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4,325 results for “real”
STOP-IT Real-Time Sensor Data Protection (RSDP)
<p>Sensors, and other devices, generate large amounts of data, which can be used for different purposes; for example, controlling the proper functioning of a critical infrastructure, performing predictive maintenance actions or making decisions that improve the productivity of an industrial plant. However, the use or analysis of erroneous or corrupt data can cause catastrophic situations. For this reason, it is very important to be able to guarantee the integrity of the data generated by sensors, or other devices, which will be used to perform relevant actions for a critical infrastructure, industrial plant, etc. The RSDP tool provides exactly that service, it checks the integrity of the data that has been previously stored in the system, and this can be guaranteed thanks to the use of Blockchain, or DLT, technologies.</p>
Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework
<p>[This repository contains the source data for the manuscript "<strong>Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework</strong>" https://nature-research-under-consideration.nature.com/users/37265-nature-communications/posts/47951-a-real-time-optical-and-electronic-chemical-sensor-based-on-a-amino-enone-linked-triazine-containing-2d-covalent-organic-framework]</p> <p>Fully-aromatic, two-dimensional covalent organic frameworks (2D COFs) are hailed as candidates for electronic and optical devices, yet to-date few applications emerged that make genuine use of their rational, predictive design principles and permanent pore structure. Here, we present a 2D COF made up of chemoresistant β-amino enone bridges and Lewis-basic triazine moieties that exhibits a dramatic real-time response in the visible spectrum and an increase in bulk conductivity by two orders of magnitude to a chemical trigger - corrosive HCl vapours. The optical and electronic response is fully reversible using a chemical switch (NH<sub>3</sub> vapours) or physical triggers (temperature or vacuum). These findings demonstrate a useful application of fully-aromatic 2D COFs as real-time responsive chemosensors and switches.</p>
A Global Database of Historic and Real-time Flood Events based on Social Media
<p>Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10.000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE are detected. All results are publicly available on <a href="http://www.globalfloodmonitor.org">www.globalfloodmonitor.org</a>.</p>
Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography
<p>This repository contains all data and code underlying the publication: J. de Wit et al. "<em>Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography</em>" in Nature Communications (2024) (https://doi.org/10.1038/s41467-024-52594-x)</p> <p><strong>--------------Code description------------------</strong></p> <p>The set of scripts is largely organized around the figures. For each (sub)figure, also from supplementary materials, that involves data and plotting, there is a script that generates the plot from data that can be found in the different zip files that are present in the Zenodo repository under https://doi.org/10.5281/zenodo.11428245.</p> <p>The scripts use the data that is contained in the ZIP folders. The ZIP folders are organized by experiment (Experiment 1, including contrast optimization; Experiment 2), one for the other data (OtherData, the validation for with Trypan blue, and the Arabidopsis, Radish and nematode) and one as a smaller dataset to explain the method on a single B-scan (Example_Bscan_dynamicOCT).</p> <p>IMPORTANT: The folder where the ZIP files are unzipped should be put in the file '<em>basepath.txt</em>', such that the data can be automatically loaded.</p> <p>Besides the figures that mention 'MakeFig...' there are a few more scripts:</p> <ul> <li><em>pointcloud_generation_experiment1.py</em>: this file makes the point clouds from the dynamic OCT images as described in Fig 2b. The resulting data is saved as maximum intensity projections and axial sums(forming the basis for Fig S4, S6 and S7) and as voxel counts (forming the basis of Fig.2c and Fig S5)</li> <li><em>pointcloud_generation_timelapses.py</em>: this file does the segmentation for experiment 2 and saves the maximum intensity projections and axial sums of the different stages in the segmentation (forming the basis of Fig3a,d,e and FigS9a,b), and saves the point clouds of the data. These point clouds were refined manually in CloudCompare as described in methods. These segmented point clouds are contained in the data zip folder of experiment 2.</li> <li><em>StatisticalTests.R</em>: This R file calculates the statistical tests for Fig.2cd and Fig.S5d. Here the path is not automatically updated, and should be manually set. The input file is contained in "Experiment1/SegmentationData/segmentationdata_samples.csv" and the output of the file is "D:/DataZenodo/Experiment1/SegmentationData/data_combined_Rstats_output.csv"</li> <li><em>example_dynamic_Bscan.py</em>: This script gives an example of the dynamic OCT processing as proposed in this paper. First it shows the process from an OCT interference spectrum to a B-scan. Then it loads 100 B-scans and applies dynamic OCT, including normalization with histograms. Finally it gives a dynamic B-scan and plots this against the average normal OCT image. This script can be used with only the zip folder "Example_Bscan_dynamicOCT", which reduces the amount of data needed to download/unzip.</li> </ul> <p>The list of other script files to load the data and generate the figures (guiding to the path of uncropped figures) is:</p> <ul> <li><em>MakeFig1bce_Fig2e.py</em></li> <li><em>MakeFig1d.py</em></li> <li><em>MakeFig1agraphs_FigureS1.py</em></li> <li><em>MakeFig3acde_S9ab.py</em></li> <li><em>MakeFigS2_determine_dynamic_range_experiment1.py</em></li> <li><em>MakeFigS8.py</em></li> <li><em>MakeFigureS4-S6-S7.py</em></li> <li><em>MakeFigureS5.py</em></li> <li><em>MakeHistFig2b_makeFigS3b-e.py</em></li> <li><em>MakePlotsFig2ab.py</em></li> <li><em>MakePlotsFig2cd.py</em></li> </ul> <p>Code was all run in Python 3 using Anaconda Spyder.</p> <p>Moreover, the zip file with the code contains the folder '<em>figures</em>' with all subfigures. Some of them are automatically saved from the scripts, others (like photos, icons, but also the Trypan blue microscopy figure) are added in the respective folder. The are logically organized by figure number.</p> <p><strong>--------------Dataset Description-----------------</strong></p> <p>As mentioned above, the data is organized in four zip folders for both experiments, the other data (validation with Trypan blue, other plant-pathogens) and one for the dynamic OCT B-scan example. The data contain the following:</p> <p><strong>Experiment 1: </strong></p> <ul> <li>DynamicOCTimages whose subfolders (organized by date) contain a folder per volume dataset in experiment 1 with a z-stack of .tif files that form the imaged volume. The lateral sampling is 3 um and the axial sampling is 1.37 um. </li> <li>ContrastOptimization: This folder contains <ul> <li><em>Bscans_with_segmentation</em>: segmented B-scans for contrast optimization (Fig S3)</li> <li><em>Bscan_figS1_fig1</em>: The B-scans and segementation for Figure S1.</li> <li><em>histogramdata_dynamicrange</em>: The histograms, bins and deducted reference data for determining the dynamic range per color channel for experiment 1 (Fig S2)</li> <li><em>logcompressed_3value_dOCT_example</em>: An example data stack for obtaining histograms (see script MakeFigS2_determine_dynamic_range_experiment1.py)</li> <li><em>overlaps_threshold-100-98-95-92-90-85-80-75-70-65-60-55-50-45perc_red-1_2_blue_-3_0_green1_filt.npy</em>: A file with intermediate data for the contrast optimization, which can also be generated with the script "<em>MakeHistFig2b_makeFigS3b-e.py</em>"</li> </ul> </li> <li>SegmentationData: This folder contains <ul> <li><em>MIP_segmentation_stages</em>: maximum intensityp projections and axial sums for all images at different stages in the segmentation (basis for Fig S4,6,7)</li> <li><em>processed_masks and StackMasks</em>: the manually obtained masks (segmented in StackMasks, made into masks in the folder 'processed_masks') for filtering out stomata, veins and artefacts.</li> <li><em>Unmasked_axialsum_th34_formanualsegmentation</em>: This folder contains the images of Fig.S4 and were used for the segmentation (we addes a small offset, such that the in segmentation we could set it to 0 and have a unique mask). </li> <li><em>overview_samples_bremiayn.csv</em>: A dataframe with the data for all the samples in experiment 1 that is used as input for the segmentation. It also contains the result of the manual check whether it has infection (Fig2c, left).</li> <li><em>segmentationdata_samples.csv</em>: This supplements the file of overview_samples_bremiayn.csv with the results from the segmentation and is output to script "<em>pointcloud_generation_experiment1.py</em>". It forms the basis of Fig.2a-c, and FigS5, as well as for the R-script to do the statistical testing.</li> <li><em>qPCR_dOCT.csv</em>: This script contains the qPCR data and is input to Fig2d. </li> </ul> </li> </ul> <p><strong>Experiment 2:</strong></p> <ul> <li><em>DynamicOCTimages</em>: This contains the z-stacks of .tif files of the volumes for experiment 2 (and one extra, where a z-slice is used in Fig.1b, bottom). Sampling step size is here again 3 um in lateral direction and 1.37 um in axial direction.</li> <li><em>.npy files </em>with the histograms (with same bins as Experiment 1), maxvalues and reference values for the dynamic range calculation.</li> <li><em>segmentation_data</em>: this folder contains: <ul> <li><em>quantification_volume_disc160_33_10.csv</em> and <em>quantification_volume_disc160_33_10.xlsx</em>: data from the manually segmented point clouds that form the basis of Fig.3c.</li> <li><em>timelapse_sampleoverview.csv</em>: overview of the samples that is used as input in the file "<em>pointcloud_generation_timelapses.py</em>"</li> <li><em>pointclouds</em>: Folder with segmented point clouds for the three leaf discs. These files could be loaded in CloudCompare.</li> <li><em>overviewMIPs</em>: folder with overview maximum intensity projections for the different steps in segmentation, which also forms the input of Fig.3a, FigS9ab.</li> <li>rawpointclouds: folder with the automatically generated point clouds from file <em>pointcloud_generation_timelapses.py </em>which were imported into CloudCompare as the basis for the segmented point clouds.</li> </ul> </li> </ul> <p><strong>OtherData:</strong></p> <p>This folder contains the z-stacks of dynamic OCT tif images for Arabidopsis (here both a normal contrast and one that has been increased to only contain the original 0-180 range); nematodes, radish (called radijs_test_PP_py_0002), spores for Fig1c (SporesImaging) and the dynamic OCT image of Fig1d. </p> <p><strong>example_Bscan_dynamicOCT:</strong></p> <p>This folder contains data to run the script example_dynamic_Bscan.py to show the dynamic OCT imaging process from raw OCT spectra.</p> <ul> <li><em>raw_spectra_exampleframe:</em> contains interference spectra, a reference spectrum and interpolation grid to show how to get from a raw OCT spectrum to a normal single B-scan.</li> <li><em>abs_images:</em> contains 100 subsequent B-scans that can be used to generate a dynamic OCT image as done in example_dynamic_Bscan.py</li> </ul> <p> </p> <p> </p>
Dataset related to the Journal Article 'A deep learning method for the prediction of ship fuel consumption in real operational conditions'
<p>This dataset contains the data used to plot the graphs and create tables corresponding to the figure/table number in the published version of the paper.<br>Paper DOI:https://doi.org/10.1016/j.engappai.2023.107425</p> <p> </p>
Artifacts supplementing the EuroUSEC '22 paper "Assessing Real-World Applicability of Redesigned Developer Documentation for Certificate Validation Errors"
<p>This upload supplements the conference EuroUSEC 2022 submission by providing the full questionnaire, anonymized dataset and all performed analyses presented in the paper specified below.</p> <ul> <li>Title: <strong>Assessing Real-World Applicability of Redesigned Developer Documentation for Certificate Validation Errors</strong></li> <li>Authors: Martin Ukrop, Michaela Balážová, Pavol Žáčik, Eric Vincent Valčík, Vashek Matyas</li> <li>Paper details: https://crocs.fi.muni.cz/public/papers/eurousec2022</li> <li>Paper abstract: <em>We face certificate validation errors commonly, yet the related tools and documentation had been shown to have very poor usability. Previous research suggests that just improving the error messages and corresponding documentation can have significantly positive effects. Our work aims at increasing the usability of certificate validation by 1) redesigning the API error messages and the corresponding documentation, and 2) validating the real-world applicability of the redesign by investigating the opinions of 180 IT professionals. We focus on the perceived obstacles, desired ideal form and overall satisfaction. The redesigned documentation exhibits a reliable significant decrease in perceived incompleteness, with a small amount of perceived bloat and tangle. The redesigned documentation, now published on a dedicated website, is preferred by 89% of our study participants.</em></li> </ul> <p>The artifacts accompanying this paper contain three major parts:</p> <ul> <li>The questionnaire used in the main study (described in Sections 3.1 and 3.2 of the paper and mostly present in Appendices A and B of the paper).</li> <li>The anonymized dataset (multiple formats) of all valid questionnaire answers and qualitative coding performed. Analyses of this dataset are the core of the paper and are present in subsection 3.4 and all parts of Sections 4 and 5.</li> <li>The set of analyses files (IBM SPSS scripts and outputs) producing all statistical results presented in the paper are included.</li> </ul> <p>More details about the artifacts can be found in the README file in the artifacts archive.</p>
Real-time ZTD and gradients from 20 IGS stations; year 2019
<p>This dataset contains multi-GNSS real-time products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties estimated every 1 minute over the entire year 2019 for 20 IGS stations: ALGO, BIK0, CAS1, CHPG, CPVG, FAA1, JPLM, KERG, KZN2, LHAZZ, LMMF, NKLG, NNOR, PNGM, POVE, REYK, RGDG, ULAD, WROC, YEL2.</p> <p>Two subsets are available that correspond to two processing strategies, i.e. common and advanced. A detailed description, validation and comparison of both strategies can be found in: https://link.springer.com/article/10.1007/s10291-020-01014-w</p> <p>Data are stored in standard Matlab MAT files. Each file contains a single table array (Matlab format) with the complete set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using in-build Matlab function "writetable.m".</p>
WikiChurches – A Fine-Grained Dataset of Architectural Styles with Real-World Challenges
<p>WikiChurches is a dataset for architectural style classification, consisting of 9,485 images of church buildings. Both images and style labels were sourced from Wikipedia. The dataset can serve as a benchmark for various research fields, as it combines numerous real-world challenges: fine-grained distinctions between classes based on subtle visual features, a comparatively small sample size, a highly imbalanced class distribution, a high variance of viewpoints, and a hierarchical organization of labels, where only some images are labeled at the most precise level. In addition, we provide 631 bounding box annotations of characteristic visual features for 139 churches from four major categories. These annotations can, for example, be useful for research on fine-grained classification, where additional expert knowledge about distinctive object parts is often available.</p> <p>Please refer to the README.md file for information about the different files contained in this dataset.</p>
Online Real-Time Delphi Survey for the research project "MENARA" - Compilation of all Comments to Closed and Open Questions
<p><strong>Looking into the Futures: Delphi Survey about the MENA region</strong></p> <p>In order to get a more realistic overview of the situation and trends, of the potentials, problems and potentials of the countries of the MENA region a Real Time Delphi survey was conducted. This is an important tool of modern future research. It was managed by the IZT- Institute for Future Studies in Berlin. A group of 139 experts and researchers from different institutes and organizations were invited to participate at the Online Real-Time Delphi Survey (RTD) about possible and likely futures of the MENA region. The experts were asked to answer questions and provide their opinions on twelve topics such as social unrest, youth unemployment, urbanization, gender equality, security etc. In this dataset all comments to the closed and the open questions are compiled.</p> <p>The output was one of the basic material used for the creation of future regional scenarios for mid-term (2025) and long-term (2050) time horizons. Focus scenarios were produced in order to exemplify selected characteristic and important future options, in terms of chances and risks (e.g. energy futures).</p>
NO2 levels inside vehicle cabins with pollen and activated carbon filters: A real world targeted intervention to estimate NO2 exposure reduction potential
<p>In-vehicle and on-road (ambient) NO<sub>2</sub> measurements in different car cabin from Birmingham, UK. This dataset was used for the publication NO2 levels inside vehicle cabins with pollen and activated carbon filters: A real world targeted intervention to estimate NO<sub>2</sub> exposure reduction potential, Science of The total environment,160395 <a href="https://doi.org/10.1016/j.scitotenv.2022.160395">https://doi.org/10.1016/j.scitotenv.2022.160395</a></p>
Processed Synthetic Real-World Data for tristate modelling
<p>This model learning dataset is created out of the <a href="https://zenodo.org/record/7409763">Raw Synthetic RWD</a> raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.</p> <p>This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset.</p> <p>The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.</p> <p>The outcome to be predicted is a tristate quantized version of the health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).</p>
Processed Synthetic Real-World Data for binary modelling
<p>This model learning dataset is created out of the <a href="https://zenodo.org/record/7409763">Raw Synthetic RWD</a> raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.</p> <p>This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.</p> <p>The outcome to be predicted is the binary quantized health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).</p>
Raw Synthetic Real-World Data
<p>Real World Data from a simulator for 1,000 people belonging to any of four behavioural groups: athletic, normal, unfit and feeble, simulated over 2 years and 3 months.</p> <p>The dataset is organised into 4 CSV files:</p> <ul> <li>aggregations.csv contains the daily physiological data, including their short- & long-term averages and their trends. Every row corresponds to a day for a person.</li> <li>answers.csv contains the answers to questions. Each row corresponds to a question answered.</li> <li>activity_summaries.csv contains summaries of the detected activities. Each row corresponds to an activity someone performed.</li> <li>participants_features.csv contains the demographic info of each simulated person. This is static info, every row corresponding to one person. An ML engineer can combine information from any of these files to setup model learning datasets. This is done in the “Processed Synthetic RWD for binary modelling” and the “Processed Synthetic RWD for tristate modelling” datasets that are also shared here.</li> </ul>
Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
<p>Raw experimental data associated with the manuscript "Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches", by Sosa-Carrillo and colleagues, bioRxiv, 10.1101/2022.11.02.514931.<br> The code to analyze this raw data and generate the figures for the manuscript is available on GitLab at https://gitlab.inria.fr/InBio/Public/yeastcybersecretion.</p>
Ecotoxicity test results for the paper entitled "Evaluation of a bioelectrochemical reductive/oxidative sequential process for chlorinated aliphatic hydrocarbons (CAHs) removal from a real contaminated groundwater"
<p>Within the frame of the ELECTRA No. 826244 H2020 project, a sequential reductive/oxidative bioelectrochemical process at a laboratory scale was tested with real groundwater from a contaminated site in Northern Italy for chlorinated aliphatic hydrocarbons (CAHs) removal. The changes in groundwater ecotoxicity were followed by a complex ecotoxicity test battery including testorganisms from various trophic levels. The datasets (supplementary to Chapter 3.5) for this publication are presented here. Referred publication: Edoardo Dell'Armi, Marco Zeppilli, Maria Letizia Di Franca, Bruna Matturro, Viktória Feigl, Mónika Molnár, Zsófia Berkl, Imre Németh, Hafsa Yaqoubi, Simona Rossetti, Marco Petrangeli Papini, Mauro Majone, Evaluation of a bioelectrochemical reductive/oxidative sequential process for chlorinated aliphatic hydrocarbons (CAHs) removal from a real contaminated groundwater, Journal of Water Process Engineering, 49, 2022, 103101, https://doi.org/10.1016/j.jwpe.2022.103101.</p>
Additional results for article "A new approach for the generation of real-time GNSS low-latitude ionospheric scintillation maps"
<p>Complete set of interpolation error and correlation metrics for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options using the SSS cross-validation scheme for the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Complete table of interpolation errors and correlation.csv”.</p> <p>Complete set of scintillation maps for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options covering the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Scintillation maps for the 10-hour dataset.zip”.</p> <p>Comparison plots of the scintillation maps generated by the approaches GDA, IDW, RBF and GPR with the pre-processing options SAR, SMR and VQI for each of the 40 intervals of time of 16 minutes covering the 10-hour dataset - file “Set of maps for all 4 approaches with the SAR, SMR and VQI sets of options.zip”.</p> <p>Sequence of scintillation maps for the 8-hour dataset generated by the GPR(VQI) approach for the three time resolutions (1, 2, and 16-minute) - file “Scintillation maps for the 8-hour comparison dataset.zip”.</p> <p>Animations corresponding to the sequence of maps generated by the GPR(VQI) approach for the 8-hour dataset, and for the three time resolutions (1, 2, and 16-minute) - file “Animations of GPR(VQI) maps for the 8-hour comparison dataset.zip”.</p>
Datasets of the work named Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform
<pre>- 1_Motion_Simulator/ - IMU_results/ - 20211028101756.csv - 20220114101543.csv - 20220117000000.csv - Rotary_Table/ - 15/ - rover_20220105.nav - rover_20220105.obs - solution_20220105_CAS.log - 360/ - rover_20211221.nav - rover_20211221.obs - solution_20211221_CAS.log - 360-15/ - rover_20220202_CAS.nav - rover_20220202_CAS.obs - solution_20220202_CAS.log - Static_Tests/ - solution_SSRA00CAS0 - solution_SSRA00WHU0 - 2_GNSS_Signal_Simulator/ - platformmov_C1.xtd - platformmov_C2.xtd - platformmov_C3.xtd - TestBetaNoneMov_C1 - TestBetaNoneMov_C1.nav - TestBetaNoneMov_C1.obs - TestBetaNoneMov_C1.ubx - TestBetaNoneMov_C2 - TestBetaNoneMov_C2.nav - TestBetaNoneMov_C2.obs - TestBetaNoneMov_C2.ubx - TestBetaNoneMov_C3 - TestBetaNoneMov_C3.nav - TestBetaNoneMov_C3.obs - TestBetaNoneMov_C3.ubx - 3_Test_Sea/ - 20220503000000.xlsx - solution_28.nav - solution_28.obs - solution_28.ubx Background: {Journal Article using this dataset} 'Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform' Paper DOI: <a href="https://doi.org/10.3390/s23020925">https://doi.org/10.3390/s23020925</a> Abstract: a low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved. </pre>
Treatment Condition Student Data: D.A.R.E./keepin' it REAL Elementary Curriculum
<p>Raw, de-identified treatment condition data for waves 1, 2, and 3 associated with an evaluation of D.A.R.E. officers' delivery of the "keepin' it REAL" prevention program. These data were compared to control cases which were generated using the "Virtual Controls" algorithm developed by Hansen, et al., 2018 (Hansen, W.B. Saldana, S., Chen, S.H., & Ip, E.H. (2018). An algorithm for creating virtual controls using integrated and harmonized longitudinal data. Evaluation and the Health Professions. 41(2), 183-215. doi: 10.1177/0163278718772882). </p>
Real state in Madrid and Barcelona: Apartments for selling
<p>El dataset contiene información sobre los inmuebles (apartamentos) puestos en venta en las localidades de Madrid y Barcelona.</p> <p>1 fila cabecera y 506 filas de datos.</p> <p>El dataset está compuesto por los siguientes campos:</p> <ul> <li>Población: indica la población del inmueble, puede ser MADRID o BARCELONA</li> <li>Título: descripción corta del anuncio</li> <li>Precio: precio del inmueble con la moneda local</li> <li>Superficie: superficie del inmueble con la unidad</li> <li>Habitaciones: número de habitaciones</li> <li>Aseos: número de aseos</li> <li>Referencia: referencia única del anuncio</li> <li>Ubicación: dirección completa del inmueble</li> <li>Consumo: consumo energético del inmueble</li> <li>Anunciante: descripción del anunciante</li> <li>Descripción: descripción completa del inmueble</li> <li>Images: json con información relativa a la primera imagen del anuncio</li> <li>Url: dirección web original del anuncio </li> </ul>
Digital Elevation Models, orthoimages and lava outlines of the 2021 Fagradalsfjall eruption: Results from near real-time photogrammetric monitoring
<p>This repository contains the data behind the work described in Pedersen et al (in review), specifically the Digital Elevation Models (DEMs), orthoimages and lava outlines created as part of the near-real time monitoring of the Fagradalsfjall 2021 eruption (SW-Iceland).</p> <p>The processing of the data is explained in detail in the Supplement S2 of Pedersen et al (2022).</p> <p>The data derived from Pléiades surveys includes only the DEMs and the lava outlines. The Pléiades-based orthoimages are subject to license. Please contact the authors for further information about this.</p> <p><strong>Convention for file naming:</strong></p> <p>Data: DEM, Ortho, Outline</p> <p>YYYYMMDD_HHMM: Date of acquisition</p> <p>Platform used: Helicopter (HEL), Pléiades (PLE), Hasselblad A6D (A6D)</p> <p>Origin of elevations in DEMs: meters above ellipsoid (zmae)</p> <p>Ground Sampling Distance: 2x2m (DEM) and 30x30cm (Ortho)</p> <p>Cartographic projection: isn93 (see cartographic specifications for further details)</p> <p> </p> <p><strong>Cartographic specifications:</strong></p> <p>Cartographic projection: ISN93/Lambert 1993 (EPSG: 3057, https://epsg.io/3057)</p> <p>Horizontal and vertical reference frame: The surveys after 18 April 2021 are in ISN2016/ISH2004, updated locally around the study area in April 2021 (after pre-eruptive deformations occurred). The rest of the surveys of late March and early April were created using several floating reference systems (see Supplement S3 for details), since no ground surveys were available during the first weeks of the data collection. The surveys of 23 March 2021, 31 March 2021 were re-procesed in Gouhier et al., 2022, using the survey done on 18 May 2021 as reference.</p> <p>Origin of elevations: Ellipsoid WGS84</p> <p>Raster data format: GeoTIFF</p> <p>Raster compression system: ZSTD (http://facebook.github.io/zstd/)</p> <p>Vector data format: GeoPackage (https://www.geopackage.org/)</p>
ScienceDex guides
Understand access before you commit
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.