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547 results for “Model Tests”
Custom language model checkpoints used in "Testing the limits of natural language models for predicting human language judgments"
<p>Checkpoint files for an RNN, LSTM, BILSTM and n-gram models used the paper "Testing the limits of natural language models for predicting human language judgments"</p>
An Experimental Test of Lanchester's Models of Combat in the Neotropical Termite Nasutitermes corniger (Blattodea: Termitidae)
<p><span>Lanchester's models of combat have been invoked to explain the mechanics of group fighting in social animals. Specifically, Lanchester's square law posits that the fighting ability of the group is proportional to the square of the number of combatants. Although used to explain a variety of ecological phenomena, the models have not been thoroughly tested. We tested the Lanchester models using group battles between colonies of the termite <em>Nasutitermes corniger</em>. Our main goals were to determine if mortality rates fit the Lanchester models, and if so, whether the behavioural mechanisms underlying a group's success match those used in deriving the model. We initiated battles between pairs of colonies with different ratios of fighters and recorded deaths over time. We found that the numerically larger army has an advantage, but that the advantage is not as pronounced as predicted by Lanchester's square law. We also video-recorded battles to analyse individual behaviour, which did not support the mechanisms invoked by Lanchester. Instead, the killing power of an individual is increased by the presence of nestmates, giving the larger group a disproportionate advantage. Although the behavioural mechanisms leading to the advantage may differ, our results still support some of the proposed ecological phenomena.</span></p>
Data associated with the manuscript "Simple statistical models can be sufficient for testing hypotheses with population time series data"
<p>This is a revised version of the archive of R code and data used in the manuscript, <em>Simple statistical models can be sufficient for testing hypotheses with population time series data. </em>The data are in three files. <em>etodata1.csv</em> and <em>etodata2.csv</em> contain two versions of the same data for shoal-dwelling fishes in the Etowah River and associated environmental covariates. <em>knz_dat</em> contains data for small mammals collected in the Konza Prairie Biological Station and associated environmental covariates. The R code consists of four primary files that call nine auxiliary files. CaseStudy1-main_code and CaseStudy2-main_code are the primary files for running the two case studies. Simulations1 and Simulations2 are the files for running the two batteries of simulations. We thank the Konza Prairie Biological Station and Konza Prairie Long-Term Ecological Research Program supported by the National Science Foundation (DEB-1440484) for collecting and providing access to mammal community data. More details are in the manuscript and supporting information. </p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (<a href="../records/6482346">https://zenodo.org/records/6482346</a>) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Fig. 1 in Testing the robustness of transmission network models to predict ectoparasite loads. One lizard, two ticks and four years
Fig. 1. Transmission networks generated with (a) a short time window of infection; and (b) a long time window of infection, from the GPS location data of the lizards in the study population in 2010. Nodes represent individual lizards and edges between nodes are directed towards the lizard that is at risk of infection. The edges are weighted as described in the main text and the thicker the line the more weight is associated with that edge.
Data Set Used in Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS
<p>This document contains the data set used for the study Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS that is currently in submission.</p>
Simulated Neutral Landscape Models and Scaling Results for Testing of Multi-Dimensional Grid-Point Scaling Algorithm
<p>The data package contains (1) simulations of neutral landscape models of categorical data for benchmark testing of scaling algorithms, and (2) scaling results for testing consistency and sensitivity of the newly developed Multi-Dimensional Grid-Point (MDGP) scaling algorithm.</p> <p>Neutral landscapes were generated using the "nlmpy" python module. The MDGP scaling algorithm and the test framework were implemented in R (https://github.com/gannd/landscapeScaling). </p>
Datasets used to train and test the Cortical Spectro-Temporal Model (CSTM).
<p>ZIP files of folders containing all the datasets (audio file corpora) employed in our research to train the Encoder Layer (EL) and the SVMs and to test the complete CSTM. This folder includes a set of 840 corpora which are distributed in 2 corpora for each configuration organized by 2 sets of synthesized voices, 3 syllabic conditions (i.e. mono-, di- and tri-syllabic English words) and 10 completely different vocabularies all distributed in 6 acoustic variants, beyond the original version of the corpora.</p> <p>The 6 acoustic variants corresponds to: two levels of white noise (19.8 dB and 13.8 dB Signal to Noise Ratio (SNR) average Root Mean Square (RMS) power rate), two levels of reverberation (Reveberation-Time 60 dB (RT-60) value of 0.61 seconds and 1.78 seconds) and variations of pitch on both directions (from E to G and from E to C).</p>
Fig. 5. Modeled 3D in Response to enantiomers of (Z3Z9)-6,7-epoxy-octadecadiene, sex pheromone component of Ectropis obliqua Prout (Lepidoptera: Geometridae): electroantennagram test, field trapping, and in silico study
Fig. 5. Modeled 3D structure and validation of EoblPBP1. (A) Sequence alignment of EoblPBP1 and template 1DQE_A. α-helices are displayed as squiggles. Identical residues are highlighted in white letters with deep blue background. (B) Overall structure of the EoblPBP1. Three disulfide bonds are in red. N-terminus, C-terminus, and α-helices are labeled. Two potential key residues: Thr117 and Arg 121 are in orange. (C) Ramachandran plot of EoblPBP1.
Data: Testing the mating system model of parasite complex life cycle evolution reveals demographically driven mixed mating
<p>Abstract: Many parasite species use multiple host species to complete development; however, empirical tests of models that seek to understand factors impacting evolutionary changes or maintenance of host number in parasite life cycles are scarce. Specifically, Brown et al.’s (2001) mating system model, which posits multi-host life cycles are an adaptation to prevent inbreeding in hermaphroditic parasites and thus, preclude inbreeding depression, remains untested. The model assumes loss of a host results in parasite inbreeding and predicts host loss can only evolve if there is no parasite inbreeding depression. <a name="_Hlk169780726"></a>We provide the first empirical tests of this model using a novel approach we developed for assessing inbreeding depression from field-collected, parasite samples. The method compares genetically-based, selfing-rate estimates to a demographic-based selfing rate, which was derived from the closed mating system experienced by endoparasites. Results from the hermaphroditic trematode <em>Alloglossidium renale</em>, which has a derived 2-host life cycle, supported both the assumption and prediction of the mating system model as this highly inbred species had no indication of inbreeding depression. Additionally, comparisons of genetic and demographic selfing rates revealed <a name="_Hlk169781073"></a>a mixed mating system that could be explained completely by the parasite’s demography, i.e., its infection intensities.</p>
Supplemental material for: Software System Testing assisted by Large Language Models: An Exploratory Study
<p>This is the supplemental material of the paper titled as “Software System Testing Assisted by Large Language Models: An Exploratory Study” presented at the 36th International Conference on Testing Software and Systems.</p> <p>It contains the raw execution data generated by both models, GPT-4o and GPT-4omini, during the exploratory study. The supplementary material includes the following files:</p> <ul> <li><em>GPT-4ominiRQ1-2ExecutionData.zip</em>: contains the JSON outputs from the OpenAI API for the GPT-4o mini model. Each output is labeled according to the research question number and the corresponding timestamp (for RQ1) or the requested test case (for RQ2), all provided in plain text format.</li> <li><em>GPT-4oRQ1-2ExecutionData.zip</em>: contains the JSON outputs from the OpenAI API for the GPT-4o model. Like the previous file, each output is named in plain text format based on the research question number and timestamp (for RQ1) or the requested test case (for RQ2).</li> </ul> <p>To cite this work: </p> <p>C. Augusto, J. Morán, A. Bertolino, C. de la Riva and J. Tuya, “S<em>oftware System Testing assisted by Large Language Models: An Exploratory Study</em>”, in <em>Testing Software and Systems</em> (pp. 239–255). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-80889-0_17</p>
Dataset from the Towing Tank Test of the B.I.O. Hespérides ship model, conducted at the CEHINAV Towing Tank, in the presence of simulated broken ice
<p>This data set presents a collection of measurements carried out at CEHINAV Towing Tank. The colletion includes several rar files described below:</p> <ul> <li><strong>Towing_test.rar</strong>: Raw data (ASCII files) containing the following information: <ul> <li>Time (Chanel 1)</li> <li>Velocity (Chanel 2)</li> <li>Resistance (Chanel 4)</li> <li>Laser position bow (Chanel 5)</li> <li>Laser position stern (Chanel 6)</li> <li>Laser position longitudinal (Chanel 7)</li> <li>Absolute carriage position (Chanel 8)</li> </ul> </li> <li><strong>Towed_self_propulsion</strong>: Raw data (ASCII files) containing the followig information: <ul> <li>Time (Chanel 1)</li> <li>Velocity (Chanel 2)</li> <li>RPM engine (Chanel 3)</li> <li>Resistance (Chanel 4)</li> <li>Laser position bow (Chanel 5)</li> <li>Laser position stern (Chanel 6)</li> <li>Laser position longitudinal (Chanel 7)</li> <li>Thrust (Chanel 8)</li> <li>Torque (Chanel 9)</li> <li>Absolute carriage position (Chanel 11)</li> </ul> </li> <li><strong>Figures_coverage</strong>: PNG files with images of block coverage tested and files containing the relative position during the test.</li> <li><strong>HESPERIDES_model.msd</strong>: Maxurf file with the geometry of the model tested.</li> </ul> <p> </p> <p>The tests were conducted in february and november of 2020.</p>
Machine learning models, and training, validation and test datasets for: "Sequence determinants of human gene regulatory elements"
<p>This record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript:</p> <p><strong>Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021).</strong></p> <p><br> This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.</p>
INSYDE model - Testing empirical and synthetic flood damage models: the case of Italy
<p>This repository contains the R code for <a href="http://www.nat-hazards-earth-syst-sci.net/16/2577/2016/">INSYDE, a synthetic, probabilistic flood damage model based on explicit cost analysis</a>.</p> <p>Correspondence to: Rui Figueiredo (<a href="mailto:rui.figueiredo@iusspavia.it">rui.figueiredo@iusspavia.it</a>)</p>
CNN models and training, validation and test datasets for "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"
<p>Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript:</p> <p>Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"</p>
Testing Flood Damage Models on three recorded flood events in Italy
<p>The package includes: 1) two excel files containing input, processing and output of the statistical analysis; 2) a geodata folder made of three sub-folders, each related to case study areas (Adda, Bacchiglione, Secchia). They include the output of flood hazard models and geometries of exposed buildings.</p>
Test-particle simulations for "Quantifying the influence of bars on action-based dynamical modelling of disc galaxies"
<p>This contains the test-particle simulations of barred galaxies with varying bar properties (bar strength, pattern speed) which have been used in the manuscript "Quantifying the influence of bars on action-based dynamical modelling of disc galaxies" by Ghosh, S., Trick, W. H., Green G. M. (2022).</p> <p>For details for the datasets, please see the README.md file.</p> <p>For any further information, please feel free to contact Ghosh, Soumavo (ghosh@mpia.de).</p> <p> </p>
Data for: Predicting age and mass at maturity from feeding behavior and diet in M. sexta: An empirical test of a life history model
<p>Feeding for most animals involves bouts of active ingestion alternating with bouts of no ingestion. In insects, the temporal patterning of bouts varies widely with resource quality and is known to affect growth, development time, and fitness. However, the precise impacts of resource quality and feeding behavior on insect life history traits is poorly understood. To explore and better understand the connections between feeding behavior, resource quality and insect life history traits, we combined laboratory experiments with a recently proposed mechanistic model of insect growth and development for a larval herbivore, <em>Manduca sexta</em>. We ran feeding trials for 4<sup>th</sup> and 5<sup>th</sup> instar larvae across different diet types (two hostplants and artificial diet) and used these data to parameterize a joint model of age and mass at maturity that incorporates both insect feeding behavior and hormonal activity. We found that the estimated durations of both feeding and non-feeding bouts were significantly shorter on low- than on high-quality diets. We then explored how well the fitted model predicted historical out-of-sample data on age and mass of <em>M</em>.<em> sexta</em>. We found that the model accurately described qualitative outcomes for the out-of-sample data, notably that a low-quality diet results in reduced mass and later age at maturity compared to high-quality diets. Our results clearly demonstrate the importance of diet quality on multiple components of insect feeding behavior (feeding and non-feeding), and partially validate a joint model of insect life history. We discuss the implications of these findings with respect to insect herbivory and discuss ways in which our model could be improved or extended to other systems.</p>
U-T training and test data for Saltblock model
<p>Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: Saltblock model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.</p>
A set of positive examples consisting of short-term schedules for testing the acquisition of MiniZinc scheduling models
<p>To test the robustness of schedule model acquisition in a variety of situations, we generated 48,000 instances of schedules with variations in the following five dimensions:</p> <p>1. task description, 2. temporal constraints, 3. resource constraints, 4. the introduction or absence of noisy columns, and 5. the number of tasks and resources in a schedule.</p> <p>The file 'schedule_robustness_dimensions.pl' contains the list of constraints used to generate each table which, thus, need to be acquired.</p> <p>The dimensions are:</p> <p>1. Different ways to describe a task:<br> a. only the start time and duration columns are part of the table, the task duration is a pre-assigned input parameter,<br> b. only the start time and end time columns are part of the table, the task duration is a pre-assigned input parameter,<br> c. only the duration and end time columns are part of the table, the task duration is a pre-assigned input parameter,<br> d. all three columns are present in the table, the task duration is a pre-assigned input parameter,<br> e. the start time, duration and end time columns are all part of the table, and the task duration is calculated using a formula,<br> f. only the start time and end time columns are part of the table, and the task duration is calculated using a formula,<br> g. only the duration and end time columns are part of the table, and the task duration is calculated using a formula,<br> h. all three columns are present in the table, and the task duration is calculated using a formula.</p> <p>2. Different ways of expressing temporal constraints between task $i$ and its successor $j$<br> a. no temporal constraints at all,<br> b. $\textit{start}_i + \textit{cst} \leq \textit{start}_j$,<br> c. $\textit{start}_i + \textit{cst} \geq \textit{start}_j$,<br> d. $\textit{start}_i + \textit{cst}_1 \leq \textit{start}_j$, $\textit{start}_i + \textit{cst}_2 \geq \textit{start}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> e. $\textit{start}_i + \textit{cst} = \textit{start}_j$,<br> f. $\textit{start}_i + \textit{cst} \leq \textit{end\_time}_j$,<br> g. $\textit{start}_i + \textit{cst} \geq \textit{end\_time}_j$,<br> h. $\textit{start}_i + \textit{cst}_1 \leq \textit{end\_time}_j$, $\textit{start}_i + \textit{cst}_2 \geq \textit{end\_time}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> i. $\textit{start}_i + \textit{cst} = \textit{end\_time}_j$,<br> j. $\textit{end\_time}_i + \textit{cst} \leq \textit{start}_j$,<br> k. $\textit{end\_time}_i + \textit{cst} \geq \textit{start}_j$,<br> l. $\textit{end\_time}_i + \textit{cst}_1 \leq \textit{start}_j$, $\textit{end\_time}_i + \textit{cst}_2 \geq \textit{start}_j$ ($\textit{cst}_2 \neq \textit{cst}_1$),<br> m. $\textit{end\_time}_i + \textit{cst} = \textit{start}_j$,<br> n. $\textit{end\_time}_i + \textit{cst} \leq \textit{end\_time}_j$,<br> o. $\textit{end\_time}_i + \textit{cst} \geq \textit{end\_time}_j$,<br> p. $\textit{end\_time}_i + \textit{cst}_1 \leq \textit{end\_time}_j$, $\textit{end\_time}_i+\textit{cst}_2\geq\textit{end\_time}_j$ ($\textit{cst}_2\neq\textit{cst}_1$),<br> q. $\textit{end\_time}_i + \textit{cst} = \textit{end\_time}_j$.<br> Note that we only generate temporal constraints that mention the start time, i.e. 2b–2m, if the start time attribute is part of the table, i.e. not in the cases 1c or 1g.</p> <p>3. Different ways of expressing resource scheduling constraints:<br> a. no scheduling constraints at all,<br> b. a DISJUNCTIVE constraint for each subset of tasks using the same resource,<br> c. a DIFFN constraint on all tasks, so that there is no overlap between tasks that will be assigned to the same resource,<br> d. a SHIFT constraint that forces the start and end times of each task to be within the same availability period, with no gap between two consecutive availability periods,<br> e. a CALENDAR constraint that forces the start and end time of each task assigned to a given resource $r$ to fall within the same period of availability of the resource $r$,<br> f. a set of DISJUNCTIVE constraints and a SHIFT constraint,<br> g. a DIFFN and a SHIFT constraint,<br> h. a set of DISJUNCTIVE constraints and a CALENDAR constraint,<br> i. a DIFFN and a CALENDAR constraint.<br> The combination of certain temporal and resource constraints may lead to infeasibility. For instance, in a temporal constraint of type 2c, the two corresponding tasks may overlap, which is incompatible with a DISJUNCTIVE constraint between these tasks, i.e. a constraint of type 3b. Therefore, we do not generate scheduling instances that mix the dimensions 2c, 2d, 2e, 2g, 2h, 2i, 2o, 2p, and 2q, with the dimensions 3b, 3c, 3f, 3g, 3h, and 3i. Note that the number of resources generated varies according to the number of tasks, as explained in Item 5.</p> <p>4. Creating noisy columns or not:<br> a. no additional noisy columns,<br> b. three extra columns with random values standing for noise.</p> <p>5. Number of tasks and resources referenced by the schedule:<br> a. 10 tasks and 2 resources,<br> b. 100 tasks and 10 resources,<br> c. 1,000 tasks and 20 resources,<br> d. 10,000 tasks and 100 resources.</p> <p><br> For each valid combination of dimensions, we generated 10 instances of schedules.</p>
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.