Find research datasets worth reusing

Information-optimal local features automatically attract covert and overt attention

COVERT ATTENTION FOLDER  The "COVERT ATTENTION" folder contains one hundred .mat files (eight files for sixteen participants).  The file name for each subject (from S01 to S16) describes: <ul> <li>Condition: Experimental or Control</li> <li>Cue Validity: 50% or 80%</li> <li>Type of trials: Neutral or Non-Neutral</li> </ul> Each matrix of Neutral trials and Non-Neutral Control trials contains 8 columns: <ul> <li>trial number</li> <li>side of presentation of the gabor: 1=SX; 2=DX </li> <li>gabor orientation: 1 = -20°; 2 = +20°</li> <li>gabor contrast</li> <li>subject's response: 1 = -20°; 2 = +20°</li> <li>correct response: 1= right; 0=wrong</li> <li>trial type: 1= Valid trial; 2 = Invalid trial</li> <li>Reaction time</li> </ul> Each matrix of Non-Neutral Experimental trials contains 10 columns: <ul> <li>trial number</li> <li>side of presentation of the gabor: 1=SX; 2=DX </li> <li>gabor orientation: 1 = -20°; 2 = +20°</li> <li>gabor contrast</li> <li>subject's response: 1 = -20°; 2 = +20°</li> <li>correct response: 1= right; 0=wrong</li> <li>trial type: 1= Valid trial; 2 = Invalid trial</li> <li>Identification code of the specific optimal feature presented</li> <li>Identification code of the specific non-optimal feature presented</li> <li>Reaction time</li> </ul>   OVERT ATTENTION FOLDER The file name for each subject (from S01 to S16) describes: <ul> <li>Condition: Experimental or Control</li> <li>Cue Validity: 50% or 80%</li> </ul> The "OVERT ATTENTION" folder contains sixty-four .EDF files (four files for sixteen participants). The EyeLink EDF* files contain many types of data, including eye movement events (BLINKS, FIXATIONS, SACCADES), messages (including the “default” ones by Eyelink, and the custom made ones, defining the single trial’s specific properties: this type of messages starts with “Trialinfo”), button presses, and eye-data samples.  Here we have used the (calibrated) x and y position data of the right eye. Custom-made matlab scripts (available upon request to the authors) have been used to parse the data into trials, filter eye position and estimate eye velocity, select exploitable trials, detect and analyze saccades, etc. The EDF file format is a highly compressed binary format, intended for use with SR Research EyeLink viewers and applications. EDF files are not readable, they need to be transformed into a readable format (typically ASCII). A tool to transform edf files into ascii is freely available on the SR Research Support online forum site: https://download.sr-support.com/dispdoc/page25.html. The folder also contains sixty-four .mat files (four files for sixteen participants), containing the relevant information of each trial. Each matrix of the Control condition contains 5 columns: <ul> <li>trial number</li> <li>side of presentation of the place holder: 1=SX; 2=DX </li> <li>Type of trial: 0 = Neutral, 1 = Valid, 2 = Invalid</li> <li>Saccade to target: 1 correct saccade 0 no saccade</li> <li>Saccade latency</li> </ul> Each matrix of Experimental condition contains 8 columns: <ul> <li>trial number</li> <li>side of presentation of the place holder: 1=SX; 2=DX </li> <li>Identification code of the specific optimal feature presented</li> <li>Identification code of the specific non-optimal feature presented</li> <li>Type of trial: 3 = neutral, 4 = valid, 5 = invalid</li> <li>Saccade to target: 1 correct saccade 0 no saccade</li> <li>Saccade latency</li> </ul>

Code and data for Nguyen Le et al. "Robust optimal control of interacting multi-qubit systems for quantum sensing"

This is the code and simulation data for the paper "Robust optimal control of interacting multi-qubit systems for quantum sensing" by Nguyen Le et al.

opencc-zeroFeb 2022View details →

dryad36/100

No impact of biocontrol agent's predation cues on development time or size of surviving Aedes albopictus under optimal nutritional availability

Cyclopoid copepods have been applied successfully to limit populations of highly invasive Aedes albopictus mosquitoes that can transmit diseases of public health importance. However, there is concern that changes in certain mosquito traits, induced by exposure to copepod predation, might increase the risk of disease transmission. In this study, third instar Ae. albopictus larvae, hereafter referred to as "focal individuals", were exposed to Megacyclops viridis predator cues associated with both consumption of newly-hatched mosquito larvae and attacks on focal individuals. The number of newly-hatched larvae surrounding each focal larva was held constant to control for density effects on size, and the focal individual's day of pupation and wing length were recorded for each replicate. Exposing late instar Ae. albopictus to predation decreased their chances of surviving to adulthood, and three focal larvae that died in the predator treatment showed signs of melanization, indicative of wounding. Among surviving focal Ae. albopictus, no significant difference in either pupation day or wing length was observed due to copepod predation. The absence of a significant sublethal impact from M. viridis copepod predation on surviving later-stage larvae in this analysis supports the use of M. viridis as a biocontrol agent against Ae. albopictus.

Optimal Control of Renewable Energy Communities with Controllable Assets: consumption and production profiles

consumption and production profiles for cases I and II used for computing simulations in Optimal Control of Renewable Energy Communities with Controllable Assets

Co-Optimization of Reservoir and Power Systems (COREGS) for Seasonal Planning and Operation

Input and output data associated with the paper Co-Optimization of Reservoir and Power Systems (COREGS) for Seasonal Planning and Operation. Can be used with the COREGS model at https://github.com/lcford2/coregs.

opencc-by-4.0Nov 2019View details →

Haploid Diploid optimization data from eCS (EVONANO)

Data-set produced by and depicted in https://arxiv.org/abs/1911.07302

opencc-by-4.0Mar 2022View details →

Time optimal low-thrust rendezvous from an asteroid belt

This dataset allows to train regression models representing the optimal time of flight of a constant acceleration low-thrust trajectory aimed at a randezvous with a target orbiting station (placed at 1.3 Astronomical Units).The version 2 of the dataset should be used, correcting a data bias towards simpler transfers present in the previous version.The attributes, X, represent the modified equinoctial parameters (p,f,g,h,k,sin(L), cos(L)) of the spacecraft (SI units) at the start of the transfer. L is the true longitude. Different representations of the initial state are likely key to improving any model.The time of flight, Y,  is given in units of TIME=5022642.890912783sThe dataset was used for value function learning in our paper:Izzo, D. and Origer, S.: "Neural representation of a time optimal, constant acceleration rendezvous" - https://arxiv.org/abs/2203.15490where we were more interested on the effects/use of the data augmentation technique called "Backward Generation of Optimal Examples" than on the accuracy of the resulting neural model. A MAE of ~25 days is obtained on the test set, when training from the augmented dataset (vs. ~34.08 days from the non augmented one). Both these numbers can likely be improved considerably, constituting a nice challenge for the community.The python pickles can be opened as:<blockquote>with open("filename.pk", "rb") as f:     X, Y = pkl.load(f)</blockquote>else , we also provide the corresponding csv files.* training: contains 3000 items* test: contains ~1000 items* training_augmented_32_0.0001: contains 96000 items (augmented from the 3000 of training)

opencc-by-4.0Mar 2022View details →

Optimal Mowing Regime in Enhancing Biodiversity in Seasonal Floodplains along Engineered Channels, Survey data

This is the survey dataset used in the publication " Optimal Mowing Regime in Enhancing Biodiversity in Seasonal Floodplains along Engineered Channels".

opencc-by-4.0Mar 2022View details →

An optimized 4C-seq protocol based on cistrome and epigenome data in the mouse RAW264.7 macrophage cell line

In this protocol, we describe the 4C-seq method in detail using RAW264.7 cells, a mouse macrophage cell line widely used to study acute and metabolic inflammation. We specifically outline how cistrome and epigenome data can be integrated into the primer designing step, critical for the entire protocol. Because intra-TAD chromatin loops are facilitated by transcription factors and coregulators, many of which are co-localized in open chromatin regions, the binding centers of those factors can be obtained by chromatin immunoprecipitation sequencing (ChIP-seq). The binding sites of these factors reflect the coherent loci of the chromatin loops and therefore can be used as references to improve the accuracy of the 4C primers. We specifically describe the 4C protocol using examples of recently identified Ccl2 enhancer and silencer as bait.

opencc-by-4.0Jul 2021View details →

Quantum Volume circuits optimized with CPLEX on a line of qubits

We provide BIP optimized circuits for QV64, 128 and 256 for qubits in a line topology. An Notebook is provided for loading the circuits. For more details see https://arxiv.org/abs/2106.06446

opencc-by-4.0Jul 2021View details →

Determination of the Optimal Parameters for Self-Healing Efficiency of Encapsulated bacteria in Concrete Simulated Subtropical Climate

Concrete is a remarkable construction material. However, its low tensile strength makes it prone to cracking, which negatively affects its durability. To address this issue, bacterial concrete has been implemented as a self-healing alternative due to its capability to seal microcracks through microbial-induced calcium carbonate precipitation (MICCP). In this study, a bacterial strain (i.e, Bacillus Pseudiformus) was encapsulated through three different methods: encapsulation through hydrogel beads, vacuum impregnation on lightweight aggregates, and attachment to cellulose nanocrystals. Furthermore, three precursor types were used, magnesium acetate, calcium lactate, and sodium lactate were implemented. Compressive strength tests and flexural strength tests were performed on mortar specimens to characterize their mechanical properties. Once the crack was induced, samples were subjected to 28 days of wet/dry cycles in which the corresponding crack width was monitored. At the end of this period, the beams were retested to determine the strength recovery of the specimens. The results showed that the specimen groups in which calcium lactate was added to the cementitious matrix displayed the highest values in compressive strength. In terms of flexural strength, no major difference was found among the specimens. Moreover, the flexural strength recovery of the specimens did not show any significant difference as well. In terms of the healing efficiency, the sample that displayed the best results was the one containing calcium lactate as a precursor along with bacteria and yeast extract encapsulated in hydrogel beads. In addition, scanning electron microscopy (SEM) along with x-ray energy dispersive spectroscopy (EDS) was performed on the cracked specimens to characterize the healing products. Furthermore, a scale study was performed on concrete samples to determine the long-term implications of adding encapsulated bacteria along with calcium lactate and yeast extract in concrete.

opencc-by-4.0Apr 2022View details →

Application of optimized CNN to fixture layout in automotive parts. Training Dataset.

Fixture layout has aroused substantial interest in the research community during the last decades. It affects the design and production of fixtures themselves, and therefore manufacturing costs. While fixturing may seem simple in conception, it requires expertise and well-trained engineers.  A general principle, usually called the 3-2-1 locating principle, ensures fixing an object, restringing its degrees of freedom. Fixtures must always comply with the principle. While most research approaches automation of the fixture layout using optimization or rule-based frameworks, this paper proposes supervised learning. The presented framework solves the 3-2-1 locating principle for sheet metal designs based on the experience of previous designs, using automotive b-pillars as a test study. There are three main contributions. 1. A novel idea to introduce sheet metal design data in a Convolutional Neural Network (CNN), projecting the geometry over a plane. The Z coordinate transforms into gray-scale pixel values, generating a topographic map. 2. The framework reuses knowledge about fixturing to layout new workpieces. The framework is an add-in integrated with the CAD environment. 3. A hyperparameter-tuned CNN for regression generates the final output. The results show high accuracy (≈ 100%) in classifying b-pillars and fast convergence in regression, proving model usability for industrial cases.   Source Code: https://colab.research.google.com/drive/18lS83mZEdwY5S-41pvcGXgUP6GbKpun_?usp=sharing

dryad36/100

Optimization of cancer risk assessment model for PM2.5-bound PAHs Application in Shanxi of China SM

To optimize the models for cancer risk assessment of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs), six models of three types utilized with high frequency in recent years, including the USEPA recommendation (Model I), inhalation carcinogen unit risk (Models IIA–IID), and three exposure pathways (inhalation, dermal, and oral) (Model III), were selected to calculate potential cancer risk using the benzo[a]pyrene toxicity equivalent method. The results indicated no significant differences in the risk values between Models IIA and III, and no significant differences were found among Models IIB, IIC, and IID. However, there were significant differences between Models I and II, and between Models I and III. Furthermore, the optimization of Model I indicated that the population exposure parameters for each country should be used and age differences should not be disregarded. However, no significant differences were observed with respect to gender. In conclusion, Model I was superior to the other models; when age differences were not considered, Model IID (the USEPA's integrated risk information system) was preferred to replace Models IIB and IIC. Models IIA and III may overestimate the risk of carcinogens, and their parameters need to be reoptimized to accurately assess cancer risks.

opencc-zeroApr 2022View details →

Data set for manuscript "Optimal utilization of PMTCT of HIV services among adolescents under group versus focused antenatal care"

The derived variables in the data set are labeled "DER what variables they are derived from" except the variable "optimal utilization which is a composite outcome of other variables depending on HIV status as described in the manuscript. The variables "...cat" are categorized or recoded from an original numeric/ categorical variable The variables bin are used when a "cat" variable exists but further recoding is done to form a binary variable

Greening the financial regulation by optimizing credit limits for renewable energy

Numerical Data used in the empirical part of the paper

Multi-objective optimization of equation of state molecular parameters: SAFT-VR Mie models for water

Supporting document containing information on the Pareto points and thermodynamic calculations.

Optimizing Continuous Integration by Dynamic Test Selection

Continuous integration (CI) is widely used in modern software engineering. However, it is  an expensive practice. Some proposed approaches only focus on either intra- or inter-build cost reduction. In this paper, we propose an adaptive technique for dynamic test selection DTS, which combines intra- and inter-build cost reduction techniques. DTS uses build features to construct machine learning models to predict the probability of a specific build failure and transform the probability into the necessary test proportion, with respect to a selected test case prioritization technique. Based on the output of prediction model, it thus selects a prioritized test suite and a variable proportion of test cases with respect to a build. We constructed a large-scale dataset with approximately 115,000 builds, and conducted a controlled experiment using the dataset. The experiment shows that DTS outperforms existing techniques significantly. It detects 19.9% to 32.5% more failed test cases, compared with state-of-the-art techniques evaluated in the experiment. At the same time, DTS performs better than all three existing peer techniques on approximately 47% of projects. Moreover, the experiment also shows that our failure prediction model has an improvement of 0.15 in Area Under Curve (AUC), compared to prior machine learning  models.

Dataset: Impact of GST thickness on GST-loaded silicon waveguides for optimal optical switching

The following files provide the dataset of the work "Impact of GST thickness on GST-loaded silicon waveguides for optimal optical switching". The description and organization of the files are explained in the file README.txt.

Optimizing Nozzle Travel Time in Proton Therapy [Dataset]

Dataset of instances taken into account by the paper, together with solutions and achieved computational time. Manuscript submitted to 2022 IEEE-CBMS. ABSTRACT - Proton therapy is an oncological therapy that is more expensive than classical radiotherapy but that is considered the gold standard in several situations. Moreover, since there is still a limited amount of delivering facilities for this techniques, it is fundamental to increase the number of treated patients over time. The objective of this work is to offer an insight on the problem of the optimization of the part of the delivery time of a treatment plan that relates to the movements of the system. We denote it as the Nozzle Travel Time Problem (NTTP), in analogy with the Leaf Travel Time Problem (LTTP) in classical radiotherapy. In particular this work: (i) describes a mathematical model for the delivery system and formalize the optimization problem for finding the optimal sequence of movements of the system (nozzle and bed) that satisfies the covering of the prescribed irradiation directions; (ii) provides an optimization pipeline that solves the problem for instances with an amount of irradiation directions much greater than those usually employed in the clinical practice; (iii) reports preliminary results about the effects of employing two different resolution strategies within the aforementioned pipeline, that rely on an exact Traveling Salesmna Problem (TSP) solver (Concorde) and an efficient heuristic Vehicle Routing Open-source Optimization Machine (VROOM).   For each combination of system features (SF1, SF2, SF3) and distance metric (L1 and Linf), 50 runs (5 session by 10 runs) with prescribed fields from 5 to 100 (step 5) have been executed. - 'grph' folder contains GTSP and ATSP instances in GraphML and txt format. - 'vrinst' folder contains ATSP instances, expressed as VRP instances, in json format, to be fed into VROOM - 'tsps' folder contains symmetric TSP instances in TSPLIB format to be fed into Concorde - 'ress' folder contains result of optimizzation obtained by Concorde (.sol and .res formats) and VROOM (.json) all the files in these folders is named as [SF#]_[distanceMetric][[prescribedFields#]_[subrun]][Session8charsCode], so that, for example "SF3_Linf[100_9]2e275d41" represents the 100 fields result of the 9th subrun of the session with code 2e275d41, where SF3 and Linf norm have been taken into account. - 'resultsNPY' folder contains .npy file about computation time and computed travel time for both solvers