Find research datasets worth reusing

Relationship-Based Threat Modeling: Evaluation Data

The full data sets used for the evaluation of our Relationship-Based Threat Modeling approach, as described in our recent submission to EnCyCriS 2022. Stef Verreydt, Laurens Sion, Koen Yskout, and Wouter Joosen. 2022. Relationship-Based Threat Modeling. In The 3rd International Workshop on Engineering and Cybersecurity of Critical Systems (EnCyCriS’22 ), May 16, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/ 3524489.3527303

opencc-by-4.0Feb 2022View details →

Supplemental data files for: Evidence for the superposition of tectonic systems in the northern Songliao Block, NE China, revealed by a 3-D electrical resistivity model

Data files for a 3-D electrical resistivity model in the northern Songliao Block, NE China, including the MT data observed there  (note the data format is for 3-D inversion using ModEM), and the preferred resistivity model. The software EMdesk from Jilin Kingti Geoexploration Tech, Ltd (Changchun, China) can be used for data analysis and modeling (http://www.kingti.net).

Model data from GRL paper "Precipitation in Northeast Mexico Primarily Controlled by the Relative Warming of Atlantic SSTs"

This folder includes monthly model data (Specified Chemistry version of the Whole Atmosphere Community Climate Model with CAM4 physics) of sea level pressure (SLP), zonal wind (U), meridional wind (V), convective precipitation rate (P), and vertical velocity (OMEGA) that were used in the Geophysical Research Letters paper "Precipitation in Northeast Mexico Primarily Controlled by the Relative Warming of Atlantic SSTs" by Wright et al. Boreal winter files include "DJFM" (December - March) in their title and boreal summer files include "JJAS" (June - September) in their title. Data from nine different experiments is included. These experiments were designed to isolate the large-scale atmospheric response to each combination of Atlantic Multidecadal sea surface temperature Variability (AMV) and Interdecadal Pacific sea surface temperature variability (IPV). Given that each climate mode can exist in a positive, negative, or neutral state, there are nine combinations of AMV and IPV. The first four letters of each filename denote the experiment ID with "A" referring to AMV, "I" referring to IPV, "C" referring to cold, "W" referring to warm, and "N" referring to neutral. For example, "ACIW_JJAS_SLP_1801-2000.nc" is the -AMV/+IPV sea level pressure during boreal summer. Vertical velocity data was lost for some experiments and we are providing what is left. Please refer to the manuscript by Wright et al. for further details on the experimental setup.

openother-openMar 2022View details →

Preprocessed Data and Pretrained Models for Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings

This is preprocessed data and pretrained models from two of our papers: "Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings," by Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, and Junichi Yamagishi. (ICASSP 2020) <a href="https://arxiv.org/abs/1910.10838">https://arxiv.org/abs/1910.10838</a>  "Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis," by Erica Cooper, Xin Wang, Yi Zhao, Yusuke Yasuda, and Junichi Yamagishi. (arXiv) <a href="https://arxiv.org/abs/2011.04839">https://arxiv.org/abs/2011.04839</a> This data is meant to be used with our open-source implementation, which can be found here:  https://github.com/nii-yamagishilab/multi-speaker-tacotron More information about the directory structure and how to use the data can be found in the READMEs on GitHub.

opencc-zeroMar 2022View details →

Data from: Mental health ecosystem of Gipuzkoa (2015) for Bayesian network modelling

This dataset include data from Mental Health network of Gipuzkoa (Spain). It is included information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript: "Almeda, N., Garcia-Alonso, C. R., Gutierrez-Colosia, M. R., Salinas-Perez, J. A., Iruin-Sanz, A., & Salvador-Carulla, L. (2022). Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem. PLoS ONE, 17(1 January), 1–16. https://doi.org/10.1371/journal.pone.0261621". This manuscript has been published in Plos One journal. This research focused on developing a formal causal model based on Bayesian network prototypes which were designed by formalizing expert knowledge (by using Expertbased Cooperative Analysis) and resulting in Direct Acyclic Graphs. The best Bayesian networks and their corresponding regression models were used to estimate the statistical ranges or confidence intervals for the dependent variable (potential effect, consequence, or output) given the independent variable values. These ranges, adjusted to delimited statistical distributions (triangular, trapezoidal and gamma), were managed by a Monte Carlo simulation engine for intervention assessment. A computer-based Decision Support System (DSS) was used to assess the status of ecosystem performance: RTE, statistical stability and entropy. Main results of the analyses pointed out that by combining causal reasoning and statistical methods, decision makers can obtain a deep view of both pre-implementing and post-implementing situations. Knowing the causal levers, it is possible to act directly to the causes in order to potentially produce de appropriate results considering the uncertainty: to provide a more balanced and integrated MH care provision in the community. In this particular case, an improvement in the outpatient workforce increases both ecosystem performance (RTE) and stability and slightly decreases entropy.

Data for Floor heating pre-on/off parameters based on Model Predictive Control feature extrapolation Paper in CLIMA2022 conference proceedings

This is a collection of time series results used to obtain all the results shown in the paper. The tags of the .csv or .mat files are self explanatory and easy to use.

In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation

Background The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted).  It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset. General description / Dataset Structure Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function. The different stenosis degrees for each degree are: ScenarioI: 100 % Area fraction (no stenosis) ScenarioII: 37,5 % Area fraction ScenarioIII: 23,4 % Area fraction ScenarioIV: 6,56 % Area fraction Data fields for each file <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>name of the scenario according to the paper, corresponds to filename</td> </tr> <tr> <td>configuration</td> <td>parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table>   <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor,  unit mmHg for type 'p' and ml/s for type 'q'  </td> </tr> <tr> <td>anatomicalPosition</td> <td> name of the corresponding anatomical position </td> </tr> </tbody> </table>

Data from: A new data-driven mathematical model dissociates attractiveness from sexual dimorphism of human faces

Human facial attractiveness is evaluated by using multiple cues. Among others, sexual dimorphism (i.e. masculinity for male faces/femininity for female faces) is an influential factor of perceived attractiveness. Since facial attractiveness is judged by incorporating sexually dimorphic traits as well as other cues, it is theoretically possible to dissociate sexual dimorphism from facial attractiveness. This study tested this by using a data-driven mathematical modelling approach. We first analysed the correlation between perceived masculinity/femininity and attractiveness ratings for 400 computer-generated male and female faces (Experiment 1) and found positive correlations between perceived femininity and attractiveness for both male and female faces. Using these results, we manipulated a set of faces along the attractiveness dimension while controlling for sexual dimorphism by orthogonalisation with data-driven mathematical models (Experiment 2). Our results revealed that perceived attractiveness and sexual dimorphism are dissociable, suggesting that there are as yet unidentified facial cues other than sexual dimorphism that contribute to facial attractiveness. Future studies can investigate the true preference of sexual dimorphism or the genuine effects of attractiveness by using well-controlled facial stimuli like those that this study generated. The findings will be of benefit to the further understanding of what makes a face attractive.

Data from: Modeling the impact of birth control policies on China's population and age: effects of delayed births and minimum birth age constraints

We consider age-structured models with an imposed refractory period between births. These models can be used to formulate alternative population control strategies to China's one-child policy. By allowing any number of births, but with an imposed delay between births, we show how the total population can be decreased and how a relatively older age distribution can be generated. This delay represents a more "continuous" form of population management for which the strict one-child policy is a limiting case. Such a policy approach could be more easily accepted by society. Our analyses provide an initial framework for studying demographics and how social constraints influence population structure. This dataset includes the raw population data for 1981 China and 2000 Japan, and some Matlab code files used to process such raw data and produce predictions.

Data for: Size spectrum model reveals importance of considering species interactions in a freshwater fisheries management context

Inland fisheries have significant cultural and economic value around the globe, providing dietary protein, income, and recreation. Consequently, methods for monitoring and managing these important fisheries are continually being refined. In marine systems, multi-species size spectrum models have been increasingly used to explore management scenarios of important fish stocks within an ecosystem-based fisheries management framework; however, these models have not been applied in freshwater systems. In this study, we developed a multi-species size spectrum model for the fish community of Lake Nipissing, a large, productive lake in Ontario, Canada. To the best of our knowledge, this is the first fully calibrated multi-species size spectrum model for an inland fishery. Using this model, we explored the impacts of different management scenarios on fish community dynamics while taking species interactions into account. Specifically, we examined how changes in fishing mortality affect: (1) species biomass; (2) community size structure; and (3) stock recovery times. We found that community dynamics following changes in fishing mortality were driven by complex interactions among species, including competition and predation. The greatest changes in biomass and community size structure were observed following changes in fishing mortality to top predators, with community size structure most strongly influenced by changes in mortality to the largest species in the community. Counter to predictions based on generation time, the smallest species in our model exhibited the longest time to recovery due to strong competition and predation. Our results demonstrate the importance of taking an ecosystem-based approach and considering species interactions in the management of inland fisheries and highlight the potential of size spectrum model use in freshwater systems.

Processed data for MethylBoostER: an XGBoost model to classify kidney cancer subtypes

This is a repository containing processed data for MethylBoostER, an XGBoost model that classifies kidney cancer subtypes. The open-source code can be found here: https://github.com/ss-lab-cancerunit/MethylBoostER.

Modeled data related to the article "Diffusive Wave Models for Operational Forecasting of Channel Routing at Continental Scale"

The data holder contains modeled data on water level and discharge from some test cases.

Data from: How well do embryo development rate models derived from laboratory data predict embryo development in sea turtle nests?

Development rate of ectothermic animals varies with temperature. Here we use data derived from laboratory constant temperature incubation experiments to formulate development rate models that can be used to model embryonic development rate in sea turtle nests. We then use a novel method for detecting the time of hatching to measure the in situ incubation period of sea turtle clutches to test the accuracy of our models in predicting the incubation period from nest temperature traces. We found that all our models overestimated the incubation period. We hypothesize three possible explanations which are not mutually exclusive for the mismatch between our modeling and empirically measured in situ incubation period: (1) a difference in the way the incubation period is calculated in laboratory data and in our field nests, (2) inaccuracies in the assumptions made by our models at high incubation temperatures where there is no empirical laboratory data, and (3) a tendency for development rate in laboratory experiments to be progressively slower as temperature decreases compared with in situ incubation.

Training dataset for "A deep learned nanowire segmentation model using synthetic data augmentation"

This image dataset contains synthetic structure images used for training the deep-learning based nanowire segmentation model presented in our work "A deep learned nanowire segmentation model using synthetic data augmentation" to be published in npj Computational materials. Detailed information can be found in the corresponding article.

Improving landscape-scale productivity estimates by integrating trait-based models and remotely-sensed foliar-trait and canopy-structural data

Assessing the impacts of anthropogenic degradation and climate change on global carbon cycling is hindered by a lack of clear, flexible, and easy-to-use productivity models along with scarce trait and productivity data for parameterizing and testing those models. We provide a simple solution: a mechanistic framework (RS-CFM) that combines remotely-sensed foliar-trait and canopy-structural data with trait-based metabolic theory to efficiently map productivity at large spatial scales. We test this framework by quantifying net primary productivity (NPP) at high-resolution (0.01-ha) in hyper-diverse Peruvian tropical forests (30,040 hectares) along a 3,322-m elevation gradient. Our analysis captures hotspots and elevational shifts in productivity more accurately and in greater detail than alternative empirical- and process-based models that use plant functional types. This result exposes how high-resolution, location-specific variation in traits and light competition drive variability in productivity, opening up possibilities to fully harness remote sensing data and reliably scale up from traits to map global productivity in a more direct, efficient, and cost-effective manner.

reference radiance model and result data for an office building near LAX

radiance scene files and metric results generated from high quality referece images for an office building near LAX. contents of lci_tempate.tar.gz: ALLSKIES:      4294 sky conditions generated by write_skydefs.py     based on LAX.epw, all conditions with solar altitude >= 2 degrees, diffnorm > 5 w/m^2 INSGEO/: (scene files that can be made into an instance using setup.sh) EXT.rad     INT.rad     LIT2.rad    baseglz.rad site.rad MATERIAL/: all.mat POINTS/: (reference view locations as sensor points) ref_views.pts RAD/: ROIglz.rad big.rad ROI/: (radiance scene polygons describing zones around each point) o1.rad o2.rad z1.rad z2.rad VIEWS/: (reference views) o1ref.vf   o1ref_r.vf o2ref.vf   o2ref_r.vf z1ref.vf   z1ref_r.vf z2ref.vf   z2ref_r.vf refs/: (source weather data and model units) lax.epw          lax.wea          sky_runorder.txt units.txt reference images generated with run_step.sh 2phs images generated with 2phase_run_auto.sh contents of ref_data.tar.gz: refimgs/ref_data_view_*_metric.txt (for each view) columns: month    day    hour    dgp    illum    ugr    ugp    dgp_t1    dgp_t2    avglum    loggcr    logpwgcr data generated with the run_step.sh file in lci_tempate.tar.gz for all sky files metrics computed with run_rayt.sh  in lci_tempate.tar.gz scripts require python3.6-3.8 on mac os or linux with the following packages installed: raytraverse v1.2.7

Hadron Shower Simulation Data for Generative Models in Fundamental Physics

Data set containing pion calorimeter showers used to train and evaluate our generative models for our Hadrons, Better, Faster, Stronger publication. Complete dataset consist of three hdf5 files: <ul> <li>pion_train_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to train the models.</li> <li>pion_eval_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to evaluate the models.</li> <li>pion_eval_steps20to90.hdf5 contains showers originating form pions with discrete energies ranging form 20 GeV to 90 GeV in steps of 10 GeV. This set was used to evaluate the models.</li> </ul> Each file contains a group called 'hcal_only'. This group has two dataset, 'energy' which contains the energy of the pions in GeV and 'layers' which contains the shower images, projected onto a 48x48x48 grid. For our model training this was reduced to 48x25x25 via slicing. The entries correspond to energy depositions in MeV.

Data from: Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model

Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and thus omit linkages between individual-level and population-level processes. We describe an integrated spatial capture-recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture-recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from < 0.75 bears/625 km2 grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53–69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multi-year integrated population model using capture-recapture and telemetry data (2008–2016; Regehr et al. 2018). Overall, the SCR-movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR-movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.

opencc-zeroNov 2022View details →

Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference

1. The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities. 2. Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species-specific estimates by leveraging detection information among species. Here we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability. 3. We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N-mixture models. Our approach substantially increases precision in estimates of abundance compared to models that do not share detection information among species. We then expand this model, and apply it to repeated detection/non-detection data collected on six species of tits (Paridae) breeding at 119 1 km2 sampling sites across a P. montanus hybrid zone in northern Switzerland (2004-2020). We find strong impacts of forest cover and elevation on population persistence and colonisation in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonisation probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies. 4. While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper, and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.