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9,113 results for “Sampling”
Princeton Handbook for Reproducible Neuroimaging: Sample Data
<p>This archive contains a raw DICOM dataset acquired (with informed consent) using the <a href="https://github.com/ReproNim/reproin">ReproIn</a> naming convention on a Siemens Skyra 3T MRI scanner. The dataset includes a T1-weighted anatomical image, four functional runs with the “prettymouth” spoken story stimulus, and one functional run with a block design emotional faces task, as well as auxiliary scans (e.g., scout, soundcheck). The “prettymouth” story stimulus created by <a href="https://doi.org/10.1177%2F0956797616682029">Yeshurun et al., 2017</a> and is available as part of the <a href="https://openneuro.org/datasets/ds002345">Narratives</a> collection, and the emotional faces task is similar to <a href="https://doi.org/10.1016/j.nicl.2015.05.004">Chai et al., 2015</a>. These data are intended for use with the <a href="https://brainhack-princeton.github.io/handbook/">Princeton Handbook for Reproducible Neuroimaging</a>. The handbook provides guidelines for BIDS conversion and execution of BIDS apps (e.g., fMRIPrep, MRIQC). The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.</p>
SAFE Sampling area borders
<b>Description: </b><p>This zipfile contains a shapefile showing the large scale boundaries of the main sampling regions used by researchers at the SAFE Project:<br></p><ul><br><li> the SAFE experimental area, </li><br><li> the Virgin Jungle Reserve (VJR), </li><br><li> the Maliau Basin Conservation Area, and </li><br><li> the Danum Valley Conservation Area</li><br></ul><br><br><p>For details of the project design, see the project description: <a href="https://doi.org/10.1098/rstb.2011.0049">https://doi.org/10.1098/rstb.2011.0049</a>.</p><p></p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3697804">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Sampling_area_borders.xlsx, Sampling_area_borders_UTM50N_WGS84.zip</p><p><b>Sampling_area_borders.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>Sampling_area_borders_UTM50N_WGS84.zip</b></p><p>Description: Contains a polygon shapefile of sampling area boundaries</p><p>This file contains 1 data tables:</p><ol><li><p></p><p><b>SAFE Sampling area borders</b> (described in worksheet Sampling_area_borders)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 2</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>name</b>: The full name of the sampling area (Field type: id)</li><li><b>shortname</b>: A sampling area code (Field type: categorical)</li></ul><p></p></li></ol><p><b>Date range: </b>2010-10-01 to 2020-03-05</p><p><b>Latitudinal extent: </b>4.6286 to 5.0588</p><p><b>Longitudinal extent: </b>116.7525 to 117.8081</p>
mixQTL sample data
<p># Introduction</p> <p>Sample data for mixQTL (https://github.com/liangyy/mixqtl).</p> <p>Data is derived from the GEUVADIS project (https://www.internationalgenome.org/data-portal/data-collection/geuvadis)</p> <p><br> # Disclaimer</p> <p>The data is provided "as is", and the authors assume no responsibility for errors or omissions. <br> The User assumes the entire risk associated with its use of these data. <br> The authors shall not be held liable for any use or misuse of the data described and/or contained herein. <br> The User bears all responsibility in determining whether these data are fit for the User's intended use. </p> <p>The information contained in these data is not better than the original sources from which they were derived,<br> and both scale and accuracy may vary across the data set. <br> These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics<br> appropriate for applications that potential users of the data may contemplate. <br> <br> The user is responsible to comply with any data usage policy from the original GWAS studies;<br> refer to the list of traits described [here](https://www.biorxiv.org/content/10.1101/814350v1)<br> to identify their respective Consortia's requirements.</p> <p><br> THE DATA IS PROVIDED WITHOUT WARRANTY OF ANY KIND,<br> EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,<br> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.<br> IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,<br> WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,<br> OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE DATA.</p>
Meeting radiation dosimetry capacity requirements of population-scale exposures by geostatistical sampling
<p>This is the data repository for the PLOS ONE Manuscript: "Meeting radiation dosimetry capacity requirements of population-scale exposures by geostatistical sampling". This repository contains the following data:</p> <p>1. "State-and-Subdivision-Boundary-Files.Edited-for-ArcMap-10.4.KML-Format.zip":</p> <p>This file contains modified U.S. state and sub-division boundary files [in KML format], which can be imported into ArcMap using its KMLtoLayer function. These files have been modified to prevent sub-division naming issues that we encountered when importing boundary data into ArcMap: A) State sub-divisions with identical names are considered a single sub-division by ArcMap (corrected by adding a letter after each sub-division of the same name, i.e. CenterA, CenterB, etc), and; B) ArcMap would only identify the sub-division by its first word if sub-division name contained spaces (corrected by converting all spaces into dashes). </p> <p>2. "HPAC-Plumes.Processed.zip" and "HPAC-Plumes.Unprocessed.zip":</p> <p>These files contain HPAC plume coordinate (WGS1984) and dose (in cGy) values for all scenarios discussed in the manuscript. We provide "processed" and "unprocessed" HPAC plume data files. The "unprocessed" HPAC plume data is provided in its original XML format, which cannot be imported into ArcMap directly. The "processed" HPAC plumes are provided in tab-delimited X,Y,Z format (Latitude, Longitude, and Dose). We have also added a "0 cGy" contour in the "processed" plumes (surrounding the HPAC plume), as the presence of unirradiated data points adjacent to the plume was found to be crucial for accurate kriging, since these points served as boundaries for kriging.</p> <p>3. "Final-Derived-Plumes.Data-Points.zip":</p> <p>This file contains geostatistically-derived plume coordinate (WGS1984) and dose (in cGy) values for all scenarios discussed in the manuscript. Data is in comma-delimited format (Latitude, Longitude, and Dose). Data points consist of a set of initial coordinates generated at random locations within each Census sub-division using the ArcMap tool, ‘CreateRandomPoints_management’, and subsequent points generated by densification (the geostatistical procedure that targets and localizes an additional small cohort of irradiated individuals to mitigate uncertainty in environmental measurements). These data points were assigned radiation level values corresponding to the adjacent outer HPAC contour by a script comparing each sample with its location within the HPAC plume of the same scenario.</p> <p>4. “Intermediate-Derived-Plumes.Data-Points.zip”</p> <p>This archive contains coordinate data (WGS1984) and dose values (in cGy) for all intermediary steps of plume development (using our geostatistical method) for all scenarios. Like (3), the data is comma-delimited (Latitude, Longitude, and Dose), and were assigned radiation level values by a script comparing sampling locations with the location of the HPAC plume of the same scenario. Scenario replicate folders contains text files for each iteration step of the plume derivation process, including a file containing just the initial random sampling (“Iteration-1”), a file containing initial sampling and sampling locations selected by the first densification step (“Iteration-2”), a file containing initial sampling and sampling locations selected by the first and second densification steps (“Iteration-3”), and so on.</p> <p>This archive also contains a Table (“Progression-of-New-Densification-Selected-Sampling-Locations-For-All-Scenarios.xslx”) which provides a categorical breakdown of how many unique densification-selected sampling locations occur within the irradiated region (i.e. overlap the HPAC plume) for each iteration of all scenario replicates. The fraction of irradiated to unirradiated sampling locations varies among each scenario and individual replicates for the same scenario. Our analysis shows that these results depend on the population densities and exact topography of the HPAC plume which is different among each scenario.</p> <p>5. "Geostatistical-Sampling-Project.All-Scripts.zip"</p> <p>This archive contains all programs required for this project. This includes Python scripts meant to be run within the ArcMap software environment (for random point generation and data extraction), and Perl scripts used to process HPAC and U.S. State and Sub-division boundary files, and to assign radiation values to sample locations based on a modified HPAC plume. A java program, “CompareReplicates.jar”, compares the overlapping areas between a pair of polygons that overlap one other using the ArcMap software environment, and requires access to the ArcGIS Runtime SDK (<a href="https://developers.arcgis.com/arcgis-runtime/">https://developers.arcgis.com/arcgis-runtime/</a>).</p>
Sample Attributes
<p>This file contains the following:</p> <p>Sample Name, Sample Title, Bioproject_accession, Organism, Host, Isolation_source, Collection_date, Geo_loc_name, Lat_lon, Ref_biomaterial, rel_to_oxygen, samp_collect_device, samp_mat_process, samp_size, source_material_id, description, sequencing_replicate, concentration_after_ampl (ng/ul), ng_sequenced, read_count</p>
Uniform Random SAT Samples
<p>Random SAT samples generated with the following <em>samplers</em>:</p> <ul> <li>Spur</li> <li>QuickSampler </li> <li>Unigen2</li> <li>Smarch </li> </ul>
Global ECMWF Fire Forecasting system - sample data for wildfires in Attica (Greece) on 23-26 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides sample datasets for the assessment of the fire danger during the Attica (Greece) wildfires occurred on 23-26 July 2018:</p> <ul> <li> <p>ECMWF_EFFIS_20180723_1200_en.tar<br> (ensemble forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723_1200_hr.tar<br> (deterministic forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_hr_e5.tar<br> (deterministic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_en_e5.tar<br> (probabilistic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_e5.tar<br> (probabilistic and deterministic reanalysis based on ERA5 issued for 2018-07-23/26, global coverage, FWI only)</p> </li> <li> <p>bbox.tar, containing 1 index (FWI) for the bounding box:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions in the period 23-26 July 2018</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts (in the period 14-26 July 2018) generated using weather forcings from the latest model cycle of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> </ul> </li> <li> <p>lon_min = 23, lon_max = 25, lat_min = 37, lat_max = 39</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
Sample Sentinel-1 SAR data for sea ice type retrieval
<p>Sample Sentinel-1 SAR data for sea ice type retrieval processed with thermal noise removal (<a href="https://ieeexplore.ieee.org/document/8126233">https://ieeexplore.ieee.org/document/8126233</a>).</p> <p>Original data is available at ESA Scientific Hub <a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu/</a></p> <p> </p>
Global ECMWF Fire Forecasting system - sample data for wildfires in Sweden on 15-20 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#%21/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides FWI sample datasets for the assessment of the wildfires occurred in Sweden on 15-20 July 2018:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 45r1 of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> <li> <p>Geographical bounding box: lon_min = 10.1, lon_max = 24.8, lat_min = 55, lat_max = 69</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
Influence of preservation methods, sample medium and sampling time on eDNA recovery in a neotropical river
<p>Environmental DNA (eDNA) has rapidly emerged as a promising biodiversity monitoring technique, proving to be a sensitive and cost‐effective method for species detection. Despite the increasing popularity of eDNA, several questions regarding its limitations remain to be addressed. We investigated the effect of sampling me‐ dium and time, and preservation methods, on fish detection performance based on eDNA metabarcoding of neotropical freshwater samples. Water and sediment sam‐ ples were collected from 11 sites along the Jequitinhonha River, Southeastern Brazil; sediment samples were stored in ethanol, while the same amounts of water per sam‐ ple (3 L) were stored in a cool box with ice, as well as by adding the cationic surfactant benzalkonium chloride (BAC). Sediment and water samples yielded a similar amount of fish MOTUs (237 vs. 239 in the first sampling event, and 153 vs. 142 in the second sampling event). Water stored in ice provided better results than those preserved in BAC (239 and 142 vs. 194 and 71 MOTUs). While documenting the effectiveness of eDNA surveys as practical tools for fish biodiversity monitoring in poorly accessible areas, we showed that keeping water samples cooled results in greater eDNA recovery and taxon detection than by adding cationic surfactants (BAC) as sample pre‐ servatives. Furthermore, by comparing two sets of samples collected from the same locations at a 3‐week interval, we highlight the importance of conducting multiple sampling events when attempting to recover a realistic picture of fish assemblages in lotic systems.</p>
M4DB Sample Data Set
<p>A sample data set for the MicroMagnetic-Metadata DataBase (M4DB).</p>
Nutrient concentration in seawater samples, collected from the underway supply, CTD and trace metal rosettes in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This dataset presents concentrations of nutrients (µmol/L): nitrous oxide (NOx), nitrate, nitrite, ammonium (NH4), phosphate (PO4) and silicic acid (Si), measured in samples of seawater during the Antarctic Circumnavigation Expedition (ACE). Samples were collected from CTD and trace metal rosette (TMR) deployments, as well as from the underway water supply on board, then analysed by flow injection following certified standards. This data was collected to support physical, chemical and biological oceanography studies in the Southern Ocean, conducted as part of ACE during the austral summer of 2016/2017.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_seawater_nutrient_data.csv, data file, comma-separated values</li> <li>data_analysis_nutrient_concentration_standards.csv, metadata, comma-separated values</li> <li>quality_checking_crm_analysis_results.csv, metadata, comma-separated values</li> <li>nutrient_concentrations_along_track_visualisation.png, metadata, portable network graphics</li> <li>ace_seawater_nutrients_comparison_plots_tmr_ctd.pdf, metadata, portable document format</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata text</li> <li>change_log.txt</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - various changes to data and metadata, described below</p> <ul> <li>added further data points to data file (those sampled from trace metal rosette)</li> <li>added ACE station number and ACE event number to data file</li> <li>reordered columns in data file</li> <li>updated README with information about sampling of trace metal rosette</li> <li>updated README with new citation, license, dataset contact and dataset contents, updated abstract</li> <li>updated data_file_header with new data fields</li> <li>added comparison plot of CTD and TMR data points</li> <li>updated caption of plot of nutrient concentrations along track (only from CTD samples)</li> </ul> <p>v1.0 - initial release of dataset</p> <p><strong>Dataset license</strong></p> <p>This nutrient concentration dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Output of Tax4Fun of predicted functional profiles. KO IDs and a KO abundance in every samples after a process of normalization.
<p>Supplementary Material Chapter 1.</p> <p> </p> <p>Table # 3: Output of Tax4Fun of predicted functional profiles. KO IDs and a KO abundance in every samples after a process of normalization.</p>
Amplicon sequence variants by sample table from Antarctic methane seeps
<p>Antarctica is estimated to contain as much as a quarter of earth's marine methane, however we have not discovered an active Antarctic methane seep limiting our understanding of the methane cycle. In 2011, an expansive (70m x 1m) microbial mat formed at 10m water depth in the Ross Sea, Antarctica and we carried out 16S rRNA gene analysis on samples collected one year and five years after the methane seep formed. The data set attached is the resulting Amplicon Sequence Variant table by sample that we used to track the community composition change during this time and in comparison to other sampling in the McMurdo sound region. </p>
Tree species reference samples in southwest of France
<p><strong>Ground tree species reference samples located in southwest of France</strong></p> <p>1 262 tree species references from a temperate forest. Suitable for remote sensing applications.<br> 8 broadleaf species, and 5 coniferous.</p> <p>We also provide the spectral values of Formosat-2 level 2A for each one-year Satellite Image Time Series from 2006 to 2014.</p> <p>Paper related to this dataset : <a href="https://doi.org/10.3390/rs11212512">https://doi.org/10.3390/rs11212512</a></p> <p><strong>For more details, please consult the README.pdf file.</strong></p> <p> </p>
Deep Generative Model Samples
<p>This dataset contains the main results of <em>Deep Generative Models for Galaxy Image Simulations</em> (Lanusse et al. 2020).</p> <p>It consists of tuples of postage stamps of HST/ACS COSMOS galaxies, corresponding parametric light profiles, and samples from a Deep Generative Model conditioned on size, magnitude, and redshift of the corresponding real galaxies. For each tuple, the dataset also contains morphological statitics computed on each stamp.</p> <p>The details of the creation of this dataset and how to use it to reproduce the plots of the paper can be found at https://github.com/McWilliamsCenter/deep_galaxy_models .</p>
Re-evaluating deep neural networks for phylogeny estimation: the issue of taxon sampling
Deep neural networks (DNNs) are powerful machine learning models that are widely used for classification problems, and have been recently proposed for quartet tree phylogeny estimation (Survorov et al. Systematic Biology 2020 and Zou et al. Molecular Biology and Evolution 2020). Here we present a study evaluating recently trained DNNs (from Zou et al., MBE 2020) in comparison to a collection of standard phylogeny estimation methods, including UPGMA, neighbor joining, maximum parsimony, and maximum likelihood, on a heterogeneous collection of 20-sequence datasets simulated under the same models that were used to train the DNNs, and also under similar conditions but with higher rates of evolution. Our study shows that using DNNs with quartet amalgamation (to combine quartet trees into a tree on the full dataset) is only more accurate than UPGMA, and otherwise is less accurate than all standard phylogeny estimation methods we explore (maximum likelihood, neighbor joining, and maximum parsimony). We further find that while DNNs can provide good quartet tree accuracy, some standard phylogeny estimation methods match or improve on DNNs for quartet accuracy, especially, but not exclusively, when used in a global manner (i.e., the tree on the full dataset is computed and then the induced quartet trees are extracted from the full tree). Thus, our study provides evidence that a major challenge impacting the utility of current DNNs for phylogeny estimation is their restriction to estimating quartet trees which must subsequently be combined into a tree on the full dataset: in contrast, global methods -- i.e., those that estimate trees from the full set of sequences -- are able to benefit from taxon sampling, and hence have higher accuracy on large datasets.
Effects of sampling seasons and locations on fish environmental DNA metabarcoding in dam reservoirs
<p>Environmental DNA (eDNA) analysis has seen rapid development in the last decade, as a novel biodiversity monitoring method. Previous studies have evaluated optimal strategies, at several experimental steps of eDNA metabarcoding, for the simultaneous detection of fish species. However, optimal sampling strategies, especially the season and the location of water sampling, have not been evaluated thoroughly. To identify optimal sampling seasons and locations, we performed sampling monthly or at two-monthly intervals throughout the year in three dam reservoirs. Water samples were collected from 15 and 9 locations in the Miharu and Okawa dam reservoirs in Fukushima Prefecture, respectively, and 5 locations in the Sugo dam reservoir in Hyogo Prefecture, Japan. One liter of water was filtered with glass-fiber filters and eDNA was extracted. By performing MiFish metabarcoding, we successfully detected a total of 21, 24, and 22 fish species in Miharu, Okawa, and Sugo reservoirs, respectively. From these results, the eDNA metabarcoding method had a similar level of performance compared to conventional long-term data. Furthermore, it was found to be effective in evaluating entire fish communities. The number of species detected by eDNA survey peaked in May in Miharu and Okawa reservoirs, and in March and June in Sugo reservoir, which corresponds with the breeding seasons of many of fish species inhabiting the reservoirs. In addition, the number of detected species was significantly higher in shore, compared to offshore samples in the Miharu reservoir, and a similar tendency was found in the other two reservoirs. Based on these results, we can conclude that the efficiency of species detection by eDNA metabarcoding could be maximized by collecting water from shore locations during the breeding seasons of the inhabiting fish. These results will contribute in the determination of sampling seasons and locations for fish fauna survey via eDNA metabarcoding, in the future.</p>
Sample data for analysis of period/frequency gradient and phase gradient in spreadouts, ex vivo models of somitogenesis
<p>Here are timelapse imaging (as .tif) of a dynamic Notch signaling reporter (i.e. LuVeLu) in spreadouts, ex vivo models of somitogenesis. Also here are the corresponding period and phase wavelet movies, generated using a wavelet analysis workflow developed by Gregor Mönke. These sample data are used to run an accompanying Python script, available at: <a href="https://github.com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/FrequencyPhase_GradientSlopeAnalysis">https://github.com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/FrequencyPhase_GradientSlopeAnalysis</a></p>
Which pitfall traps and sampling effort to choose to evaluate cropping system effects on spider and carabid assemblages?
<p>Dataset and example of the script (R) used in the simulation approach.</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.