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858 results for “monitoring data”
Six years of high-resolution monitoring data of 40 borehole heat exchangers
<p>This dataset provides six years of monitoring data of the ground heat exchanger that supplies heat and cold to the E.ON Energy Research Center located in Aachen, Germany. The data belong to the following publication:</p> <blockquote> <p>Heim, E., Stoffel, P., Müller, D., & Klitzsch, N. (2024). <em>Six years of high-resolution monitoring data of 40 borehole heat exchangers</em>. Scientific Data, 11(1), 1334. <a href="https://doi.org/10.1038/s41597-024-04241-9" rel="nofollow">https://doi.org/10.1038/s41597-024-04241-9</a></p> </blockquote> <p>The ground heat exchanger consists of 40 double-U-loop borehole heat exchangers (BHE), that are arranged in three subfields. Each subfield is connected to an underground vault, in which sensors for the inlet fluid temperature, the outlet fluid temperature and the volume flow of each BHE are placed. The sensors record data in 30-second intervals. Coherent data is provided from July 1, 2018 to June 30, 2024 in two time resolutions and processing levels:</p> <ul> <li><strong>Raw data</strong>: The raw data in its initial resolution, aligned to coherent 30-second timestamps.</li> <li><strong>Prepared data</strong>: The raw data was resampled to 5 minute intervals using the weighted mean, with the volume flow as weight. Moreover, data periods that are not representative of the thermal exchange process in the BHE were masked (e.g., no-flow periods).</li> </ul> <p>Both raw and prepared data are provided as .csv files for each month individually. Temperature measurements are given in °C, the volume flow rate in l/min. </p> <p>In addition, a <strong>supporting file</strong> indicating the BHE locations is provided. It also contains the length of the horizontal connecting pipes going from the underground vaults (where the sensors are placed) to the BHE heads. </p> <p><strong>Code </strong>explaining how to open and work with the data is provided in a separate github repository:<strong> </strong><a href="https://github.com/elimh/ERC_BHEfield_Data_Code">https://github.com/elimh/ERC_BHEfield_Data_Code</a></p>
TSE surveillance and Atypical scrapie intensified monitoring data
<p>GENERAL_DATA_AS_REPORT.xls: statutory TSE surveillance data of sheep and goats 2013-2019 from 22 countries.</p> <p>MANDATE_DATA_RAW_AS_REPORT.xls: raw data collated by the EC for the mandate EFSA-Q-2020-00662, including the results of the intensified monitoring implemented in flocks/herds from 22 countries in which a case of atypical scrapie has been confirmed</p> <p>MANDATE_DATA_ANALYSIS_AS_REPORT.xls: : data collated by the EC for the mandate EFSA-Q-2020-00662, including the results of the intensified monitoring implemented in flocks/herds from 22 countries in which a case of atypical scrapie has been confirmed, aggregated for analysis.</p>
Data for "Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing"
<p>This dataset includes the experiment and simulation data of a new structural health monitoring system using distributed fiber optic sensing (DFOS) and Isogeometric Analysis (IGA).</p> <p>The experiment setup was a 5mm thick PVC pipe with a fiber optic cable wrapped around the outer surface of the pipe. The PVC pipe was subjected to an applied deformation and the distributed strains along the optical fiber was measured with a Neubrescope (NBX7031) instrument using Rayleigh backscattering technology.</p> <p>The simulation was performed using the in-house code JWRIAN-IGA developed in Joining and Welding Research Institute, Osaka University. The simulated data includes deformation, stress and strain distributions of the pipe, and projected one-dimensional fiber strains. The visualization files are post-processed with ParaView software.</p>
Data from: Predicting bushmeat biomass from species composition captured by camera traps: implications for locally-based wildlife monitoring
<p>The 'StatAnalysis.zip' contains the data and model files. We used it for the four analyses below.</p> <p>First, we estimated population densities, the mean body mass and camera-trap capture rates of five main bushmeat targets in a rainforest of southeast Cameroon: Peters's duikers (<em>Cephalophus callipygus</em>), bay duikers (<em>C. dorsalis</em>), blue duikers (<em>Philantomba monticola</em>), brush-tailed porcupines (<em>Atherurus africanus</em>) and Emin's pouched rats (<em>Cricetomys emini</em>). Second, on the basis of the density and body mass estimates, we estimated bushmeat biomass—the total biomass of the five bushmeat species—and its spatial variation. Third, we calculated six bushmeat indicators based on the capture rate estimates. Lastly, we examined the correlation between bushmeat biomass and the indicators.</p> <p>The ZIP file consists of 16 R script files, three CSV files (in the 'data' subfolder) and 135 stan files (in the 'stan' subfolders). It also has two empty folders, 'figure' and 'res', where the figures and R objects of model results will be stored following the analyses. Please see the document 'README.txt' before performing the analysis. This text file gives the ZIP file structure and brief descriptions of the files.</p>
Transitioning from microsatellites to SNP-based microhaplotypes in genetic monitoring programs: lessons from a 20-year time series of paired data.
<p>Many long-term genetic monitoring programs began before next-generation sequencing became widely available. Older programs can now transition to new marker systems usually consisting of 1000s of SNP loci, but there are still important questions about comparability, precision, and accuracy of key metrics estimated using SNPs. Ideally, transitioned programs should capitalize on new information without sacrificing continuity of inference across the time series. We combined existing microsatellite-based genetic monitoring information with SNP-based microhaplotypes obtained from archived samples of Rio Grande silvery minnow (<em>Hybognathus amarus</em>) across a 20-year time series to evaluate point estimates and trajectories of key genetic metrics. Demographic and genetic monitoring bracketed multiple collapses of the wild population, and included cases where captive-born repatriates comprised the majority of spawners in the wild. Even with smaller sample sizes, microhaplotypes yielded comparable and in some cases more precise estimates of variance genetic effective population size, multilocus heterozygosity and inbreeding compared to microsatellites because many more microhaplotype loci were available. Microhaplotypes also recorded shifts in allele frequencies associated with population bottlenecks. Trends in microhaplotype-based inbreeding metrics were associated with the fraction of hatchery-reared repatriates to the wild, and should be incorporated into future genomic monitoring. Although differences in accuracy and precision of some metrics were observed between marker types, biological inferences and management recommendations were consistent.</p>
Source Data for Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips
<p>This data accompanies the study "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips" published in (Nature) Communications Engineering. This paper focuses on using large and inexpsensive datasets for obtaining information on the dynamics of bridges. In this study, data is collected by smartphones in moving vehicles as the cross over a bridge, in three distinct applications. Smartphone data was collected in controlled field experiments and uncontrolled Uber rides on a long-span suspension bridge in the USA (The Golden Gate Bridge) and an analytical method was developed to accurately recover modal properties. The method was also successfully applied to partially-controlled crowdsourced data collected on a short-span highway bridge in Italy. The results suggest that larve and inexpensive datasets collected by smartphones could play a role in monitoring the health of existing transportation infrastructure.</p> <p>The data provided includes the source data for the figures in the publication as well as the "controlled data" referenced in the study.</p>
Geochemical, timescales data in orthopyroxenes from Kizimen 2010 eruption and database of studies linking diffusion timescales with monitoring signals.
<p>This dataset comprises the geochemical data on whole rock analysis, melt inclusions, residual glasses, magnetites and orthopyroxenes in samples from the 2010 eruption of Kizimen volcano (Kamchatka), as well as the timescales modelled in the orthopyroxenes and a database of studies linking diffusion timescales with monitoring signals, associated with the publication: Ostorero, L., Balcone-Boissard, H., Boudon, G. <em>et al.</em> Correlated petrology and seismicity indicate rapid magma accumulation prior to eruption of Kizimen volcano, Kamchatka. <em>Commun Earth Environ</em> <strong>3</strong>, 290 (2022). https://doi.org/10.1038/s43247-022-00622-3</p> <p> </p>
Raw data: Wildbee Monitoring along two elevational gradients in Germany
<p><a name="_Hlk94689048"></a><span>Global warming is assumed to restructure mountain insect communities in space and time. Theory and observations along climate gradients predict that insect abundance and richness, especially of small-bodied species, will increase with increasing temperature</span>.<span> However, the specific responses of single species to rising temperatures, such as spatial range shifts, may weaken such prediction, asking for intensive monitoring of real-world communities over time. Here, we examined the temporal and spatial change in wild bee communities and its drivers along two largely well-protected elevational gradients (alpine grassland vs. prealpine forest), each resampled within the last decade. Along both gradients, we detected clear upward shifts in bee communities,</span> with cold-adapted bumblebee species<em> </em><span>reacting particularly sensitive, demonstrating the speed with which mobile organisms can respond to climatic changes. Mean annual temperature (MAT) was identified as the main driver of species richness in both regions. Accordingly, and in large overlap with expectations under climate warming, we detected an increase in bee richness and abundance, and an increase of small-bodied species in low- and mid-elevations along the grassland gradient. While changes in upward shifts, species richness, abundance and body size in the prealpine forest gradient were partly consistent with the alpine grassland system, they were generally weaker, which could be due to a much less severe warming trend than in the alpine grassland system or due to the variable and possibly confounding effect of canopy cover. Our study highlights the use of <a name="_Hlk94772843"></a>accurate assessed abundance data revealing rapid changes in bee communities over only one decade. We conclude, that in well-protected temperate regions, small-bodied bees may initially profit from warming temperatures, by getting more abundant and diverse.</span></p>
Data for: Spatial monitoring of flying insects over a Swedish lake using a CW lidar system
<p>Data for a field experiment on remote sensing of flying insects are supplied. A bistatic CW lidar system of the Scheimpflug type was employed, and echoes from flying insects over and close to a Swedish lake were recorded as read-out files from an array detector making observations along the emitted laser beam. The activated pixels of the detector could be converted into range data by triangulation. With a high detector read-out frequency, data on numerous insects could be obtained. Special emphasis was put on distinguishing between large and small insects (from the intensities of the recorded echoes) and the insect positions with regard to the shores of the lake. </p>
Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring
<p><span>Our dataset, Nest Monitoring of the Kagu, consists of around ten days (253 hours) of continuous monitoring sampled at 25 frames per second. Our proposed dataset aims to facilitate computer vision research that relates to event detection and localization. We fully annotated the entire dataset (23M frames) with spatial localization labels in the form of a tight bounding box. Additionally, we provide temporal event segmentation labels of five unique bird activities: Feeding, Pushing leaves, Throwing leaves, Walk-In, and Walk-Out. The feeding event represents the period of time when the birds feed the chick. The nest-building events (pushing/throwing leaves) occur when the birds work on the nest during incubation. Pushing leaves is a nest-building behavior during which the birds form a crater by pushing leaves with their legs toward the edges of the nest while sitting on the nest. Throwing leaves is another nest-building behavior during which the birds throw leaves with the bill towards the nest while being, most of the time, outside the nest. Walk-in and walkout events represent the transitioning events from an empty nest to incubation or brooding, and vice versa. We also provide five additional labels that are based on time-of-day and lighting conditions: Day, Night, Sunrise, Sunset, and Shadows. In our manuscript, we provide a baseline approach that detects events and spatially localizes the bird in each frame using an attention mechanism. Our approach does not require any labels and uses a predictive deep learning architecture that is inspired by cognitive psychology studies, specifically, Event Segmentation Theory (EST). We split the dataset such that the first two days are used for validation, and performance evaluation is done on the last eight days.</span></p>
King's College Cambridge wildflower meadow monitoring data: biodiversity, climate change and society
<p class="MsoNormal">The biodiversity and climate crises are critical challenges of this century. Wildflower meadows in urban areas could provide important nature-based solutions, addressing the biodiversity and climate crises jointly, and benefitting society in the process. King's College Cambridge (England, UK) established a wildflower meadow over a portion of its iconic Back Lawn in 2019, replacing a fine lawn first laid in 1772.</p> <p class="MsoNormal">We used biodiversity surveys, Wilcoxon signed rank, and ANOVA models to compare species richness, abundance, and composition of plants, spiders, bugs, bats, and nematodes supported by the meadow, and remaining lawn, over three years. We estimated the climate change impact of meadow vs lawn from maintenance emissions, soil carbon sequestration, and reflectance effect. We surveyed members of the university to quantify the societal benefits of, and attitudes towards, increased meadow planting on the collegiate university estate.</p> <p class="MsoNormal">In spite of its small size (0.36 ha), the meadow supported approximately three times more plant species, three times more spider and bug species and individuals, and bats were recorded three times more often over the meadow than the remaining lawn. Terrestrial invertebrate biomass was 25 times higher in the meadow compared with the lawn. Fourteen species with conservation designations were recorded on the meadow (six for lawn), alongside meadow specialist species.</p> <p class="MsoNormal">Reduced maintenance and fertilising associated with meadow reduced emissions by an estimated 1.36 Mg CO<sub>2</sub>-e per hectare per year compared with lawn. Relative reflectance increased by 25-34% for meadow relative to lawn. Soil carbon stocks did not differ between meadow and lawn.</p> <p class="MsoNormal">Respondents thought meadows provided greater aesthetic, educational, and mental well-being services than lawns. In open responses, lawns were associated with undesirable elitism and social exclusion (most colleges in Cambridge restrict lawn access to senior members of the college), and respondents proved overwhelmingly in favour of meadow planting in place of lawns on the collegiate university estate.</p> <p class="MsoNormal">This study demonstrates the substantial benefits of small urban meadows for local biodiversity, cultural ecosystem services, and climate change mitigation, supplied at a lower cost than maintaining conventional lawn.</p>
Data from: Monitoring temporal and spatial trends of illegal and legal fishing in Canada's marine conservation areas using vessel tracking datasets
<p>Expansion of marine conservation areas (CA) necessitates resource-efficient and achievable strategies for monitoring and evaluation of ongoing fishing activity at national levels. To demonstrate and explore such a strategy, we conducted the first extensive analysis of fishing activity within Canada's static, geographically defined marine CAs with fishing regulations (n = 264 areas). We used eight years of Automatic Identification System data to estimate fishing effort across three oceans and conducted temporal and spatial comparisons specific to each CA's regulations and enactment date. We addressed questions on CA effectiveness, fishing displacement, fishing the line behavior, and relationships between fishing activity and spatial CA attributes. We estimated 22,000 hours of fishing activity within CAs after enactments, 22% of which was identified as illegal. CA effectiveness appeared to be lowest for Atlantic CAs based on illegal fishing effort density within CAs. Fishing displacement and fishing the line was generally not apparent as buffer areas around CAs tended to already have higher fishing effort prior to enactments. CA effectiveness and responses to CAs varied considerably, as was visualized using timeseries plots and maps developed for each CA. Our evaluation of a nation's full suite of CAs provides managers with a foundation and approach for continued monitoring and reporting.</p>
Coffee Leaf Rust monitoring data on Hawaii Island
<p><span>CLR was detected in Hawaii for the first time in 2020 and quickly spread across the state. We initiated a CLR monitoring program in Kona, West Hawaii Island to track the spread of this new invasive disease across a broad elevational gradient. The goals of the program were to assist growers in the early detection of CLR, to characterize patterns of disease incidence across the region, and to collect information on farm agronomics, management practices, and costs to apply fungicides, all of which can be used to develop IPM strategies for this pathogen. We monitored 30 coffee lots in Kona, located between 204 and 875 m elevation. Average CLR incidence remained below 4% early in the season and increased to 36% during harvest. We observed no significant difference in CLR incidence between low-, mid- and high-elevation farms. A significant reduction in the number of leaves per branch was observed at the end of the harvest season, and a significant negative correlation was found between the number of leaves per branch and maximum CLR severity. Mean disease incidence and mean severity were observed to have a significant positive correlation. Incidence increased above threshold levels (5%) despite most growers applying preventative fungicides 3-10 times throughout the season, suggesting that improved coverage and timing of applications is needed along with the addition of systemic fungicides. Our study provides the first insights into CLR disease patterns under the unique and variable conditions under which Hawaiian coffee is grown and will aid in the development of IPM programs that can be used to sustain Hawaii's coffee industry under this new threat. </span></p>
Sensed Data of Indoor Monitoring Framework in Aedes Architecture Forum Exhibition called Living Prototypes
<p>The presented Dataset is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy - CITA in Copenhagen, Denmark. The collected data is part of Indoor Monitoring Framework sensed data (temperature and humidity) stored in InfluxDB in CSV format for Living Prototype Exhibition at Aedes Forum in Berlin for a period of 35 days. The dataset can be used in InfluxDB and Grafana for visualization. </p>
Figure 1 in Preliminary data on the chironomid fauna of the Middle Volga region within the Republic of Tatarstan (Russia) based on hydrobiological monitoring studies
Figure 1. Map of the region. Sampling sites are given in black circles.
Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy - Raw Data
<p>Raw data for publication Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy</p>
An infoveillance analysis of public interest, national data and wastewater monitoring in Wales, UK
<p>Infoveillance, wastewater and national data.R - The R script used for the analyses and figures present in the manuscript.</p> <p> </p> <p>R functions:</p> <p>flattenCorrMatrix.R -A function for correlation analysis</p> <p>cormtest.R - A function for correlation analysis</p> <p> </p> <p>Data files:</p> <p>Medical data.csv - Data extracted from the UK government portal for COVID-19 cases, deaths and vaccinations</p> <p>South Wales qPCR data.csv - qPCR data for different South Wales sites on each date, with ‘signal’ denoting the signal of SARS-CoV-2 detection in wastewater samples</p> <p>Wales GT data.csv - Google Trends relative search volume data extracted directly from Google Trends for the region of Wales</p>
Population dynamics and seasonal migration patterns of Spodoptera exigua in northern China based on 11 years of monitoring data
<p>The beet armyworm, <em>Spodoptera exigua </em>(Hübner) is an important migratory pest worldwide that has caused serious economic losses in the main crop-producing areas of China. To effectively monitor and control this pest, it is necessary to investigate its interannual and seasonal migration patterns in northern China. In this study, we weekly monitored the population dynamics of <em>S. exigua</em> using sex pheromone traps in Shenyang, Liaoning Province from 2012 to 2022 and simulated the migration trajectories using the HYSPLIT model. Overall, the migration numbers varied significantly among years, with large migrations in 2018 and 2020 that resulted in a total catch of more than 2000 individuals. </p>
Data from: Plant-derived environmental DNA complements diversity estimates from traditional arthropod monitoring methods but outperforms them detecting plant-arthropod interactions
<p>Our limited knowledge about the ecological drivers of global arthropod decline highlights the urgent need for more effective biodiversity monitoring approaches. Monitoring of arthropods is commonly performed using passive trapping devices, which reliably recover diverse communities, but provide little ecological information on the sampled taxa. Especially the manifold interactions of arthropods with plants are barely understood. A promising strategy to overcome this shortfall is environmental DNA (eDNA) metabarcoding from plant material on which arthropods have left DNA traces through direct or indirect interactions. However, the accuracy of this approach has not been sufficiently tested. In four experiments, we exhaustively test the comparative performance of plant-derived eDNA from surface washes of plants and homogenized plant material against traditional monitoring approaches. We show that the recovered communities of plant-derived eDNA and traditional approaches only partly overlap, with eDNA recovering various additional taxa. This suggests eDNA as a useful complementary tool to traditional monitoring. Despite the differences in recovered taxa, estimates of community α- and β-diversity between both approaches are well correlated, highlighting the utility of eDNA as a broad scale tool for community monitoring. Last, eDNA outperforms traditional approaches in the recovery of plant-specific arthropod communities. Unlike traditional monitoring, eDNA revealed fine-scaled community differentiation between individual plants and even within plant compartments. Especially specialized herbivores are better recovered with eDNA. Our results highlight the value of plant derived eDNA analysis for large-scale biodiversity assessments that include information about community level interactions.</p>
Monitoring data for oyster reefs and nearby controls, including high frequency environmental parameters, carbonate chemistry, oyster growth, and other data
<p>Data for Oyster reefs' control of carbonate chemistry - implications for oyster reef restoration in estuaries subject to coastal ocean acidification. </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.