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96 results for “Time processing”
North Temperate Lakes LTER Processed eddy covariance time series fluxes from tower located on roof of the CFL building oriented toward Lake Mendota 2012 - current
We calculated eddy covariance based fluxes of CO2, H2O, heat, and momentum to study lake-atmosphere exchanges since 2012. These data were collected by Ankur Desai from 2012 to present using a CSAT-3 sonic anemometer and LI-7500 gas analyzer located on the roof of the CFL building. A footprint model (Kljun) was used to screen for lake only data.
Labeled Time Series Data of Force/Torque for Monitoring Assembly Processes with a Delta Robot
<p>This dataset comprises 524 recordings of 6-dimensional time series data, capturing forces in three directions and torques in three directions during the assembly of small car model wheels. The data was collected using an equidistant sampling method with a sampling period of 0.004 seconds. Each time series represents the process of assembling one wheel, specifically the placement of a tire onto a rim, and includes a label indicating whether the assembly was successful (OK). The wheels were assembled in batches of four, and the recordings were obtained over six different days. The labels of recordings from two (days 3 and 4) of the six days are invalid as described in [1]. The labels presented in this data set are only binary (they do not describe the reason of the failure). The labels of recordings from days 5 and 6 are created by human while the other labels came from a convolutional neural network based computer vision classifier and can be inaccurate as described in section 5.4 of [1]. </p> <h4>Dataset Structure:</h4> <ul> <li><strong>File:</strong> <code>ForceTorqueTimeSeries.csv</code> <ul> <li><strong>Columns:</strong> <ul> <li><code>idx (1-524)</code>: Index of the recording corresponding to the assembly of one wheel.</li> <li><code>label (true/false)</code>: Indicates whether the assembly was successful (TRUE = product is OK).</li> <li><code>meas_id (1-6)</code>: Identifier for the day on which the recording was made (refer to Table 2.1 in [1]).</li> <li><code>force_x</code>: X-component of the force measured by the sensor mounted on the delta robot's end effector.</li> <li><code>force_y</code>: Y-component of the force.</li> <li><code>force_z</code>: Z-component of the force.</li> <li><code>torque_x</code>: X-component of the torque.</li> <li><code>torque_y</code>: Y-component of the torque.</li> <li><code>torque_z</code>: Z-component of the torque.</li> </ul> </li> </ul> </li> </ul> <h4>Additional Files:</h4> <ul> <li><strong><code>IMG_3351.MOV</code>:</strong> A video demonstrating the assembly process for one batch of four wheels.</li> <li><strong><code>F3-BP-2024-Trna-Ales-Ales Trna - 2024 - Anomaly detection in robotic assembly process using force and torque sensors.pdf</code>:</strong> Bachelor thesis [1] detailing the dataset and preliminary experiments on fault detection.</li> <li><strong><code>F3-BP-2024-Hanzlik-Vojtech-Anomaly_Detection_Bachelors_Thesis.pdf</code>:</strong> Bachelor thesis [2] describing the data acquisition process.</li> </ul> <h3>References:</h3> <ol> <li>Trna, A. (2024). <em>Anomaly detection in robotic assembly process using force and torque sensors</em> [Bachelor’s thesis, Czech Technical University in Prague].</li> <li>Hanzlik, V. (2024). <em>Edge AI integration for anomaly detection in assembly using Delta robot</em> [Bachelor’s thesis, Czech Technical University in Prague].</li> </ol>
Videos of the processed microscope images and time series of the petrophysical parameters from image processing and geochemical simulation and of the measured induced polarisation [Video][Dataset]
<p>Supporting Information for the manuscript <em>Microfluidics and spectral induced polarization for direct observation and petrophysical modeling of calcite dissolution</em> published in Geophysical Research Letters</p> <ul> <li><strong>Data Set S1.</strong> Porosity, water saturation, and calcite sample perimeter from image<br>processing.</li> <li><strong>Data Set S2.</strong> Porosity, water conductivity, and pH from geochemical simulation.</li> <li><strong>Data Set S3.</strong> Real and imaginary components of the complex electrical conductivity at<br>2.5 Hz and CEC from petrophysical modeling.</li> <li><strong>Movie S1.</strong> Dissolution of the calcite sample with the detected contour superimposed in<br>white on the grayscale images. Time, length scale, and flow direction are indicated. In<br>case of problems launching the file, we recommend using VLC Media Player software.</li> <li><strong>Movie S2.</strong> Segmented images of the CO2 bubbles produced by the calcite dissolution.<br>Time, length scale, and flow direction are indicated. In case of problems launching the<br>file, we recommend using VLC Media Player software.</li> </ul>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
CCE LTER process cruise, in the California Current region, event log records including date, time, position and activity for use in post-cruise data integration based on co-sampling indexes. From 2006 to 2019 CCE LTER used a locally developed event logging system. During P2107, CCE LTER started to utilize the R2R Event Logger on UNOL ships, 2006 - 2024 (ongoing).
The event logger program developed and maintained by the California Cooperative Oceanic Fisheries Investigations, SIO, program is used aboard CCE LTER process cruises to create indexes with temporal, spatial and activity information for post-cruise data integration. The event log is configured aboard the ship for the recording of sampling events by both ship crew personnel on the bridge, and research personnel in the lab. The event log is processed post-cruise to correct for various errors.
Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping
<p>This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model.</p> <p>The detail information on the products were given below:</p> <p>Name: DSMst-RothC 5-km global topsoil SOC stock products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.041666667 degree</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Extent: -180°, -90°: 180°, 90°</p> <p>Data format: GeoTIFF</p> <p>Compression: LZW</p> <p>Data type: Float32</p> <p>Unit: t C ha<sup>-1</sup></p>
Pre-processed and modeled GNSS time-series after the 2011 Tohoku Earthquake
<p>The raw, pre-processed, and modeled GNSS time-series of the 213 GEONET sites in the Tohoku region, Japan, from Mar. 12, 2011 to Nov. 20, 2021, relative to the Okhotsk plate (Argus et al., 2011, <em><em>Geochemistry, Geophysics, Geosystems</em></em>).</p> <p>The original GNSS time-series are F5 solutions, which are distributed by Geospatial Information Authority of Japan (GSI, https://www.gsi.go.jp/). The details and availability of F5 solutions are written in Takamatsu et al. (2023, Earth, Planets, and Space) https://doi.org/10.1186/s40623-023-01787-7.</p> <p>The GNSS time-series processing was performed by Tomita (submitted), and the following signals were excluded from the raw time-series: seasonal variation, coseismic step, antenna maintenance offset, and common mode errors. Then, the pre-processed time-series were modeled by a trajectory modeling method considering postseismic deformation of the 2011 Tohoku earthquake, the Boso SSEs, and postseismic deformations due to aftershocks and L-ASE (long-term aseismic slip event) since late 2019.<br> <br> "sitelist.txt" - Site information file<br> column 1: Full site ID<br> column 2: 4digits site ID<br> column 3: Longitude [deg]<br> column 4: Latitude [deg]<br> column 5: Height [m] <br> <br> "pre-process/xxxx.txt" - Time-series at xxxx (4digits site ID) site<br> column 1: days from Mar. 12, 2011 (1 corresponds to Mar. 12, 2011)<br> column 2: raw East-West displacement [m]<br> column 3: raw North-South displacement [m]<br> column 4: raw Up-down displacement [m]<br> column 5: pre-processed East-West displacement [m]<br> column 6: pre-processed North-South displacement [m]<br> column 7: pre-processed Up-down displacement [m]</p> <p>"model/xxxx/prediction_yy.txt" - Time-series for yy component (yy=EW, NS, UD) at xxxx (4digits site ID) site<br> column 1: days from Mar. 12, 2011 (1 corresponds to Mar. 12, 2011)<br> column 2: modeled displacement excluding the Boso SSEs [m]<br> column 3: modeled displacement excluding the Boso SSEs and postseismic deformation due to aftershocks caused one year after the 2011 Tohoku Eq. [m]<br> column 4: modeled displacement excluding the Boso SSEs, postseismic deformation due to aftershocks caused one year after the 2011 Tohoku Eq. and the 2019 L-ASE [m]</p> <p><br> The displacement on Mar. 12, 2011 was initially set to be zero before the pre-processing, but the removal of the above factors provided some deviation from zero.</p> <p>The raw time-series excluded outliers from the original F5 solutions, and the raw time-series were transformed into the Okhotsk plate reference.</p> <p>Following the above trajectory modeling, the fully-relaxed postseismic displacement fields due to 2015 Feb. 17 Sanriku-oki earthquake ("Table_displacement1.xlsx"), the 2015 May 13 Miyagi-oki earthquake ("Table_displacement2.xlsx"), and summation of the 2021 Feb. 13 Fukushima-oki, the 2021 Mar. 20 Miyagi-oki, and the 2021 May 1 earthquakes ("Table_displacement2.xlsx") were calculated. Moreover, the cumulative displacement field due to the 2019 L-ASE since Nov. 25, 2019 was also calculated. In those files, the estimation errors are also shown as 1σ standard deviation obtained from diagonal components of the model covariance matrices. </p> <p> </p> <p>The details of these data are introduced in the corresponding paper (Tomita, submitted).</p>
Process Planning with Uncertain Process Time
<p>The generated data represents instances for a project, with variations in the number of items, stations, workers, tasks, and other parameters. Each instance file contains information such as the number of items, stations, workers, tasks, and the Takt time used. Additionally, it includes details about the mean and standard deviation of process times for tasks, equipment costs, and precedence relations between tasks. These instance files can be used as input for various analyses or simulations related to the project, providing a diverse range of scenarios to explore and evaluate.</p> <p><<This data is generated for the paper under review with the tile: "Mixed-Model Assembly Lines with Process Time Uncertainty>></p> <p> </p>
Data-Driven Identification and Analysis of Waiting Times in Business Processes: A Systematic Literature Review
<p>Supplementary Material for Systematic Literature Review titled "Data-Driven Identification and Analysis of Waiting Times in<br> Business Processes: A Systematic Literature Review"</p>
Data and scripts for 'Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry'
<p>This repository contains three zipped elements used in the study <em>Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry (</em>https://doi.org/10.5194/tc-2022-81):</p> <p>1. The time-lapse DEMs (original and flow-corrected), orthomosaics (not flow-corrected) and cliff outlines (original and flow-corrected) of the 24K and Langtang survey areas used in this study. The spatial resolution is the same as used in the analysis. These zipped files also contain a .csv file (time_selection.csv) indicating for each index (indicated in the file name) the serial date number in days (date origin January 0, 0000).</p> <p>2. The R and Python scripts (Scripts_final.zip) used to process the DEMs and orthomosaics from the time-lapse images as well as the script to calculate the slope-perpendicular melt. These scripts come with .csv and .txt files that serve as template for the required input data format.</p>
Supplementary material 1 from: Dainese M, Poldini L (2012) Does residence time affect responses of alien species richness to environmental and spatial processes? NeoBiota 14: 47-66. https://doi.org/10.3897/neobiota.14.3273
Supplementary material 1 from: Dainese M, Poldini L (2012) Does residence time affect responses of alien species richness to environmental and spatial processes? NeoBiota 14: 47-66. https://doi.org/10.3897/neobiota.14.3273
Figure 5. Sensory score and period of storage for processed cheese-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>R2 was found to be 96.5 percent of the total variation as explained by sensory scores. Period<br> of storage (days) for which the processed cheese has been in the shelf can be determined based on<br> sensory score (Fig. 5).</p>
Figure 4. Comparison of ASS and PSS for multilayer model R-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>TDNN models with single and multi layers were developed taking soluble nitrogen, pH,<br> standard plate count, yeast & mould count, spore count as input parameters, and sensory score as<br> output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square<br> Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were<br> used in order to compare the prediction ability of the developed TDNN models. Regression<br> equations were developed for predicting the shelf life of processed cheese, which came out as 28.25<br> days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence<br> from the study it can be concluded that TDNN artificial neural network models are quite efficient in<br> predicting shelf life of processed cheese.</p>
Figure 2. Training pattern of TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>The Neural Network Toolbox under MATLAB software was used for developing the TDNN<br> models. Training pattern of TDNN models is presented in Fig.2.</p>
Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for<br> training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate<br> count, yeast & mould count, and spore count were taken as input parameters, and sensory score as<br> output parameter for developing TDNN single and multilayer models (Fig.1).</p>
Figure 3. Comparison of ASS and PSS single layer model-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>TDNN models with single and multi layers were developed taking soluble nitrogen, pH,<br> standard plate count, yeast & mould count, spore count as input parameters, and sensory score as<br> output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square<br> Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were<br> used in order to compare the prediction ability of the developed TDNN models. Regression<br> equations were developed for predicting the shelf life of processed cheese, which came out as 28.25<br> days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence<br> from the study it can be concluded that TDNN artificial neural network models are quite efficient in<br> predicting shelf life of processed cheese.</p>
Pre-Processed Power Grid Frequency Time Series
<p><strong>Overview</strong><br> This repository contains ready-to-use frequency time series as well as the corresponding pre-processing scripts in python. The data covers three synchronous areas of the European power grid:</p> <ul> <li>Continental Europe</li> <li>Great Britain</li> <li>Nordic</li> </ul> <p>This work is part of the paper "Predictability of Power Grid Frequency"[1]. Please cite this paper, when using the data and the code. For a detailed documentation of the pre-processing procedure we refer to the supplementary material of the paper.</p> <p><strong>Data sources</strong><br> We downloaded the frequency recordings from publically available repositories of three different Transmission System Operators (TSOs).</p> <ul> <li><strong>Continental Europe </strong>[2]: We downloaded the data from the German TSO <em>TransnetBW GmbH,</em> which retains the Copyright on the data, but allows to re-publish it upon request [3].</li> <li><strong>Great Britain </strong>[4]: The download was supported by National Grid ESO Open Data, which belongs to the British TSO <em>National Grid</em>. They publish the frequency recordings under the NGESO Open License [5].</li> <li><strong>Nordic</strong> [6]: We obtained the data from the Finish TSO <em>Fingrid</em>, which provides the data under the open license CC-BY 4.0 [7].</li> </ul> <p><strong>Content of the repository</strong></p> <p><strong>A) Scripts</strong></p> <ol> <li>In the "Download_scripts" folder you will find three scripts to automatically download frequency data from the TSO's websites.</li> <li>In "convert_data_format.py" we save the data with corrected timestamp formats. Missing data is marked as NaN (processing step (1) in the supplementary material of [1]).</li> <li>In "clean_corrupted_data.py" we load the converted data and identify corrupted recordings. We mark them as NaN and clean some of the resulting data holes (processing step (2) in the supplementary material of [1]).</li> </ol> <p>The python scripts run with Python 3.7 and with the packages found in "requirements.txt".</p> <p><strong>B) Yearly converted and cleansed data</strong><br> The folders "<year>_converted" contain the output of "convert_data_format.py" and "<year>_cleansed" contain the output of "clean_corrupted_data.py".</p> <ul> <li><strong>File type</strong>: The files are zipped csv-files, where each file comprises one year.</li> <li><strong>Data format</strong>: The files contain two columns. The second column contains the frequency values in Hz. The first one represents the time stamps in the format <em>Year-Month-Day Hour-Minute-Second</em>, which is given as naive local time. The local time refers to the following time zones and includes Daylight Saving Times (python time zone in brackets): <ul> <li>TransnetBW: Continental European Time (<em>CE)</em></li> <li>Nationalgrid: Great Britain (<em>GB</em>)</li> <li>Fingrid: Finland (<em>Europe/Helsinki</em>)</li> </ul> </li> <li><strong>NaN representation</strong>: We mark corrupted and missing data as "NaN" in the csv-files.</li> </ul> <p><strong>Use cases</strong><br> We point out that this repository can be used in two different was:</p> <ul> <li><strong>Use pre-processed data</strong>: You can directly use the converted or the cleansed data. Note however, that both data sets include segments of NaN-values due to missing and corrupted recordings. Only a very small part of the NaN-values were eliminated in the cleansed data to not manipulate the data too much.</li> </ul> <ul> <li><strong>Produce your own cleansed data</strong>: Depending on your application, you might want to cleanse the data in a custom way. You can easily add your custom cleansing procedure in "clean_corrupted_data.py" and then produce cleansed data from the raw data in "<year>_converted".</li> </ul> <p><strong>License</strong></p> <p>This work is licensed under multiple licenses, which are located in the "LICENSES" folder.</p> <ul> <li>We release the code in the folder "Scripts" under the MIT license .</li> <li>The pre-processed data in the subfolders "**/Fingrid" and "**/Nationalgrid" are licensed under CC-BY 4.0.</li> <li>TransnetBW originally did not publish their data under an open license. We have explicitly received the permission to publish the pre-processed version from TransnetBW. However, we cannot publish our pre-processed version under an open license due to the missing license of the original TransnetBW data.</li> </ul> <p><strong>Changelog</strong><br> Version 2:</p> <ul> <li>Add time zone information to description</li> <li>Include new frequency data</li> <li>Update references</li> <li>Change folder structure to yearly folders</li> </ul> <p>Version 3:</p> <ul> <li> <p>Correct TransnetBW files for missing data in May 2016</p> </li> </ul>
Time-Lapse Self-Potential Signals from Microbial Processes: a Laboratory Perspective
<p>#Datasets for 3D self-potential monitoring experiments for microbial processes under laboratory-controlled conditions</p> <p>The three datasets presented here are the measured time-lapse self-potential (SP) data described in the paper 'Time-Lapse Self-Potential Signals from Microbial Processes: a Laboratory Perspective'.</p> <p>The data contains three SP datasets and resistivity data. Each SP dataset contains 193 columns, the data in the first column is the time and the unit is seconds, and columns 2 to 193 are SP data in mV corresponding to the 192 electrode measurements.</p> <p>1) Stability test in water: The measured data is named water_background_data.txt, which record the time-lapse SP signals measured by the SP monitoring system in the stability test experiment.</p> <p>2) Microbial Processes monitoring experiment 2: The measured data is named Experiment2_data.txt, which record the time-lapse SP signals measured by microbial processes in organic matter.</p> <p>3) Microbial Processes monitoring experiment 3: The measured data is named Experiment3_data.txt, which record the time-lapse SP signals measured by microbial processes in organic matter.</p> <p>4) Resistivity data: The measured data is named Resistivity_measurement_data.txt, which record three experimental materials' resistivity measured by the DER25718 instrument.</p>
Ecological network structure in response to community assembly processes over evolutionary time
Open the record for dataset details and reuse information.
Integrating floral trait and flowering time distribution patterns help reveal a more dynamic nature of co-flowering community assembly processes
<p>Species' floral traits and flowering times are known to be the major drivers of pollinator-mediated plant-plant interactions in diverse co-flowering communities. However, their simultaneous role in mediating plant community assembly and plant-pollinator interactions is still poorly understood. Since not all species flower at the same time, inference of facilitative and competitive interactions based on floral trait distribution patterns should account for fine phenological structure (intensity of flowering overlap) within co-flowering communities. Such an approach may also help reveal the simultaneous action of competitive and facilitative interactions in structuring co-flowering communities.</p> <p>Here we used modularity within a co-flowering network context, as a novel approach to detect convergent and/or over-dispersed patterns in floral trait distribution and pollinator sharing. Specifically, we evaluate differences in floral trait and pollinator distribution patterns within (high temporal flowering overlap) and among co-flowering modules (low temporal flowering overlap). We further evaluate the consistency of observed floral trait and pollinator sharing distribution patterns across space (three geographic regions) and time (dry and rainy seasons).</p> <p>We found that floral trait similarity was significantly higher in plant species within co-flowering modules than in species among them. This suggests pollinator facilitation may lead to floral trait convergence, but only within co-flowering modules. However, our results also revealed seasonal and spatial shifts in the underlying interactions (facilitation or competition) driving co-flowering assembly, suggesting that the prevalent dominant interactions are not static.</p> <p>Synthesis: Overall, we provide strong evidence showing that the use of flowering time and floral trait distribution alone may be insufficient to fully uncover the role of pollinator-mediated interactions in community assembly. Integrating this information along with patterns of pollinator sharing will greatly help reveal the simultaneous action of facilitative and competitive pollinator-mediated interactions in co-flowering communities. The spatial and temporal variation in flowering and trait distribution patterns observed further emphasize the importance of adopting a more dynamic view of community assembly processes.</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.