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33 results for “data visualisation”
Galaxy Training Tutorial: "Divers and Adaptable Visualisations of Metabarcoding Data Using ampvis2"
<p><span>This tutorial teaches you how to filter data for significant information, visualise it effectively, and adapt plots to your needs. You will explore multiple visualisation methods to gain deeper insights from your data.</span></p> <p><a href="https://training.galaxyproject.org/training-material/"><span>Galaxy Training Material Website</span></a></p>
Aircraft Aerosol Data and MATLAB Script for Visualisation on 17/06/22
<p>A dataset containing all data from an ultralight aircraft over several flights between Feb 21 and June 22, including flight log data, MCPC, STAP and OPC data. Also includes a data visualisation script used for the manuscript "Evolution of the planetary boundary layer over Copenhagen investigated using aircraft aerosol measurements and the model DEHM".</p>
HMC_visualisation_and_source_data_SS_v1.0.2
<p>This is the the code and data for:</p> <p>"Rethinking a hybrid malaria chemoprevention delivery strategy for children in sub-perennial settings: a modelling study integrating age- and seasonally-targeted delivery"</p> <p>Swapnoleena Sen1,2, David Schellenberg3, Melissa A Penny4,5*</p> <p>1Swiss Tropical and Public Health Institute, Allschwil, Switzerland</p> <p>2University of Basel, Basel, Switzerland</p> <p>3London School of Hygiene and Tropical Medicine, London, United Kingdom</p> <p><span lang="EN-IN">4The Kids Research Institute Australia, Nedlands, WA, Australia</span></p> <p><span lang="EN-IN">5Centre for Child Health Research, The University of Western Australia, Crawley, WA, Australia</span></p> <p>*Correspondence to: Prof Melissa A Penny (<a href="mailto:melissa.penny@uwa.edu.au">melissa.penny@uwa.edu.au</a>)</p> <p>In this study, using a validated individual-based malaria model combined with pharmacological models of drug action (OpenMalaria), we examined the potential public health impact of a proposed hybrid malaria chemoprevention (HMC, for children 03-24 months), and an age-expanded HMC (referred as HMC+, for children 03-36 months), under different assumptions of drug sensitivity, coverage, and prevalence (5-70%).</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_HMC_modeling_SS" repository (DOI: 10.5281/zenodo.13804293). </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>
Figure 3 from: Mesa-Varona O, Plaza-Rodríguez C, Valentin L, Filter M (2024) WarenstromInfo: a tool for the easy extraction and visualisation of trade flow data. Research Ideas and Outcomes 10: e112227. https://doi.org/10.3897/rio.10.e112227
Figure 3 WI User Interfaces: UI3. In the UI3, countries involved in the query can be selected (I and J). Input data from UI3 are included in the query by clicking "Next" (K).
Figure 5 from: Mesa-Varona O, Plaza-Rodríguez C, Valentin L, Filter M (2024) WarenstromInfo: a tool for the easy extraction and visualisation of trade flow data. Research Ideas and Outcomes 10: e112227. https://doi.org/10.3897/rio.10.e112227
Figure 5 A screenshot of the BACI SQLite database builder workflow. The workflow presented here is organised into two main sections, with interconnected KNIME nodes displayed in each section. The yellow-boxed nodes are responsible for creating and loading the database where the data are stored. The green-boxed section shows: Locate BACI csv files (BACI files must have been previously downloaded and stored in a folder that is pointed in the "List Files/Folders" KNIME node). Split the files according to the HS data provided and Load csv files, filtering and storage in SQLite database (carried out in each consecutive metanode). In this last section, the user can decide not to filter the data or customise the filter criteria of the BACI database, including more types of data apart from those agrifood data that are included in the default configuration of the workflow. Variable flow connections (red lines) can be removed in the second green-boxed section, if an HPC (High Performance Computing) service is available. This will allow the workflow to run much faster as the nodes will not be executed one after the other, but all at the same time.
Figure 4 from: Mesa-Varona O, Plaza-Rodríguez C, Valentin L, Filter M (2024) WarenstromInfo: a tool for the easy extraction and visualisation of trade flow data. Research Ideas and Outcomes 10: e112227. https://doi.org/10.3897/rio.10.e112227
Figure 4 WI User Interfaces: UI4. The UI4 provides an overview of the final WI outputs, displaying a list with the initial input variables (L), three maps displaying trade flows (a world map, a world map focused on Europe and an European map) (M), download options (N), slide filter bars for value and weight preview (O) and trade flow data in the preview table (P).
Figure 1 from: Mesa-Varona O, Plaza-Rodríguez C, Valentin L, Filter M (2024) WarenstromInfo: a tool for the easy extraction and visualisation of trade flow data. Research Ideas and Outcomes 10: e112227. https://doi.org/10.3897/rio.10.e112227
Figure 1 WI User Interfaces: UI1. In the UI1, users can select the desired database (EUROSTAT/BACI) (A). The database selection is concluded by clicking "Next" (B), which will lead the user to the following UI.
Figure 2 from: Mesa-Varona O, Plaza-Rodríguez C, Valentin L, Filter M (2024) WarenstromInfo: a tool for the easy extraction and visualisation of trade flow data. Research Ideas and Outcomes 10: e112227. https://doi.org/10.3897/rio.10.e112227
Figure 2 User Interfaces: UI2. In the UI2, the following input options can be selected: standard selection (C), time range of the search (D), the email option (E), the pre-filter option (F) and the table with the embedded live filter (G). Input data from UI2 are processed by clicking "Next" (H).
Data from: Exploring and visualising spaces of tree reconciliations
Tree reconciliation is the mathematical tool that is used to investigate the coevolution of organisms, such as hosts and parasites. A common approach to tree reconciliation involves specifying a model that assigns costs to certain events, such as cospeciation, and then tries to find a mapping between two specified phylogenetic trees which minimises the total cost of the implied events. For such models, it has been shown that there may be a huge number of optimal solutions, or at least solutions that are close to optimal. It is therefore of interest to be able to systematically compare and visualise whole collections of reconciliations between a specified pair of trees. In this paper, we consider various metrics on the set of all possible reconciliations between a pair of trees, some that have been defined before but also new metrics that we shall propose. We show that the diameter for the resulting spaces of reconciliations can in some cases be determined theoretically, information that we use to normalise and compare properties of the metrics. We also implement the metrics and compare their behaviour on several host parasite datasets, including the shapes of their distributions. In addition, we show that in combination with multidimensional scaling, the metrics can be useful for visualising large collections of reconciliations, much in the same way as phylogenetic tree metrics can be used to explore collections of phylogenetic trees. Implementations of the metrics can be downloaded from: https://team.inria.fr/erable/en/team-members/blerina-sinaimeri/reconciliation-distances/
Data from: Exploring and visualising spaces of tree reconciliations
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Data from: A comprehensive and user-friendly framework for 3D-data visualisation in invertebrates and other organisms
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Data associated with "RxTrends: An R Shiny Application for Visualising Open Data on Prescribed Medications in Ireland"
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RxTrends: An R Shiny Application for Visualising Open Data on Prescribed Medications in Ireland
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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.