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8,460 results for “MONITORING”
EV Data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds. </p>
Goose monitoring data from the Meise Botanic Garden, Belgium
<p>This data deposit gives details and routes of the goose monitoring at Meise Botanic Garden, Meise, Belgium. Here you will find details of the survey routes and areas where the geese were counted. </p> <p>These data can be used with the observation data published on the Global Biodiversity Information Facility. However, a snapshot of the data is included here.</p> <p>Groom Q (2019). Waterbirds of the Botanic Garden Meise. Version 3.6. Botanic Garden Meise. Occurrence dataset https://doi.org/10.15468/cgffyq accessed via GBIF.org on 2019-01-01.</p> <p>The files contain the following...</p> <p><strong>0021579-181108115102211.zip</strong></p> <p>The download of data from the Global Biodiversity Information Facility</p> <p>Groom Q (2018). Waterbirds of the Botanic Garden Meise. Version 3.4. Botanic Garden Meise. Occurrence dataset https://doi.org/10.15468/cgffyq accessed via GBIF.org on 2019-01-01.</p> <p><strong>dataonsectors2019.txt; sectorsdbf.txt</strong></p> <p>A tab-delimited text file containing data on the surveyed sectors of the garden.</p> <p><strong>gardenSectors3.shp; ods.dbf</strong></p> <p>A shapefile and data file describing the extents and positions of the surveyed sectors of the Botanic Garden.</p> <p><strong>lakes.zip</strong></p> <p>A shapefile describing the extents of the lakes in the Botanic Garden. This file is not particularly precise and is only used to create maps.</p> <p><strong>route1.zip; route2.zip; route3.zip; route4.zip</strong></p> <p>Shapefiles giving the survey routes within the Botanic Garden. Routes were always walked in a clockwise direction.</p> <p> </p> <p> </p>
A map describing routes used in goose monitoring at Meise Botanic Garden
<p>The geese living in Meise Botanic Garden were surveyed between October 11, 2011 - July 10, 2017. Each day that the geese were monitored one route was chosen at random. This map describes those routes</p> <p><strong>routemap.png</strong></p> <p>A map of the survey routes within the Botanic Garden. CC BY-SA</p> <p>The background map is derived from the OpenStreetMap see <a href="https://www.openstreetmap.org/copyright">https://www.openstreetmap.org/copyright</a></p>
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds.</p>
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds.</p>
Domain Analysis Monitoring Public Dataset Dec 5
<p>Complex and heterogeneous software systems need to be monitored as their full behavior often only emerges at runtime, e.g., when interacting with other systems or the environment. Software monitoring approaches observe and check properties or quality attributes of software systems during operation. Such approaches have been developed in diverse communities and for various kinds of systems and purposes. For instance, requirements monitoring aims to check at runtime whether a software system adheres to its requirements, while resource or performance monitoring collects information about the consumption of computing resources by the monitored system. Many venues publish research on software monitoring, often using diverse terminology, focusing on different monitoring aspects and phases. The lack of a comprehensive overview of existing research often leads to re-inventing the wheel. We provide a domain model to structure and systematize the field of software monitoring, starting with requirements and resource monitoring. For more information on the domain model, please refer to the authors' publication lists. In this dataset we provide details on 47 approaches we analyzed with the model to assess its coverage.</p> <p>Please note that this is version 1.1 from January 30, 2019 and a more recent version might be available (link on top of dataset).</p>
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds.</p>
Sentinel-1 Satellite Imagery Based Ice Road Detection and Monitoring
<p>Canada’s northern ice roads in winter which is more than 3300 miles are freezing later and melting earlier, drastically reducing the forecasting capabilities for the safe use. An attractive Area of Interest (AoI) could be around the region “Yellowknife” in Canada, which is also mentioned in the recent TV show (<a href="https://en.wikipedia.org/wiki/Ice_Road_Truckers">https://en.wikipedia.org/wiki/Ice_Road_Truckers</a>). This area could be used to train the algorithm to detect the current situation and to forecast the time window to close and open the ice tracks to the citizens. Mockup could be achieved by building a screenshot mockup on mobile device (Smartphone/Tablet).</p>
Monitoring of urban areas on Sentinel-1
<p>Data from Sentinel-1 SLC product was used to determine the extent of the urbanised area. Such a solution is necessary in the case of rapidly developing cities, as in the case of the capital of India - New Dehli.</p> <p>Links to the presentation:</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/Urban-area-on-S-1.pdf</p> <p> </p>
MAJA look-up tables for Sentinel-2 A&B sensors, for Copernicus Atmosphere Monitoring Service aerosol types
<p>The archive contains the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors. These look-up tables correspond to the aerosol types used by Copernicus Atmosphere Monitoring Service (CAMS). However, the default continental model is also provided.</p> <p>Version 1.1 has new LUT for water vapour estimates, which corrects for a bias observed for large water vapour contents (above 2.5 g/cm2)</p> <p>Version 1.2 just changed the Folder name for a better integration with Start_maja.</p> <p>Version 1.3 added the Header files</p>
A Methodology for the Fast Identification and Monitoring of Microplastics in Environmental Samples using Random Decision Forest Classifiers
<p>This short video shows the results of the application of a classifier for microplastics as described by Hufnagl et al. (2019).</p> <p> </p> <p>If you reuse this video please cite</p> <p> </p> <p>Hufnagl, B., Steiner, D., Renner, Löder, M. G. J., Laforsch, C. and Lohninger, H. <em>A Methodology for the Fast Identification and Monitoring of Microplastics in</em><em> Environmental Samples using Random Decision Forest Classifiers,</em> Analytical Methods, 2019, DOI:10.1039/C9AY00252A</p>
Monitoring data from implementation phase
<p>WP6 collected qualitative and quantitative information and data in order to monitor the implementation of the measures of the 11 URBANWASTE pilot cases. Data and information collected are related to the following issues: </p> <ul> <li>Monitoring of actions, items and stakeholders involved</li> <li>Information on materials and flows supporting the LCA analyisis</li> <li>Waste production: weight (daily or 3 sampling weeks) or, in case the weighting of waste is not possible, bins/bags filled in (daily or 3 sampling weeks) and bins/bags dimension.</li> <li>Gender information</li> </ul> <p>All the information collected was reported in the deliverable D6.2 and was also used to support the evaluation of pilot measures (D7.1) and the refinement of the forms related the 11 pilot cities and the 22 measures that are included in deliverable 4.1.</p>
VGAM: Compact and Low Power Mass Spectrometer-based Instrumentation for Volcanic Gas Monitoring
<p>Compact mass spectrometers can provide simultaneous multi-species analysis with high sensitivity and precision.<br> For volcanic gas monitoring in situ, instrumental mass spectrometry requires reliability and ruggedness combined<br> with low power usage and portability. The Volcanic Gas Analytical Monitor (VGAM) is capable of quantitative<br> molecular analysis of a variety of atmospheric and volcanic gases in a single sensor by ion trap mass<br> spectrometry. These gases include: H 2 , He, H 2 O, N 2 , O 2 , Ar, NO, N 2 O, CO, CO 2 , H 2 S, SO, SO 2 , and CH 4 . Unlike<br> previous field instruments using magnetic sector and quadrupole mass spectrometers (MS) with vacuums backed<br> by compact turbomolecular pumps, the VGAM uses a low-power autoresonant ion trap MS and NEG-Ion vacuum<br> that operate at only 25 W total power, which is often the power requirement for just the mass spectrometer. Ratio-<br> metric mass spectral response is combined with total pressure measurements to report absolute partial<br> pressures. Data are generated in real time and are recorded to internal flash memory. Relatively low power (&lt;1<br> Watt) data telemetry to remote sites is possible. Analysis of volcanic plumes, fumaroles, and solfatara fields is<br> accomplished by direct inlet of gases, after trapping as much excess water vapor in both the external and internal<br> foreline as possible. We report herein on the VGAM auto-run and post-processing procedures, initial calibration<br> results for CO 2 , and the results of a brief field deployment at Sulphur Banks solfatara field, Kilauea Volcano,<br> Hawaii.</p>
ScintPi: A low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives
<p>These data sets contain ionospheric scintillation (GPS L1) observations (S4 indices) collected by a ScintPi prototype during 2018 at a low magnetic latitude station (Presidente Prudente). ScintPi is a low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives.</p> <p>The file named "ScintPi_PPR_S4_2018.mat" contains S4 values (s4mat) for 2018 as a function of universal time (utmat), day-of-year (doymat), GPS satellite identifier number (prnmat), GPS satellite elevation (elmat) and azimuth (azmat) angles.</p> <p>The file named "ScintPi_PPR_20180211.mat" contains example raw (10 Hz) measurements made by ScintPi on February 11, 2018. The file contains values of receiver's altitude, latitude and longitude (variables alt,lat, and lon), GPS satellite azimuth and elevation (variables el and az), GPS identifier number (prn), signal-to-noise ratio (snr), and day-of-year (doy).</p>
Summary Table of AHG Parameters, Hydraulics, Morphological and geophysical variables, and Suspending Sediment Concentration Derived at 1246 USGS River Monitoring Stations
<p>This table contains information about:</p> <p>1) At-A-Station Hydraulic Geometry Parameters,</p> <p>2) Hydraulics Variables,</p> <p>3) Morphological and Geophysical Variables, and</p> <p>4) Suspending Sediment Concentration and Fraction of Sand, Silt, and Clay,</p> <p>derived at 1246 USGS river monitoring stations across the conterminous United States. </p>
uGIM: week monitorization data of a microgrid with five agents (04/08/2019 - 10/08/2019)
<p>uGIM is a microgrid intelligent management software that can represent individual microgrid’s players using a multi-agent approach. This dataset has data regarding a week (from 04-08-2019 to 10-08-2019) of a microgrid with five players (all offices). All agents have consumption and generation data. One of the agents also has sensor data, such as temperature, movement and humidity.</p> <p>In uGIM, agents are deployed in the player’s facilities using single-board computers. All the data in this dataset is read and stored in five single-board computers. Each agent integrates several resources. In this microgrid deployment, all resources use TCP/IP communication. However, uGIM supports more protocols, such as Modbus/RTU, and Modbus/TCP.</p> <p>uGIM related publications:<br> - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. <a href="http://doi.org/10.1016/j.measurement.2019.107427">https://doi.org/10.1016/j.measurement.2019.107427</a><br> - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. <a href="http://doi.org/10.1109/ACCESS.2020.2985254">https://doi.org/10.1109/ACCESS.2020.2985254</a><br> - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. <a href="http://doi.org/10.14201/gredos.144238">https://doi.org/10.14201/gredos.144238</a><br> - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. <a href="http://doi.org/10.1016/j.jclepro.2019.118154">https://doi.org/10.1016/j.jclepro.2019.118154</a></p> <p> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Laser triangulation measurement for AFP monitoring
<p>This data set was acquired in the context of EU project ZAero. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 721362. Project duration: 2016/10/01 - 2019/09/30. This data set contains a HDF5 file with data used for evaluation in a SAMPE 2019 conference paper [1].</p> <p>The main data in this package is contained in a HDF5 file: zaeroFTD1.h5. There are two entries in this file:<br> /raw: contains the raw laser range data as acuqired during AFP lay-up<br> /seg: contains the manually defined segmentation that corresponds to the laser range data</p> <p>The data was acquired for preliminary test runs for AFP monitoring in the ZAero project. It contains different regions that correspond to the following labels (as defined in /seg):<br> 1 ... gap<br> 2 ... regular tow<br> 3 ... overlap<br> 4 ... fuzzball</p> <p>This data is mainly intended for testing of algorithms that perform defect detection on laser range images of AFP data.</p> <p>For more information about the HDF5 format, please visit the HDF5 Group website:<br> https://www.hdfgroup.org/solutions/hdf5/</p> <p>An example for loading and visualizing the data in Python comes with this data set:<br> readDataExample.py</p> <p>=====================================<br> REFERENCES<br> =====================================</p> <p>[1]<br> @inproceedings{Zambal2019_SAMPE}<br> author = {Sebastian Zambal and Christoph Heindl and Christian Eitzinger},<br> title = {Machine Learning for CFRP Quality Control},<br> booktitle = {Conference of the Society for the Advancement of Material and Process Engineering (SAMPE), Nantes, France},<br> year = {2019}<br> }</p> <p>[2]<br> @inproceedings{Zambal2019_QCAV,<br> author = {Sebastian Zambal and Christoph Heindl and Christian Eitzinger and Josef Scharinger},<br> title = {End-to-End Defect Detection in Automated Fiber Placement Based on Artifcially Generated Data},<br> booktitle = {Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111721G},<br> doi = {DOI: 10.1117/12.2521739},<br> year = {2019}<br> }</p> <p> </p>
Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for independent validation study)
<p>Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.</p> <p>This dataset contains data collected during the independent validation study: derived data from raw accelerometry data, and summary performance data.</p> <p>The full dataset, including raw accelerometry data, is available here: <a href="https://mueller-et-al-2019.s3.amazonaws.com/index.html">https://mueller-et-al-2019.s3.amazonaws.com/index.html</a></p>
The Politecnico di Torino rolling bearing test rig: description of the open-access data for vibration monitoring and diagnostics
<p>Accelerometric measurements from the rolling bearing test rig of the Dynamic and Identification Research Group (DIRG), Department of Mechanical and Aerospace Engineering, Politecnico di Torino.</p> <p>Goals:</p> <p> • Vibration Monitoring, Bearing Diagnostics, Damage detection, Damage localization, Damage classification, Damage assessment.</p> <p>Features:</p> <p> • high-speed spindle driving a hollow shaft supported by a couple of identical roller bearings B1 and B3. B1 is the bearing under analysis and features various damages.</p> <p> • two damage types (indentations on a roller and on the inner ring) and severities (0, 150, 250, 450 µm).</p> <p> • a central, larger roller bearing (B2) is loaded through a sledge generating a controlled radial force measured by a load cell.</p> <p> • lubrication is obtained by oil injection into the hollow shaft.</p> <p> • a K-type thermocouple is used to monitor the temperature (manually recorded).</p> <p> • two triaxial accelerometers are mounted on the supports of bearings B1 and B2.</p> <p>Dataset:</p> <p> • stationary acquisitions at different speed & load combinations (speed: 0, 100, 200, 300, 400, 500 Hz; load: 0, 1000, 1400, 1800 N).</p> <p> • endurance acquisitions of the bearing featuring the 450µm roller indentation. Monitoring of the damage evolution for about 230 hours under the same speed and load condition.</p> <p> </p> <p>The extended description of the dataset can be found in the attached pdf "Description and analysis of open access data" or in:</p> <p>A.P. Daga, A. Fasana, S. Marchesiello, L. Garibaldi, The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data, Mechanical Systems and Signal Processing 120 (2019) 252–273. doi:10.1016/j.ymssp.2018.10.010.</p>
Figure 6 in Necromys lasiurus (Cricetidae: Sigmodontinae) from open areas of the Atlantic Forest of Rio de Janeiro: Population structure and implications for the monitoring of hantaviruses
Figure 6. Results of the Bayesian Analysis of Population Structure (BAPS) of the Necromys lasiurus Cytochrome b sequences compiled in the present study, showing the four genetic clades, which are color-coded. The vertical black lines separate the sample groups. Insert map shows the Brazilian biomes.
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.