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33 results for “data visualisation”
Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach"
<p>Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach", published in Journal of Catalysis 2022 408:1–8, doi: <a href="https://doi.org/10.1016/j.jcat.2022.02.014">10.1016/j.jcat.2022.02.014</a></p> <p>Folder names describe the type of data content.</p>
Water quality and diarrhoea bibliometric data and visualisation
<p>This repository contains bibliometric data and its visualisations:</p> <p>Bibliometric data</p> <ul> <li>Keyword: "water quality" AND diarrhoea</li> <li>Database: Scopus</li> <li>Date taken: 28 June 2017</li> <li>Formats: bib, csv, ris</li> <li>Reference manager: Jabref and Zotero</li> </ul> <p>Visualisations</p> <ul> <li>Tools: VosViewer (http://VosViewer.com)</li> <li>Tool's citation: Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. (paper, preprint, supplementary material) (http://dx.doi.org/10.1007/s11192-009-0146-3)</li> <li>Mindmap of analysis procedures using Freeplane https://www.freeplane.org/wiki/index.php/Main_Page</li> </ul>
Basic data visualisations for Figshare State of Open Data 2021 survey
<p>R markdown files for:</p> <ul> <li>Downloading and cleaning data from the State of Open Data survey 2021</li> <li>Basic visualisations of responses to questions in the State of Open Data survey 2021</li> <li>HTML file of those visualisations.</li> </ul> <p>Free text fields are included in the markdown but have been turned off for knitting and in the HTML file.</p>
Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation
<p>Scanning transmission electron microscopy data related to paper "Scanning transmission electron microscopy data related to paper "Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation", <a href="https://doi.org/10.1017/S1431927620024307">https://doi.org/10.1017/S1431927620024307</a>.</p>
Patient-specific processed data, code and visualisations for "Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions"
<p>Processed data and code for reproducing the main results and figures of the paper "<strong>Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions</strong>".</p> <p>We analysed publicly available data from subjects with drug-resistant focal epilepsy. A total of 2656 hours of long-term intracranial electroencephalography (iEEG) from 18 subjects was obtained using the "The SWEC-ETHZ iEEG Database and Algorithms" (available at <a href="http://ieeg-swez.ethz.ch">http://ieeg-swez.ethz.ch</a>) (Burrello et al., 2019).</p> <p>Reference<br> A. Burrello, L. Cavigelli, K. Schindler, L. Benini, A. Rahimi, <strong>‘‘</strong>Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms<strong>’’</strong> <em>in proceedings of the</em> <em>ACM/IEEE Design, Automation, and Test in Europe Conference (DATE)</em>, Florence, Italy, March 25-29, 2019. </p>
Source data belonging to "Visualisation of dCas9 target search in vivo using an open-microscopy framework"
<p>Source data corresponding to "Visualisation of dCas9 target search <em>in vivo</em> using an open-microscopy framework". Contains pTarget and pNonTarget raw datasets, as well as all localization data, cell UV intensity data, cell outline data, and analysed diffusion coefficient lists.</p>
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
<p><b>Background</b> </p> <p>RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.</p> <p><b>Results</b></p> <p>With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.</p> <p><b>Conclusion</b></p> <p>RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.</p>
A simplified palaeoceanography archiving system (PARIS) and GUI for storage and visualisation of marine sediment core proxy data vs age and depth.
<p>Scientific discovery can be aided when data is shared following the principles of findability, accessibility, interoperability, reusability (FAIR) data (Wilkinson et al., 2016). Recent discussions in the palaeoclimate literature have focussed on defining the ideal database format for storing data and associated metadata. Here, we highlight an often overlooked primary process in widespread adoption of FAIR data, namely the systematic creation of machine readable data at source (i.e. at the field and laboratory level). We detail a file naming and structuring method that was used at LSCE to store data in text file format in a way that is machine-readable, and also human-friendly to persons of all levels of computer proficiency, thus encouraging the adoption of a machine-readable ethos at the very start of a project. Thanks to the relative simplicity of downcore palaeoclimate data, we demonstrate the power of this simple but powerful file format to function as a basic database in itself: we provide a Matlab-based GUI tool that allows users to search and visualise data by sediment core location, proxy type and species type. The adoption of similarily accessible, machine-readable file formats at other laboratories will promote data sharing within projects, while also allowing for the automation of submission of data to online database repositories with particular formatting and/or metadata requirements, thus reducing post-hoc workload.</p>
Data from: Spectroscopic approach to correction and visualisation of bright-field light transmission microscopy biological data
<p>The most realistic information about the transparent sample such as a live cell can be obtained only using bright-field light microscopy. At high-intensity pulsing LED illumination, we captured a primary 12-bit-per-channel (bpc) response from an observed sample using a bright-field wide-field microscope equipped with a high-resolution (4872x3248) image sensor. In order to suppress data distortions originating from the light interactions with undesirable elements in the optical path, poor sensor reproduction (geometrical defects of the camera sensor and some peculiarities of sensor sensitivity), this uncompressed 12-bpc data underwent a kind of correction after simultaneous calibration of all the parts of the experimental arrangement. Moreover, the final intensities of the corrected images are proportional to the photon fluxes detected by a camera sensor. It can be visualized in 8-bpc intensity depth after the Least Information Loss compression [Lect. Notes Bioinform. 9656, 527 (2016)].</p>
Reprocessing script and data for the Bilzingsleben antlers dataset to visualise it with the archeoViz web application
<p>This files were created to be used for visualisation with the <a href="https://analytics.huma-num.fr/archeoviz/bilzingsleben"><em>archeoViz</em> application</a>.</p> <p>This record includes:</p> <ul> <li>Reprocessed data from: Vollbrecht, Jürgen. 2000. “The antler finds at Bilzingsleben, excavations 1969-1993”, <em>Internet Archaeology</em>, 8. DOI: <a href="https://doi.org/10.11141/ia.8.1">10.11141/ia.8.1</a></li> <li>and an R script with the code used to the reprocess this dataset.</li> </ul> <p>In the resulting files,</p> <ul> <li><strong>bilzingsleben-data.csv</strong>: partialy reprocessed Vollbrecht's data.</li> <li><strong>bilzingsleben.csv</strong>: output file of the reprocessing: the objects (antlers) are associated with the ranges of coordinates of the square they were found, links to their record in Vollbrecht's database are included.</li> <li><strong>bilzingsleben-refits.csv</strong>: refitting data are reformated in a convenient format.</li> <li><strong>bilzingsleben-timeline.csv</strong>: the year the squares were excavated were deduced using Vollbrecht 2000 and recoded. This made it possible to generate:</li> <li><strong>bilzingsleben-timeline-map.jpg</strong>: a map of the site giving the year the squares were excavated and the labels arbitrary attributed for the <em>archeoViz</em> visualisation.</li> </ul> <p> </p>
Data from: Spectroscopic approach to correction and visualisation of bright-field light transmission microscopy biological data
Open the record for dataset details and reuse information.
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
Open the record for dataset details and reuse information.
Genomic Data Visualisation with JBrowse
<p>The data provided here is part of the Galaxy Training Network tutorial for visualising genomic data with JBrowse.</p>
PMC_visualisation_and_source_data_SS_v1.0.7
<p>This is the first release of the code and data for:</p> <p>"Public health impact of current and proposed age-expanded perennial malaria chemoprevention: a modelling study"</p> <p>Swapnoleena Sen1,2, Lydia Braunack-Mayer3,4, Sherrie L Kelly1, Thiery Masserey1,2, Josephine Malinga4,5, Joerg J Moehrle2, Melissa A Penny4,5*</p> <p>1 Swiss Tropical and Public Health Institute, Allschwil, Switzerland<br>2 University of Basel, Basel, Switzerland<br>3 <span>Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland </span><br>4 Telethon Kids Institute, Nedlands, WA, Australia<br>5 Centre for Child Health Research, The University of Western Australia, Crawley, WA, Australia</p> <p>*Correspondence to: Prof Melissa A Penny (melissa.penny@uwa.edu.au)</p> <p>In this study, we integrated an individual-based model of malaria (OpenMalaria) with pharmacological models of drug action to assess the public health impact and cost-effectiveness of perennial malaria chemoprevention (PMC), and the added benefit of further age-expanded dosing schedule (referred as PMC+).</p> <p>The details of running OpenMalaria model, data generation and analysis (including R scripts used for preparing the source data files) for this study can be found in a separate "OpenMalaria_workflow_PMC_modeling" repository (DOI:10.5281/zenodo.12721515). </p> <p>Here the plotting functionalities are described. The repository is strcutured based on figures reported in the manuscript. Each figure has a folder as per its name that includes: 1) R code to plot figure 2) source data files and 3) one PNG and one PDF version of the figure. </p> <p>Please note: i) "dependencies.R" specifies all package information and dependencies in which the simulation, analysis scripts and plotting scripts are tested and stable. <br>ii) The R scripts rely on the folder structure and working directories used by the researchers. To replicate figures, you will need to adjust the file paths.</p>
Analytics, Visualisation and Machine Learning of General Practitioner Prescribing using Open Health Data
<p>Open Prescription data used in Postgraduate project into Northern Ireland General Practice prescribing.</p>
Analyses, data and figures related to: "Connecting ships: Using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and visualise wood use patterns"
<p>Analyses, data and figures related to: "Connecting ships: using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and to visualise patterns of wood use" by Ronald M. Visser (Saxion University of Applied Sciences, Deventer, the Netherlands) and Yardeni Vorst (Vorst wood research, Zaandam, the Netherlands) submitted to the International Journal of Wood Culture</p>
Dataset for the 20181106 study group lesson (data visualisation)
<p>We want to measure the effect of a bacteria (E.coli) on the immune system of plants. <br> To do so, we measure the fluorescence of leaf disks at two different wavelenghts and at three different time points (0,2 and 4 hours after inoculation). <br> In the provided dataset, each row is a measurement of the fluorescence (column `fluorescence`) of one leaf disc. There is also a column (`fluo_normalised`) of the normalised fluorescence (normalised relatively to timepoint 0h). </p> <p> </p>
Data and visualisation code from 'Effects and avoidance of photoconversion-induced artefacts in confocal and STED microscopy' by Dasgupta et al (2024)
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PMC_visualisation_and_source_data_SS_v1.0.6_copy
<p>This is an erronous copy of the original submission of code and data for:</p> <p>"Public health impact of current and proposed age-expanded perennial malaria chemoprevention: a modelling study"</p> <p>Please refer to DOI <a href="https://doi.org/10.5281/zenodo.12722070">10.5281/zenodo.12722070 </a></p>
Data Visualisation_Robert
<p>A short video_Data Visualisation </p>
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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.