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Dataset results
94 results for “Training Materials”
Nanopore sequence analysis - Galaxy Training Material
<p>Twelve MDR plasmids harboring samples were prepared according to the MinION library construction protocols, followed by library sequencing. After 8 hours of sequencing run, a total of 287 725 reads ranging from dozens to tens of thousands of bases in length were obtained, covering a total of 493 Mbp. The raw data were subjected to several stages of processing, including basecalling, de-multiplexing, fasta sequence extraction. For this tutorial one out of the twelve samples is chosen as example.</p> <p>This dataset is extracted of a project studying the Efficient generation of complete sequences of MDR-encoding plasmids by rapid assembly of MinION barcoding sequencing data (<a href="https://doi.org/10.1093/gigascience/gix132">https://doi.org/10.1093/gigascience/gix132</a>)</p>
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>
Training material - NBC- newspapers FR CH
<p>List of articles used to training a naive Bayes classifier, selected from French language Swiss newspapers, extracted from the impresso app. </p>
Detection of SARS-CoV-2 variants by genomic analysis of wastewater ampliconic samples (Galaxy Training Material)
<p>The tutorial aims to train how to run workflows to analyze lineages abundances in SAR-CoV-2 wastewater ampliconic samples. (https://training.galaxyproject.org/training-material/)</p>
Detection of SARS-CoV-2 variants by genomic analysis of wastewater metatranscriptomic samples (Galaxy Training Material)
<p>The tutorial aims to train how to run workflows to analyze lineages abundances in SAR-CoV-2 wastewater metatranscriptomic samples. (https://training.galaxyproject.org/training-material/)</p>
Scanpy Parameter Iterator - Galaxy Training Material
<p>Input dataset for the Scanpy Parameter Iterator tutorial on Galaxy Training Network. It is an extension of <a href="https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.html">Filter, Plot and Explore Single-cell RNA-seq Data</a> tutorial.</p>
Decoratype-based materials informatics: Polaritype identification, convex hull DFT calculations, training data, and predicted compounds data
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Downsampled AnnData input file - Galaxy Training Material
<p>Downsampled input dataset for the Single Cell Data Formats Conversion tutorial on Galaxy Training Network. </p>
CDS input for Monocle3 tutorial - Galaxy Training Material
<p>CDS input file for Monocle3 trajectory analysis tutorial. Created from AnnData object from the upstream pre-processing.</p>
Supplementary material for the paper "A bibliometric investigation on didactic proposals in interpreter training (2001-2020)"
<p>Supplementary material for the article entitled "A bibliometric investigation on didactic proposals in interpreter training (2001-2020)". It includes a readme file with instructions. VOSviewer is required to view the data (https://www.vosviewer.com). </p>
Combining datasets after Alevin pre-processing - Galaxy Training Material
<p>These are the downsampled datasets for one of the single cell case study tutorials in Galaxy: "Combining single cell datasets after pre-processing". </p>
Training data for 'Maximum Likelihood Phylogeny Reconstruction'' (Galaxy Training Material)
<p>This data is used for Galaxy Training Network (GTN) training 'Maximum Likelihood Phylogeny Reconstruction'. It consists of 173 amino acid alignments of orthologs found in chromosome 5 of four strains of S. cerevisiae. Original sequence data (https://zenodo.org/record/6610704) was processed in Galaxy following GTN 'Preparing genomic data for phylogeny reconstruction' training (10.48546/workflowhub.workflow.359.1) to generate alignments of orthologs.</p>
Trajectory Analysis using Monocle3 - Galaxy Training Material
<p>Input datasets for the trajectory analysis tutorial from the case study series.<br> https://training.galaxyproject.org/training-material/topics/transcriptomics/ </p>
Calculating α and β diversity from microbiome taxonomic data - Galaxy Training Material
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Training material for Calling variants in non-diploid systems
<p>The majority of life on Earth is non-diploid and represented by prokaryotes, viruses and their derivatives such as our own mitochondria or plant’s chloroplasts. In non-diploid systems allele frequencies can range anywhere between 0 and 100% and there could be multiple (not just two) alleles per locus. The main challenge associated with non-diploid variant calling is the difficulty in distinguishing between sequencing noise (abundant in all NGS platforms) and true low frequency variants. </p>
Supplementary material 7 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.
Supplementary material 3 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
The informed consent request and workshop survey questions given to participants after the workshop each day for 4 consecutive days.
Supplementary material 2 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
This document shows just the questions we asked the applicants who applied to participate in this Georeferencing for Research Use workshop. We used a Google Form to deliver these questions and collect responses. It is both an application and serves as our pre-workshop survey.
Supplementary material 8 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Summary of desired future workshop topics that were listed by participants on the last day of the workshop.
Supplementary material 6 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Summary of topics to be covered in an ideal workshop as identified by workshop applicants in the workshop call for participation. We incorporated as many as possible that also fit our scope.
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.