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1,221 results for “Aggregators”
Highly feminised sex-ratio estimations for the world's third-largest nesting aggregation of loggerhead sea turtles
<p>All data uploaded is in a CSV format.</p> <p>TempVariation contains the daily average temperatures for each island (pooling data between years and beaches).</p> <p>LumTempYear contains the comparison between temperature and luminosity for each beach for all 3 years of data collection.</p> <p>LumTemp contains pools the data from LumTempYear so that there is one average temperature reading per beach. The weightings column states the percentage nesting occurring on that beach.</p> <p>BeachHist builds upon LumTemp including the estimated proportion of nests experiencing critically high temperatures, based on the daily temperatures received from the TempVariation.</p> <p> </p>
Replication Package for: Scalable and Reliable Multi-Dimensional Aggregation of Sensor Data Streams
<p>This repository contains a replication package and experimental results for our study on <em>Scalable and Reliable Multi-Dimensional Aggregation of Sensor Data Streams</em>.</p> <p>It features the presented implementation with Kafka Streams, tools for load generation and data collection, scripts for executing the presented evaluations as well as our raw results and script for analysis. A detailed description is given in the top-level README.md file.</p>
FIGURE 2 in Agrimonia eupatoria subsp. major stat. nov. (Rosaceae) and notes on the Agrimonia eupatoria aggregate
FIGURE 2. Lectotype of the name Agrimonia eupatoria var. major Boiss. (G -00330003!).
FIGURE 5 in Hieracia balcanica XV. Taxonomic and nomenclatural notes on Hieracium pilosissimum and H. divaricatum, with remarks on the H. heldreichii aggregate (Asteraceae)
FIGURE 5. Lectotype of Hieracium thessalum (BRNM 7309/36).
FIGURE 4 in Hieracia balcanica XV. Taxonomic and nomenclatural notes on Hieracium pilosissimum and H. divaricatum, with remarks on the H. heldreichii aggregate (Asteraceae)
FIGURE 4. Lectotype of Hieracium rumelicum (BRNM 7304/36).
FIGURE 3 in Hieracia balcanica XV. Taxonomic and nomenclatural notes on Hieracium pilosissimum and H. divaricatum, with remarks on the H. heldreichii aggregate (Asteraceae)
FIGURE 3. Holotype of Hieracium divaricatum (LE 01017994).
First aggregation and settling for f = 10 Hz, accelerated 60x
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Supporting data for Liquid fragmentation induced by particle aggregation during two-phase flow in 3D porous media
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Particle size and velocity distributions from an OTT Parsivel optical disdrometer located at Mario Zucchelli Station, aggregated to 5min, monthly netCDF archive
<p>Disdrometric data from an OTT Parsivel with 32 size classes and 32 velocity classes located at Mario Zucchelli Station (Antarctica, -74.6945 N, 164.1152 E, 15 m a.s.l.). Mid values and widths of the classes are provided. Spectra are sampled every 5 minutes (after merging five 1-minute measurements) and saved in monthly netCDF files. Note that this data has been processed regardless of precipitation type. Particle Size distribution (PSD) is calculated for every data entry. Monthly cumulated spectra and PSD are also provided.</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/api/records/ed6ea5bf-3f59-467b-9914-f4db387bb55d">https://antarcticdatacenter.cnr.it/geonetwork/srv/api/records/ed6ea5bf-3f59-467b-9914-f4db387bb55d</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <p> </p> <h2>File "MZS_PAR_201612_5min.nc"</h2> <pre><strong> dimensions</strong>: <em>diameter </em>= 32; <em>velocity </em>= 32; <em>time </em>= UNLIMITED; // (7488 currently) <strong>variables</strong>: long <em>time_UTC</em>(time=7488); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>diameters</em>(diameter=32); :description = "Mid values of the size classes"; :units = "mm"; float <em>velocities</em>(velocity=32); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float <em>diameters_width</em>(diameter=32); :description = "Width of the size classes"; :units = "mm"; float <em>velocities_width</em>(velocity=32); :description = "Width of the velocity classes"; :units = "m s^-1"; int <em>spectrum</em>(diameter=32, velocity=32, time=7488); :description = "Matrix of particle counts in each of the 32 diameter sizes and 32 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 32U, 32U, 1U; // uint float <em>PSD</em>(diameter=32, time=7488); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 32U, 1U; // uint double <em>monthlySpectrum</em>(diameter=32, velocity=32); :description = "Matrix of particle counts in each of the 32 diameter sizes and 32 velocity ranges over the entire month."; :units = "counts"; double <em>monthlyPSD</em>(diameter=32); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; // <strong>global attributes</strong>: :<em>title </em>= "OTT Parsivel disdrometer data, aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "Particle counts diveded in 32 size classes and 32 velocity classes. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Dec 2016"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :<em>source </em>= "OTT Parsivel disdrometer at MZS (Antarctica)"; :<em>location </em>= "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "04-Nov-2024 15:40:53 UTC"; :<em>coverage </em>= "Monthly coverage (Dec 2016): 83.871 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created from raw OTT telegram, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN.";</pre> <p> </p>
Data from: Aggregation but not organo-metal complexes contributed to C storage in tidal freshwater wetland soils
One of the many goals of wetland restoration is to promote the long-term storage of carbon (C) in the terrestrial biosphere. Unfortunately, soil C reservoirs in restored wetlands are slow to accumulate even after hydrology and plant communities are reestablished. Oftentimes wetland restoration changes the soil matrix and thus can dramatically alter how soil C is stored and processed. Our research investigated whether soil organic matter (SOM) preservation theories derived from studies in non-wetland soil systems can be extended to wetland soils. We examined C associated with water-stable soil aggregates, minerals, and metal oxides within habitats of one natural and one restored tidal freshwater wetland. This study revealed that a majority of the soil C in the natural site was associated with large macroaggregates (> 2000 μm), and soils from the restored site stored more C in small macroaggregates (> 250 to < 2000 μm). Despite these different associations, the chemical composition of SOM followed similar patterns across each aggregate-size class. Results from the sequential extraction procedure suggest organo-metal oxide complexes do not contribute to C stabilization in these habitats. This research is one of the few studies that have examined C stabilization related to soil structure in wetland soils. Our results suggest soil aggregate formation may be an important mechanism driving C stabilization, and that disruption to macroaggregates may limit C accumulation in restored wetlands. Additional empirical research and long-term field monitoring are needed to confirm linkages between aggregate-C stabilization and accumulation in wetland soils.
Data from: Aggregation of infective stages of parasites as an adaptation and its implications for the study of parasite-host interactions
The causes and consequences of aggregation among conspecifics have received much attention. For infecting macroparasites, causes include variation among hosts in susceptibility, and/or whether infective stages are aggregated in the environment. Here, we link these two phenomena and explore whether aggregation of infective stages in the environment is adaptive to parasites encountering host condition-linked defenses, and what effect such aggregations have for parasite-host interactions. Using simulation models, we show that parasite fitness is increased by aggregates attacking a host, particularly when investment into defenses is high. The fitness benefit of aggregation remains despite inclusion of factors that should curb the benefits of aggregation: namely, mortality of low condition hosts (those hosts expected to be most susceptible to parasitism) and costs of high coinfection. Using sample sizes common in studies, aggregation of infective stages reduces the likelihood of detecting host condition-parasitism relations, even when host condition is the only other factor in models affecting parasitism. Thus, it is not surprising that the expected inverse relations between host condition and parasitism, commonly a premise in studies of parasite-host interactions, are inconsistently found. An understanding of how parasites encounter hosts is thus needed for developing theory for parasite-host ecological and evolutionary interactions.
Data from: The interaction between ambush predators, search patterns of herbivores and aggregations of plants
<p>While predators benefit from spatial overlap with their prey, prey strive to avoid predators. I used an individual-based simulation comprising sit-and-wait predators, widely-foraging herbivores, and plants, to examine the link between predator ambush location, herbivore movement, and plant aggregation. I used a genetic algorithm to reach the best strategies for all players. The predators could ambush herbivores either inside or outside plant patches. The herbivores could use movement of varying directionality levels, with a change in directionality following the detection of plants. When the predators were fixed outside plant patches, the herbivores were selected to use a directional movement before plant encounter followed by a tortuous movement afterwards. When predators were fixed inside patches, herbivores used a continuous directional movement. Predators maintained within-patch positions when the herbivores were fixed to use the directional-tortuous movement. The predator location inside patches led to higher plant aggregations, by changing the herbivore movement. Finally, I allowed half of the predators to search for herbivores and let them compete with sit-and-wait predators located inside plant patches. When plants were clumped and herbivores used a directional-tortuous movement, with a movement shift after plant detection, ambush predators had a higher success relative to widely-foraging predators. In all other scenarios, widely-foraging predators did much better than ambush predators. The findings from my simulation suggest a behavioral mechanism for several observed phenomena of predator-prey interactions, such as a shorter stay by herbivores in patches when predators ambush them nearby, and a more directional movement of herbivores in riskier habitats.</p>
Figure 4 in Phylogenetic analysis of the Niphargus orcinus species- aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa
Figure 4. Distribution of characters ''coxa II-shape'' (character 30, CI50.5, RI50.86; left) and ''coxa III shape'' (character 40, CI50.33, RI50.78; right). The difference in the distribution of the two characters contradicts the notion that they might be serial homologues.
Figure 3 from: Devigne C, Broly P, Mullier R, Deneubourg J (2012) Aggregation in woodlice: social interaction and density effects. ZooKeys 176: 133-144. https://doi.org/10.3897/zookeys.176.2258
Figure 3 - Dynamics of aggregation under shelters. Average proportion of woodlice aggregated under the "winning" and the "losing" shelter for experiments showing a clear choice of one of both shelters (Binomial test, difference from an equal distribution of woodlice between shelters).
Figure 4 from: Devigne C, Broly P, Mullier R, Deneubourg J (2012) Aggregation in woodlice: social interaction and density effects. ZooKeys 176: 133-144. https://doi.org/10.3897/zookeys.176.2258
Figure 4 - Dynamics of aggregation under the "winning" shelter. Evolution of the average number of woodlice under the winning shelter as a function of time for the four densities tested and for experiments showing a clear choice of one of both shelters. Standard deviations are presented for each 4 minutes. Horizontal lines below the graph indicated the statistical differences between densities; these differences were pointed out by a Kruskal-Wallis followed by a Dunn's tests for each minute of the experiments.
Figure 2 from: Devigne C, Broly P, Mullier R, Deneubourg J (2012) Aggregation in woodlice: social interaction and density effects. ZooKeys 176: 133-144. https://doi.org/10.3897/zookeys.176.2258
Figure 2 - Choice of one shelter. Proportion of choice of a shelter at the end of the experiments as a function of woodlice density.
Figure 2 from: Mesibov R (2013) A specialist's audit of aggregated occurrence records. ZooKeys 293: 1-18. https://doi.org/10.3897/zookeys.293.5111
Figure 2 - Exclusions from the GBIF and ALA datasets (see text for details). A not identified to species or only tentatively identified to species B undescribed species C non-native species D no latitude and longitude E manuscript names F miscellaneous duplicates G Penicillata H ALA preliminary exclusions (not in Australia, images, provider K observations). D and F categories do not include records already excluded for taxonomic reasons
Figure 1 from: Mesibov R (2013) A specialist's audit of aggregated occurrence records. ZooKeys 293: 1-18. https://doi.org/10.3897/zookeys.293.5111
Figure 1 - Illustration of 'uncertainty', 'distance' and 'offset'. In MoA, spatial uncertainty is defined using the point-radius method, where a site is assumed to be at the centre of a circle whose radius is the uncertainty u. In both diagrams, d is the Euclidean distance between the MoA estimate of the site's location (blue cross) and the aggregator estimate (red square). The offset o is the distance d minus the uncertainty u. In diagram 1 the aggregator site is within the circle of uncertainty surrounding the MoA site, and the offset is negative. In diagram 2 the aggregator site is outside the circle of uncertainty and the offset is positive
Figures 19-22 from: Johansson N, Achterberg C van (2016) Revision of the Palaearctic Gasteruption assectator aggregate, with special reference to Sweden (Hymenoptera, Gasteruptiidae). ZooKeys 615: 73-94. https://doi.org/10.3897/zookeys.615.8857
Figures 19-22 - Lectotype of Gasteruption nigritarse (Thomson). 19 habitus lateral 20 labels 21 head anterior 22 head ventral.
Figures 8-11 from: Johansson N, Achterberg C van (2016) Revision of the Palaearctic Gasteruption assectator aggregate, with special reference to Sweden (Hymenoptera, Gasteruptiidae). ZooKeys 615: 73-94. https://doi.org/10.3897/zookeys.615.8857
Figures 8-11 - Lectotype of Gasteruption boreale (Thomson). 8 habitus lateral 9 head dorsal 10 labels 11 head anterior.
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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.