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392 results for “tutorial”
Tutorial Data Bundle 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</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>This is the <strong>lightweight</strong> data bundle to be used for the <a href="https://pypsa-eur.readthedocs.io/en/latest/tutorial.html">PyPSA-Eur tutorial</a>. It excludes large bathymetry and natural protection area datasets.</p> <p>While the <a href="https://github.com/PyPSA/PyPSA-eur">code</a> in PyPSA-Eur is released as free software under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a>, <strong>different licenses and terms of use</strong> apply to the various input data, which are summarised and linked below:</p> <p><strong>corine/*</strong></p> <ul> <li>CORINE Land Cover (CLC) database</li> <li><strong>Source:</strong> <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/">https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Access to data is based on a principle of full, open and free access as established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013. This regulation establishes registration and licensing conditions for GMES/Copernicus users and can be found here. Free, full and open access to this data set is made on the conditions that:</p> <ul> <li> <p>When distributing or communicating Copernicus dedicated data and Copernicus service information to the public, users shall inform the public of the source of that data and information.</p> </li> <li> <p>Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the Union.</p> </li> <li> <p>Where that data or information has been adapted or modified, the user shall clearly state this.</p> </li> <li> <p>The data remain the sole property of the European Union. Any information and data produced in the framework of the action shall be the sole property of the European Union. Any communication and publication by the beneficiary shall acknowledge that the data were produced “with funding by the European Union”.</p> </li> </ul> </blockquote> <ul> <li><a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata">https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata</a></li> </ul> <p><strong>eez/*</strong></p> <ul> <li>World exclusive economic zones (EEZ)</li> <li><strong>Source:</strong> <a href="http://www.marineregions.org/sources.php#unioneezcountry">http://www.marineregions.org/sources.php#unioneezcountry</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Marine Regions’ products are licensed under CC-BY-NC-SA. Please contact us for other uses of the Licensed Material beyond license terms. We kindly request our users not to make our products available for download elsewhere and to always refer to marineregions.org for the most up-to-date products and services.</p> </blockquote> <ul> <li><a href="http://www.marineregions.org/disclaimer.php">http://www.marineregions.org/disclaimer.php</a></li> </ul> <p><strong>naturalearth/*</strong></p> <ul> <li>World country shapes</li> <li><strong>Source:</strong> <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/">https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>All versions of Natural Earth raster + vector map data found on this website are in the public domain. You may use the maps in any manner, including modifying the content and design, electronic dissemination, and offset printing. The primary authors, Tom Patterson and Nathaniel Vaughn Kelso, and all other contributors renounce all financial claim to the maps and invites you to use them for personal, educational, and commercial purposes.</p> <p>No permission is needed to use Natural Earth. Crediting the authors is unnecessary.</p> </blockquote> <ul> <li><a href="http://www.naturalearthdata.com/about/terms-of-use/">http://www.naturalearthdata.com/about/terms-of-use/</a></li> </ul> <p><strong>NUTS_2013_60M_SH/*</strong></p> <ul> <li>Europe NUTS3 regions</li> <li><strong>Source:</strong> <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>In addition to the general copyright and licence policy applicable to the whole Eurostat website, the following specific provisions apply to the datasets you are downloading. The download and usage of these data is subject to the acceptance of the following clauses:</p> <ol> <li> <p>The Commission agrees to grant the non-exclusive and not transferable right to use and process the Eurostat/GISCO geographical data downloaded from this page (the "data").</p> </li> <li> <p>The permission to use the data is granted on condition that: the data will not be used for commercial purposes; the source will be acknowledged. A copyright notice, as specified below, will have to be visible on any printed or electronic publication using the data downloaded from this page.</p> </li> </ol> </blockquote> <ul> <li><a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a></li> <li><a href="https://ec.europa.eu/eurostat/about/policies/copyright">https://ec.europa.eu/eurostat/about/policies/copyright</a></li> </ul> <p><strong>ch_cantons.csv</strong></p> <ul> <li>Mapping between Swiss Cantons and NUTS3 regions</li> <li><strong>Source:</strong> <a href="https://en.wikipedia.org/wiki/Data_codes_for_Switzerland">https://en.wikipedia.org/wiki/Data_codes_for_Switzerland</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Creative Commons Attribution-ShareAlike 3.0 Unported License</p> </blockquote> <ul> <li><a href="https://en.wikipedia.org/wiki/Data_codes_for_Switzerland">https://en.wikipedia.org/wiki/Data_codes_for_Switzerland</a></li> </ul> <p><strong>EIA_hydro_generation_2000_2014.csv</strong></p> <ul> <li>Hydroelectricity generation per country and year</li> <li><strong>Source:</strong> <a href="https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016">https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Public domain and use of EIA content: U.S. government publications are in the public domain and are not subject to copyright protection. You may use and/or distribute any of our data, files, databases, reports, graphs, charts, and other information products that are on our website or that you receive through our email distribution service. However, if you use or reproduce any of our information products, you should use an acknowledgment, which includes the publication date, such as: "Source: U.S. Energy Information Administration (Oct 2008)."</p> </blockquote> <ul> <li><a href="https://www.eia.gov/about/copyrights_reuse.php">https://www.eia.gov/about/copyrights_reuse.php</a></li> </ul> <p><strong>hydro_capacities.csv</strong></p> <p>Hydroelectricity generation and storage capacities</p> <ul> <li><strong>Source:</strong> <ul> <li> <p>A. Kies, K. Chattopadhyay, L. von Bremen, E. Lorenz, D. Heinemann, RESTORE 2050 Work Package Report D12: Simulation of renewable feed-in for power system studies., Tech. rep., RESTORE 2050 (2016).</p> </li> <li> <p>B. Pfluger, F. Sensfuß, G. Schubert, J. Leisentritt, Tangible ways towards climate protection in the European Union (EU Long-term scenarios 2050), Fraunhofer ISI. <a href="https://www.isi.fraunhofer.de/content/dam/isi/dokumente/ccx/2011/Final_Report_EU-Long-term-scenarios-2050.pdf">https://www.isi.fraunhofer.de/content/dam/isi/dokumente/ccx/2011/Final_Report_EU-Long-term-scenarios-2050.pdf</a></p> </li> </ul> </li> </ul> <p><strong>je-e-21.03.02.xls</strong></p> <ul> <li>Population and GDP data for Swiss Cantons</li> <li><strong>Source:</strong> <a href="https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html">https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Information on the websites of the Federal Authorities is accessible to the public. Downloading, copying or integrating content (texts, tables, graphics, maps, photos or any other data) does not entail any transfer of rights to the content.</p> <p>Copyright and any other rights relating to content available on the websites of the Federal Authorities are the exclusive property of the Federal Authorities or of any other expressly mentioned owners.</p> <p>Any reproduction requires the prior written consent of the copyright holder. The source of the content (statistical results) should always be given. Anyone who intends on using statistical results for commercial purposes or gain must obtain an authorisation pursuant to Art. 13 of the Fee Ordinance and is liable to pay an indemnity. Please contact the FSO for this purpose.</p> </blockquote> <ul> <li><a href="https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html">https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html</a></li> <li><a href="https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html">https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html</a></li> </ul> <p><strong>nama_10r_3gdp.tsv.gz</strong></p> <ul> <li>Gross domestic product (GDP) by NUTS3 region</li> <li><strong>Source:</strong> <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3gdp&lang=">http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3gdp&lang=</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Eurostat has a policy of encouraging free re-use of its data, both for non-commercial and commercial purposes. All statistical data, metadata, content of web pages or other dissemination tools, official publications and other documents published on its website, with the exceptions listed below, can be reused without any payment or written licence provided that:</p> <ul> <li> <p>the source is indicated as Eurostat;</p> </li> <li> <p>when re-use involves modifications to the data or text, this must be stated clearly to the end user of the information.</p> </li> </ul> <p>Exceptions</p> <ul> <li> <p>The permission granted above does not extend to any material whose copyright is identified as belonging to a third-party, such as photos or illustrations from copyright holders other than the European Union. In these circumstances, authorisation must be obtained from the relevant copyright holder(s).</p> </li> <li> <p>Logos and trademarks are excluded from the above mentioned general permission, except if they are redistributed as an integral part of a Eurostat publication and if the publication is redistributed unchanged.</p> </li> <li> <p>When reuse involves translations of publications or modifications to the data or text, this must be stated clearly to the end user of the information. A disclaimer regarding the non-responsibility of Eurostat shall be included.</p> </li> </ul> </blockquote> <ul> <li><a href="https://ec.europa.eu/eurostat/about/policies/copyright">https://ec.europa.eu/eurostat/about/policies/copyright</a></li> </ul> <p><strong>nama_10r_3popgdp.tsv.gz</strong></p> <ul> <li>Population by NUTS3 region</li> <li><strong>Source:</strong> <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en">http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Eurostat has a policy of encouraging free re-use of its data, both for non-commercial and commercial purposes. All statistical data, metadata, content of web pages or other dissemination tools, official publications and other documents published on its website, with the exceptions listed below, can be reused without any payment or written licence provided that:</p> <ul> <li> <p>the source is indicated as Eurostat;</p> </li> <li> <p>when re-use involves modifications to the data or text, this must be stated clearly to the end user of the information.</p> </li> </ul> <p>Exceptions</p> <ul> <li> <p>The permission granted above does not extend to any material whose copyright is identified as belonging to a third-party, such as photos or illustrations from copyright holders other than the European Union. In these circumstances, authorisation must be obtained from the relevant copyright holder(s).</p> </li> <li> <p>Logos and trademarks are excluded from the above mentioned general permission, except if they are redistributed as an integral part of a Eurostat publication and if the publication is redistributed unchanged.</p> </li> <li> <p>When reuse involves translations of publications or modifications to the data or text, this must be stated clearly to the end user of the information. A disclaimer regarding the non-responsibility of Eurostat shall be included.</p> </li> </ul> </blockquote> <ul> <li><a href="https://ec.europa.eu/eurostat/about/policies/copyright">https://ec.europa.eu/eurostat/about/policies/copyright</a></li> </ul> <p><strong>time_series_60min_singleindex_filtered.csv</strong></p> <ul> <li>ENTSO-E hourly per-country load profiles</li> <li><strong>Source:</strong> <a href="https://data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv">https://data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Attribution in Chicago author-date style should be given as follows: "Open Power System Data. 2019. Data Package Time series. Version 2019-06-05. <a href="https://doi.org/10.25832/time_series/2019-06-05">https://doi.org/10.25832/time_series/2019-06-05</a>. (Primary data from various sources, for a complete list see URL)."</p> </blockquote> <ul> <li><a href="https://data.open-power-system-data.org/time_series/2019-06-05/README.md">https://data.open-power-system-data.org/time_series/2019-06-05/README.md</a></li> </ul>
Training data for 'Beacon' tutorial (Galaxy Training Material)
<p>The data files are from the 1000 Genomes Project (1000HG) and GDC database. These datasets will be utilized in the Galaxy training session titled "Working with Beacon V2: A Comprehensive Guide to Creating, Uploading, and Searching for Variants with Beacons and Querying the University of Bradford GDC Beacon Database for Copy Number Variants (CNVs)." This training aims to equip participants with the skills necessary to construct Beacons, prepare and transform data into Beacon-compatible formats, seamlessly import data, and proficiently query Beacons for genetic variants. The provided data sets are integral for hands-on practice and will guide users through working with Beacon V2.</p>
Example data sets to accompany SeismicUnixGui tutorial (>V87.1)
<h1>SeismicUnixGui example data set for use with SeismicUnixGui (>V87.x)</h1> <p>The data set that is included was collected (2018) during a week-long summer orientation internship program hosted (RESESS-Research Experience in Solid Earth Science for Students ) operated by the Earthscope Consortium. The data set accompanies a tutorial for use of SeismicUnixGui.</p> <p><br>SeismicUnixGui is a graphical user interface (GUI) to select parameters for Seismic Un*x (SU) modules. <br>Seismic Un*x (Stockwell, 1999) is a widely distributed free software package for processing seismic reflection<br>and signal processing. Perl/Tk is a mature, well-documented and free object-oriented graphical user interface for Perl. <br>In a classroom environment, shell scripting of SU modules engages students and helps focus on the theoretical limitations<br>and strengths of signal processing. However, complex interactive processing stages, e.g., selection of optimal <br>stacking velocities, killing bad data traces, or spectral analysis requires advanced flows beyond the scope of <br>introductory classes. In a research setting, special functionality from other free seismic processing software <br>such as SioSeis (UCSD-NSF) can be incorporated readily via an object-oriented style to programming.<br>An object oriented approach is a first step toward efficient extensible programming of multi-step processes, <br>and a simple GUI simplifies parameter selection and decision making. Currently, in SeismicUnixGui, Perl 5 packages <br>wrap nearly 300 SU modules that are used in teaching undergraduate and first-year graduate student <br>classes (e.g., filtering, display, velocity analysis and stacking). Perl packages (classes) can advantageously<br>add new functionality around each module and clarify parameter names for easier usage. For example, through <br>the use of methods, packages can isolate the user from repetitive control structures, as well as replace the <br>names of abbreviated parameters with self-describing names. Moose, an extension of the Perl 5 object system, <br>greatly facilitates an object-oriented style. Perl wrappers are self-documenting via Perl programming document <br>markup language.</p>
Reproduction Package for FM 2024 Article `Software Verification with CPAchecker 3.0: Tutorial and User Guide'
<p>This package allows you to check the claims of our FM 2024 tutorial paper<br><em>Software Verification with CPAchecker 3.0: Tutorial and User Guide</em>.</p> <p>See the <strong>README.html</strong> for more information.</p> <p>Abstract:<br><em>This tutorial provides an introduction to CPAchecker for users. CPAchecker is a flexible and configurable framework for software verification and testing. The framework provides many abstract domains, such as BDDs, explicit values, intervals, memory graphs, and predicates, and many program-analysis and model-checking algorithms, such as abstract interpretation, bounded model checking, Impact, interpolation-based model checking, k-induction, PDR, predicate abstraction, and symbolic execution. This tutorial presents basic use cases for CPAchecker in formal software verification, focusing on its main verification techniques with their strengths and weaknesses. An extended version also shows further use cases of CPAchecker for test-case generation and witness-based result validation. The envisioned readers are assumed to possess a background in automatic formal verification and program analysis, but prior knowledge of CPAchecker is not required. This tutorial and user guide is based on CPAchecker in version 3.0. This user guide’s latest version and other documentation are available at <a href="https://cpachecker.sosy-lab.org/doc.php">https://cpachecker.sosy-lab.org/doc.php</a>.</em></p> <p> </p>
Testing tomogram for TracET tutorial
<p>Data for test the software tracET. This compress file has three folders, each of one with data of every kind of structure: membrane, filament and blob.</p>
Clay tutorial images #3
<p>Clay tutorial images</p>
Reproducing the FeS2 Example from the Demeter Tutorials
<p>These files are provided to complement the learning materials for managing RO-Crates in Galaxy.</p> <p>The files included are:</p> <ul> <li>A crystal structure file for performing a feff fit (1564889.cif).</li> <li>A XAS spectra file to process and analyse (fes2_rt01_mar02.xmu).</li> <li>A Galaxy workflow file to perform the process and analysis (Galaxy-Workflow-FeS2_Analysis.ga)</li> <li>A Galaxy RO-Crate file which can be restored to see the processing and analysis directly.</li> </ul> <p>The workflow and RO-Crate reproduce the EXAFS fitting example for athena and artemis as described by <a href="https://github.com/bruceravel/demeter/tree/master/examples/recipes/FeS2">Bruce Ravel</a>. Instead of using the FeS2.inp file in the original example, the workflow uses a crystal structure file (<a href="https://www.crystallography.net/cod/cif/1/56/48/1564889.cif">1564889.cif</a>) from the Crystallography Open Database (COD).</p> <p>This RO is published as part of the research data submitted for the paper <strong>Facilitating Reproducibility in Catalysis Research with Managed Workflows and RO-Crates: A Galaxy Case Study</strong>, ChemCatChem, DOI: 10.1002/cctc.202401676.</p>
Tutorial output for Tourmaline amplicon sequence processing workflow
<p>Tutorial output for the <a href="https://github.com/aomlomics/tourmaline">Tourmaline</a> amplicon sequence processing workflow.</p> <p>Tourmaline was run on the test data provided in the directory <a href="https://github.com/aomlomics/tourmaline/tree/master/00-data">00-data</a>, which were downloaded along with the rest of the repository using this command:</p> <pre><code>git clone https://github.com/aomlomics/tourmaline</code></pre> <p>Reference data were downloaded and symlinked using these commands:</p> <pre><code>cd tourmaline/01-imported wget https://data.qiime2.org/2021.2/common/silva-138-99-seqs-515-806.qza wget https://data.qiime2.org/2021.2/common/silva-138-99-tax-515-806.qza ln -s silva-138-99-seqs-515-806.qza refseqs.qza ln -s silva-138-99-tax-515-806.qza reftax.qza</code></pre> <p>Paths in 00-data/manifest_pe.csv and 00-data/manifest_se.csv were edited to match the local paths.</p> <p>Output for all modes of the workflow were then generated in series:</p> <pre><code>conda activate qiime2-2021.2 snakemake dada2_pe_report_unfiltered snakemake dada2_pe_report_filtered snakemake dada2_se_report_unfiltered snakemake dada2_se_report_filtered snakemake deblur_se_report_unfiltered snakemake deblur_se_report_filtered</code></pre> <p> </p>
Datasets for Galaxy Collection Operations Tutorial
<p>This repository contains datasets using in Galaxy tutorial introducing users to dataset collection operations: https://training.galaxyproject.org/training-material/topics/galaxy-interface/tutorials/collections/tutorial.html</p>
Tangos tutorial database
<p>This is a test database for use with the <a href="https://pynbody.github.io/tangos/data_exploration.html">tangos tutorial</a>.</p>
OpenFWI tutorial data
<p>OpenFWI tutorial data for https://colab.research.google.com/drive/17s5JmVs9ABl8MpmFlhWMSslj9_d5Atfx#scrollTo=am6FFr41ZAsu</p>
Galaxy tutorial for reference-based RNA-seq analysis
<p>https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html#functional-enrichment-analysis-of-the-de-genes</p> <p>the `compute` tool in the tutorial doesn't work. I used R to process the dataset and provide it so the students can continue with the tutorial.</p>
MODELING.VIS: Video Tutorial and Instructions to use the Graphical User Interface Toolbox
<p>INTRODUCTION: MODeLING.Vis was designed as an attempt to perform interactive data analyses. Given the software's effectiveness in extracting valuable information from the experimental data presented in this study, the applied methods and principles have been presented together with the analysis of results, and the code has been shared. Note, however, that MODeLING.Vis is not commercial, which constrains efforts behind scientific investigations.</p> <p>HYPOTHESIS: For a better understanding of the Graphical User Interface Toolbox, a demo and user manual of the toolbox should be provided for the convenience of users. The electrophoretic dataset should be published together with the tutorial for ease of access. </p> <p>METHODOLOGY: Creation of a practical video tutorial demonstrating how to download, install, run and operate MODeLING.Vis. Direct access to the electrophoretic dataset (protLabled.xls) is provided. </p>
Multimodal faces dataset for PracticalMEEG handson tutorials
<p>The dataset analyzed in PracticalMEEG tutorials is part of a dataset recorded by Rik Hanson and colleagues. During the hands-on sessions we will mainly work with the MEG data of a single representative subject, for the group statistics we will work with source-level processed data from all subjects.</p> <p>The “mmfaces” dataset contains EEG, MEG, functional MRI and structural MRI data from research participants that were recorded in multiple runs of a simple task performed on a large number of Famous, Unfamiliar and Scrambled faces. It is described in more detail the data descriptor publication <a href="https://www.nature.com/articles/sdata20151">doi:10.1038/sdata.2015.1</a> and analyzed in detail in <a href="http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00076/abstract">doi:10.3389/fnhum.2011.00076</a>.</p> <p>The original multimodal dataset included simultaneous MEG/EEG recordings on 19 healthy subjects. In the original study, three subjects (sub001, sub005, sub016) were excluded from further analysis.</p> <p>The dataset used to be available from the MRC-CBU FTP server, but is nowadays maintained on <a href="https://openneuro.org/datasets/ds000117">OpenNeuro</a>.</p> <p>Stimulation details</p> <ul> <li>The start of a trial was indicated with a fixation cross of random duration between 400 to 600 ms</li> <li>The face stimuli was superimposed on the fixation cross for a random duration of 800 to 1,000 ms</li> <li>Inter-stimulus interval of 1,700 ms comprised a central white circle</li> <li>Two types of stimulation patterns: <ul> <li>Immediate: The image was presented consecutively</li> <li>Long: The two images were presented with 5-15 intervening stimuli</li> </ul> </li> <li>For the purposes of our analysis, we treat these two stimulation patterns of stimuli together</li> <li>To maintain attention, subjects were asked to judge the symmetry of the image and respond with a keypress</li> </ul> <p>MEG/EEG acquisition details</p> <p>The MEG data consist of 102 magnetometers and 204 planar gradiometers from a Neuromag/Elekta/Megin VectorView system. The same system was used to simultaneously record EEG data from 70 electrodes (using a nose reference), which are stored in the same “FIF” format file. The above FTP site includes a raw .fif file for each run/subject, but also a second .fif file in which the MEG data have been “cleaned” using Signal-Space Separation as implemented in MaxFilter 2.1.</p> <p>A Polhemus was used to digitize three fiducial points and a large number of other points across the scalp, which can be used to co-register the M/EEG data with the structural MRI image. Six runs of approximately 10 minutes each were acquired for each subject, while they judged the left-right symmetry of each stimulus (face or scrambled), leading to nearly 300 trials in total for each of the 3 conditions.</p> <ul> <li>Sampling frequency: 1100 Hz</li> <li>Stimulation triggers: The trigger channel is STI101 with the following event codes:</li> <li>Famous faces: 5 (first), 6 (immediate), and 7 (long)</li> <li>Unfamiliar faces: 13 (first), 14 (immediate), and 15 (long)</li> <li>Scrambled faces: 17 (first), 18 (immediate), and 19 (long)</li> <li>Sensors <ul> <li>102 magnetometers</li> <li>204 planar gradiometers</li> <li>70 electrodes recorded with a nose reference (Easycap conforming to extended 10-20% system)</li> <li>Two sets of bipolar electrodes were used to measure vertical (left eye; EEG062) and horizontal Electro-oculograms (EEG061). Another set was used to measure ECG (EEG063)</li> </ul> </li> <li>A fixed 34 ms delay exists between the appearance of a trigger in the trigger channel STI101 and the appearance of the stimulus on the screen</li> </ul> <p>MRI acquisition details</p> <p>The MRI data were acquired on a 3T Siemens TIM Trio, and include a 1x1x1mm T1-weighted structural MRI (sMRI) as well as a large number of 3x3x4mm T2*-weighted functional MRI (fMRI) EPI volumes acquired during 9 runs of the same task (performed by same subjects with different set of stimuli on a separate visit). (The FTP site also contains DTI and ME-FLASH MRI images from the same subject, which could be used for improved head modeling for example, but these are not used here.) For full description of the data and paradigm, see README.txt on the FTP site or <a href="http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00076/abstract">Wakeman & Henson</a>.</p>
CamaraLab/ConDecon : Tutorial Data
<p><strong>ConDecon</strong> is a clustering-independent deconvolution method for estimating cell abundances in bulk tissues using single-cell RNA-seq data. The aim of ConDecon is to infer a probability distribution across a reference single-cell dataset that represents the likelihood for each cell in the reference data to be present in the query bulk tissue. ConDecon enables previously elusive analyses of dynamic cellular processes in bulk tissues and represents an increase in functionality and phenotypic resolution with respect to current methods for gene expression deconvolution. We anticipate that these features will improve our understanding of tissue cell composition by facilitating the inference of cell state abundances within complex bulk tissues, particularly in the context of evolving systems like development and disease progression.</p> <p>In the <a href="https://github.com/CamaraLab/ConDecon">ConDecon GitHub repository</a>, we demonstrate ConDecon's utility by applying it to transcriptomic data (ConDecon_B_cell_Tutorial), spatial transcriptomic data (ConDecon_Spatial_RNA_Tutorial), and chromatin accessibility data (ConDecon_ATAC_Tutorial). These datasets were downloaded from open-source data repositories (referenced below) and re-processed by the Camara lab. For convenience, the re-processed data associated with these tutorials has been uploaded below.</p> <p>Aubin, R. G., Montelongo, J., Hu, R., Camara, P. G. <em>Clustering-independent estimation of cell abundances in bulk tissue using single-cell RNA-seq data</em>. <strong>Biorxiv</strong> (2023).</p>
Mock datasets for MPoL tutorials and tests
<p>`*.npz` and `*.asdf` files containing visibilities are in the TMS format (opposite that of CASA).</p> <p>logo_cube.noise.npz visibilities have been rescaled such that data - model / sigma follows the expected Gaussian envelope.</p> <p>HD 143006 continuum visibilities have flagged outliers removed and weights rescaled such that the data - model / sigma follows the expected Gaussian envelope, for each spectral window.</p> <p>AS 209 continuum visibilities have been averaged across frequency and have their weights rescaled such that the data - model / sigma follows the expected Gaussian envelope, for each spectral window.</p>
DARIAH Campus Geographical Text Analysis Tutorial - Resources
<p>These datasets are part of the Geographical Text Analysis tutorial hosted on DARIAH-Campus, and produced as part of the GeoHumanities Working Group activities. </p>
Traing Data for "Assembly of metagenomic sequencing data" tutorial
<p>Metagenomics involves the extraction, sequencing and analysis of combined genomic DNA from <strong>entire microbiome</strong> samples. It includes then DNA from <strong>many different organisms</strong>, with different taxonomic background.</p> <p>Reconstructing the genomes of microorganisms in the sampled communities is critical step in analyzing metagenomic data. To do that, we can use <strong>assembly</strong> and assemblers, <em>i.e.</em> computational programs that stich together the small fragments of sequenced DNA produced by sequencing instruments.</p> <p>Assembling seems intuitively similar to putting together a jigsaw puzzle. Essentially, it looks for reads “that work together” or more precisely, reads that overlap. Tasks like this are <strong>not straightforward</strong>, but rather complex because of the complexity of the genomics (specially the repeats), the missing pieces and the errors introduced during sequencing.</p> <p>In this tutorial, we will learn how to run metagenomic assembly tool and evaluate the quality of the generated assemblies. To do that, we will use data from the study: <a href="https://www.ebi.ac.uk/metagenomics/studies/MGYS00005630#overview">Temporal shotgun metagenomic dissection of the coffee fermentation ecosystem</a>. For an in-depth analysis of the structure and functions of the coffee microbiome, a temporal shotgun metagenomic study (six time points) was performed. The six samples have been sequenced with Illumina MiSeq utilizing whole genome sequencing.</p> <p>Based on the 6 original dataset of the coffee fermentation system, we generated mock datasets for this tutorial.</p>
GTN Tutorial: Cancer alignment
<p>Data belonging to the GTN tutorial on cancer alignment. This tutorial is based on the Bioinformatics.ca cancer workshop</p>
SARS-CoV-2 Viral Samples and Reference Genome for Galaxy Training Network SARS with Galaxy on AnVIL Tutorial
<p>In the lab activity, we'll see if there are genomic differences in the collected sample compared to the original SARS-CoV-2 genome (the reference). We need three files to do this:</p> <ul> <li><strong>SARS-CoV-2_reference_genome.fasta</strong> : the reference genome</li> <li><strong>VA_sample_forward_reads.fastq.gz</strong>: 1 of 2 raw read data files</li> <li><strong>VA_sample_reverse_reads.fastq.gz</strong>: 2 of 2 raw read data files</li> </ul> <p>The sample for this activity was derived from data collected at Virginia Commonwealth University in Richmond, VA. The researchers collected the sample with the goal of being able to track the spread and evolution of this virus state-wide, nationally, and internationally. You can download the original data <a href="https://www.ncbi.nlm.nih.gov/sra/?term=XGTK449087">here</a>.</p> <p>You can download the reference genome <a href="https://www.ncbi.nlm.nih.gov/nuccore/1798174254">here</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.