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392 results for “Tutorial”
Remote Sensing with TerrSet Guide Tutorial Data
<p>The Remote Sensing Guide provides a comprehensive introduction to the TerrSet remote sensing software package. With clear instructions and more than 300 color illustrations, the text is ideal for students and professionals seeking a hands-on and guided exploration of the fundamental issues in remote sensing and image processing. This latest version is digital only (<a href="https://www.amazon.com/Remote-Sensing-TerrSet-2020-IDRISI-ebook/dp/B08V1LBT15/ref=sr_1_12?crid=34HSK8ZIPQ9PG&dchild=1&keywords=remote+sensing+guide&qid=1611854020&s=books&sprefix=remote+sens%2Cstripbooks%2C148&sr=1-12">buy on Amazon</a>) and works with TerrSet liberaGIS release as well as also previous versions of TerrSet. You can download data for each chapter separately, or use the Download All button to download all of them toether. Please note that all files are zipped.</p>
Pylake tutorial dataset: Force calibration
<p>A dataset acquired on a LUMICKS C-Trap used to demonstrate passive and active force calibration of an optical trap.</p> <p>The dataset contains:</p> <p>- A passive force calibration deep in the flowcell.</p> <p>- An active calibration near the surface of the flowcell.</p> <p>- A calibration on a fast detector.</p> <p>- A calibration spectrum with a noise peak.</p> <p>- A calibration with a noise floor.</p> <p>- A calibration item with the data inside the calibration marker.</p>
Tutorial files for CICLOPv2_waterdynamics
<p>The end to end solution for identifying and characterizing protein cavities and the water within them</p> <p>CICLOP v2.0 is a an extension of CICLOP (<a href="https://ciclop.raylab.iiitd.edu.in/" rel="nofollow">https://ciclop.raylab.iiitd.edu.in/</a>), a robust Python-based molecular dynamics trajectory analysis tool tailored for identifying and characterizing protein cavities (internal cavities) and the water contained within them. This tool takes molecular dynamics simulation trajectories or a single pdb file as its input. Here, we provide a concise guide on how to use CICLOP to characterize water within protein cavities.</p> <p>CICLOP is built on python3.11. The following are the dependencies required to run CICLOP</p> <ol> <li>numpy, scipy, math, pandas, sys, os, time</li> <li>MDAnalysis</li> <li>tqdm</li> <li>multiprocessing</li> <li>shapely</li> <li>gc</li> <li>argparse</li> <li>concurrent.futures</li> <li>collections</li> </ol> <p>The above dependncies can be installed seperately or using the yml file provided. And viola! You are ready to delve into the vast world of protein cavities.</p> <p>A detailed turotial is also provided to help you get accustomed to the tool's usage.</p> <p>For more help with input flags : CICLOP -h or python3 CICLOP.py -h</p> <p>For basic usage: CICLOP -w tip3p -f input.pdb -o Output.pdb</p> <p> </p>
Tutorial "Fundamentals of the laser flash thermal diffusivity measurements"
<p>E-learning module about the fundamentals of the laser flash thermal diffusivity measurements which is part of a tutorial series prepared in the framework of the Hi-TRACE project. </p>
Tutorial "LFA front side measurement"
<p>E-learning module about front side measurements using the laser flash method which is part of a tutorial series prepared in the framework of the Hi-TRACE project. </p>
Tutorial "Uncertainty assessment for thermal diffusivity measurement by laser flash method"
<p>E-learning module about the assessment of uncertainty associated with thermal diffusivity measurement performed at high temperature by the laser flash method. Part of a tutorial series prepared in the framework of the Hi-TRACE project. </p>
Tutorial "Thermal diffusivity of solid materials using the flash method"
<p>E-learning module about the thermal diffusivity measurement of different types of solid materials using the flash method. Part of a tutorial series prepared in the framework of the Hi-TRACE project. </p>
Dataset to test the Nextflow Tutorial
<p>Tutorial: <a href="https://telatin.github.io/microbiome-bioinformatics/Nextflow-start/">https://telatin.github.io/microbiome-bioinformatics/Nextflow-start/</a></p> <p>Repository: <a href="https://github.com/telatin/nextflow-example">https://github.com/telatin/nextflow-example</a></p>
Tangos tutorial datasets
<p>These files contain output from sample cosmological simulations, suitable for use in the <a href="https://pynbody.github.io/tangos/first_steps.html">tangos tutorials</a> </p>
Example Data for PlotsOfData Tutorial
<p>Example Data for PlotsOfData Tutorial</p>
Die Mini-Präsentation - Tutorial
<p>Tutorial zur Mini-Präsentation - Standard-Übung in Lehrveranstaltungen von Prof. Dr. De Rentiis</p>
Dataset for a tutorial to quantify BAM earthquake using SNAP
<p><strong>A tutorial to quantify BAM earthquake using SNAP</strong></p> <p>Differential InSAR is a satellite-based remote sensing technique that can be used to quantify small displacements of the Earth's surface. This is due to the interferometric phase being much more sensitive to the ground motion than to the elevation difference. This practical session will explain how to apply it to real-world Envisat ASAR images, with user-oriented open-source SNAP software. The main goal is able to generate ground motion from a pair of SAR images to map the Earthquake of 2003 in BAM city.</p> <p>More information can be found here: </p> <p>Video: https://youtu.be/Uc-5F9Vz04w</p> <p>https://github.com/BAMInSAR</p> <p>https://www.facebook.com/groups/RadarInterferometry</p>
Salmon SPLiT-seq tutorial data
<p>This is the data required to run the salmon and alevin-fry tutorial on SPLiT-seq data. </p>
Tutorial for the 2022 ACM SIGMOD Conference: Spatial Data Quality in the IoT Era: Management and Exploitation
<p>Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications.</p>
Test data for the Large Genome Assembly tutorial
<p>A set of test data to use for the Galaxy Training Network tutorial, Large genome assembly. This data is publicly available in other sources, but has been combined here and subsampled for easier use in the tutorial. We do not claim ownership of this data - please see the full attribution to each of the data sources explained below.</p> <p><strong>Sequencing reads:</strong></p> <p>From the Snow gum: <em>Eucalyptus pauciflora</em>. From NCBI BioProject number: PRJNA450887; Paper: Wang W, Das A, Kainer D, Schalamun M, Morales-Suarez A, Schwessinger B, Lanfear R; 2020, doi: 10.1093/gigascience/giz160.</p> <p>From NCBI, three read files were imported into Galaxy for this tutorial: nanopore reads (SRR7153076), and paired Illumina reads (SRR7153045). For the test data set: these were randomly subsampled to 10% of the original file size, and reads mapping to related chloroplast gene sequences (rbcL sequence: accession KM360776.1; matK sequence: accession KT632904.1) were excluded. </p> <p>Files: Nanopore reads; Illumina reads, R1 and R2</p> <p><strong>Reference genome: </strong></p> <p><em>Arabidopsis thaliana. </em>Although this is not the same species as above, we can use it as an example for a comparison step in the tutorial. This has been downloaded from The Arabidopsis Information Resource at <a href="https://www.arabidopsis.org/index.jsp">https://www.arabidopsis.org/index.jsp</a> from Genes: Download: TAIR10 genome release: TAIR10 chromosome files: file TAIR10_chr_all.fas.gz. Then unzipped into a fasta file. </p>
Tutorial to explore riverbank erosion along Jamuna River, Bangladesh, with the Google Earth Engine
<p>This short tutorial shows the different features and data that are available in a tool that allows you to explore riverbank erosion in Bangladesh. The tool has been developed by: Freihardt & Frey (under submission): Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine.</p> <p>The tool can be accessed via this link: <a href="https://code.earthengine.google.com/3ea8f1fd5d771accc621550d744a914e?hideCode=true">https://code.earthengine.google.com/3ea8f1fd5d771accc621550d744a914e?hideCode=true</a></p> <p>To access the video tutorial without downloading it: <a href="https://youtu.be/_b9AAPDw7Wk">youtu.be/_b9AAPDw7Wk</a></p>
CG Fast Track thanks for tutorial
Still leraning blender to make my own 3d indie game. CG Fast Track has one of the best tutorials that helps me to create more stuffs for my game. Thanks Source: Objaverse 1.0 / Sketchfab
For Student Tutorial Example
Tutorial Step: 1.Basic Modeling 2.To ZBrush Sculpting 3.Retopology 4.Bake(Marmoset or Substance Painter) 5.Paint Material 6.To Unity←Finish (ↀДↀ)✧ Source: Objaverse 1.0 / Sketchfab
Week 4 - Excel 2007 Tutorial Creating Charts
<p>Video for Week 4 ICT Tourism & Management </p> <p>Step by step for creating charts with Microsoft Excel 2007</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>
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.