Find research datasets worth reusing

opencc-by-4.0Jul 2021View details →

Model catalogues and histograms of KSVZ axion models with multiple heavy quarks

This record contains the files pertaining to the results mentioned in the linked work Plakkot & Hoof, “Anomaly Ratio Distributions of KSVZ Axion Models with Multiple Heavy Quarks.” The contents are: <ul> <li>4 histogram files</li> <li>2 compressed <code>tar.gz</code> files containing detailed catalogues,</li> <li>2 Python scripts to extract information from the files (requires Python 3 and the <code>numpy</code>, <code>matplotlib</code> and <code>h5py</code> packages)</li> </ul> Histogram files The columns of the histogram files (listed below) correspond to the numerator of E, denominator of E, numerator of N, denominator of N, and the number of models (frequency) with that E/N ratio. The file <code>histogram_complete_NQ_1_to_9.txt</code> contains additional columns showing the number of models per entry for each NQ. <ol> <li><code>histogram_all_LP_allowed_models.txt</code> for all LP-allowed models</li> <li><code>histogram_additive_LP_allowed_models.txt</code> for LP-allowed additive models</li> <li><code>histogram_same_reps_LP_allowed_models.txt</code> for LP-allowed additive models where all new quarks live on the same representation</li> <li><code>histogram_complete_NQ_1_to_9.txt</code> for all possible models with NQ ≤ 9, regardless of the LP criterion</li> </ol> Catalogues The catalogues are contained in the two compressed <code>HDF5</code> files listed below. The catalogues contain groups for different NQ, each with subgroups for numerators and denominators of E and N, model representation (as a list). The file <code>catalogue_additive_models_NQ_1_to_9.tar.gz</code> contains additionally the energy scale at which the first LP appears. The integers in the model lists represent the quark representations (integer m for the representation rm in the text), and the negative signs indicate opposite PQ charge. <ol> <li><code>catalogue_additive_models_NQ_1_to_9.tar.gz</code> contains the catalogue for additive models with NQ ≤ 9 (9 groups, 6 subgroups)</li> <li><code>catalogue_all_LP_allowed_models.tar.gz</code> Contains catalogues for all LP-allowed models (28 groups, 5 subgroups)</li> </ol> Scripts The two scripts are: <ol> <li><code>print_histogram_info.py</code> is a sample script to extract information from the histogram files. The default choice for NQ can be adjusted by e.g. invoking <code>python print_histogram_info.py 5</code> for NQ = 5</li> <li><code>read_catalogue.py</code> is a sample script to extract data from the catalogue files (which need to be unpacked first). The default settings can be overwritten by e.g. invoking <code>python read_catalogue.py 5 file.h5</code> for NQ = 5 and the catalogues contained in <code>file.h5</code></li> </ol>

opencc-by-4.0Apr 2018View details →

A multiple model high-resolution head-related impulse response database for aided and unaided ears (HDF5 format)

The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation).  The data here is stored in HDF5 files, the SOFA files are published in a separate dataset <a href="https://zenodo.org/record/2582553">doi:10.5281/zenodo.2582553</a>.

opencc-by-4.0Oct 2021View details →

Dataset for: Modelling the filtration efficiency of a woven fabric: The role of multiple lengthscales

This is data for: "Modelling the filtration efficiency of a woven fabric: The role of multiple lengthscales", on <a href="https://arxiv.org/abs/2110.02856">arXiv</a> Files are (this is also in README file): 1) FinalFused.tif : stack of slices taken with confocal at Bristol by Ioatzin Rios de Anda. This is the imaging data of the fabric used 2) processDataTo3D_PAPER.py : Python code to analyse 1) to produce mask of fibre voxels needed for LB simulation, by Jake Wilkins 3) LBregionstack.tiff : image stack for region in LB simulations 4) masknx330ny280nz462_t10.txt : mask in right format to be read in to Palabos LB code to specify which voxels are fibre and so need bounce-back 5) Ioatzin3D.cpp : C++ code for Palabos LB. NB need Palabos LB code: https://palabos.unige.ch/, should go in directory "~/palabos-v2.2.0/examples/Ioatzin/3D ". Needs 4) 6) make_pkl.py : converts output of LB code into Python pickled format for .py codes below. 7) IoatzinDarcy_pkl.py : takes pickled output of LB code and computes Darcy k etc 8) traj2_pkledge.py : computes trajectories of particles and so filtration efficiency, needs pickled output of LBC code and 9) 9) lattice_params.yaml : parameter values for 7) and 8) 10) eff_filter_edges.txt : filtration efficiencies computed by 8) WITH inertia 11) eff_filter0Stokes.txt : filtration efficiencies computed by 8) WITHOUT inertia 12) plot_filtration.py : plots 10) and 11) 13) Final_render.mp4 : rotating animation showing region simulated by LB code, by Jake Wilkins 14) alpha_ofz.txt : alpha - fraction of fibres voxels as function of z 15) plot_justalpha.py : plots 14) 16) vtk01.vti : flow field velocity field in vti format - as used by Paraview 17) vel3D.pkl : flow field velocity field in Python's pkl format 18) slice_heatmap.py : produces heatmap of velocities in xy slice through the flow field 19) plot_sigma_streamlines.py : plots Sigma (curvature lengthscale) from 20), 21), 22), 23) 20) stream4.txt: streamline for flow field 21) stream5.txt: streamline for flow field 22) stream6.txt: streamline for flow field 23) stream7.txt: streamline for flow field 24) plot_Stokes.py : plots Stokes number as function of particle diameter 25) 0traj20.0_47.xyz : trajectory in format that Paraview can read 26) intraj20.0_47.xyz : another trajectory 27) streamlines_pkl.py : calculates streamlines, eg 20), 21), 22) and 23)  28) this README file Abstract of that work: During the COVID-19 pandemic, many millions have worn masks made of woven fabric, to reduce the risk of transmission of COVID-19. Masks are essentially air filters worn on the face, that should filter out as many of the dangerous particles as possible. Here the dangerous particles are the droplets containing virus that are exhaled by an infected person. Woven fabric is unlike the material used in standard air filters. Woven fabric consists of fibres twisted together into yarns that are then woven into fabric. There are therefore two lengthscales: the diameters of: (i) the fibre and (ii) the yarn. Standard air filters have only (i). To understand how woven fabrics filter, we have used confocal microscopy to take three dimensional images of woven fabric. We then used the image to perform Lattice Boltzmann simulations of the air flow through fabric. With this flow field we calculated the filtration efficiency for particles around a micrometre in diameter. We find that for particles in this size range, filtration efficiency is low ($\sim 10\%$) but increases with increasing particle size. These efficiencies are comparable to measurements made for fabrics. The low efficiency is due to most of the air flow being channeled through relatively large (tens of micrometres across) inter-yarn pores. So we conclude that our sampled fabric is expected to filter poorly due to the hierarchical structure of woven fabrics.

opencc-by-4.0Jul 2021View details →

Mainshock+aftershock M4.95+ seismicity forecasts derived from the Regional Earthquake Likelihood Models (RELM) and the multiplicative hybrid earthquake models developed by Rhoades et al. (2014)

Contains six mainshock+aftershock seismicity forecasts developed by the Working Group of the Regional Earthquake Likelihood Models (RELM) experiment, sixteen multiplicative hybrid forecasts created by Rhoades et al. (2014), and the 2011-2020 M4.95+ ANSS earthquake catalog for California. Six additional forecast files are included to properly conduct the comparative tests implemented in the Collaboratory for the Study of Earthquake Predictability (CSEP) testing centre. Forecasts are stored in tab separated value files with the following fields (the first row of data is shown as an example): <pre>LON_0 LON_1 LAT_0 LAT_1 DEPTH_0 DEPTH_1 MAG_0 MAG_1 RATE FLAG -125.4 -125.3 40.1 40.2 0.0 30.0 4.95 5.05 5.8499099999999998e-04 1 </pre> Forecast are described in detail by the following publications: Bird, P., and Z. Liu (2007). Seismic Hazard Inferred from Tectonics: California. Seismological  Research Letters, 78(1):37-48. Ebel, J. E., D. W. Chambers, A. L. Kafka, and J. A. Baglivo (2007). Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California. Seismological  Research Letters, 78(1): 57-65. Helmstetter, A., Y. Y. Kagan, and D. D. Jackson (2007). High-resolution Time-independent Grid-based Forecast for M >= 5 Earthquakes in California. Seismological  Research Letters, 78(1): 78-86. Holliday, J., Chen, C., Tiampo, K., Rundle, J., Turcotte, D., and Donnellan, A. (2007). A RELM earthquake forecast based on pattern informatics. Seismological Research Letters, 78(1):87–93. Kagan, Y. Y., D. D. Jackson, and Y. Rong (2007). A Testable Five-Year Forecast of Moderate and Large Earthquakes in Southern California Based on Smoothed Seismicity. Seismological  Research Letters, 78(1): 94-98. Rhoades, D.A., Gerstenberger, M.C., Christophersen, A., Zechar, J.D., Schorlemmer, D., Werner, M.J. and Jordan, T.H., 2014. Regional earthquake likelihood models II: Information gains of multiplicative hybrids. Bulletin of the Seismological Society of America, 104(6):3072-3083. Shen, Z.-K., D. D. Jackson, and Y. Y. Kagan (2007). Implications of Geodetic Strain Rate for Future Earthquakes, with a Five-Year Forecast of M5 Earthquakes in Southern California. Seismological  Research Letters, 78(1):116-120. Ward, S. (2007). Methods for evaluating earthquake potential and likelihood in and around California. Seismological Research Letters, 78(1):121–133. Wiemer, S. and Schorlemmer, D. (2007). ALM: An asperity-based likelihood model for California. Seismological Research Letters, 78(1):134–140.

opencc-by-4.0Mar 2023View details →

ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'

ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'

opencc-by-4.0Jul 2023View details →

Dataset for the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest"

Datasets to accompany the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest". Written by J. Elgy and P. D. Ledger (Keele University, 2023). The datasets include data files, meshes, and source code for generating figures from the paper. This requires the open source MPT-Calculator software available at <a href="https://github.com/MPT-Calculator/MPT-Calculator%7D">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch). The authors gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1

opencc-by-4.0Mar 2019View details →

A multiple model high-resolution head-related impulse response database for aided and unaided ears (SOFA format)

The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation).  The data here is stored in SOFA format, with the data identical to the HDF5 files in <a href="https://zenodo.org/record/1226873">doi:10.5281/zenodo.1226873</a>.

opencc-by-4.0Sep 2020View details →

Planmap's Deliverable 6.2- 3D geo-models based on multiple datasets of the Moon (implicit or explicit modelling)

Outputs of the 3D geomodelling of the shallow-surface layered deposits on the Chang'e 3 landing site. This is  based on the Yutu rover GPR channel 2B data gathered along its traverse in Sinus Iridum on the Moon.   Notebooks at https://doi.org/10.5281/zenodo.4055213

opencc-zeroOct 2023View details →

The road to success and the fences to be crossed: Considering multiple infrastructure in landscape connectivity modelling (Dataset and script)

Linear infrastructure represent a barrier to movement for many species, reducing the connectivity of the landscapes in which they reside. Of all linear infrastructure, roads and fences are two of the most ubiquitous, and are understood to reduce landscape connectivity for wildlife. However, what is often neglected consideration is a holistic approach of modelling the effects of multiple types of linear infrastructure simultaneously. Few studies have examined this, typically assessing the impacts of a singular kind of infrastructure on landscape connectivity. Therefore, the aim of this study is to address the relative importance of considering multiple kinds of linear infrastructure in landscape connectivity modelling. We utilised presence data of red deer Cervus elaphus and wild boar Sus scrofa in Doñana Biosphere Reserve (Spain) to generate a sequential approach of scenarios of landscape connectivity; firstly only with environmental variables, secondly with roads as the sole infrastructure, thirdly with the addition of fences, and finally with the further addition of fences and wildlife road-crossing structures. We found that the connectivity of the landscape was greatly affected by the addition of fences and wildlife road-crossing structures in both species, with fences in particular causing considerable alterations to estimated movement pathways. Our finding impresses a need to consider multiple different types of linear infrastructure when modelling landscape connectivity to enable a more realistic view of wildlife movement and inform mitigation and conservation measures more accurately.

opencc-by-4.0Aug 2023View details →

Supplementary Material on "Processes, Methods, and Tools in Model-based Engineering --- A Qualitative Multiple-Case Study"

This dataset provides the supplementary material that we applied for all interviews conducted in the context of our qualitative study resulting in the JSS article mentioned in the title: <ul> <li>The semi-structured interview guide,</li> <li>the codebook,</li> <li>the blank consent form that our interviewees signed,</li> <li>and the blank invitation mail that we used to ask our interviewees to participate in our study.</li> </ul>

opencc-by-4.0Sep 2018View details →

A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance

Simulated data associated with the paper "A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance". The main dataset is contained within the mermaids.csv, with columns explained within the associated README file. Epigenetic and social network information are contained within the other two datasets

opencc-by-nc-sa-4.0Aug 2019View details →

Coffee3D - 3-D models for coffee trees using multiple-view stereo

This is a set of images, odometry data, and 3-D point clouds for 8 coffee tree specimens. Images were captured by a Logitech C920 camera. Odometry data was produced using monocular visual SLAM by Mur-Artal & Tardos' <a href="https://github.com/raulmur/ORB_SLAM2">ORB-SLAM2 </a>SLAM system under <a href="https://github.com/thsant/3dmcap">3dmcap</a>. Two multiple-view stereo systems were employed in 3-D reconstruction: Furukawa & Ponce <a href="https://www.di.ens.fr/pmvs">PMVS</a> and Schöenberger's <a href="https://colmap.github.io">COLMAP</a>.

opencc-zeroNov 2022View details →

Input data to model multiple effects of large-scale deployment of grass in crop-rotations at European scale

This is the input dataset to a Python script (<a href="https://github.com/oskeng/MF-bio-grass">https://github.com/oskeng/MF-bio-grass</a>) used to model the effects of widespread deployment of grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts. For more information about the dataset and the study, see the original article: Englund, O., Mola-Yudego, B., Börjesson, P., Cederberg, C., Dimitriou, I., Scarlat, N., Berndes, G. Large-scale deployment of grass in crop rotations as a multifunctional climate mitigation strategy. GCB Bioenergy

opencc-zeroMay 2023View details →

Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases.