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576 results for “monitoring system”
Regional drinking water quality monitoring program: long-term monitoring of water quality in select canals, reservoirs, and treatment plants of the greater Phoenix, Arizona metropolitan area drinking water system, ongoing since 1998
Regional Drinking Water Quality Monitoring Program ================================================== Arizona Statue University (ASU) has been working with regional water providers (Salt River Project (SRP), Central Arizona Project (CAP)) and metropolitan Phoenix cities since 1998 on algae-related issues affecting drinking water supplies, treatment, and distribution. The results have improved the understanding of taste and odor (T&O) occurrence, control, and treatment, improved the understanding of dissolved organic and algae dynamics, and initiated a forum to discuss and address regional water quality issues. The monitoring benefits local Water Treatment Plants (WTPs) by optimizing ongoing operations (i.e., reducing operating costs), improving the quality of municipal water for consumers, facilitating long-term water quality planning, and providing information on potentially future-regulated compounds. ASU has been monitoring water quality in terminal reservoirs (Lake Pleasant, Saguaro Lake, and Bartlett Lake) continuously from 1998 to the present for algae-related constituents (taste and odors, and more recently metals from the upper reservoirs), nutrients, and disinfection by-product precursors (i.e., total and dissolved organic carbon and organic nitrogen). Additional monitoring has been conducted in the SRP and CAP canal systems and in water treatment plants in Phoenix, Tempe and Peoria. During this work the Valley has been in a prolonged drought and recently one above average wet year, and this data provides important baseline data for development of new or expanded WTPs and management of existing WTPs in the future. The current work has improved the understanding of T&O sources and treatment, but additional research and monitoring into the future is necessary. Reservoir monitoring is conducted once per month at Bartlett Lake, Saguaro Lake, and Lake Pleasant, and quarterly at Roosevelt, Apache, and Canyon Lakes. Samples are depth integrated in the epilimnion a
SBC LTER: Daily averages of modeled significant wave height (Hs) and peak wave period (Tp) in the Santa Barbara Coastal area from the Coastal Data Information Program - Monitoring and Prediction System (CDIP MOP)
From http://cdip.ucsb.edu: The Coastal Data Information Program (CDIP) is a research group at Scripps Institution of Oceanography that monitors coastal waves and nearshore sand levels on regional scales. CDIP maintains a network of optimally-placed, directional wave buoys from San Diego to Eureka. The buoy measurements are used to initialize a high spatial resolution (100m x 100m) linear spectral wave propagation model. The resulting hourly hindcasts and nowcasts of CA coastal wave conditions have a level of accuracy that is not possible with more traditional wind-wave generation models that are initialized with modeled wind fields.
EPSRC HEED Data Repository: Footfall Monitoring System
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. </p> <p>As part of the project, we deployed a Footfall Monitoring System in the Uttargaya settlement in Nepal. Footfall monitors are designed to measure the step count of passers-by with the aim to: Evaluate the level of activity in an area by measuring footfall count and Evaluate the effect of streetlights on the level of activity.</p> <p>For the purpose of this study, the 7 footfall monitors are deployed beside 7 streetlights. The footfall monitors were deployed prior to commissioning of streetlights to gather baseline data and evaluate the impact of streetlights on the footfall count. The key constituents of footfall monitors are: Raspberry Pi 3B and Case; PiFace Real Time Clock and CAM008 70º night vision camera. The total cost of a monitor is £92.88. The Raspberry Pi is the central unit of the system that runs a program to sense the footfall count as measured by the IR sensor. The IR sensor counts footfall by tracking the number of times a horizontal beam of light is “broken” when a person crosses a threshold. If new data is recorded by the sensor, the updated footfall count along with the direction of movement and the current time (measured from PiFace RTC) is stored onto an SD card. A packet containing the updated values is also transmitted to the heed-data server hosted at Coventry University.</p> <p>Post Deployment Challenges:</p> <ul> <li><strong>Damage to footfall</strong>: In April 2019, footfall monitor 7 was damaged due to a gust of storm and heavy rains in the camp. This monitor was replaced in May 2019.</li> <li><strong>Power outages:</strong> These are common in the camp. Data is lost during this time as the devices have no access to power.</li> <li><strong>Internet connectivity: </strong>The availability and reliability of Wi-Fi continue to be an issue for the transmission of data to heed-data server.</li> </ul>
STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.2 System Level Matrix dataset
<p>This dataset contains the final set of indicators selected for the system level of the new monitoring system. The data is part of the D3.2 "Report on additional indicators<br>of monitoring system". The indicators are organised by category, principles, criteria, requirements and references. </p> <p> </p>
STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C1
<p><span>This appendix presents the full set of LCA indicators identified in D3.1 for the environmental pillar. These 56 indicators are the result of a search limited to highly relevant sources in the field (specified in D3.1) and were used for the final selection according to pre-established criteria.</span></p>
STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C3
<p><span>This appendix presents the full set of LCA indicators identified in D3.1 for the social pillar. These 571 indicators are the result of the analysis of 43 relevant articles and were used for the final selection according to pre-established criteria.</span></p>
STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C2
<p><span>This appendix presents the full set of LCA indicators identified in D3.1 for the economic pillar. These 22 indicators are the result of the consultation of various books and scientific articles aligned with the LCC and TEA methodology (specified in D3.1) and were used for the final selection according to pre-established criteria.</span></p>
STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C4
<p><span>This appendix includes some recent research reports that have applied LCA methodology and circularity analysis to different sectors of the bioeconomy. A total of 30 research articles covering the environmental, social and economic pillars of sustainability, as well as circularity assessments, were analyzed</span>.</p>
Water year 2019 monitoring of the inorganic carbon system (pH and total alkalinity) in the Upper Clark Fork River (Montana, USA)
These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river inorganic carbon system to the floodplain restoration. Data are lab analyses of pH and total alkalinity in samples of well-mixed river thalweg water. Data are from the 2019 water year (1 Oct 2018 to 30 Sep 2019). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at 13 project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. Lab analyses include high-precision colorimetric pH analysis and gran titration of alkalinity.
Water year 2017-18 monitoring of the inorganic carbon system (pH and total alkalinity) in the Upper Clark Fork River (Montana, USA)
These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river inorganic carbon system to the floodplain restoration. Data are lab analyses of pH and total alkalinity in samples of well-mixed river thalweg water. Data are from the 2017 and 2018 water years (1 Oct 2016 to 30 Sep 2018). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. Lab analyses include high-precision colorimetric pH analysis and gran titration of alkalinity.
Water year 2020 monitoring of the inorganic carbon system (pH and total alkalinity) in the Upper Clark Fork River (Montana, USA)
These data were collected by the University of Montana and Montana State University to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) program. The original analytical intent for these data was to assess the response of river inorganic carbon system to the floodplain restoration. Data are lab analyses of pH and total alkalinity in samples of well-mixed river thalweg water. Data are from the 2020 water year (1 Oct 2019 to 30 Sep 2020). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. Lab analyses include high-precision colorimetric pH analysis and gran titration of alkalinity.
EPSRC HEED Data Repository: Stove Use Monitoring System
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people.</p> <p>As part of the project, we deployed Stove Use Monitoring Systems (SUM) on clay cook stoves in Kigeme camp, Rwanda. The aim was to (a) measure and evaluate temperature profiles within stove enclosure and on the surface of stoves (b) evaluate frequency and duration of stove use. The SUM consisted of 2 sensors - a thermocouple (to measure temperature within the stove) and a Si7021 sensor (to measure temperature and humidity outside the stove), connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in values exceeded a set threshold for either of the readings. The SUM was powered by a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.</p> <p>The study was conducted in 2 phases. In phase 1 (02 July 2019 to 30 September 2019), data was collected from 15 SUM and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (02 October 2019 to 17 October 2019), data was collected from 9 SUM and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. The data from both phases of study is deposited here along with the metadata.</p>
EPSRC HEED Data Repository: Lantern Monitoring System
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people.</p> <p>As part of the project, we deployed Lantern Monitoring Systems in Nyabiheke camp, Rwanda. The aim was to (a) evaluate lantern usage pattern – static or mobile (b) evaluate lantern charge and discharge pattern to understand consumption behaviour. The mobile lantern monitors comprise of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device is the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL is used to generate activity and freefall interrupts based on acceleration readings when the lantern is in use. These interrupts are, in turn, processed to calculate the step count of the user. Additionally, the voltage of lantern battery is measured using an in-house designed voltage monitor to evaluate the discharging and charging patterns. The updated values of step count, rate of change of steps and device and lantern battery voltage are stored only if a significant change in the step count is detected. The device is packaged within the lantern casing and powered through a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.</p> <p>The study was conducted in 2 phases. In phase 1 (03 July 2019 to 30 September 2019), data was collected from 60 lanterns and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. Moreover, several incidents of theft and device failures were reported leading to loss of data. In phase 2 (09 October 2019 to 18 December 2019), the design of lantern monitors was modified to fix these issues. The data was collected from 54 lanterns data and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board and internal watchdog timer was used to reset the device in case of failures. While certain failures persisted, the data yield was considerably higher than phase 1 of the study. The data from both phases of study is deposited here along with the metadata.</p>
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
<p>This dataset contains the video clips used to produce the results presented in:</p><p>Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.</p>
ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems
<p>Contained within this directory is the latest dataset utilized in the research titled 'ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems'. We are providing a downloadable ZIP file that includes the survey questionnaire, the amassed data, and the Jupyter Notebooks utilized for our analytical process.</p>
Reproduction package for the paper "Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stae1315">"Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring" by Sutlieff et al. (2024)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.3 Content Level Matrix dataset
<p>This dataset contains the final set of indicators selected for the content level of the new monitoring system. It is part of the D3.2 "Report on additional indicators of monitoring system" of the STAR4BBS Project (Appendix 6.3). The list of indicators are accompanied by guidance notes, the specific sector and value chain to which they apply as well as potential examples of indicators are suggested. </p>
Data and results for manuscript "Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems "
<p>Root systems are essential in nutrient uptake and translocation, but are difficult to characterize non-invasively with existing methods. We propose electrical impedance tomography (EIT) as a new tool for the imaging and monitoring of crop root systems. In a laboratory experiment we demonstrate the capability of the method to capture physiological responses of root systems with high spatial and temporal resolution. We conclude that EIT is a promising functional imaging technique for crop roots.</p> <p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript.</p>
GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification system from 1985 to 2022
<div> <p>GLC_FCS30D is the first global fine land cover dynamic product at a 30-meter resolution that adopts continuous change detection. It utilizes a refined classification system containing 35 land-cover categories and covers the time span from 1985 to 2022. Before the year 2000, the update cycle was every 5 years, while after 2000, it is updated annually. In specific, it developed by combining the continuous change detection method, local adaptive updating models and the spatiotemporal optimization algorithm from dense time-series Landsat imagery, and was validated to achieve an overall accuracy of 80.88% (±0.27%) for the basic classification system 10 major land-cover types) and 73.24% (±0.30%) for the LCCS level-1 validation system (17 LCCS land-cover types).</p> <p>The GLC_FCS30D has been compressed into 36 zip files, and<strong> <em>the details about the GLC_FCS30D can be found in the User's guides.</em></strong></p> </div>
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.