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94 results for “Training Materials”
Supplementary material 5 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
Questions we asked in the Georeferencing for Research Follow Up Survey done 3 months after the workshop.
Supplementary material 4 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
Three months after the workshop, participants were surveyed to assess what workshop-related knowledge and materials were being used and disseminated to others. This document summarized data collected in this particular survey.
Supplementary material 1 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
Darwin Core Archive file downloaded from the iDigBio portal for use in the Georeferencing for Research Use workshop. Total 25,429 records, accessed on 2016-08-29. Collections contributing to the record set are listed in the archive records.citation.txt file. Dataset GUID: a69d1541-4726-465d-84ad-50c7ed556eee
STOC population trend - Galaxy training material
<p>Galaxy training material example file</p> <p>https://galaxyproject.github.io/training-material/</p> <p> </p>
SSHOC Training Material Video 4 - Contributing metadata to the Ethnic and Migrant Minorities' (EMM) Survey Registry as a professional polling/survey company - training video in English, French and Spanish
<p>Contributing metadata to the Ethnic and Migrant Minorities' (EMM) Survey Registry as a professional polling/survey company</p> <p>A training video targeting professional polling/survey companies to entice them to document their surveys on the EMM Survey Registry</p> <p><strong>Target Audience for the video: </strong>Professional polling/survey companies producing quantitative surveys on ethnic and migrant minorities’ integration and/or inclusion</p>
Trajectory Analysis: Monocle3 in RStudio - Galaxy Training Material
<p>The input dataset for the trajectory analysis tutorial using Monocle3 in RStudio, which is a follow-up to the case study tutorials that can be found on the <a href="https://training.galaxyproject.org/training-material/topics/single-cell/">Galaxy Training Network</a>.</p>
Training material for the mapping and quantification of single-cell ATAC-seq 10X Datasets
<p>The data provided here is part of the Galaxy Training Network tutorial that analyses 10x genomics single-cell ATAC-seq data from the 10x platform. </p> <p>Due to time constraints during training, the datasets were subsampled to reads that map to chrY.</p> <p>The 10x Genomics Datasets follow the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution</a> license.</p>
Illustration for "Ten simple rules for making training materials FAIR"
<p>Illustration for paper "Ten simple rules for making training materials FAIR"</p>
Seurat object input file - Galaxy Training Material
<p>A Seurat input file for conversion to AnnData.</p>
Using the IBM analog in-memory hardware acceleration kit for neural network training and inference - Supplementary Material
<p>Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripheral circuitry in AIMC chips require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this Tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, a platform that provides the benefits of using the AIHWKit simulation in a fully managed cloud setting along with physical AIMC hardware access, freely available at https://aihw-composer.draco.res.ibm.com. Finally, we show examples of how users can expand and customize AIHWKit for their own needs. This Tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from <a href="https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial" target="_blank" rel="noopener">https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial</a>.</p> <p> </p>
Training and Support for Student Library Employees in a Tiered Reference Service Model: Supporting Materials
Open the record for dataset details and reuse information.
Training material for Galaxy 101 tutorial
<p>The data provided here are part of a Galaxy tutorial "Galaxy 101". The files contain BED-formatted 1) coding exons on chromosome 22 of human genome version hg38, 2) SNPs from dbSNP version 153 located in whole genes, and 3) repeats from hg38 chr22.</p>
Metagenomic assembly (Galaxy Training Material)
<p>The tutorial aims to train how to perform assembly of metagenomic samples. (https://training.galaxyproject.org/training-material/)</p>
Establish the uSI Values and End-user Training Material That Will be Used to Guide Oxytocin Dosing Actions
ClinicalTrials.gov study NCT04548453. IPD Sharing: NO. Countries: 1. Publications: 0.
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.