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.
32
datasets available to search
ShareScore release 0.9.0
Dataset results
32 results for “similarity prediction”
SNSynergy: similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations
Open the record for dataset details and reuse information.
ADE prediction using Drug Similarities
Open the record for dataset details and reuse information.
Couple-Wise Functional Connectivity Similarity Between Auditory And Salience Network Predicts Marital Satisfaction
Open the record for dataset details and reuse information.
ASVspoof 2019 LA Listening Test Data for Partial Rank Similarity MOS Prediction
<p>This dataset is a derivitave work of the ASVSpoof 2019 LA condition listening test data found here:<br> https://datashare.ed.ac.uk/handle/10283/3336<br> -> LA.zip</p> <p>"ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech" <br> Xin Wang, Junichi Yamagishi, Massimiliano Todisco, Héctor Delgado, Andreas Nautsch, Nicholas Evans, Md Sahidullah, Ville Vestman, Tomi Kinnunen, Kong Aik Lee, Lauri Juvela, Paavo Alku, Yu-Huai Peng, Hsin-Te Hwang, Yu Tsao, Hsin-Min Wang, Sébastien Le Maguer, Markus Becker, Fergus Henderson, Rob Clark, Yu Zhang, Quan Wang, Ye Jia, Kai Onuma, Koji Mushika, Takashi Kaneda, Yuan Jiang, Li-Juan Liu, Yi-Chiao Wu, Wen-Chin Huang, Tomoki Toda, Kou Tanaka, Hirokazu Kameoka, Ingmar Steiner, Driss Matrouf, Jean-François Bonastre, Avashna Govender, Srikanth Ronanki, Jing-Xuan Zhang, Zhen-Hua Ling.<br> Computer Speech and Language Colume 64, 2020.</p> <p>This form of the data was used for the PRS paper accepted to ASRU 2023:</p> <p>"Partial Rank Similarity Minimization Method for Quality MOS Prediction of <br> Unseen Speech Synthesis Systems in Zero-shot and Semi-supervised Setting."<br> Hemant Yadav, Erica Cooper, Junichi Yamagishi, Sunayana Sitaram, Rajiv Ratn Shah.</p> <p>Modifications to the original data include converting audio from flac -> wav, sv56 normalization, conversion of labels from an 0-9 rating scale to a 1-5 scale, and creation of training/development/testing splits.</p>
Does evolutionary relatedness predict ecological similarity?
Open the record for dataset details and reuse information.
Development of quantitative direct prediction algorithm for the human target organ similarity of human pluripotent stem cell-derived organoids and cells
GEO Series GSE178858. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
A pan-immunotherapy signature to predict intratumoral CD8+ T cell expansions [bulkTCR_Trilateral_similarity]
GEO Series GSE303232. Mus musculus. 36 samples. Type: Other.
Development of quantitative direct prediction algorithm for the human target organ similarity of human pluripotent stem cell-derived organoids and cells [RNA-seq]
GEO Series GSE178855. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Similarity Regression predicts evolution of transcription factor sequence specificity
GEO Series GSE121420. synthetic construct. 682 samples. Type: Other.
Data from: Morbidity rate prediction of dengue hemorrhagic fever (DHF) using the support vector machine and the Aedes aegypti infection rate in similar climates and geographical areas
Background: In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly, and thus, they cannot enhance the capability of the predictive model's learning algorithm. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings: We identified the study areas in three provinces (Nakhon Pathom, Ratchaburi, and Samut Sakhon) of central Thailand that were reported to have a high incidence of dengue outbreaks. Prior to being added to the model, the infection data of the dengue vector, Aedes aegypti, the climate parameters, and the population density were collected from various sources and standardized. This process ensured that the data were not overwhelmed by each other in terms of the distance measures and to enhance the model effectiveness. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Aedes aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. The support vector machine (SVM), a machine learning technique, has been receiving attention in many research areas due to its remarkable generalization performance. In this research, we applied the SVM with the radial basis function (RBF) kernel, referred to as the SVM-R, to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions: The infection rates of the Aedes aegypti female mosq uitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. This approach is more reliable and practical for monitoring dengue outbreaks, particularly in locations with similar climates because it does not rely on only climate factors. In addition, we demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE).
Data from: Morbidity rate prediction of dengue hemorrhagic fever (DHF) using the support vector machine and the Aedes aegypti infection rate in similar climates and geographical areas
Open the record for dataset details and reuse information.
Development of quantitative direct prediction algorithm for the human target organ similarity of human pluripotent stem cell-derived organoids and cells [scRNA-seq]
GEO Series GSE178857. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
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.