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.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “knowledge transfer”
Figure 2: Direct and indirect paths of knowledge transfer to New Zealand to manage sand drifting in the nineteenth and twentieth centuries.
<p>Figure 2 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
Data for "Transferring Chemical and Energetic Knowledge Between Molecular Systems With Machine Learning"
<p>Data used in the paper "Transferring Chemical and Energetic Knowledge Between Molecular Systems With Machine Learning."</p> <p>The following is a description of each file:</p> <p>- ala_dipep_full.zip contains the JSON files for alanine dipeptide</p> <p>- ala_dipep_full.txt contains the free energy values for alanine dipeptide</p> <p>- trialanine.zip contains the JSON files for trialanine</p> <p>- trialanine.txt contains the free energy values for trialanine</p> <p>- decaalanine.zip contains the JSON files for decaalanine, broken into groups</p> <p> </p> <p>Each JSON file contains the following properties:</p> <p>- atom_types: describing the short strings used for various types of atoms via their mass and radius.</p> <p>- atoms: describing each individual atom, with their type via the short string in atom_types, their partial charge, and coordinates.</p> <p>- angles: describing the angles formed between three atoms, their atom indices, as well as their angular value.</p> <p>- dihedrals: describing the dihedrals formed between four atoms, their atom indices, as well as their dihedral value.</p> <p>- bonds: describing the existence of pairwise bonds between atoms via a binary number.</p> <p>- van_der_waals: describing the van der Waals forces between pairs of atoms.</p> <p>- coulomb: describing the Coulomb forces between pairs of atoms.</p>
Efficient Drug-Target Interactions Prediction Framework via Transferable Knowledge Fusing
<p><span>Drug-target integrations (DTI) prediction is a niche in drug discovery, streamlining the search for potential drugs. Computer-aided drug discovery (CADD) has gained traction for its precise predictions, efficiency, and adaptability across various situations. Yet, the computational demands of current top CADD models hinder their practical use due to heavy resource needs.</span></p> <p><span>In this research, we introduce TransFusE DTI, an effective framework for predicting DTIs that leverages pre-trained knowledge to construct models that optimize predictive accuracy while minimizing computational demands. The encoder uses a pre-extracted embedding vector from ProtBERT to reduce computational load and adapts a smaller ProtBERT model. It also includes target-related functional text to boost predictive accuracy. We evaluate the performance of TransFusE DTI using three widely-recognized benchmark datasets: BIOSNAP, DAVIS, and BindingDB, and compare its results to prior studies.</span></p> <p><span>Our results demonstrate that TransFusE DTI exhibits superior predictive performance on the BIOSNAP and BindingDB datasets. Notably, the model's parameter count is only 60% of that of the previous top-performing model by </span><span><a href="https://www.mdpi.com/1999-4923/14/8/1710"><span>Kang et al. (2022)</span></a></span><span>, and it operates efficiently with a learning rate of 26%. Furthermore, the model's video memory requirement is 11.2 GB, rendering it suitable for use on general-purpose Graphics Processing Units (GPUs). </span></p>
When and How to Transfer Knowledge in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments of the paper below:</p> <p>Ruan, G., Minku, L.L., Menzel, S., Sendhoff, B., Yao, X., "When and How to Transfer Knowledge in Dynamic Multi-objective Optimization," 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 2019, pp. 2034-2041. </p> <p> </p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems.</p>
Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments from the paper below:</p> <p>Ruan, G., Minku, L., Menzel, S., Sendhoff, B., Yao., “Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization” <em>2020 IEEE Congress on Evolutionary Computation</em></p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the ‘inner’ optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.</p>
Data used in Knowledge Transfer Research for Drone Navigation
<p>Data used in Knowledge Transfer Research for Drone Navigation.</p>
A federated learning framework based on transfer learning and knowledge distillation for targeted advertising-Click-Through Rate Prediction Dataset
<p>https://www.kaggle.com/c/avazu-ctr-prediction</p>
Data from: From strategy to action: A qualitative study on salient factors influencing knowledge transfer in project-based experiential learning in healthcare organizations in Kenya
Open the record for dataset details and reuse information.
How do students respond to different gestures produced by animated pedagogical agents? An investigation of attention, narrative recall, and knowledge transfer within a personalized learning context
Open the record for dataset details and reuse information.
A federated learning framework based on transfer learning and knowledge distillation for targeted advertising-Ad Display/Click Data on Taobao.com dataset
<p>https://tianchi.aliyun.com/dataset/56</p>
Exploration of an Online Education Program to Support Caregivers' Knowledge Transfer
ClinicalTrials.gov study NCT07377331. IPD Sharing: YES. Countries: 1. Publications: 0.
Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics
GEO Series GSE73638. Homo sapiens. 102 samples. Type: Expression profiling by array.
Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics [Cell lines]
GEO Series GSE73637. Homo sapiens. 52 samples. Type: Expression profiling by array.
Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics [Tumor]
GEO Series GSE73551. Homo sapiens. 50 samples. Type: Expression profiling by array.
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.