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Dataset results
59 results for “Network monitoring”
In-situ monitoring of the Seine River based on the MeSeine network
<p>In Europe, the management of freshwater ecosystem is governed by the Water Framework Directive (2000/60/CE) and its transposing legislation in France (2006-1772 of December 2006). The good ecological status of water are evaluated using a combination of several indicators such as biological and physico-chemical parameters. The Seine River crosses several important urbanized areas of France, including the Parisian conurbation (9 millions inhabitants). To ensure the good ecological status of the Seine River, the Greater Paris Conurbation Sanitation Authority (SIAAP), has constructed and operated the MeSeine network since 1990. MeSeine constitutes a tool for evaluating the quality of the Seine river and its tributaries (Marne, Oise) in terms of physico-chemistry, bacteriology, micro-contamination and faunal diversity.</p> <p>The MeSeine network extends along 125 km of the Seine River (from Choisy to Méricourt) and over 13 km along the Marne River (frome Champigny to Alfortville). It is structured around tree pillars:</p> <ul> <li>Real time monitoring of the physico-chemical composition of the Seine river using in situ sensor, in particular dissolved oxygen and temperature sensors</li> <li>Sampling and laboratory analysis campaigns to monitor watercourses quality and comply with the quality standards as defined by the Water Framework Directive (good ecological and chemical parameters)</li> <li>Biota monitoring to appreciate the diversity of fish populations, macro-invertebrates and diatoms.</li> </ul> <p>The aim of this work is to provide dissolved oxygen and temperature data of the Seine, generated by the in-situ sensors of the MeSeine network, via the open platform Zenodo.</p>
Monitoring of the physico-chemical composition of the Seine River based on the MeSeine network
<p>In Europe, the management of freshwater ecosystem is governed by the Water Framework Directive (2000/60/CE) and its transposing legislation in France (2006-1772 of December 2006). The good ecological status of water are evaluated using a combination of several indicators such as biological and physico-chemical parameters. The Seine River crosses several important urbanized areas of France, including the Parisian conurbation (9 millions inhabitants). To ensure the good ecological status of the Seine River, the Greater Paris Conurbation Sanitation Authority (SIAAP), has constructed and operated the MeSeine network since 1990. MeSeine constitutes a tool for evaluating the quality of the Seine river and its tributaries (Marne, Oise) in terms of physico-chemistry, bacteriology, micro-contamination and faunal diversity.</p> <p>The MeSeine network extends along 125 km of the Seine River (from Choisy to Méricourt) and over 13 km along the Marne River (frome Champigny to Alfortville). It is structured around tree pillars:</p> <ul> <li>Real time monitoring of the physico-chemical composition of the Seine river using in situ sensor, in particular dissolved oxygen and temperature sensors</li> <li>Sampling and laboratory analysis campaigns to monitor watercourses quality and comply with the quality standards as defined by the Water Framework Directive (good ecological and chemical parameters)</li> <li>Biota monitoring to appreciate the diversity of fish populations, macro-invertebrates and diatoms.</li> </ul> <p>The aim of this work is to provide data on the physico-chemical quality of the Seine generated by the MeSeine observatory via the open platform Zenodo.</p>
Supplementary Material to "Analysis of nationwide groundwater monitoring networks using lumped-parameter models and groundwater"
<p>This repository contains the Supplementary Material to "Analysis of nationwide groundwater monitoring networks using lumped-parameter models and groundwater", submitted to Journal of Hydrology for review.</p> <p> </p>
Supporting information for "Incoming Neutron Flux Corrections for Cosmic-ray Soil and Snow Sensors Using the Global Neutron Monitor Network"
<p>This dataset includes 2 files that represent supporting information for McJannet, D and Desilets, D (Submitted 2023)"Incoming Neutron Flux Corrections for Cosmic-ray Soil and Snow Sensors Using the Global Neutron Monitor Network" Water Resources Research.</p> <p>File 1 - Supporting Information 1 - Example calculation: Excel sheet showing demonstration calaculations using the neutron intensity correction described in the paper</p> <p>File 2 - Supporting information 2 - List of neutron moniotr stations used in the paper and acknowledgment of their contribtuion</p>
Data from "Quantification of major particulate matter species from a single filter type using infrared spectroscopy – Application to a large-scale monitoring network"
Open the record for dataset details and reuse information.
Monitoring Eastern flower thrips and soybean thrips (Thysanoptera: Thripidae) and the generalist predator, insidious flower bug (Hemiptera: Anthocoridae) in the American Midwest Suction Trap Network
Open the record for dataset details and reuse information.
Data from: A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams
Open the record for dataset details and reuse information.
Lérins islands islanding system : MV distribution network monitoring data
<p>U and I measurment at Gridf Forming Unit connexion point during an islanding and when connected to the continent, and global consumption of Lérins islands.</p>
Data from: Systematic site selection for multispecies monitoring networks
The importance of monitoring biodiversity to detect and understand changes throughout time and to inform management is increasingly recognized. Monitoring schemes should be globally unified, spatially integrated across scales, long term, and cost-efficient. We propose a framework to design optimized multispecies-targeted monitoring networks over large areas. The method builds upon previous developments on systematic conservation planning in terms of optimizing resource allocation in space, and comprises seven steps: (a) determine which questions will be addressed, (b) define species to be monitored, (c) compile occurrence data for all defined species, (d) predict the overall distribution of each species, (e) collect relevant environmental data and identify homogeneous strata, (f) set targets for the minimum number of monitoring sites per species and/or stratum and (g) identify optimal monitoring sites. We tested whether the monitoring networks designed with our framework have increased performance when compared to networks obtained with simple-random or stratified-random sampling by using a set of different indicators. To that end, we designed monitoring networks using optimized and non-optimized sampling schemes, applied to a case study in Portugal, where the goal was to design a monitoring network for amphibians and reptiles, to complement the one currently established in Spain. Results allowed us to conclude that monitoring networks designed with our method tend to outperform the non-optimized ones, in terms of higher species diversity (i.e. higher number of species and equity across monitoring sites), higher representation of environmental strata, and particularly higher coverage of rare species, with less survey effort. Synthesis and applications. We developed a framework to allocate monitoring sites for multiple species at broad scales using predictive models and optimization algorithms currently applied in systematic conservation planning. This framework presents field survey cost-efficiency advantages when compared to other standard sampling designs and can significantly contribute to improving the design of monitoring schemes. Thus, we recommend its application to design new multispecies monitoring networks or to extend existing ones.
Irradiance monitoring network data
<p>The data.tar.gz archive contains data from an irradiance monitoring network in Tucson, Arizona for the period 2014-04-05 to 2014-06-30. It includes a sensor metadata csv, csv files for the measurements on each day, and csv files for the clearsky-profiles for each sensor on each day. This data was used to make short-term forecasts of solar irradiance.</p>
Irradiance monitoring network data and wind motion vectors
<p>The data.tar.gz archive contains data from an irradiance monitoring network in Tucson, Arizona for the period 2014-04-05 to 2014-06-30. It includes a sensor metadata csv, csv files for the measurements on each day, csv files for the clearsky-profiles for each sensor on each day, and a time-series of the expected wind motion vectors obtained from a numerical weather model. This data was used to make short-term forecasts of solar irradiance.</p>
Pressure monitoring dataset and frequency domain analysis in Padua water distribution network
<p>The dataset includes:</p><ul><li>the acquired pressure signal at measurement section P036 with a high sampling frequency (indicated by "fre") for 24 hours;</li><li>the frequency domain analysis of three pressure datasets within a certain range, "omega", of frequencies</li></ul>
Datasets for reproducing "Robust Modelling of Internet Delay and Smart Monitoring Schemes for the Automation of Overlay Networks"
<p>This upload contains the datasets necessary to reproduce the figures and the results of my PhD thesis titled "<a href="https://tel.archives-ouvertes.fr/tel-03666771/document">Robust Modelling of Internet Delay and Smart Monitoring Schemes for the Automation of Overlay Networks</a>" (2020).</p> <p>These datasets are derived from public sources: <a href="https://www.caida.org/projects/manic/">CAIDA MANIC</a> and <a href="https://atlas.ripe.net/">RIPE Atlas</a>.</p>
Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the fifth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</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/egINC9hJifI</p>
Dataset for publication 'Reconnecting Stochastic Methods with Hydrogeological Applications: Uncertainty Analysis and Risk Assessment for the Design of Optimal Monitoring Networks'
<p>This dataset includes all data and information on how to reproduce the results and the figures of the paper 'Reconnecting Stochastic Methods with Hydrogeological Applications: Uncertainty Analysis and Risk Assessment for the Design of Optimal Monitoring Networks'.</p>
Data for: Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network [Version 2]
<p>This package provides material that can be openly published for the paper "Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network". It consists in the code used to generate results and figures as well as the weights of the deep convolutional neural networks trained to segment water in the surveillance camera images.</p>
Brazilian academic network monitoring data
<p>The <a href="https://rnp.br/en/ipe-network">Ipê Network</a> is an academic network connecting several education, research, and health institutes across Brazil. This dataset is composed of a collection of JSON files made available by the monitoring tool <a href="https://viaipe.rnp.br">Via Ipê</a>. Each file contains information related to a specific minute. The period covers November 2020, with 55 minutes missing from the dataset. The folder hierarchy defines each file's corresponding time. For instance, the file 11/10/8/20/1d.json.gz corresponds to the JSON file for the monitoring information at 8:20 am on November 10th, 2020.<br> More details and useful code can be found on <a href="https://github.com/VitorSpa/ViaIpe-Tools">GitHub</a>.</p>
Evaluation of Changes in Weight, Sleep, and Other Psycho-behavioural Parameters During Covid-19 Confinement in Subjects Monitored by the RNPC Network
ClinicalTrials.gov study NCT04409197. IPD Sharing: Not stated. Countries: 1. Publications: 1.
OptiLink HF Study: Optimization of Heart Failure Management Using Medtronic OptiVol Fluid Status Monitoring and CareLink Network
ClinicalTrials.gov study NCT00769457. IPD Sharing: NO. Countries: 1. Publications: 2.
CrescNet - Growth Monitoring Network
ClinicalTrials.gov study NCT03072537. IPD Sharing: NO. Countries: 1. Publications: 20.
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.