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Data from: Improving short-term information spreading efficiency in scale-free networks by specifying top large-degree vertices as the initial spreaders.
The positive function of initially influential vertices could be exploited to improve spreading efficiency for short-term spreading in scale-free networks. However, the selection of initial spreaders depends on the specific scenes. The selection of initial spreaders needs to offer low complexity and low power consumption for short-term spreading. In this paper, we propose a selection strategy for efficiently spreading information by specifying a set of top large-degree vertices as the initially informed vertices. The essential idea behind the proposed selection strategy is to exploit the significant diffusion of the top large-degree vertices at the beginning of spreading. To evaluate the positive impact of initially influential vertices, we first build an information spreading model in the Barabási-Albert (BA) scale-free network; next, we design 54 comparative Monte Carlo experiments based on a benchmark strategy and the proposed selection strategy in different BA scale-free network structures. Experimental results indicate that (1) the proposed selection strategy significantly can improve spreading efficiency in the short-term spreading and (2) both network size and number of hubs have a strong impact on spreading efficiency, while the number of initially informed vertices has a weak impact. The proposed selection strategy can be employed in short-term spreading, such as sending warnings or crisis information spreading or information spreading in emergency training or realistic emergency scenes.
Top-down determinants of the numerosity-time interaction
<p>This is the dataset related to the article:"Top-down determinants of the numerosity-time interaction"</p> <p>The file contains subjects data for the four experiments<br><br>For the .csv file:</p> <p>sub_ID is the participant's identification number</p> <p>PSE indicates the Point of Subjective Equality for the Specified Condition</p> <p>SD indicates the Standard Deviation of the PSE in the adjacent column</p> <p> </p> <p>For the .mat files:</p> <p>The name of the .mat is subID_WhichExperiment_WhichCondition<br><br>3rd column: Condition<br>4th column: Test Duration<br>8th column: Response (Exp2 only)<br>10th column: Response (Exp1 only)</p>
Replication Files for "Decomposing the Growth of Top Wealth Shares"
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Aggregated dataset on preferences about top persuasive strategies and principles to influence pro-environmental behaviour change
<p>This dataset is an aggregation of two datasets, in total, it has 743 entries.</p> <p>For a full immersion on the data collected in both surveys. The Greensoul dataset is made up of a pre-pilot dataset with 303 responses and a post-pilot with 65 responses.</p> <ul> <li> <p>https://zenodo.org/records/2610102</p> </li> <li> <p><a href="../records/3565757">https://zenodo.org/records/3565757</a></p> </li> </ul> <p><strong></strong>The second one through the Prolific platform (https://www.prolific.co/) with 375 new responses from the same four countries (Spain, Austria, UK and Greece). Furthermore, we used a similar screening methodology employed in the first survey during the GreenSoul project.</p>
Supplementary material 1 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
The code documentation for the ALMaSS Population_Manager class
Figure 7 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 7 Results of implementing different scouting and foraging strategies on the performance of model colonies in pollen collection. Three different foraging strategies (i.e. distance, quality or random) were tested for each scouting strategy (i.e. distance, quantity and random). The total amount of collected pollen, the mean number of daily foraging flights, the number of foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.
Figure 5 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 5 Scout, recruit and foragers behaviour rules. Without private and social information, model bees become scout bees. When there is no private information because they never performed a foraging flight or because the flight was unsuccessful, model bees become recruits and will search for social information. If model bees have private information, they are considered forager bees even if no social information is available in the colony. In the presence of social information, scout and forager bees can change foraging locations (50% chance).
Figure 4 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 4 Available foraging hours and weather variables (temperature and solar radiation) for each simulation day throughout the year. Rain and wind variables are not shown, but were used to calculate the number of available foraging hours.
Figure 3 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 3 Example of nectar (in yellow on the left side) and pollen (in blue on the right side) spatial and temporal distribution through the season. In each snapshot, a brighter colour indicates a higher amount of the resource in the polygon. A total of 12 snapshots were taken every 30 days, starting on day 15 of the simulation.
Figure 2 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 2 The total mass of floral resources (i.e. sugar and pollen) in the studied landscape available to bees in all the simulations. The mass of floral resources was calculated, based on the production and phenology of the individual plant species comprising the habitats present in the studied landscape and the landscape composition. Pollen availability started on simulation day 20 and nectar was available from day 39.
Figure 1 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 1 Components in ALMaSS landscape model. The blue arrow represents the access to landscape information at a 1 m2 resolution. In this example, one element has woody habitats, while the other is an arable field. The information about each element depends on its type and the temporal factors described in the green boxes. The orange box shows some of the factors derived from the landscape element type, its management and the weather.
Figure 6 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 6 Results of the implementation of different scouting and foraging strategies on the performance of model colonies in terms of nectar collection. For each scouting strategy (i.e. distance, quality or random), four different foraging strategies (i.e. distance, energy efficiency, quality and random) were tested. The total amount of sugar collected, the mean number of daily foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.
Figure 2 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 2 The time step processes. The three parts of the time step (BeginStep, Step, EndStep) process can run in multithreaded mode for each object 1 to n, extant at that time and are separated by customisable methods for reporting or list management by the Population_Manager class.
Figure 1 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 1 The current class hierarchy for beetle population managers, starting with the parent class Population_Manager_Base.
Figure 3 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 3 Change in the maximum and minimum sizes and population numbers for two scenarios using the Theoretical1 species, N = do nothing, Rand = randomise the execution order.
Drone photos of Szczeliniec Wielki mesa top surface in the Piekiełko canyon area
<p>This research was funded by National Science Centre, Poland, research project no. 2020/39/D/ST10/00861.</p> <p>The compressed folder contains 225 photos taken by a Phantom 4 Pro drone, presenting the top surface of Szczeliniec Wielki in the Piekiełko canyon area.</p> <p>Photos were taken 6 October 2022 by Aleksandra Michniewicz as a part of Q-MESA project by Filip Duszyński.</p>
DAVLAT ISHTIROKIDAGI KORXONALARNI TRANSFORMATSIYA QILISH UCHUN TOP MENEJERLARNI JALB QILISH
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Bridgmanite is nearly dry at the top of the lower mantle
<p>Bridgmanite is nearly dry at the top of the lower mantle</p>
Synaptic mechanisms of top-down control in the non-lemniscal inferior colliculus
<p>Corticofugal projections to evolutionarily ancient, sub-cortical structures are ubiquitous across mammalian sensory systems. These "descending" pathways enable the neocortex to control ascending sensory representations in a predictive or feedback manner, but the underlying cellular mechanisms are poorly understood. Here we combine optogenetic approaches with in vivo and in vitro patch-clamp electrophysiology to study the projection from mouse auditory cortex to the inferior colliculus (IC), a major descending auditory pathway that controls IC neuron feature selectivity, plasticity and auditory perceptual learning. Although individual auditory cortico-collicular synapses were generally weak, IC neurons often integrated inputs from multiple corticofugal axons that generated reliable, tonic depolarizations even during prolonged presynaptic activity. Latency measurements in vivo showed that descending signals reach the IC within 30 ms of sound onset, which in IC neurons corresponded to the peak of synaptic depolarizations evoked by short sounds. Activating ascending and descending pathways at latencies expected in vivo caused a NMDA receptor dependent, supra-linear EPSP summation, indicating that descending signals can non linearly amplify IC neurons' moment-to-moment acoustic responses. Our results shed light upon the synaptic bases of descending sensory control, and imply that heterosynaptic cooperativity contributes to the auditory cortico-collicular pathway's role in plasticity and perceptual learning.</p>
Zebrafish Larvae top view
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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.