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232 results for “time measurement”
Figure. Measurement data plotted for all nestlings as a function of age for the black stork: a) wing length (WL), b) head length (HL), c) bill length (BL), and d) tarsus length. in Age estimation of black stork (Ciconia nigra) nestlings from wing, bill, head, and tarsus lengths at the time of ringing
Figure. Measurement data plotted for all nestlings as a function of age for the black stork: a) wing length (WL), b) head length (HL), c) bill length (BL), and d) tarsus length.
Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
Measuring Space-Time Accessibility: Hansen's Model Vs. Machine Learning
<p>This dataset consists of Hansen accessibility data in 2011 and 2020 in the municipalities of Lombardia and Emilia Romagna (Italy). Neural Network (NN) learns to predict Hansen accessibility based on the number of residents, employed people and travel time.</p>
Time Series Measurement Data of Office Building in Jakarta Indonesia - Incoming Transformer of 20 kV | 0.4 kV
<p>Time Series Measurement Data of Office Buildings in Jakarta Indonesia - Incoming Three Transformers of 2 MVA rating (20 kV | 0.4 kV)<br> The data was taken by Standardized Power Quality Analyzer within minutes span data during eight days in 2016. <br> The data consist of Voltage, Current, Active/Reactive/Apparent Power, Power Factor, Total Harmonic Distortion (THD) within three-phase measurement. Related data was also included from with it i.e., calculated data for active power losses, Unbalanced Voltage per phase, efficiency etc</p>
Time Series Measurement Data of Medium Voltage | Low Voltage [MV|LV] Feeders in Jabodetabek Regions Indonesia – District Incoming Transformers of 630 kVA & 400 kVA (20 kV | 0.4 kV)
<p>Time Series Measurement Data of Medium Voltage | Low Voltage (MV|LV] Feeders in Jabodetabek Regions Indonesia – District Incoming Transformers of 630 kVA & 400 kVA (20 kV | 0.4 kV)<br> Standardized Power Quality Analyzer took the data within 1- and 5 minutes span data during seven days in 2016. <br> The data consist of Voltage, Current, Active/Reactive/Apparent Power, Power Factor, and Total Harmonic Distortion (THD) within three-phase measurement. Related data was also included with it i.e., calculated data for active power losses, Unbalanced Voltage per phase, efficiency etc</p>
Time Series Measurement Data of Residentials | Jabodetabek Regions Indonesia – 220|230 Volt (1300|2200|3300|4400|5500) VA
<p>Time Series Measurement Data of Residentials | Jabodetabek Regions Indonesia – Voltage rating from 220|230|400 Volt within Power Contract/Limiter of (1300|2200|3300|4400|5500) VA.<br> The data was collected with Standardized EDMI Read Head Optical Read Head for Data Communication (FLAG IEC-62056-21) within 5, 10, and 15 minutes. The data were then levelized into 1 minute for computation needs through the interpolation method.</p> <p>The data consist of Apparent Power (S, kVA) and Time (in minutes) within one-phase measurement, representing the behavioral patterns and social practices around load consumption in the rural, semi-urban, and urban areas of Jabodetabek.</p>
3-D T1 relaxation time measurements in an equine model of subtle post-traumatic osteoarthritis using MB-SWIFT
<p>This dataset contains Key analysis and plotting scripts, data, and sample images.</p> <p>3-D T1 relaxation time measurements in equine model of post-traumatic osteoarthritis using MB-SWIFT</p> <p>Journal of Orthopaedic Research | DOI: 10.1002/jor.25629</p> <p>Swetha Pala (1), Nina Hänninen (1,2), Ali Mohammadi(1), Mohammadhossein Ebrahimi (1,2), Nikae C.R. te Moller(3), Harold Brommer(3), P. René van Weeren (3), Janne T.A. Mäkelä (1), Rami K. Korhonen (1), Isaac O. Afara(1), Juha Töyräs (1,4,5), Santtu Mikkonen (1), Mikko J. Nissi (1*), Olli Nykänen (1,2)</p> <p> 1Department of Applied Physics, University of Eastern Finland <br> 2Research Unit of Medical Imaging, Physics and Technology, University of Oulu<br> 3Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University<br> 4Science Service Center, Kuopio University Hospital, Kuopio, Finland<br> 5School of Information Technology and Electrical Engineering, The University of Queensland </p> <p><br> *Corresponding author<br> Mikko J. Nissi<br> Department of Technical Physics<br> University of Eastern Finland, Kuopio Finland<br> POB 1627<br> 70211 Kuopio<br> mikko.nissi@uef.fi<br> +358-50-5955517<br> Running title: ‘3D T1 of mild PTOA using MB-SWIFT’</p> <p><br> Keywords: Quantitative MRI, T1 relaxation, equine model, post-traumatic osteoarthritis, proteoglycan content.</p> <p>Included folders and files are:<br> - Article_figures: all figures published in the manuscript (.svg format)<br> - Data: Raw MRI data files per flip angle (phase & magnitude images) from 28 samples and corresponding fitted T1 maps within respective folders. SPSS structured data files used for statistical analysis.<br> - Matlab scripts: Matlab functions used for data processing and T1 computation, aedes plugins, and data analysis with subfolders and files:<br> - aedes_plugins: plugins for aedes (http://aedes.uef.fi) and scripts for calculation of surface visualisations from relaxation time maps and auto-segmented mesh. <br> - Data processing and T1 computation: Scripts for non-linear 3D T1 fitting. <br> - Analysis: Key scripts used for analysis and plotting.</p> <p>- README.txt: this file describing the contents of the dataset.</p> <p><br> See more info in separate readme files included in sub-folders.</p> <p><br> (Swetha Pala, 31 May 2023)</p>
Data from: A user-friendly guide to using distance measures to compare time series in ecology
<p>Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g. assigning species to individual bird calls); clustering (e.g. clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g. accuracy of model predictions to original time series data); and anomaly detection (e.g. detecting possible catastrophic events from population time series). These common tasks in ecological research rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored.</p> <p>Here we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology.</p> <p>Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends.</p> <p>The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series, and help us answer new ecological questions.</p>
Dataset For "Nyiragongo crater collapses measured by multi-sensor SAR amplitude time series"
<p>This archive contains the input ant results files used with PickCraterSAR for publication "Nyiragongo crater collapses measured by multi-sensor SAR amplitude time series" submitted to JGR-SE.</p> <p>It also contains crops of each amplitude images used in this study in ENVI format with corresponding headers.</p> <p>At least, it contains the ash index values derives from SEVIRI data analysis.</p>
The Feasibility of Home-Based Measurement of Circadian Timing for Veterans With TBI and Insomnia
ClinicalTrials.gov study NCT05665764. IPD Sharing: YES. Countries: 1. Publications: 0.
Interstitial cortisol measurements aligned by wake time, healthy volunteers
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Area and Timing data and R script for: 3D scanning as a tool to measure growth rates of live coral microfragments used for coral reef restoration
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Data from: A user-friendly guide to using distance measures to compare time series in ecology
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Data and source code for: ClinVar and HGMD genomic variant classification accuracy has improved over time, as measured by implied disease burden
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Demographic measures of Hypericum cumulicola (Hypericaceae) in 15 populations in Florida Rosemary Scrub patches with different time-since-fire, at Archbold Biological Station, Highlands County, Florida from 1994-2015
We collected demographic data comprising 38,313 unique observations from a sample of 10,910 individuals of H. cumulicola, from 15 independent Florida rosemary scrub patches with different time-since-fire at Archbold Biological Station, Highlands County, Florida. Plants were censused during their peak of reproduction annually in July and August between 1994 and 2015.
Multidecadal Time Series of Measured Chlorophyll-a in Lakes and Estuarine-Coastal Ecosystems, 1966-2024
The photosynthetic pigment chlorophyll-a is a commonly measured index of phytoplankton biomass and water quality across all aquatic ecosystem types. Some monitoring and research programs have sustained chlorophyll-a measurements for decades at monthly or higher frequency. Each of these time series is an invaluable record of phytoplankton variability at a particular location. The patterns of that variability have been essential for identifying the underlying processes of phytoplankton change at time scales of days, months, seasons, years and decades. Multidecadal series are rare and valuable because they provide empirical records of phytoplankton changes over the recent decades of unprecedented global change. These records also provide an empirical basis for comparing patterns and rates of change across geographic regions and ecosystem types. This data package contains multidecadal time series of measured chlorophyll-a concentration in three ecosystem types: 134 freshwater lakes (including a small number of reservoirs) that do not freeze; 78 high latitude lakes that do freeze; and 176 coastal ecosystems defined as water bodies where freshwater and seawater mix, including estuaries, coastal bays and lagoons, tidal rivers, and the Baltic Sea. Although there are other published compilations of chlorophyll-a time series, this package was compiled specifically to report observations made at monthly or higher frequency and sustained over multiple decades. The mean time series duration in this package is 33 years, and the mean number of sampling dates per site was 503 (range 186 to 2381). Thus, this data package provides an empirical basis for analyses to measure and compare decadal-scale patterns and rates of phytoplankton biomass variability between inland lakes and water bodies at the land-ocean interface. All chl-a measurements reported here were accessed from published repositories, except these four sites. We acknowledge and thank the following data providers for perm
Interior Alaska managed sites: seedling data measured one time in either summer 2011, 2012, or 2013
This dataset includes counts of seedlings (presented by species) within quadrats of a specified size, measured in black spruce forest and adjacent areas that had been either thinned or shearbladed (clearcut). Basal diameter measurements for select seedlings is also included.
Interior Alaska managed sites: soil organic layer and mineral soil nutrient concentrations and additional characteristics measured one time in either summer 2012 or 2013
This dataset contains soil organic layer and mineral soil depth, C and N concentrations, and other soil characteristics collected at all managed and adjacent unmanaged study sites.
Interior Alaska managed sites: soil organic layer depth and thaw depth measured one time in either summer 2012 or 2013
This dataset contains estimates of soil organic layer depth and thaw depth along transects measured at a suite of managed and unmanaged areas in interior Alaska in summers 2012 and 2013.
Interior Alaska managed sites: tree inventory measured one time in either summer 2012 or 2013
This dataset contains the diameter of all trees (diameter at breast height) and shrubs (basal diameter) measured in managed areas and adjacent unmanaged black spruce forest, in Summers 2012 and 2013.
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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.