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ShareScore release 0.9.0
Dataset results
24 results for “Predictive Analytics”
RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).
<p>This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a></p> <p>The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki</p>
RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)
<p>This is an RDFied version of the dataset published in Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. <em>Nanomaterials</em> <strong>2020</strong>, <em>10</em>, 2017.</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.3390/nano10102017">https://doi.org/10.3390/nano10102017</a></p> <p>The Original publication authors: Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A.</p>
TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING
<p>In recent years, the fintech industry has experienced rapid growth, driven by technological advancements and evolving consumer expectations. Fintech companies offer innovative financial services, such as digital banking, investment platforms, and payment solutions, catering to the needs of a tech-savvy customer base. However, as competition intensifies, customer retention has emerged as a critical challenge for these companies. According to a study by Ransom (2021), acquiring a new customer can cost five times more than retaining an existing one, making it imperative for fintech organizations to focus on strategies that enhance customer loyalty. The financial technology (fintech) sector has experienced unprecedented growth in recent years, fundamentally transforming how individuals and businesses access and manage financial services. Characterized by the integration of technology with financial services, fintech encompasses a wide array of offerings, including digital banking, peer-to-peer lending, robo-advisory services, and payment processing. As of 2023, the global fintech market was valued at approximately $309 billion and is projected to reach around $1.5 trillion by 2030, according to a report by Fortune Business Insights. This remarkable growth is largely attributed to advancements in digital technology, increasing smartphone penetration, and a growing consumer preference for online financial solutions. Moreover, the COVID-19 pandemic accelerated the adoption of digital financial services, as consumers sought contactless transactions and remote banking options.</p>
Data and analytical codes for: Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure
<p>Data and analytical codes for: Ishizawa et al. (2023) Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure, bioRxiv, 2023.07.04.546222</p> <p> https://www.biorxiv.org/content/10.1101/2023.07.04.546222v1</p> <p> </p>
Predictive Analytics and Behavioral Nudges to Improve Palliative Care in Advanced Cancer
ClinicalTrials.gov study NCT05590962. IPD Sharing: NO. Countries: 1. Publications: 1.
Predicting the effects of climate change on the fertility of aquatic animals using a meta-analytic approach
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Analytic dataset informing prediction of subterranean cave and mine ambient temperatures
<p>Caves and other subterranean features provide unique environments for many species. The importance of cave microclimate is particularly relevant at temperate latitudes where bats make seasonal use of caves for hibernation. White-nose syndrome (WNS), a fungal disease that has devastated populations of hibernating bats across eastern and central North America, has brought renewed interest in bat hibernation and hibernaculum conditions. A recent review synthesized current understanding of cave climatology, exploring the qualitative relationship between cave and surface climate with implications for hibernaculum suitability. However, a more quantitative understanding of the conditions in which bats hibernate and how they may promote or mediate WNS impacts is required. We compiled subterranean temperatures from caves and mines across the western United States and Canada to: a) quantify the hypothesized relationship between mean annual surface temperature (MAST) and subterranean temperature and how it is influenced by measurable site attributes, and b) use readily available gridded data to predict and continuously map the range of temperatures that may be available in caves and mines. Our analysis supports qualitative predictions that subterranean winter temperatures are correlated with MAST, that temperatures are warmer and less variable farther from the surface, and that even deep within caves temperatures tend to be lower than MAST. Effects of other site attributes (e.g., topography, vegetation, precipitation) on subterranean temperatures were not detected. We then assessed the plausibility of model-predicted temperatures using knowledge of winter bat distributions and preferred hibernaculum temperatures. Our model unavoidably simplifies complex subterranean environments, and is not intended to explain all variability in subterranean temperatures. Rather, our results offer researchers and managers improved broad-scale estimates of the geographic distribution of potential hibernaculum conditions compared to reliance on MAST alone. We expect this information to better support range-scale estimation of winter bat distributions and projection of likely WNS impacts across the West. We suggest that our model predictions should serve as hypotheses to be further tested and refined as additional data become available. </p>
Analytical prediction of scattering properties of spheroidal dust particles with machine learning
<p>This respository includes the data used in the paper "Analytical prediction of scattering properties of spheroidal dust particles with machine learning".</p> <ol> <li>"alldata.zip" represents the extinction and absorption coefficients, and phase matrix elements of spheroids dust particles for both training and test data. These data comes from the Oleg Dubovik's group: <a href="https://www.grasp-open.com/products/spheroid-package-release">https://www.grasp-open.com/products/spheroid-package-release</a>/.</li> <li>"<a href="/api/files/7c2cdcdd-dc47-4a19-adc8-59c944e76e54/tmat_Jacall_norm_intg.pickle?versionId=59294030-26a8-4683-9b26-8e8584c2b44b">tmat_Jacall_norm_intg.pickle</a>" contains the Jacobians simulated from linearized T-matrix model used for training in the paper and "<a href="/api/files/7c2cdcdd-dc47-4a19-adc8-59c944e76e54/tmat_fine_n_intg.pickle?versionId=3d20954d-4318-40bb-a900-a93627915c2e">tmat_fine_n_intg.pickle</a>" involves Jacobians used for testing.</li> </ol>
Predicting Weather Disruptions for the ICC Champions Trophy 2025 in Pakistan Using Machine Learning and Data Analytics
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Use of a Predictive Analytics Algorithm to Optimize Weaning of Inotropes Following Pediatric Cardiac Surgery
ClinicalTrials.gov study NCT04600700. IPD Sharing: NO. Countries: 1. Publications: 1.
Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits
ClinicalTrials.gov study NCT06245694. IPD Sharing: NO. Countries: 1. Publications: 14.
Predictive Analytics for Theranosis in RA
ClinicalTrials.gov study NCT02928276. IPD Sharing: NO. Countries: 2. Publications: 1.
Predictive Analytics and Peer-Driven Intervention for Guideline-based Care for Sleep Apnea
ClinicalTrials.gov study NCT03345524. IPD Sharing: NO. Countries: 1. Publications: 23.
Predictive Analytics and Computer Visualization Enhances Patient Safety to Prevent Falls
ClinicalTrials.gov study NCT06339125. IPD Sharing: NO. Countries: 0. Publications: 9.
Pathogen Reduction Evaluation & Predictive Analytical Rating Score
ClinicalTrials.gov study NCT02783313. IPD Sharing: UNDECIDED. Countries: 3. Publications: 2.
Analytic dataset informing prediction of subterranean cave and mine ambient temperatures
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Teva Asthma Predictive Analytics Study
ClinicalTrials.gov study NCT04997304. IPD Sharing: NO. Countries: 1. Publications: 0.
Autoantibody Epitope Spreading in the Pre-Clinical Phase Predicts Progression to Rheumatoid Arthritis [ANALYTE: Cytokine or chemokine]
GEO Series GSE32019. Homo sapiens. 556 samples. Type: Protein profiling by protein array.
Autoantibody Epitope Spreading in the Pre-Clinical Phase Predicts Progression to Rheumatoid Arthritis [ANALYTE: ANTIGEN]
GEO Series GSE32016. Homo sapiens. 559 samples. Type: Protein profiling by protein array.
Pre-analytical considerations in quantifying circulating miRNAs that predict end-stage kidney disease in diabetes
GEO Series GSE266819. Homo sapiens. 32 samples. Type: Non-coding RNA profiling by high throughput sequencing.
ScienceDex guides
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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.