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opencc-by-4.0Aug 2024View details →

Final geometries and energies, statistical analysis and estimated errors of single metals and bimetallics for CO2 to methanol conversion

The dataset accommodate all the extra data discussed in: Pisal, P., Krejčí, O. & Rinke, P. Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion. npj Comput Mater 11, 213 (2025). https://doi.org/10.1038/s41524-025-01664-9  The datased contains four types of data: <ol> <li>All the final geometries and energies of adsorbated (*H, *O, *OCHO & *OCH3) and all the 158 single metals and bimetallic alloys on all the surfaces with Miller indices in {-2, -1, ... 2} optimized with Open Catalyst Project (OCP) 20 equiformer_V2 machine-learned force-field model. These are in the <a href="https://zenodo.org/api/records/15587232/draft/files/geometries_and_energies.zip/content" target="_blank" rel="noopener noreferrer">geometries_and_energies.zip</a> file organized by the metal/alloys name, with the final geometries and enerigies in a json file, using a json ASE format.</li> <li>All the estimated mean absolute errors (MAE) of predicted adsorption energies for all the considered metals and bimetallic alloys in <a href="https://zenodo.org/api/records/15587232/draft/files/Estimated_MAEs_metals_bimetallics.csv/content" target="_blank" rel="noopener noreferrer">Estimated_MAEs_metals_bimetallics.csv</a> and xlsx file. The data content is identical, files differs only by a format.</li> <li>All the adsorption energy disctibutions (AEDs) for all the 158 metals/alloys and adsorbates in <a href="https://zenodo.org/api/records/15587232/draft/files/AEDs_metals_bimetallics.csv/content" target="_blank" rel="noopener noreferrer">AEDs_metals_bimetallics.csv</a> and xlsx files. The data content is identical, files differs only by a format.</li> <li>All the statistical information of the adsorption energies for all the 158 metals/alloys and adsorbates in <a href="https://zenodo.org/api/records/15587232/draft/files/Statistics_AEDs_metals_bimetallics.csv/content" target="_blank" rel="noopener noreferrer">Statistics_AEDs_metals_bimetallics.csv</a> and xlsx files. The data content is identical, files differs only by a format.</li> </ol>

FIGURE 3 in Distinguishing between Leptidea sinapis and L. reali (Lepidoptera: Pieridae) using a morphometric approach: impact of measurement error on the discriminative characters

FIGURE 3. Schematic drawings of characters used in morphometric analysis: A) male genitalia (lateral view), B) female genitalia (lateral view) showing both papille analis and anterior apophyses.

FIGURE 2 in Distinguishing between Leptidea sinapis and L. reali (Lepidoptera: Pieridae) using a morphometric approach: impact of measurement error on the discriminative characters

FIGURE 2. Male specimen bivariate scatter plot using aedoeagus (≈ aedeagus ≈ phallus) and saccus length as discriminative characters.

FIGURE 4 in Distinguishing between Leptidea sinapis and L. reali (Lepidoptera: Pieridae) using a morphometric approach: impact of measurement error on the discriminative characters

FIGURE 4. PC1 and PC2 scatter plot of male specimens: A) "size-and-shape" PCA, B) "size-adjusted" PCA.

FIGURE 5 in Distinguishing between Leptidea sinapis and L. reali (Lepidoptera: Pieridae) using a morphometric approach: impact of measurement error on the discriminative characters

FIGURE 5. PC1 and PC2 scatter plot of female specimens: A) "size-and-shape" PCA, B) "size-adjusted" PCA.

opencc-zeroNov 2023View details →

created in error

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opencc-zeroNov 2023View details →

created in error

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opencc-by-4.0Dec 2023View details →

Demonstration of fault-tolerant Steane quantum error correction

Source data underlying the graphical representations used in the figures and corresponding executed quantum circuits.

opencc-by-4.0Dec 2023View details →

Stim circuits and collected data for "Error-corrected Hadamard gate simulated at the circuit level" manuscript

The stim circuits which we sampled to obtain logical failure probabilities, and the collected data organised in an excel file.

opencc-by-4.0Oct 2023View details →

Intracranial and behavioral data from "Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex"

Preprocessed intracranial EEG and behavioral data from Hoy, Quiroga-Martinez, et al. manuscript titled "Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex" published in Nature Communications (2023). Source data files for figures are included as well.

opencc-by-4.0Apr 2024View details →

AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error (Dataset)

This upload contains the dataset used in <a href="https://arxiv.org/abs/2401.17879" target="_blank" rel="noopener">AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error</a>. Further details and instructions are provided in the <a href="https://github.com/jonasricker/aeroblade" target="_blank" rel="noopener">project's Github repository</a>.

restrictedcc-by-4.0Nov 2024View details →

Error Related Potential in motion to stop the gait with a lower limb exoskeleton

<h2>Description</h2> This dataset contains EEG signals from experiments designed to evoke Error-Related Potentials (ErrP) in motion, during gait using a Brain-Computer Interface (BCI) to control a lower limb exoskeleton. The ErrP is elicited using Tactile stimuli that activates while walking and, once it deactivates, the exoskeleton stops. The experiment consists in a circuit with color marks on the floor, with 4 regions: 2 Gait Regions, where the subject has to keep walking, and 2 Stop Regions, where the subject tries to stop the exoskeleton using motor imagination. At the beginning of each repetition, the exoskeleton activates automatically and, while walking, they must perform two mental tasks: Relax (R), at Gair Regions to keep walking, and Motor Imagery (I) of stop, at Stop Regions to deactivate the exoskeleton and stop. These tasks can be executed correctly (RC, IC) or incorrectly (RE, IE). Since it is an open-loop experiment and the subject is never in control of the system, tasks are correctly performed 70% of the time (RC, IC), while the remaining 30% are incorrect (RE, IE).  For instance, one the subject enters in the Gait Region and maintains an idle state while walking to continue with the gait. In RC (Relax Correct) they cross the region without any stop, but in RE (Relax Error) the feedback activates and the exoskeleton erroneously stops. Conversely, in IC (Imagination Correct, the subject imagines the sensation of stop walking in their muscles, and the feeedback activates and the exoskeleton stops the gait, but during IE (Imagination Error), they walk through the region and the exoskeleton does not stop despite the motor imagery. Therefore, ErrP is elicited by the stimuli in RE and can be compared with the absence of ErrP (NoErrP) in IC, where the stimulus activates but should not evoke an error. Each subject participates in three sessions, consisting of 20 trials. In each trial, 4 mental tasks are performed, 2 Relax and 2 Imagination, interleaved. Thus, in each session, a total of 12 ErrP and 28 NoErrP signals are recorded in the dataset. Except subject R06, who only participated in 2 sessions.    <h2>Data information</h2> A trial consists of a Matlab structure that stores all information related to the trial experiment.  <ul> <li>data_EEG: Original EEG signals recorded with a sampling rate of 250Hz, where each row is a channel (1-28 EEG, 29-32 EOG, 33-35 inertial electrodes).</li> <li>data_preprocessed_EEG: Matrix that contains the preprocessed signals for each channel. Rows 1-35 are the original signals and then, the preprocessed signals in blocks of 35. Find the indexes of each filter in session.conf.info.preprocessingSteps.ListPreprocessingSteps.</li> <li>trigger_EEG: Information related to signal quality and missing data while recording. </li> <li>data_EXO: Exoskeleton recorded data with a sampling rate of 250Hz.</li> <li>data_preprocessed_EXO: The same data recorded by the exoskeleton in data_EXO, since it does not require the application of any filter.</li> <li>trigger_EXO: Empty vector. </li> <li>data_Actuators:  Arduino response when activates (1) and deactivates (-1) the feedback. </li> <li>data_preprocessed_Actuators: The same Arduino resposes recorded in data_Actuators, because it does not require any filter application. </li> <li>trigger_Actuators: Empty vector. </li> <li>task_EEG: Vector that associates a task to each signal sample.</li> <li>task_index_EEG: Zero vector with negative peaks at the samples indicating the start of a task. Each peak decrements by one unit with each task.  </li> <li>task_order_EEG: Vector that increments a unit with each task change. </li> <li>event_EEG: Vector of commands to activate (1) and deactivate (-1) the feedback in Arduino. </li> <li>conf: Configuration employed for data acquisition and preprocessing. <ul> <li>acquisition: User and signals acquisition information. <ul> <li>user_code: User code name.</li> <li>feedback: Trial in openloop (User do not have control of the system).</li> <li>feedbackErrP: Feedback type employed during the trial.</li> <li>readfile: Path to read files after its acquisition.</li> <li>saveSession_Script: Script used to save the recorded data.</li> <li>writeResults: Path to save the recorded data.</li> <li>device: List of connected devices during the trial and their related information, such as name, sampling rate, connection order, etc.  </li> <li>task: Information about tasks occurring during the trial. <ul> <li>task_list: Decodes tasks numbers. The first number is the global task/mental activity, the second one is the physiological state of the user, and the third one indicates the task version (preparation or basic task).</li> <li>sequence_tasks: List of tasks in order of execution.</li> <li>sequence_times: List with the duration of each task in the sequence.</li> </ul> </li> <li>deviceOutput: List of devices that receive commands to execute orders, such as the exoskeleton for walking and stopping and the VibroLed for turning feeedback on and off.</li> <li>eye_index: Indexes of EOG electrodes.</li> <li>EEG_index: Indexes of EEG electrodes.</li> <li>inertial_index: Indexes of inertial electrodes.</li> <li>file_name: Trial name.</li> <li>num_epochs: Number of epochs within a trial. An epoch is the half of sampling rate (250Hz), this means that an epoch has a duration of 0.5s and 125 samples.  </li> </ul> </li> <li>preadjustment: Empty list. </li> <li>preprocessing: Information of the preprocessing filters, parameters and order of application.</li> <li>processing: Not necessary for this analysis. </li> <li>static: Information used internally by the architecture for its correct operation.</li> <li>info: Important information about filters, their order and indexes in data_processed_EEG.</li> </ul> </li> <li>times: Struct with information of the devices synchronization and preprocessing times.</li> <li>times_processing: Processing duration times. </li> </ul>

opencc-by-4.0Dec 2023View details →

Error Analysis of Kernel EDMD for Prediction and Control in the Koopman Framework

This repository contains code and data to re-create the numerical results shown in "Error analysis of kernel EDMD for prediction and control in the Koopman framework"  <a href="http://arxiv.org/abs/2312.10460">http://arxiv.org/abs/2312.10460</a> Please see the README file for detailed description of the codes in this repository.

msd-errors - Data for the comparison Arrhenius figures for AgCrSe2

Results of the Arrhenius analysis of AgCrSe2 systems using Arrhenius and super-Arrhenius functions at a series of temperatures where only some subset of the full simulation is analysed. Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/fc1f0cb96213ea5d811087618920b879fea11e9a">this GitHub repo</a>.

msd-errors - Data for diffusion coefficient and offset figures3

Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk. Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/900ea562a3de64ba9ec05ddfdf2d4c41f611c37c">this GitHub repo</a>.

msd-errors - Data for scaling figures2

Data for a figure that shows the scaling of uncertainty as a function of simulation length and number of atoms and step size, for both true and anitcorrelated random walks. This compares the kinisi approach (on just 32 unique walks) with the numerical values from (1024 unique walks). Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/900ea562a3de64ba9ec05ddfdf2d4c41f611c37c">this GitHub repo</a>.

msd-errors - Data for diffusion coefficient and offset figures

opennotspecifiedOct 2019View details →

Subspecies and Distribution. L. J fons E. Geoffroy Saint-Hilaire, 1810 — Senegal and Gambia E to Nigeria, Cameroon, and Central African Republic, and S to SW Republic ofthe Congo. L.f.affinisK.Andersen&Wroughton,1907—Chad,Sudan,SouthSudan,NUganda,andextremeNEDRCongo. L. f. rex G. S. Miller, 1905 — Eritrea, Ethiopia, and Somalia S through E DR Congo, Kenya, and Tanzania (including Unguja I in Zanzibar Archipelago) to N Zambia and N Malawi. A published report from Namibia is considered an error. in Megadermatidae

Subspecies and Distribution. L. J fons E. Geoffroy Saint-Hilaire, 1810 — Senegal and Gambia E to Nigeria, Cameroon, and Central African Republic, and S to SW Republic ofthe Congo. L.f.affinisK.Andersen&Wroughton,1907—Chad,Sudan,SouthSudan,NUganda,andextremeNEDRCongo. L. f. rex G. S. Miller, 1905 — Eritrea, Ethiopia, and Somalia S through E DR Congo, Kenya, and Tanzania (including Unguja I in Zanzibar Archipelago) to N Zambia and N Malawi. A published report from Namibia is considered an error.

msd-errors - Data for variance and covariance figure

Data for a figure that presents the variance and covariance for a true random walk. There is a comparison shown between the numerical result from 1024 unique random walks and the analysis, by kinisi, of the same 1024 random walks. Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="git@github.com:arm61/msd-errors/tree/6c81f0e8e5ee08bb2eaebd520ceb59213c5026f5">this GitHub repo</a>.