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47 results for “Choice Modelling”

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zenodo44/100

Destination Choice Model including panel datausing WiFi localization in a pedestrian facility

A minimal example of a destination choice model including panel data on EPFL campus. It is based on the output of Danalet et al. (2014).  It runs on Pythonbiogeme. Some variables are removed from the dataset due to privacy issues. Thus, some parameters may not be significant.

opencc-zeroJun 2015View details →

zenodo44/100

Model results: Model-based decision support for the choice of active spring frost protection measures in apple production

Background: Apple producers are dealing with weather related risks affecting their production. One important risk, is the damage of buds or young fruits by late spring frosts. Fruit growers can protect their apple orchards against this risk in various ways. With a probabilistic model (available on Git Hub: <a href="https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection">https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection</a>, <a href="https://doi.org/10.5281/zenodo.11473204">https://doi.org/10.5281/zenodo.11473204</a>), we want to support the decision between several active frost protection measures. The measures considered in the model are: overhead irrigation, below-canopy irrigation, stationary wind machines, mobile wind machines, tractor-mounted gas heaters, portable gas heaters, candles and pellet heaters. As case studies, we parameterized the model for two German apple production regions (Rhineland and Lake Constance region). Repository content: This repository contains the simulation results of 100,000 Monte Carlo runs with the model. The results are provided as .RDS and .csv files. The .RDS files are suitable to be uses with the Code on Git Hub to follow the Post-Hoc analysis and figure plotting.

opengpl-3.0-or-laterJun 2024View details →

zenodo40/100

From optimality to prestige: Investigating human-animal interactions at Late Mesolithic Hoge Vaart-A27 (Almere, the Netherlands) using a Prey Choice Model

<h2>This dataset is from my Research Master's Thesis from Groningen University. </h2> The dataset includes; <ul> <li>The CSV data necessary to recreate all models and graphs from the thesis</li> <li>The R.Script with all codes</li> </ul> Used packages and programming language:  <ul> <li>Kassambara, A. (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots (R package version 0.6.0) [R; Rstudio]. R Foundation for Statistical Computing.<a href="https://doi.org/%3Chttps://CRAN.R-project.org/package=ggpubr%3E."> <https://CRAN.R-project.org/package=ggpubr>.</a></li> <li>Mei, W., Yu, G., & Greenwell, B. M. (2022). ggtrendline: Add Trendline and Confidence Interval to ‘ggplot’ (R package version 1.0.3) [R; Rstudio]. R Foundation for Statistical Computing.<a href="https://cran.r-project.org/package=ggtrendline"> https://CRAN.R-project.org/package=ggtrendline</a></li> <li>Pedersen, T. L. (2024). patchwork: The Composer of Plots (R package version 1.2.0) [R; RStudio]. R Foundation for Statistical Computing.<a href="https://cran.r-project.org/package=patchwork"> https://CRAN.R-project.org/package=patchwork</a></li> <li>R Core Team. (2022). R: A Language and Environment for Statistical Computing (R version 4.2.1) [R; RStudio]. R Foundation for Statistical Computing.<a href="https://www.r-project.org/"> https://www.R-project.org/</a></li> <li>Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC.</li> <li>Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.<a href="https://ggplot2.tidyverse.org"> https://ggplot2.tidyverse.org</a></li> <li>Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686.<a href="https://doi.org/10.21105/joss.01686"> https://doi.org/10.21105/joss.01686</a></li> </ul> Abstract The research of the faunal assemblage from Hoge Vaart-A27 (Almere, the Netherlands) provides a new perspective on investigating the connections between foraging strategies and socio-cultural dynamics of the Late Mesolithic and Early Swifterbant communities in Northwest Europe. The significance of this topic lies in its potential to help elucidate the broader socio-cultural aspects of foraging and human-animal interactions during this period. Despite extensive research, there remains a gap in comprehending how prestige influenced prey selection alongside optimal foraging strategies. The study aims to address this gap by employing zooarchaeological methods combined with prey choice modelling to investigate prey selection complexities in Flevoland’s transitioning cultural and physical landscape. The methods include analyses of species abundance and detailed examination of red deer, horse, aurochs and wild boar. The key findings reveal that while (optimal) foraging strategies were practised at Hoge Vaart-A27, they were significantly influenced by motivations such as prestige. The results contribute significantly to research by offering a glimpse into how Northwest European foragers could have perceived and interacted with big game beyond subsistence.  Keywords human-animal relationships, prestige, profitability, prey choice, Swifterbant Culture, optimal foraging theory, costly signalling theory

opencc-by-4.0Jun 2024View details →

zenodo40/100

Water migration through enzyme tunnels is sensitive to the choice of explicit water model (DhaA)

This repository contains data for the haloalkane dehalogenase DhaA. Data underpinning analyses of alditol oxidase (AldO) and cytochrome P450 2D6 (CYP2D6) are available from the related repository: <a href="https://doi.org/10.5281/zenodo.11545455">https://doi.org/10.5281/zenodo.11545455</a>   Content: tt_conda.yml -> conda environment used for the calculations.          Usage :          conda env create -f tt_conda.yml          conda activate tt_conda.yml  01_MD_simulations.tar.gz -> the files to run simulation, out and restart files from simulation and simulation analysis results, organized by models and Tunnel Conformational Groups (TCGs). ├── 01_inputs │   ├── opc │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   ├── scripts │   ├── tip3p │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   └── tip4pew │       ├── TCG_d1.0_o1.1 │       ├── TCG_d1.4_o1.2 │       ├── TCG_d1.8_o1.4 │       ├── TCG_d2.5_o2.1 │       └── TCG_d3.0_o2.5 ├── 02_outputs │   ├── opc │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   ├── tip3p │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   └── tip4pew │       ├── TCG_d1.0_o1.1 │       ├── TCG_d1.4_o1.2 │       ├── TCG_d1.8_o1.4 │       ├── TCG_d2.5_o2.1 │       └── TCG_d3.0_o2.5 └── 03_analysis               02_caver.tar.gz -> results of CAVER calculations, organized by models and TCGs. ├── config_files ├── opc │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 ├── tip3p │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 └── tip4pew     ├── TCG_d1.0_o1.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.4_o1.2     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.8_o1.4     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d2.5_o2.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     └── TCG_d3.0_o2.5         ├── 1         ├── 2         ├── 3         ├── 4         └── 5 03_aquaduct.tar.gz -> results of AQUA-DUCT calculations, organized by models and TCGs.   ├── opc │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 ├── tip3p │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 └── tip4pew     ├── TCG_d1.0_o1.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.4_o1.2     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.8_o1.4     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d2.5_o2.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     └── TCG_d3.0_o2.5         ├── 1         ├── 2         ├── 3         ├── 4         └── 5            04_transport_tools.tar.gz -> the results of TransportTools and analysis done from TransportTools results. ├── bottleneck_analyses │   ├── data │   │   └── super_clusters │   ├── statistics │   │   └── comparative_analysis │   └── visualization │       ├── comparative_analysis │       └── sources ├── overall_results │   ├── data │   │   ├── exact_matching_analysis │   │   └── super_clusters │   ├── statistics │   │   └── comparative_analysis │   └── visualization │       ├── comparative_analysis │       └── sources └── scripts     ├── bottleneck_residues     ├── presence_of_tunnels     └── water_transport_analysis         └── output 05_hbonds.tar.gz -> contains raw results of hydrogen bond analysis of transported waters for P1 tunnel of DhaA     06_control_NVE_calculations.tar.gz -> data obtained from NVE simulations of DhaA TCG o1.4d1.8 and files necessary to reproduce ├── 01_MD_simulations │   ├── 01_inputs │   │   ├── configs │   │   │   ├── NVE_equil.in │   │   │   ├── NVE_prod.in │   │   │   ├── NVT_cool.in │   │   │   └── NVT_equil.in │   │   ├── opc │   │   ├── tip3p │   │   └── tip4pew │   └── 02_outputs │       ├── opc │       ├── tip3p │       └── tip4pew ├── 02_caver │   ├── configs │   │   ├── calculate_tunnels.txt │   │   └── clustering.txt │   ├── opc │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── tip3p │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── tip4pew │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 ├── 03_aquaduct │   ├── opc │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── tip3p │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── tip4pew │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 └── 04_transport_tools     ├── data     ├── statistics     ├── transport_tools.log     ├── tt_config.in     └── visualization

opencc-zeroOct 2024View details →

dryad40/100

Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses

1. Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect non-zero correlations has rarely been evaluated and these few studies have not focused on tag-recovery data, which is a common data type. 2. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (Anas platyrhychos) tag-recovery dataset and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate more robust summary statistics for tag-recovery datasets than total individuals tagged. 3. Different priors lead to substantially different estimates of correlation from the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations could not estimate strongly negative correlation with useful precision or accuracy. Many correlation estimates spanned the available parameter space (–1,1) and underestimated the magnitude of negative correlation. Only one prior combined with our most intensive monitoring scenario provided reliable results. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery. 4. The inadequacy of prior distributions and sample size combinations previously assumed adequate for obtaining robust inference from tag-recovery data represents a concern in the application of Bayesian hierarchical models to tag-recovery data. Our analysis approach provides a means for examining prior influence and sample size on hierarchical models fit to capture-recapture data while emphasizing transferability of results between empirical and simulation studies.

opencc-zeroFeb 2023View details →

zenodo40/100

dataset of: "Glacier projections sensitivity to temperature-index model choices and calibration strategies"

data for Schuster, L., Rounce, D. R., and Maussion, F.: Glacier projections sensitivity to temperature-index model choices and calibration strategies, Annals of Glaciology, 2023, <a href="https://doi.org/10.1017/aog.2023.57" target="_blank" rel="noopener">https://doi.org/10.1017/aog.2023.57 </a> When using this dataset, please refer to the original publication in addition to this Zenodo repository. You can find the code to create the data and plot the figures in the <a href="https://github.com/lilianschuster/oggm_mb_sandbox_option_intercomparison">oggm_mb_sandbox_option_intercomparison GitHUB repository</a>.

opencc-by-4.0Feb 2023View details →

dryad40/100

Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses

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publicAug 2024View details →

dryad36/100

Data from: Mate choice strategies in a spatially-explicit model environment

Decisions about the choice of a mate can greatly impact both individual fitness and selection processes. We developed a novel agent-based model to investigate two common mate choice rules that may be used by female gray treefrogs (Hyla versicolor). In this model environment, female agents using the minimum-threshold strategy found higher quality mates and traveled shorter distances on average, compared with female agents using the best-of-n strategy. Females using the minimum-threshold strategy, however, incur significant lost opportunity costs, depending on the male population quality average. The best-of-n strategy leads to significant female:female competition that limits their ability to find high quality mates. Thus, when the sex ratio is 0.8, best-of-5 and best-of-2 strategies yield mates of nearly identical quality. Although the distance traveled by females in the mating task varied depending on male spatial distribution in the environment, this did not interact with female choice for the best-of-n or minimum-threshold strategies. By incorporating empirical data from the frogs in this temporally- and spatially-explicit model, we thus show the emergence of novel interactions of common decision-making rules with realistic environmental variables.

opencc-zeroDec 2017View details →

dryad36/100

Patient preferences for HIV service delivery models: A discrete choice experiment in Kisumu, Kenya

Background: Novel "differentiated service delivery" models for HIV treatment that reduce clinic visit frequency, minimize waiting time, and deliver treatment in the community promise retention improvement for HIV treatment in Sub-Saharan Africa. Quantitative assessments of differentiated service delivery (DSD) feature most preferred by patient populations do not widely exist but could inform the selection and prioritization of different types of DSD models. Methods: We used a discrete choice experiment (DCE) to elicit patient preferences for HIV treatment services and how they differ across DSD models. We surveyed adults aged >18 years, enrolled in HIV care for >6 months between February and March 2019 at four facilities in Kisumu County, Kenya. DCE offered patients a series of comparisons between three treatment models, each of which varied in seven attributes: ART refill location, the quantity of ART dispensed at each refill, medication pick-up hours, type of adherence support, clinical visit frequency, staff attitude, and professional cadre of the person providing ART refills. We used a hierarchical Bayesian model to estimate attribute importance and the relative desirability of care characteristics, latent class analysis (LCA) for groups of preferences, and mixed logit model for willingness to trade analysis. Results: Of 242 patients, 128 (53.8%) were females and 150 (62.8%) lived in rural areas. Patients placed the greatest importance on ART refill location [19.5% (95%CI 18.4, 20.6)] and adherence support [19.5% (95%CI 18.7, 20.3)], followed by staff attitude [16.1% (95%CI 15.1, 17.2)]. In the mixed logit, patients preferred the nice attitude of the staff (coefficient=1.60), refill ART health center (Coeff=1.58), and individual adherence support (Coeff=1.54), 3 or 6 months for ART refill (Coeff=0.95 & 0.80, respectively) and pharmacists (instead of lay health workers) providing ART refill (Coeff=0.64). No differences were observed by gender or urbanicity. LCA revealed two distinct groups (59.5% vs.40.5%). Conclusions: Participants preferred 3 to 6-month refill intervals or clinic visit spacing, which DSD provides for stable patients. While DSD has also encouraged community ART group options, our results suggest strong patient preferences for ART refills from health centers or by pharmacists over lay caregivers or community members. These preferences held across gender and urban/rural subpopulations.

opencc-zeroOct 2022View details →

zenodo36/100

Replication package for: DEMAND AND WELFARE ANALYSIS IN DISCRETE CHOICE MODELS WITH SOCIAL INTERACTIONS

This replication package includes the dataset and stata code needed to produce the empirical results in the paper.

opencc-by-4.0Dec 2022View details →

dryad36/100

Data from: Mate choice strategies in a spatially-explicit model environment

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publicAug 2019View details →

dryad36/100

Quantitative variables related to color, territory, behavior, and morphology for male lesser prairie-chickens used in discrete choice models in mate choice study

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publicApr 2021View details →

dryad36/100

Patient preferences for HIV service delivery models: A discrete choice experiment in Kisumu, Kenya

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publicOct 2022View details →

dryad32/100

Data from: Influence of device accuracy and choice of algorithm for species distribution modelling of seabirds: a case study using black-browed albatrosses

Species distribution models (SDM) based on tracking data from different devices are used increasingly to explain and predict seabird distributions. However, different tracking methods provide different data resolutions, ranging from < 10m to >100km. To better understand the implications of this variation, we modeled the potential distribution of black-browed albatrosses Thalassarche melanophris from South Georgia that were simultaneously equipped with a Platform Terminal Transmitter (PTT) (high resolution) and a Global Location Sensor (GLS) logger (coarse resolution), and measured the overlap of the respective potential distribution for a total of nine different SDM algorithms. We found slightly better model fits for the PTT than for GLS data (AUC values 0.958±0.048 vs. 0.95±0.05) across all algorithms. The overlaps of the predicted distributions were higher between device types for the same algorithm, than among algorithms for either device type. Uncertainty arising from coarse-resolution location data is therefore lower than that associated with the modeling technique. Consequently, the choice of an appropriate algorithm appears to be more important than device type when applying SDMs to seabird tracking data. Despite their low accuracy, GLS data appear to be effective for analyzing the habitat preferences and distribution patterns of pelagic species.

opencc-zeroDec 2016View details →

dryad32/100

Data from: Objective choice of phylogeographic models

Phylogeography seeks to discover the evolutionary processes that have given rise to organismal and genetic diversity. This requires explicit hypotheses (i.e., models) to be evaluated with genetic data in order to identify those hypotheses that best explain the data. In recent years, advancements in the model-based tools used to estimate phylogeographic parameters of interest such as gene flow, divergence time, and relationships among groups have been made. However, given the complexity of these models, available methods can typically only compare a handful of possible hypotheses, requiring researchers to specify in advance the small set of models to consider. Without formal quantitative approaches to model selection, researchers must rely on their intuition to formulate the model space to be explored.We explore the adequacy of intuitive choices made by researchers during the process of data analysis by reanalyzing 20 empirical phylogeographic datasets using PHRAPL, an objective tool for phylogeographic model selection. We show that the best models for most datasets include both gene flow and population divergence parameters, and that species tree methods (which do not consider gene flow) tend to be overly simplistic for many phylogeographic systems. Objective approaches to phylogeographic model selection offer an important complement to researcher intuition.

opencc-zeroDec 2016View details →

dryad32/100

Data from: Discrete choice modelling of natal dispersal: "choosing" where to breed from a finite set of available areas

1. Classic natal dispersal studies focus mainly on distance travelled. Although distances capture some of the main selective pressures related to dispersal, this approach cannot easily incorporate the properties of the actual destination vs. the available alternatives. Recently, movement ecology studies have addressed questions on movement decisions in relation to availability of resources and/or availability of suitable habitats through the use of discrete choice models (DCMs), a widely used type of models within econometrics, which explains individual choices as a function of the properties of a finite number of alternatives. 2. In this contribution, we show how the dispersal discrete choice model (DDCM) can be used for analysing natal dispersal data in patchy environments given that the natal and the breeding area of the disperser are observed. We test this method using a case study on Great Tits (Parus major) in an archipelago of small woodlots. 3. Our results show that DDCMs are able to capture the results of classic distance-based approaches and simultaneously allow testing hypotheses on how departure and settlement are affected by variables that characterize the disperser, the natal patch and the breeding area, as well as their interactions. 4. DDCMs can be applied to any other species and system that uses some form of discrete breeding location or a certain degree of discretization can be applied.

opencc-zeroDec 2014View details →

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Data from: Trial marriage model – female mate choice under male interference

<ol> <li>In sexually reproducing animals, the process of mate choice by females is often mixed with the process of male-male competition. Current models of female male choice focus mainly on how females identify the higher quality of males, but neglect the effect of male-male competition on the mate choice of females. Therefore, it remains controversial what is the relative importance of two processes in forming a social bond.</li> <li>We propose a new "trial marriage" model for females' mate choice. The model assumes that females unconditionally accept any male they first encounter as their mating partner, and then conditionally switch mates to a new male of higher quality than their current partner when male-male competition occurs. This model was tested in the green weevil, Hypomeces squamosus, by exploring how females switched mates when males' mating interference was experimentally induced.</li> <li>The likelihood that females switched mates, as well as their conditional acceptance criteria of a new mate, was both raised with the intensity of males' mating interference that was manipulated in an enhanced encounter rate experiment, and in male introduction or stepwise removal experiments. These experimental findings confirm that a "trial marriage" strategy occurs during females' mate choice.</li> <li>Compared with other strategies, it is more beneficial for females to choose a better mate without paying the costs of identifying males as suggested by the "trial marriage" strategy. More importantly, by using the current partner quality as the conditional acceptance threshold of new mates, females can choose better males in future encounters with potential mates. In the green weevils, males' preference for larger females and the higher possibility of the largest male winning an interference are mixed together when males' mating interference reaches a higher intensity. Therefore, the consequence of a male interference will determine which male could be chosen by a female. Under this condition, conditional acceptance of the winner becomes the most beneficial strategy of females in choosing their mates. We thus suggest that the "trial marriage" strategy would be more efficient when males' mating interference becomes the determinant factor of females' mate choice.</li> </ol>

opencc-zeroApr 2020View details →

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Data from: Testing the validity of functional response models using molecular gut content analysis for prey choice in soil predators

Analysis of predator - prey interactions is a core concept of animal ecology, explaining structure and dynamics of animal food webs. Measuring the functional response, i.e. the intake rate of a consumer as a function of prey density, is a powerful method to predict the strength of trophic links and assess motives of prey choice, particularly in arthropod communities. However, due to their reductionist set-up, functional responses, which are based on laboratory feeding experiments, may not display field conditions, possibly leading to skewed results. Here, we tested the validity of functional responses of centipede predators and their prey by comparing them with empirical gut content data from field-collected predators. Our predator - prey system included lithobiid and geophilomorph centipedes, abundant and widespread predators of forest soils and their soil-dwelling prey. First, we calculated the body size-dependent functional responses of centipedes using a published functional response model in which we included natural prey abundances and animal body masses. This allowed us to calculate relative proportions of specific prey taxa in the centipede diet. In a second step, we screened field-collected centipedes for DNA of eight abundant soil-living prey taxa and estimated their body size-dependent proportion of feeding events. We subsequently compared empirical data for each of the eight prey taxa, on proportional feeding events with functional response-derived data on prey proportions expected in the gut, showing that both approaches significantly correlate in five out of eight predator - prey links for lithobiid centipedes but only in one case for geophilomorph centipedes. Our findings suggest that purely allometric functional response models, which are based on predator-prey body size ratios are too simple to explain predator - prey interactions in a complex system such as soil. We therefore stress that specific prey traits, such as defence mechanisms, must be considered for accurate predictions.

opencc-zeroDec 2016View details →

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Data from: Exploring foraging decisions in a social primate using discrete choice models

There is a growing appreciation of the multiple social and nonsocial factors influencing the foraging behavior of social animals, but little understanding of how these factors depend on habitat characteristics or individual traits. This partly reflects the difficulties inherent in using conventional statistical techniques to analyze multi-factor, multi-context foraging decisions. Discrete choice models provide a way to do so, and we demonstrate this by using them to investigate patch preference in a wild population of social foragers (chacma baboons, Papio ursinus). Data were collected from 29 adults across two social groups encompassing 683 foraging decisions over a six-month period, and the results interpreted using an information theoretic approach. Baboon foraging decisions were influenced by multiple nonsocial and social factors, and were often contingent on the characteristics of the habitat or individual. Differences in decision-making between habitats were consistent with changes in interference competition costs but not changes in social foraging benefits. Individual differences in decision-making were suggestive of a trade-off between dominance rank and social capital. Our findings emphasize that taking a multi-factor, multi-context approach is important to fully understand animal decision-making. We also demonstrate how discrete choice models can be used to achieve this.

opencc-zeroDec 2011View details →

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Data from: Examining temporal sample scale and model choice with spatial capture-recapture models in the common leopard Panthera pardus

Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the "true" explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.

opencc-zeroDec 2014View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record