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146 results for “agent based”
Simulated results from an agent-based model examining inequality and innovation in social networks
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Cystargolide-based amide and ester Pz analogs as proteasome inhibitors and anticancer agents
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Data from: Rapid evolution of prehistoric dogs from wolves by natural and sexual selection emerges from an agent-based model
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When should bees be flower constant? An agent-based model highlights the importance of social information and foraging conditions
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Falls Lake Agent-based Model Critical Transitions Dataset
<p>Data files are provided here. They are output from the Falls Lake ABM at <a href="https://github.com/maskarb/FallsLakeABM">https://github.com/maskarb/FallsLakeABM</a>. Four management scenarios are simulated for 16 shifting climate scenarios. Management Scenario 1 is no management. Management Scenario 2 is retrofitting. Management Scenario 3 is retrofitting with light drought restrictions. Management Scenario 4 is retrofitting with severe drought restrictions. The 16 shifting climate scenarios correspond to shifting factors equal to 0.25 - 1.0, increasing in 0.05 increments. For each of the 64 settings (16 climate settings X 4 management scenarios), 100 simulations were run to capture stochasticity.</p> <p>Files in Results-mgmt-x folders show the agent-based modeling results for each simulation. Data files (for example, res-mgmt-1-s-0.25-5.txt) describe the storage and deficit for each of the simulations in million cubic meters, reported at a monthly timestep. Columns 5-9, inflow, storage, outflow, totalWaterSupply, waterSupply, report monthly flows. </p> <p>Files in EWS-mgmt-X-window-25 shows the value of each early warning signals indicator calculated for log-transformed reservoir storage for each of 100 simulations, for management strategies 1-4 and shifting factors. Winsize is set at 25%, which means that EWS indicators are not calculated until time step 300, or 25% of 1200 time steps.</p>
Field-based ecological studies to assess prospective biological control agents for invasive alien plants: an example from giant rat's tail grass
<p>1. Biological control (biocontrol) of invasive alien plants is a widely utilised weed management tool. Prospective biocontrol agents are typically assessed through host-specificity testing and pre-release efficacy studies performed in quarantine. However, rearing of the potential biocontrol agents and/or test plants is often difficult or impossible under quarantine conditions. Moreover, practitioners may attain laboratory-artefacts in quarantine, which may result in the potential agent being needlessly rejected. Field-based studies in the weed's indigenous distribution could overcome these issues.</p> <p>2. Sporobolus pyramidalis and Sporobolus natalensis (giant rat's tail grass; Poaceae) are indigenous in Africa but have become problematic invasive alien plants in Australia. A previous biocontrol programme was terminated because the candidate agent could not be reared and tested in quarantine. We performed field-based host-specificity and efficacy studies for prospective biocontrol agents in South Africa (indigenous distribution). Forty-seven non-target grass species were sampled during host-specificity assessments. Candidate agent efficacy was estimated based on damage to the target weeds, for each host-specific candidate individually and in combination with other host-specific candidates.</p> <p>3. Three species of endophagous wasps were deemed host-specific. Efficacy assessments identified an undescribed stem-boring wasp (Tetramesa sp.) species as the most damaging candidate. A second Tetramesa species was much less damaging alone but had a cumulative impact on the plant in combination with the more damaging Tetramesa species. Both Tetramesa species are recommended for importation into quarantine in Australia for confirmatory host-specificity testing with a significantly reduced test plant list.</p> <p>4. Synthesis and applications: Similar field-based assessments in the indigenous distribution of weeds targeted for biocontrol could be included in future programmes. Where rearing of potential agents and/or test plants is difficult or impossible under quarantine conditions, our field-based method provides an alternative. Where quarantine-based testing is feasible, this method ensures that only candidates that have passed an ecologically realistic host-specificity and potential efficacy screening are imported into quarantine. This may reduce the number of agents that are imported and the length of time each agent is kept in quarantine. This is advantageous because quarantine space is highly valuable and is usually a limiting factor in pre-release assessments of biocontrol agents.</p>
Data from: Modelling mobile agent-based ecosystem services using kernel weighted predictors
1. Agriculture benefits from ecosystem services provided by mobile agents, such as biological pest control by natural enemies and pollination by bees. However, methods that can generate spatially explicit predictions and maps of these ecosystem services based on empirical data are still scarce. 2. Here we propose a generic statistical model to derive kernel functions to characterize the spatial distribution of ecosystem services provided by mobile agents. The model is similar in spirit to a generalized linear model, and uses data of landscape composition and ecosystem services assessed at target sites to estimate parameters of the kernel. The approach is tested in a simulation study and illustrated by an empirical case study on parasitism rates of the diamondback moth Plutella xylostella. 3. The simulation study shows that the scale parameter of the exponential power kernel can be estimated with limited bias, whereas estimation of the shape parameter is difficult. For the case study the model provides biologically relevant estimates for the kernel associated with parasitism of Plutella xylostella. These estimates can be used to generate ecosystem service maps for existing or planned landscapes. The case study reveals that predictions can be sensitive to the parameter values for the width and shape of the kernel, and to the link function used in the statistical model. 4. In the last two decades numerous empirical studies assessed ecosystem services at target sites and related these to the surrounding landscape. Our method can take advantage of these data by estimating underlying kernels that can be used to map the spatial distribution of ecosystem services. However, empirical data that can discriminate between alternative kernel shapes remain critical.
A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems - Plots
<p>A collection of all plots generated for deriving the conclusions seen in "<span><span>A comparison of model-based and model-free agents in solving semi-automatically generated PPDDL problems</span></span>".</p>
Dataset for publication "Comparative Analysis of the Pharmacokinetics of a New Migliol-Based Antiviral Agent Campecin and Investigation of Degradation Mechanisms"
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Simulation results of the agent-based model of urban insurgence with the effect of gathering sites and Koopman mode analysis
<p>The data set contains the simulation results of the agent-based model of urban insurgence with the effect of gathering sites and Koopman mode analysis. Some details on the agent-based model (without gathering sites) can be found in [Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham, Nonlinear Dynamics of Crime and Violence in Urban Settings, Journal of Artificial Societies and Social Simulation, 15(1), 2, http://jasss.soc.surrey.ac.uk/15/1/2.html, DOI: 10.18564/jasss.1921].</p> <p>Files in folder "0bar" are related to the case with 0 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "1bar" are related to the case with 1 preferential gathering site.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and average distance from the preferential gathering site. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "2bars" are related to the case with 2 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars2.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "3bars" are related to the case with 3 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars3.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "4bars" are related to the case with 4 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars4.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "5bars" are related to the case with 5 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "10bars" are related to the case with 10 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars10.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "20bars" are related to the case with 20 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars20.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "30bars" are related to the case with 30 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars30.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob25" are related to the case with 5 preferential gathering sites and 25% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.25.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob50" are related to the case with 5 preferential gathering sites and 50% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.50.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob75" are related to the case with 5 preferential gathering sites and 75% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.75.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "KMD" have detailed information for the case with 3 preferetial gathering sites and lattice size 200x200.</p> <p>File "2016_Actives_LD200_seed111_bars3.txt" has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens.</p> <p>Each file with name starting with Day for each active / intimidated (jailed) / non-intimidated (notjailed) agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship. Each file with name starting with Day for each LEO (cop) has his/her x-coordinate, y-coordinate, agent's state and -1, -1, -1. Last 3 entrees are not used. These files are provided for each time step of the simulation.</p>
Simulation results of two agent-based models of logistics systems
<p>The data set contains the simulation results of the two different agent-based models of logistics systems: <br> 1. A medical treatment facility (MTF) model, consisting of agents representing wounded soldiers and fixed sites representing medical facilities.<br> 2. A ship fueling (SF) simulation, consisting of agents representing fuel transport ships and fixed sites representing fuel-using bases.</p> <p>Folders in folder "MTF" are related to the MTF model.</p> <p>Files in folder "casualty_rateX" are related to the case with casualty rate of X new casualties per time step, where X = 30, 50, 70, . . . , 330.</p> <p>Each file "RunN_casualtyX_fullness.txt" has the "fullness" (defined as the ratio of the number of patients at a site to the total patient capacity of that site) of each site for each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Each file "RunN_casualtyX_dow.txt" has the total number of Dead Of Wounds that occur in all sites in each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Folders in folder "SF" are related to the SF model.</p> <p>Files in folder "siteMaxX" are related to the case with an initial (and maximum) site fuel value of X units, where X = 25, 50, 75, . . . , 200.</p> <p>Each file "assetTowedFuelHistory_siteMaxX_N.txt" has the number of towed fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "assetUseFuelHistory_siteMaxX_N.txt" has the number of onboard fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "siteHistory_siteMaxX_N.txt" has the number of fuel units at each site for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p>
A simple framework for agent-based modeling with extracellular matrix: Simulation results
<h1>Data for "A simple framework for agent-based modeling with extracellular matrix"</h1> <p> </p> <div>Metzcar, J., Duggan, Ben S., Fischer, B., Murphy, M., Heiland, R., Macklin, P. A simple framework for agent-based modeling with extracellular matrix. bioRxiv. doi: 10.1101/2022.11.21.514608</div> <div> </div> <div><a href="https://www.biorxiv.org/content/10.1101/2022.11.21.514608">Link to preprint</a></div> <div> <p>This repository contains the data for each subfigure (and video) in the preprint cited and linked abvoe, as well as the original figures and videos themselves. We have tried to include original model file (<code>PhysiCell_settings.xml</code> or other <code>*.xml</code> file) used to generate each results with each file set as well as the code (see python scripts) to make images and videos that appear in the pre-print - both within the files and base modules and examples in a separate folder.</p> <p>The data can be regenerated using release 2.0, <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.1">2.1</a>, and <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.2.1">2.2.1</a>. Release 2.1 is recommended for reproducing more exactly the results for the fibrosis, invasive carcinoma, and series of leader-follower results as the results archived here were produced using a set of two random number generators (one from PhysiCell and one from BioFVM). 2.2.1, used to produce the invasive cellular front results, consolidates the use of random number generators to just one (the PhysiCell one). As such, in 2.2.1, the random number generator seed may need changed to produce stochastic replicates, even when multithreading.</p> </div> <p> </p> <div>The following file sets are in this download:</div> <ul> <li>Fig2_SM_3_simple_tests.zip <ul> <li>Has results for Figure 2 and SM Figure 3</li> </ul> </li> <li>Fig3_fibrosis.zip <ul> <li>Results from fibrosis simulation (originally Figure 3, now Figure 4)</li> </ul> </li> <li>Fig4_invasive_carcinoma.zip <ul> <li>Results from invasive carcinoma simulation (originally Figure 4, now Figure 5)</li> </ul> </li> <li>Fig5_collective_migration_initial_tests.zip <ul> <li>Results from initial leader follower model development (originally Figure 5, now Figure 6)</li> </ul> </li> <li>Fig6_instant_remodeling.zip <ul> <li>Results from instant remodeling leader follower model scenariods (originally Figure 6, now Figure 7)</li> </ul> </li> <li>Fig7b_leader_follower.zip <ul> <li>Results from leader-follower collective migration scenario (origianlly Figure 7b, now Figure 8b)</li> </ul> </li> <li>images_and_vidoes_for_paper.zip <ul> <li>has all the images, videos, and some figures from the main body of the paper and the supplmental material. Updated in this version.</li> </ul> </li> <li>Invasive_cellular_front.zip <ul> <li>Has results for each ECM scenario: random, parallel, and perpindicular orientations and mixed ECM conditions. New to this data repository.</li> </ul> </li> <li>python_imaging.zip <ul> <li>Base modules and examples for producing images (tested on Python 3.9). Updated in in this verison</li> </ul> </li> <li>SM_Fig_4b_leader_follower_decreased_remodeling.zip</li> <li>stochastic_replicates.zip <ul> <li>Contains stochastic replicates for the collective migration, fibrosis, and invasive carcinoma models and source code for all.</li> </ul> </li> <li>stochastic_replicates_invasive_cellular_front.zip <ul> <li>Contains stochastic replicates for the invasive cellular front scenarios. New to this data repository.</li> </ul> </li> </ul>
Cellular population data (Agent-based model CRC)
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Data and Scripts for "Timing matters in Macrophage / CD4+ T cell interactions: An agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals"
<p>This contains the data and graphing scripts necessary to recreate all figures in the paper "Timing matters in Macrophage / CD4+ T cell interactions: An agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals". Supplemental Material for the paper is also provided here. Please refer to the README.md for instructions on how to use. The model can be found at: https://github.itap.purdue.edu/ElsjePienaarGroup/LTBINaiveinvitroModel/ along with the uncalibrated parameter files and scripts to run on HPCs.</p>
Data from: Computational research on mobile pastoralism using agent-based modeling and satellite imagery
Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.
FIGURE 42 in Acarine biocontrol agents Neoseiulus californicus sensu Athias-Henriot (1977) and N. barkeri Hughes (Mesostigmata: Phytoseiidae) redescribed, their synonymies assessed, and the identity of N. californicus (McGregor) clarified based on examination of types
FIGURE 42. Neoseiulus californicus sensu Athias-Henriot (1977). Variation in the shape of the spermathecae of specimens from: (a, b) slide lot #23 (Table 1); (c, d) lot #22; (e, f) lot #28; (g, h) lot #25; (i, j) lot # 30; (k, l) lot #34; (m–o) lot #35; (p, q) lot #36; (r, s) lot #39; (t, u) lot #37; (v) lot #41; (w) lot #42; (x) lot #45; (y) holotype of N. chilenensis, lot #43 (image is the result of a montage (merging) of photos taken by Diana Goernert, Senckenberg Museum of Natural History Görlitz). All figures at the same scale, shown in (a). Legend: AU—Australia, CA—California (USA), CAN—Canada, CH—Chile, FL— Florida (USA), KE—Kenya, SK—South Korea.
FIGURES 29–35 in Acarine biocontrol agents Neoseiulus californicus sensu Athias-Henriot (1977) and N. barkeri Hughes (Mesostigmata: Phytoseiidae) redescribed, their synonymies assessed, and the identity of N. californicus (McGregor) clarified based on examination of types
FIGURES 29–35. Neoseiulus californicus sensu Athias-Henriot (1977). Male: 29. Dorsal idiosoma (slide lot #26, Table 1); 30. Ventral idiosoma (lot #28); 31. Dorsal and 32. ventrianal shields of a paratype of N. chilenensis (conspecific with specimens of N. californicus sensu Athias-Henriot; lot #43); 33. Sternal shields (lots #25 and 28); 34. Ventrianal shield (lot #26); 35. Chelicerae and spermatodactyls: (a–b) lateral aspect (a: antiaxial, b: paraxial) (c–d) dorsal aspect; specimens from (a–c) lots #40–41 and (d) lot #28. [n.b. Figs 33–35 are at the same scale (shown as 50 µm, between Figs. 33 and 35), but are also at the same scale as Figs 29–30 (scale shown as 100 µm). Figs 31–32 are on the same scale].
FIGURES 19–24 in Acarine biocontrol agents Neoseiulus californicus sensu Athias-Henriot (1977) and N. barkeri Hughes (Mesostigmata: Phytoseiidae) redescribed, their synonymies assessed, and the identity of N. californicus (McGregor) clarified based on examination of types
FIGURES 19–24. Gnathotectum (19, 20) and subcapitulum (21–24). Neoseiulus barkeri: 19, 21. Female, 23. Male (specimens from slide lot #17, Table 1). Neoseiulus californicus sensu Athias-Henriot (1977): 20, 22. Females (from lot #27), 24. Male (lot #28). Legend: dd—deutosternal denticles; im—internal malae; lo—hyaline lobe, present in males only; gt—gnathotectum. All figures at the same scale, shown in Fig. 20.
FIGURES 4–8 in Acarine biocontrol agents Neoseiulus californicus sensu Athias-Henriot (1977) and N. barkeri Hughes (Mesostigmata: Phytoseiidae) redescribed, their synonymies assessed, and the identity of N. californicus (McGregor) clarified based on examination of types
FIGURES 4–8. Males: 4. Chelicerae and spermatodactyls of (a) Neoseiulus californicus holotype; (b) N. barkeri paralectotype (paraxial view); (c) other N. barkeri from Finland (slide lot #16, Table 1); (d) South Korea (dorsal view; slide lot #17). 5–6. Detail of N. barkeri male idiosoma (5) dorsum and (6) venter, based on a specimen from California with similar collection data to that of N. californicus type (slide lot #3, Table 1). 7. Detail of gland opening gv3 (between JV2 setae) on N. barkeri male ventrianal shield region (from slide lot # 15). 8. Detail of N. barkeri gland openings gd4 of (a) paralectotype; (b–e) Finland specimens (slide lot #16, Table 1); (f) South Korea specimens (slide lot #17). [n.b. 8a, c, e present both left and right gd4 of individual males, at different depths, while 8b, d, f present only left gd4; green arrows indicate the point where the gland duct leads deeper into the body. Poroids id5, id2, idm1–6, and idx shown in 5 are based on observations on male from slide lots #15– 17; poroid idm5 is not shown here in 5, being near the posterior edge of the shield, which is curved on the specimen illustrated; poroids ivo, ivp and sigillae in 6 are mostly based on observations on males from slide lots #16 and 17.]
FIGURES 25–28. Neoseiulus barkeri. Legs I–IV in Acarine biocontrol agents Neoseiulus californicus sensu Athias-Henriot (1977) and N. barkeri Hughes (Mesostigmata: Phytoseiidae) redescribed, their synonymies assessed, and the identity of N. californicus (McGregor) clarified based on examination of types
FIGURES 25–28. Neoseiulus barkeri. Legs I–IV, based primarily on the holotype of N. mckenziei (=jun. syn. of N. barkeri; slide lot #9, Table 1). 25–28. includes femora, genua, and tibiae of legs I, II, III, IV, respectively; 28. Basitarsus IV also included. All setae are labelled except dorsals (ad1–2, pd1–2) of genu II–IV to avoid incumbrance.
ScienceDex guides
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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.