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392 results for “tutorial”
bollito: a flexible pipeline for comprehensive single-cell RNA-seq analyses - Melanoma tutorial
<p>Downsampled version of the melanoma dataset originally published by <em><a href="https://genome.cshlp.org/content/28/9/1353">Ho et al </a>(1)</em>. The dataset is composed by cells from the 451Lu cell line. There are two samples available:</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> <td><strong>R1/R2</strong></td> </tr> <tr> <td>451LU</td> <td>Parental cell line</td> <td>2500K_451LU_L003_R*_001.fastq.gz</td> </tr> <tr> <td>451LUBR3</td> <td>Vemurafenib-resistant sample treated with targeted BRAF inhibitors</td> <td>500K_451LUBR3_L004_R*_001.fastq.gz</td> </tr> </tbody> </table> <p><br> (1) Ho YJ, Anaparthy N, Molik D, et al. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. <em>Genome Res</em>. 2018;28(9):1353-1363. doi:10.1101/gr.234062.117</p>
IPBES Data Management Tutorials - Session 3.5: Data management report details: Sensitive data, anonymization, and ethical considerations
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session on <em>data management report details: Sensitive data, anonymization, and ethical considerations </em>captures specific considerations and processes for IPBES experts regarding sensitive data and Indigenous and local knowledge within data management reports. </p>
IPBES Data Management Tutorials - Session 2.3: Roles and responsibilities
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management Policy </em>chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy. </p> <p>This session on <em>roles and responsibilities </em>outlines the responsibilities of all involved players as stipulated in the IPBES data management policy. These are discussed in light of the importance for IPBES experts.</p> <p>Following version 2.0 of the data and knowledge management policy, a new PDF supplement has been added which covers the roles and responsibilities of the task force and technical support unit on Indigenous and local knowledge. </p>
IPBES Data Management Tutorials - Session 2.2: Why a data management policy for IPBES and how it concerns experts
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management policy </em>chapter provides an introduction of the IPBES data and knowledge management policy. It discusses why IPBES has a data and knowledge management policy and who is responsible for what in the implementation and further development of this policy.</p> <p>This session, <em>Why a data management policy for IPBES and how it concerns experts, </em>provides background on what IPBES wants to achieve with its data and knowledge management policy and how it impacts experts within IPBES. The session now has a supplement which covers the development of the policy from version 1.0 to 2.0. </p> <p>Please note that the IPBES data and knowledge management policy is the second version of the IPBES data management policy.</p>
FEL source input for simex_platform tutorial
<p>Simulated x-ray pulse for European XFEL SASE1 beamline 20pC bunch charge (ca. 3 fs pulse duration), undulator length nzc=35, 4.96 keV photon energy.</p> <p>To be used as input file for the simex_platform tutorial on single particle imaging (www.github.com/eucall-software/simex_platform/wiki/SimEx-Tutorial)</p>
Imalys: ESIS-Software-Tools – Tutorial Data
<p>Imalys is a <a href="https://codebase.helmholtz.cloud/esis/Imalys">software library</a> in the <a href="https://doi.org/10.3390/rs16071139">ESIS project</a>. The Imalys library includes a <a href="https://codebase.helmholtz.cloud/esis/Imalys/tutorial">tutorial</a> that shows how image and vector data can be analyzed and transformed into new products. The examples in the tutorial refer to the sample data in <strong>Imalys_tutorial_data.zip</strong>.</p> <p>The software repository with source code, binaries, documents and the tutorials is available on <a href="https://codebase.helmholtz.cloud/esis/Imalys">GitLab</a> and in an earlier version on <a href="https://github.com/c7sepe2/Imalys_ESIS-Software-Tools">GitHub</a>.</p> <p>In the <a href="https://doi.org/10.3390/rs16071139">ESIS project</a> we are trying to put environmental indicators on a well-defined and reproducible basis. The ESIS software library "Imalys" is supposed to generate the remote sensing products defined for ESIS. Landscape diversity, change and different landuse types can be analyzed in time and space. Landuse borders can be delineated and typical landscape structures can be characterized by a self adjusting process.</p> <p><strong>Acknowledgements:</strong></p> <p>We thank the Helmholtz Association and the Federal Ministry of Education and Research (BMBF) for supporting the DataHub Initiative of the Research Field Earth and Environment. The DataHub enables an overarching and comprehensive research data management, following FAIR principles, for all topics in the Program Changing Earth – Sustaining our Future.</p> <p>Parts of the work was funded by the Federal Ministry for Digital and Transport of Germany (BMDV, grant no. 45KI19D041, Artificial Intelligence and Mobility - AIAMO project).</p>
Files from TCGA-KIRC Study for Body Part Regression Tutorial
<p>The data here are in whole based upon data generated by the TCGA Research Network: <a href="https://cancergenome.nih.gov/">http://cancergenome.nih.gov/</a>.</p> <p><br> The DICOM files from the <a href="https://wiki.cancerimagingarchive.net/display/Public/TCGA-KIRC#580038695f8cd691bda43dda71b4093c69c7318">TCGA-KIRC </a>study were converted to nifti files. Moreover, the nifti files with greater size than 35 MB and smaller size than 5 MB were removed (to reduce the size of the dataset and to remove the files with few slices). Furthermore, the metadata from the DICOM files is saved in a separate excel-file.</p>
Downsampling of CT-Lymph-Node Dataset for Body Part Regression Tutorial
<p>Down sampling of the<a href="https://wiki.cancerimagingarchive.net/display/Public/CT+Lymph+Nodes#19726546f04e74ab3631480694fcb72cac2e5477"> CT Lymph Node</a> dataset from the TCIA.<br> The files were down sampled to a pixel spacing of 7 mm/pixel. Through zero padding and cropping, all images are provided in the size of 64px x 64 px. Moreover, the HU values were clipped between -1000 HU and 1500 HU and rescaled to -1 and 1. To avoid aliasing effects, an additional Gaussian smoothing filter was applied before down sampling.</p> <p>This dataset was created for a Body Part Regression tutorial.</p>
Si data files for Galaxy materials science tutorials
<p>This is a training dataset for use in Galaxy materials science tutorials. These files can be used to demonstrate the AIRSS (Ab-Initio Random Structure Searching) method for finding muon stopping sites, using the UEP (Unperturbed Electrostatic Potential) technique for the optimisation stage of that method.</p> <p>The files included are:</p> <ul> <li><strong>Si.cell:</strong> structure file containing atom locations</li> <li><strong>Si.den_fmt:</strong> electron density data, generated with CASTEP</li> <li><strong>Si.castep:</strong> CASTEP log file for the electron density calculation</li> <li><strong>Si-muairss-uep.yaml:</strong> configuration file for the AIRSS / UEP workflow</li> </ul>
Tutorial and dataset for gigapixel-like imaging strategies for dental anthropology
<p>This tutorial and image dataset to accompany the following publication: Willman JC, Lozano M, Hernando R, Vergès JM. Gigapixel-like imaging strategies for dental anthropology: Applications for scientific communication and training in digital image analysis. Quaternary International, <a href="https://doi.org/10.1016/j.quaint.2020.05.027">https://doi.org/10.1016/j.quaint.2020.05.027</a>. Part of the Special Issue: Not Only Use.</p> <p><strong>Contains: </strong>tutorial,<strong> </strong>183 images files for reconstructing three examples of gigapixel-like images, and one “READ ME” file describing the images.</p> <p>The tutorial is meant to be used as a guideline for the creation of gigapixel-like (GPL) images of dental surfaces based on our experience. The methodology can be extrapolated to other types of materials and surfaces, but you may need to augment these guidelines according to the specificity of your own research needs. We hope that the inclusion of this supplement will stimulate other researchers to include specific guidelines and step-by-step processes for how they created their own GPL images. While this study concentrates on scanning electron microscopy (SEM) images, there are many other ways to acquire two-dimensional images (e.g., digital photography, optical light microscopy, etc.) that can be used to create GPL images. Likewise, the number of software packages and their numerous built-in parameters for creating extended focus and mosaic images vary greatly. Therefore, more tutorials/guidelines will surely improve the transparency and accessibility of the GPL methodology in the archaeological sciences, biological anthropology, and allied fields.</p>
Restart dataset for a single location in Norway ALP1 (61.0243N,8.12343E) for CTSM/FATES EMERALD Galaxy tutorial
<p>Restart files for CLM-FATES version 2.0.1 for <a href="https://github.com/NordicESMhub/ctsm/releases/tag/release-emerald-platform2.0.1">CLM-FATES EMERALD version 2.0.1</a>.</p> <p>CTSM_FATES-EMERALD_on_inputdata_version2.0.0_ALP1.tar_(restart_info):<br> - ALP1_refcase.datm.r.2300-01-01-00000.nc <br> - ALP1_refcase.datm.rs1.2300-01-01-00000.bin<br> - ALP1_refcase.cpl.r.2300-01-01-00000.nc <br> - ALP1_refcase.clm2.r.2300-01-01-00000.nc </p> <p>This dataset is being used in the <a href="https://training.galaxyproject.org/training-material/topics/climate/tutorials/fates/tutorial.html">Galaxy Training tutorial on CLM-FATES</a>.</p> <p> </p> <p>This work has been done in in collaboration with <a href="https://usegalaxy.eu/">Galaxy Europe</a> and <a href="https://www.eosc-life.eu/">EOSC-Life</a>:<br> - Within the 1st EOSC-Life Training Open Call, <a href="https://galaxyproject.eu/posts/2020/09/08/training-wp9-eosc-life/">two out of four proposals</a> have been awarded to the European Galaxy team to develop climate science e-learning material and mentoring and training opportunities for our communities.</p> <p>CLM-FATES documentation can be found <a href="https://fates-docs.readthedocs.io/en/latest/">here</a>.</p>
A video tutorial on CHADA preliminary development and use
<p>This video is intended to give practical guidelines on how to prepare a CHADA case study, based on the guidelines and templates that are already available in this Zenodo community.</p>
IPBES Data Management Tutorials - Chapter 5: Tools for data management
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management Policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>Please note that the IPBES data and knowledge management policy is the second version of the IPBES data management policy.</p> <p>This chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle. The chapter includes the following sessions: </p> <ul> <li>Session 5-1: Introduction to tools for data management (<a href="https://doi.org/10.5281/zenodo.4018639">10.5281/zenodo.4018639</a>)</li> <li>Session 5-2: Tools to find and attribute DOIs (<a href="https://doi.org/10.5281/zenodo.4018643">10.5281/zenodo.4018643</a>)</li> <li>Session 5-3: Literature access tools (<a href="https://doi.org/10.5281/zenodo.4014824">10.5281/zenodo.4014824</a>)</li> <li>Session 5-4: Processing and analysis (<a href="https://doi.org/10.5281/zenodo.4018647">10.5281/zenodo.4018647</a>)</li> <li>Session 5-5: References and citation manager: Zotero (<a href="https://doi.org/10.5281/zenodo.4018653">10.5281/zenodo.4018653</a>)</li> <li>Session 5-6: Publishing and sharing (<a href="https://doi.org/10.5281/zenodo.4018655">10.5281/zenodo.4018655</a>)</li> </ul>
Covid-19 CT dataset for Body Part Regression Tutorial
<p>The dataset is a subset of CT scans from the <a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226443">Covid-19-AR</a> dataset from the Cancer Image Archive. The data were converted from the DICOM file format to the NifTI file format for better and easier handling. The converted dataset was created for a tutorial of the <a href="https://github.com/MIC-DKFZ/BodyPartRegression">bpreg</a> python package.</p> <p>Acknowledgment:<br> The dataset was funded with federal funds from the National Center for Advancing Translational Sciences UL1 TR003107 and the National Cancer Institute, Contract No. 75N91019D00024, Subcontract 20X023F. </p>
Fatiando a Terra data v1.0.0: A curated collection of open geophysics data for tutorials and documentation
<p>This repository holds curated sample datasets that can be used in the documentation and tutorials of the <a href="https://www.fatiando.org/">Fatiando a Terra</a> project. All datasets are cleaned and formatted versions of openly available data under permissive licenses or in the public domain.</p> <p>More information about datasets and the code for cleaning, formatting, and preprocessing the data can be found at: <a href="https://github.com/fatiando/data">https://github.com/fatiando/data</a></p> <p>See the README.md file for information on data sources and their original licenses.</p> <p><strong>NOTE:</strong> This collection uses <a href="https://semver.org/">semantic versioning</a> (i.e., MAJOR.MINOR.BUGFIX). Major releases mean that backwards incompatible changes were made to the data. Minor releases add new data without changing existing files. Bug fix releases fix errors in a previous release that makes the data unusable. Changes to the current data files will always be published as a major release unless the file(s) in the previous release was unusable/corrupted.</p>
Data for time series tutorial
<p>These are sample data files to be used in the time series tutorial found here: <a href="https://github.com/abigailStev/timeseries-tutorial">https://github.com/abigailStev/timeseries-tutorial </a></p> <p>They are public datasets from the NICER X-ray Timing Instrument of a black hole, MAXI J1535-571, and a neutron star, Swift J0243.6+6124. There are also Good Time Intervals I created for each of the photon event lists.</p>
Dataset for a tutorial dedicated to the Sankey diagram
<p>The dataset is a standard table representing steps of patient care. It contains 4 standard variables : a patient identifier, the label of the step, the start date and the end date of the step. One patient may have several steps. The step labels are synthetic (i.e., A, B, C, D, E, F) and may correspond to passages in care unit, successive administrations of drugs or carrying out of medical procedures.</p> <p>This dataset is used for a tutorial dedicated to the Sankey diagram : https://gitlab.com/d8096/health_data_science_tutorials/-/tree/main/tutorials/sankey_diagram</p>
Sample datasets for Galaxy RNA-seq tutorial
<p>This is downsampled dataset from http://dx.doi.org/10.1038/nprot.2016.095. It was prepared as follows:</p> <ol> <li>Mapping data from http://dx.doi.org/10.1038/nprot.2016.095 against hg38 using HISAT2</li> <li>Restricting resulting BAM datasets to chrX:70,000,000-80,000,000</li> <li>Extracting reads using picard SamToFastq tool</li> </ol> <p>File rnaseq_sex.tab contains mapping between accession numbers and sex of the sequences individuals. </p>
BrainIAK Tutorials: Condensed Datasets
<p>This is a collection of datasets used by BrainIAK <a href="https://brainiak.org/tutorials">tutorials</a>. These datasets are pre-processed and ready to use. They have been condensed, by reducing the number of subjects from the original studies, to keep the file size small. Each tutorial is paired with a dataset as listed below. The file brainiak_datasets.zip contains the data for all the tutorials, and in unzipped form uses 18GB of space. If you wish to download data for specific tutorials, use the list below to find the correct dataset to download and use.</p> <p>Tutorial 2: VDC (Kim et al., 2017) and 02-data-handling (this is a simulated dataset)</p> <p>Tutorials 3-5: VDC (Kim et al., 2017)</p> <p>Tutorial 6: Ninety Six (Kriegeskorte et al., 2008)</p> <p>Tutorials 7: Face-scene (Turk-Browne et al., 2012). The script for within subject searchlight uses the VDC dataset.</p> <p>Tutorial 9: Face-scene (Turk-Browne et al., 2012)</p> <p>Tutorial 8: Latatt (Hutchinson et al., 2016)</p> <p>Tutorial 10: Pieman2 (Simony et al., 2016)</p> <p>Tutorial 11: Raider (Haxby et al., 2011) and Pieman2 (Simony et al., 2016)</p> <p>Tutorial 12: Sherlock_processed (Chen et al., 2017)</p>
Datasets for Collections: Rule Based Uploader tutorial
<p>Datasets for Collections: Rule Based Uploader tutorial at <a href="https://galaxyproject.github.io/training-material/topics/galaxy-data-manipulation/tutorials/upload-rules/tutorial.html">https://galaxyproject.github.io/training-material/topics/galaxy-data-manipulation/tutorials/upload-rules/tutorial.html</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.