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392 results for “tutorial”
Training material for Genome assembly quality control (Galaxy Training Network tutorial)
<p>This Zenodo repository includes the required datasets for following the GTN: Genome assembly quality control.</p>
Tutorial video for: A toolbox for the retrodeformation and muscle reconstruction of fossil specimens in Blender
<p>Accurate muscle reconstructions can offer new information on the anatomy of fossil organisms and are also important for biomechanical analysis (multibody dynamics and finite element analysis). For the sake of simplicity, muscles are often modeled as point-to-point strands or frustra (cut off cones) in biomechanical models. However, there are cases in which it is useful to model the muscle morphology in 3D, to better examine the effects of muscle shape and size. This is especially important for fossil analyses, where muscle force is estimated from the reconstructed muscle morphology (rather than based on data collected in vivo). The two main aims of this paper are as follows. First, we created a new interactive tool in the free open access software Blender to enable interactive 3D modeling of muscles. This approach can be applied to both palaeontological and human biomechanics research to generate muscle force magnitudes and lines of action for finite element analysis. Second, we provide a guide on how to use existing Blender tools to reconstruct distorted or incomplete specimens. This guide is aimed at palaeontologists but can also be used by anatomists working with damaged specimens or to test functional implication of hypothetical morphologies.</p>
Training data for 'Repeat masking with RepeatMasker' tutorial (Galaxy Training Material)
<p>Data needed for the 'Repeat masking with RepeatMasker' tutorial (Galaxy Training Material).</p> <p>The assembly was generated following the 'Genome assembly using PacBio data' tutorial</p>
Training data for 'From peaks to gene' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes peaks from a study published by Li et al., 2012 (DOI:10.1016/j.stem.2012.04.023) to identify target genes</p>
Training data for 'Reference based RADSeq ' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes RAD-seq data from a study published by Hohelnlohe et al., 2010 (DOI:10.1371/journal.pgen.1000862) to identify and type single nucleotide polymorphisms (SNPs) in each of 100 individuals from two oceanic and three freshwater populations and thus estimate genetic diversity and differentiation among populations. </p>
Training data for 'Long non-coding RNAs (lncRNAs) annotation with FEELnc' tutorial (Galaxy Training Material)
<p>Data needed for the 'Long non-coding RNAs (lncRNAs) annotation with FEELnc' tutorial (Galaxy Training Material).<br>The assembly was generated following the 'Genome assembly using PacBio data' tutorial.<br>The annotation was generated following the 'Genome annotation with Funannotate ' tutorial.</p> <p>The bam file is RNASeq SRR8534859_1.fastq.gz and SRR8534859_2.fastq.gz mapping on the genome assembly.</p>
Example Ptychography Data for the PtyPy Tutorials
<p>Experimental ptychography data collected at different X-ray instruments and electron microscopes at the Diamond Light Source. The purpose of this data deposition is to provide relevant experimental ptychography data for a comprehensive collection of tutorials for the PtyPy software framework, all which are available at <a href="https://ptycho.github.io/tutorials" target="_blank" rel="noopener">https://ptycho.github.io/tutorials</a>.</p>
ERA5 data to run example tutorials in RASCAL
<p>This is data from 2000 to 2023 to run some examples of reconstructions with the RASCAL model. It contains the geopotential height at 925hPa, and the temperature at 2m</p>
Tutorial DAPCy: the Plasmodium falciparum (Pf7) genotype dataset from MalariaGEN
Open the record for dataset details and reuse information.
Barycentered NICER event list from ObsID 1200120106, used in Stingray tutorial
<p>This is a NASA NICER observation of the accreting black hole MAXI 1820+070 during its 2018 outburst</p> <p>The raw X-ray event data in FITS format were obtained from the NICER archive at HEASARC:</p> <p>https://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/w3hdprods.pl?files=Preview&Coordinates=Equatorial&Equinox=2000&CheckSize=1&showgifs=1&Target=heasarc%5Fnicermastr%7C%7C%7C%5F%5Frow%3D30877%7C%7C&popupFrom=&querytime=1708425079</p> <p>Processing: <br>We ran the barycorr FTOOL, using the JPL DE 430 ephemeris (all details of processing can be found in the header of the FITS file). </p> <p>We distribute it to be used as practice data for Spectral Timing tutorials. Scientific use might require better processing, involving a re-run of the Level-2 data pipeline.<br><br>The data come in three versions:</p> <ol> <li>the original ~2.4GB FITS file</li> <li>a reduced ~720MB HDF5 file containing only part of the data, to help with slow connections</li> <li>a further reduced ~370MB HDF5 file, containing even less data but still adequate for most purposes in the tutorial.</li> </ol>
Open Access Tutorial
<p>Das Video ist in einem Werkstattprojekt (Forschendes Lernen) zum Thema Open Access mit Studierenden der FH Potsdam am Fachbereich Informationswissenschaften entstanden.</p> <p>Die Aufgabenstellung war es, ein Video-Tutorial zu Open Access zu produzieren, welches die Entwicklung, ebenso wie Vor- und Nachteile von Open Access erläutert und aufgrund der Ausführungen zur Open Access Publikation motiviert. </p> <p>Das Video gibt einen Überblick über die Geschichte des wissenschaftlichen Publizierens und die Ursprünge der Open Access-Bewegung. Daran anknüpfend werden verschiedene Strategien vorgestellt: der goldene und der grüne Weg. Die Vor- und Nachteile von Open Access werden insbesondere in Hinblick auf Article Processing Charges diskutiert.</p> <p>Umgesetzt wurde das Video mit Simpleshow: https://www.mysimpleshow.com/de/ </p> <p>Urheber des Videos: Studierende Fachhochschule Potsdam, Fachbereich 5 Informationswissenschaften<br> (https://www.fh-potsdam.de/studieren/fachbereiche/informationswissenschaften/)</p> <p>Werkstattleitung: Prof. Dr. jur. Ellen Euler,LL.M. zusammen mit Tutorin: Dorothea Strecker</p>
Sample datasets for Transposon insertion sequencing analysis tutorial
<p>The dataset contains five files:</p> <ol> <li>Tnseq-Tutorial-reads.fastqsanger.gz - A subset of TnSeq reads published in `Santiago, M., Matano, L. M., Moussa, S. H., Gilmore, M. S., Walker, S., & Meredith, T. C. (2015). A new platform for ultra-high density Staphylococcus aureus transposon libraries. <em>BMC Genomics</em>, <em>16</em>(1), 1–18. http://doi.org/10.1186/s12864-015-1361-3`</li> <li>condition_barcodes.fasta - Set of barcodes to separate reads from different experimental conditions</li> <li>construct_barcodes.fasta - Set of barcodes to separate reads from different transposon constructs</li> <li>staph_aur.fasta : Genome file for <em>Staphylococcus aureus </em></li> <li>staph_aur.fasta : Annotation file for <em>Staphylococcus aureus </em></li> </ol>
Training data for 'Somatic variant calling' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that demonstrates identification of somatic and germline variants from tumor and normal sample pairs.</p>
Video tutorial for Multi-Template-Matching implementation in Fiji and KNIME
<p>Set of tutorial videos on how to use Multi-Template-Matching as implemented in Fiji and KNIME by Thomas LSV and Gehrig J.</p> <p>Test datasets are also available on Zenodo</p> <p>Contact: l.thomas(at)acquifer.de, j.gehrig(at)acquifer.de</p>
Tutorial Dataset from the I22 beamline at Diamond Light Source
<p>A tutorial dataset generated by the I22 beamline at Diamond Light Source to accompany the I22 beamline data reduction and analysis manual.</p>
Data and simulations files for the tutorial article "Brillouin Optomechanics in Nanophotonic Structures"
<p>Data and simulations files for the tutorial article "Brillouin optomechanics in nanophotonic structures".<br> Published in APL Photonics Special issue "Optoacoustics—Advances in high-frequency optomechanics and Brillouin scattering" - DOI: 10.1063/1.5088169</p>
Training data for 'Genome annotation with Apollo' tutorial (Galaxy Training Material)
<p>Published scaffolds from the Apis mellifera assembly Amel_4.5 and Official Gene Set 3.2.</p> <p>Source: <a href="http://hymenopteragenome.org/beebase/?q=download_sequences">http://hymenopteragenome.org/beebase/?q=download_sequences</a></p>
CBRAIN Tutorial #1 Image Files
<p>These are files that can be used for the CBRAIN Tutorial at <a href="https://github.com/aces/cbrain/wiki/Tutorial-%231:-Getting-Started-with-CBRAIN">https://github.com/aces/cbrain/wiki/Tutorial-%231:-Getting-Started-with-CBRAIN</a>.</p>
Tutorial Weather Data Cutouts for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a>, since git is not suited for handling large changing files. Instead we provide separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-eur.readthedocs.io/en/latest/installation.html">documentation</a>.</p> <p>The provided lightweight <strong>cutouts </strong>are spatiotemporal subsets of the German weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset for March 2013 to be used for the <a href="https://pypsa-eur.readthedocs.io/en/latest/tutorial.html">PyPSA-Eur tutorial</a>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Video Tutorials for Using the Solidipes Curation Tool
<h2>Contributions</h2> <ul> <li>Emmanuelle Denove realized the project, conceived the videos and proceeded to the curated export into Zenodo</li> <li>Guillaume Anciaux and Son Pham-Ba supervised the realization by attending brainstorming meetings and by providing feedbacks.</li> </ul> <h2>Data structure and information</h2> <p>This dataset contains 12 videos exported as .mp4 files, as well as their source .prproj (Premiere Pro Project) files and all media used in them. For each individual video, the name of the .mp4 file, raw file and media folder is identical. The structure of the data inside the "data" folder is as follows :</p> <ul> <li>Folder/files structure: <ul> <li><code>final_videos</code> - folder containing the final exported videos <ul> <li><code>solidipes-*.mp4</code> - the individual videos</li> </ul> </li> <li><code>raw_files</code> - folder containing the Premiere Pro source files <ul> <li><code>solidipes-*.prproj</code> - source files</li> </ul> </li> <li><code>media</code> - folder containing the media folder for every video <ul> <li><code>solidipes-*</code> - folders containing the raw media for each video</li> </ul> </li> </ul> </li> </ul> <h2>Funding</h2> <p><a href="https://ethrat.ch/en/measure-1-calls-for-field-specific-actions/">ETH-Board ORD, measure 1</a>, grant n°22945: DCSM - Cloud and web based platform for dissemination of computational solid mechanics</p> <h2>Video details</h2> <p>This section gives a short description of each video as well as its identifier in the repository.</p> <ul> <li>Creating a dataset curation: <ul> <li>identifier : <code>dcsm</code></li> <li>This video shows how to upload a dataset to be hosted on DCSM's internal servers, from which they can directly be curated with solidipes.</li> </ul> </li> <li>Initiate solidipes: <ul> <li>identifier : <code>solidipes-initialise</code></li> <li>This video shows how to initialise a directory as a solidipes curation from the terminal.</li> </ul> </li> <li>Intro and installation: <ul> <li>identifier : <code>solidipes-installation</code></li> <li>This video gives a brief intro to solidipes and shows how to install it from the terminal using pip.</li> </ul> </li> <li>What is solidipes ? : <ul> <li>identifier : <code>solidipes-intro</code></li> <li>This video gives a detailed introduction into what solidipes does and what its goals are.</li> </ul> </li> <li>Data curation from the terminal : <ul> <li>identifier : <code>solidipes-terminal-curation</code></li> <li>This video shows how a dataset can be curated on the terminal using solidipes.</li> </ul> </li> <li>Download from Zenodo : <ul> <li>identifier : <code>solidipes-terminal-download</code></li> <li>This video shows how to download a dataset from Zenodo from the terminal using solidipes.</li> </ul> </li> <li>Upload to Zenodo from the terminal : <ul> <li>identifier : <code>solidipes-terminal-export</code></li> <li>This video shows how to export a dataset curated with solidipes to Zenodo from the terminal.</li> </ul> </li> <li>Acquisition on the web service : <ul> <li>identifier : <code>solidipes-web-acquisition</code></li> <li>This video shows the aspects of the "acquisition" step of the solidipes web service.</li> </ul> </li> <li>Curation on the web service : <ul> <li>identifier : <code>solidipes-web-curation</code></li> <li>This video shows the aspects of the "curation" step of the solidipes web service.</li> </ul> </li> <li>Exporting from the web service : <ul> <li>identifier : <code>solidipes-web-export</code></li> <li>This video shows how to export a dataset to Zenodo from the solidipes web service.</li> </ul> </li> <li>Edit metadata on the web service : <ul> <li>identifier : <code>solidipes-web-metadata</code></li> <li>This video shows the aspects of the "metadata" step of the solidipes web service.</li> </ul> </li> <li>Starting the web service + intro : <ul> <li>identifier : <code>solidipes-web-overview</code></li> <li>This video shows how to start the web service from the terminal, and gives an overview of its aspects.</li> </ul> </li> </ul> <h2>Image credit</h2> <p>The following graphics were used in some of the videos :</p> <ul> <li><a href="https://www.svgrepo.com/svg/533602/arrow-narrow-left">https://www.svgrepo.com/svg/533602/arrow-narrow-left</a></li> <li><a href="https://www.svgrepo.com/svg/145033/text-file-document">https://www.svgrepo.com/svg/145033/text-file-document</a></li> <li><a href="https://www.svgrepo.com/svg/184892/scientist">https://www.svgrepo.com/svg/184892/scientist</a></li> <li><a href="https://icons8.com/icon/12245/image-file">https://icons8.com/icon/12245/image-file</a></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.