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858 results for “monitoring data”
Multisensor monitoring data of Hochebenkar Rock Glacier
<p>HRG_DSM_stack.tif: Area-wide monitoring time series of Hochebenkar Rock Glacier (Oetz valley, Tyrol, Austria) based on multitemporal digital surface models (DSMs) derived from</p> <ul> <li>photogrammetry using historical arerial imagery (1953, 1971, 1977, 1990, 1997)</li> <li>airborne laser scanning (2006, 2009, 2010, 2011, 2017)</li> <li>and unmanned aerial vehicle-based laser scanning (2018, 2019, 2020, 2021).</li> </ul> <p>The airborne laser scanning data from 2006 and 2017 were provided by the Federal Government of Tyrol and are licenced under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.de) and must be used according to the following conditions of use: https://www.tirol.gv.at/data/nutzungsbedingungen/</p> <p>HRG_HS_stack.tif: Shaded reliefs computed from the multitemporal DSMs, which were used for applying the image correlation technique to derive displacement vectors.</p> <p>HRG_DDSM_stack.tif: Differential digital surface models computed by subtracting the subsequent DSMs. The uncertainty of the individual DDSMs are masked.</p> <p>Shapefiles in HRG_velocity_vectors.zip: Mean velocity vectors (m/yr) computed from the displacement vectors by dividing the vector length by the time period between the acquisition campaigns.</p> <p>All datasets are provided in the Austrian GK West projection (EPSG 31254). The spatial resolution of the raster datasets is 1 metre.</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>
Data for: Condition monitoring system for in situ crack detection based on thermal emissions
<p>The advent of packaged infra-red (IR) bolometer detectors has led to thermography-based techniques becoming popular for non-destructive evaluation of aerospace structures. These packaged bolometers are relatively compact and cost about 10% the price of high-resolution IR photovoltaic effect detectors. In this work, a condition monitoring system for in situ crack detection has been presented which utilises an original equipment manufacturer (OEM) microbolometer detector. The proposed system cost approximately 1% the price of a state-of-the-art photovoltaic effect detector system and has the potential to transform the use of IR imaging for condition monitoring in the aerospace industry and elsewhere. The proposed system performs crack detection based on the principles of thermoelastic stress analysis (TSA), which is a well-established non-destructive thermography technique. Proof-of-concept lab tests were performed on open-hole aluminium specimens to compare the performance of the proposed system against an IR photovoltaic effect detector system and demonstrate its potential application for in situ crack detection in industrial environments. It was demonstrated that crack detection is possible from loading waveform signals with frequencies as low as 0.3 Hz. This represents a significant advance in the viability of TSA-based crack detection in large-scale structural tests where loading frequencies are usually lower than 1 Hz.</p> <p> </p>
Monitoring Data SRC
<p>The uploaded data was generated during the EU-India funded PAVITR Project, which was funded by the European Union through its Horizon 2020 research and innovation program, grant number 821410 and the Department of Science and Technology in India through the DST/IMRCD/India-EU. </p> <p>The dataset contains monitoring data from weather station and sensors installed onsite. Real time data is available at https://www.fieldclimate.com. Login details included in the PAVITR DMP and Final report.</p>
Figures and Data for "CO2 rock physics modeling for reliable monitoring of geologic carbon storage"
<p>The following data includes all data used to generate figures in this study. We will update the link to the paper once it is published. It has been accepted in Nature Comm Earth and Environment. LANL has approved this release: LA-UR-24-25434.</p>
Lobster deployment, mortality and growth monitoring data at Lehanagh Pool_IMTA lab Ireland
<p>Lobster deployment, mortality and growth monitoring data at Lehanagh Pool_IMTA lab Ireland</p>
Data from: High nest failure in a zebra finch population and persistent predation of a nest by a monitor lizard
<p>Predation is well known to have substantial effects on behaviour and fitness in many animals. In songbirds, nest predation is rarely observed directly, so that research focusses primarily on the consequences of predation and less on the behaviour of the predator. Here, we report predation data in a zebra finch (<em>Taeniopygia catanosis</em>) nest box population, highlighting a 22-min-long sequence, captured on video, of a sand goanna (<em>Varanus gouldii</em>) predating a zebra finch nest in the wild. This monitor lizard appeared to be extremely persistent with climbing and jumping up to the next box nine times, including three successive unsuccessful attempts that lead to a change in approach strategy. It removed all six nestlings from the nest box during those repeated approaches and consumed them. In combination with overall high predation rates in the study population we document here, the findings highlight the role that a single predator species can have on nest success and, thus potentially also breeding decisions and social organisation of the prey population. Specifically so in a species like the zebra finch which synchronises reproductive attempts through the use of social information acquired through nest inspections and which uses social hotspots where they could gather information on changes in local social composition due to the individualised signals they use.</p>
Monitoring Campaign of Environmental Data in Spotrs Centres of Catalonia, Spain
<p>The dataset contains the environmental data collected during a monitoring campaign between 2022 and 2023 in Catalonia, Spain. The work is further described in the research paper "Calculating comfort indexes and applying comfort models to predict thermal sensation vote in sports centres" published in Indoor Air by Hindawi-Wiley (<a href="https://doi.org/10.1155/1970/9142303">https://doi.org/10.1155/1970/9142303</a>).</p>
Data and Code for the manuscript "Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space"
<p>This repository includes the data and code to reproduce the results for the study "<em>Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space</em>". The study shows that leaf angle dynamics systematically confound widely applied vegetation indices. Moreover, it is demonstrated that these effects are not random but tightly linked to abiotic environmental conditions. These findings demonstrate both challenges and opportunities for using VIs for vegetation monitoring.</p>
Quantitatively Monitoring of Seasonal Frozen Ground Freeze-thaw Cycle Using Ambient Seismic Noise Data
<p>This is the electronic supplemental data for the publication entitled </p> <p>"<strong>Quantitatively Monitoring of Seasonal Frozen Ground Freeze-thaw Cycle Using Ambient Seismic Noise Data</strong>"</p> <p>submitted to <strong>Seismological Research Letters (SRL)</strong>. </p> <p>The names of the compressed files represent the experiment number and station number. For example, "1_2" indicates data collected from the second station during the first experiment. Each compressed file contains seismic raw data in the ".SAC" format. The filenames include the UTC end time of data collection. For instance, "453003616.00000001.2021.10.20.06.40.22.000.z.sac" indicates that data collection ended at 06:40:22 on October 20, 2021. Each complete .sac file contains 4 days of data with a sampling interval of 0.002 seconds.</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>
Data for "Statistical Process Monitoring of Isolated and Persistent Defects in Complex Geometrical Shapes"
<p>Simulated data used in the paper "Statistical Process Monitoring of Isolated and Persistent Defects in Complex Geometrical Shapes" by Daniele Zago, Bianca Maria Colosimo, and Giovanna Capizzi.</p> <p>The files includes MATLAB code used to generate the simulated noisy meshes.</p>
Data for "Entanglement phases, localization and multifractality of monitored free fermions in two dimensions".
<p>This folder contains the numerical data to reproduce all figures in the paper "Entanglement phases, localization and multifractality of monitored free fermions in two dimensions". All data are in csv format and were read and plotted on Jupyter notebooks. </p> <p>Each subfigure has an own subfolder where data is stored. For figures with insets there is a separate subfolder for the main plot and the inset, except the cases where inset and main use the same data (e.g. Fig. 3b). All data is obtained directly from simulations as outlined in the main text except fit data (e.g. Fig. 5a) or data obtained from a scaling collapse (inset of Fig. 3b).</p>
Replication Package: Anomaly detection via runtime monitoring data for structural equation modeling
<p>This replication package contains the following information:</p> <ul> <li><strong>Data extraction from literature & interviews: </strong><em>Generation Structural & Measurement Model via literature and interviews.xlsx</em> - here you can find the mapping of the extracted phrases to inductively summarise information regarding the structural and measurement models.</li> <li><strong>Dataset</strong> of runtime monitoring data extracted from TrainTicket via EvoMaster: <br> <ul> <li><em>TrainTicket faults classification.xlxs:</em> Describes the datasets and their faults, in which microservice the fault is injected for better explainability of the obtained results</li> <li><em>IndicatorDescriptionbasedonAnomalyDetectionToolsInterviews.xlsx:</em> description and mapping of selected indicators to the defined parameters from <a href="https://arxiv.org/abs/2408.07816" target="_blank" rel="noopener">previous work </a></li> <li>Unfortunately, the size of the datasets generated via EvoMaster and their injected faults are too big to upload here, thus, they will be available here: <a href="https://uibkacat-my.sharepoint.com/:f:/g/personal/monika_steidl_uibk_ac_at/EjLMt8SYWwtJtp2YuSaqavcBKJoCQ3b5H_l_OY0ifbVRCA?e=fatKyD" target="_blank" rel="noopener">Datasets with injected anomalies</a><br> <ul> <li>the error description can be found <a href="https://github.com/FudanSELab/train-ticket/wiki/Fault-Description" target="_blank" rel="noopener">here</a></li> <li>the datasets are named ts-error-<em>indicatorOfError</em>-reset.zip because the databases are getting reset so that no anomalies are introduced with wrong database entries</li> </ul> </li> </ul> </li> <li><strong>Code</strong> for handling and transforming data to extract indicators describing the whole system's and microservices' behavior from the collected runtime monitoring data collected from TrainTicket:<br> <ul> <li><a href="https://github.com/moniSt13/ConTest-Parsing" target="_blank" rel="noopener">link to the Github repository</a></li> </ul> </li> <li><strong>reports</strong> regarding the established PLS-SEM model using previously handled and transformed runtime monitoring data. Please be aware that opening the reports can leas to out of memory due to their size: <ul> <li><em>Assessment of Measurement Model: MeasurementModel_TrainTicket_erorcleaned.zip & MeasurementModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em> </li> <li><em>Assessment of Structural Model: StructuralModel_TrainTicket_errorcleaned.zip & StructuralModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em></li> </ul> </li> </ul> <p><br><br>---------------------------------------</p> <p><em>Future work </em>not elaborated in the associated paper due to space restrictions:</p> <ul> <li><strong>reports regarding F5 error</strong>: PLS-SEM model results without interpretation and further mediating effects between microservices included: F5_error.zip</li> </ul> <p> </p> <p> </p> <p> </p>
Bathymetry data from the Western Australian Southwest Weland Monitoring Program
<p>The Microsoft excel file contains lake bathymetry data for 23 lakes in southwest Western Australia. Each sheet conatins volume and or lake area data as a function of elevation above the lowest point of each lake. The names of sheets correspond to the Code field that can be found in the file SiteSummary.xlsx, storred at <span><a href="https://zenodo.org/records/14015326">https://zenodo.org/records/14015326</a>.</span></p>
Data from: Spatio-temporal monitoring of deep-sea communities using metabarcoding of sediment DNA and RNA
We assessed spatio-temporal patterns of diversity in deep-sea sediment communities using metabarcoding. We chose a recently developed eukaryotic marker based on the v7 region of the 18S rRNA gene. Our study was performed in a submarine canyon and its adjacent slope in the Northwestern Mediterranean Sea, sampled along a depth gradient at two different seasons. We found a total of 5,569 molecular operational taxonomic units (MOTUs), dominated by Metazoa, Alveolata and Rhizaria. Among metazoans, Nematoda, Arthropoda and Annelida were the most diverse. We found a marked heterogeneity at all scales, with important differences between layers of sediment and significant changes in community composition with zone (canyon vs slope), depth, and season. We compared the information obtained from metabarcoding DNA and RNA and found more total MOTUs and more MOTUs per sample with DNA (ca. 20% and 40% increase, respectively). Both datasets showed overall similar spatial trends, but most groups had higher MOTU richness with the DNA template, while others, such as nematodes, were more diverse in the RNA dataset. We provide metabarcoding protocols and guidelines for biomonitoring of these key communities in order to generate information applicable to management efforts.
Data from: How much would it cost to monitor farmland biodiversity in Europe?
To evaluate progress on political biodiversity objectives, biodiversity monitoring provides information on whether intended results are being achieved. Despite scientific proof that monitoring and evaluation increase the (cost) efficiency of policy measures, cost estimates for monitoring schemes are seldom available, hampering their inclusion in policy programme budgets. Empirical data collected from 12 case studies across Europe were used in a power analysis to estimate the number of farms that would need to be sampled per major farm type to detect changes in species richness over time for four taxa (vascular plants, earthworms, spiders and bees). A sampling design was developed to allocate spatially, across Europe, the farms that should be sampled. Cost estimates are provided for nine monitoring scenarios with differing robustness for detecting temporal changes in species numbers. These cost estimates are compared with the Common Agricultural Policy (CAP) budget (2014–2020) to determine the budget allocation required for the proposed farmland biodiversity monitoring. Results show that the bee indicator requires the highest number of farms to be sampled and the vascular plant indicator the lowest. The costs for the nine farmland biodiversity monitoring scenarios corresponded to 0·01%–0·74% of the total CAP budget and to 0·04%–2·48% of the CAP budget specifically allocated to environmental targets. Synthesis and applications. The results of the cost scenarios demonstrate that, based on the taxa and methods used in this study, a Europe-wide farmland biodiversity monitoring scheme would require a modest share of the Common Agricultural Policy budget. The monitoring scenarios are flexible and can be adapted or complemented with alternate data collection options (e.g. at national scale or voluntary efforts), data mobilization, data integration or modelling efforts.
Data from: Interspecies interference and monitoring duration affect detection rates in chew cards
Pest monitoring methods should provide unbiased accurate estimates of pest densities and locations, while also minimizing time-in-field and costs. Recent pest mammal monitoring studies have found that chew cards are more effective than conventional mammal monitoring methods, but little experimental work has been done to determine optimal experimental duration or quantify the risks of saturation by one species biasing detections of other species. Here, we used chew cards in three sites within Awarua wetland (Southland, New Zealand) to investigate the optimal amount of time required to detect targeted pest species (rats, possums and mice), and to examine the potential of rats and possums to bias detection rates of other species. We found depressed detections of possums and rats where a contraspecific had been detected on a card, which is consistent with previous studies of a similar duration on interspecies interference. This experiment is the first to analyse the rates at which species detections accrue over the course of a survey, and we found rat detections lagged behind possums for the first four nights. We modelled the effect of survey duration and relative rat abundance on the likelihood of further possum detections. Duration and rat abundance interacted, meaning there are trade-offs to be considered with regard to duration: shorter durations may avoid the risk of saturation in areas of high pest density, but risk not sampling sparse or neophobic populations. Our data suggest that chew cards remain one of the most sensitive pest monitoring tools for rats and possums, compared to conventional methods such as tracking tunnels and wax tags. In areas of moderate pest densities, we suggest that a duration of five nights is optimal for detecting pests. However, in areas of high pest density the sensitivity of chew cards may render them unsuitable because of saturation and interspecies interference effects.
Data and Code for Shriver et al. 2021, Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using range-wide monitoring data
<p>Data and code for demographic analyses in Shriver et al. 2021. See paper and ReadMe for analysis description and further details. </p> <p>Shriver, R.K., C.B. Yackulic, D.M. Bell, J.B. Bradford. (2021)Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using range-wide demographic models. Ecology. <a href="https://doi.org/10.1002/ecy.3425">https://doi.org/10.1002/ecy.3425</a></p> <p> </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>
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.