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1,045 results for “Generated Data”
Assessment of Fair Trade education programs in France: data 2021 from the control group and those from the two experimental fields (the Fair Generation scheme and the Fair Trade Universities Label)
<p><span>see the technical report (period 2019-2021) on researchgate:</span></p> <p><span><a href="https://www.researchgate.net/publication/380823820_Evaluation_of_the_fair-trade_education_programs_Results_of_the_first_phase_of_the_Fair_Future_program">(PDF) Evaluation of the fair-trade education programs. Results of the first phase of the Fair Future program (researchgate.net)</a></span></p>
"Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", scripts and data
<p>This repository contains the data, scripts and results for the paper "Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", https://doi.org/10.1016/j.esr.2024.101329.</p> <p>Results in the paper are divided into three sections, corresponding to the numbers of the folders inside this dataset. They are described as follows:</p> <p>1 - EU policy impact: What is the impact on the Italian electricity of the european proposal of cutting power demand and shifting it during peak hours on gas consumption, system costs and emissions?</p> <p>2 - Gas cost sensitivity: Which would be Italy’s most convenient power system considering different gas prices?</p> <p>3 - DSM in mitigation: What could be the role of demand side measures in power systems with a high penetration of RES?</p>
Generative Data by Generative Agents - First Simulation Data
<p>"Generative Data by Generative Agents" is a project that aims to create a simulation architecture for virtual agents with LLMs, based on the article “Generative Agents: Interactive Simulacra of Human Behavior” (Park et. all, 2023). This simulation aims to subsequently generate synthetic data from the agent.</p> <p>This publication consists of data related to the first simulation test, with the initial simulation parameters, logs obtained and simulation summary.</p> <p>The project repository contains the simulation code and more information.</p>
Data from an empirical study on generative artificial intelligence painting
<p>This is an empirical study on generative artificial intelligence painting, including research methods, basic data of 17 subjects and their AI art painting, and quantitative data after their data analysis.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 7 . Test Accuracy with prograess generation
<p>In the training phase, the neural network weights errors are minimized and network design<br> problem which the objective function to an acceptable level. In test step we have better results<br> because weights of neural network are adjusted by genetic algorithm and back propagation method.<br> Of course achievement to accuracy with 83.5% is reason using of good feature with minimum error.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 6. Training Accuracy with prograess generation
<p>There are many features will reduce the efficiency of the algorithm and its complexity.<br> Among the methods for selecting the appropriate features, the algorithm is a GA.<br> One of the important parameters for testing methods is accuracy rate on progress generation.<br> In fact accuracy is reverse error in algorithm results. As reader can compare the results of our paper<br> with another works. Figure 4 show that accuracy present for Training step. We achieve to best<br> answers of 800 generation to after generation.</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 1. The AGLO online assessment approach
<p>In Figure 1, we present the lifetime of AGLOs in the context of online student assessment following a set of steps. In the backend, the tutor develops an AGLO model respecting a predefined meta-model. The model is intuitive, it has a few sections where symbols are defined using formulas and random numbers and then used in a section of a presentation for the student. When such models are created they are stored in a storage facility like a database to be selected by the student through the web application frontend. In the frontend, the students access the web application using a web browser from a workstation, tablet or smartphone. In the assessment process, the student will access several AGLOs. At this step, the accessed AGLOs are instantiated with random numbers, formulas are evaluated to fulfill the designed learning or testing scenario and to create the presentation content for the student. Nevertheless, the instantiated symbols will be used for the automatic assessment of the answers correctness</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 4. Online test assessment example
<p>Thus, applying these restrictions the computed solution is C, E, G, J, L, H, I and is unique. Node C is the starting node since it is the first from the lexicographical point of view. The first step CE is the only choice coping with the restrictions from the [CE, CG, and CJ] edges. Next, EG is the first edge in the list of [EG, EJ]. The next step is GJ which is the only choice. Edge JL is another unique choice. Edge LH is the next step from the list [LH, LI]. Finally, the last edge is obtained by backtracking to node L and then taking edge LI. These restrictions allow us to drive the student to build only one solution from the possible set of solutions. This will determine an easier way of comparing the student’s answer with the answer of the computer. Another more general solution is to use validation functions which require implementation in domain libraries written in JavaScript. </p>
Data Set Used in Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS
<p>This document contains the data set used for the study Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS that is currently in submission.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 7. Avatar 3D model generation
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data
<p>In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 16. Failed Generations
<p>For some images there is a chance to get stuck where fitness function maxed, but we are not near to the original image like in figure 16, both row and column fitness matched. But image lost a key portion from original image, in these cases we should increase the weight of fitness function Fx(X), which will solve the issue.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 12. After few generation
<p>In figure 11 it is the initial population showed and figure 12 the population started to change and figure 13 we reached a convergence.</p>
Data and code for article "Mid-infrared frequency comb via coherent dispersive wave generation in silicon nitride nano-photonic waveguides"
<p>This dataset contains the data presented in the figures of the article "Mid-infrared frequency comb via coherent dispersive wave generation in silicon nitride nano-photonic waveguides" (doi:10.1038/s41566-018-0144-1).</p> <p>The raw data in figures (curved plots) is packaged as an independent OriginLab project file (.opj). </p> <p>The layout of the design of the silicon nitride nano-photonic waveguide is presented. Fabrication process card (shown as a diagram) is provided as well.</p> <p>The source code for simulations presented in the article is also presented.</p>
Supporting Data for "Proton transfer in nonpolar solvents: an approach to generate electrolytes in aprotic media" (Phys. Chem. Chem. Phys., doi:10.1039/c8cp02349b)
<p>Conductivity data for all cation-anion pairs [units given in the header for each column].</p> <p>Small-angle neutron scattering (SANS) data (Q [1/Å], I(Q) [SAXS - arbitrary, SANS - 1/cm], error I(Q) [same units]) of PLMA48 as a 2 wt % solution in n-dodecande-d26.</p>
Cloud management platform evaluation data generated by CMP²
<p>This repository contains exemplary results from using the CMP² (Comparing Cloud Management Platforms) testbed on CloudcheckR, ManageIQ, MistIO, Boto and Libcloud. The results give insight into the performance of multi-cloud middleware. All experiments were conducted as research in education linked to the Cloud Accounting and Billing research initiative at Service Prototyping Lab, Zurich University of Applied Sciences, Switzerland. Apart from the raw data in JSON format, generated graphs are also included.</p> <p> </p>
Bias correction of simulated Brazilian wind power generation based on reanalysis data
<p>Available data:</p> <p>- Brazilian wind power generation time series derived from MERRA-2 reanalysis data with wind speed and wind power bias correction.</p> <p>- Wind speed correction factors derived from INMET wind speeds (http://www.inmet.gov.br/portal/) as well as wind power correction factors dervied from ONS wind power generation time series are also provided.</p> <p>- Simulation of about 38 years of wind power generation with fixed capacity.</p> <p>Data used for validation:</p> <p>- Historical wind power generation data, which were used for validation of simulated time series, can be found at the ONS homepage (http://ons.org.br/Paginas/resultados-da-operacao/historico-da-operacao/geracao_energia.aspx).</p> <p> </p> <p>Other Links:</p> <p>- Information on this will soon be found here: https://refuel.world/</p> <p>- Code for generating time series, validation and analysis: https://github.com/KatharinaGruber/BrazilWind</p> <p>- Master thesis belonging to data: https://doi.org/10.5281/zenodo.1471221</p>
Synchronous generator experimental data (voltage, current and rotor position data)
<p>This database is composed of stator voltages and currents, field voltage and rotor position of a three-phase synchronous generator under resistive load. The data were acquired by means of two Tektronix MSO 2014B oscilloscopes with 4 channels each. Data were collected on 8 channels corresponding to phase voltages (Va,Vb,Vc), phase currents (Ia,Ib,Ic), field current of the generator (Ifd) and a pulse signal for angular position reference of the rotor (theta_m). For the simultaneous collection of the signals, a trip circuit was designed and implemented, the output of which was used as the triggering signal for the oscilloscopes. The synchronous generator was connected to a synchronous motor (Y-Y) and to a resistive circuit (18 units of 40W lamps) which served as loads. Voltage data were collected by means of a Keysight N2791 voltage probes; current data were collected using Tektronix A622 current tips. An PHCT203 optical key was used to collect rotor position pulse signal. The generator model is MOTROM M610-75-B-1K8-GS of 0.5 cv, 1800 rpm, 4 poles. The generator parameters obtained by means of physical bench tests were:</p> <p>Rs = 32.5 ohms (stator winding resistance)</p> <p>Rfd = 358.9 ohms (field winding resistance)</p> <p>Ld = 0.803H (direct axis stator inductance)</p> <p>Lq = 0.691H (quadrature axis stator inductance)</p> <p>Lls = 0.12H (stator winding leakage inductance)</p> <p>Lfd = 2.23H (field winding inductance)</p> <p>Vf = 64V (field voltage applied during the experiment, supplied by an regulated DC source)</p> <p> </p> <p>The database is composed by the following files of preprocessed data sampled at 10kHz in which the following variables are given, respectively, time, Va, Vb, Vc, Theta_r, Ia, Ib, Ic:</p> <p>1) data0001.txt</p> <p>2) data0002.txt</p> <p>3) data0003.txt</p> <p>4) data0001.csv</p> <p>5) data0002.csv</p> <p>6) data0003.csv</p> <p> </p> <p>Further, the database contains the following files of raw data:</p> <p>1) T0001A.txt</p> <p>2) T0001B.txt</p> <p>3) T0002A.txt</p> <p>4) T0002B.txt</p> <p>5) T0003A.txt</p> <p>6) T0003B.txt</p> <p>Each realization is contained in two files (suffixes A,B) and contain:</p> <p>Suffix A</p> <p>time</p> <p>CH1 (Va)</p> <p>CH1_peak (Va peak)</p> <p>CH2 (Vb)</p> <p>CH2_peak (Vb peak)</p> <p>CH3 (Vc)</p> <p>CH3_peak (Vc peak)</p> <p>CH4 (Theta_r)</p> <p>CH4_peak (Theta_r peak)</p> <p> </p> <p>Suffix B</p> <p>time</p> <p>CH1 (Ia)</p> <p>CH1_peak (Ia peak)</p> <p>CH2 (Ib)</p> <p>CH2_peak (Ib peak)</p> <p>CH3 (Ic)</p> <p>CH3_peak (Ic peak)</p> <p>CH4 (EMPTY)</p> <p>CH4_peak (EMPTY)</p> <p> </p> <p>When using the raw data, it is important to consider that the values were acquired in the following conditions:</p> <p>- Voltage probe scale: 100:1</p> <p>- Current probe scale: 100mV/A</p> <p>- Oscilloscope probe scale: 10x<br> <br> - Oscilloscope configuration: check header of raw files.</p> <p> </p> <p>Contact information: jose.grzybowski@uffs.edu.br</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.