Find research datasets worth reusing

opencc-by-4.0May 2024View details →

Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning

It contains reference training and test set configurations (and corresponding energy, forces, and virial stress values) for ZIF-8 and MOF-5.

opencc-by-4.0May 2024View details →

Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives

In this study, we investigated the correlation between the electronic properties of anticholinesterase compounds and their biological activity. While the methodology of such correlation is well-established and has been effectively utilized in previous studies, we employed a more sophisticated approach: machine learning. Initially, we focused on a set of 22 molecules sharing a common chalcone skeleton and categorized them into two groups based on their IC50 indices: high activity and low activity. Utilizing the open-source software Orca, we conducted calculations to determine the geometries and electronic structures of these molecules. Over a hundred parameters were collected from these calculations, serving as the foundation for the features used in machine learning. These parameters included the Mulliken and Lowdin electronic populations of each atom within the skeleton, molecular orbital energies, and Mayer's free valences. Through our analysis, we developed numerous models and identified several successful candidates for effectively distinguishing between the two groups. Notably, the most informative descriptor for this separation relied solely on electronic populations and orbital energies. By understanding which computationally calculated properties are most relevant to specific biological activities, we can significantly enhance the efficiency of drug development processes, saving both time and resources.

opencc-by-4.0May 2024View details →

Experimental and Computational Study Towards Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials

SnOx has received great attention as an electrocatalyst for CO2 reduction reaction (CO2RR), however, it still suffers from low activity. Moreover, the atomic-level SnOx structure and the nature of the active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, we first enhance its CO2RR performance by supporting SnO2 nanoparticles on two common supports, Vulcan Carbon and TiO2 . Then, electrolysis of CO2 at various temperatures in a neutral electrolyte reveals that the application window for this catalyst is between 12 and 30 °C. Furthermore, our study introduces a machine learning interatomic potential method for the atomistic simulation to investigate SnO 2 reduction and establish a correlation between SnO x structures and their CO 2 RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of *H and *CO2−, where both are correlated with the presence of oxygen on the nanoparticle surface. This study offers in-depth insights into the rational design and application of SnOx -based electrocatalysts for CO2RR.

openmit-licenseMay 2024View details →

Dataset: Active learning streamlines development of high performance catalysts for higher alcohol synthesis

In this repository there are five Excel files, three Jupyter notebook files, and a zip archive containing Origin files.  The Full_catalytic_performance_data.xlsx file comprises all the experimental and computational catalytic data complied as a part of the research work titled "Active learning streamlines development of high performance catalysts for higher alcohol synthesis" carried out at the Advanced Catalysis Engineering group, ETHZ. The Source_data.xlsx file and .opju files contain the raw data used to create the display items in the manuscript. The repository contains three additional files "Modelling_Data_Phase_1.xlsx", "Modelling_Data_Phase_2.xlsx", "Modelling_Data_Phase_3.xlsx" which contain the curated data to run the Gaussian process -Bayesian Optimization algrotihm across three specific active learning Phases devised in this study. The python codes necessary to run the model are provided as Jupyter Notebook (.ipynb) files and are also available on GitHub in the link provided below.

opencc-by-4.0Feb 2019View details →

Separation of memory span and learning rate: evidence from behavior and spontaneous brain activity in older adults

The data file includes two small files. The first file is Behavioral data. The second file is fMRI data.

opencc-by-4.0Aug 2024View details →

Accelerating Discovery of Mechanically Stable Metal−Organic Frameworks for Vinylidene Fluoride Storage by Active Learning

Supplementary data including dataset and python scripts for "Accelerating Discovery of Mechanically Stable Metal−Organic Frameworks for Vinylidene Fluoride Storage by Active Learning"

opencc-by-4.0Sep 2024View details →

Latent space active learning with message passing neural network: the case of HfO_{2}

<div>A dataset containing the structures obatined from the active learning process and their calculated energies and forces. See more details in our papr "Latent space active learning with message passing neural network: the case of HfO_{2}".</div>

opencc-by-4.0Sep 2024View details →

Identification of crashworthy designs combining active learning and the solution space methodology - Validation and training dataset

Dataset and Python codes for training a crashworthiness classifier.

opencc-by-4.0Nov 2022View details →

All data support the published articel "Loop-optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade-off through cross-strategy between machine learning and the B-factor analysis"

Trichoderma reesei endoglucanases (EGs) have limited industrial applications due to its low thermostability and activity. Here, we aimed to improve the thermostability of EGs from T.reesei without reducing its activity counteracting the activity-stability trade-off. A cross-strategy combination of machine learning and B-factor analysis was used to predict beneficial amino acid substitution in EG loop optimization. Experimental validation showed single-site mutated EG concomitantly improved enzymatic activity and thermal properties by 17.21%–18.06% and 49.85%–62.90%, respectively, compared with wild-type EGs. Furthermore, the mechanism explained mutant variants had lower RMSD values and a more stable overall structure than the wild type. According to this study, EGs loop optimization is crucial for balancing the activity-stability trade-off, which may provide new insights into how loop region function interacts with enzymatic characteristics. Moreover, the cross-strategy between machine learning and B-factor analysis improved superior enzyme activity-stability performance, which integrated structure-dependent and sequence-dependent information.

opencc-by-4.0Jan 2023View details →

CalcAMP: A new machine learning model for the accurate pre-diction of antimicrobial activity of peptides

Datasets used for the publication:  CalcAMP: A new machine learning model for the accurate prediction of antimicrobial activity of peptides

Active Learning Prototypes for Teaching Game AI

Supplementary materials for “Active Learning Prototypes for Teaching Game AI” Overview This package contains the following files and folders: <ul> <li><code>LICENSE_CODE.txt</code> - The license for the included code.</li> <li><code>LICENSE_DATA.txt</code> - The license for the included data.</li> <li><code>README.md</code> - Package description.</li> <li><code>requirements.txt</code>- Specifies the Python dependencies required for running the included notebook.</li> <li><code>survey_analysis.ipynb</code> - Notebook used for analyzing the survey data.</li> <li><code>survey_data.ods</code>- Fully anonymized survey data.</li> </ul> Reproducibility of results The results presented in the research paper “Active Learning Prototypes for Teaching Game AI”, published in the Proceedings of the IEEE Conference on Games 2023, and authored by Nuno Fachada, Filipa F. Barreiros, Phil Lopes and Micaela Fonseca, can be reproduced with the Jupyter notebook included in this package. Licenses <ul> <li>The code in the Jupyter Notebook is made available under the <a href="https://opensource.org/licenses/MIT">MIT</a> license (see <code>LICENSE_CODE.txt</code>).</li> <li>The non-code materials are made available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license (see <code>LICENSE_OTHER.txt</code>).</li> </ul>

Artifact for paper "An Experimental Evaluation of Conformance Testing Techniques in Active Automata Learning" at MODELS 2023

This is the archival artifact intended for artefact evaluation of the paper "An Experimental Evaluation of Conformance Testing Techniques in Active Automata Learning" accepted at MODELS 2023.

Valar: Streamlining Alarm Ranking in Static Analysis with Value-Flow Assisted Active Learning

static analysis alarm data

Valar: Streamlining Alarm Ranking in Static Analysis with Value-Flow Assisted Active Learning

static analysis alarm dataset

opencc-by-4.0Oct 2023View details →

Data base: case-based learning with or without Escape Room activities as an active learning approach for improving academic performance and satisfaction among university students of psychology of groups

Data base Database that collects data on gender, age, grade at the beginning of the course, grades of the activity and satisfaction with the activity. An experimental study using randomisation of team work groups was developed. Some student groups developed CBL activities in combination with Escape Room activities, and other student groups developed CBL activities alone. The latter can be considered a control group. This innovative teaching project was performed by social work students at the University of Zaragoza (Spain). This degree comprises 240 ECTS credits spread out over four years. Specifically, this experimental study was created for "Social Work with Groups" , a compulsory subject taught during the second semester of the second academic year of the Social Work degree programme. It is divided into two parts: the first one is presented from a social psychology perspective, and it is made up of five course curriculum topics. The  second one is taught from a social work/social services perspective, which focuses more on the specifics of the profession (four course curriculum topics). This experiment was conducted in February and March 2023, during the delivery of the social psychology part of the course. There are taught five course curriculum topics that fall within the domain of social psychology (psychology of groups). These topics are: 1) group meaning and types; 2) group growth processes, cohesion, conflict, obedience and group violence,  group decision-making; 3) group structure: definition, status, roles, norms, group culture; 4) leadership and 5) group characteristics such as communication and empathy. The participants were students enrolled in the “Social Work with Groups” course at the University of Zaragoza (Spain) during the 2022-2023 academic year. The sample size was 111 students: 56 performed CBL activities with Escape Room activities, and 55 performed CBL activities without Escape Room activities. The variable outcome of this experimental study was academic performance, assessed by the grade obtained in the mark for CBL activities with a rating from 0 to 10, where the higher score indicated a better performance. This mark showed the number of correct concepts that were identified and extracted from the case. This score was translated to a categorical assessment going from fail (between 0 and 4.9), to pass (between 5.0 and 6.9), to merit (between 7.0 and 8.9), to outstanding (between 9.0 and 10). Secondary outcomes The secondary variables were: 1) quantitative and qualitative exam score (on the psychology of groups´ contents) 2) students´ satisfaction with the activity, and 3) time needed for performing the activities. The academic performance data were collected using the exam score for the subject (psychology of groups´ contents). This exam consisted of 40 multiple-choice questions with three response options, taking the chance factor into account (so marks were deducted for wrong answers). The quantitative rating of each academic score can range between 0 and 10, with a higher score denoting a higher percentage of correct answers. The categorical holistic assessment of achievement goes from fail (between 0 and 4.9), to pass (between 5.0 and 6.9), to merit (between 7.0 and 8.9), to outstanding (between 9.0 and 10). The data on students´ satisfaction with the activity performed were collected using a self-reporting questionnaire made up of seven statements on the course and teaching methodology used (Gómez-Poyato et al. 2020; Oliván-Blázquez et al. 2022; Olivan-Blázquez et al. 2019), which were answered on a Likert scale from 0 to 4, with 0 meaning not at all and 4 meaning to a great extent. The statements to be evaluated were as follows: the teaching methodology used has encouraged new knowledge acquisition; it has favoured deep learning; it has helped me to think more critically; it has helped me to apply theoretical content to practice; it has helped me to apply theoretical content to assessments; it has helped me to understand concepts better; I believe it is an appropriate teaching methodology. A free response section was also included so that students could express themselves openly. The data for the time used to carry out the activities were also collected, measured in minutes used for finishing the activities. Age, gender and university admittance mark data were also obtained in order to   to determine if the student groups were in the same conditions regarding these values  at the start of the analysis.

opencc-by-4.0Oct 2023View details →

Quantifying the Effects of Vagus Nerve Stimulation on Gastric Myoelectric Activity in Ferrets Using an Interpretable Machine Learning Approach

Raw data

closedIPD-NOFeb 2026View details →

ACtive Children Enhance LEaRning and AttenTION: A Randomized Controlled Trial (RCT)

ClinicalTrials.gov study NCT05794360. IPD Sharing: NO. Countries: 1. Publications: 4.

restrictedIPD-UNDECIDEDFeb 2026View details →

Effects of Sativex(Registered Trademark) and Oral THC on Attention, Affect, Working Memory, Reversal Learning, Physiology and Brain Activation

ClinicalTrials.gov study NCT01037608. IPD Sharing: Not stated. Countries: 1. Publications: 3.

controlledIPD-YESFeb 2026View details →

The Role of Dopamine, Reward Learning and Prefrontal Activity in Expectation-induced Mood Enhancement

ClinicalTrials.gov study NCT05208294. IPD Sharing: YES. Countries: 1. Publications: 0.