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94 results for “Training Materials”
Training data for 'Genome annotation with Funannotate' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for genome annotation with funannotate.</p> <p>Genome was assembled following the GTN Flye assembly tutorial, then masked with RepeatMasker.</p> <p>RNASeq data: SRR8534859 reads were mapped to the genome using STAR (toolshed.g2.bx.psu.edu/repos/iuc/rgrnastar/rna_star/2.7.8a+galaxy0), then the bam was downsampled (10% with toolshed.g2.bx.psu.edu/repos/devteam/picard/picard_DownsampleSam/2.18.2.1) to reduce the size of the dataset. Fastq files were then extracted from the resulting bam file (toolshed.g2.bx.psu.edu/repos/devteam/picard/picard_SamToFastq/2.18.2.1).</p> <p>SwissProt_subset.fasta is a subset of SwissProt proteins that are known to have some similarity with the genome (found using Diamond against the genome, then extracting sequences matching with e-value < 0.0001).</p>
Training data for 'Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads' tutorial (Galaxy Training Material)
<p>The data here is a copy of the corresponding SRR records in the NCBI SRA. The duplication serves a dual purpose:</p> <ol> <li>as a backup should there be problems connecting to NCBI servers, e.g., during Galaxy user trainings.</li> <li>to illustrate how to obtain raw sequencing data from alternative sources, and to organize the data into the same collection structure in a Galaxy history that is generated by specialized Galaxy SRA download tools.</li> </ol>
Training material for RNA sequencing
<p><strong>RNA-seq</strong> (<strong>RNA sequencing</strong>) uses high-throughput (HTS) data to reveal the presence and quantity of RNA in a biological sample at a given moment in time.</p> <p>RNA-Seq is used to analyze the continually changing cellular transcriptome. Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/SNPs and changes in gene expression.<sup>[4]</sup> In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as miRNA, tRNA, and ribosomal profiling.</p>
Training material for RNA sequencing
<p><strong>RNA-seq</strong> (<strong>RNA sequencing</strong>) uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment in time.</p> <p>RNA-Seq is used to analyze the continually changing cellular transcriptome. Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/SNPs and changes in gene expression.<sup>[4]</sup> In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as miRNA, tRNA, and ribosomal profiling.</p>
Training material for exome sequencing
<p>Exome sequencing means that all protein-coding genes in a genome are sequenced.</p> <p>In Humans, there are ~180,000 exons that makes up 1% of the human genome which contain ~30 million base pairs. Mutations in the exome have usually a higher impact and more severe consequences, than in the remaining 99% of the genome.</p> <p>With exome sequencing, one can identify genetic variation that is responsible for both Mendelian and common diseases without the high costs associated with whole-genome sequencing. Indeed, exome sequencing is the most efficient way to identify the genetic variants in all of an individual's genes. Exome sequencing is cheaper also than whole-genome sequencing. </p> <p> </p> <p>For training on exome sequencing data analysis, the Galaxy community proposes two tutorials (https://github.com/bgruening/training-material/tree/master/Exome-Seq). Here, you can find the needed datasets for these tutorials.</p>
Alevin in commandline - Galaxy Training Material
<p>Input datasets for Generating a single cell matrix using Alevin (bash + R) tutorial on Galaxy Training Network. </p>
Supplementary material for the publication: Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"
Open the record for dataset details and reuse information.
[Supplementary Material] Personalised Route Training for People with Cognitive Impairments through Co-Creation
<p>Supplementary material for the paper ' Paving the Way: Personalised Route Training for People with Cognitive Impairments through Co-Creation' submitted to MUC 2024.</p>
Supplementary material S22: An instance of a honeycomb pulse with a successive train of peaks.
<p>An instance of an accelerometer jolting pulse exhibiting multiple successive peaks, registered from within the honeycomb substrate. Six consecutive traces can clearly be seen within a short time period, lasting approximately 20ms. The white bars either side of the accelerometer trace on the top panel refer to the cropped time period seen in the bottom panel. The maximum magnitude of the pulse waveform is 2.3x10<sup>-3 </sup>m/s<sup>2</sup>. In the spectrogram, the magnitude of the acceleration is logarithmic (to the base 10) with a maximum acceleration of 4.4x10<sup>-4</sup> m/s<sup>2</sup> and the lowest acceleration magnitude corresponding to 1/20 of the maximum. The maximum signal is here forced be the maximum found in the <em>Varroa </em>pulse, as the purpose of this figure is to best showcase the signal peaks.</p>
Training course materials: Introduction to Remote Sensing of Water Quality in Lakes
<p>Dataset including ESA Sentinel-2 MSI and Sentinel-3 OLCI L1B scenes (L1B), atmospherically corrected reflectance products (C2RCC), image subsets, and intermediate water quality products from the Copernicus Land Service and ESA Lakes_cci processing chain <em>Calimnos. </em>Data are in support of a recorded course introducing the theoretical and practical basis for water quality observation of lakes through remote sensing. See https://monocle-h2020.eu/Resources/Training</p> <p>The data package contains the following:</p> <p><strong>input_data.zip </strong>- unprocessed (L1B / L1C) satellite scenes of MSI and OLCI instruments over the Razelm/Sinoe lagoon and Black Sea</p> <p><strong>Output_S3_OLCI.zip - </strong>all outputs derived from the Sentinel-3 OLCI scene, including C2RCC and <em>Calimnos </em>(including POLYMER atmospheric correction and biogeochemical estimates as in the Lakes_cci)</p> <p><strong>Output_S2_MSI_Full_Scene_C2RCC.zip</strong> - atmospherically corrected (C2RCC algorithm) full Sentinel-2 MSI scene</p> <p><strong>Output_S2_MSI_Subset_C2RCC.zip </strong>- subset of Sentinel-2 MSI scene and atmospherically corrected results (C2RCC algorithm) </p> <p><strong>Output_S2_MSI_Calimnos_PML.zip </strong>- subset of Sentinel-2 MSI scene atmospherically corrected using POLYMER and biogeochemical estimates, using <em>Calimnos </em>as in the Copernicus Land Service. </p>
Documenting And Assessing Open Innovation: Co-creation Of An Open Data Model For Surgical Training (Additional materials, tables 2 & 3)
<p>Challenge competitions have recently resurged for promoting open innovation in areas where markets fail to provide incentives, such as the Sustainable Development Goals (SDGs). Challenges call for the general public to contribute novel solutions to a well-defined problem, in exchange for prizes, credentials and the promise of further development of selected solutions. The aim of this paper is to report on the development of an open and collaborative data model to document and evaluate innovations in the context of a challenge competition, while also being compatible with the work of other open source communities to validate and improve them. By reusing open documentation standards and embedding them into a semantic collaborative platform, the model aimed to be flexible enough to respond to the evaluation needs of the project organisers and self-assessment for participants. We expect our experience provides insights on the potential of semantic, collaborative platforms and standards for increasing the impact of innovations towards the SDGs.</p> <p>The developer team defined the goal and scope of the ontology in collaboration with the GSTC organisers. This was done by agreeing on scenarios where the ontology will be used and establishing competency questions that the ontology has to be able to respond to. Table 2 describes the four motivating scenarios, including actors involved, requirements, sequence of actions and main problems identified. Table 3 details the competency questions for each scenario.</p>
Additional Material for Debiasing architectural decision-making: a workshop-based training approach
<p>This additional material was created to enable full replication of the experiment described in the paper:<br> "Klara Borowa, Maria Jarek, Gabriela Mystkowska, Weronika Paszko and Andrzej Zalewski: <strong>Debiasing architectural decision-making: a workshop-based training approach</strong>".<br> The paper was accepted for publication at ECSA 2022 (The 16th European Conference on Software Architecture).<br> <br> The folder contains the following files:<br> Coding-details.xlsx - the coding scheme, as well as coding results. Contains all code counts for each team participating in the experiment.<br> Experiment-meeting-plan.pdf - contains the plan for the 3-hour meeting during which the experiment was conducted, as well as a timetable of when each meeting took place and who organised it.<br> Debiasing-workshop-slides.pdf - slides used during the debiasing workshop.<br> Debiasing-workshop-plan.pdf - detailed instruction for workshop organisers, what should be said and done during the workshop.<br> Participants_questionnaire_and_results.xlsx - questionnaire questions used to obtain information about participants, as well as questionnaire results for all 44 participants.</p>
The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data
<p>Dataset used in the research presented in the article:</p> <p>Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" <em>Materials</em> 15, no. 10: 3727. https://doi.org/10.3390/ma15103727</p> <p>The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.</p>
SSHOC Training Material Video 5 - Leveraging the the Ethnic and Migrant Minorities' (EMM) Survey Registry for public policy - training video in English, French and Spanish
<p>EMM Survey Registry - D9.4 - Video 5 Live Demo - Policy Making Stackholders (front- and back-end)</p> <p><strong>The video will begin by reiterating what the EMM Survey Registry is and why policy-making stakeholders (e.g. civil society and think tank communities; policymakers) interested in quantitative surveys on ethnic and migrant minorities’ integration and/or inclusion should be using this service. It will then explain how this target user group can both leverage the EMM Survey Registry for finding and learning about surveys, as well as showcasing surveys they have produced and/or have been involved in.</strong></p>
Training data for 'Mapping-by-sequencing' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that demonstrates mapping-by-sequencing analysis and represent a subsample of the data used in Sun & Schneeberger, 2015 (DOI:10.1007/978-1-4939-2444-8_19).</p>
Trained Potentials for Article "Computationally Efficient Machine-Learned Model for GST Phase Change Materials via Direct and Indirect Learning"
<p>We provide 8 files here to get started using our trained potentials:</p> <p>1) *.yaml files for each trained potential. These are the outputs of the PACE training process.</p> <p>2) *.yace files for each trained potential. These are read by LAMMPS to use the trained potential. They can be obtained from the *.yaml files using the command line command: "pace_yaml2yace *.yaml".</p> <p>3) GST_config.data -- a starting configuration of GST to be read by LAMMPS. This configuration contains 504 atoms at density 5.85 g/cm^3.</p> <p>4) sample.inp -- a sample LAMMPS input file using the trained potentials. This currently uses "ACE-Indir2.yace" to run the starting configuration "GST_config.data" for 10 ps at 1200 K. When run, it outputs a log file "test.log" and a dump file "test.dump". The choice of trained potential can be changed in the "pair_coeff" section.</p>
Galaxy Hi-C Training material dm3
<p>Hi-C data for Galaxy training, dm3 cells.</p>
Training data for 'Genetic map RADSeq ' tutorial (Galaxy Training Material)
<p>The data provided here are part of a study published by Amores<em> et al.</em> (2011) (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3176089/">doi 10.1534/genetics.111.127324</a>), exploiting massively parallel DNA sequencing to develop meiotic maps by genotyping F<sub>1</sub> offspring of a single female and a single male spotted gar (<em>Lepisosteus oculatus</em>).</p>
Training material for Genome Annotation
<p>Genome annotation is the process of attaching biological information to sequences. It consists of three main steps:</p> <ul> <li>identifying portions of the genome that do not code for proteins</li> <li>identifying elements on the genome, a process called gene prediction, and</li> <li>attaching biological information to these elements.</li> </ul>
Training material for analysis small RNA-seq data (Galaxy Training Network tutorial)
<p>The data provided here is part of the Galaxy Training Network tutorial for analysis of small RNA-seq (sRNA-seq) data using mirdeep2 and miranda. This dataset is provided by INRA (Le Rheu, France).</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.