Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
29
datasets available to search
ShareScore release 0.9.0
Dataset results
29 results for “Virtual Machine”
Beware of the generic machine learning-based scoring functions in structure-based virtual screening
<p>Data sets and the rescoing scores utilized in the paper "Beware of the generic machine learning-based scoring functions in structure-based virtual screening" (DOI: 10.1093/bib/bbaa070)</p>
Accuracy or novelty: what can we gain from target-specific machine learning-based scoring functions in virtual screening?
<p>Datasets, features, and some representative scripts utilized in the paper "Accuracy or novelty: what can we gain from target-specific machine learning-based scoring functions in virtual screening?" </p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCA P1)
<p>This repository contains the dataset for the Missing PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
AGORA virtual machine
<p>This virtual machine contains the software developed and the experimental results of the paper AGORA: Automated Generation of test Oracles for REST APIs, accepted for publication in the technical track of the <em>ACM SIGSOFT International Symposium on Software Testing and Analysis 2023 (ISSTA 2023).</em> To use the most up-to-date version of AGORA, please refer to the official GitHub repository: https://github.com/isa-group/Beet</p> <p>user: agora</p> <p>password: agora</p>
Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers
<p>The 12 VS scenarios considered in this study employing six training-test data partitions<strong> </strong>(A-F). All training sets employ the same set of 371 actives (WO2015160641A2), but differ on the considered set of inactives and hence are uniquely identified by the latter (either TrueInactives, DeepCoys, RandomDecoys or ActivesOnly). Likewise, all test sets employ the same 297 actives (WO201503820A1), none of them also included in the training set, but different sets of inactives (TrueInactives or DeepCoys). </p> <p> </p> <table align="center"> <caption>Table 1. Six virtual screening scenarios corresponding to six pairs of training-test data for each type of SFs (classification or regression)</caption> <thead> <tr> <th scope="col">Partition ID</th> <th scope="col">Training set</th> <th scope="col">Test set</th> <th scope="col">Type</th> </tr> </thead> <tbody> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Regression</td> </tr> </tbody> </table> <p> </p>
A Comprehensive Study of Bugs in Embedded WebAssembly Virtual Machines
<p>This contains all the experimental code, data sets, and result files for our experiments.</p>
A Comprehensive Study of Bugs in Embedded WebAssembly Virtual Machines
<p>This contains all the experimental code, data sets, and result files for our experiments.</p>
An Empirical Study of Embedded WebAssembly Virtual Machine
<p>This contains all the experimental code, data sets, and result files for our experiments.</p>
A Comprehensive Study of Bugs in Embedded WebAssembly Virtual Machines
<p>This contains all the experimental code, data sets, and result files for our experiments.</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.