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Dataset results
392 results for “tutorial”
Training material for small RNA-seq data analysis (Galaxy Training Network tutorial)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes small RNA-seq (sRNA-seq) data from a study published by Harrington et al. (DOI:10.1186/s12864-017-3692-8) to detect differential abundance of various classes of endogenous short interfering RNAs (esiRNAs). The goal of this study was to investigate "connections between differential retroTn and hp-derived esiRNA processing and cellular location, and to investigate the potential link between mRNA 3’ end cleavage and esiRNA biogenesis." To this end, sRNA-seq libraries were constructed from triplicate <em>Drosophila</em> tissue culture samples under conditions of either control RNAi or RNAi knockdown of a factor involved in mRNA 3’ end processing, <em>Symplekin</em>. This dataset (GEO Accession: GSE82128) consists of single-end, size-selected, non-rRNA-depleted sRNA-seq libraries. Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to a subset of interesting transcript features including: (1) transposable elements, (2) <em>Drosophila</em> piRNA clusters, (3) <em>Symplekin</em>, and (4) genes encoding mass spectrometry-defined protein binding partners of <em>Symplekin</em> from Additional File 2 in the indicated paper by Harrington et al. More details on features 1 and 2 can be found here: https://github.com/bowhan/piPipes/blob/master/common/dm3/genomic_features (piRNA_Cluster, Trn). All features are from the <em>Drosophila</em> genome Apr. 2006 (BDGP R5/<em>dm3</em>) release.</p>
Video tutorial for creating uploads on EFSA's Knowledge Junction community of Zenodo
<p>The Knowledge Junction is a curated, open repository for the exchange of evidence and supporting materials used in food and feed safety risk assessments, with the goal of improving transparency, reproducibility and evidence reuse. The content of this repository can be used by EFSA's panels and working groups and any other interested parties when preparing for new risk assessments. </p> <p>The video tutorial describes the process of creating a new upload along with filling out the metadata based on the requirements for publishing objects in Knowledge Junction. </p> <p><strong>Objects suitable for submission to the repository</strong></p> <ul> <li>Objects which already have a DOI should <strong>not</strong> be published in the repository</li> <li>Objects which are subject to copy right restrictions should <strong>not</strong> be published in the repository</li> <li>All other evidence and supporting materials of relevant for food and feed safety will be accepted if the metadata provided is completed according to the instructions below</li> </ul>
Immcantation Introductory Tutorial Data
<p>Necessary datasets to run the <a href="http://immcantation.org">Immcantation</a> Introductory Tutorial. Below is the description of the files in the data set.</p><ul><li>input.fasta: Example data from "Identification of Subject-Specific Immunoglobulin Alleles From Expressed Repertoire Sequencing Data" (Laserson et al. (2014)). Processed B cell receptor reads from one healthy donor (PGP1) 3 weeks after flu vaccination. As part of the processing, each sequence has been annotated with the isotype.</li></ul>
Auto-MISCHBARES: Tutorial & Demonstration
<p>Tutorial and demonstration of Auto-MISCHBARES.</p> <p> </p> <p>Additional links:</p> <p>MISCHBARES: <a title="MISCHBARES" href="https://github.com/fuzhanrahmanian/MISCHBARES">https://github.com/fuzhanrahmanian/MISCHBARES</a></p> <p>MADAP: <a title="MADAP" href="https://github.com/fuzhanrahmanian/MADAP">https://github.com/fuzhanrahmanian/MADAP</a></p> <p>HELAO: <a title="HELAO" href="https://github.com/helgestein/helao-pub">https://github.com/helgestein/helao-pub</a></p> <p> </p>
Designing Smartphone Surveys: A Series of Video Tutorials
<p>A series of 8 step-by-step videos to assit in the desing and implementation of smartphone surveys.</p>
FastEddy-model Tutorials: Moist Dynamics, Example04_BOMEX
<p>This archive contains the data required for the FastEddy-model tutorial on moist dynamics validation. The dataset includes a FastEddy initial conditions file for the validation case, FE_BOMEX.0 along with simulation results from the 11 models that participated in the original Siebesma et al. 2003 model intercomparison.</p>
Ecosystem-based marine spatial planning assessment tool video tutorial
<p><span>Aiming at promoting the capacity building of competent authorities, scientists and consultants, a novel tool is proposed for assessing the alignment of marine spatial planning processes with ecosystem-based approach principles and to guide its operationalization.</span></p> <p><span>The EB-MSP assessment tool is developed with the ambition of providing a fit-for-purpose tool to overcome the ecosystem-based marine spatial planning implementation barriers reported by experts and managers.</span></p> <p><span>The EB-MSP assessment tool is designed to apply to any spatial plan, regardless of its stage of development: assessment of existing plans, plans in progress; or different national plans in a transboundary region.</span></p> <p><span>The EB-MSP tool can be accessed at https://aztidata.es/EB-MSP</span></p> <p><span>The EB-MSP assessment tool leads users through a step-by-step procedure for evaluating a specific plan.</span></p> <p><span>Upon first access, the user must register. This way, the users can conduct multiple assessments in a single session or different sessions, and can retrieve the information from previous sessions.</span></p> <p><span>The evaluation can be conducted by documenting the actions adopted during the planning process stages, or by examining how ecosystem-based marine spatial planning cross-cutting topics, have been incorporated into the plan.</span></p> <p><span>The user has to provide information about 130 statements that reflect the actions or tasks adopted during the planning process.</span></p> <p><span>For each action, six complementary fields of information have to be provided by the user:</span></p> <ol> <li><span><span><span> </span></span></span><span>the degree of implementation of the action; </span></li> <li><span><span><span> </span></span></span><span>the relevance of the action for the assessed planning site; </span></li> <li><span><span><span> </span></span></span><span>the main source of knowledge base supporting the action; </span></li> <li><span><span><span> </span></span></span><span>the respondent's confidence; </span></li> <li><span><span><span> </span></span></span><span>approaches, tools and methods implemented; and </span></li> <li><span><span><span> </span></span></span><span>justification of the responses</span></li> </ol> <p><span>Once the assessment is performed, the results are displayed as dynamic graphs that can be downloaded.</span></p> <p><span>The responses are also stored in a table, which can be downloaded as an Excel spreadsheet.</span></p>
Example data used in tutorial of HEIG (v1.2.0)
<p>The repository contains example data used in tutorial of HEIG v1.2.0. Note, this data is not compatible with v1.0.0, and some options are supported in v1.1.0. HEIG is a statistical framework for efficiently conducting joint analysis for large-scale imaging and genetic data.</p> <p>The dataset has the following file structures:</p> <ul> <li>input <ul> <li>images1: 500 simulated images</li> <li>images2: 500 simulated images</li> <li>genotype: genotype data in PLINK bfile</li> <li>misc: miscellaneous files</li> <li>ldr_sumstats: 19 LDR GWAS summary statistic files of the superior fronto-occipital fasciculus</li> <li>ld_regu8580: LD matrices with regularization {85%,80%}, including ~460,000 genotype array SNPs</li> <li>ld_regu7570: LD matrices with regularization {75%,70%}, including ~460,000 genotype array SNPs</li> <li>visualization: example files for visualization</li> </ul> </li> <li>output <ul> <li>fpca: results of functional PCA</li> <li>genotype: hail.MatrixTable of genotype data</li> <li>gwas: LDR GWAS results generated by HEIG</li> <li>herigc: results of heritability and (cross-trait) genetic correlation analysis</li> <li>images: preprocessed 1000 images in H5DF file</li> <li>ldr: LDRs constructed from 1000 images</li> <li>sumstats: preprocessed 19 LDR GWAS summary statistic files of the superior fronto-occipital fasciculus</li> <li>visualization: visualization results</li> <li>voxelgwas: voxel-level GWAS results</li> </ul> </li> </ul> <p>The results in ouput are produced by data in input, which can verify if the user runs the code correctly.</p>
Supplementary datasets, data analysis code, and R tutorials for: Phylogenetic analysis of adaptation in comparative physiology and biomechanics: overview and a case study of thermal physiology in treefrogs
<p>Comparative phylogenetic studies of adaptation are uncommon in biomechanics and physiology. Such studies require collecting data from many species, a challenge when data collection is experimentally intensive. Moreover, researchers struggle to employ the most biologically appropriate phylogenetic tools for identifying adaptive evolution. Here, we detail an established but greatly underutilized phylogenetic comparative framework—the Ornstein-Uhlenbeck process—that explicitly models long-term adaptation. We discuss challenges in implementing and interpreting the model, and we outline potential solutions. We demonstrate use of the model through studying the evolution of thermal physiology in treefrogs. Frogs of the family Hylidae have twice colonized the temperate zone from the tropics, and such colonization likely involved a fundamental change in physiology due to colder and more seasonal temperatures. However, which traits changed to allow colonization is unclear. We measured cold-temperature tolerance and characterized thermal performance curves in jumping for twelve species of treefrogs distributed from the Neotropics to temperate North America. We then conducted phylogenetic comparative analyses to examine how tolerances and performance curves evolved and to test whether that evolution was adaptive. We found that tolerance to low temperatures increased with the transition to the temperate zone. In contrast, jumping well at colder temperatures was unrelated to biogeography and thus did not adapt during dispersal. Overall, our paper shows how comparative phylogenetic methods can be leveraged in biomechanics and physiology to test the evolutionary drivers of variation among species.</p>
A tutorial dataset for TransportTools engine
<p>This file includes the input data necessary to follow the tutorial for TransportTools standard workflow.</p> <p>- two trajectories and topologies of MD simulations</p> <p>- templates for CAVER and AQUA-DUCT analyses of these simulations</p> <p>- precomputed results of CAVER and AQUA-DUCT analyses</p>
Training material for flye genome assembly (Galaxy Training Network tutorial)
<p>Datasets are subsets of 3 public datasets (Mucor mucedo Fresen. NRRL 3635 Standard Draft genome sequencing with PacBio technology)</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336965[accn]</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336964[accn]</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336963[accn]</p> <p> </p> <p> </p>
Dataset for the tutorial of supeRbaits R package
<p>Dataset for the tutorial of the R package "supeRbaits" (https://github.com/BelenJM/supeRbaits)</p>
Long reads training material for 'Quality Control' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for reads Quality Control.</p> <p>PacBio HiFi reads were provided by PacBio - GIAB sample HG002 (https://www.pacb.com/smrt-science/smrt-resources/datasets/) and was downsampled using seqtk (https://github.com/lh3/seqtk)</p> <p>Nanopore reads were provided by Tim Kahlke as part of "Long-Read, long reach Bioinformatics Tutorials" (https://timkahlke.github.io/LongRead_tutorials/) and was basecalled using Guppy v5.0.2 (dna_r9.4.1_450bps_sup.cfg).</p>
Tracking focal adhesions with TrackMate and Weka - tutorial dataset 2
<p>This folder contains data used to illustrate the utility of Weka detector in TrackMate.</p> <p>- classifier.model: trained Weka classifier.<br> - image data: <strong> human dermal microvascular blood endothelial cells expressing GFP-paxillin</strong></p> <p>More detail on using these files can be found here: https://imagej.net/plugins/trackmate/trackmate-weka.</p>
Training data for the "Biodiversity data exploration" Galaxy-E tutorial
<p>Dataset sample from Reef life survey initiative https://reeflifesurvey.com/ to serve as a training set for "Biodiversity data exploration" tutorial for Galaxy and notably Galaxy for ecology initiative</p>
MIntO - Tutorial
<p>MIntO tutorial.</p> <p>Subset of 10 human gut microbiome samples, matching Illumina metagenomic (<strong>metaG</strong>) and metatranscriptomic (<strong>metaT</strong>) data, to illustrate the use of MIntO.</p> <p>The subset of 10 metagenomes from the Inflammatory Bowel Disease Multi’omics Database (IBDMDB) correspond to two participants with Crohn’s disease (<code>CD1</code> and <code>CD2</code>). </p> <p>- Samples from <code>CD1</code>: CD136, CD138, CD140, CD142, CD146.</p> <p>- Samples from <code>CD2</code>: CD237, CD238, CD240, CD242 and CD244.</p> <p> </p> <p>There are two files:</p> <p>- IBD_tutorial_raw.tar.gz comprises the raw reads to run this tutorial! This samples have been filtered from the original ones and only include reads matching to 5 genomes (<em>Faecalibacterium prausnitzii</em>, <em>Parabacteroides distasonis</em>, <em>Bacteroides fragilis</em>, <em>Ruminococcus bromii</em> and <em>Bacteroides_uniformis</em>)</p> <p>- IBD_tutorial_output.tar.gz` comprises the output, after running MIntO.</p> <p> </p>
2D Sound Navigation - Tutorial Materials
<p>Materials presented to the experiment participants to familiarize them with the navigation controls and auditory guidance.</p>
Tutorial videos of MEFisTo software and other UVic ECE 340 course content
<p>An assemblage of tutorial videos and content that document the usage of MEFisTo software and detail example questions from the <em>Fundamentals of Applied Electromagnetism</em> textbook by Ulaby <strong>2014</strong>. These materials were used for the ECE 340 course at the University of Victoria in Winter 2021.</p>
Training data for 'Functional annotation of protein sequences' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for functional annotation of protein sequences.</p>
Training data for 'Refining Manual Genome Annotations with Apollo (eukaryotes)' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for manual curation of eukaryotic genome annotation using Apollo.</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.