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153 results for “Behavioral analysis”
Replication Data for: "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis"
<p>This data package contains all the data relevant to reproduce the results presented in the publication "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis".</p>
An experimental data set for the analysis of the thermophysical behavior of a single-story naturally ventilated double-skin façade (DSF) under summer boundary conditions
<p>Double-skin facades (DSFs) are adaptive building envelope elements that offer the possibility to dynamically interact with the heat and mass flow between indoor and outdoor environments. Though designed to provide better performance compared to more conventional envelope solutions, these façade systems may, in some cases, underperform and lead to an increase in energy use or in thermal discomfort if not properly designed and operated. One of the known problems is the risk of overheating, in hot periods, in the ventilated cavity. In order to analyze this effect, we have systematically investigated the performance of a single-story, naturally ventilated DSF. The DSF is operated in the so-called outdoor air curtain mode and has venetian blinds installed in the 20 mm deep ventilated cavity. Tests were carried out under a steady-state regime corresponding to relevant summertime conditions. In an effort to enable the scientific community to access experimental data to analyze this problem further or for model validation purposes, we released together with the open-access paper entitled "<strong>Characterization of a naturally ventilated double-skin façade through the design of experiments (DOE) methodology in a controlled environment</strong>," the entire set of experimental data collected during the tests. The data set contains the results of a series of experimental runs where different configurations of the DSF, as detailed below, have been subjected to various boundary conditions through a climate simulator facility equipped with a solar simulator device. The database is supported by a guide ("Guide.pdf"), where further explanations about how to read data and schematic drawings of the sensor layout are provided. Additional information about the original aims of the experiments, the detailed methods, and other data processing procedures can be found in the article mentioned above. The collection of experimental tests in this data set covers:</p> <ul> <li>49 steady-state measurements where the following factors were changed using different experimental designs: solar irradiance (0, 350, and 700 Wm<sup>-2</sup>), outdoor chamber temperature (15, 25, and 35 ℃), opening size (7, 21, and 42 dm<sup>2</sup>), and venetian blinds angle (closed blinds θ=0 º, θ=45 º, and open blinds θ=90 º) [file name: "Complete_data.csv"],</li> </ul> <p>Any inquiries about the experimental data can be sent to: <a href="mailto:aleksandar.jankovic@ntnu.no">aleksandar.jankovic@ntnu.no</a></p> <p>The activities presented in this paper were carried out within the research project "REsponsive, INtegrated, VENTilated - REINVENT – windows," supported by the Research Council of Norway through the research grant 262198, and the partners SINTEF, Hydro Extruded Solutions, Politecnico di Torino and Aalto University.</p>
Bio-logger Ethogram Benchmark: A benchmark for computational analysis of animal behavior, using animal-borne tags
<p>This repository contains the datasets and experiment results presented in our <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a>:</p> <blockquote> <p>B. Hoffman, M. Cusimano, V. Baglione, D. Canestrari, D. Chevallier, D. DeSantis, L. Jeantet, M. Ladds, T. Maekawa, V. Mata-Silva, V. Moreno-González, A. Pagano, E. Trapote, O. Vainio, A. Vehkaoja, K. Yoda, K. Zacarian, A. Friedlaender, "A benchmark for computational analysis of animal behavior, using animal-borne tags," 2023.</p> </blockquote> <p>Standardized code to implement, train, and evaluate models can be found at <a href="https://github.com/earthspecies/BEBE/">https://github.com/earthspecies/BEBE/</a>. </p> <p>Please note the licenses in each dataset folder.</p> <p><strong>Zip folders beginning with "formatted":</strong> These are the datasets we used to run the experiments reported in the benchmark paper. </p> <p><strong>Zip folders beginning with "raw": </strong>These are the unprocessed datasets used in BEBE. Code to process these raw datasets into the formatted ones used by BEBE can be found at <a href="https://github.com/earthspecies/BEBE-datasets/">https://github.com/earthspecies/BEBE-datasets/</a>.</p> <p><strong>Zip folders beginning with "experiments": </strong>Results of the cross-validation experiments reported in the paper, as well as hyperparameter optimization. Confusion matrices for all experiments can also be found here. Note that dt, rf, and svm refer to the feature set from Nathan et al., 2012.</p> <p><em>Results used in Fig. 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (deep neural networks vs. classical models)</em><br>{dataset}_ harnet_nogyr<br>{dataset}_CRNN<br>{dataset}_CNN<br>{dataset}_dt<br>{dataset}_rf<br>{dataset}_svm<br>{dataset}_wavelet_dt<br>{dataset}_wavelet_rf<br>{dataset}_wavelet_svm</p> <p><em>Results used in Fig. 5D of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (full data setting)<br></em>If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs):<br>{dataset}_harnet_nogyr<br>{dataset}_harnet_random_nogyr<br>{dataset}_harnet_unfrozen_nogyr<br>{dataset}_RNN_nogyr<br>{dataset}_CRNN_nogyr<br>{dataset}_rf_nogyr<br><br>Otherwise:<br>{dataset}_harnet_nogyr<br>{dataset}_harnet_unfrozen_nogyr<br>{dataset}_harnet_random_nogyr<br>{dataset}_RNN_nogyr<br>{dataset}_CRNN<br>{dataset}_rf</p> <p><em>Results used in Fig. 5E of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (reduced data setting)<br></em>If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs):<br>{dataset}_harnet_low_data_nogyr<br>{dataset}_harnet_random_low_data_nogyr<br>{dataset}_harnet_unfrozen_low_data_nogyr<br>{dataset}_RNN_low_data_nogyr<br>{dataset}_wavelet_RNN_low_data_nogyr<br>{dataset}_CRNN_low_data_nogyr<br>{dataset}_rf_low_data_nogyr</p> <p>Otherwise:<br>{dataset}_harnet_low_data_nogyr<br>{dataset}_harnet_random_low_data_nogyr<br>{dataset}_harnet_unfrozen_low_data_nogyr<br>{dataset}_RNN_low_data_nogyr<br>{dataset}_wavelet_RNN_low_data_nogyr<br>{dataset}_CRNN_low_data<br>{dataset}_rf_low_data<br><br></p> <p><strong>CSV files</strong>: we also include summaries of the experimental results in experiments_summary.csv, experiments_by_fold_individual.csv, experiments_by_fold_behavior.csv. </p> <p><em>experiments_summary.csv - results averaged over individuals and behavior classes<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>f1_mean (float): mean of macro-averaged F1 score, averaged over individuals in test folds<br>f1_std (float): standard deviation of macro-averaged F1 score, computed over individuals in test folds<br>prec_mean, prec_std (float): analogous for precision<br>rec_mean, rec_std (float): analogous for recall<em><br><br>experiments_by_fold_individual.csv - results per individual in the test folds<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fold (int): test fold index<br>individual (int): individuals are numbered zero-indexed, starting from fold 1<br>f1 (float): macro-averaged f1 score for this individual<br>precision (float): macro-averaged precision for this individual<br>recall (float): macro-averaged recall for this individual<em><br></em></p> <p><em>experiments_by_fold_behavior.csv - results per behavior class, for each test fold<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fold (int): test fold index<br>behavior_class (str): name of behavior class<br>f1 (float): f1 score for this behavior, averaged over individuals in the test fold<br>precision (float): precision for this behavior, averaged over individuals in the test fold<br>recall (float): recall for this behavior, averaged over individuals in the test fold<br>train_ground_truth_label_counts (int): number of timepoints labeled with this behavior class, in the training set<em><br></em></p>
Dataset: An Empirical Analysis of Pool Hopping Behavior in the Bitcoin Blockchain
<p>We provide the first empirical analysis of pool hopping behavior among 15 mining pools throughout Bitcoin's history. Bitcoin mining is a critical activity that keeps the Bitcoin system secure, valid, and stable. Mining pools have emerged as major players that ensure that the Bitcoin system stays secure, valid, and stable. Individual miners join mining pools to benefit from a more stable and predictable income. Many questions remain open regarding how mining pools have evolved throughout Bitcoin's history and when and why miners join or leave mining pools. We propose a heuristic algorithm to extract the payout flow from mining pools and detect the pools' migration of miners. Our results showed that reward rules and pool fees influence miners' decisions to join, change, or exit from a mining pool, thus affecting the dynamics of mining pool market shares. Our analysis provides evidence that mining activity becomes an industry as miners' decisions follow classical economic rationale. </p>
An experimental data set for analysis of the thermophysical behavior of a single-story mechanically ventilated double-skin façade (DSF) in fixed boundary conditions corresponding to winter/mid-season and summer cases
<p>Double-skin facades (DSFs) are dynamic and flexible building envelopes that employ a ventilated cavity to either prevent or reduce the solar-induced cooling load or exploit solar energy for passive solar heating. The mechanical ventilation of the cavity offers higher flexibility and control than natural ventilation, as the latter largely depends on stochastic and unpredictable external conditions. Furthermore, when mechanical ventilation rates are combined with the operation of a shading device, the possibilities for controlling the accumulated heat in the cavity of the DSF increase further. Therefore, this experimental campaign systematically investigates how these two important features interact in controlling the cavity's thermal load and airflow conditions. The measurement collected during the experiments constitutes a dataset that contains the results of a series of experimental runs where the different configurations of DSF, in terms of mechanical ventilation rate and venetian blinds, have been subjected to two representative boundary conditions through a climate simulator facility equipped with a solar simulator device. The full-scale DSF mock-up, which includes venetian blinds installed in a 200 mm ventilated cavity, is operated in this experiment in two modes: outdoor air curtain (OAC) and supply air (SA) mode. Tests were carried out under a steady-state regime with different boundary conditions. For the analysis of the utilization of the excess heat accumulated in the cavity and prevention of DSF overheating, boundary conditions corresponding to g-value calculations were selected. For the analysis of air preheating in the DSF cavity, the boundary conditions corresponding to late winter/mid-season weather (cold outdoor air and low-to-moderate solar irradiance) were chosen. The entire set of experimental data collected during the tests is made publicly available to enable the scientific community to access experimental data to further analyze this problem or for model validation purposes. The data set supplements the open-access paper entitled "<strong>Control of heat transfer in single-story mechanically ventilated facades</strong>" (<a href="https://doi.org/10.1016/j.enbuild.2022.112304">https://doi.org/10.1016/j.enbuild.2022.112304</a>), where additional information about the aims of the experiments, the detailed methods, and other data processing procedures can be found. The database is supported by a guide ("Guide.pdf"), where further explanations about how to read data and schematic drawings of the sensor layout are provided. The collection of experimental tests is divided into two files, according to two considered cases:</p> <ul> <li><strong>DSF operating in outdoor air curtain mode </strong>(24 steady-state measurements). The following factors were changed: mechanical ventilation rate (0, 10, 15, 20, 30, 40, 50, and 100 % of maximum fan power) and venetian blind configuration (closed θ=0 º, semi-open θ=45 º, and raised blinds). The outdoor and indoor temperatures, 30 ℃ and 25 ℃, and solar irradiance of 500 Wm<sup>-2</sup> were replicated. [file name: "Summer.csv"],</li> <li><strong>DSF operating in supply air mode</strong> (27 steady-state measurements). The following factors were changed: mechanical ventilation rate (0, 10, 15, 20, 30, 40, 50, 75, and 100 % of maximum fan power) and venetian blind configuration (closed θ=0 º, semi-open θ=45 º, and raised blinds). The outdoor and indoor temperatures, 10 ℃ and 25 ℃, and solar irradiance of 300 Wm<sup>-2</sup> were replicated. [file name: " Winter_MidSeason.csv"]</li> </ul> <p>Any inquiries about the experimental data can be sent to: <a href="mailto:aleksandar.jankovic@ntnu.no">aleksandar.jankovic@ntnu.no</a></p> <p>The activities presented in this paper were carried out within the research project "REsponsive, INtegrated, VENTilated - REINVENT – windows," supported by the Research Council of Norway through the research grant 262198, and the partners SINTEF, Hydro Extruded Solutions, Politecnico di Torino and Aalto University.</p>
Adaptive Behavior of Farmers Under Consecutive Droughts Results In More Vulnerable Farmers: A Large-Scale Agent-Based Modeling Analysis in the Bhima Basin, India
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A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction
<p>This file contains human-robot interaction data acquired during an experiment conducted at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). The campaign focuses on collecting non-identifying data, such as torso trajectories and the internal state of the system, from people in the proximity of a robot. The study spans three days in two different environments at the University Campus Est in Lugano, Switzerland.</p> <div> <div> <div> <div> <p>The campaign adheres to ethical guidelines and is approved by SUPSI's local ethics committee.</p> <p>Duration: Total of 5 hours and 7 minutes.</p> <p>Participants: 1777 individuals tracked.</p> <p><strong>Environments:</strong></p> <ul> <li>Entrance to the campus canteen (demographically diverse, including students and staff).</li> <li>Corridor between classrooms (mainly attended by students).</li> </ul> <p><strong>Data Types</strong>:</p> <ul> <li>Robot Sensor: Timestamps, user ID, 3D torso pose in Robot Sensor frame, interaction intention detector output.</li> <li>Environment Sensor: Timestamps, user ID, 3D poses of torso and hands in Environment Sensor frame, 2D torso positions in the sensor’s field of view.</li> <li>Robot State: Currently selected behavior, state (idle or performing an offering motion).</li> </ul> <p><strong>Key Events</strong>:</p> <ul> <li>Pick Motion: User's hand movement within 0.3 meters of the box.</li> <li>Robot Offer: Robot begins an offering motion.</li> <li>Successful Offer: Pick Motion within 6 seconds of a Robot Offer.</li> </ul> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.
<p>Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.</p> <p>v1.2: typos corrected and all files available in a single .zip file for download</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - Mouse feeding behavior dataset
<p>Dataset contains feeding and drinking behavioral recordings of C57BL6/J male mice. Mice were distributed into 2 groups (9 control mice and 8 high-fat diet mice) and tracked individually on Phecomp cages for 9 weeks. During the first experimental week all animals were given <em>ad libitum</em> access to a standard chow (habituation phase). After this first week, control mice continued with the same diet regime while high-fat mice were exclusively given <em>ad libitum</em> access to a high-fat chow. Data was used originally in this publication <a href="http://onlinelibrary.wiley.com/doi/10.1111/adb.12595/abstract">10.1111/adb.12595.</a></p> <p>The data set consist in:</p> <p>- a "mouse_recordings" folder containing a CSV file containing mouse recordings and the files.</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "phases" folder containing a CSV file containing experimental phases.</p> <p>- a "chromHMM_files" folder containing a cellmarkfiletable table used by chromHMM to learn a HMM model</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - D. melanogaster behavior dataset obtained with JAABA
<p>Dataset contains <em>Drosophila melanogaster </em>behavioral annotations of chasing. The dataset is formed by 20 GAL4 line flies group from a TrpA activation screen with increased propensity to chasing and by 19 pBDPGAL4U line (control) flies group. Ctrax motor trajectories derived from the original video-recordings (1000 seconds) were downloaded from this <a href="https://sourceforge.net/projects/jaaba/files/Sample%20Data/sampledata_v0.1.zip/download">link</a> and used to obtain the chasing behavioral annotations using <a href="http://jaaba.sourceforge.net/">JAABA</a> <a href="https://www.nature.com/articles/nmeth.2281">10.1038/nmeth.2281</a>: </p> <p>The data set consist in:</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "perframe_TrpA" folder containing mat files with three Ctrax derived variables (dnose2ell, dtheta and velmag) from the motor trajectories of the GAL4 line.</p> <p>- a "perframe_pBDPGAL4" folder containing mat files with three Ctrax derived variables (dnose2ell, dtheta and velmag) from the motor trajectories of the control line.</p> <p>- a "scores" folder including the JAABA chasing annotations in two mat file one for each fly line.</p>
Companion for Visual Performance Analysis of Memory Behavior in a Task-Based Runtime on Hybrid Platforms
<p>This is the companion data for the CCGRID2019 submission paper entitled: Visual Performance Analysis of Memory Behavior in a Task-Based Runtime on Hybrid Platforms by Lucas Leandro Nesi, Samuel Thibault, Luka Stanisic and Lucas Mello Schnorr. All the data, source code, and images generation scripts used in the paper are presented here.</p>
Figure 3 in A behavioral analysis of achromatic cue perception by the ant Cataglyphis aenescens (Hymenoptera; Formicidae)
Figure 3. Angular distributions and tracks of foragers on the orientation platform during intensity threshold experiments with 370 nm (a, b) and 440 nm (c, d). Only the results of the control tests and the last critical tests with which the ants' homeward orientations were lost were given for each stimulus. a) 370 nm control test, I = 1.1 × 1011 photons, P <0.0005; b) last critical test, I = 0.44 × 1010 photons, P> 0.05, not significant (n.s.); c) 440 nm control test, I = 1.1 × 1011 photons, P <0.01; d) last critical test, I = 1.1 × 1010 photons, P> 0.05, n.s. The triangle above each circle indicates the home angle. The dots around the circumference show the actual distribution of angles of foragers. Sample size = 30; h.a. = home angle; a = mean vector angle; r = mean vector length; u = critical values of the V test; d = deviation values around the 95% confidence interval. The dashed lines denote the 95% confidence interval around each sample mean.
Data and Analysis Scripts for "Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds"
<p>This document contains the raw data and analysis scripts for the paper "Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds", including <em>Drosophila melanogaster</em> circling and handedness behavior at multiple timepoints and across genotypes and experimental conditions manipulating serotonin. It also contains code used to run ecological simulations in the paper and the results of those simulations, as well as code to generate figures for the paper.</p>
Data for: Simulation and social network analysis provide insight into the acquisition of tool behavior in hybrid macaques
<p>The pathways through which primates acquire skills are a central focus of cultural evolution studies. The roles of social and genetic inheritance processes in skill acquisition are often confounded by environmental factors. Hybrid macaques from Koram Island, Thailand provide an opportunity to examine the roles of inheritance and social learning to skill acquisition within a single ecological setting. These hybrids are a cross between tool-using Burmese long-tailed (<em>Macaca</em> <em>fascicularis</em> <em>aurea</em>) and non-tool-using common long-tailed macaques (<em>Macaca</em> <em>fascicularis</em> <em>fascicularis</em>). This population provides an opportunity to explore the roles of social learning and inheritance processes while being able to exclude underlying ecological factors. Here, we investigate the roles of social learning and inheritance in tool use prevalence within this population using social network analysis and simulation. Agent-based modeling (ABM) is used to generate expectations for how social/asocial learning and inheritance structure the patterning in a social network. The results of the simulation show that various transmission mechanisms can be differentiated based on associations between individuals in a social network. The results provide an investigative framework for discussing tool-use transmission pathways in the Koram social network. By combining ABM, network analysis, and behavioral data from the field we can investigate the roles social learning and inheritance play in tool acquisition in wild primates. </p>
Data from: Assessing the association between animal color and behavior: A meta-analysis of experimental studies
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Data for: Simulation and social network analysis provide insight into the acquisition of tool behavior in hybrid macaques
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Ultimate strength assessment of stiffened panel using non-linear mechanical behavior of an equivalent single layer: grillage FE model used for analysis
<p>This example shows how the ESL can be applied in the ultimate strength structural analysis in Abaqus finite element software. In other words, ESL methodology is applied only in some parts of the structure while larger structural supporting components like girders and webframes are still modeled explicitly. FIles include also the Full_3D_FEM model used for validating the ESL model.</p> <p>Dataset includes following files:</p> <p>1. ESL_nonlinear_grillage.inp - this is Abaqus input file for running the ESL nonlinear grillage model.</p> <p>2. ugensFINALv_master.for - this defines the nonlinear stiffness or ABD matrix. This is called by input file (ESL_nonlinear_grillage.inp ).</p> <p>3. Full_3D_FEM.inp - Full_3D_FEM model used for validating the ESL model.</p> <p> </p>
Data from: Novel sources of (co)variation in nestling begging behavior and hunger at different biological levels of analysis
<p>Biological hypotheses predicting patterns of offspring begging typically concern the covariance with hunger and/or development at specific hierarchical levels. For example, hunger drives within-individual patterns of begging, but begging also drives food intake among individuals within broods, and begging and food intake can covary positively or negatively among genotypes or broods. Testing biological phenomena that occur at multiple levels therefore requires the partitioning of covariance between traits of interest to ensure that each level-specific relationship is appropriately assessed. We performed a partial cross-fostering study on a wild population of great tits (Parus major), then used multivariate mixed-models to partition variation and covariation in nestling begging effort and two metrics of nestling hunger within versus among individual nestlings and broods. At the within-individual level, we found that nestlings begged more intensely when hungrier (positive correlation between begging and hunger). However, among individuals, nestlings that were fed more frequently also begged more intensely on average (negative correlation between begging and hunger). Variation in nestling mass did not give rise to the negative correlation between begging and hunger among nestlings, but we did find that lighter nestlings begged more intensely than their heavier biological siblings, suggesting that this effect may be driven by a genetic component linked to offspring size. Our study illustrates how patterns of covariance can differ across biological levels of analysis and addresses biological mechanisms that could produce these previously obscured patterns.</p>
Qualitative raw data and behavioral analysis for understanding VMMC policy decision-making
<p>Faced with declining donor funding for HIV, low- and middle-income countries must identify efficient and cost-effective ways to integrate HIV prevention programs into public health systems for long-term sustainability. In Zambia, donor support to the voluntary medical male circumcision (VMMC) program, which previously funded non-governmental organizations as implementing partners, is increasingly being directed through government structures instead. We developed a framework to understand how the behaviors of individual decision-makers within the government could be barriers to this transition. We interviewed key stakeholders from the national, provincial, and district levels of the Ministry of Health, and from donors and partners funding and implementing Zambia's VMMC program, exploring the decisions required to attain a sustainable VMMC program and the behavioral dynamics involved at personal and institutional levels. Using pattern identification and theme matching to analyze the content of the responses, we derived three core decision-making phases in the transition to a sustainable VMMC program: 1) developing an alternative funding strategy, 2) developing a policy for early-infant (0-2 months) and early-adolescent (15-17 years) male circumcision, which is crucial to sustainable HIV prevention; and 3) identifying integrated and efficient implementation models. We formulated a framework showing how, in each phase, a range of behavioral dynamics can form barriers that hinder effective decision-making among stakeholders at the same level (e.g., national ministries and donors) or across levels (e.g., national, provincial and district). Our research methodology and the resulting framework offer a systematic approach for in-depth investigations into organizational decision-making in public health programs, as well as development programs beyond VMMC and HIV prevention. It provides the insights necessary to map organizational development and policy-making transition plans to sustainability, by explaining tangible factors such as organizational processes and systems, as well as intangibles such as the behaviors of policymakers and institutional actors.</p>
BEHAV3D: A 3D live imaging platform for comprehensive analysis of engineered T cell behavior and tumor response
<p>The use of patient-derived material and immune cell co-cultures in modeling immune-oncology has gained significant interest for understanding and manipulating immune cell tumor targeting in a patient-specific context. However, current protocols have limitations in visualizing and analyzing the dynamic cellular features of these living culture systems. We recently developped a workflow names BEHAV3D, that combines multi-color live 3D imaging and computational tools to analyze cell death dynamics, classify T cell behavior, and generate data-informed 3D images and videos. Here we provide some example pre-processed dataset of videos of two co-culture set ups: breast cancer Patient Derived Organoids with αβ T cells engineered to express a γδ TCR (TEGs) and Acute Lymphoblastic Leukemia cells with CD19 CART cells.</p>
ScienceDex guides
Understand access before you commit
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.