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.
87
datasets available to search
ShareScore release 0.9.0
Dataset results
87 results for “Learning Design”
Developing Deep Learning Approaches to Find and Classify Architectural Design Decisions in Issue Tracking Systems
<p>This upload contains three files:</p> <ol> <li>mongodump-JiraRepos_2023-03-07-16 00.archive: Archive containing the issue data pulled from the Jira API.</li> <li>mongodump-MiningDesignDecisions.archive: Archive containing the data of our deep learning models and the labelled issues.</li> <li>mongodump-MiningDesignDecisions-lite.archive: Similar to the archive above, except this one only contains the best trained model (BERT). Also, it does not contain any embeddings or other files.</li> </ol> <p>This archive contains the data of our deep learning models and the labelled issues (MiningDesignDecisions archive).</p>
A fast machine-learning-guided primer design pipeline for selective whole genome amplification
Open the record for dataset details and reuse information.
Inverse design of soft materials via a deep-learning-based evolutionary strategy
Open the record for dataset details and reuse information.
The AI Economist: Taxation policy design via two-level deep reinforcement learning
Open the record for dataset details and reuse information.
Data for: Lessons learned from the design and operation of a small-scale cross-flow tidal turbine
Open the record for dataset details and reuse information.
Data from: Active learning design: Modeling force output for axisymmetric soft pneumatic actuators
Open the record for dataset details and reuse information.
Deep learning guided design of dynamic proteins
Open the record for dataset details and reuse information.
Supporting user preferences in Search-Based Product Line Architecture Design using Machine Learning
<p>Presentation of the paper Supporting user preferences in Search-Based Product Line Architecture Design using Machine Learning to the SBCARS 2020.</p>
Predictive Design of Ultrastretchable Electrodes with Strain-Insensitive Performance via Robotics- and Machine Learning-Integrated Workflow
Open the record for dataset details and reuse information.
Open Data Package: Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science.
<p>Open Data Package for the paper: Stefanie Betz, Birgit Penzenstadler, Leticia Duboc, Ruzanna Chitchyan, Sedef Akinli Kocak, Ian Brooks, Shola Oyedeji, Jari Porras, Norbert Seyff, and Colin C. Venters. 2024. Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science. ACM Trans. Softw. Eng. Methodol. 24 00, JA, Article 00 (March 2024), 39 pages. https://doi.org/10.1145/3649597 25</p>
Data for Machine Learning Guided Design of Nerve-on-A-Chip Platforms with Promoted Neurite Outgrowth
Open the record for dataset details and reuse information.
Data for "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection"
<p>Once decompressed, the file contains a folder which contains:</p> <ul> <li>The files "s100_Nth.fasta" (where "N" is 5, 6, 7 or 8), which are the output of the SELEX experiment described in the paper with DOI: <a href="https://doi.org/10.1002/cbic.201900265">10.1002/cbic.201900265</a>. They are standard fasta files, and the descriptor of each sequence is of the form "seqX-Y", where "X" is an increasing label, and "Y" is the number of times "seqX" has been obtained (number of counts of "seqX").</li> <li>The file "Aptamer_Exp_Results.csv", which contains the sequences tested experimentally for the paper "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection" (preprint available at https://doi.org/10.1101/2022.03.12.484094), with the following experimental results for each sequence: (i) whether the sequence was able to bind thrombin ('B' for binders, 'NB' for non-binders); (ii) the thrombin exosite used for binding ('I' for exosite I, 'II' for exosite II, 'n/a' for sequences not tested).</li> </ul> <p>Examples of usage of the data are available at https://github.com/adigioacchino/RBMsForAptamers.</p>
Codes and results for self-learning entropic population annealing for interpretable materials design
<p>Codes that can reproduce the results in the paper entitled "self-learning entropic population annealing for interpretable materials design". Results, when the number of particles is 50, are included.</p>
Data and code for paper: Design Optimization of Geometric-Confined Cardiac Organoids Enabled by Machine Learning Techniques
<p>Collection of the code for the generation of figures, analysis and data for the paper: <strong>Design Optimization of Geometric-Confined Cardiac Organoids Enabled by Machine Learning Techniques.</strong></p> <p>Ensemble learning classifiers, PACMAP & Trimap code in python file. All figures and additional analysis in R code. </p> <p> </p>
Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization
<p>This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article <em>Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization</em> by Zijun Deng and Lev Sarkisov.</p>
Using Deep Learning to Design High Aspect Ratio Fusion Devices (Dataset)
<p>This dataset was developed for the work entitled Using Deep Learning to Design High Aspect Ratio Fusion Devices. A small explanation is hereby given:</p> <p>The design of fusion devices is typically based on computationally expensive simulations. This can be alleviated using high aspect ratio models that employ a reduced number of free parameters, especially in the case of stellarator optimization where non-axisymmetric magnetic fields with a large parameter space are optimized to satisfy certain performance criteria. However, optimization is still required to find configurations with properties such as low elongation, high rotational transform, finite plasma beta, and good fast particle confinement. In this work, we train a machine learning model to construct configurations with favorable confinement properties by finding a solution to the inverse design problem: obtaining a set of model input parameters for given desired properties. Since the solution of the inverse problem is non-unique, a probabilistic approach, based on mixture density networks, is used. It is shown that optimized configurations can be generated reliably using this method.</p>
Identification of crashworthy designs combining active learning and the solution space methodology - Validation and training dataset
<p>Dataset and Python codes for training a crashworthiness classifier.</p>
A Deep Learning Approach to the Forward Prediction and Inverse Design of Plasmonic Metasurface Structural Color - Raw Data
<p>Reflection spectra of PDMS - Al nanorod metamaterials were collected using LUMERICAL FDTD simulations. PDMS material properties were defined using a refractive index of 1.41 and Al material properties were defined using frequency selective permittivities from the handbook of Palik. A total of 4620 structures were simulated, sweeping the following dimension parameters:</p> <ul> <li>Aluminium thickness (t)</li> <li>Pillar height (h)</li> <li>Pillar diameter (d)</li> </ul> <p>Reflectance spectra were converted into CIE 1931 chromaticity values (x,y). This dataset is comprehensive and allows for the development of deep learning models for the forward and inverse design of the given metamaterial structure as detailed in the associated manuscript. The associated manuscript and supporting documentation provide extensive details of data collection and processing methods.</p> <p> </p>
Aspen Plus v12 models for design a project-based learning on developing a novel technology for slurry management
<p>Preliminary investigation on the likelihood of trapping CO<sub>2</sub> and NH<sub>3</sub>, which are emitted at a rate of 132 mg/h and up to 5.1 – 510 mg/h during the storage of manure at 25 °C, in brines of NaCl and CaCl<sub>2</sub> was conducted in Aspen Plus® v12. The supersaturated solution of 0.5 kg H<sub>2</sub>O and 10 kg salt was considered to account for any possible absorption in the liquid film that is generated during the deliquescence phenomenon. Only in the case of using the CaCl<sub>2</sub> as dehydrating agent (anhydrous salt) in the prototype, the simulation in the commercial package predicted the absorption of CO<sub>2</sub> and NH<sub>3</sub> and formation of CaCO<sub>3</sub> and NH<sub>4</sub>Cl. On the other hand, the extents of formation of NaHCO<sub>3</sub>, and even NH<sub>4</sub>HCO<sub>3</sub> or NH<sub>4</sub>COONH<sub>2</sub> were found to be negligible under the conditions that the prototype operates. Prediction of the solubilities of NaCl, NH<sub>4</sub>Cl, NaHCO<sub>3</sub>, CaCO<sub>3</sub>, Ca(OH)<sub>2</sub>, CaCl<sub>2</sub>, CaCl<sub>2</sub>·H<sub>2</sub>O, CaCl<sub>2</sub>·2H<sub>2</sub>O, CaCl<sub>2</sub>·4H<sub>2</sub>O, and CaCl<sub>2</sub>·6H<sub>2</sub>O in the temperature range 0 – 100 °C at 1 atm.</p>
Web-Based Intervention Designed to Educate and Improve Adherence Through Learning to Use Continuous Glucose Monitoring
ClinicalTrials.gov study NCT03367351. IPD Sharing: NO. Countries: 1. Publications: 1.
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.