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858 results for “monitoring data”
Data for Seismic Noise and Subsurface Velocity Characterization for a Unique Bedload Monitoring Observatory in a Dryland Ephemeral Channel
<p>Seismic dataset used in submitted manuscript "Seismic Noise and Subsurface Velocity Characterization for a Unique Bedload Monitoring Observatory in a Dryland Ephemeral Channel".</p>
Supporting data for "Geodetic monitoring of elastic and inelastic deformation in compacting reservoirs due to subsurface operations"
<p>Supporting data for "Geodetic monitoring of elastic and inelastic deformation in compacting reservoirs due to subsurface operations" </p> <p>Yuexin Li1*, Mateo Acosta1, Krittanon Sirorattanakul1, Stephen Bourne2, and Jean-Philippe Avouac1</p> <p>1 Division of Geological and Planetary Sciences, California Institute of Technology, USA<br>2 Shell Global Solutions International, Amsterdam, The Netherlands</p> <p>Nov 2024<br>Contact: Yuexin Li (yuexinli@berkeley.edu, yuexinli.sar@gmail.com)</p> <p> </p> <p>This study was supported by the NSF/Industry-University Collaborative Research Center 'Geomechanics and Mitigation of Geohazards' (National Science Foundation award # 1822214), and the Enhancement Project GMG-3 funded by Nederlandse Aardolie Maatschappij (NAM). M.A acknowledges funding from the Swiss National Science Foundation through grant P2ELP2195127 and from Caltech’s Resnick sustainability institute. We would like to acknowledge NAM for granting the access and permitting to present the InSAR and GNSS data.</p>
Code and Training Data for "Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands based on In-situ Internet of Things Monitoring"
Open the record for dataset details and reuse information.
Monitoring data 2022-2024 northern Mongolia
<p>This file contains temperature data monitored in three caves (Zuun Khargana, Khavtsgait agui, and Alag Erdene 9) in northern Mongolia. In addition, it also contains temperature data from a meteorological observatory of the administration of the Khuvsgul National Park, Hatgal.</p> <p>The data were collected between 2022 and 2024 and are presented and discussed in Box et al. (Cave and Karst Science).</p>
Raw data for "Application of biological early warning systems in wastewater treatment plants: Introducing a promising approach to monitor changing wastewater composition"
Open the record for dataset details and reuse information.
Acoustic monitoring data Roaringwater Bay, SW Ireland.
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Online water quality monitoring data from full scale CS#3 DWDN for the DBP prediction model
<p>Online water quality data though the drinking water distribution network. More than 1 year of data.</p> <p>SCADA data source.</p> <p>Provide water quality of the whole system at selected locations.</p>
2021 DATA SET OF TRAFFIC COUNTES BASED ON CITIZEN SENSORS TO MONITOR DAILY TRAFFIC SOUTHERN INNER BYPASS RING IN LJUBLJANA
<p>Traffic counters for Southern inner bypass ring in Ljubljana, Slovenia. Corresponding roads: Aškerčeva road, Zoisova road, Karlovška road and Roška road.</p>
South Greenland Peregrine Falcon population monitoring data (1981-2021)
<p>The Peregrine Falcon (<em>Falco peregrinus tundrius</em>) population in South Greenland has been monitored annually 1981-2021 (except 1993, 2004 and 2020). At visits to known breeding sites we recorded presence/absence of territorial falcons as well as their breeding outputs (number of eggs and/or young).</p> <p>The file named S_Greenland_Peregrine_monitoring_data-1981-2021.csv contains the raw data from 835 site checks (sometimes several per site per year).</p> <p>The file named S_Greenland_Summary_occupancy_and_productivity-1981-2021.csv contains a <em>summary</em> of the raw data, providing annual estimates of occupancy, productivity and average brood size (young/ successful nest).</p> <p>The respective ReadMe files specify the contents.</p>
Data set - Monitoring light pollution with an unmanned aerial vehicle
<p>Dataset used in the study Monitoring light pollution with an unmanned aerial vehicle. A digital camera and a sky quality meter mounted on a UAV have been used to study the relationship between indices computed on night images and night ground brightness (NGB) measured by an optical device pointed downward towards the ground. Both measurements were taken contemporarily during flights at 70 meter and 100 meters altitude, and also varying exposure time.</p>
Geophysical data from: "Lasting effects of soil compaction on soil water regime confirmed by geoelectrical monitoring"
<p>The geophysical data presented herein were collected in the Soil Structure Observatory (see, Keller et. al. 2017) and were used in the manuscript titled: "Lasting effects of soil compaction on soil water regime confirmed by geoelectrical monitoring". The data set includes:</p> <p> </p> <p>1. Seasonal and bi-hourly DC-resistivity data:</p> <p>DC-res.ods</p> <p> </p> <p>2. Standard deviation of seasonal and bi-hourly DC-resistivity data:</p> <p>DC-res-std.ods</p> <p> </p> <p>3. Volumetric water content data inferred from TDR data using Topp's equation at 10, 20, 40 and 70 cm.</p> <p>Water_content.ods</p> <p> </p> <p>4. Temperature data at 10, 20, 40 and 70 cm.</p> <p>Temperature.ods</p> <p> </p> <p>5. Meteorological data from 2013 to 2018 collected at the SSO:</p> <p>meteo.ods</p> <p> </p> <p>Data sets 1, 2, 3 and 4 were collected at the compacted ley (CL), non-compacted ley (NL) and compacted bare soil (CB) and non-compacted ley (NB)</p> <p> </p> <p> </p> <p> </p> <p>Keller, T., Colombi, T., Ruiz, S., Manalili, M. P., Rek, J., Stadelmann, V., ... & Schymanski, S. (2017). Long-Term Soil Structure Observatory for Monitoring Post-Compaction Evolution of Soil Structure. Vadose Zone Journal, 16(4).</p>
Raw data for "Plot-scale variability of organic carbon in temperate agricultural soils - Implications for soil monitoring"
<p>This dataset is the raw data that belongs to a peer-reviewed study on the small-distance variability of soil organic carbon in agricultural soils in Germany. It consists of three different files. The first file gives the coordinates of the 16 soil cores that were taken at each of the 16 sites (eight cropland and eight grassland sites). The second file gives the soil properties measured at each individual core (n=16 per site) and the third file the soil properties measured at each indivdual soil profile (n=6 per site).</p>
Data from: Scat DNA provides important data for effective monitoring of mammal and bird biodiversity
<p>Fauna has long been neglected in the monitoring of ecological restoration, despite the key role they play in ecosystem function. Vertebrate surveys can be time consuming and costly, often requiring multiple methodologies and taxonomic expertise, making comprehensive monitoring cost prohibitive. Here we evaluate a new method of assessing mammal and bird diversity through the genetic identification of scat collections. Using DNA metabarcoding of scat collections from three bioregions we generated bird and mammalian assemblage data and distinguished between sites with different restoration histories. However, scat detectability was affected by environmental conditions (e.g. rainfall and soil), suggesting that our approach is most applicable at certain times of year or in arid (or semi-arid) environments with rocky soils, where conditions are favourable for scat preservation. Taken together these data provide a pathway to: plan, monitor and establish best-practice when restoring landscapes and add to the growing body of literature on the value of DNA metabarcoding in biomonitoring applications.</p>
Data on monitoring the formation of chemical cocktails in urban streams in response to Freshwater Salinization Syndrome
<p>Data include the concentration of base cations and nutrients over time and over space in urban streams near the University of Maryland campus in College Park, Maryland, USA. Data were collected longitudinally along streams and over 24-hour time periods. Data on the retention and release of base cations and trace elements were also collected through incubation experiments. </p>
Data from: Integrating water quality monitoring and diatom community trends to determine landscape-level change in protected lakes.
<p>These data represent water quality measurements and diatom counts from surface sediments collected from lakes in five National Park units in the Great Lakes Region (USA) between 2005 and 2018. Water quality measurements were made three times annually during the growing season; data are presented as growing season means. Surface sediment samples were collected from the same lakes every 3-5 years over more than a decade to analyze diatom community turnover. Diatom data are presented as proportions relative to total diatom counts in each sample; only taxa occuring at 2% or greater in a given park were included. </p>
Supplementary data for camera-based monitoring of Bogong moths
<p>Supplementary data to be used in conjunction with the images available from the following repositories:</p> <p>Cabramurra 2019 dataset: <a href="http://doi.org/10.5281/zenodo.4950570">https://doi.org/10.5281/zenodo.4950570</a>,<br> Ken Green Bogong 2019–2020 dataset: <a href="http://doi.org/10.5281/zenodo.4971714">https://doi.org/10.5281/zenodo.4971714</a>,<br> Mt Kosciuszko 2019–2020 dataset: <a href="http://doi.org/10.5281/zenodo.5039891">https://doi.org/10.5281/zenodo.5039891</a>,<br> Ken Green Bogong 2020–2021 dataset: <a href="https://doi.org/10.5281/zenodo.4972022">https://doi.org/10.5281/zenodo.4972022</a>,<br> Mt Kosciuszko 2020–2021 dataset: <a href="http://doi.org/10.5281/zenodo.5040011">https://doi.org/10.5281/zenodo.5040011</a>,<br> Mt Gingera 2020–2021 dataset: <a href="https://doi.org/10.5281/zenodo.5040018">https://doi.org/10.5281/zenodo.5040018</a>.</p> <p>20210823_14_model.pth (the trained pytorch model used by camfi) is also available from <a href="https://github.com/J-Wall/camfi/releases/download/v2.1.4/20210823_14_model.pth">https://github.com/J-Wall/camfi/releases/download/v2.1.4/20210823_14_model.pth</a> and is included here for posterity.</p> <p> </p>
Missing data in sea turtle population monitoring: a Bayesian statistical framework accounting for incomplete sampling
<p>Monitoring how populations respond to sustained conservation measures is essential to detect changes in their population status and determine the effectiveness of any interventions. In the case of sea turtles, their populations are difficult to assess because of their complicated life histories. Ground-derived clutch counts are most often used as an index of population size for sea turtles; however, data are often incomplete with varying sampling intensity within and among sites and seasons. To address these issues, we: (1) develop a Bayesian statistical modelling framework that can be used to account for sampling uncertainties in a robust probabilistic manner within a given site and season; and (2) apply this to a previously unpublished long-term sea turtle dataset (n = 17 years) collated for the Republic of the Congo, which hosts two sympatrically nesting species of sea turtle (leatherback turtle [<em>Dermochelys coriacea</em>] and olive ridley turtle [<em>Lepidochelys olivacea</em>]). The results of this analysis suggest that leatherback turtle nesting levels dropped initially and then settled into quasi-cyclical levels of interannual variability, with an average of 573 (mean, 95% prediction interval: 554–626) clutches laid annually between 2012 and 2017. In contrast, nesting abundance for olive ridley turtles has increased more recently, with an average of 1,087 (mean, 95% prediction interval: 1,057–1,153) clutches laid annually between 2012 and 2017. These findings highlight the regional and global importance of this rookery with the Republic of the Congo, hosting the second largest documented populations of olive ridley and the third largest for leatherback turtles in Central Africa; and the fourth largest non-arribada olive ridley rookery globally. Furthermore, whilst the results show that Congo's single marine and coastal national park provides protection for over half of sea turtle clutches laid in the country, there is scope for further protection along the coast. Although large parts of the African coastline remain to be adequately monitored, the modelling approach used here will be invaluable to inform future status assessments for sea turtles given that most datasets are temporally and spatially fragmented. </p>
Data derived state probabilities for Z. noltei monitoring study. Observed variables were shoot density at four sites in this study
<p>1. In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well-known situation. Model transferability and adaptability may be extremely beneficial - approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context.</p> <p>2. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer-reviewed literature to identify which components needed adjustment including parameterisation and quantification of the model, and desired outcomes. We adopted both linguistic labels and scenario-based elicitation to elicit from experts the conditional probabilities used to quantify the DBN.</p> <p>3. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterised the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses.</p> <p>4. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximise model reuse and minimise re-development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.</p>
Dataset from: Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring
<p>Estimating habitat and spatial associations for wildlife is common across ecological studies, and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inferences in an effort to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eularian) data for seven species collected simultaneously in eastern Washington (2018 – 2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differ depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, though specific habitat relationships differed. We hypothesized the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.</p>
Data of carcass monitoring in rewilding mountain landscapes
<p><span>Rural abandonment and subsequent vegetation regeneration ('passive rewilding') are expected to increase worldwide, producing cascades of dynamic socioeconomic, landscape and biological changes. Although landscape characteristics strongly influence the structure and functioning of scavengers, little is known about the ecological consequences of passive rewilding due to woody encroachment (i.e., 'landscape closure') on scavenging assemblages. We investigated differences in 'scavenger assemblage composition' (species richness and abundances) and 'scavenging efficiency' (scavenging frequency, detection and consumption times and consumption rates) in a mountain agroecosystem (Pyrenees) undergoing passive rewilding.</span> <span>We monitored 178 carcasses in three landscapes: 'open', 'shrubland' and 'forest', and evaluated the effects of landscape type on 'scavenger assemblage composition' and 'scavenging efficiency' at the community and species levels, while accounting for the influences of carcass size, type and placement time. We also examined whether the locally most abundant and efficient scavenger (i.e., the griffon vulture <em>Gyps fulvus</em>) affects scavenging patterns. We found that landscape type was the main factor governing scavenging dynamics. Overall and average scavenger richness were similar in open and shrubland landscapes, while forests contained the lowest number of scavengers, mainly comprising mammals. Unlike mammals, avian scavenging frequency decreased as vegetation cover increased, especially for obligate scavengers (i.e., vultures). Scavenger abundances were highest in open landscapes, and carcasses were detected and consumed more rapidly in these landscapes. Carcass size did not influence detection and consumption times, although it did affect average scavenger richness, abundances and consumption rates. Consumption rates were higher in open landscapes and were strongly associated with the presence of griffon vultures. Interestingly, we found that griffon vultures influenced scavenging dynamics via facilitation processes. However, woody encroachment could </span><span>reduce the scavenging role of this species</span><span>, while favoring mammalian facultative scavengers. Finally, our findings highlight the pivotal role of griffon vultures, mediated by landscape characteristics, in reducing carcass persistence.</span></p>
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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.