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391 results for “remote monitoring”
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2020 in China
<p>In this dataset, the MODIS vegetation index and land surface temperature products are processed into NDVI and LST monthly time series with a spatial resolution of 1 km, and the final precipitation data of GPM IMERG are downscaled, unified at a spatial resolution of 1 km. And after a standardization process, using the spatial distance model, a remote sensing drought monitoring dataset in China from 2001 to 2020 was produced based on the Temperature Vegetation Precipitation Dryness Index. For the specific construction process of this data, please refer to https://linkinghub.elsevier.com/retrieve/pii/S0034425720303278</p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2021 in China (v2.0)
<p>The Enhanced Vegetation Index (EVI), Land Surface Temperature (LST) and Precipitation (P) were used as new data sources based on the spatial distance model to construct an optimized multi-source remote sensing dryness index named Temperature-Vegetation-Precipitation Dryness Index based on the shortcomings of the TVPDIorigin (i.e., TVPDI<sub>o</sub>) data source. The TVPDI<sub>n</sub> of the long time series was also compared and analyzed with the classical drought index - Standardized Precipitation Evapotranspiration Index (SPEI-3) on a 3-month scale, different drought response level products of Solar-Induced Chlorophyll Fluorescence (SIF), soil moisture (SM) from ESA CCI (European Space Agency's Climate Change Initiative), and total crop yield, then the sensitivity and validity of the TVPDI<sub>n</sub> for wetness and dryness monitoring were synthesized and validated. On this basis, here are the results of the verification:</p> <p>(1) Compared with the original data source TVPDI<sub>o</sub> using the new multi-source remote sensing data source of precipitation and vegetation index to construct TVPDI<sub>n</sub>, the overall correlation between the two and SPEI-3 was good, with a maximum of 0.57 and 0.56, respectively (p< 0.1), but the overall TVPDI<sub>n</sub> constructed in this study had a better fit compared to the original data source TVPDI<sub>o</sub> and was more sensitive to the monitoring of dry and wet conditions.</p> <p>(2) According to the comparison of TVPDI<sub>n</sub> with ESA CCI sm, TVPDI<sub>n</sub> showed a high correlation of more than 0.9 with soil water content, which proved that TVPDI<sub>n</sub> was highly consistent with soil moisture; compared with SIF, 54.5% of the regional correlation coefficients were greater than 0.8 (p< 0.01), and spatially, the correlation results were better in the northwest than in the east, indicating that the response of TVPDI<sub>n</sub> to vegetation productivity is more agile in regions with continental climate such as the northwest. The results of correlation with grain yield comparison showed that good positive correlations were presented with TVPDI<sub>n</sub> in Liaodong Peninsula, northern North China Plain, and most of Qilian Mountains, southern edge of Qinling Mountains, middle and lower reaches of Yangtze River, and South China, indicating that TVPDI<sub>n</sub> has a high consistency in the changes of agricultural grain production in the above mentioned regions, and also proving the index in monitoring agricultural aridity and guiding agricultural production The good performance of the index in monitoring agricultural aridity and guiding agricultural production.</p> <p> This dataset is version 2.0, and covers all of China's territory, but the temperature-vegetation- precipitation dryness index of the open water surface are often set to a null value. Note:The data format is "TIF", the spatial resolution is "1 km", the time resolution is "1 month" and dimensionless. The pixel value is the NTVPDI value, and the closer the pixel value is to 0, the drier it is, and the larger the data, the wetter the land surface. The practical utility of this dataset is to compare the degree of dryness and wetness of China's land, to monitor short-term and medium-term droughts, and to substitute model parameters related to soil moisture. This is of great value to the impartial formulation of China's environmental and economic policies, regular monitoring and evaluation of drought and flood conditions. This product will be freely available to all users worldwide and will be continuously improved to suit new goals and needs.</p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
Photosynthetically Active Radiation data taken with the Delta-T SunScan wand every 15 cm of 1m x 1m chamber flux and point frame plots as well as four remotely monitored canopies at the Toolik Field Station in AK, Summer 2012.
Within-canopy PAR was measured with a Delta-T SunScan wand every 15 cm from the ground to above the canopy under both direct and diffuse light. The data includes all outputs from the SunScan wand: time of measurement, spread of PAR sensors, total irradiance, total diffuse light, and individual outputs of 64-PAR sensors on the SunScan wand. These measurements were taken for 1m x 1m chamber flux (n=14) and point frame (n=19) plots as well as sites four montitored remotely by PAR sensors located above, within, and below shrub canopies. All plots were dominated either by tall Salix pulchra and Betula nana species and were located near the LTER shrub plots at Toolik Field Station , AK in the summer of 2012.
Raspberry Pi nest cameras – an affordable tool for remote behavioural and conservation monitoring of bird nests
<p><span><span><span><span><span><span><span><span><span><span><span>1. Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioural ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. Here we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof – especially to humidity and salt spray. The camera was paired with a passive infra-red sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviours and interspecific interactions of two sympatric species of breeding storm-petrel (Monteiro's storm-petrel <i>Hydrobates monteiroi</i> and Madeiran storm-petrel <i>Hydrobates castro</i>) during their chick-rearing periods.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30 s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Here we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently the camera-build methods and scripts could be applied easily to many different species that also utilise cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviours and interspecies interactions.</span></span></span></span></span></span></span></span></span></span></span></p>
Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading)
<p>This video is the fifth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading)</p> <p>Bio: Beth Harvey is currently a master’s student in digital health and data analytics at the Henley Business School. Having completed her bachelor’s in biomedical science, Beth has then gone on to work in the medical device and IVD regulatory sector with a consultancy firm in Vancouver. After moving back to the UK in 2019 Beth worked with multiple UK manufacturers in the digital health space which spurred her interest in the field she is now studying. Beth’s research interests are in healthcare innovation remote patient monitoring, and data analysis. She is currently finishing her dissertation on the opportunities and challenges for remote blood monitoring in oncology in collaboration with the Future Blood Network.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/8pbO3N9IQKM</p>
Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)
<p>This video is the seventh talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)</p> <p>Bio: Professor Kelvin Tsoi is an Epidemiologist specialized in Digital Health. His research interests focus on digital innovation in chronic disease management, including mobile and telecare application for hypertension management, technological implementation and social engagement for cognitive screening, artificial intelligent application on electronic health records. He also works as the traditional epidemiologist on evidence-based medicine and population cohort studies. He obtained his Bachler Degree from Department of Statistics and Doctor of Philosophy from School of Public Health in the Chinese University of Hong Kong. He further received post-doctoral training in the Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics. He was also appointed as a Director of CUHK JC Bowel Cancer Education Centre to promote colorectal cancer screening. In 2011, he worked as a research scientist in Hospital Authority. He led projects covering a wide range of service areas on chronic diseases, such as service demand projection for schizophrenia and dementia. The experience of database management enhanced his understanding of the HA database structures. In 2013, he was invited to join the interdisciplinary team for Big Data research and worked closely with a team of engineers and data scientists. Currently, Professor Tsoi is an Associate Professor in JC School of Public Health and Primary Care, SH big Data Decision Analytics Research Centre and JC Institute of Ageing. I matriculated as a medical student at Fitzwilliam College in 1993. My interest in biomedical research was developed during my Part II year studying Anatomy A (neurosciences and developmental biology) and I subsequently enrolled on the MB-PhD programme. I completed my doctoral thesis in neurophysiology of circadian rhythms in 2000 and qualified as a medical doctor in 2001. While studying for my PhD, I pursued an active interest in teaching and started supervising undergraduates at Fitzwilliam (and other colleges) in 1998. I served as MCR President in 1999, became a Fellow in 2003 and Director of Studies in Clinical Medicine in 2004. I pursued a career in surgery after graduation and was appointed as a University Lecturer in Transplant Surgery in 2012.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ANZKGxj87E0</p>
FUDGE-5G Remote Monitoring data
<p>This data was generated from the 5G Virtual Office trials. The data was collected from the vertical application, which include biosensors data, environmental data and other vertical related data.</p>
Raspberry Pi nest cameras – an affordable tool for remote behavioural and conservation monitoring of bird nests
Open the record for dataset details and reuse information.
Monitoring recent changes in the Beaufort Sea coast using very high resolution remote sensing
<p>Arctic permafrost coasts are major carbon (Schuur et al., 2015) and mercury pools (Schuster et al., 2018). They represent about 34% of the Earth’s coastline, with long sections affected by high erosion rates (Fritz et al, 2017), increasingly threatening coastal communities. Year-round reduction in Arctic sea ice is forecasted and by the end of the 21st century, models indicate a decrease in sea ice area from 43 to 94% in September and from 8 to 34% in February (IPCC, 2014). An increase of the sea-ice free season leads to a longer exposure of coasts to wave action. Further, climate warming is also expected to modify the contribution of terrestrial erosion (Fritz et al., 2015, Ramage et al., 2018, Irrgang et al., 2018). Within the project EU Horizon2020 project NUNATARYUK, we are updating the mapping of the Arctic coast, with the Canadian Beaufort coast as a case-study. The surveying methodology includes: i. a high resolution update of the coastline mapping and change rates using Pleiades (CNES) satellite acquisitions from 2018, ii. a survey using RTK-UAV aerial imagery of long-term monitoring sites from the Canada-US border to King Point, and iii. the experimental use of TerraSAR-X staring spotlight scenes and PAZ at key sites to monitor intraseasonal dynamics of cliff edge retreat. This research is funded by the EC H2020 Project NUNATARYUK. Support on remote sensing imagery access by the WMO Polar Space Task Group.</p>
Design and development remote monitoring agent for energy service companies in the telecommunication industry
<p>Data set downloaded from the remote monitoring agent (RMA) over its local webpage using WiFi connection.</p>
Remote camera monitoring and arboreal trapping data for a reintroduced population of red-tailed phascogales (Phascogale calura)
<p>Effective monitoring methods are required to evaluate the success of wildlife reintroduction programs. To improve the threat status of the Vulnerable red-tailed phascogale (<em>Phascogale calura</em>), the Australian Wildlife Conservancy reintroduced the species to a fenced reserve at Mt. Gibson Wildlife Sanctuary. After trialing a variety of post-release monitoring methods, remote camera monitoring and arboreal trapping with an extensive period of pre-luring provided the most information with which to evaluate the success of the reintroduction. To date, reintroduced red-tailed phascogales have increased in both occupancy and population size following releases which began at Mt. Gibson in 2017. Other managers of red-tailed phascogale populations may find the described methods useful, particularly in the context of multi-species reintroductions where trap saturation can reduce capture rates of smaller species, such as phascogales.</p>
Data for: Beyond signal quality: The value of unmaintained pH, dissolved oxygen, and oxidation-reduction potential sensors for remote performance monitoring of on-site sequencing batch reactors
<p>Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89 to 91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors' type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring smalls-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.</p>
Supplementary Files: "Improved baited remote underwater video (BRUV) for 24h re-al-time monitoring of surface and deep-sea marine species"
<p>In the supplementary material you will find: Figure S1: Photographs of the different parts of the innovative BRUV design; Video S1: Video footage of a bluntnose sixgill shark; Video S2: Types of markings in blue sharks bodies to identify individuals; Video S3: Other marine pelagic species.</p>
Remote Monitoring in Obstructive Sleep Apnea
ClinicalTrials.gov study NCT01678560. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Remote Patient Monitoring on Chronic Disease Management
ClinicalTrials.gov study NCT03127852. IPD Sharing: NO. Countries: 1. Publications: 2.
Feasibility and Effectiveness of a Remote Monitoring Program for the Treatment of Diabetic Foot Ulcers
ClinicalTrials.gov study NCT05579743. IPD Sharing: NO. Countries: 1. Publications: 1.
Testing State of the Art Remote Glucose Monitoring at Diabetes Camp
ClinicalTrials.gov study NCT01680653. IPD Sharing: NO. Countries: 1. Publications: 1.
Remote Glucose Monitoring System in Hospitalized Patients With Diabetic Ketoacidosis (DKA)
ClinicalTrials.gov study NCT05439928. IPD Sharing: NO. Countries: 1. Publications: 11.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.