Find research datasets worth reusing

opencc-zeroJul 2019View details →

Data from: A multifactor coupling prediction model for the failure depth of floor rocks in fully mechanized caving mining: a numerical and in situ study

To study the mining-induced failure depth of floor rocks in a fully-mechanized mining caving field affected by different coal seam pitches, mining face lengths, burial depths and aquifer water pressures, multifactor coupled orthogonal numerical tests on the failure depth of floor rocks were conducted. The numerical results show that the failure depth of floor rocks increases with increasing mining face length, coal seam pitch and burial depth. According to the relationship between failure depth and these impact factors, a multifactor coupled prediction model for the failure depth of floor rocks was established. In addition, the in-situ measurement of the failure depth of floor rocks in the Yitang Coal Mine in Huoxi coal field in Shanxi Province, China, was performed, and the in-situ failure depths of floor rocks in the 100502 (80 m) and 100502 (180 m) mining faces were approximately 12.50~14.65 m and 17.50~19.20 m, in good agreement with the results of the multifactor prediction model. Furthermore, the sensitivity of each impact factor in the prediction model of the floor failure depth was further analysed by F-test and range analysis, and the impact order of studied factors on the floor failure depth is coal seam pitch>mining face length>burial depth>aquifer water pressure.

opencc-zeroDec 2014View details →

Data from: Computing the local field potential (LFP) from integrate-and-fire network models

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

opencc-zeroDec 2015View details →

Data from: Modeling the perception of audiovisual distance: Bayesian causal inference and other models

Studies of audiovisual perception of distance are rare. Here, visual and auditory cue interactions in distance are tested against several multisensory models, including a modified causal inference model. In this causal inference model predictions of estimate distributions are included. In our study, the audiovisual perception of distance was overall better explained by Bayesian causal inference than by other traditional models, such as sensory dominance and mandatory integration, and no interaction. Causal inference resolved with probability matching yielded the best fit to the data. Finally, we propose that sensory weights can also be estimated from causal inference. The analysis of the sensory weights allows us to obtain windows within which there is an interaction between the audiovisual stimuli. We find that the visual stimulus always contributes by more than 80% to the perception of visual distance. The visual stimulus also contributes by more than 50% to the perception of auditory distance, but only within a mobile window of interaction, which ranges from 1 to 4 m.

opencc-zeroJul 2019View details →

Data from: A time series model for estimating temporal variation in phenotypic selection on laying dates in a Dutch great tit population

[No abstract entered]

opencc-zeroDec 2013View details →

Data from: Model choice for phylogeographic inference using a large set of models

Model-based analyses are common in phylogeographic inference because they parameterize processes such as population division, gene flow and expansion that are of interest to biologists. Approximate Bayesian Computation is a model-based approach that can be customized to any empirical system and used to calculate the relative posterior probability of several models, provided that suitable models can be identified for comparison. The question of how to identify suitable models is explored using data from Plethodon idahoensis, a salamander that inhabits the North American inland northwest temperate rainforest. First, we conduct an ABC analysis using five models suggested by previous research, calculate the relative posterior probabilities, and find that a simple model of population isolation has the best fit to the data (PP = 0.70). In contrast to this subjective choice of models to include in the analysis, we also specify models in a more objective manner by simulating prior distributions for 143 models that included panmixia, population isolation, change in effective population size, migration, and range expansion. We then identify a smaller subset of models for comparison by generating an expectation of the highest posterior probability that a false model is likely to achieve due to chance and calculate the relative posterior probabilities of only those models that exceed this expected level. A model that parameterized divergence with population expansion and gene flow in one direction, offered the best fit to the P. idahoensis data (in contrast to an isolation only model from the first analysis). Our investigation demonstrates that the determination of which models to include in ABC model choice experiments is a vital component of model-based phylogeographic analysis.

opencc-zeroDec 2015View details →

Data from: Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids

In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP) and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation.

opencc-zeroJul 2022View details →

Functional and ecomorphological evolution of orbit shape in Mesozoic archosaurs is driven by body size and diet: Geometric morphometric data, 3D models (stl files), FEA models (Hypermesh, Abaqus files)

The orbit is one of several skull openings in the archosauromorph skull. Intuitively, it could be assumed that orbit shape would closely approximate the shape and size of the eyeball resulting in a predominantly circular morphology. However, a quantification of orbit shape across Archosauromorpha using a geometric morphometric approach demonstrates a large morphological diversity despite the fact that the majority of species retained a circular orbit. This morphological diversity is nearly exclusively driven by large (skull length > 1000 mm) and carnivorous species in all studied archosauromorph groups, but particularly prominently in theropod dinosaurs. While circular orbit shapes are retained in most herbivores and smaller species, as well as in juveniles and early ontogenetic stages, large carnivores adopted elliptical and keyhole-shaped orbits. Biomechanical modeling using finite element analysis reveals that these morphologies are beneficial in mitigating and dissipating feeding-induced stresses without additional reinforcement of the bony structure of the skull.

Supplementary data: "Evaluación de Tierras: Elaboración de un modelo de aptitud de uso agrícola para Kernza (Thinopyrum intermedium) en agroecosistemas con distinto grado de artificialización en el Partido de Azul, Provincia de Buenos Aires, Argentina"/"Land Evaluation: an agricultural suitability model for Kernza (Thinopyrum intermedium) in agroecosystems with different land uses intensities in Azul distric, Buenos Aires, Argentina.

This is the data generated for the achievement of the objectives of my thesis and publications.

Visualization Data for Topic Modeling in the Field of Biotechnology (Bachelor's Thesis)

Visualization Data for Topic Modeling in the Field of Biotechnology (Bachelor's Thesis at UPM)

Base Data for Damped Wave Net Models in PortHamiltonianBenchmarkSystems

Mat-files for default damped wave net configurations

opencc-zeroAug 2022View details →

Spectrum data and thermal model data of comet/103P (v1.0)

Hyperactive comets have attracted attention due to their high water production rate with an unclear mechanism, though some hypotheses are proposed to explain it. Based on the thermal theories of the comet nuclei, this paper studied a comet surface thermal model considering the sublimation of H2O. In this paper, a method for solving the sublimation rate of water ice by infrared spectra is proposed. The method adopts the assumption of comet nucleus surface temperature roughness and uses the numerical solution of the Fredholm equation. We use the HRI-IR spectrum (1.05-4.8 μm) data by EPOXI to analyze the pixel water sublimation rate of hyperactive comet 103P/Hartley2. The results show that sublimation exists in most areas of the surface with or without surface roughness, and most of the water production rate (70% ~ 90%) may come from the comet nucleus. According to the sublimation law, it is estimated that the sublimation temperature of water ice on 103P is above 180K. If the dust-to-ice volume ratio is 3:1, the sublimation temperature is about 200-210K, which indicates that the water ice may sublimate underneath. This may explain why exposed water ice on the surface can hardly be observed while the active fraction of this comet is up to 100%.

Supplementary data: "Evaluación de Tierras: Elaboración de un modelo de aptitud de uso agrícola para Kernza (Thinopyrum intermedium) en agroecosistemas con distinto grado de artificialización en el Partido de Azul, Provincia de Buenos Aires, Argentina" (II) /"Land Evaluation: an agricultural suitability model for Kernza (Thinopyrum intermedium) in agroecosystems with different land uses intensities in Azul distric, Buenos Aires, Argentina. (II)

Decision trees for the land suitability model for Kernza.

opencc-by-4.0Aug 2022View details →

Data for Herb-paths, a network and statistical model to explore health-beneficial effects of herbs and herbal constituents

Results data for the manuscript "Herb-paths, a network and statistical model to explore health-beneficial effects of herbs and herbal constituents".

opencc-by-4.0Aug 2022View details →

Laboratory evidence supporting a mechanical model describing the dynamic formation of fault damage asymmetry [DATA]

Data and metadata for the publication "Laboratory evidence supporting a mechanical model describing the dynamic formation of fault damage asymmetry", published in Geophysical Research Letters.

opencc-by-4.0Aug 2022View details →

Adaptive Empirical Modeling Data for "Thin Current Sheet Formation and Reconnection at $X\sim$-10\,R$_E$ during the Main Phase of a Magnetic Storm"

The ZIP file contains results of AM03 model run for the magnetic storm event on 17 June 2012.

opencc-ncJun 2009View details →

EIAH data model: semantic interoperability between distributed digital repositories

The authors described their information architecture project aimed at improving access to the Encyclopaedia of Iranian architectural history (EIAH) by signalling relationships between concepts and between concepts and documents. The outcome will be presented in a semantic portal or might be used for complex search queries by end users.

opencc-by-4.0Oct 2022View details →

Base Data for port-Hamiltonian Elasticity Models

Base data for the creation of port-Hamiltonian elasticity models.

opencc-zeroOct 2022View details →

Data for: Modeling climate-driven range shifts in populations of two bird species limited by habitat independent of climate

Ranges of species around the world are expected to contract in response to climate change. Species distribution models (SDMs) are a powerful tool for predicting changes in habitat availability, but the variables selected to create SDMs influence their performance. In addition to climate, habitat characteristics and species traits can play a role in predicted species distribution. In this paper, we consider how variable selection influences the accuracy of SDMs when applied to isolated subpopulations of two widely distributed bird species: the great gray owl (Strix nebulosa) and the willow flycatcher (Empidonax traillii). In the Sierra Nevada of California, these species are restricted largely to discrete patches of meadow habitat within a forest matrix, providing the potential to identify specific locations to target conservation efforts. We contrast predictions made by SDMs that consider climatic variables alone with those that incorporate both climate and geophysical variables. Adding geophysical variables resulted in differing model predictions. For willow flycatchers, adding geophysical variables improved predictive performance. In the case of great gray owls, models with and without geophysical variables had nearly identical performance under historical conditions but differed starkly in their predictions. The full model (climatic and geophysical variables) predicted habitat availability to decrease moderately, whereas the climate-only model predicted nearly complete loss of favorable habitat by 2099. The climate-only model is consistent with expectations based on previous SDMs of birds across North America, but previous studies also assume homogeneity in species traits and range-wide habitat requirements. The full model appears more consistent with recent trends in great gray owl numbers in the Sierra Nevada specifically, where the population has remained relatively stable over recent decades. Given contradictions in our model predictions, care should be taken when trying to apply similar SDM models to other systems.

opencc-by-4.0Oct 2022View details →

MAgPIE model input data sets: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections

These MAgPIE input data sets include harmonized crop yield projections from several crop models (9 crop models and 5 climate models). Additionally, regional, validation, and calibration data sets are also reported.