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392 results for “tutorials”
Data for DeepRank2 tutorials
<p>This data contains raw PDB files and other metadata that can be used to run <a href="https://github.com/DeepRank/deeprank2">DeepRank2</a> tutorials for processing and later training on protein-protein interfaces and protein structures containing single residue variants.</p> <p>The PDB files in data_raw/ppi/ folder have been generated using <a href="https://github.com/X-lab-3D/PANDORA">PANDORA</a> software.</p> <p>The PDB files in data_raw/srv/ folder have been retrieved from <a href="https://doi.org/10.3389/fmolb.2023.1204157">Ramakrishnan et al.</a></p> <p> </p>
Introduction to static monitoring - PAMGuard tutorial dataset
<h1>Introduction to static monitoring - PAMGuard tutorial dataset</h1> <h2>GENERAL INFORMATION</h2> <p>This is a dataset to be used with the PAMGuard tutorial <em>Introduction to static monitoring in PAMGuard</em>. The dataset is from a single autonomous acoustic logger (SoundTrap) recording off the Scottish West Coast and contains a wealth of acoustic data including dolphin whistles and clicks, sonar, baleen whales and porpoises. The tutorial text and other related files can be found on the <a href="https://github.com/PAMGuardLearning/Intro2Pamguard">PAMGuardLearning GitHub page</a> or<a href="https://www.pamguard.org/69_Tutorials.html"> PAMGuard website</a>.</p> <h3>Year of data collection:</h3> <p>2018</p> <h3>Geographic location of data collection:</h3> <p>Stanton Banks, Scotland</p> <h3>Funding sources that supported the collection of the data:</h3> <p>The COMPASS project has been supported by the EU’s INTERREG VA Programme, managed by the Special EU Programmes Body. The views and opinions expressed in this document do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).</p> <h3>Recommended citation for this dataset:</h3> <p>Risch, D., Quer, S., Edwards, E., Beck, S., Macaulay, J., Calderan, S. (2018). Acoustic data from the Scottish west coast recorded with a single-element recording unit doi: 10.5281/zenodo.13880212</p> <h2>DATA & FILE OVERVIEW</h2> <h3>Description of dataset</h3> <p>The dataset contains three days of sample recordings from a deployment of an acoustic recording device (SoundTrap ST300) with a battery pack. The SoundTrap was running an on-device click detector on data at 576kHz sample rate and saving 96kHz raw acoustic data. The click detector detects any transient sound and saves a snippet of the waveform. </p> <p>The dataset also contains processed data from the entire deployment. There data have been processed in PAMGuard software (www.pamguard.org) for low frequency moans, dolphin whistles, clicks noise and longterm spectral averages. The detection and soundscape data has been saved in and raw acoustic recordings discarded. </p> <h3>Files</h3> <p>There are two directories - _audio_ and _viewer_. *audio* contains .sud files whicha re compressed audio and metadata files. They contain raw audio, click detection data and metadata. Data from sud files can be decompressed using SoundTrap Host software (www.oceaninstruments.co.nz). </p> <p>The _viewer_ folder contains a PAMGuard database and associated detection files in the _PAMBinary_ folder and sub folders. The database contains PAMGuard settings and some basic metadata. The detection .pgdf files are non human readable files that contain detection data such as detected clicks, frequency contours of whistles, moans and other tonal sounds and a time series of soundscape metrics. These files can be opened with PAMGuard software (www.pamguard.org), MATLAB (https://github.com/PAMGuard/PAMGuardMatlab) and R (https://github.com/TaikiSan21/PamBinaries). </p> <p>The folder structure is as follows</p> <p><code>├── audio #Three example days of acoustic recordings</code><br><code>│ ├── D2_SB_189411</code><br><code>│ ├── D2_SB_189415 </code><br><code>│ └── D2_SB_189428</code><br><code>├── README.md #The readme file</code><br><code>└── viewer #Processed data from a whole deployment</code><br><code> ├── compass_database_D2_stanton.sqlite3 #PAMGuard database</code><br><code> └── PAMBinary</code><br><code> ├── 20180301 #Folders containing detection files</code><br><code> ├── 20180302</code><br><code> ├── '''</code></p> <h2>SHARING AND ACCESS</h2> <p>These data are open source under Creative Commons Attribution 4.0 International. This means that these data can be used in other tutorials as long as the original authors are credited. Using these data for scientific purposes requires the permission of the authors. </p>
Visualization and perception of data gaps in the context of Citizen Science projects: Video tutorial support
<p>Online experiment about the influence of the availability of a video tutorial on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
Tutorial datasets for Dictys
<p>This includes several tutorial datasets for Dictys' context specific and dynamic gene regulatory network inference and analysis. See Dictys and its tutorial instructions at https://github.com/pinellolab/dictys.</p>
Onboarding/Registration (TRIPLE Video Tutorial Series)
<p>In this tutorial series, researcher Agnieszka Szulińska and Research Infrastructure Coordinator Edward Gray discuss gotriple.eu. This episode explores the opportunities GoTriple offers for registered users.</p>
MuSpinSim data files for Galaxy materials science tutorials
<p>This is a training dataset for use in Galaxy materials science tutorials. These files can be compared to the output of simulations by MuSpinSim for dissipation of muon spins.</p> <p>The files included are:</p> <ul> <li><strong>dissipation_theory.dat:</strong> theoretical values formatted as a MuSpinSim output</li> <li><strong>experiment.dat:</strong> mock experimental values formatted as a MuSpinSim output</li> </ul>
IMMERSE Horizon 2020 Project Downstream User Toolbox – data for tutorial on impact of wave coupling on surface particle dispersion simulations
<p>Exemplary data for tutorial on impact of wave coupling on surface particle dispersal simulations<br> <a href="https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU">https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU</a><br> created as part of the downstream user toolbox of the IMMERSE Horizon 2020 project (<a href="https://immerse-ocean.eu/">https://immerse-ocean.eu/</a>).</p> <p>In the tutorial the impact of new options for the representation of wave-current interactions in the NEMO ocean model (<a href="https://www.nemo-ocean.eu/">https://www.nemo-ocean.eu/</a>) on surface particle simulations are tested in a case study for the Mediterranean Sea. The tutorial consists of two jupyter notebooks: Parcels_CalcTraj.ipynb and CompTraj_uncoupledVScoupled.ipynb. Parcels_CalcTraj.ipynb calculates Lagrangian particle trajectories based on velocity output from ocean only as well as coupled ocean-wave model simulation by making use of the OceanParcels software (<a href="https://oceanparcels.org/">https://oceanparcels.org/</a>). CompTraj_uncoupledVScoupled.ipynb compares dispersal statistics of Lagrangian particle trajectories calculated from ocean-only vs coupled ocean-wave model simulations.</p> <p>This repository contains the surface velocity and ocean model grid data needed to run Parcels_CalcTraj.ipynb, as well as the trajectory data produced by Parcels_CalcTraj.ipynb, which is needed to run CompTraj_uncoupledVScoupled.ipynb. The surface velocity data stems from two simulations with a regional high-resolution (1/24° horizontal resolution) model configuration for the Mediterranean Sea: a coupled ocean-wave model simulation and a complimentary ocean-only simulation. These model simulations make use of the NEMO v4.2-RC ocean model, the Wave Watch 3 v.6.07 wave model, the OASIS3-MCT coupler, and ECMWF atmospheric fields; they are described in detail in IMMERSE deliverable D5.7 “Assessment of wave-current effects on the circulation in theMed-MFC system”<strong>.</strong></p>
Accurate and Efficient Estimation of Local Heritability using Summary Statistics and LD Matrix -- Demo datasets for the HEELS tutorials
<p>We introduced a new estimator for local heritability, "HEELS", which attains comparable statistical efficiency as the REML estimator (such as those produced by GCTA and BOLT-REML) but only requires summary-level statistics – Z-scores from marginal association tests and the empirical LD. Our method has been implemented into an open-source Python-based command line tool. </p> <p>The datasets released here can be downloaded to test the two main functions of our software package: 1) estimating local heritability; 2) computing the low-dimensional representation of the LD matrix. They are meant to accompany the HEELS tutorials we have posted onto the wiki pages of our github repository: https://github.com/huilisabrina/HEELS/wiki.</p> <p> </p>
Data supporting Tutorial: EPI Distortion Correction
<p>This data support Tutorial: EPI Distortion Correction by Barbara Dymerska for the Weekend Course during the ISMRM 2023 session: Artifacts & Correction Strategies on Sunday, 04 June 2023:</p> <p>https://github.com/fil-physics/ISMRM2023_educational</p> <p> </p>
Training data for 'Exome sequencing data analysis' tutorial (Galaxy Training Material)
<p>The data used in this tutorial are a subset of the data published previously in <a href="https://zenodo.org/record/3243160">Training material for the course "Exome analysis with GALAXY"</a>. Credit for uploading the original data goes to Paolo Uva and Gianmauro Cuccuru!</p> <p>Specifically, you may need the following datasets for following the tutorial:</p> <p><strong>Raw sequencing reads</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/father_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/father_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/father_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/father_R2.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/mother_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/mother_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/mother_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/mother_R2.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/proband_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/proband_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/proband_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/proband_R2.fq.gz</a></li> </ul> <p><strong>Premapped sequencing reads</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_father.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_father.bam</a></li> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_mother.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_mother.bam</a></li> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_proband.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_proband.bam</a></li> </ul> <p><strong>Reference sequence (human chromosome 8)</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz?download=1">https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz</a></li> </ul> <p> </p> <p>If you would just like to play with GEMINI rather than work through the full tutorial, you'll find below a prebuilt GEMINI database (for GEMINI version 0.20.1) for the family trio. You can start exploring this database without having to run GEMINI load and, in fact, without having to install GEMINI's bundled annotation data.</p>
Hands on analysis tutorial
Open the record for dataset details and reuse information.
Data and R-script for a tutorial that explains how to convert spreadsheet data to tidy data.
<p>Data and R-script for a tutorial that explains how to convert spreadsheet data to tidy data. The tutorial is published in a blog for The Node (https://thenode.biologists.com/converting-excellent-spreadsheets-tidy-data/education/)</p>
deep21 tutorial data
<p>Sample data for training and testing the deep21 deep learning model for 21cm cosmology. The object of the experiment is to separate radio cosmological signal from foreground contaminants, with a Principal Component Analysis (PCA) preprocessing step. Data were originally generated in .fits file format via <a href="http://intensitymapping.physics.ox.ac.uk/CRIME.html">CRIME Simulation Package</a> (see <a href="https://arxiv.org/abs/1405.1751">Alonso et al. 2014</a> for details).</p> <p>Included are binary numpy (.npy) files for 5 full-sky simluations of the cosmological signal, observed signal, and reference PCA-subtracted maps. Loading files with Numpy will yield arrays of shape (<span class="math-tex">\(N_{\rm voxels}, N_x, N_y, N_\nu\)</span>) = (950, 64, 64, 64), with 192 voxels per simulation.</p> <p>Designed for use with the <a href="https://colab.research.google.com/drive/1wQnmelM33Qjq-nHeVD9JkTHXER1PAJM0?hl=en#scrollTo=rUk2wvTTulLY">browser-based deep21 tutorial</a> on Google Colab (fuller explanation of experiment also available). Full-scale processing scripts are available on the <a href="https://github.com/tlmakinen/deep21">deep21 GitHub repository</a>.</p> <p> </p>
Training data for 'Genome annotation with Maker' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for genome annotation with Maker.</p> <p>It is based on data used in <a href="http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/MAKER_Tutorial_for_WGS_Assembly_and_Annotation_Winter_School_2018">another Maker tutorial</a>.</p> <p>The full genome was <a href="https://www.ncbi.nlm.nih.gov/genome/?term=Schizosaccharomyces%20pombe[Organism]&cmd=DetailsSearch">downloaded from NCBI</a>, and mitochondria sequence removed from it for simplicity.</p>
Tutorial Photonics Explorer Module 7: Interference and Diffraction
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes about Photonics - the Photonics Explorer - in order to promote the potential of photonics to enliven physics lessons. This video shows several experiments on the subject of interference and diffraction.</p>
Tutorial Photonics Explorer Module 5: Polarisation
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes - the Photonics Explorer- about Photonics in order to promote the potential of photonics to enliven physics lessons. This video is concerned with the topic polarisation and optical activity. </p>
Tutorial Photonics Explorer Module 1: total internal reflection
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes about Photonics - the Photonics Explorer - in order to promote the potential of photonics and to enliven physics lessons. This video tutorial demonstrates and explains the principals of total internal reflection.</p> <p> </p>
Tutorial Photonics Explorer Module 3 part 2: lenses, imaging rules, optical setups and telescopes
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes about Phoronics - the Photonics Explorer- in order to promote the potential of photonics to enliven physics lessons. This video is concerned with the topic polarisation and optical activity.</p> <p> </p>
Tutorial Photonics Explorer Module 3 part 1: lenses, imaging rules, optical setups and telescopes
<p>Photonics Austria (PhAu) has conducted Teacher Training Programmes about Photonics - the Photonics Explorer- in order to promote the potential of photonics to enliven physics lessons. This video tutorial contains several experiments designed to illustrate imaging equation and the laws of lenses.</p>
Bacterial training dataset for Galaxy training network tutorials on Genome assembly
<p>This training dataset is from an imaginary <em>Staphylococcus aureus</em> bacterium with a miniature genome. There is a reference genome in various formats as well as some fastq reads of a closely related but also imaginary mutant strain.</p> <p>It is a useful dataset for demonstrating:</p> <ul> <li>de novo genome assembly</li> <li>read mapping and variant calling</li> <li>genome annotation</li> </ul> <p>The files included are:</p> <ul> <li><strong>wildtype.fna</strong>: the reference genome sequence of the wildtype strain in fasta format (a header line, then the nucleotide sequence of the genome.)</li> <li><strong>wildtype.gff</strong>: the reference genome sequence of the wildtype strain in general feature format (a list of features - one feature per line, then the nucleotide sequence of the genome.)</li> <li><strong>wildtype.gbk</strong>: the reference genome sequence in genbank format.</li> <li><strong>mutant_R1.fastq</strong> and <strong>mutant_R2.fastq</strong>: Fastq sequence reads of a closely related mutant strain. <ul> <li>The reads are paired-end.</li> <li>Each read is 150 bases long.</li> <li>The number of bases sequenced is equivalent to 19x the genome sequence of the wildtype strain. (Read coverage 19x - rather low!).</li> </ul> </li> </ul>
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.