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1,271 results for “Data Flow”
Data from: Measuring the biodiversity of microbial communities by flow cytometry
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Data from: Transitions in the flow patterns and aerodynamic characteristics of the flow around staggered rows of cylinders
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Data from: How mechanisms of habitat preference evolve and promote divergence with gene flow
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Data from: Microstructure and cerebral blood flow within white matter of the human brain: a TBSS analysis
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Data from: Optic flow odometry operates independently of stride integration in carried ants
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Data from: Role of selection and gene flow in population differentiation at the edge vs. interior of the species range differing in climatic conditions
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Expression data from E12.5 ePet-EYFP rostral and caudal serotonin (5HT) neurons purified by flow cytometry
GEO Series GSE19474. Mus musculus. 12 samples. Type: Expression profiling by array.
Expression data from monocytes of individuals with different collateral flow index CFI
GEO Series GSE7638. Homo sapiens. 160 samples. Type: Expression profiling by array.
Expression Data from MC3T3-E1 cells subjected to laminar fluid flow (LFF)
GEO Series GSE138160. Mus musculus. 4 samples. Type: Expression profiling by array.
Expression data for normal flow sorted hematopietic cell subpopulations
GEO Series GSE19599. Homo sapiens. 16 samples. Type: Expression profiling by array.
Data for modeling flow rate and temperature changes in a hot spring
<p>Flow rate ,temperature and meteorological data in a hot spring that used for modeling. </p>
Figure 3 in Optimizing biodiversity informatics to improve information flow, data quality, and utility for science and society
Figure 3. Examples of ways in which aggregators can make uncertainties and biases visually available to users of Primary Biodiversity Data. Such information can be employed to filter data and to quantify and correct for biases in sampling effort, respectively. (a) Georeferenced localities of a given species are simply plotted in geographic space (black dots; current practice). (b) Those same localities appear using symbologies that provide additional information; a hazy cloud indicates the radius of error for localities holding information regarding uncertainty of the georeference, and localities lacking such data appear only as hollow black circles. (c) Information appears that reflects the results of sampling effort, by showing in gray the georeferenced localities for all species belonging to a more inclusive target group (i.e., all species detected with the same techniques as the species of interest; conventions the same as in b). Note that the right-hand side of the study region lacks records for any species of the target group, suggestive of very low sampling effort there.
Figure 2 in Optimizing biodiversity informatics to improve information flow, data quality, and utility for science and society
Figure 2. Use of individual and collective Stable Unique Identifiers (e.g., DOIs) in biodiversity informatics. (a) Individual Stable Unique Identifier (I-SUI) allows linking diverse data domains for a given organism. In this example, an I-SUI links the voucher specimen and associated Primary Biodiversity Data (e.g., date and locality) of an individual mammal to information regarding various aspects of molecular- to population-level biology. (b) Collective Stable Unique Identifier (C-SUI) denotes a set (i.e., a list) of individual identifiers. For example, a C-SUI could indicate the n individual records used in a given analysis.
Figure 1 in Optimizing biodiversity informatics to improve information flow, data quality, and utility for science and society
Figure 1. Simplified overview of the interactions and flow of data among providers, aggregators, and users in biodiversity informatics. Numbers indicate the typical order of actions: 1. Aggregator receives data uploads (and periodic updates) from providers; 2. User makes a data query to aggregator's online portal; 3. Aggregator responds to query by making data available on portal (for viewing and/or download). Note that by querying a single aggregator, a user can receive data from multiple providers. Additionally, multiple intermediate aggregators typically exist, feeding into the largest ones most commonly consulted by users (e.g., GBIF).
Supplementary data: "Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI"
<p>This repository contains processed data and result files for the paper <a href="https://arxiv.org/abs/2204.10727">Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI</a>. The code for producing the processed data and the results is <a href="https://github.com/sebastianptz/XAI-for-HVDC">available at github</a>.</p> <p><strong>Data</strong></p> <p>The data folder contains the feature and target data used to train the ML model:</p> <ul> <li>stability_input: A Folder containing training and test sets for the stability model for each area.</li> <li>flow_input: A Folder containing training and test sets for the flow model for each link.</li> <li><em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features.</li> <li><em>input_forecast.h5 </em>and<em> input_actual.h5: </em>The day-ahead available (forecast) and ex-post available (actual) data of external features including the engineered features.</li> <li><em>indicators.h5 </em>: The grid frequency stability indicators.</li> <li><em>documentation_of_data_download</em>: Information files concerning the ENTSO-E raw data and its aggregation.</li> <li>HVDClinks: A Folder containing preprocessed time series for scheduled and unscheduled HVDC flows</li> </ul> <p><strong>Data sources</strong></p> <p>Most of the data is derived from ENTSO-E Transparency Platform data [1]. The grid stability indicators (indicators<em>.h5</em>) are based on publicly available data from the German Transmission System Operators (TSOs) [2].</p> <p><strong>Results</strong></p> <p>The stability_results and the flow_results folder contain the results of hyperparameter optimization, model prediction and interpretation via SHAP for the respective models.</p> <ul> <li><em>cv_results_gtb_full.csv</em> : Performance results for each combination in the hyperparameter optimization.</li> <li><em>cv_best_params_gtb_full.csv</em> : Hyperparameters used in the final (optimized) model.</li> <li><em>shap_values_gtb_full.npy</em> : First-order SHAP values calculated on different data sets: The train set, the randomized test set and the continuous test set.</li> <li><em>y_pred.h5/y_pred_links.h5</em> : Predictions of daily profile predictor and Machine Learning models.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The data might be subject to copyright or related rights. Please consult the primary data owner.</p> <p> </p> <p> </p> <div> </div>
Data for "Flow adaptation to velocity changes in transitional clay suspension flows"
<p>This dataset details experimental results of unsteady clay-laden suspension open-channel flows, focussing on the flow adaptation to changes in velocity within these transitional clay flows. </p> <p><br> </p>
SuperDARN data for manuscript "Multi-instrument observations of the evolution of polar cap patches associated with flow shears and particle precipitation"
<p>SuperDARN在14年15月12日2015-<> UT期间的电离层对流数据。</p>
Data set of flow-sediment processes in the Yarlung Zangbo River (2021)
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Exploratory Study on Artifact Denoising of Cerebral Blood Flow and EEG Data in Ambulance Settings
ClinicalTrials.gov study NCT06701708. IPD Sharing: NO. Countries: 0. Publications: 0.
Analyzing Flow Cytometry or Targeted Gene Expression Data Influences Clinical Discoveries — Profiling Blood Samples of Pancreatic Ductal Adenocarcinoma Patients
GEO Series GSE241957. Homo sapiens. 88 samples. Type: Other.
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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.