Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Two-time correlation function based on speckle patterns from x-ray photon correlation spectroscopy associated with "Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass" (scientific article published in Nature Communications, 2024)

This dataset consists of contrast data, i.e., the two-time correlation function, based on speckle patterns measured at the at the 8ID-E beamline of the Advanced Photon Source at Argonne National Laboratory. Experimental details are stated in the paper specified under "related work" and in the accompanying supplementary information. You are welcome to use this dataset in compliance with the CC BY 4.0 licence assigned to this dataset. Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful. ____________________________________________________________________ The data consists of 32 text files in total, which correspond to the main and lower panel Figure 2 of the main publication.  30 of these text files are contrast data, which are named "contrast_DT250s_nn.text" wiith "nn" as the identifier of consecutive data sets going from 1 to 30. Each data set consists of p rows and q columns, DT250s denotes the time resolution of data points, which is 250 s along both row and column values. The data set called "Time_Contrast_1to30s.txt" states the start time in seconds of the first data point of each of the thirty contrast data set. The data set called "ScatteredIntensity.txt" states the scattered intensity at full time resolution, i.e. 2.5 s. The files are plain text files with the data points separated by "space" along rows and "new line" along columns.

opencc-by-4.0Mar 2023View details →

Dataset from: Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions

Measures of  functionality, proprioception, self reported status and joint acoustic emissions (AE) were recorded for a sample of general population. Specifically, threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant. Knee AE were recorded using two sensors in different frequency ranges and three modes of AE event detection were investigated during cycling with 30 and 60 rpm cadences.

opencc-by-4.0Dec 2022View details →

Data and code for "Phase transitions in inorganic halide perovskites from machine learning potentials: The impact of size, rate, and the underlying exchange-correlation functional"

This record contains databases with data from density functional theory calculations used for training a series of neuroevolution potentials (NEPs), which are also included here. Information is also included for how to access the databases and run the NEP models. Databases The <code>*.db</code> files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see <a href="https://wiki.fysik.dtu.dk/ase/tutorials/tut06_database/database.html">here</a> for more information. The <code>demo-database-access.py</code> script illustrates the most basic access. Models The neuroevolution potential (NEP) models described in the publication can be found in the <code>nep-*.txt</code> files. They can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD. Primitive structures Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP models included here. The <code>demo-for-using-structures-and-models.py</code> script illustrates how to access the structures and models.

opencc-by-4.0Aug 2023View details →

Data of the publication "Real-time broadening of bath-induced density profiles from closed-system correlation functions"

The Lindblad master equation is one of the main approaches to open quantum systems. While it has been widely applied in the context of condensed matter systems to study properties of steady states in the limit of long times, the actual route to such steady states has attracted less attention yet. Here, we investigate the nonequilibrium dynamics of spin chains with a local coupling to a single Lindblad bath and analyze the transport properties of the induced magnetization. Combining typicality and equilibration arguments with stochastic unraveling, we unveil for the case of weak driving that the dynamics in the open system can be constructed on the basis of correlation functions in the closed system, which establishes a connection between the Lindblad approach and linear response theory at finite times. In this way, we provide a particular example where closed and open approaches to quantum transport agree strictly. We demonstrate this fact numerically for the spin-1/2 XXZ chain at the isotropic point and in the easy-axis regime, where superdiffusive and diffusive scaling is observed, respectively.

opencc-by-4.0May 2022View details →

Machine learning the quantum flux-flux correlation function for catalytic surface reactions

This dataset contains information on each of the 14 reactions used in the paper, the geometries for these reactions, the product of the quantum reaction rate constant and canonical reactant partition function and the flux-flux correlation function time series values for each reaction-temperature combination. reaction_details.csv This is a .csv file containing additional details on the reactions used in this paper. Each row contains one reaction/temperature combination, of which there are 55.   Column descriptions: <ul> <li>reaction_number: Reaction identifier number used in this work</li> <li>reaction: The chemical reaction equation</li> <li>metal_surface: atomic symbol of metal surface</li> <li>facet_number: Miller indices of surface</li> <li>reactants: Python dictionary object of reactants and their quantities</li> <li>products: Python dictionary object of products and their quantities</li> <li>reaction_energy [eV]: reaction energy in electron-volts</li> <li>activation_energy [eV]: activation energy of reaction in electron-volts</li> <li>temperature [K]: The randomly assigned temperature a  calculation was run for</li> <li>kQ_Cff [1/au]: The calculated integrated reaction rate product   at corresponding temperature {1,2,3,4} in units 1/(au time).</li> <li>reaction_split: Train/test placement of that reaction/temperature combination for reaction split</li> <li>temperature_split: Trian/test placement of that reaction/temperature combination for temperature split</li> <li>catalysishub_reactionID: Catalysis Hub reaction ID identifier for referencing catalysis hub database</li> <li>doi: digital object identifier of original publication for which DFT calculations were performed</li> </ul>     Flux_flux_correlation_functions: Directory containing flux-flux correlation function time series values for each reaction temperature combination. Values are organized in subdirectories, one for each of the 14 reaction. In each subdirectory .csv files are labeled by reaction number and temperature in Kelvin. Each csv file contains a column with time points [au of time] and the corresponding flux-flux correlation function value in units [1/(au of time)2].   Geometries: Directory containing geometry files for each reaction. Geometries of reactants on the surface were shifted respect to those supplied by catalysis hub to create continuous reaction pathways where necessary. Geometry files are organized in subdirectories for each reaction. When complete nudged elastic band (NEB) minimum energy paths (MEP) were not available ,subdirectories contain a products.xyz, reactants.xyz, and TSstar.xyz file (reactions 1 to 11) otherwise the complete set of NEB MEP images labeled neb{n}.xyz is given (reactions 12, 13, 14).

opencc-by-4.0May 2024View details →

Green Function Database in ak135 for synthetic cross-correlation computation in WMSAN.

## Description This file is an HDF5 file containing synthetic seismic waveforms computed with AxiSEM in an axisymmetric Earth in model ak135f. It contains waveforms at various distances for a vertical point force source of 1E20 N. ## Parameters Distance range from 0° to 180° with a 0.1° step. Source location latitude  = 90°, longitude = 0°. Sampling frequency 1Hz.  Duration 3600s. Dominant period 1s. ## Architecture Network  "L"  Station "SYNTH0000" : station at distance = 0° from the source location. <pre>|-- <a href="../records/11126562" target="_blank" rel="noopener">NOISE_vertforce_dirac_0-ak135f_1.s_3600s.h5</a>/ │ └── L/ │ └── SYNTH0000/ │ └── ... │ └── SYNTH1800/ │ └── _metadata/</pre>

Fig. 1 in Fig. 6. Pair correlation function g in Parascorpaena poseidon Chou and Liao 2022

Fig. 1. Location of the study area in Southern Benin (West Africa). The red points represent visual contacts GPS locations of the red-bellied monkey (N = 22) used in this study. Source: Field work, 2019 and Benin IGN Map, 2018. Spatial reference system: WGS 1984 UTM Zone 31.

Fig. 5 in Fig. 6. Pair correlation function g in Parascorpaena poseidon Chou and Liao 2022

Fig. 5. Size distribution of suitable habitats in the study area in southern Benin. The number above each bar represents the percentage of the suitable habitat size class considered.

Fig. 4 in Fig. 6. Pair correlation function g in Parascorpaena poseidon Chou and Liao 2022

Fig. 4. Plots of the response curves for each variable depending on the probability of presence and map of their spatial distribution. Each plot represents a Maxent model using only the corresponding variable. The plots are given for the six biophysical variables (A to F) with highest permutation importance> 2% (percent shown on plot). The plots show the average response (red line) and the standard deviation (blue interval around the average). For Land Cover Class, two shades (blue and green) represent upper and lower limit defined by standard deviation.

Fig. 3 in Fig. 6. Pair correlation function g in Parascorpaena poseidon Chou and Liao 2022

Fig. 3. Probability of red-bellied monkey presence (A) and suitable habitats (upper 0.0898 of red-bellied monkey presence probability) identified with the Maximum Training Sensitivity and Specificity (Max TSS) threshold (B).

Fig. 6. Pair correlation function g in Fig. 6. Pair correlation function g in Parascorpaena poseidon Chou and Liao 2022

Fig. 6. Pair correlation function g(r) of suitable habitats. Where the observed gRipley (black line) or gTranslate (red dashed line) is greater than gPois (green dashed line) we can expect more clustering than expected and where the observed gRipley or gTranslate is less than gPois we can expect more dispersion than expected.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 15. Drawing of autocorrelation function and partial correlation of the residues for males and females primary stage students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for males and females of primary stage students, through applying the compute (Ljung-Box Q) to check the model accommodation on the Function level 0.05 so the Q value occurs of males and females of primary stage students: Ljung-Box Q' = 1.10306, With p-value = P(Chi-square(1) > 1.10306) = 0.2936 Note that the Tabulated value   equals 3.841 while the Q value is less than   Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions)  of the residues for male females primary stage, in which the residues  value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 13. Drawing of autocorrelation Function and partial correlation of the residues for primary stage males students

It's possible to notice the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for males primary stage, in which the residues  value is located within the confidence interval limits which means the residues series is random and the Evaluated Model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm- Figure 12. Drawing of autocorrelation function and partial correlation for females primary stage students

We use the Unit Radix Dickey-Fuller Test to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.369693, Statistic Test =1.01829, P-Value=0.9194 We notice from the values above P-Value = 0.9194 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, we notice that the stability of the time series has been achieved. See Figure 11.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 10. Drawing of autocorrelation function and partial correlation for Males and Females primary stage students

The instability of the time series is recognized, and to be more accurate, we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (10).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 14. Drawing of autocorrelation function and partial correlation of the residues for primary stage males students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for primary stage female students, through applying the compute (Ljung- Box Q) to check the model accommodation on the function level 0.05 so the Q value occurs of primary stage female students: Ljung-Box Q' = 0.966626, With p-value = P(Chi-square(1) > 0.966626) = 0.3255 However, the Tabulated value   equals 3.841 whilst the Q value is less than Tabulated value, so it accepts the Null Hypothesis which indicates the emptiness of the evaluated model out of the contrast accordance trouble. It's possible to notice the two parameters functions (Autocorrelation and Partial Correlation Functions)  of the residues for  females primary stage students, in which the residues  value is located within the confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is shown.