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864 results for “numerative”
Data on grammatical gender and numeral classifiers
<p>This material contains the dataset from the <a href="https://version.helsinki.fi/hals/sinnemaki/sinnemaki2019">gitlab repository</a> of the following article. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius. 2019. On the distribution and complexity of gender and numeral classifiers. In Di Garbo, Francesca, Bernhard Wälchli & Bruno Olsson (eds.), <em>Grammatical Gender and Linguistic Complexity, Volume II: World-wide Comparative Studies</em> (Studies in Diversity Linguistics 27), 133–200. Berlin: Language Science Press.</p>
Numerical convergence of model Cauchy-Characteristic Extraction and Matching (data)
<p>This dataset was used to produce the convergence plots in the paper "Numerical convergence of model Cauchy-Characteristic Extraction and Matching", as well as additional convergence tests that can be found in the repository https://github.com/ThanasisGiannakopoulos/model_CCE_CCM_public. The data can be used to reproduce the aforementioned convergence plots or for comparison against data obtained if one performs the same simulations independently.</p>
Numerical Atlas Database
<p>Database containing numerical astrophysical measurements extracted from arXiv publications, as described in Crossland et al. 2021. File is an SQLite3 database containing extracted data and article abstract texts.</p>
Data and scripts for reproducing "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow", currently under review.</p>
Probability of Detection applied to X-ray inspection using numerical simulations
<p>In this work, we apply and adapt established Probability of Detection (POD) methods on inline inspection of aluminium cylinder heads using X-ray computed tomography. The CT simulation tool SimCT [4] is used to acquire virtual images of the specimens including artificial defects, which avoids the manufacturing of calibrated defects of known type (e.g., pore, inclusion, crack etc.), size and location. One of the exemplary defects is discussed as representative result together with the generated POD curves as well as its characteristics (i.e., the minimum detected defect, the maximum missed defect, POD(a90) =0.90 and a90/95).</p>
Results of the numerical experiments of the HyPaD algorithm
<p>This dataset is supplementary material for [1]. It provides the data obtained for the numerical experiments of the Hybrid Patch Decomposition (HyPaD) algorithm [2] that are presented in that paper. The following data is contained in the different zip-archives:</p> <ul> <li><strong>SNIA - full enumeration:</strong> A collection of .mat files with the output data for each of the 35 test instances using the full enumeration approach to realize the SNIA procedure.</li> <li><strong>SNIA - dynamic boxes:</strong> A collection of .mat files with the output data for each of the 35 test instances using the dynamic boxes approach to realize the SNIA procedure.</li> <li><strong>SNIA - fixed boxes [4]:</strong> A collection of .mat files with the output data for each of the 35 test instances using the fixed boxes approach with b=4 branching steps (i.e., 16 boxes in total) to realize the SNIA procedure.</li> <li><strong>MOMIX:</strong> A collection of folders that contain the .mat files with the results for the MOMIX (and MOMIX light) algorithm [3] which have been used as a reference point in the publication.</li> <li><strong>Figures:</strong> The .fig files used in [1].</li> </ul> <p> </p> <p><strong>References:</strong></p> <ol> <li>Gabriele Eichfelder and Leo Warnow, <strong>On implementation details and numerical experiments for the HyPaD algorithm to solve multi-objective mixed-integer convex optimization problems</strong>, <a href="http://www.optimization-online.org/DB_HTML/2021/08/8538.html">Optimization Online</a>, 2021.</li> <li>Gabriele Eichfelder and Leo Warnow, <strong>A hybrid patch decomposition approach to compute an enclosure for multi-objective mixed-integer convex optimization problems</strong>, <a href="https://link.springer.com/article/10.1007/s00186-023-00828-x">Mathematical Methods of Operations Research</a>, 2021.</li> <li>Marianna De Santis, Gabriele Eichfelder, Julia Niebling and Stefan Rocktäschel, <strong>Solving Multiobjective Mixed Integer Convex Optimization Problems</strong>, <a href="https://epubs.siam.org/doi/10.1137/19M1264709">SIAM Journal on Optimization</a>, 2020.</li> </ol>
H2020 ENODISE: SISW Numerical Aeroacoustic Database Configuration B2
<p>This database contains the aeroacoustic numerical results generated by Siemens Industry Software (SISW) for the B2 configuration investigated in the framework of the European project ENODISE. In this configuration, a six-blade propeller mounted on a support is studied for two operating points with and without flow. The operating points correspond to conditions 65 and 66 of the experimental campaign carried out at Ecole Centrale of Lyon (see H2020 Enodise: Experimental dataset configuration B2 ECL, https://zenodo.org/record/7925336).</p> <p>The two investigated operating points are:</p> <ul> <li>Uinf = 0 studied numerically using detached-eddy simulation combined with finite-element method for acoustics</li> <li>Uinf = 22 m/s studied numerically using large-eddy simulation combined with finite-element method for acoustics</li> </ul> <p>The SISW aeroacoustic results have been obtained using CFD software Simcenter STAR-CCM+ coupled with acoustic software Simcenter 3D Acoustics. The hydrodynamic and acoustic interactions between the propeller and its support are taken into account in the numerical simulations.</p> <p>More information about the numerical setup and results can be found in the documentation included in the database.</p> <p>The data are saved in dat files.</p>
H2020 ENODISE: ONERA Numerical Aeroacoustic Database of Configuration C
<p>This database contains the acoustic prediction results realized by ONERA of the configuration C investigated in the framework of the European project ENODISE. In this configuration, contra-rotating propellers are investigated in static condition with and without shroud. The results presented here can be compared to the measurements carried out by von Karman Institute for Fluid Dynamics in anechoic facility ALCOVES (https://zenodo.org/record/7966211).</p> <p>The investigated operating point as calculated in the prediction is:</p> <ul> <li>Shrouded, RPM=6000, d=3cm.</li> <li>Unshrouded, RPM=6000, d=3cm.</li> </ul> <p>Predictions were obtained using the ProLB code based on a Lattice Botlzmann Method.</p> <p>Description of the database is proposed in the document <em>Database_ENODISE_ONERA_ConfigurationC.pdf.</em></p>
Numerical simulations and experimental measurements of the ULB semi-industrial furnace for the development of a Digital Twin
<p>This dataset contains the numerical and experimental data used to build the Digital Twin in Aversano et al. (https://doi.org/10.1016/j.proci.2020.06.045) and the adaptive Digital Twin in Procacci et al. (https://doi.org/10.1016/j.proci.2022.07.029).</p> <p>The directory "Numerical_data" includes 45 text files containing the data coming from the CFD simulations of the ULB furnace. <br> In each file, for each computational cell the features reported are: <br> - the cell's position in x, y, z coordinates and in meters.<br> - the cell's temperature in K. <br> - the cell's species mass fraction of NO (mf-pollut-pollutant-0), CO, OH, H2, H2O, CO2, O2, CH4.<br> The details of the setup of the numerical simulations are reported in Aversano et al.</p> <p>The numerical simulations have been computed for different values of the equivalence ratio (phi), blend of H2-CH4 (H2) and <br> inlet diameter (D).<br> The simulations for different inlet diameter where computed using different meshes, with slightly different numbers of cells.<br> In the file 'cases_parameters.csv', the value of the parameters is reported for of each simulation. There is a <br> discrepancy between the naming of the simulations in Aversano et al. and the one used in naming the files, so both are reported.</p> <p>The experimental measurements used to validate the numerical simulations can be found in the directory "Experimental_data". Each<br> file contains the value of the measured temperature along with the position in x and z in meters (y being 0). The temperature is<br> in K. The experimental uncertainty is estimated at 10 K.</p> <p>The file 'grid.vtu' contains the computational grid used to solve the CFD simulations. It can be opened using VTK-based software <br> such as Paraview or Pyvista.</p> <p>Changelog:</p> <p>- In version V1, some simulations were corrupted during data export.<br> - Added the grid file in V3</p>
H2020 ENODISE: NLR Numerical aeroacoustic database configuration B3-flap
<p>This dataset considers numerical aerodynamic and aeroacoustic data for an over-the-wing mounted propeller, denoted configuration B3-flap as defined in the H2020 ENODISE project (https://www.vki.ac.be/index.php/about-enodise).</p> <p>The dataset consists of</p> <p>- Aerodynamic data: propeller thrust and torque</p> <p>- Acoustic data: pressure Fourier modes at 1, 2, and 3 BPF</p> <p>both for the isolated propeller and the over-the-wing mounted propeller.</p> <p>An overview of the performed numerical simulation and a description of the dataset is given in the included PDF file (ENODISE_NLR_B3_flap_URANS.pdf). This file also includes the aerodynamic data.</p> <p>Experiments have also been performed by NLR for this configuration. See https://zenodo.org/record/8283630 for details and the experimental dataset.</p>
CLDF dataset derived from Bowern et al.'s "Diversity in the Numeral Systems of Australian Languages" from 2012
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bowern, Claire, and Jason Zentz (2012): Diversity in the Numeral Systems of Australian Languages. Anthropological Linguistics, 2012. http://www.jstor.org/stable/23621076</p> </blockquote>
Numerical modeling of the seismic cycle for normal and reverse faulting earthquakes in Italy
<p>Results of the numerical models expressed in terms of nodal stresses, strains and displacements.</p> <p>Data Set S1. Nodal values of the modelled displacements for the L’Aquila 2009 earthquake.</p> <p>Data Set S2. Nodal values of the modelled strain tensor for the L’Aquila 2009 earthquake.</p> <p>Data Set S3. Nodal values of the modelled stress tensor for the L’Aquila 2009 earthquake.</p> <p>Data Set S4. Nodal values of the modelled displacements for the Norcia 2016 earthquake.</p> <p>Data Set S5. Nodal values of the modelled strain tensor for the Norcia 2016 earthquake.</p> <p>Data Set S6. Nodal values of the modelled stress tensor for the Norcia 2016 earthquake.</p> <p>Data Set S7. Nodal values of the modelled displacements for the Emilia 2012 earthquake.</p> <p>Data Set S8. Nodal values of the modelled strain tensor for the Emilia 2012 earthquake.</p> <p>Data Set S9. Nodal values of the modelled stress tensor for the Emilia 2012 earthquake.</p>
Does ring current heating generate the observed O+ shell?: Numerical information for the figures
<p>This archive contains data files needed to reproduce selected figures from the following manuscript:</p> <p>Does ring current heating generate the observed O+ shell?<br> by J. Krall, J. D. Huba, and M.-C. Fok</p> <p>This manuscript was submitted to Geophyscial Research Letters in April, 2020</p> <p>In addition, this archive contains CIMI code output giving Coulomb heating losses from the ring current<br> and a SAMI3 subroutine that describes the heating function used to mimic ring current heating.</p>
Numerical code and data for: Suppressed Charge Dispersion via Resonant Tunneling in a Single-Channel Transmon
<p>The numerical code and data accompanying the analysis of Figs. 4 and 5 of Suppressed Charge Dispersion via Resonant Tunneling in a Single-Channel Transmon, Phys. Rev. Lett. (2020)</p>
NUMERICAL INVESTIGATION OF CONDENSING FLOWS IN A SUPERSONIC SEPARATOR A LOW PRESSURE.
<p>As a contribution to overcoming the challenges of numerical simulation involving the phenomenon of condensation, this work aims to investigate the nucleation of simple molecules, through the classical nucleation theory (CNT) associated with the Hill’s Moments (MoM) models . The main objective of this simulations was to investigate the numerical precision of the CFD open source code - Stanford University Unstructured(SU2) - to correctly capture the occurrence of a condensation wave, and to analyze thenucleation of liquid droplets through the moments of order zero to three</p>
Supplementary data: Quantifying and numerically representing recharge and flow components in a karstified carbonate aquifer
<p>The following data and information is related to the paper "Quantifying and numerically representing recharge and flow components in a karstified carbonate aquifer":</p> <p>Observed and estimated hydrological time series (hourly spring discharge, hourly rainfall, daily minimum and maximum air temperature and evapotranspiration) and modelling results (hourly discharges and flows) using an InfoWorks ICM pipe network model, as well as R code to compute Fourier transform / power spectrum of time series.</p> <p>See 'readme_Schuler_etal_WRR_supplementary data.pdf' for more information.</p>
Numerically Perturbed Structural Connectomes from 100 individuals in the NKI Rockland Dataset
<p>This dataset contains the derived connectomes, discriminability scores, and classification performance for structural connectomes estimated from a subset of the Nathan Kline Institute Rockland Sample dataset, and is associated with an upcoming manuscript entitled: <em>Numerical Instabilities in Analytical Pipelines Compromise the Reliability of Network Neuroscience</em>. The associated code for this project is publicly available at: <a href="https://github.com/gkpapers/2020ImpactOfInstability">https://github.com/gkpapers/2020ImpactOfInstability</a>. For any questions, please contact Gregory Kiar (gkiar07@gmail.com) or Tristan Glatard (tristan.glatard@concordia.ca).</p> <p>Below is a table of contents describing the contents of this dataset, which is followed by an excerpt from the manuscript pertaining to the contained data.</p> <ul> <li>impactofinstability_connect_dset25x2x2x20_inputs.h5 : Connectomes derived from 25 subjects, 2 sessions, 2 subsamples, and 20 MCA simulations with input perturbations.</li> <li>impactofinstability_connect_dset25x2x2x20_pipeline.h5 : Connectomes derived from 25 subjects, 2 sessions, 2 subsamples, and 20 MCA simulations with pipeline perturbations.</li> <li>impactofinstability_discrim_dset25x2x2x20_both.csv : Discriminability scores for each grouping of the 25x2x2x20 dataset.</li> <li>impactofinstability_connect+feature_dset100x1x1x20_both.h5 : Connectomes and features derived from 100 subjects, 1 sessions, 1 subsamples, and 20 MCA simulations with both perturbation types.</li> <li>impactofinstability_classif_dset100x1x1x20_both.h5 : Classification performance results for the BMI classification task on the 100x1x1x20 dataset.</li> </ul> <p><strong>Dataset</strong><br> The Nathan Kline Institute Rockland Sample (NKI-RS) dataset [1] contains high-fidelity imaging and phenotypic data from over 1,000 individuals spread across the lifespan. A subset of this dataset was chosen for each experiment to both match sample sizes presented in the original analyses and to minimize the computational burden of performing MCA. The selected subset comprises 100 individuals ranging in age from 6 – 79 with a mean of 36.8 (original: 6 – 81, mean 37.8), 60% female (original: 60%), with 52% having a BMI over 25 (original: 54%).</p> <p>Each selected individual had at least a single session of both structural T1-weighted (MPRAGE) and diffusion-weighted (DWI) MR imaging data. DWI data was acquired with 137 diffusion directions; more information regarding the acquisition of this dataset can be found in the NKI-RS data release [1].</p> <p>In addition to the 100 sessions mentioned above, 25 individuals had a second session to be used in a test-retest analysis. Two additional copies of the data for these individuals were generated, including only the odd or even diffusion directions (64 + 9 B0 volumes = 73 in either case). This allows an extra level of stability evaluation to be performed between the levels of MCA and session-level variation.</p> <p>In total, the dataset is composed of 100 diffusion-downsampled sessions of data originating from 50 acquisitions and 25 individuals for in depth stability analysis, and an additional 100 sessions of full-resolution data from 100 individuals for subsequent analyses.</p> <p><strong>Processing</strong><br> The dataset was preprocessed using a standard FSL [2] workflow consisting of eddy-current correction and alignment. The MNI152 atlas was aligned to each session of data, and the resulting transformation was applied to the DKT parcellation [3]. Downsampling the diffusion data took place after preprocessing was performed on full-resolution sessions, ensuring that an additional confound was not introduced in this process when comparing between downsampled sessions. The preprocessing described here was performed once without MCA, and thus is not being evaluated.</p> <p>Structural connectomes were generated from preprocessed data using two canonical pipelines from Dipy [4]: deterministic and probabilistic. In the deterministic pipeline, a constant solid angle model was used to estimate tensors at each voxel and streamlines were then generated using the EuDX algorithm [5]. In the probabilistic pipeline, a constrained spherical deconvolution model was fit at each voxel and streamlines were generated by iteratively sampling the resulting fiber orientation distributions. In both cases tracking occurred with 8 seeds per 3D voxel and edges were added to the graph based on the location of terminal nodes with weight determined by fiber count.</p> <p><strong>Perturbations</strong><br> All connectomes were generated with one reference execution where no perturbation was introduced in the processing. For all other executions, all floating point operations were instrumented with Monte Carlo Arithmetic (MCA) [6] through Verificarlo [7]. MCA simulates the distribution of errors implicit to all instrumented floating point operations (flop).</p> <p>MCA can be introduced in two places for each flop: before or after evaluation. Performing MCA on the inputs of an operation limits its precision, while performing MCA on the output of an operation highlights round-off errors that may be introduced. The former is referred to as Precision Bounding (PB) and the latter is called Random Rounding (RR).</p> <p>Using MCA, the execution of a pipeline may be performed many times to produce a distribution of results. Studying the distribution of these results can then lead to insights on the stability of the instrumented tools or functions. To this end, a complete software stack was instrumented with MCA and is made available on GitHub through https://github.com/gkiar/fuzzy.</p> <p>Both the RR and PB variants of MCA were used independently for all experiments. As was presented in [8], both the degree of instrumentation (i.e. number of affected libraries) and the perturbation mode have an effect on the distribution of observed results. For this work, the RR-MCA was applied across the bulk of the relevant libraries and is referred to as Pipeline Perturbation. In this case the bulk of numerical operations were affected by MCA.</p> <p>Conversely, the case in which PB-MCA was applied across the operations in a small subset of libraries is here referred to as Input Perturbation. In this case, the inputs to operations within the instrumented libraries (namely, Python and Cython) were perturbed, resulting in less frequent, data-centric perturbations. Alongside the stated theoretical differences, Input Perturbation is considerably less computationally expensive than Pipeline Perturbation.</p> <p>All perturbations were targeted the least-significant-bit for all data (t=24and t=53in float32 and float64, respectively [7]). Simulations were performed between 10 and 20 times for each pipeline execution, depending on the experiment. A detailed motivation for the number of simulations can be found in [9].</p> <p><strong>Evaluation</strong><br> The magnitude and importance of instabilities in pipelines can be considered at a number of analytical levels, namely: the induced variability of derivatives directly, the resulting downstream impact on summary statistics or features, or the ultimate change in analyses or findings. We explore the nature and severity of instabilities through each of these lenses. Unless otherwise stated, all p-values were computed using Wilcoxon signed-rank tests.</p> <p> <strong>Direct Evaluation of the Graphs</strong><br> The differences between simulated graphs was measured directly through both a direct variance quantification and a comparison to other sources of variance such as individual- and session-level differences.</p> <p>Quantification of Variability – Graphs, in the form of adjacency matrices, were compared to one another using three metrics: normalized percent deviation, Pearson correlation, and edgewise significant digits. The normalized percent deviation measure, defined in [8], scales the norm of the difference between a simulated graph and the reference execution (that without intentional perturbation) with respect to the norm of the reference graph. The purpose of this comparison is to provide insight on the scale of differences in observed graphs relative to the original signal intensity. A Pearson correlation coefficient was computed in complement to normalized percent deviation to identify the consistency of structure and not just intensity between observed graphs. Finally, the estimated number of significant digits for each edge in the graph was computed. The upper bound on significant digits is 15.7 for 64-bit floating point data.</p> <p>The percent deviation, correlation, and number of significant digits were each calculated within a single session of data, thereby removing any subject- and session-effects and providing a direct measure of the tool-introduced variability across perturbations. A distribution was formed by aggregating these individual results.</p> <p>Class-based Variability Evaluation – To gain a concrete understanding of the significance of observed variations we explore the separability of our results with respect to understood sources of variability, such as subject-, session-, and pipeline-level effects. This can be probed through Discriminability [10], a technique similar to ICC which relies on the mean of a ranked distribution of distances between observations belonging to a defined set of classes.</p> <p>Discriminability can then be interpreted as the probability that an observation belonging to a given class will be more similar to other observations within that class than observations of a different class. It is a measure of reproducibility, and is discussed in detail in [10].</p> <p>This definition allows for the exploration of deviations across arbitrarily defined classes which in practice can be any of those listed above. We combine this statistic with permutation testing to test hypotheses on whether differences between classes are statistically significant in each of these settings.</p> <p>With this in mind, three hypotheses were defined. For each setting, we state the alternate hypotheses, the variable(s) which will be used to determine class membership, and the remaining variables which may be sampled when obtaining multiple observations. Each hypothesis was tested independently for each pipeline and perturbation mode, and in every case where it is possible the hypotheses were tested using the reference executions alongside using MCA.</p> <ol> <li>Individual Variation<br> HA: Individuals are distinct from one another.<br> Class definition: Subject ID.<br> Experiments: Session (1 subsample), Direction (1 subsample), MCA (1 subsample, 1 session).</li> <li>Session Variation<br> HA: Sessions within an individual are distinct.<br> Class definition: Session ID | Subject ID.<br> Experiments: Subsample, MCA (1 subsample).</li> <li>Subsample Variation<br> HA: Direction subsamples within an acquisition are distinct.<br> Class definition: Subsample | Subject ID, Session ID.<br> Experiments: MCA.</li> </ol> <p>As a result, we tested 3 hypotheses across 6 MCA experiments and 3 reference experiments on 2 pipelines and 2 perturbation modes, resulting in a total of 30 distinct tests.</p> <p><strong> Evaluating Graph-Theoretical Metrics</strong><br> While connectomes may be used directly for some analyses, it is common practice to summarize them with structural measures, which can then be used as lower-dimensional proxies of connectivity in so-called graph-theoretical studies [11]. We explored the stability of several commonly-used univariate (graphwise) and multivariate (nodewise or edgewise) features. The features computed and subsequent methods for comparison in this section were selected to closely match those computed in [12].</p> <p>Univariate Differences – For each univariate statistic (edge count, mean clustering coefficient, global efficiency, modularity, assortativity, and mean path length) a distribution of values across all perturbations within subjects was observed. A Z-score was computed for each sample with respect to the distribution of feature values within an individual, and the proportion of "classically significant" Z-scores, i.e. corresponding to p < 0.05, was reported and aggregated across all subjects. The number of significant digits contained within an estimate derived from a single subject were calculated and aggregated.</p> <p>Multivariate Differences – In the case of both nodewise (degree distribution, clustering coefficient, betweenness centrality) and edgewise (weight distribution, connection length) features, the cumulative density functions of their distributions were evaluated over a fixed range and subsequently aggregated across individuals. The number of significant digits for each moment of these distributions (sum, mean, variance, skew, and kurtosis) were calculated across observations within a sample and aggregated.</p> <p><strong> Evaluating A Complete Analysis</strong><br> Though each of the above approaches explores the instability of derived connectomes and their features, many modern studies employ modeling or machine-learning approaches, for instance to learn brain-behavior relationships or identify differences across groups. We carried out one such study and explored the instability of its results with respect to the upstream variability of connectomes characterized in the previous sections. We performed the modeling task with a single sampled connectome per individual and repeated this sampling and modelling 20 times. We report the model performance for each sampling of the dataset and summarize its variance.</p> <p>BMI Classification – Structural changes have been linked to obesity in adolescents and adults [13]. We classified normal-weight and overweight individuals from their structural networks (using for overweight a cutoff of BMI > 25 [14]). We reduced the dimensionality of the connectomes through principal component analysis (PCA), and provided the first N-components to a logistic regression classifier for predicting BMI class membership, similar to methods shown in [14], [15]. The number of components was selected as the minimum set which explained > 90% of the variance when averaged across the training set for each fold within the cross validation of the original graphs; this resulted in a feature of 20 components. We trained the model using k-fold cross validation, with k = 2, 5, 10, and N (equivalent to leave-one-out; LOO).</p>
Supplementary data to *Benchmarking of numerical integration methods for ODE models of biological systems*
<p>This archive contains supplementary data and code for the manuscript <strong>Benchmarking of numerical integration methods for ODE models of biological systems </strong>by<strong> Städter et al. 2020</strong>. It contains</p> <ul> <li>scripts to automatically download and install all required packages and models,</li> <li>scripts to compile the models and to perform the study,</li> <li>value files containing all data underlying the analyses in the manuscript,</li> <li>scripts to generate the manuscript figures.</li> </ul> <p>There is a <strong>README.md </strong>file with further information, in particular on what scripts to execute to reproduce the study.</p>
Evidence for individual discrimination and numerical assessment in collective antipredator behaviour in wild jackdaws (Corvus monedula)
Collective responses to threats occur throughout the animal kingdom but little is known about the cognitive processes underpinning them. Antipredator mobbing is one such response. Approaching a predator may be highly risky, but the individual risk declines and the likelihood of repelling the predator increases in larger mobbing groups. The ability to appraise the number of conspecifics involved in a mobbing event could therefore facilitate strategic decisions about whether to join. Mobs are commonly initiated by recruitment calls, which may provide valuable information to guide decision-making. We tested whether the number of wild jackdaws responding to recruitment calls was influenced by the number of callers. As predicted, playbacks simulating three or five callers tended to recruit more individuals than playbacks of one caller. Recruitment also substantially increased if recruits themselves produced calls. These results suggest that jackdaws use individual vocal discrimination to assess the number of conspecifics involved in initiating mobbing events, and use this information to guide their responses. Our results show support for the use of numerical assessment in antipredator mobbing responses and highlight the need for a greater understanding of the cognitive processes involved in collective behaviour.
Numerical experiments for PDO
<p>The data named "JGR-CTRL-omega-50yr.nc", "JGR-CTRL-TS-50yr.nc", "JGR-CTRL-uwnd-50yr.nc" and "JGR-CTRL-vwnd-50yr.nc" are the output of the specific variables for the control experiment, which runs for 50 model years.The data named "JGR-SENS-omega-50yr.nc", "JGR-SENS-TS-50yr.nc", "JGR-SENS-uwnd-50yr.nc" and "JGR-SENS-vwnd-50yr.nc" are the corresponding output for the sensitive experiment, which also runs for 50 model years.</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.