paper '<strong>Mandrake: visualising microbial population structure by embedding millions of genomes into a low-dimensional representation</strong>'</p> <p>Files:</p> <ul> <li>616k* - Files for the analysis of 661k bacterial genomes from the SRA (note typo 616-661k). Includes mandrake output and input files (.npz)</li> <li>gps_acc - Files for the analysis of 20k S. pneumoniae accessory genomes from the GPS project. Original accessory matrix is gps_gene_presence_absence.Rtab</li> <li>sc2million_v1* - Files for the analysis of ~1M SARS-CoV-2 genomes. sc2million_v3.npz are the input distances.</li> <li>sce<commit hash>.qdrep - Nvidia systems profile of code at that commit hash</li> <li>sce<commit hash>.ncu-rep - Nvidia kernel profile of code at that commit hash</li> </ul>
Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America.
<p>Köppen - Geiger scripts and resulting datasets for the publication entitled "Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America." This scripts can be adapted to any geographic scale and region. Works with climate change scenarios.</p> <p>Original publication: <a href="https://doi.org/10.1016/j.gloplacha.2023.104155">https://doi.org/10.1016/j.gloplacha.2023.104155</a></p> <p>Dataset description</p> <p><strong>Scripts.rar</strong>: R Scripts used in this publication, as well they are reproducible</p> <p><strong>Readme_Köppen.txt</strong>: README file that explain the requisites and data formatting to run the scripts</p> <p><strong>Output datasets.zip</strong>: Output GIS datasets of this publication. Coordinate system GCS WGS 1984</p> <p> </p>
Stand structure and tree population dynamic attribute dataset of long abandoned strict forest reserves
<p>We provide an integrated dataset of two consecutive forest inventories, both containing plot-level, and individual tree-level data. The first provides the descriptions and measuring units (or categories) of plot-level variables (Table 1). The plot level table contains 233 records (rows), one for each selected permanent plot of six strict forest reserves located in Hungary. This dataset is georeferenced and contains information on inventories and basic stand structure attributes (Table_1_Plots ESRI shape format). </p> <p>The individual tree-level datasets were acquired by the sampling procedure, detailed in section 2.2. Species, dendrometric attributes, relative crown position, health, and decay status were documented for each tree belonging to the samples. Table 2 provides the descriptions and measuring units (or categories) of tree-level datasets in detail. Furthermore, it provides a tree history classification based on the interpretation of tree status changes. According to a simple scheme of the life and dead history of a tree, it could be classified into four main phases: establishment/regeneration phase; developmental phase; death and gradual decay of the tree trunk; terminated in decomposed/disintegrated state. The main events along these phases are ingrowth regeneration; death of the tree (mortality); disaggregation and decomposition of deadwood. We classify each sampled tree individuals into tree history categories (events and phases, Table 3) that can provide population dynamic aspects at stand level by appropriate tree aggregation functions.</p> <p>Relational link can be set between the plot-level and tree-level datasets based on the unique identification code of the site and sampling plots.</p>
Supplementary material for "Productivity drives the dynamics of a red kite source population that depends on immigration"
<p>Data files, code and custom functions for all analyses and figures presented in the paper. The seven data files are provided in csv format (CMRJuvRing.csv, CMRJuvDraht.csv, CMRAdDraht.csv, CMRAdSat.csv, RingRecoveries.csv, Bruten.csv, Condition.csv). The code file (RedKiteCode.txt) and the function file (Custom_functions.txt) are space delineated text files. The code file is written for R, but some models are run in JAGS from R. The code file also contains descriptions of the data files and code for data management.</p> <p> </p>
Data to support Whitney JL, Coleman RR, Deakos MH "Genomic evidence indicates small island-resident populations and sex-biased behaviors of Hawaiian Reef Manta Rays"
<p>Datasets supporting the manuscript: Whitney JL, Coleman RR, Deakos MH "Genomic evidence indicates small island-resident populations and sex-biased behaviors of Hawaiian Reef Manta Rays". <em>BMC Ecology and Evolution </em><strong>23</strong>, 31 (2023). https://doi.org/10.1186/s12862-023-02130-0</p> <p>Nuclear data:</p> <p>"Mobula-alfredi_nuclear_reference_RAD_contigs.fasta" is a fasta of 359,751 contigs that serve as the reference for nuclear alignment of genotypes to RAD loci. Contigs begin and end with GATC cut site.</p> <p>Mobula-alfredi_nuclear_all_2048snps_38genotypes.vcf is a VCF file with all 2048 nuclear SNPs in final filtered SNP dataset. 38 genotypes are included from Maui Nui and Hawaii Island. This 2048 SNPs includes both 2038 neutral and 10 outlier SNPs. </p> <p>Mobula-alfredi_nuclear_neutral_2038snps_38genotypes.vcf is a VCF file with 2038 neutral nuclear SNPs genotyped in 38 individuals from Maui Nui and Hawaii Island. </p> <p>Mobula-alfredi_nuclear_outliers_10snps_38genotypes.vcf is a VCF file with 10 outlier SNPs genotyped in 38 individuals from Maui Nui and Hawaii Island. </p> <p>Structure (.str) files are also provided in addition to VCFs. In all files Population prefixes M=Maui Nui and K=Hawaii Island. </p> <p>Mitochondrial data:</p> <p>Mobula-alfredi_mitogenome_34haplotypes_9sites_min4x.vcf is a VCF file with 9 variant sites across the mitogenome haplotyped in 34 individuals from Maui Nui and Hawaii Island. </p> <p>Mobula-alfredi_mitogenome_34haplotypes_allsites_min4x.fasta is a FASTA file with whole mitogenomes aligned to OP562409 [https://www.ncbi.nlm.nih.gov/nuccore/OP562409]. Sites with less than 4x coverage were masked with Ns. </p> <p>Mobula-alfredi_mitogenome_reference_OP562409.fasta is a FASTA file containing the <em>Mobula alfredi</em> reference mitogenome OP562409 [https://www.ncbi.nlm.nih.gov/nuccore/OP562409].</p> <p> </p>
Kabadayi_Boykov_Sefer_Gerrits_Ottoman_NFS_Gazetteer_23112022_16296_populated_places_version_1
<p>This gazetteer is a joint product of two research projects, UrbanOccupationsOETR (European Research Council - Starting Grant, Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000, <a href="https://urbanoccupations.ku.edu.tr/">https://urbanoccupations.ku.edu.tr</a>, Grant agreement ID: 679097) and POPGEO_BG (Marie Skłodowska-Curie Individual Fellowship, Population Geography of Bulgaria, 1500- 1920: A Historical Spatial Analysis, <a href="https://popgeo.ku.edu.tr/">https://popgeo.ku.edu.tr</a>, Grant agreement ID: 867474) both funded by the European Commission.</p> <p>M. Erdem Kabadayı and Grigor Boykov have been the principal investigators of UrbanOccupationsOETR and POPGEO_BG, respectively.</p> <p>This unprecedented large-scale Ottoman gazetteer is based on mid-nineteenth century population registers (NFS.d. with their archival fond) available at the Presidency of the Republic of Türkiye, Directorate of State Archives. Its current first version enlists 16.296 populated places (12.285 and 4.011 geolocated within UrbanOccupationsOETR and POPGEO_BG, respectively) extracted from 764 population registers dating between 1830 and 1849. For details of the geolocation procedure, especially for UrbanOccupationsOETR but also valid to a large extent POPGEO_BG, see:</p> <p>Ma, Jilian, Akın Sefer, and M. Erdem Kabadayı. “Geolocating Ottoman Settlements: The Use of Historical Maps for Digital Humanities.” In <em>Proceedings of the 8th International Symposium of the International Cartography Commission on the History of Cartography “Mapping the Ottoman Realm: Travelers, Cartographers and Archaeologists” 21–23 April 2020, Istanbul, Turkey (Rescheduled for December 2021, Florence, Italy)</em>, edited by Imre Josef Demhardt, Vol. 3. Göttingen: Copernicus Publications, 2021. <a href="https://doi.org/10.5194/ica-proc-3-10-2021">https://doi.org/10.5194/ica-proc-3-10-2021</a>.</p> <p>The gazetteer is available in the format of a spreadsheet with 15 columns. It provides the following information for all of the 16.296 enlisted populated places:</p> <ul> <li>longitude</li> <li>latitude</li> <li>register_date_in_Hicri (calendar)</li> <li>doc_type (<em>mufassal </em>or <em>icmal</em> population registers)</li> <li>NFS.d. register_number (as in the archival fond)</li> <li>project (acronym)</li> <li>toponym_modern (current toponym in various languages depending on the location of the populated place today). For 797 populated places, toponym_modern is entered as ‘vanished.’ These populated places are all geolocated with coordinates by using georeferenced historical maps. However, no current settlements could be identified in their locations.</li> <li>toponym Ottoman in NFS.d. (typed as accurately as possible in Arabic script copying from the Ottoman population registers)</li> <li>toponym transcribed (original handwritten Ottoman to Latin script, as a principle, following the Redhouse lexicon) from NFS.d. </li> <li><em>kaza</em> (the Ottoman administrative unit on the sub-district level as stated) in NFS.d.</li> <li><em>kaza_1848_1264</em> and <em>liva_1848_1264</em> correspondences of enlisted <em>kaza</em> information in NFS.d. registers according to the official Ottoman yearbook dating to 1848 (1264 in Hicri): <ul> <li><em>Salname-i Devlet-i Aliye-i Osmaniye</em>. 2nd edition. Istanbul: Darü’t-Tıbaatü’l-Amire, 1264.</li> </ul> </li> <li>(Unique) populated place id (number)</li> </ul> <p>As well as two additional administrative levels, <em>divan</em> and <em>nahiye</em> for the limited number of populated places.</p> <p>POPGEO_BG was an individual fellowship; therefore, Grigor Boykov has contributed to the construction of this section of the gazetteer single-handedly, from its conceptualization to the data entry and control.</p> <p>UrbanOccupationsOETR, on the other hand, sustained a large research team between 01.10.2016 and 30.09.2022 at Koç University and several team members contributed to the gazetteer in varying capacities:</p> <p>M. Erdem Kabadayı conceptualized and designed the research with support from Grigor Boykov. M. Erdem Kabadayı, Akın Sefer, Grigor Boykov, and Piet Gerrits supervised the project. Piet Gerrits designed and maintained graphical user interfaces for geolocation and data entry. Akın Sefer as a team leader, located, evaluated, and selected the population registers, supervised and controlled geolocation and data entry, and conducted data curation, coding, control, and cleaning. Efe Erünal, Aysel Yıldız, and Semih Çelik also conducted source selection. Efe Erünal was also active in geolocation and data entry control.</p> <p>Following UrbanOccupationsOETR team members in alphabetical order geolocated populated places and entered data into our geospatial databases: Akın Sefer, Aysel Yıldız, Barış Yıldırım, Deniz Ali Uyan, Efe Erünal, Fulya Özturan, Jilian Ma, Mertkan Karaca, Nikola Rakovski, Semih Çelik, Şehnaz İyibaş.</p> <p>External researchers Furkan Elmas and Alper Kara conducted Ottoman transcription corrections and new entries under the supervision of Akın Sefer.</p> <p>Piet Gerrits maintained the digital research infrastructure of the UrbanOccupationsOETR and merged the POPGEO_BG dataset to construct the gazetteer. Furthermore, he also prepared the GeoPackage and data visualization interface available at: <a href="https://urbanoccupations.ku.edu.tr/gazetteer/">https://urbanoccupations.ku.edu.tr/gazetteer/</a>.</p> <p>This gazetteer is a work in progress, and we would appreciate critical feedback to improve it in its following versions. Please get in touch with <a href="mailto:mkabadayi@ku.edu.tr">mkabadayi@ku.edu.tr</a> / <a href="mailto:mekabadayi@gmail.com">mekabadayi@gmail.com</a> / <a href="mailto:grigor.boykov@univie.ac.at">grigor.boykov@univie.ac.at</a> / <a href="mailto:griboykov@yahoo.com">griboykov@yahoo.com</a> for your correction suggestions and inquiries.</p> <p>If you would like to use the gazetteer in further publication, please use the credentials specified below:</p> <div> <div>Kabadayı, M. Erdem, Akın Sefer, Grigor Boykov, and Piet Gerrits. 2022. “Making of a Mid-Nineteenth Century Ottoman Gazetteer and Mapping and Examining Late Ottoman Population Geography.” <em>Journal of the Ottoman & Turkish Studies Association</em> 9 (2): 179–204. <a href="https://doi.org/10.2979/jottturstuass.9.2.25">https://doi.org/10.2979/jottturstuass.9.2.25</a>.</div> </div>
Single Scattering properties at W-band of ice populations
<p>The file contains coefficients of the polynomials that approximate radar observables at the W-band for a population of ice particles. </p> <p> </p> <p> </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.