Find research datasets worth reusing

opencc-by-4.0Oct 2021View details →

Scripts to perform dynamic optimization of a model of invasive aspergillosis

Matlab scripts to perform optimization and analyses of a dynamic optimization model of invasive aspergillosis: "Dynamic optimization reveals alveolar epithelial cells as key mediators of host defense in invasive aspergillosis" Jan Ewald, Flora Rivieccio, Luká&scaron; Radosa, Stefan Schuster, Axel A. Brakhage, Christoph Kaleta https://doi.org/10.1101/2021.05.12.443764 File structure: --- Core optimization files --- -- simulation.cpp -- Description of ODE model in 'C', used by solver to calculate the time course given the parameters, initial states and control variables -- simulation.mexa64 -- Compiled mex-File from simulation.cpp used as interface by MATLAB to 'C' files. -- optFun.m -- Definition of the objective function. Uses a control vector 'u' to calculate the, objective function value F, the time course of state variables 'x' and gradients used by the solver to determine the optimal control of 'u'. -- single_opt.m -- Function which performs an optimization run for a given parameter set and a randomized initial solution u0. Optimization can be conducted by SNOPT or in conjunction with IPOPT. Both solver are not provided here (proprietary). --- Additional files --- -- solve_with_ipopt.m -- Configuration file and interface to call the solver IPOPT. -- lognorm_mu.m -- Function to provide log-normal distributed samples given the standard deviation, reference value (mode of distribution) and number of samples which should be calculated. -- mice_param_alv.m -- Reference parameter set of the model for mice as host organism and cell numbers normalized per alveolus. -- parameter_model.m -- Definition of parameter container to handle interface to optFun-call by solvers which do not allow parameter in function call. --- Optimization Runs --- -- rand_params.m -- Performs optimization runs for multiple randomized parameter sets and several scenarios to determine parameter sensitivity. -- doseresponse.m -- Performs optimization runs for a sequnce of initial conidial dosages to determine the dose response of the system. --- Scripts for post-analysis --- -- rand_analysis.m -- Loads the data created by the optimization runs (rand_params.m) and performs parameter sensitivity analysis as well as produces plots for analysis related to parameter influence. -- doseanalysis.m -- Loads the data created by the optimization runs (doseresponse.m ) and produces dose response curves as well as additional plots and analyses. --- Optimization output files --- -- rand_param_out/ -- Folder contains optimization results as MATLAB session-file for randomized parameters used for parameter sensitivity analysis. -- dose_out/ -- Folder contains optimization results as MATLAB session-file for dose response curve calculation.

opencc-by-4.0Oct 2021View details →

Advanced Optimal Sensor Placement for Kalman-based multiple-input estimation

Data set for journal publication "Advanced Optimal Sensor Placement for Kalman-based input estimation".

opencc-by-4.0Dec 2018View details →

Dataset for "Convex modeling of pumps in order to optimize their energy use" article

This is the data set about pump optimization used in the submitted article "Convex modeling of pumps in order to optimize their energy use" .  The proposed optimization method is shown, and also the two methods to which it is compared to.

opencc-by-4.0Oct 2022View details →

FPCA - From mobile app-based crowdsourcing to crowd-trusted food price estimates in Nigeria: pre-processing and post-sampling strategy for optimal statistical inference

Timely and reliable monitoring of commodity food prices is an essential requirement for the assessment of market and food security risks and the establishment of early warning systems, especially in developing economies. However, data from regional or national systems for tracking changes of food prices in sub-Saharan Africa lacks the temporal or spatial richness and is often insufficient to inform targeted interventions. In addition to limited opportunity for [near-]real-time assessment of food prices, various stages in the commodity supply chain are mostly unrepresented, thereby limiting insights on stage-related price evolution. Yet, governments and market stakeholders rely on commodity price data to make decisions on appropriate interventions or commodity-focused investments. Recent rapid technological development indicates that digital devices and connectivity services are becoming affordable for many, including in remote areas of developing economies. This offers a great opportunity both for the harvesting of price data (via new data collection methodologies, such as crowdsourcing/crowdsensing — i.e. citizen-generated data — using mobile apps/devices), and for disseminating it (via web dashboards or other means) to provide real-time data that can support decisions at various levels and related policy-making processes. However, market information that aims at improving the functioning of markets and supply chains requires a continuous data flow as well as quality, accessibility and trust. More data does not necessarily translate into better information. Citizen-based data-generation systems are often confronted by challenges related to data quality and citizen participation, which may be further complicated by the volume of data generated compared to traditional approaches. Following the food price hikes during the first noughties of the 21st century, the European Commission's Joint Research Centre (JRC) started working on innovative methodologies for real-time food price data collection and analysis in developing countries. The work carried out so far includes a pilot initiative to crowdsource data from selected markets across several African countries, two workshops (with relevant stakeholders and experts), and the development of a spatial statistical quality methodology to facilitate the best possible exploitation of geo-located data. Based on the latter, the JRC designed the Food Price Crowdsourcing Africa (FPCA) project and implemented it within two states in Northern Nigeria. The FPCA is a credible methodology, based on the voluntary provision of data by a crowd (people living in urban, suburban, and rural areas) using a mobile app, leveraging monetary and non-monetary incentives to enhance contribution, which makes it possible to collect, analyse and validate, and disseminate staple food price data in real time across market segments. The granularity and high frequency of the crowdsourcing data open the door to real-time space-time analysis, which can be essential for policy and decision making and rapid response on specific geographic regions. <a href="https://datam.jrc.ec.europa.eu/datam/perm/news/870?rdr=1666109837893">Link to the project</a>

opencc-by-4.0Oct 2022View details →

Reaction Optimizer Spreadsheet tool

Synergistic data analysis tool for green chemistry. Includes visual kinetic analysis (variable time normalization analysis), a linear solvation energy relationship calculator to determine solvent effects, and green chemistry metrics.

opencc-zeroNov 2022View details →

The giant clam photosymbiosis is a physically optimized photoconversion system for the most intense sunlight on Earth

Giant clams are photosymbiotic with unicellular algae ("zooxanthellae") organized in the clam's mantle tissue. This tissue has an especially low albedo for a photosynthetic system, generally less than 10\% at all visible wavelengths. This efficient absorbance of light occurs in the ecological context of the high solar irradiances in intertidal habitats near the equator. At these light levels, photosynthetic systems typically adapt to absorb less light in order to prevent radiative damage to chloroplasts. Giant clams are therefore unusual. If the giant clam photosymbiosis proves to be simultaneously efficient at absorption and at phototransduction at these irradiances, they are potentially remarkably productive and an important source of bioinspiration. We showed previously that the clams organize algae into vertical pillars in the mantle tissue. The clams' iridocytes, or optically structured skin cells on the surface of the tissue, then function to evenly distribute incoming solar irradiance along the vertical faces of the pillars. The result is that zooxanthellae in the system absorb solar power at lower rates than that of incoming solar flux. The overall energetic performance of this phtooconversion scheme has, however, been difficult to characterize given the complex three-dimensional structure and the fact that it is coupled to a much more voluminous, respiring animal. Here we use a combination of photochemical characterization and new quantitative modeling of data from the literature to estimate the photochemical efficiency as a function of incoming irradiance of the initial electron-transfer events. Our approach is to consider the clam mantle tissue in isolation as a meta-material for photoconversion. To do this, we developed a method to directly measure the system's photochemical efficiency with spatial resolution of 10's of microns using optical microprobes threaded through the tissue. These experimental efficiency data then serve as ground-truthing for a subsequent reanalysis of photosynthesis-irradiance curves of clams taken from the literature. For this quantitative re-analysis, we incorporated the clam system's quantum efficiency as a function of irradiance per cell into a Monte Carlo model of radiative transfer among cells to find the tissue's area-specific oxygen evolution apart from any sinks. We found that cells located within the dense clam system had fluorescence transients (i.e., Kautsky curves), a direct measure of the efficiency of PS II) that were very slow and of low intensity, particularly for a dense system, consistent with photochemical efficiencies generally greater than 50\% and often greater than 90\%. When incorporated into a larger computational model, we found that mature Tridacnid clams can efficiently perform photoconversion of light energy into chemical energy at light intensities many times more intense than the maximum time-averaged environmental radiance, or even the solar constant. The intensities to which the clam is adapted, however, can be found in strong wave-lensed pulses of irradiance that are characteristic of the clams' habitats. This surprising result makes sense if the system has evolved to both avoid damage from and utilize the power in the intense pulses of light that result from wave-lensing. Our model predicts that by evolving to compensate for the intense pulses of solar energy produced by wave-lensing, the clam system can perform photochemical conversion of radiation at intensities many times greater than the solar constant at around 90\% quantum efficiency. This result in turn suggests a strategy for organic, engineered materials performing photoconversion under solar concentration.

opencc-by-4.0May 2021View details →

Data repository of FDTD GPR antenna optimization by Sam Stadler

This is the data repository for the article by Sam Stadler and Jan Igel by the name "Developing realistic FDTD GPR antenna surrogates via full-waveform inversion by means of particle swarm optimization". In this repository, all the measurement data, simulated data, and high-resolution figures are stored for further use.

opencc-zeroNov 2022View details →

Data from: Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents

Uncertainty in reconstruction accuracy is a critical problem faced in the current traffic accident reconstruction process. The purpose of this study is to explore the use of an improved optimization algorithm combined with MAthematical DYnamic MOdels (MADYMO) multibody simulations and crash data to conduct accurate reconstructions of vehicle–pedestrian accidents. The performance of three commonly employed multiobjective optimization algorithms, including nondominated sorting genetic algorithm-II (NSGA-II), neighbourhood cultivation genetic algorithm (NCGA) and multiobjective particle swarm optimization (MOPSO) were compared and evaluated. The effects of the number of objective functions, the selection of different objective functions and the optimal number of iterations are also investigated. The present study indicated that NSGA-II had better convergence and generated more noninferior solutions and better final solutions than NCGA and MOPSO. And multibody simulations coupled with optimization algorithms can be used to accurately reconstruct vehicle-pedestrian collisions.

opencc-by-4.0Nov 2022View details →

Bearing capacity semi-empirical methods inputs for optimization

These datasets presents inputs for bearing capacity prediction methods. These methods are four well-known semi-empirical models for predicting bearing capacity of piles. The data was collected from the works of Lobo (2005), Vianna (2000) and Jr. (1988) and includes load tests and SPT measures taken from severam Brazilian regions.  The suffix 'DQ' and 'MH', 'Av' and 'Tx' represents which method this input was obtained from, corresponding respectively to Decourt and Quaresma (1978) and Meyerhof (1983). The columns indexes represents: N_pile - Pile number (for reference); N_l - SPT result for the pile lenght; N_p - SPT result for the pile tip L - pile lenght; D - pile diameter; U - pile perimeter; A - pile section area Qu - Pile bearing capacity, obtained through NBR 6122 load test. When using this dataset, please cite the following paper: //<a href="http://soilsandrocks.com/sr-2021-074921">soilsandrocks.com/sr-2021-074921</a> DOI: 10.28927/SR.2021.074921

opengpl-2.0Sep 2022View details →

The role of vegetation optimality in the Budyko framework

In this study, we test how vegetation optimality explains convergence on the Budyko-curve. This is done for five study sites along the North Australian Tropical Transect, five catchments in Australia and a selection of catchments of the CAMELS-dataset. We test the following hypotheses: - Model simulations based on vegetation optimality lead to a better reproduction of the empirical Budyko curve than model simulations without self-optimized vegetation. - The empirical parameter n stays constant as climate changes, as long as vegetation cover and rooting depths stay constant. - Changes in n values are a result of slowly varying, long-term vegetation properties. How to use this project This project contains all pre- and post-processing scripts for the model runs to test our hypotheses. All final figures are in the notebooks, as well as the supplementary analyses. These scripts can be re-used for other applications. This is also a Renku project. Hence, to use this repository for your own analysis, create a free login for renku, then fork the project and modify it to your liking. The simplest way to start this project is right from the Renku platform - just click on the `Environments` tab and start a new session. This will start an interactive environment right in your browser. To work with the project anywhere outside the Renku platform, click the `Settings` tab where you will find the git repo URLs - use `git` to clone the project on whichever machine you want. Python and conda packages are in `requirements.txt` and `environment.yml`. Please consult the various documentation sources for renkulab(https://renkulab.io/).

Large-area periodically-poled lithium niobate wafer stacks optimized for high-energy narrowband terahertz generation - Dataset

Dataset for the publication "Large-area periodically-poled lithium niobate wafer stacks optimized for high-energy narrowband terahertz generation".

opencc-zeroDec 2022View details →

Combining sampling gear to optimally inventory species highlights the efficiency of eDNA metabarcoding

Biodiversity surveys may require the use of multiple types of sampling gear to maximize the efficiency of species detections, yet few studies have investigated how to optimally distribute effort among gear. In this study, we conducted eDNA metabarcoding and capture-based sampling surveys (electrofishing, fyke netting, gillnetting, and seining) to sample fish species richness in a large northern temperate lake. We evaluated the success of the sampling methods individually and in combination to determine the allocation of effort and cost across sampling gear that provides the optimal approach for lake-wide species inventories. We found that eDNA metabarcoding detected more species than any other sampling method, including 11 species that were not detected with any capture-based approach. Optimal gear combination analyses revealed that detected species richness is maximized when most of the effort or budget is allocated to eDNA metabarcoding, with smaller allocations to seining and fyke netting. eDNA metabarcoding and capture sampling gear showed similar patterns of spatial heterogeneity in the fish community across habitat types, with pelagic samples forming a group that was distinct from nearshore samples. Our results indicate that eDNA metabarcoding is a rapid and cost-efficient tool for biodiversity monitoring and that assessing the complementarity of multiple sampling types can inform the development of optimal approaches for measuring fish species richness.

Switching between Numerical Black-box Optimization Algorithms with Warm-starting Policies - Reproduction artifacts

# Reproducibility instructions This document details the steps to reproduce the results presented in the paper 'Chaining of Numerical Black-box Algorithms: Investigating the Impact of Warm-Starting and the Switching Point' ## Algorithm code The code for the individual algorithms and the switching routines is included in the 'dynas.zip' folder.   ## Data collection To collect the performance data for the static algorithms, the file 'data_collection.py' can be used. This file runs all 5 static algorithm on the function suite used in the paper, using the 'ioh' package. To run it, please modify the 'ioh_dir' variable to a place where the data should be stored.  The resulting data is made available under the 'static.zip' file. ## Processing code The code which processes the performance data into the list of usecases is part of the 'Processing_R' notebook.  This results in a set of rds-files with the data in IOHanalyzer's internal format, and in a csv file ('Split_performances_relative.csv') with the selected usecases.  This csv file can then be read in using the 'visualization' notebook, which can generate Figure 1 from the paper. This notebook can then be used to count the specific usecases, which is the data shown in Table 1. The csv-file containing the selected usecases ('usecases.csv') is also generated here. The 'usecases' csv file can be used in the 'Data_collection_switch' notebook to run the dynamic configurations. This results in the 'Dynamic.zip' data, which can again be read using IOHanalyzer in the 'Processing_R' notebook. This then generates several csv-files, for both the static and dynamic case, containing the per-run hitting times ('RT_samples.zip'). These are then used in the 'Visualization' notebook to generate figure 3 and 4, as well as table 2.  ## Countour plots The code for generating Figure 5 is included in the end of the 'Visualization' notebook. ## Performance comparison plot Figure 6 has been generated using IOHanalyzer web-interface (iohanalyzer.liacs.nl), by uploading the files in 'cma_bfgs.zip'.  ## Switch point impact plot Figure 7 has been generated using the 'split_experiment' notebook

opencc-by-4.0Feb 2022View details →

Data for LEELO-LA (Long-term Energy Expansion Linear Optimization - Latin America)

This repository contains the data for LEELO-LA (Long-term Energy Expansion Linear Optimization - Latin America)

Optimization Framework for Temporal Interference Current Tibial Nerve Stimulation in Tibial Nerves based on In-Silico Studies

Online Resource 1: All MR-based 3D models and cross-sections. n = 29.   Online Resource 2: Comparison of changes in electrode pairs according to anatomical differences (independent sample t-test for Online Resource 2, *p<0.0001) When Electrode opt TI was applied, the model in which the combination of electrodes was changed relative to Parallel opt TI was referred to as the Change group, and the model that was unaltered was called the Non-change group. In the case of the Change group, PR values increased in Electrode opt compared with those in the Parallel opt group. When comparing the ratio of nerve depth to the total length of the model section, we confirmed the Change group exhibited a smaller ratio. Since the sample size of each group was different, the difference of average in the PR value of each group was evaluated using an independent sample t-test. Purple dots present raw data. Mean and standard deviation of the changes are shown in black.

Data for the paper "Direct Microstability Optimization of Stellarator Devices"

This archive contains data and source code used for the paper "Direct Microstability Optimization of Stellarator Devices".

Dataset for Comparing Three Generations of D-Wave Quantum Annealers for Minor Embedded Combinatorial Optimization Problems

Dataset for the paper titled "Comparing Three Generations of D-Wave Quantum Annealers for Minor Embedded Combinatorial Optimization Problems" LA-UR-23-20367

opencc-by-4.0Feb 2023View details →

Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms - Code and Data

This repository contains the code and data for reproducibility of the paper 'Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms'.  The following files are included: - TA.zip and DEM.zip: The code used for the TA and DEM algorithms respectively. - experiment_runner: Python file which was used to run the black-box optimization algorithms on the discrepancy problems from IOHexperimenter (requires package 'ioh', version 0.3.6 or higher). This generates data in IOH-format, which is included in 'raw_data.zip' - process_stardicr.R: R script which uses IOHanalyzer to extract the performance from the raw data into csv files for visualization. The resulting csvs are included in 'csv_with_pos' for the final results including the corresponding coordinates and 'csv_perf.zip', which contains the convergence information. - Found_Values: The discrepancy values found by TA and DEM, separated by sampler. - A csv file of the relative performance of each of the optimizers compared to the values found by TA is included in 'final_precision_table.csv' - Plot_StarDiscr: the python notebook used to generate all figures, except figure 3 which was created using the IOHanalyzer GUI (iohanalyzer.liacs.nl). The full dataset is available on the website under the source 'star_discrepancy' - Figures: some additional figures which were not included in the paper because of space constraints + higher quality versions of some of the landscape plots.

Optimal policy for uncertainty estimation concurrent with decision making

Dataset for "Optimal policy for uncertainty estimation concurrent with decision making". The dataset should be merged into the code folder, thus the program can work.