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8,460 results for “MONITORING”
DCASE 2024 Challenge Task 10 Development Dataset: Acoustic-based Traffic Monitoring
<h3><strong>Directory structure:</strong></h3> <p><strong>engine-sounds.zip</strong><br>|----- car [<em>car engine sounds</em>]<br>|----- cv [<em>commercial vehicles engine sounds</em>]</p> <p><strong>locX.zip</strong><br>|----- meta.json [<em>contains meta information of traffic condition and sensor setup corresponding to the location</em>]<br>|----- train [<em>train flac files inside</em>]<br>|----- train.csv<br>|----- val [<em>val flac files inside</em>]<br>|----- val.csv</p> <p><strong>simulation.zip</strong><br>|----- locX<br> |----- car<br> | |_____ left [<em>flac files and label csv inside</em>]<br> | |_____ right [<em>flac files and label csv inside</em>]<br> |----- cv<br> |_____ left [<em>flac files and label csv inside</em>]<br> |_____ right [<em>flac files and label csv inside</em>]</p> <p><strong>NOTE:</strong> We split zip files for large size folders (i.e., loc1, loc3, loc6). Please make sure to download <em><strong>all splits</strong></em> before unzipping the data! For example, run </p> <p><code>zip -s 0 loc1.zip --out unsplit-loc1.zip</code></p> <p><code>unzip unsplit-loc1.zip</code></p> <p>in your terminal once you have downloaded <code>loc1.z01</code>, <code>loc1.z02</code>, <code>loc1.z03</code> and <code>loc1.zip</code>.</p> <p> </p> <h3><strong>Contact:</strong></h3> <p>If there is any question, please contact:</p> <ul> <li>Luca Bondi: <a href="mailto:luca.bondi@us.bosch.com">luca </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:luca.bondi@us.bosch.com"> bondi@us (dot) bosch (dot) com</a></li> <li>Shabnam Ghaffarzadegan: <a href="mailto:shabnam.ghaffarzadegan@us.bosch.com">shabnam </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> ghaffarzadegan@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> com</a></li> <li>Wei-Cheng (Winston) Lin: <a href="mailto:winston.lin@us.bosch.com">wei-cheng </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> lin@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> com</a></li> </ul> <p> </p> <h3><strong>Acknowledgement:</strong></h3> <p><em>This work has partially received funding from the European Union's Horizon 2020 research and innovation programme under the Marie </em><em>Skłodowska</em><em>-Curie grant agreement No. 956962 and from the European Research Council under the European Union's Horizon 2020 research and innovation program / ERC Consolidator Grant: SONORA (no. 773268). This work reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.</em></p> <p> </p>
Monitoring the intensity distribution of earth-air activity around the Mogao Grottoes
Open the record for dataset details and reuse information.
Microscopy and flow cytometry raw data for: A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution
<p><br>This repository contains the microscopy and flow cytometry raw data for the following publication:</p> <p><strong>A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution</strong></p> <p>Andreas Kaczmarczyk, Simon van Vliet, Roman Peter Jakob, Raphael Dias Teixeira, Inga Scheidat, Alberto Reinders, Alexander Klotz, Timm Maier, Urs Jenal</p> <p>Biozentrum, University of Basel, 4056 Basel, Switzerland</p> <p>Correspondence to: urs.jenal[at]unibas.ch, andreas.kaczmarczyk[at]unibas.ch</p>
Development of an IoT based smart potato leaf diseases monitoring and controlling system with image processing
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Coatings Characterization and Monitoring
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Laboratory water quality monitoring data from full scale CS#3 DWDN for the DBP prediction model
<p>Lab measurements of water quality based on manual sampling including ordinary monitoring and special monitoring performed in SafeCREW project.</p> <p>Laboratory LIMS database.</p> <p>Provide water quality measurements based on the planned monitoring schedule. Schedule tailored to SafeCREW project needs plus usual legal water sampling.</p>
Non-freely available datasets for the following work: "Comparison of consumption data and phenotypical antimicro-bial resistance in E. coli isolates of human urinary samples and of weaning and fattening pigs from surveillance and monitor-ing systems in Germany."
<p>Human and animal databases used for the creation of the work "Comparison of consumption data and phenotypical antimicrobial resistance in E. coli isolates of human urinary samples and of weaning and fattening pigs from surveillance and monitoring systems in Germany."</p>
Towards on-device dehydration monitoring using machine learning from wearable device's data
<p>Body signals data such as accelerometer, gyroscope, magnetometer, galvanic skin response, photoplethysmography, temperature and barometric pressure were recorded for 11 subjects using Shimmer3 GSR unit. Some data were recorded while fasting for periods reaching 15 hours without water or food intake in Muslims' fasting time (starting fasting time for each subject sample is recorded in fastingTimes.csv for each subject from which last drinking time can be calculated and for the other non-fasting samples, drinking was done in the last hour before recording the sample, so the last drinking time was set to be 1 hour.</p> <p>I have received from some researchers that the zip folder is corrupted, you can find the right version here: https://zenodo.org/record/7099266.</p> <p>Please kindly contact the authors if you have any further concerns</p>
Pasagshak River Sockeye Salmon (Oncorhynchus nerka) Escapement Monitoring 2019–2021
<p>Since 2011, sockeye salmon (<em>Oncorhynchus nerka)</em> escapement through the Pasagshak River into Lake Rose Teed on the Kodiak Island road system has been estimated with a weir. A continuation of funding from the Alaska Sustainable Salmon Fund (AKSSF, Project #52009) allowed the Alaska Department of Fish and Game (ADF&G), Division of Commercial Fisheries to operate a PVC floating weir near the mouth of Lake Rose Teed during the 2019 through 2021 seasons. This data set includes the number and species of fish counted through the weir, and the age, sex, and length (ASL) information gathered from sockeye salmon captured and sampled at the weir.</p>
Durham Cathedral bat ultrasound monitoring data 2017-19
<p>The dataset lists the number of sound files in zero-crossing format extracted per hour from continuous recording of bat ultrasound using two different types of 'Anabat' bat detectors. Each one hour period is denoted by a date and the time in GMT at the beginning of the period.</p>
Water infiltration monitoring in chalks through time-lapse GPR
<p>These time-lapse GPR measurement data are used to investigate the water flow in near surface chalks.</p>
Figure 2 from: Leese F, Altermatt F, Bouchez A, Ekrem T, Hering D, Meissner K, Mergen P, Pawlowski J, Piggott J, Rimet F, Steinke D, Taberlet P, Weigand A, Abarenkov K, Beja P, Bervoets L, Björnsdóttir S, Boets P, Boggero A, Bones A, Borja Á, Bruce K, Bursić V, Carlsson J, Čiampor F, Čiamporová-Zatovičová Z, Coissac E, Costa F, Costache M, Creer S, Csabai Z, Deiner K, DelValls Á, Drakare S, Duarte S, Eleršek T, Fazi S, Fišer C, Flot J, Fonseca V, Fontaneto D, Grabowski M, Graf W, Guðbrandsson J, Hellström M, Hershkovitz Y, Hollingsworth P, Japoshvili B, Jones J, Kahlert M, Kalamujic Stroil B, Kasapidis P, Kelly M, Kelly-Quinn M, Keskin E, Kõljalg U, Ljubešić Z, Maček I, Mächler E, Mahon A, Marečková M, Mejdandzic M, Mircheva G, Montagna M, Moritz C, Mulk V, Naumoski A, Navodaru I, Padisák J, Pálsson S, Panksep K, Penev L, Petrusek A, Pfannkuchen M, Primmer C, Rinkevich B, Rotter A, Schmidt-Kloiber A, Segurado P, Speksnijder A, Stoev P, Strand M, Šulčius S, Sundberg P, Traugott M, Tsigenopoulos C, Turon X, Valentini A, van der Hoorn B, Várbíró G, Vasquez Hadjilyra M, Viguri J, Vitonytė I, Vogler A, Vrålstad T, Wägele W, Wenne R, Winding A, Woodward G, Zegura B, Zimmermann J (2016) DNAqua-Net: Developing new genetic tools for bioassessment and monitoring of aquatic ecosystems in Europe. Research Ideas and Outcomes 2: e11321. https://doi.org/10.3897/rio.2.e11321
Figure 2 - Overview of the structure of DNAqua-Net and interplay between the five Working Groups and the Management Committee.
Fig. 2. A in SHORT COMMUNICATION Monitoring a population of Cruziohyla craspedopus (Funkhouser, 1957) using an artificial breeding habitat
Fig. 2. A) ABHab P-2.1 with empty egg mass; B) Tadpole of Cruziohyla craspedopus (preserved).
Lake water quality monitoring data from 8287 northern lakes
<p>This dataset contains water quality monitoring data and lake morphometry data from 8287 lakes collected between 1960 and 2022 in Finland, Sweden, Norway, Canada and the United States (Alaska). Data was sourced from governmental databases and non-profit organizations via data portals and data requests (see Metadata). The dataset contains over 2.6 million standardized and quality-controlled observations of dissolved oxygen, water temperature, total nitrogen, total phosphorus, water colour and ammonia, as well as sampling location, sampling date, sampling depth and key lake properties including lake altitude, surface area, mean depth and maximum depth. We excluded lakes undergoing physical or chemical treatments against hypoxia, lakes in which experiments have been conducted (e.g. nutrient addition). and reservoirs.</p> <p>The consumer of these data ("Data User" herein) should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is". The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors, original data providers and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. The data user may not create derivatives of this dataset or re-distribute these data to a third party.</p> <p><strong>Note: </strong>if you would like to use this data in your own study, you must contact the author (joachim.jansen@helsinki.fi) to ensure compliance with requirements of the contributors to this synthesis dataset, </p>
DCASE 2024 Challenge Task 10 Evaluation Dataset: Acoustic-based Traffic Monitoring
<h3><strong>Directory structure:</strong></h3> <p><strong>locX.zip</strong><br>|----- test [<em>test flac files inside</em>]</p> <p> </p> <p><strong>NOTE:</strong> We split zip files for large size folders (i.e., loc1, loc3, loc6). Please make sure to download <em><strong>all splits</strong></em> before unzipping the data! For example, run </p> <p><code>zip -s 0 loc6.zip --out unsplit-loc6.zip</code></p> <p><code>unzip unsplit-loc6.zip</code></p> <p>in your terminal once you have downloaded <code>loc6.z01</code>, <code>loc6.z02</code> and <code>loc6.zip</code>.</p> <p> </p> <h3><strong>Contact:</strong></h3> <p>If there is any question, please contact:</p> <ul> <li>Luca Bondi: <a href="mailto:luca.bondi@us.bosch.com">luca </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:luca.bondi@us.bosch.com"> bondi@us (dot) bosch (dot) com</a></li> <li>Shabnam Ghaffarzadegan: <a href="mailto:shabnam.ghaffarzadegan@us.bosch.com">shabnam </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> ghaffarzadegan@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> com</a></li> <li>Wei-Cheng (Winston) Lin: <a href="mailto:winston.lin@us.bosch.com">wei-cheng </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> lin@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> com</a></li> </ul> <p> </p> <h3><strong>Acknowledgement:</strong></h3> <p><em>This work has partially received funding from the European Union's Horizon 2020 research and innovation programme under the Marie </em><em>Skłodowska</em><em>-Curie grant agreement No. 956962 and from the European Research Council under the European Union's Horizon 2020 research and innovation program / ERC Consolidator Grant: SONORA (no. 773268). This work reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.</em></p>
Experimental-data-driven deep learning strategy for Structural Health Monitoring of a plate in acoustic fields
Open the record for dataset details and reuse information.
Figure 2 from: Garzon-Lopez C, Hattab T, Skowronek S, Aerts R, Ewald M, Feilhauer H, Honnay O, Decocq G, Van De Kerchove R, Somers B, Schmidtlein S, Rocchini D, Lenoir J (2018) The DIARS toolbox: a spatially explicit approach to monitor alien plant invasions through remote sensing. Research Ideas and Outcomes 4: e25301. https://doi.org/10.3897/rio.4.e25301
Figure 2 Overlook of the "iSDM" R package functions presented in the toolbox.
Figure 4 from: Garzon-Lopez C, Hattab T, Skowronek S, Aerts R, Ewald M, Feilhauer H, Honnay O, Decocq G, Van De Kerchove R, Somers B, Schmidtlein S, Rocchini D, Lenoir J (2018) The DIARS toolbox: a spatially explicit approach to monitor alien plant invasions through remote sensing. Research Ideas and Outcomes 4: e25301. https://doi.org/10.3897/rio.4.e25301
Figure 4 Image reconstruction process work flow. This process is done using GRASS GIS.
Figure 2 from: Assou D, Segniagbeto GH, Radji R, Akiti J, Pando F (2018) Monitoring data of marine turtles on the Togolese coast during 2012–2013. ZooKeys 779: 109-118. https://doi.org/10.3897/zookeys.779.26967
Figure 2 Monitoring program sites (Segniagbeto et al. 2017).
Figure 3 from: Assou D, Segniagbeto GH, Radji R, Akiti J, Pando F (2018) Monitoring data of marine turtles on the Togolese coast during 2012–2013. ZooKeys 779: 109-118. https://doi.org/10.3897/zookeys.779.26967
Figure 3 Distribution of marine turtle occurrences by species on the Togolese coast.
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.