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151 results for “Reference dataset”
Dataset of first appearances of the scholarly bibliographic references on English Wikipedia articles as of 1 March 2017 and as of 1 October 2021
<p><strong>Abstract</strong></p> <p>We developed a methodology to detect the oldest scholarly reference added to Wikipedia articles by which a certain paper is uniquely identifiable as the "first appearance of the scholarly reference." We identified the first appearances of 923,894 scholarly references (611,119 unique DOIs) in180,795 unique pages on English Wikipedia as of March 1, 2017, and stored them in the dataset. Moreover, we assessed the precision of the dataset, which was and it was a high precision regardless of the research field. In this version, it is available not only the dataset of English Wikipedia as of March 1, 2017, but also English Wikipedia as of October 1, 2021, generated by using the same methodology.</p> <p> </p> <p><strong>Data Records</strong></p> <p>The data format of the dataset is JSON lines, where each line is a single record. In this dataset, we detected the first appearance of each scholarly reference added to Wikipedia articles. If there are multiple references corresponding to the same paper on the same page, only the oldest one is collected. Sample of the record is the following.</p> <ul> <li>doi -- DOI corresponding to the paper (String), e.g., "10.1006/anbe.1996.0497"</li> <li>paper_type -- Document type of the paper (String), e.g., "journal-article"</li> <li>paper_container_title -- Journal title, book title, or proceedings title (Array of String), e.g., ["Animal Behaviour"]</li> <li>paper_publisher -- Publisher name (String), e.g., "Elsevier BV"</li> <li>paper_title -- Paper title (Array of String), e.g., ["Push or pull: an experimental study on imitation in marmosets"]</li> <li>paper_published_year -- Published year (String), e.g., "1997"</li> <li>paper_issue -- Issue number (String), e.g., "4"</li> <li>paper_volume -- Volume number (String), e.g., "54"</li> <li>paper_page -- Page numbers (String), e.g., "817-831"</li> <li>paper_author -- Authors information consisted of given and family names, sequences (order in author names), and affiliations (Array of JSON), e.g., [{"given":"THOMAS", "family":"BUGNYAR", "sequence":"first", "affiliation":[]}, {"given":"LUDWIG", "family":"HUBER", "sequence":"additional", "affiliation":[]}]</li> <li>issn -- ISSN related to the paper (Array of String), e.g., ["0003-3472"]</li> <li>research_field -- Research fields from ESI categories (Array of String), e.g., ["PLANT & ANIMAL SCIENCE"]</li> <li>page_id -- Page id (String), e.g., "577858"</li> <li>page_title -- Page title (String), e.g., "Imitation"</li> <li>revision_id -- Revision id (String), e.g., "203309031"</li> <li>revision_timestamp -- Revision timestamp (String), e.g., "2008-04-04 15:54:09 UTC"</li> <li>revision_comment -- Revision comment (edit summary) (String), e.g., "/* Animal Behaviour */"</li> <li>editor_name -- Wikipedia editor's name (String), e.g., "Nicemr"</li> <li>editor_type -- Type of the editor (String), e.g., "User"</li> </ul> <p><strong>References</strong></p> <ul> <li>Kikkawa, J., Takaku, M. & Yoshikane, F. "Dataset of first appearances of the scholarly bibliographic references on Wikipedia articles", Scientific Data, Vol. 9, Article number 85, pp. 1-11, 2022. <a href="https://doi.org/10.1038/s41597-022-01190-z">https://doi.org/10.1038/s41597-022-01190-z</a>.</li> </ul> <p><strong>FUNDING</strong></p> <ul> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K12543">JP20K12543</a></li> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K21303">JP21K21303</a></li> </ul>
Data Steward Professional: Reference dataset of Data Steward related job vacancies for competences assessment
<p>Data Stewardship vacancies collection to support FAIRsFAIR Data<br> Stewardship Professional Competence Framework<br> <br> This dataset is provided to validate and support the analysis of Data<br> Stewardship competences.<br> The dataset includes a collection of vacancies from the popular job search<br> website <a href="http://indeed.com/">indeed.com</a> that responded to the search term "Data Steward".<br> <br> <strong>Acknowledgment</strong><br> The research leading to these results has received<br> funding from the Horizon2020 projects FAIRsFAIR<br> (grant number 831558)<br> <br> <strong>References</strong><br> FAIRsFAIR Project Deliverable D7.3 Data Stewardship<br> Professional Competence Framework, Work in Progress.<br> To be published Feb 2021<br> Yuri Demchenko, Lennart Stoy, Research Data Management and Data<br> Stewardship Competences in University Curriculum, In Proc. Data Science<br> Education (DSE), Special Session, EDUCON2021 – IEEE Global Engineering<br> Education Conference, 21-23 April 2021, Vienna, Austria</p>
Dataset for Reference-free Isotropic Super-resolution For Volumetric Fluorescence Microscopy
<p>Dataset for a research paper titled "Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy". The images were acquired using two modalities: confocal fluorescence microscopy (CFM) and open-top light-sheet microscopy (OT-LSM). For details about the imaging, please refer to the paper (Link to be uploaded later). </p> <p>A. CFM</p> <ul> <li>CFM image of a cortical region of a Thy 1-eYFP mouse brain. </li> <li>Lateral resolution estimated as 1.24 micron and Z-depth interval of 3 micron</li> </ul> <ol> <li>Input image ["CFM_input_xy-view.tif] [Figure 2] </li> <li>Reference image acquired by rotating the sample by 90 degrees ["CFM_rotated-and-registered_xz-view.tif'] [Figure 2] </li> </ol> <p>B. OT-LSM </p> <ul> <li>OT-LSM image of a cortical region of a Thy 1-eYFP mouse brain.</li> <li>Lateral resolution estimated as 0.5 micron and axial resolution estimated as 4.6 micron. </li> <li>For testing of artifact correction, the microscope was poorly calibrated on purpose. </li> </ul> <ol> <li>Input image for artifact correction ["OT-LSM_artifact-correction_input_volume_xy-view.tif"] [Figure 4]</li> <li>Ground-truth image for artificial blurring ["OT-LSM_artificial-blurring_GT.tif"][Supplementary Figure 14]</li> <li>Input image for artificial blurring ["OT-LSM_artificial-blurring_gau-z-blurred-std-10.tif"][Supplementary Figure 14]</li> <li>Input image for PSF deconvolution ["input_volume_PSF-deconvolution.tif"][Figure 3]</li> </ol> <p>C. Simulation </p> <ul> <li>Jupyter notebook to generate a 3D image volume for simulation ["Data Generator for Simulation.ipynb"] [Figure 1] </li> </ul>
Artificial Intelligence: Professional reference dataset of Artificial Intelligence professional competences analysis based on the job market
<p>Artificial Intelligence vacancies collection to support FAIRsFAIR Artificial Intelligence Professional Competences<br> <br> This dataset is provided as validation and support for the analysis of Artificial Intelligence competences.<br> The dataset includes a collection of vacancies from the job application<br> website <a href="http://indeed.com/">indeed.com</a> that responded to the search term "Artificial Intelligence".</p> <p>The used search term could be easily adjusted in the provided code at <a href="https://github.com/atomcracker/Competence_analysis.git">Github Repository</a>. The heavy extensive research analysis is reflected in graphs, described and reflected in <a href="https://scripties.uba.uva.nl/search?id=727184">Thesis</a>.</p>
Full dataset of several mechanical tests on an S355 steel sheet as reference data for digital representations
<p>The dataset provided in this repository comprises data obtained from a series of characterization tests performed to a sheet of typical S355 (material number: 1.0577) structural steel (designation of steel according to DIN EN 10025-2:2019). The tests include methods for the determination of mechanical properties such as, e.g., tensile test, Charpy test and sonic resonance test. This dataset is intended to be extended by the inclusion of data obtained from further test methods. Therefore, the entire dataset (concept DOI) comprises several parts (versions), each of which is addressed by a unique version DOI.</p> <p>The data were generated in the frame of the digitization project Innovationplatform Material<em>Digital </em>(PMD) which, amongst other activities, aims to store data in a semantically and machine understandable way. Therefore, data structuring and data formats are focused in addition to aspects in the field of material science and engineering (MSE). Hence, this data is supposed to provide reference data as basis for experimental data inclusion, conversion and structuring (data management and processing) that leads to semantical expressivity as well as for MSE experts being generally interested in the material properties and knowledge.</p>
Dataset for a Feasibility Study for Accelerated Reference Point Determination Using Close Range Photogrammetry
<p>The Global Geodetic Observing System (GGOS) aims for an accuracy of 1 mm in position concerning a global geodetic reference frame such as the International Terrestrial Reference Frame (ITRF). To derive a global frame, several space geodetic techniques are combined. The combination procedure requires the geometric relations between the invariant reference points of these techniques, the so-called local tie vectors. Each space geodetic technique defines its reference point individually, so the determination of the position of the reference point varies significantly between techniques. Within the international GeoMetre project, measurement systems and analysis strategies are developed to improve the quality of local tie vectors and, thus, the quality of the resulting global frame.</p> <p>The use of close range photogrammetry to determine the reference point of a telescope used for Satellite Laser Ranging (SLR) at the GGOS core station Wettzell in September 2020 is considered as a milestone in this project. This contribution deals with a novel approach for an accelerated reference point determination using close range photogrammetry. In comparison to the conventional photogrammetric approach, published so far, this new approach leads to a significant reduction in recording time. However, in case of inappropriate measurement configuration the approach also bears the risk of biased results. Most importantly, the new approach has the potential to be automated, which is one of the primary calls of GGOS for reference point determinations.</p>
[opendc-sc18-dataset] A Reference Architecture for Datacenter Scheduling: Data Artifacts
<p>This release contains the data artifacts of the paper A Reference Architecture for Datacenter Scheduling presented at <a href="https://sc18.supercomputing.org/">Supercomputing 2018</a></p> <p>For the paper, experiments have been run on the following traces:</p> <ul> <li><strong>Askalon (W-Eng)</strong> - <code>askalon_workload_ee</code></li> <li><strong>Chronos (W-Ind)</strong> - <code>chronos_exp_noscaler_ca</code></li> </ul> <p>Each of the directories for the traces have the following structure:</p> <ul> <li><strong>/setup.txt</strong><br> This text file describes the trace used for the experiment in addition to the amount of times the experiment was repeated and the amount of warm-up experiments.</li> <li><strong>/setup.json</strong><br> This JSON file describes the topology of the datacenter used in the experiments. Each item represents the identifiers of the resource (here, CPU type) to use in the machine. The available CPU types are (1) Intel i7 (4 cores, 4100 MHz) and (2) Intel i5 (2 cores, 3500 MHz).</li> <li><strong>/trace</strong><br> This directory contains the trace used in the simulation. The trace is stored in the Grid Workload Format. See the <a href="http://gwa.ewi.tudelft.nl/">Grid Workload Archive</a> for more information.</li> <li><strong>/data/experiments.csv</strong><br> A CSV file containing information of all simulations that have been run on the OpenDC platform for this experiment.</li> <li><strong>/data/job_metrics.csv</strong><br> A CSV file containing metrics (NSL, JMS, etc.) for each job that ran during the simulations.</li> <li><strong>/data/stage_measurements.csv</strong><br> A CSV file containing timing measurements for the scheduling stages that ran during the simulations.</li> <li><strong>/data/task_metrics.csv</strong><br> A CSV file containing metrics for each task that ran during the simulations.</li> <li> <p><strong>/data/tasks.csv</strong><br> A CSV file containing information about the tasks (submit time, runtime, etc.) that ran during the simulations as extracted from the traces.</p> <p>Additionally, we describe the format of each data file in the associated metadata file.</p> </li> </ul> <p><strong>Hardware</strong></p> <p>The hardware used for running the experiments is a MacBook Pro with a 2,9 GHz Intel Core i7 processor and 16 GB 2133 MHz LPDDR3 internal memory.</p> <p><strong>Reproduction</strong></p> <p>This section describes the instructions for reproducing the paper results using a provided Docker image. Please make sure you have <a href="https://www.docker.com/">Docker</a> installed and running.</p> <p>For reproduction, you will run the following experiments:</p> <ul> <li><code>askalon_workload_ee</code><br> This is the large experiment of the paper and will take approximately 4 hours to complete similar hardware.</li> <li><code>chronos_exp_noscaler_ca</code><br> This is the smaller experiment of the paper and will take approximately 5 minutes to complete on similar hardware.</li> </ul> <p>The Docker image <a href="https://hub.docker.com/r/atlargeresearch/sc18-experiment-runner/"><code>atlargeresearch/sc18-experiment-runner</code></a> can be used for running the experiments. A volume can be attached to the directory <code>/home/gradle/simulator/data</code> to capture the results of the experiments.</p> <p>Make sure you have, in your current working directory, the following files:</p> <ul> <li><strong>/setup.json</strong><br> This JSON file describes the topology of the datacenter and can be found in this archive at <code>askalon_workload_ee/setup.json</code>.</li> <li><strong>/askalon_workload_ee.gwf</strong><br> This file contains the trace for the Askalon workload. This file can be found in the archive at <code>askalon_workload_ee/trace/askalon_workload_ee.gwf</code>.</li> <li><strong>/chronos_exp_noscaler_ca.gwf</strong><br> This file contains the trace for the Chronos workload. This file can be found in the archive at <code>chronos_exp_noscaler_ca/trace/chronos_exp_noscaler_ca.gwf</code>.</li> </ul> <p>Then, you can start the Askalon experiments as follows:</p> <pre><code>$ docker run -it --rm -v $(pwd):/home/gradle/simulator/data atlargeresearch/sc18-experiment-runner -r 32 -w 4 -s data/setup.json data/askalon_workload_ee.gwf </code></pre> <p>The experiment runner can be configured with the following options</p> <ul> <li><strong>-r</strong>, <strong>--repeat</strong><br> The amount of times to repeat an experiment for each scheduler.</li> <li><strong>-w</strong>, <strong>--warm-up</strong><br> The amount of times to warm-up the simulator for each scheduler.</li> <li><strong>-p</strong>, <strong>--parallelism</strong><br> The number of experiments to run in parallel.</li> <li><strong>--schedulers</strong><br> The list of schedulers to test, separated by spaces. The following schedulers are available: <code>SRTF-BESTFIT</code>, <code>SRTF-FIRSTFIT</code>, <code>SRTF-WORSTFIT</code>, <code>FIFO-BESTFIT</code>, <code>FIFO-FIRSTFIT</code>, <code>FIFO-WORSTFIT</code>, <code>RANDOM-BESTFIT</code>, <code>RANDOM-FIRSTFIT</code>, <code>RANDOM-WORSTFIT</code>.</li> </ul> <p>After the Askalon experiments have been finished, you can start the Chronos experiments. <strong>Make sure</strong> you have a copy of the result files in your directory as the result files will be overwritten.</p> <pre><code>$ docker run -it --rm -v $(pwd):/home/gradle/simulator/data atlargeresearch/sc18-experiment-runner -r 32 -w 4 -s data/setup.json data/chronos_exp_noscaler_ca.gwf </code></pre>
Cochrane DTA Reference Dataset
<p>Dataset described in <em>Data Extraction and Synthesis in Systematic Reviews of Diagnostic Test Accuracy: A Corpus for Automating and Evaluating the Process, </em>AMIA Annual Symposium Proceedings 2018.</p>
SSUsearch reference dataset
<p>This is the reference databases for SSUsearch, including the files below. If you want to updated version of SILVA, please look into the <strong>"readme"</strong> file.</p> <p>Ali_template.ssu.silva.fasta: alignment template for mothur.align<br> Copy_db_cc.ssu.copyrighter.txt: SSU rRNA gene copy DB # in copyrighter<br> Gene_db_cc.ssu.greengene_rep.fasta: SSU rRNA gene referecne DB in Greengene<br> Gene_db.ssu.silva_108_rep.fasta: SSU rRNA gene ref DB in SILVA<br> Gene_model_org.ssu.fasta: E.coli SSU rRNA gene sequence, 16s_ecoli_J01695.<br> Gene_tax_cc.ssu.greengene_rep.tax: SSU rRNA gene taxon ref DB in Greengene<br> Gene_tax.ssu.silva_108_rep_family.tax: SSU rRNA gene taxon ref DB in SILVA<br> Hmm.ssu.hmm: 2 hmms for bac + arc and euk</p> <p> </p> <p>Please cite the original paper: </p> <p>Microbial Community Analysis with Ribosomal Gene Fragments from Shotgun Metagenomes</p> <p>Jiarong Guo, James R. Cole, Qingpeng Zhang, C. Titus Brown, James M. Tiedje</p> <p>Appl. Environ. Microbiol. Dec 2015, 82 (1) 157-166; DOI: 10.1128/AEM.02772-15</p>
D-PLACE dataset derived from Binford 2001 'Constructing Frames of Reference'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Binford, L. 2001. Constructing Frames of Reference: An Analytical Method for Archaeological Theory Building Using Hunter-gatherer and Environmental Data Sets. University of California Press</p> </blockquote>
Structure and Excitation Spectra of Third-Row Transition Metal Hexafluorides Based on Multi-Reference Exact Two-Component Theory: Dataset
<p>This dataset collects the unprocessed (= outputs from calculations) results discussed in the paper titled "Structure and Excitation Spectra of Third-Row Transition Metal Hexafluorides Based on Multi-Reference Exact Two-Component Theory", by Ayaki Sunaga.</p>
Linked collectors and determiners for: A Distribution and Taxonomic Reference Dataset of Geranium (Geraniaceae) in the New World.
Natural history specimen data linked to collectors and determiners held within, "A Distribution and Taxonomic Reference Dataset of Geranium (Geraniaceae) in the New World". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/26d72d3b-4544-4645-aa56-27aa8a669c6f">https://bionomia.net/dataset/26d72d3b-4544-4645-aa56-27aa8a669c6f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/26d72d3b-4544-4645-aa56-27aa8a669c6f">https://gbif.org/dataset/26d72d3b-4544-4645-aa56-27aa8a669c6f</a>. Formatted as a Frictionless Data package.
Reference sheet for the dataset: TechOceanS ALR B7600021 BDOC Deployment 642
<p>This reference sheet directs to the British Oceanographic Data Centre's Deployment Catalogue repository, where the full metadata for deployment 641 is available. This reference sheet and the repository both provide contact details for acquiring the raw data.</p>
REFERENCES DATASET: A SYSTEMATIC REVIEW OF THE EDUCATIONAL USE OF MOBILE PHONES IN TIMES OF COVID-19
<p>The article "A systematic review of the educational use of mobile phones in times of COVID-19" aims to review what research has delved into the educational use of mobile phones during the COVID-19 pandemic. To do this, 38 papers indexed in the Journal Citation Reports database between 2020 and 2021 were analyzed. These works were categorized into the following categories: the mobile phone as part of educational innovation, improvement of results and academic performance, positive attitude towards mobile phone use in education, and risks and/or barriers to mobile phone use. The conclusions show that most teaching innovation experiences focus more on the device than on the student. Beyond its innovative nature, the mobile phone became a tool to allow access and continuity of training during the pandemic, especially in post-compulsory and higher education.</p> <p>This data set is composed of the table with the references used for the review.</p> <p> </p>
Bearings damage dataset for the 5 MW reference drivetrain on spar type floating wind turbine
<p>This dataset contains simulated acceleration measurements for the 5MW reference drivetrain model installed on a spar-type floating wind turbine. Measurements are one-hour simulations with a sample rate of 200 Hz. See Data description file for details.</p> <p>How to cite: Dibaj, Ali, & Nejad, Amir. (2023). Bearings damage dataset for the 5 MW reference drivetrain on spar type floating wind turbine [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7674842</p>
Phylogenetic profile of 100 annotated low complexity proteins against the Uniprot Reference Proteome dataset
<p>Phylogenetic profile of 100 human proteins with characteristic compositional bias, previously recorded by Mier et al (2020) against the Uniprot Reference Proteome, containing a total of 11297 proteomes, excluding viruses. The counts for each protein correspond to homologs found in each proteome. </p> <p>Detailed description of included columns:</p> <p><strong>ref_proteome_identifier</strong>: The<strong> </strong>Uniprot Reference Proteome identifier</p> <p><strong>ncbi_taxid</strong>: The NCBI taxonomy ID</p> <p><strong>species_name</strong>: NCBI common name corresponding to taxonomy ID</p> <p><strong>species_code</strong>: internal species code composed of 9 characters</p> <p><strong>taxonomic domain</strong>: E/B/A for Eukaryota/Bacteria/Archaea classification of proteome</p> <p> </p>
Natural grasslands across mainland France: a dataset including a 10 m raster and ground reference points
<p>The data provided here include the first 10 m raster of natural grasslands across mainland France and related ground reference points. The latter consist of 1,770 field observations that describe natural and artificial grasslands from respectively a compilation of hundreds of field-based vegetation maps and the European Union Land Parcel Identification System (LPIS). The raster data of natural grasslands were derived from five annual 10 m land cover maps of France from 2016-2020.</p> <p>More details can be found on the following reference : Panhelleux, L., Rapinel, S., Hubert-Moy, L., 2023. Natural grasslands across mainland France: a dataset including a 10 m raster and ground reference points. Data in Brief 109348. https://doi.org/10.1016/j.dib.2023.109348</p>
Genomic datasets of Laminaria digitata: Paired-end reads from dd-RADseq, reference genome assembly and filtered VCF
<p>The long-term persistence of species in the face of climate change can be evaluated by examining the interplay between selection and genetic drift in the contemporary evolution of populations. In this study, we focused on spatial and temporal genetic variation in four populations of the cold-water kelp Laminaria digitata using thousands of SNPs (ddRAD-seq). These populations were sampled from the center to the south margin in the North Atlantic at two different time points, spanning at least two generations. By conducting genome scans for local adaptation from a single time point, we successfully identified candidate loci that exhibited clinal variation, closely aligned with the latitudinal changes in temperature. This finding suggests that temperature may drive the adaptive response of kelp populations, although other factors, such as the species' demographic history should be considered. Furthermore, we provided compelling evidence of selection through the examination of allele frequency changes over time, by taking into the impact of genetic drift. Specifically, we detected candidate loci exhibiting temporal differentiation that surpassed the levels typically attributed to genetic drift at the south margin, confirmed through simulations. This finding was in sharp contrast with the lack of detection of outlier loci based on temporal differentiation in a population from the North Sea, exhibiting low and decreasing levels of genetic diversity. These contrasting evolutionary scenarios among populations can be primarily attributed to the differential prevalence of selection relative to genetic drift. In conclusion, our study highlights the potential of temporal genomics to gain deeper insights into the contemporary evolution of marine foundation species in response to rapid environmental changes.</p>
Reference datasets for consistency tests of GENAPOPOP 1.0 software: a user-friendly software to analyse genetic diversity and structure in partially clonal and selfed polyploid organisms.
<p>Datasets companion of the manuscript entitled GenAPoPop 1.0: a user-friendly software to analyse genetic diversity and structure in partially clonal and selfed polyploid organisms, used to achieve consistency test with Spagedi 1.5 software, and used as reference datasets to demonstrate the new possibilities allowed by GenAPoPop software.</p> <p>Raw datasets used for testing GenAPoPop 1.0, A user-friendly software for easily compute genetic analyses of autopolyploid populations packaged for Linux, MacOS and Windows; Results obtained from Spagedi 1.5 (Hardy & Vekemans 2001) and GenAPoPop1.0.</p> <p>Four pseudo-observed genotyping autotetrapolyploid SNP datasets, corresponding respectively to panmictic (A), highly clonal (B), highly selfed (C) and half-clonal-half-selfed (D) reproductive mode scenario. In all these four scenarios, we simulated two populations of 100 individuals each, connected with a migration rate of 0.01 and mutating at a rate of 0.01, genotyped at 10 SNPs. Datasets were recorded 1000 generations after an initial randomly drawing population with equal allele frequencies.</p> <p>One SNP tetraploid genotyping dataset from two French <em>Ludwigia grandiflora subsp. hexapetala</em> populations (aquatic plant from the Angiosperm clade): two populations in which we collected 75 individuals, each genotyped with 36 SNPs using the Hiplex method allowing confident allele dosage (Delord et al. 2018).</p> <p>One microsatellite tetraploid genotyping dataset on two Aulactinia stella populations (sea-anemone from the Cnidaria phylum), sampled on the coast of the arctic ocean. One population of 21 individuals and one population of 15 individuals, both genotyped with 10 microsatellites.</p> <p>We also report here the consistency tests with GenAlex and Spagedi, results of analyses (GPP tab) on 6300 independant simulations and inferences of the quantitative reproductive modes using the bayesian method on CEMP tab made on 6300 another independant simulations.</p>
Dataset of Universal 20 patterns with references
<p>A continuously growing dataset of more than 2000 languages, with references. A previous sample of 1687 languages is also available at the Terraling website <a href="http://terraling.com/groups/15">https://terraling.com/groups/15</a>.</p>
ScienceDex guides
Understand access before you commit
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.