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8,460 results for “MONITORING”
A national scale BioBlitz using citizen science and eDNA metabarcoding for monitoring coastal marine fish
<p>Marine biodiversity is threatened by human activities. To understand the changes happening in aquatic ecosystems and to inform management, detailed, synoptic monitoring of biodiversity across large spatial extents is needed. Such monitoring is challenging due to the time, cost, and specialized skills that this typically requires. In an unprecedented study, we here combined citizen science with eDNA metabarcoding to map coastal fish biodiversity at a national scale. We engaged 360 citizen scientists to collect filtered sea water samples from 100 sites across Denmark over two seasons (1 pm on September 29<sup>th</sup> 2019 and May 10<sup>th</sup> 2020), and by sampling at nearly the exact same time across all 100 sites, we obtained an overview of fish biodiversity largely unaffected by temporal variation. This would have been logistically impossible for the involved scientists without the help of volunteer citizens. We obtained a high return rate of 94% of the samples, and a total richness of 52 fish species, representing approximately 80% of coastal Danish fish species and approximately 25% of all Danish marine fish species. We retrieved distribution patterns matching known occurrence for both invasive, endangered, and cryptic species, and detected seasonal variation in accordance with known phenology. Dissimilarity of eDNA community compositions increased with distance between sites. Importantly, comparing our eDNA data with National Fish Atlas data (the latter compiled from a century of observations) we found positive correlation between species richness values and a congruent patterns of community compositions. These findings support the use of eDNA-based citizen science to detect patterns in biodiversity, and our approach is readily scalable to other countries, or even regional and global scales. We argue that future large-scale biomonitoring will benefit from using citizen science combined with emerging eDNA technology, and that such an approach will be important for data-driven biodiversity management and conservation.</p>
Long-term monitoring in endangered woodlands shows effects of multi-scale drivers on bird occupancy
<p>Occupancy predictor data, detection predictor data, and species detections from sites in remnant Box Gum Grassy Woodland patches in south-eastern Australia. Only sites, species, and predictors used in our statistical analysises included. For privacy, predictors have been standardised (mean = 0, standard deviation = 1) and latitude and longitude have been offset by random vectors.</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>
Continuous bite monitoring method (GPEP)
<p>Determining herbage intake is pivotal for studies on grazing ecology. Direct observation of animals allows describing the interactions of animals with the pastoral environment along the complex grazing process. The objectives of the study were to evaluate the reliability of the continuous bite monitoring (CBM) method in determining herbage intake in grazing sheep compared to the standard double-weighing technique (DW) method during 45-min feeding bouts; evaluate the degree of agreement between the two techniques; and to test the effect of different potential sources of variation on the reliability of the CBM. The CBM method has been used to describe the intake behaviour of grazing herbivores. In this study, we evaluated a new approach to this method, i.e., whether it is a good proxy for determining the intake of grazing animals. Three experiments with grazing sheep were carried out in which we tested for different sources of variations, such as the number of observers, level of detail of bite coding grid, forage species, forage allowance, sward surface height heterogeneity, experiment site, and animal weight, to determine the short-term intake rate (45 min). Observer (<i>P<sub>exp1</sub></i> = 0.018, <i>P<sub>exp2</sub></i> = 0.078 and <i>P<sub>exp3</sub></i> = 0.006), sward surface height (<i>P<sub>exp2</sub></i> < 0.001), total number of bites observed per grazing session (<i>P<sub>exp2</sub></i> < 0.001 and P<i><sub>exp3</sub></i> < 0.001) and sward depletion (<i>P<sub>exp3</sub></i> < 0.001) were found to affect the absolute error of intake estimation. The results showed a high correlation and agreement between the two methods in the three experiments, although intake was overestimation by CBM on experiment 2 and 3 (181.38 and 214.24 units, respectively). This outcome indicates the potential of CBM to determining forage intake with the benefit of a greater level of detail on foraging patterns and components of the diet. Furthermore, direct observation is not invasive nor disrupts natural animal behavior.</p>
Digital health monitoring and digital endpoints in oncology
<p>Raw data set from GLOBOCAN and www.clinicaltrials.gov used for thesis work of "<strong>Digital health monitoring and digital endpoints in oncology</strong>"</p>
The Potential of GNSS-R for Freeze/Thaw Surface State Monitoring in the New Space Era
<p><em><strong>GNSS-R based F/T surface state maps over the Tibet Plateau, Andes Mountains, and Rocky Mountains covering two full F/T cycles 2019-2020:</strong> </em></p> <p><em><strong>(a) </strong>Number of frozen months during 2019</em></p> <p><em><strong>(b)</strong> number of thawed months during 2019</em></p> <p><em><strong>(c) </strong>number of frozen months during 2020</em></p> <p><em><strong>(d)</strong> number of thawed months during 2020</em></p> <p><em><strong>(e) </strong>difference of the number of frozen months between 2019 and 2020</em></p> <p><em><strong>(f) </strong>difference of the number of thawed months between 2019 and 2020</em></p> <p><em><strong>(g) </strong>number of frozen months during the 2019 freezing period (September to December)</em></p> <p><em><strong>(h) </strong>number of thawed months during the 2019 thawing period (January to June)</em></p> <p><em><strong>(i) </strong>number of frozen months during the 2020 freezing period (September to December)</em></p> <p><em><strong>(j)</strong> number of thawed months during the 2020 thawing period (January to June)</em></p> <p><em><strong>(k) </strong>difference of the number of frozen months between 2019 and 2020 during the freezing period (September to December)</em></p> <p><em><strong>(l) </strong>difference of the number of thawed months between 2019 and 2020 during the thawing period (January to June)</em></p>
syslrn: Learning What to Monitor for Efficient Anomaly Detection [Dataset]
<p>This repository includes the dataset for the paper:</p> <p><em><a href="http://doi.org/10.1145/3517207.3526979">D. Sanvito, G. Siracusano, S. Santhanam, R. Gonzalez, R. Bifulco</a></em><br> <strong><em><a href="http://doi.org/10.1145/3517207.3526979">syslrn: Learning What to Monitor for Efficient Anomaly Detection </a></em></strong><br> <em><a href="http://doi.org/10.1145/3517207.3526979">ACM EuroMLSys 2022</a></em></p> <p>The dataset contains two directories at the root level:</p> <ul> <li><em><strong>raw_dataset</strong></em></li> <li><strong><em>processed_dataset</em></strong></li> </ul> <p>Each folder in the <strong><em>raw_dataset</em> </strong>directory contains the raw monitoring data used to generate the graph associated to a single experiment together with additional metadata files.<br> Each folder in the <strong><em>processed_dataset</em> </strong>directory contains the graph associated to a single experiment as a set of three CSV files: two for the graph edges (<em>pid_childof_pid_df.csv</em> and <em>pid_speakswith_pid_df.csv</em>) and one for the graph nodes (<em>proc_df.csv</em>).<br> We provide below a code snippet to parse a graph from <strong><em>processed_dataset</em> </strong>directory.</p> <p>In both folders the name of each sub-folder is based on the following schema: <strong><em>[SCENARIO]_[W]wl/test_[TEST_ID]</em></strong> where:</p> <ul> <li><em>[SCENARIO]</em> reports the target component for the failure injection (<em>cinder_failure</em>, <em>neutron_failure</em>, <em>nova_failure</em>). <em>ff</em> indicates instead a failure-free execution</li> <li><em>[W]</em> reports the number of concurrent workloads</li> <li><em>[TEST_ID] </em>reports the ID of the specific failure scenario injected (same ID selected by the OpenStack failure injection framework [1] )</li> </ul> <p>Each experiment includes the following data in the <strong><em>raw_dataset</em></strong> sub-folders:</p> <ul> <li><em>audit_raw_logs_[TEST_ID]/</em>: raw audit monitoring data</li> <li><em>bpf_tools_[TEST_ID]/</em>: raw ebpf tools monitoring data</li> <li><em>instance-[INSTANCE_ID]/</em>: workload-specific metadata files, e.g. stdout/stderr (generated by the OpenStack failure injection framework [1] )</li> <li><em>logs_workload_[TEST_ID]/:</em> OpenStack application logs</li> <li><em>perf_tools_[TEST_ID]/</em>: raw perf tools monitoring data</li> <li><em>audit_filtered_[TEST_ID].log:</em> audit data pre-processed by <em>ausearch</em> (e.g. numerical entities are resolved to symbols)</li> <li><em>failure_[TEST_ID].info</em>: metadata information about the specific failure scenario (generated by the OpenStack failure injection framework [1] )</li> <li><em>timestamps_[TEST_ID]:</em> timing information</li> </ul> <p><em>[1] D. Cotroneo, L. De Simone, P. Liguori, R. Natella, N. Bidokhti - How Bad Can a Bug Get? An Empirical Analysis of Software Failures in the OpenStack Cloud Computing Platform [ACM ESEC/FSE 2019]</em></p> <p> </p> <p>Example: parsing a graph from <strong><em>processed_dataset</em> </strong>directory</p> <pre><code class="language-python">import pandas as pd import networkx as nx def parse_csv(path): processes_df = pd.read_csv('%sproc_df.csv' % path, index_col=0).reset_index(drop=True) speakswith_edges_df = pd.read_csv('%spid_speakswith_pid_df.csv' % path, index_col=0) speakswith_edges_df['type'] = 'speaksWith' childof_edges_df = pd.read_csv('%spid_childof_pid_df.csv' % path, index_col=0) childof_edges_df['type'] = 'childOf' return processes_df, pd.concat([speakswith_edges_df, childof_edges_df], ignore_index=True) def make_graph(nodes_df, edges_df): G = nx.MultiGraph() for _, node in nodes_df.iterrows(): G.add_node(node.pid, **node) for _, edge in edges_df.iterrows(): G.add_edge(edge.pid1, edge.pid2, type=edge.type) return G PATH = 'processed_dataset/ff_1wl/test_1/' nodes_df, edges_df = parse_csv(PATH) G = make_graph(nodes_df, edges_df) nx.draw_networkx(G, node_size=10, with_labels=False)</code></pre> <p> </p> <p>If you use this dataset for your research, please cite the following paper:</p> <pre><code>@inproceedings{sanvito2022syslrn, title={syslrn: Learning What to Monitor for Efficient Anomaly Detection}, author={Sanvito, Davide and Siracusano, Giuseppe and Santhanam, Sharan and Gonzalez, Roberto and Bifulco, Roberto}, booktitle={2nd European Workshop on Machine Learning and Systems (EuroMLSys '22)}, year={2022}, address = {Rennes, France}, publisher = {ACM}, month = apr, } </code></pre>
Monitoring of Halyomorpha halys populations in Bordeaux vineyards
<p>Monitoring of Halyomorpha halys in Bordeaux vinyeards in 2021</p>
"Towards skin-acetone monitors with selective sensitivity: dynamics of PANI-CA films" Raw Videos
<p>These are the original, unedited videos from which all data in the paper "Towards skin-acetone monitors with selective sensitivity: dynamics of PANI-CA films" was extracted by image analysis. This paper was published in <em>PLOS One</em>.</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>
Subsurface Multi-Physical Monitoring of a Reservoir Landslide with the Fiber-Optic Nerve System
<p>All data used in the study to support this research is available on repository via 10.5281/zenodo.6541529</p>
Figures for "Towards Improved Satellite Monitoring of Armed Conflicts"
<p>Figure 1. From space, VHR imagery from Planet and Maxar reveals the landscape effects of Russia’s invasion of Ukraine. 1. Cars waiting at the Ukrainian-Hungarian border (Maxar technologies); 2. Active fires in Chernihiv (PlanetScope-2022/03/21); 3a. The North Crimean Channel without water pre-invasion (PlanetScope-2022/02/21); 3b. Filled with water post-invasion (PlanetScope-2022/03/24); 4. Burning apartment buildings in Mariupol (Maxar technologies). Maxar technologies images were obtained from https://www.maxar.com/ on April 5th, 2022. PlanetScope data were obtained under the University of Copenhagen license agreement PL-0030875_2021/2022. Because Maxar and Planetscope original images are commercial they can not be publicly shared but can be requested from the Maxar and Planet with the additional assistance of authors.</p> <p>Figure 2. A village and surrounding farmland in Ukraine’s Lviv Oblast are more difficult to discern in imagery from medium-resolution civilian Landsat-8 (USGS-2021/05/04) and Sentinel-2 (ESA-2021/05/11) satellites than in imagery from their commercial VHR counterparts (Dove/PlanetScope -2021/05/09 and SkySat-2021/05/09, both of which belong to Planet). Landsat-8 (USGS) and Sentinel-2 (ESA) are available via glovis.usgs.gov. PlanetScope data were obtained under the University of Copenhagen license agreement PL-0030875_2021/2022. SkySat imagery was obtained under special request from Planet. PlanetScope and SkySat original images are commercial they can not be publicly shared but can be requested from Planet with the additional assistance of authors.</p> <p> </p> <p> </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>
Systematic characterization of wing mechanosensors that monitor airflow and wing deformations
<p>Animal wings deform during flight in ways that can enhance lift, facilitate flight control, and mitigate damage. Monitoring the structural and aerodynamic state of the wing is challenging because deformations are passive, and the flow fields are unsteady; it requires distributed mechanosensors that respond to local airflow and strain on the wing. Without a complete map of the sensor arrays, it is impossible to model control strategies underpinned by them. Here, we present the first systematic characterization of mechanosensors on the dragonfly's wings: morphology, distribution, and wiring. By combining a cross-species survey of sensor distribution with quantitative neuroanatomy and a high-fidelity finite element analysis, we show that the mechanosensors are well-placed to perceive features of the wing dynamics relevant to flight. This work describes the wing sensory apparatus in its entirety and advances our understanding of the sensorimotor loop that facilitates exquisite flight control in animals with highly deformable wings.</p> <p> </p>
Supplementary material 3 from: Anđelković AA, Lawson Handley L, Marchante E, Adriaens T, Brown PMJ, Tricarico E, Verbrugge LNH (2022) A review of volunteers' motivations to monitor and control invasive alien species. NeoBiota 73: 153-175. https://doi.org/10.3897/neobiota.73.79636
List of recommendations for designing projects to ensure maximum recruitment and volunteer retention extracted from the studies used in the analysis (for their full references, please see Suppl. material 1) and their link to our recommendations
Monitoring resistance and resilience using carbon trajectories: analysis of forest management-disturbance interactions
<p>A changing climate is altering ecosystem carbon dynamics with consequences for natural systems and human economies, but there are few tools available for land managers to meaningfully incorporate carbon trajectories into planning efforts. To address uncertainties wrought by rapidly changing conditions, many practitioners adopt resistance and resilience as ecosystem management goals, but these concepts have proven difficult to monitor across landscapes. Here, we address the growing need to understand and plan for ecosystem carbon with concepts of resistance and resilience. Using time series of carbon fixation (n=103), we evaluate forest management treatments and their relative impacts on resistance and resilience in the context of an expansive and severe natural disturbance. Using subalpine spruce-fir forest with a known management history as a study system, we match metrics of ecosystem productivity (net primary production, g·C·m<sup>2</sup>·y<sup>-1</sup>) with site-level forest structural measurements to evaluate (1) whether past management efforts impacted forest resistance and resilience during a spruce beetle (<em>Dendroctonus rufipennis</em>) outbreak, and (2) how forest structure and physiography contribute to anomalies in carbon trajectories. Our analyses have several important implications. First, we show that the framework we applied was robust for detecting forest treatment impacts on carbon trajectories, closely tracked changes in site-level biomass, and was supported by multiple evaluation methods converging on similar management effects on resistance and resilience. Second, we found that stand species composition, site productivity, and elevation predicted resistance, but resilience was only related to elevation and aspect. Our analyses demonstrate application of a practical approach for comparing forest treatments and isolating specific site and physiographic factors associated with resistance and resilience to biotic disturbance in a forest system, which can be used by managers to monitor and plan for both outcomes. More broadly, the approach we take here can be applied to many scenarios, which can facilitate integrated management and monitoring efforts.</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>
The Bug in a teacup – Monitoring arthropod-plant associations with environmental DNA from dried plant material
<p class="MsoNormal">Environmental DNA analysis has revolutionized the field of biomonitoring in the past years. Various sources have been shown to contain eDNA of diverse organisms, for example water, soil, gut content and plant surfaces. Here we show that dried plant material is a highly promising source for arthropod community eDNA. We designed a metabarcoding assay to enrich diverse arthropod communities, while preventing amplification of plant DNA. Using this assay, we analyzed various commercially produced teas and herbs. These samples recovered ecologically and taxonomically diverse arthropod communities, a total of over a thousand species in more than 20 orders, many of them specific to their host plant and its geographic origin. Atypically for eDNA, arthropod DNA in dried plants shows a very high temporal stability, opening up plant archives as a source for historical arthropod eDNA. Considering these results, dried plant material appears excellently suited as a novel tool to monitor arthropods and arthropod-plant interactions, detect agricultural pests, and identify the geographic origin of imported plant material. The ability to detect highly diverse arthropod communities from all over the world in tea bags also highlights the utility of our approach for outreach purposes and to raise awareness about biodiversity.</p>
FORM: A Dataset for Driver Monitoring System
<p>The dataset is categorized into four classes (forward, on_phonecall, rotated, mobile_usage,) depending on the driver’s head position.</p>
SOC Fuel, Steam, and Air starvation monitoring using advanced tools
<p>Polarisation, impedance and THD data of fuel, steam, and air starvation of SOC with different sensitivity analyses</p>
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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)
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.