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9,786 results for “selection”
Raw gel images accompanying the publication: Koralewska et al, NAR 2024, Short 2'-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production in vitro and in vivo, DOI 10.1093/nar/gkae284
<p>A set of raw gel images used in the article: Koralewska <em>et al</em>. Short 2’-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production <em>in vitro</em> and <em>in vivo, </em>NAR 2024, DOI 10.1093/nar/gkae284.</p>
Highly parallel genomic selection response in replicated Drosophila melanogaster populations with reduced genetic variation
<p>Many adaptive traits are polygenic and frequently more loci contributing to the phenotype are segregating than needed to express the phenotypic optimum. Experimental evolution with replicated populations adapting to a new controlled environment provides a powerful approach to study polygenic adaptation. Since genetic redundancy often results in non-parallel selection responses among replicates, we propose a modified Evolve and Resequence (E&R) design that maximizes the similarity among replicates. Rather than starting from many founders, we only use two inbred <em>Drosophila melanogaster</em>strains and expose them to a very extreme, hot temperature environment (29°C). After 20 generations, we detect many genomic regions with a strong, highly parallel selection response in 10 evolved replicates. The X chromosome has a more pronounced selection response than the autosomes, which may be attributed to dominance effects. Furthermore, we find that the median selection coefficient for all chromosomes is higher in our two-genotype experiment than in classic E&R studies. Since two random genomes harbor sufficient variation for adaptive responses, we propose that this approach is particularly well-suited for the analysis of polygenic adaptation.</p> <p>See the README.txt file to get a description of the uploaded files. Scripts.zip contains annotated command lines and scripts for the project (see internal README.txt file).</p>
Data and script: Community size can affect the signals of ecological drift and niche selection on biodiversity
<p>Updated version of the code. Data files are the same. This is the final version of the code, associated with a manuscript published in Ecology (doi: 10.1002/ecy.3014). A preprint is also available: https://www.biorxiv.org/content/10.1101/515098v1.abstract</p> <p>This is a unique dataset on insect communities sampled identically in a total of 200 streams in climatically highly different regions (100 in Brazil and 100 in Finland). The sampling design included 5 streams (communities) per watershed and provided us replicates of metacommunities (watersheds). Data also include information on in-stream variables (such as current velocity (m/s), depth (cm), stream width (cm), % of sand (0.25-2 mm), gravel (2-16 mm), pebble (16-64 mm), cobble (64-256 mm), and boulder (256-1024 mm), % of canopy cover by riparian vegetation, pH, conductivity, total nitrogen, and total phosphorus) and catchment level variables (such as average slope, % of native forest cover, pasture, agriculture, planted forests, urban areas, mining, water bodies, bare soil, secondary forest cover, and mixed land uses).</p> <p>In addition to the dataset, here we also provide and R code used to investigate the relationship between beta diversity and community size. This code calculates 4 types of beta-diversity metric for each of 100 watersheds (5 streams) in Brazil and Finland. Beta diversity: Sorensen and Bray-Curtis dissimilarity between all pairs. Beta deviation from null models: Raup-Crick (vegan version) and Bray-Curtis beta-deviation (based on the scripts by Chris Catano and Jonathan Myers). These beta diversity metrics are modelled against community size, environmental heterogeneity and spatial extent.</p> <p> </p>
Selection against admixture and gene regulatory divergence in a long-term primate field study
<p><strong>Selection against admixture and gene regulatory divergence in a long-term primate field study</strong><br> <em>Vilgalys & Fogel et al. (bioRxiv)</em></p> <ul> <li><a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/Panubis1.0_to_hg38.chain.gz">Panubis1.0_to_hg38.chain.gz</a>; <a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/hg38_to_Panubis1.0.chain.gz">hg38_to_Panubis1.0.chain.gz</a>: Liftover chain files between Panubis1.0 and hg38. </li> <li><a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/amboseli_LCLAE_tracts.txt.gz">amboseli_LCLAE_tracts.txt.gz</a>: Local ancestry calls for 442 wild, hybrid baboons studied as part of the Amboseli Baboon Research Project. Local ancestry was called using LCLAE and is represented by a 0 for homozygous yellow ancestry, 2 for homozygous anubis ancestry, and 1 for heterozygous ancestry. Each row in the file has a genomic position (chromosome, start, and end), local ancestry call, and the individual for whom the call was made. </li> <li><a href="https://zenodo.org/api/files/7cb721ac-b8e0-4dc0-a91b-fd54cc70f8d7/masked_yellow_and_anubis.vcf.gz?versionId=25e26878-bca3-4667-9668-9e19424bc23e">masked_yellow_and_anubis.vcf.gz</a>: Genotype calls for non-Amboseli yellow and anubis baboons, after masking to remove putative introgressed ancestry. </li> <li>A time-stamped version of the code is included here, and also available on GitHub at <a href="http://github.com/TaurVil/VilgalysFogel_Amboseli_admixture">github.com/TaurVil/VilgalysFogel_Amboseli_admixture</a>. </li> </ul>
Supporting data set for: Simulations of the Electrochemical Oxidation of Shape-Selected Nanoparticle Catalysts
<p>This dataset contains input and output files for simulations of the oxidation of a set of shape-selected, 3 nm platinum nanoparticles associated with the manuscript found at https://arxiv.org/abs/2201.07605.</p> <p>The simulations are performed using a grand-canonical Monte-Carlo algorithm[1,2] in combination with the ReaxFF reactive force field method as implemented in the Amsterdam Density Functional (ADF) software package version 2017.106 by Software for Chemistry and Materials (SCM). The Pt/O ReaxFF force field parameterized by Fantauzzi et al. was used for the simulations.[3] Simulations were performed at oxygen chemical potential conditions corresponding to 200-1000 K at ultra-high vacuum (UHV, <em>p</em><sub>O2</sub> = 10<sup>-10</sup> mbar) and 400-1200 K at near-ambient pressure (NAP, <em>p</em><sub>O2</sub> = 1 mbar) conditions. The following nanoparticle shapes were used as input structures for the simulations: (111)-indexed octahedron, (100)-indexed cube, (110)-indexed dodecahedron, (111)- and (100)-indexed cuboctahedron, mixed-indexed sphere, and (730)-indexed tetrahexahedron.</p> <p>The folder structure is as follows:<br> Particle shape -> pressure condition -> temperature condition -> simulation input and output files</p> <p>The simulation input and output files are of the following filetypes:<br> control: Input parameters for the ReaxFF software.<br> control_MC: Input parameters for the GCMC subroutine that interacts with the ReaxFF software.<br> geo: Atomic input coordinates in BGF file format.<br> geo_MCXXXXXX: Atomic output coordinates in BGF file format and ReaxFF total energy result for GCMC step XXXXXX.</p> <p>Simulations were performed for a total of 25,000 iterations. Only accepted GCMC steps result in the creation of a geo_XXXXXX output file. Therefore, the index XXXXXX is not continuous since output files are not written at every iteration. Other ReaxFF-specific output has been filtered in order to declutter the dataset.</p> <p>[1] T. P. Senftle, R. J. Meyer, M. J. Janik, A. C. T. van Duin, J. Chem. Phys. 2013, 139, 044109.<br> [2] T. P. Senftle, M. J. Janik, A. C. T. van Duin, J. Phys. Chem. C 2014, 118, 4967–4981.<br> [3] D. Fantauzzi, J. Bandlow, L. Sabo, J. E. Mueller, A. C. T. van Duin, T. Jacob, Phys. Chem. Chem. Phys. 2014, 16, 23118–23133.</p>
Dataset: A New Infrared Criterion for Selecting Active Galactic Nuclei to Lower Luminosities
<p>The dataset accompanying the paper "A New Infrared Criterion for Selecting Active Galactic Nuclei to Lower Luminosities" (Hviding et al. in prep)</p>
Data set for the journal article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO2-to-CO conversion"
<p>In the article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO<sub>2</sub>-to-CO conversion" we demonstrated that Fe impurities in a hybrid CoPc@MWCNT catalyst lead to its performance deterioration during long-term CO<sub>2</sub> electrolysis. Here we present the dataset the work was based on. The data are divided into four groups:<br> (i) Current transients and gas chromatography data for short-term electrolysis at different potentials (in an excel file we give the numbers of chromatograms for each potential; current transients are given as an origin file with datasets and plots inside)<br> (ii) Current transients and gas chromatography data for long-term electrolysis at different potentials and with different catalysts (in respective excel files we give the numbers of chromatograms; figure numbers are given in the folder names)<br> (iii) Electron microscopy images and EDX datasets (the images and datasets are collected in the folders with respective figure numbers used in the paper)<br> (iv) Calibration curves for ICP-MS</p>
Global and tropical band averages for a selection of CMIP5 and CMIP6 models: piControl and abrupt-4xCO2 experiments
<p>This dataset provides post-processed spatial averages for a selection of CMIP5 and CMIP6 models. The experiments contained in this dataset are only the pre-industrial controls (piControl) and the experiments with a four-fold increase in the atmospheric CO$_{2}$ concentration in relation to the pre-industrial level (abrupt-4xCO2). The spatial averages are global and tropical bands from x°S to x°N, where the x value is between 5 and 40 in increments of 5°. This dataset was created to study climate sensitivity in general and the effect of stratospheric circulation changes on the tropical equilibrium climate sensitivity. It contains the following variables:</p> <ul> <li>incoming (d) short-wave (SW, s) radiative flux (RF, r) at the top of the atmosphere (TOA, t): rsdt</li> <li>outgoing (u) SW RF at TOA: rsut</li> <li>outgoing long-wave (LW, l) RF at TOA: rlut</li> <li>net (n) RF at TOA: rnt</li> <li>incoming SW RF at the surface (s): rsds</li> <li>outgoing SW RF at the surface: rsus</li> <li>incoming LW RF at the surface: rlds</li> <li>outgoing LW RF at the surface: rlus</li> <li>net RF at the surface: rns</li> <li>sensible heat flux (hfs) at the surface: hfss</li> <li>latent heat flux (hfl) at the surface: hfls</li> <li>surface temperature (t): ts</li> <li>atmospheric temperature: ta</li> <li>specific humidity: hus</li> <li>zonal component of wind: ua</li> <li>meridional component of wind: va</li> <li>lagrangian tendency of pressure (vertical component of wind in pressure per time dimensions): wap</li> <li>surface pressure: ps</li> <li>geopotential height: zg</li> </ul>
Megalo-Cavitands: Synthesis of Acridane[4]arenes and Formation of Large, Deep Cavitands for Selective C70 Uptake
<p>Data underlying the figures in the publication “Megalo-Cavitands: Synthesis of Acridane[4]arenes and Formation of Large, Deep Cavitands for Selective C70 Uptake”, published in <em>Angew. Chem. Int. Ed.</em> <strong>2022</strong>, <em>61</em>, e202209885. <a href="https://doi.org/10.1002/anie.202209885">https://doi.org/10.1002/anie.202209885</a></p>
Datasets for "Flavour-selective localization in interacting lattice fermions"
<p>This submission includes the datasets shown in the figures of journal article</p> <p>"Flavour-selective localization in interacting lattice fermions" by D. Tusi et al.<br> DOI: 10.1038/s41567-022-01726-5</p> <p>The naming of the files corresponds to the figure numbering in the original article.</p>
Raw Data - Photoelectrolysis of TiO2 is highly localized and the selectivity is affected by the light
<p>The dataset contains raw data that complements the article:</p> <p>Photoelectrolysis of TiO<sub>2</sub> is highly localized and the selectivity is affected by the light, <em>Chemical Engineering Journal</em>, 2022, 136995.</p> <p>C. Iffelsberger, S. Ng, and M. Pumera*</p> <p>https://doi.org/10.1016/j.cej.2022.136995</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Global ice drilling and archive location data for select ice cores
<p>This document includes ice drill site information and ice core repository information for select ice cores retrieved between 1958 and 2022. Included data are not representative of all ice cores drilled during this time period, nor are they representative of all ice core samples collected and maintained by all of the contributing programs and facilities. Data are presented as they were provided by contributing facilities in 2022, when they were used to generate a figure for an article in Past Global Changes Magazine (doi.org/10.22498/pages.30.2.98).</p> <p>The data describe ice core drilling sites (latitude, longitude, elevation, site name), ice core samples (bottom depth, bottom age, core diameter, core completion date, corresponding publications), and ice core storage facilities (latitude, longitude, name).</p> <p>Contributing facilities include the following: Alfred Wegener Institute (Germany), Australian Antarctic Division (Australia), Australian Antarctic Program Partnership (Australia), Byrd Polar Center - University of Ohio (United States of America), Canadian Ice Core Lab (Canada), Chiba University (Japan), Commonwealth Scientific and Industrial Research Organization (Australia), Institute of Environmental Geosciences - University of Grenoble (France), Institute of Low Temperature Science - University of Hokkaido (Japan), Institute of Polar Science and Engineering - Jilin University (China), Karakoram International University (Pakistan), Lanzhou Institute of Glaciology and Geocryology (China), Nagoya University (Japan), National Institute of Polar Research (Japan), National Science Foundation Ice Core Facility (United States of America), New Zealand National Ice Core Facility (New Zealand, Physics of Ice Climate and Earth - University of Copenhagen (Denmark), Polar Research Institute of China (China), Research Institute for Humanity and Nature (Japan), and Tibet University. </p> <p>We are grateful to each of these facilities for contributing details of their ice core collections for this work. </p> <p> </p> <p> </p> <p>Electronic data accessibility and sample request procedures for a few of these facilities of which the authors are aware are listed below.</p> <p>Australia: data can be obtained from the Australian Antarctic Data Centre (<a href="https://urldefense.com/v3/__https://data.aad.gov.au/__;!!K-Hz7m0Vt54!k4oxTmHZ_w1LKmpFwH8LzlfLDG73TEDLZwozl9Q6dL-wfS_EQG7S75R9T3faMQA7BHyK5mv3Br0-kyWRnumedvhR$">https://data.aad.gov.au</a>); access to ice from the Australian Antarctic Program is via application (see <a href="https://urldefense.com/v3/__https://www.antarctica.gov.au/science/information-for-scientists/__;!!K-Hz7m0Vt54!k4oxTmHZ_w1LKmpFwH8LzlfLDG73TEDLZwozl9Q6dL-wfS_EQG7S75R9T3faMQA7BHyK5mv3Br0-kyWRnosG8VPm$">https://www.antarctica.gov.au/science/information-for-scientists/)</a></p> <p>Denmark: data can be obtained from <a href="https://www.iceandclimate.nbi.ku.dk/data/">www.iceandclimate.nbi.ku.dk/data</a>; the ice sampling request procedure is listed here: <a href="https://www.iceandclimate.nbi.ku.dk/data/samplingprocedure/">https://www.iceandclimate.nbi.ku.dk/data/samplingprocedure/</a> </p> <p>United States: many ice core datasets can be found at the NOAA World Data Center (<a href="https://www.ncei.noaa.gov/products/paleoclimatology/ice-core">https://www.ncei.noaa.gov/products/paleoclimatology/ice-core</a>); the allocation policy for ice core samples can be found here: <a href="https://icecores.org/policy">https://icecores.org/policy</a>.</p>
Tracking Selection using Temporal Population Genomics Data
<p>This repository contains the implementation of a pipeline to run the simulations and to produce a reference table for the ABC-RF inference of demography and selection. In its new release, this repository contains the whole-genome polymorphism of contemporary and museum specimens of <em>Apis mellifera</em> feral populations analyzed by Cridland et al. (2018).</p>
Auditory Selective Attention Switch in a Virtual Reality Classroom Environment
<p><strong>General</strong></p> <p>The audio-visual Auditory Selective Attention VR Proof of Concept (asaVRpoc) project serves to investigate the auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data on the voluntary switching of auditory selective attention in a virtual reality classroom scenario.</p> <p>The dataset contains:</p> <ul> <li>Unity project for visual display and the experiment structure</li> <li>Matlab code for experiment preparation and HpFT measurement</li> <li>Data collected in the experiment (experiment performance, head tracking, questionnaires)</li> </ul> <p><strong>Experiment preparation using Matlab</strong></p> <p>The code and software used to prepare the experiment is provided in the folder<em> "matlab_asaVRpoc"</em>.</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox commit hash: 598675ef704c178365f53d41e03ff4b11dea390f). A developmental version of Virtual acoustics (VA) 2020b (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p>Software requirements:</p> <ul> <li>Matlab 2019a or higher</li> <li>ITA Toolbox for Matlab installed</li> </ul> <p> </p> <p><strong>Experiment conduction in Unity</strong></p> <p>The Unity project is provided in the folder<em> "unity_pc_asaVRpoc"</em>.</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>Note that the <em>acoustic stimuli are NOT provided</em> with this Unity project. The stimuli are available on request from the Institute for Hearing Technology and Acoustics, RWTH Aachen University.</p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br> The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/).</p> <p>Software requirements:</p> <ul> <li>Unity 2019.4.21.f1.</li> <li>SteamVR 1.19.7</li> <li>Virtual Acoustics v2021a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</li> </ul> <p> </p> <p><strong>Data evaluation</strong></p> <p>The collected data is provided in the folder<em> "dataEvaluation_asaVRpoc"</em>. This folder contains three types of data: the raw data collected in the experiment (reaction times and error rates), the head tracking data and responses from the simulator sickness questionnaire (before and after the experiment) and the igroup presence questionnaire (after the experiment). Matlab code for the evaluation of the head tracking data and the questionnaires is provided.</p> <p> </p>
ERA5-Land selected indicators daily aggregates for Africa, 2010
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2010.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2013
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2013.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2015
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2015.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2016
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2016.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2017
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2017.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2014
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2014.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</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.