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9,786 results for “selection”
CFP01 Fish population on selected watersheds at Konza Prairie
Fishes were collected by habitat (pool or riffle) at 6 sites in the Kings Creek watershed with a single-pass electrofishing survey with one person operating the electrofisher and two people dipnetting. Collections were made seasonally.
Phrynus habitat selection
This data set comprises a single data file, which contains data on the abundance and distribution of the whipspider Phrynus longipes on the Luquillo Forest Dynamics Plot in July 2001. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Chlorophyll and phaeopigments from water column samples, collected at selected depths at Palmer Station Antarctica, during the Palmer LTER field seasons, 1991-2025.
Phytoplankton chlorophyll sampling was led by Smith from the 1991-1992 season through the 2001-2002 season, and then by Vernet from the 2002-2003 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Phaeopigments are non-photosynthetic pigments that are degradation products of phytoplankton chlorophylls which form during and after phytoplankton blooms. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Beginning in the 2020-2021 season, Station B is no longer sampled. Chlorophyll and phaeopigment concentrations are determined by filtration, extraction, and fluorometric detection of samples. The primary source of error for phaeopigment measurement is Chlorophyll b. If high amounts of Chlorophyll b are present in the sample, phaeopigments may be overestimated. There was no field season in 2021-2022.
Chlorophyll and phaeopigments from water column samples, collected at selected depths aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 1991-2024.
Phytoplankton chlorophyll sampling was led by Smith from 1991-2002, and then by Vernet from 2003-2008. Schofield is the third, and current lead, beginning in 2009. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Phaeopigments are non-photosynthetic pigments that are degradation products of phytoplankton chlorophylls which form during and after phytoplankton blooms. Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. Chlorophyll and phaeopigment concentrations are determined by filtration, extraction, and fluorometric detection of samples. The primary source of error for phaeopigment measurement is Chlorophyll b. If high amounts of Chlorophyll b are present in the sample, phaeopigments may be overestimated.
SBC LTER: Beach: Kelp export to select sandy beaches, Isla Vista, 2005-2006
The composition, cover and wet biomass of macroalgal wrack accumulated in the intertidal zone measured on selected sandy beaches of the mainland coast of the Santa Barbara Channel.
Robust functional mapping of layer-selective responses in human lateral geniculate nucleus with high-resolution 7T fMRI
Open the record for dataset details and reuse information.
EEG: Probabilistic Selection and Depression
Open the record for dataset details and reuse information.
Dataset containing the results of the selection process of DSH and CSS articles 2018-2020
<p>Dataset containing the results of the selection process of Digital Scholarship of Humanities and Computational Social Science articles. It shows which articles use data and have a clear data section. These are used to create a corpus to help build and validate and evaluate the data model of data scopes.</p>
Figures S1-S7. SMR-HEIDI analysis results for 8q24.21 locus between BP and selected phenotypes.
<p><strong>Supplementary Figures S1-S7. </strong><strong>SMR-HEIDI analysis results for rs6651255 between BP and selected phenotypes.</strong></p> <p>This project contains the following figures:</p> <ul> <li> <p>Figure S1. SMR-HEIDI analysis results for rs6651255 between BP and LDH.</p> </li> <li> <p>Figure S2. SMR-HEIDI analysis results for rs6651255 between BP and <em>GSDMC</em> expression in skeletal muscle (GTEx v6).</p> </li> <li> <p>Figure S3. SMR-HEIDI analysis results for rs6651255 between BP and <em>FAM49B</em> expression in Brain anterior cingulate cortex BA24 (GTEx v6).</p> </li> <li> <p>Figure S4. SMR-HEIDI analysis results for rs6651255 between BP and <em>FAM49B</em> expression in CD8 cell line (CEDAR).</p> </li> <li> <p>Figure S5. SMR-HEIDI analysis results for rs6651255 between BP and heel bone mineral density (UKBB).</p> </li> <li> <p>Figure S6. SMR-HEIDI analysis results for rs6651255 between BP and disc problem phenotype (UKBB)</p> </li> <li> <p>Figure S7. SMR-HEIDI analysis results for rs6651255 between BP and height (UKBB).</p> </li> </ul> <p> </p> <p><strong>Figures legend:</strong></p> <p>Each figure consists of four parts (1 – top left; 2- top right; 3- bottom left; 4 – bottom right):</p> <ol> <li> <p>Regional association plots for GWAS-1 (in our case BP GWAS) and GWAS-2 (expression or complex trait). Blue triangles represent SNPs used to calculate HEIDI test. Crossed triangle is leading SNP for which SMR test was computed.</p> </li> <li> <p>Z-Z plot (GWAS-1 on y-axis and GWAS-2 on x-axis).</p> </li> <li> <p>Visualization of LD matrix for SNPs used in calculation of HEIDI test.</p> </li> <li> <p>Plot of SMR regression coefficient estimates. The plot visualizes the heterogeneity of SMR coefficient. Blue color represents SNPs used to calculate HEIDI test.</p> </li> </ol>
A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods
<p>Non-targeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection (and refinement of it) is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of:</p> <ul> <li>51 converted mass spectral files in mzML format</li> <li>An .RData-file containing the extracted ion chromtograms (EICs)</li> <li>The randomly selected subset and the original output table of MZmine in .csv-format</li> <li>Example .xlsx files for the classification</li> <li>2 central classification tables</li> <li>Several tables with additional information about the sampling, chemical analysis and expert jugdement on EICs</li> </ul> <p>For a full description of the experiment and the data set, please read the related Data Descriptor with the title "A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods" in Metabolites (https://www.mdpi.com/journal/metabolites; DOI: https://doi.org/10.3390/metabo10040162).</p>
Kin selection explains the evolution of cooperation in the gut microbiota, by Simonet & McNally, 2020, Dataset S1 and codes for statistical analysis and figures production
<p>Dataset S1 contains all raw and processed material referred to in the published article "Kin selection explains the evolution of cooperation in the gut microbiota". R codes files provide all codes to replicate the analysis. Please refer to the README file for a description of all code files. The manifest files are those obtained by accessing the HMP portal on April 2020 under Project > HMP, Body Site > feces, Studies>WGS-PP1, File Type > WGS raw sequences set, File format > FASTQ.</p> <p>We also provide access to these data and codes at our GitHub (https://github.com/CamilleAnna/HamiltonRuleMicrobiome gitRepos.git) which can be cloned to directly re-run this analysis. </p> <p><strong>Legends for Dataset S1:</strong></p> <ul> <li>Sheet 1: Metagenomic samples used and access links.</li> <li>Sheet 2: Reference on bacterial cooperation retrieved from Web of Science search: TI¯((microb* OR bacter* OR microorganis* OR micro-organis*) AND (coop* OR social*)</li> <li>Sheet 3: Retained bacteria cooperation keywords</li> <li>Sheet 4: GOs identified by annotating all MIDAS database genomes (5944 genomes) with PANNZER2.</li> <li>Sheet 5: Full list of potential bacterial cooperation GO terms and description of manual curation decisions.</li> <li>Sheet 6: Final list of bacterial cooperation GO used for the analysis</li> <li>Sheet 7: Genomic diversity of the bacterial population within and across host. Computed from MIDAS snp_diversity.py pipeline.</li> <li>Sheet 8: final dataset for statistical analysis.</li> <li>Sheet 9: per-gene annotation of cooperation.</li> </ul>
Forward selection in a maritime pine polycross progeny trial using pedigree reconstruction.
<p>These two excel files gather genotyping data used in the following publication:</p> <p>Vidal M, Plomion C, Raffin A, Harvengt L, Bouffier L (2017) Forward selection in a maritime pine polycross progeny trial using pedigree reconstruction. Annals of Forest Science, 74(1). DOI 10.1007/s13595-016-0596-8</p> <p>The dataset describes genotyping profiles (with 56 or 63 SNPs) for the G1 and G2 individuals sampled in this paper. For each individual, the following information is mentioned: identity, preselection option (only for G2 individuals), the generation to which the individual belongs, pedigree (only for G2 individuals), alleles for each SNP.</p>
Datasets to article "Selection history alters attentional filter settings persistently and beyond top-down control"
<p>Single-Subject Behavioral and ERP mean amplitude data for Experiments 1 to 3.</p>
Supplementary Data - Impacts of selective digestive decontamination on the pangenome composition of ESBL-E. coli
<p>Supplementary Data of:</p> <p><span>Impacts of selective digestive decontamination on the pangenome composition of ESBL<em>-E. coli</em></span></p>
Selected properties of galaxy, MBHs and MBHBs populations (Izquierdo-Villalba et al. 2022)
<pre>This is a catalogue of galaxies, massive black holes (MBHs) and massive black hole binaries (MBHBs) <br>generated with L-Galaxies semi-analytical model in the version of Izquierdo-Villalba et al. 2022<br>and the dark matter merger trees extracted from the Millennium simulation (Springel et al. 2005). <br>The catalogue is created by making use of only 9 sub-volumes of the Millennium box (~2% of the whole<br>simulation, Volume = 5562144.30 Mpc3) and it contains galaxies, MBHs and MBHBs at<br>51 different redshifts (0 < z < 12.5). This catalogue is suited for studying the population<br>of MBHBs and their hosts. The properties stored in this catalogue are the following:<br> Redshift: Redshift of the galaxy/MBH/MBHB SnapNum: Snapnum of the simulation Pos: Position of the galaxy/MBH/MBHB inside the comoving box. It is an array of dimension 3. [Mpc/h] Vel: Velocity of the galaxy/MBH/MBHB inside the comoving box. It is an array of dimension 3. [km/s] Mvir: Virial mass of the dark matter sub-halo [1e10 Msun/h] Rvir: Virial radius of the dark matter sub-halo [Mpc/h] Vvir: Virial velocity of the dark matter sub-halo [km/s] Vmax: Maximum circular velocity of the dark matter halo [km/s] HotRadius: Radius of the hot gas atmosphere that surrounds the galaxy [1e10 Msun/h] ColdGas: Cold gas component of the galaxy [1e10 Msun/h] BulgeMass: Stellar mass of the bulge component [1e10 Msun/h] DiskMass: Stellar mass of the disc component [1e10 Msun/h]. The total stellar mass of the galaxy should be BulgeMass+DiskMass HotGas: Hot gas component of the galaxy [1e10 Msun/h] BlackHoleMass: Mass of the primary MBH of the galaxy [1e10 Msun/h] Lbol: Bolometric luminosity of the primary MBH of the galaxy [1e40 erg/s] fEDD: Ratio between the Lbol of the primary and the Eddington luminosity (<=1) [No dimensions] spin: Spin of the primary MBH [0,1] M_dot_acc: Accretion rate of the primary MBH of the galaxy [Msun/yr] BlackHoleMassSec: Mass of the secondary MBH (if exists) of the galaxy [1e10 Msun/h] LbolSec: Bolometric luminosity of the secondary MBH (if exists) of the galaxy [1e40 erg/s] fEDDSec: Ratio between the Lbol of the secondary MBH (if exists) and the Eddington luminosity (<=1) [No dimensions] spinSec: Spin of the primary MBH (if exists) [0,1] M_dot_acc_sec: Accretion rate of the secondary MBH (if exists) of the galaxy [Msun/yr] BinarySemiMajorAxis: Semi-major axis of the MBHB [Mpc/h] BinaryEccentricity: Eccentricity of the MBHB Sfr: Star formation rate of the galaxy [Msun/yr] BulgeSize: Size of the bulge stellar component [Mpc/h] StellarDiskRadius: Scale length of the disc stellar component [Mpc/h] GasDiskRadius: Scale length of the disc gas component [Mpc/h] The file can be read as follows: import h5py hf = h5py.File('LGal_IzquierdoVillalba2022_SubVol_0_9.h5', 'r')</pre>
Challenges of constructing and selecting the "perfect" initial and boundary conditions for the LES model PALM
<p><strong>README</strong></p> <p>All the supplementary data needed for the reproduction of the experiment described in the manuscript are provided on this ZENODO repository. The supplementary data includes the following:<br>1. IBC-pre-post-process-revised.zip which contains:<br> - Radio sounding data used for vertical profile statistical and visual comparison. They are stored as "CHMU-soundings.dat" in the CHMU_soundings directory<br> - code for making the figures for vertical profile comparison between the WRF and PALM model<br> - code for performing the statistical analysis for the vertical profiles of PALM and the WRF model<br> - code for making the scatter plots of PALM and WRF vertical profiles<br> - code for making the heatmaps of the PALM model data</p> <p>2. PALM_code.zip contains the source code for the current version of the PALM model used for this experiment</p> <p>3. palm_inputs.zip contains:<br> - static driver file<br> - dynamic driver file<br> - configuration files for the first PALM run (p3d), and the configuration files for the restart runs (p3dr)<br>for each of the performed simulations</p> <p>4. postproc.zip contains:<br> - the code for performing statistical analysis for minimum (min), average (Avg), and maximum (max) three-day averaged differences for the WRF and PALM model outputs<br> - the code for making figures of the differences between selected pairs of WRF and PALM model outputs</p> <p>5. wrf_namelist.zip contains:<br> - list of files in which the setups/configuration for the WRF ensemble used in this experiment</p> <p><strong>PALM MODEL INSTALLATION AND USAGE GUIDE</strong></p> <p>A. Installation:</p> <p>1. First, make sure to satisfy the Software Requirements. On Debian-based Linux Distributions, this can be achieved by the following command:</p> <p><code>sudo apt-get install gfortran g++ make cmake coreutils libopenmpi-dev openmpi-bin libnetcdff-dev netcdf-bin libfftw3-dev python3-pip python3-pyqt5 flex bison ncl-ncarg</code></p> <p>2. Also, some additional python dependencies are needed, which can be installed using pip. In case you want to use a virtual environment for these dependencies, please make sure to create one first. Afterwards, you can install the python dependencies by executing the following command:</p> <p><code>python3 -m pip install -r requirements.txt</code></p> <p>3. Now the PALM model system can be installed with the following commands (please replace with the desired installation directory):</p> <p><code>export install_prefix=""</code><br><code>bash install -p ${install_prefix}</code><br><code>export PATH=${install_prefix}/bin:${PATH}</code></p> <p>4. The following optional command permanently adds this installation to your bash environment:</p> <p><code>echo "export PATH=${install_prefix}/bin:\${PATH}" >> ~/.bashrc</code></p> <p>5. Type <code>bash install -h</code> to get all available options of the install script. During installation, the script calls the respective install script of all packages in this repository and installs them to the chosen directory. Therefore, it is not necessary to manually install any of the packages.</p> <p>You can test your installation with the following commands:</p> <p><code>palmtest --cases urban_environment_restart --cores 4</code></p> <p>B. Usage:</p> <p>After a successful installation, the executables for all packages have been linked into the directory /bin and a default PALM configuration file can be found at /.palm.config.default. In case you have installed the python dependencies inside a virtual environment, that environment needs to be active whenever you wand to use PALM. For usage of each of the packages, please refer to their individual documentation. Next, you need to create your first PALM setup in order to start a simulation. To get a simple preconfigured setup and start your first PALM simulation, please execute the following sequence of commands:</p> <p><code>mkdir -p "${install_prefix}/JOBS/example_cbl/INPUT"</code><br><code>cp "packages/palm/model/tests/cases/example_cbl/INPUT/example_cbl_p3d" "${install_prefix}/JOBS/example_cbl/INPUT/"</code><br><code>cd ${install_prefix}</code><br><code>palmrun -r example_cbl -c default -a "d3#" -X 4 -v -z</code></p>
SEM atlas of selected carbon nanomaterials
<p>Atlas of SEM images of commercially available carbon nanomaterials. Magnifications: x2000 - x100000.</p>
Polarized, color-selective and semi-transparent organic photodiode of aligned merocyanine H-aggregates
<p>Data to report <a href="https://doi.org/10.1039/D4TC00678J">https://doi.org/10.1039/D4TC00678J</a>:</p> <p><span><span>Highly anisotropic thin films of H-type coupled dipolar merocyanines </span></span><span><span>with large dichroic ratios of over 50 </span></span><span><span>were </span></span><span><span>deposited by solution shearing. These layers were incorporated into simultaneously color- and polarization-selective organic photodiodes. Using a transparent non-fullerene acceptor, polarization-sensitive planar-heterojunction devices with an average visible transmittance of 93% were obtained.</span></span></p> <p> </p>
Data from: Selective social interactions and speed-induced leadership in schooling fish
<p>Experimental datasets for the manuscript:</p> <div>Puy, A., Gimeno, E., Torrents, J., Bartashevich, P., Miguel, M. C., Pastor-Satorras, R., & Romanczuk, P. (2024). Selective social interactions and speed-induced leadership in schooling fish. <em>Proceedings of the National Academy of Sciences</em>, <em>121</em>(18), e2309733121.</div> <div> </div> <p>The datasets provide trajectories of fish. There are 2 recordings with N=39 fish (60 minutes duration) and 6 recordings with N=8 fish (30 minutes duration). The columns are as follows:</p> <ul> <li>Time [frame]: Time of the trajectory in frames.</li> <li>X_0 [px]: Position in the x-coordinate in pixels of the trajectory of individual 0.</li> <li>Y_0 [px]: Position in the y-coordinate in pixels of the trajectory of individual 0.</li> <li>X_1 [px]: Position in the x-coordinate in pixels of the trajectory of individual 1.</li> <li>Y_1 [px]: Position in the y-coordinate in pixels of the trajectory of individual 1.</li> <li>...</li> </ul> <p>Conversion to international units:</p> <ul> <li>50 frames = 1 s.</li> <li>2745 px= 100 cm.</li> </ul>
3-hourly water level records (selected high flow events) for the River Garry at Invergarry (Inverness-shire), Scotland
<p>3-hourly records of stage (water level) for the River Garry at Invergarry (Inverness-shire), Gauge A2, for selected high-flow events 1936-1940. Extracts from a record spanning the period 1936-10-01 to 1944-09-30.</p> <p>Data collected by Capt. W. N. McClean via his organisation River Flow Records and with the assistance of local observers.</p> <p>We acknowledge the sponsorship of Scottish Hydro-Electric and the Scottish Environment Protection Agency in suporting the costs of creating digital time series.</p> <p>Subsequent to the colletion of these records, the River Garry was developed by the construction of dams and hydro power stations below Loch Quoich and Loch Garry.</p> <p>The Scottish Environment Protection Agency (SEPA) subsequently opened a river flow gauging station on the River Garry at Craigard in 1997, approximately 3 km upstream of McClean's gauge, operated until 2011.</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.