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Dataset results
392 results for “Tutorial”
GenVarLoader Tutorial: Geuvadis Chromosome 22
<p>Chromosome 22 genotypes and BigWigs for the Geuvadis subset of the 1000 Genomes Project. The 451 BigWigs come from recount3 using GRCh38 and GENCODE v29, with samples deduplicated to one per individual by choosing the samples with highest library size. The genotypes are 451 individuals from the 100 Genomes that are represented in the Geuvadis study.</p>
Práctica Metagenómica: Tutorial de microbiomas usando QIIME2 / Practical Metagenomics: Microbiome tutorial with QIIME 2
<p>Data for the e-learning tutorial Práctica Metagenómica: Tutorial de microbiomas usando QIIME2 </p> <p>Datos para el tutorial e-learning <a href="https://f1000research.com/documents/10-798">Practical Metagenomics: Microbiome tutorial with QIIME 2</a></p> <p> </p>
Tutorial data for Imaging in neuroscience: with a focus on MEG and EEG methods
<p>MEG/EEG/MRI tutorial data for the PhD course Imaging in neuroscience: with a focus on MEG and EEG methods at Karolinska Institutet, Stockholm, Sweden. For more information, see: https://github.com/natmegsweden/meeg_course</p> <p>Data from a single subject. The participant received 160 tactile stimulation to all five fingers of the right hand at a rate of 0.3 Hz while watching a silent movie. Continuous HPI was measured for the duration of the recording.</p> <p>MEG was recorded with a Neuromag Triux MEG scanner with 102 magnetometers and 204 planar gradiometers at a sample rate of 1000Hz. Simultaneously recorded electroencephalography (EEG) with 128 channels. Electrocardiogram (ECG) or electrooculogram (EOG) was measured together with MEG to control for artefacts from heartbeats and eye-blinks. Structural MRI.</p>
Source code for R tutorials and dataset for empirical case study on Malurus elegans (red-winged fairy wren)
<p>Biological processes exhibit complex temporal dependencies due to the sequential nature of allocation decisions in organisms' life-cycles, feedback loops, and two-way causality. Consequently, longitudinal data often contain cross-lags: the predictor variable depends on the response variable of the previous time-step. Although statisticians have warned that regression models that ignore such covariate endogeneity in time series are likely to be inappropriate, this has received relatively little attention in biology. Furthermore, the resulting degree of estimation bias remains largely unexplored.</p> <p>We use a graphical model and numerical simulations to understand why and how regression models that ignore cross-lags can be biased, and how this bias depends on the length and number of time series. Ecological and evolutionary examples are provided to illustrate that cross-lags may be more common than is typically appreciated and that they occur in functionally different ways.</p> <p>We show that routinely used regression models that ignore cross-lags are asymptotically unbiased. However, this offers little relief, as for most realistically feasible lengths of time series conventional methods are biased. Furthermore, collecting time series on multiple subjects–such as populations, groups or individuals—does not help to overcome this bias when the analysis focusses on within-subject patterns (often the pattern of interest). Simulations (R tutorial 1 & 2), a literature search and a real-world empirical example on fairy wrens (data archived here with analyses presented in R-tutorial 3) together suggest that approaches that ignore cross-lags are likely biased in the direction opposite to the sign of the cross-lag (e.g. towards detecting density-dependence of vital rates and against detecting life history trade-offs and benefits of group living). Next, we show that multivariate (e.g. structural equation) models can dynamically account for cross-lags, and simultaneously address additional bias induced by measurement error, but only if the analysis considers multiple time series.</p> <p>We provide guidance on how to identify a cross-lag and subsequently specify it in a multivariate model, which can be far from trivial. Our tutorials with data and R code of the worked examples provide step‐by‐step instructions on how to perform such analyses.</p> <p>Our study offers insights into situations in which cross-lags can bias analysis of ecological and evolutionary time series and suggests that adopting dynamical models can be important, as this directly affects our understanding of population regulation, the evolution of life histories and cooperation, and possibly many other topics. Determining how strong estimation bias due to ignoring covariate endogeneity has been in the ecological literature requires further study, also because it may interact with other sources of bias.</p>
CT Images from the APOLLO-5-LSCC study for Body Part Regression Tutorial
<p>The dataset includes CT images from the <a href="https://wiki.cancerimagingarchive.net/display/Public/APOLLO-5-LSCC#95224279953c510266704797bf4c0bb2e8e7e04f">APOLLO-5-LSCC</a> study for a tutorial from the <a href="https://github.com/MIC-DKFZ/BodyPartRegression">Body Part Regression</a> python package. The CT images are saved in the npy-format. Moreover, an additional Excel file exists, which saves for each image the corresponding pixel spacings in x, y and z-direction.</p> <p>The original data was generated by the Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) Research Network, a Federal Precision Oncology and Cancer Moonshot Program of the Department of Defense, Department of Veterans Affairs, and National Cancer Institute.</p>
Tracking focal adhesions with TrackMate and Weka - tutorial dataset 1
<p>This folder contains data used to illustrate the utility of Weka detector in TrackMate.</p> <p>- classifier.model: trained Weka classifier.<br> - MDA231 paxillin DMSO 1 min.czi - MDA231 paxillin DMSO 1 min.czi #01_t1_t40_crop.tif: example image.</p> <p>More detail on using these files can be found here: https://imagej.net/plugins/trackmate/trackmate-weka.</p> <p> </p>
SnowEx Hackweek 2021 Tutorial Data
<p>Datasets used for tutorials during 2021 SnowEx Hackweek https://snowex-hackweek.github.io/website/tutorials/index.html. Datasets are documented in each tutorial Jupyter Notebook, which have code examples for common analysis tasks. </p>
H2MM Tutorial
<p>A set of tutorials for using H2MM_C.</p> <p>One is generic, the others specific to using FRETBursts, one for nsALEX measurements, the other for <em>𝜇</em>sALEX.</p>
"Wissenschaftlichkeit" und "Literarizität": Tutorial zu den "Grundlagen der Literaturwissenschaft (Einführung)", Version ohne Hintergrundmusik
<p>"Wissenschaftlichkeit" und "Literarizität": Tutorial zu den "Grundlagen der Literaturwissenschaft (Einführung)" als Hilfestellung für die Übung "Jagen & Sammeln 1" (Version ohne background Musik).</p>
"Wissenschaftlichkeit" und "Literarizität": Tutorial zu den "Grundlagen der Literaturwissenschaft (Einführung)", Version mit Hintergrundmusik
<p>"Wissenschaftlichkeit" und "Literarizität": Tutorial zu den "Grundlagen der Literaturwissenschaft (Einführung)", Hilfestellung zu "Jagen & Sammeln", intermediale Version (=mit Hintergrundmusik Getting Over It).</p>
Slightly widely sliced cubic insulin data sets for Diamond / CCP4 workshop tutorials
<p>A 450 x 0.2° image data set from a cubic insulin crystal recorded at Diamond Light Source beamline i03 for the 2022 CCP4 workshop. This is to accompany tutorials so that students can download data and work through tutorials at home</p> <p> </p> <p>Tutorial home space is https://github.com/graeme-winter/dials_tutorials</p> <p> </p>
Multilingualism (TRIPLE Video Tutorial Series)
<p>In this tutorial series, researcher Agnieszka Szulińska and Research Infrastructure Coordinator Edward Gray discuss gotriple.eu. This episode explores the opportunities GoTriple offers for multilingual research. </p> <p>Filmed and edited by Fabian Fess.</p>
Discovery (TRIPLE Video Tutorial Series)
<p>In this tutorial series, researcher Agnieszka Szulińska and Research Infrastructure Coordinator Edward Gray discuss gotriple.eu. This episode explores the opportunities GoTriple offers for multilingual research. </p> <p>Filmed and edited by Fabian Fess.</p>
Video Tutorial for the FAIR SAMPLES Template
<p>This video tutorial with audio contains FAIR WISH expert demonstrations and explanations for using the <em>FAIR SAMPLES Template</em>” of the project <strong>FAIR W</strong>orkflows to establish <strong>I</strong>GSN for <strong>S</strong>amples in the <strong>H</strong>elmholtz Association (FAIR WISH) funded by the Helmholtz Metadata Collaboration (HMC). It is part of Deliverable D3 of work package 3 “<em>Domain-specific metadata for terrestrial and aquatic Geo-Bio samples (vegetation, sediment, water, rocks</em>)”.</p> <p>The ultimate goal of the video tutorial is to instruct users how to use the FAIR SAMPLES Template. The video begins with an introduction to the Helmholtz Metadata Collaboration (HMC), the FAIR WISH project, the International Generic Sample Number (IGSN) and the overall motivation for making samples uniquely identifiable and citable by IGSN assignment. The content then changes to the main part of the tutorial, focussing on the FAIR SAMPLES “Excel” Template, its content and functionalities. In this part, the speaker guides the audience on how to use the template with the different categories of metadata for a sample.</p> <p><a title="The FAIR WISH project" href="https://dataservices-cms.gfz-potsdam.de/samples/the-fair-wish-project">FAIR WISH</a> is a joint project between the Helmholtz Centres GFZ, AWI and Hereon. It was was funded 2022-2023 by the Initiative and Networking Fund of the Helmholtz Association within the <a title="https://helmholtz-metadaten.de/en/projects/hmc-projects-2020" href="https://helmholtz-metadaten.de/en/projects/hmc-projects-2020" target="_blank" rel="noopener noreferrer">HMC Project Cohorte 2020</a> of the <a title="https://helmholtz-metadaten.de/en" href="https://helmholtz-metadaten.de/en" target="_blank" rel="noopener noreferrer">Helmholtz Metadata Collaboration Platform HMC</a>.</p>
Dataset for the Galaxy Training Network (GTN) Tutorial "Viewing Cancer Alignments in a Genome Browser"
<p>Datasets for the Galaxy Training Network (GTN) Tutorial "Viewing Cancer Alignments in a Genome Browser"</p>
wn2vec tutorial data
<p>Input data for the wn2vec tutorial (https://github.com/TheJacksonLaboratory/wn2vec).</p>
Spacepy/Pybats Data for PyHC 2022 Tutorial
<p>This dataset accompanies the tutorial found at https://github.com/spacepy/examples. It is a subset of the simulation set produced as part of this study: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020SW002489. A full description of the dataset, including reading and visualization instructions, can be found in the tutorial.</p>
Pylake tutorial dataset: Kymograph
<p>A kymograph of protein binding acquired on a LUMICKS C-Trap. The system consists of </p> <ul> <li>lambda DNA with two Atto647N fluorophores attached at specific locations (red channel)</li> <li>protein labeled with an Atto565 fluorophore (green channel)</li> </ul>
Pylake tutorial dataset: ImageStack
<p>A set of files of protein binding acquired on a LUMICKS C-Trap. The system consists of </p> <ul> <li>lambda DNA tethered between two polystyrene beads</li> <li>protein labeled with an Atto565 fluorophore (green channel)</li> </ul> <p>TIF files are image stacks acquired from an sCMOS camera and the .h5 file is time-correlated force data. All data was acquired with LUMICKS Bluelake software.</p>
Pylake tutorial dataset: Piezo tracking
<p>Force extension curve of Adenosine Kinase (AdK) with DNA handles tethered between polystyrene beads acquired with the LUMICKS C-Trap.</p> <p> </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.