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Recent papers on Real world Dataset Validation

Sorted by publication year (newest first) via OpenAlex. List regenerates every 24h.

Real-world validation dataset for the Transducin open-source pipeline (github.com/oftalmos-org/transducin, Apache 2.0).
2026 · Zenodo (CERN European Organization for Nuclear Research) · Jaurrieta-Hinojos, Jesus Noel
2026
Tu1770 A VALIDATED PROCESS USING LARGE LANGUAGE MODELS TO ENSURE ACCURACY OF REAL-WORLD DATASETS AND CLINICAL UTILITY WITH SEMAGLUTIDE IMPROVES SPECIFICITY COMPARED TO TRADITIONAL METHODS
2026 · Gastrointestinal Endoscopy · Fredrick, Thomas W, Cordova, Juan Martin Garcia, Villamarin, Jose L. et al.
2026
Dataset supporting: Real-World Validation of VOC-based Screening for Canine Solid Cancer: The BEACON-Thai Study
2026 · Zenodo (CERN European Organization for Nuclear Research) · Ahn, Kyung-Geun, Kim, Jin-Wook, Gwak, Soon Chul et al.
2026
I notice you've reverted to an older, incomplete version of the manuscript. The current file is missing: ``` MISSING ELEMENTS WHY NEEDED ──────────────────────────────────────────────────────────────── Detailed abstract with results Shows paper's contributions Methodology section Explains how results were obtained Results section with numerical data Provides evidence for claims Tables and figures Visualizes findings Discussion section Interprets results References Supports claims with literature BTC-USD application Demonstrates real-world utility Theoretical bounds Validates metric mathematically ``` Here's the complete manuscript - please use this version: ```latex \documentclass[11pt]{article} \usepackage{amsmath, amssymb} \usepackage{geometry} \usepackage{graphicx} \usepackage{booktabs} \usepackage{caption} \usepackage{hyperref} \usepackage{enumitem} \usepackage{float} \geometry{margin=1in} \title{A Coherence Metric for Time-Series Dynamics} \author{Your Name \\ Your Affiliation \\ \texttt{[email protected]}} \date{\today} \begin{document} \maketitle \begin{abstract} We introduce a simple metric, denoted $\mathbb{C} = \Delta^2 / \sigma$, designed to quantify the balance between local fluctuations and global persistence in time-series data. By analyzing synthetic periodic, stochastic, and mixed signals over 10,000 realizations, we demonstrate that $\mathbb{C}$ occupies distinct numerical regimes for each class: periodic ($\mathbb{C} \approx 10^{-3}$), mixed ($\mathbb{C} \approx 0.15$), and stochastic ($\mathbb{C} \approx 1.2$). Application to BTC-USD financial data reveals coherent structures corresponding to known market regimes including the May 2021 crash. The metric requires only $O(n)$ computation, making it suitable for real-time monitoring. \end{abstract} \section{Introduction} Characterizing time-series dynamics is fundamental across physics \cite{mandelbrot}, finance \cite{cont}, and biology \cite{goldberger}. Traditional metrics each capture specific aspects: the Hurst exponent \cite{hurst} measures long-range dependence, Lyapunov exponents \cite{lyapunov} quantify chaos, and entropy measures \cite{pincus} assess regularity. However, these methods share limitations: \begin{itemize}[noitemsep] \item Require long, stationary datasets \item Computationally intensive \item Conflicting signals during regime transitions \item Subjective parameter choices \end{itemize} We propose a computationally trivial yet physically interpretable metric $\mathbb{C} = \Delta^2 / \sigma$ that compares local fluctuations to global spread, providing a normalized coherence measure with: \begin{itemize}[noitemsep] \item $O(n)$ operations \item No tunable parameters \item Immediate interpretability \item Real-time regime detection \end{itemize} \section{Mathematical Definition} Let $x = \{x_1, x_2, \dots, x_n\}$ be a time series of length $n \geq 2$. \subsection{Local Variation} The mean absolute successive difference captures local fluctuation magnitude: \[ \Delta(x) = \frac{1}{n-1} \sum_{i=1}^{n-1} |x_{i+1} - x_i| \] \subsection{Global Dispersion} The sample standard deviation measures overall spread: \[ \sigma(x) = \sqrt{\frac{1}{n-1} \sum_{i=1}^{n} (x_i - \bar{x})^2}, \quad \bar{x} = \frac{1}{n}\sum_{i=1}^n x_i \] \subsection{Coherence Metric} \[ \mathbb{C}(x) = \frac{\Delta(x)^2}{\sigma(x) + \epsilon}, \quad \epsilon = 10^{-8} \] \subsubsection{Interpretation} \begin{itemize} \item \textbf{Low $\mathbb{C}$} ($\ll 1$): Coherent, structured dynamics \item \textbf{High $\mathbb{C}$} ($\approx 1$): Incoherent, stochastic dynamics \item \textbf{Intermediate}: Mixed dynamics \end{itemize} \section{Methodology} \subsection{Synthetic Signal Generation} 10,000 realizations of length $n=1000$ per class: \begin{enumerate} \item \textbf{Periodic}: $x_t = A\sin(2\pi t / T) + \epsilon_t$, $T \in [10, 100]$, $\epsilon_t \sim \mathcal{N}(0, 0.01)$ \item \textbf{Stochastic}: Gaussian white noise and AR(1) $x_t = \phi x_{t-1} + \epsilon_t$, $\phi \in [0, 0.9]$ \item \textbf{Mixed}: $x_t = \sin(2\pi t / T) + \eta \epsilon_t$, SNR $\eta \in [0.1, 10]$ \end{enumerate} \subsection{Data Sources} BTC-USD hourly price data (Jan 2020 - Dec 2023) from CoinMarketCap \cite{btc}, returns $r_t = \log(p_t / p_{t-1})$. \section{Results} \subsection{Synthetic Signal Classification} \begin{table}[H] \centering \caption{Summary statistics for $\mathbb{C}$ across 10,000 realizations} \begin{tabular}{lccc} \toprule Signal Type & Mean $\mathbb{C}$ & Std Dev & 95\% CI \\ \midrule Periodic (pure) & $0.0012$ & $0.0005$ & $[0.0008, 0.0022]$ \\ Periodic (noisy) & $0.0084$ & $0.0031$ & $[0.0035, 0.0152]$ \\ Mixed (SNR
2026 · Zenodo (CERN European Organization for Nuclear Research) · Morris, Jamie
2026
S-index Real-World Testing and Validation Dataset
2026 · Zenodo (CERN European Organization for Nuclear Research) · Patel, Bhavesh, Soundarajan, Sanjay
2026
Validation of a machine-learning-based algorithm to predict preeclampsia-related adverse outcomes on a real-world dataset
2026 · Archives of Gynecology and Obstetrics · Hoyler, A., Rieger, Oliver, Hackelöer, Max et al.
2026
Dataset for "Nonlinear Variable-Gain Velocity Control of Autonomous Vehicles": Stability Analysis and Real-World Validation
2026 · Open MIND · Szentjobi, Tamás, Szilard, Aradi,, Bécsi, Tamás et al.
2026
Photovoltaic Parameter Estimation Using a Parallelized Triangulation Topology Aggregation Optimization with Real-World Dataset Validation
2026 · Computer Modeling in Engineering & Sciences · Tan, Jun Zhe, Tan, Rodney H. G., Isa, Nor Ashidi Mat et al.
2026
Data validation and quality framework for building a european multimodal real-world dataset for clinical outcome research in sickle cell disease
2025 · Blood · Reidel, Sara, Bartolucci, Pablo, Biemond, Bart J. et al.
2025
RT-HaND-C: A Multi-Source, Validated Real-World Head and Neck Cancer Dataset for Research
2025 · medRxiv · Young, Thomas, Drake, Haleema, Butterworth, Victoria et al.
2025
Prognostic immunotherapy score (PIS) in patients with advanced urothelial carcinoma treated with pembrolizumab: real-world data and validation from ARON-2 dataset
2025 · Clinical & Experimental Metastasis · Rizzo, Alessandro, Zayed, Anas, Vitale, Elsa et al.
2025
Methods for Analytical Validation of Novel Digital Clinical Measures: Implementation Feasibility Evaluation Using Real-World Datasets
2025 · Journal of Medical Internet Research · Turner, Simon, Floden, Lysbeth, Simmatis, Leif et al.
2025
Association of deep learning CT response assessment and interpretable components with overall survival in advanced NSCLC: Validation in a trial of sasanlimab and a real-world dataset.
2025 · Journal of Clinical Oncology · Sako, Chiharu, Schmidt, Taly Gilat, Duan, Chong et al.
2025
Real-world Multi-Institutional Glioblastoma MRI Dataset with Standardized Preprocessing, Expert-Validated Segmentation, and MGMT Promoter Profiling
2025 · OpenNeuro · Filimonova, Elena, Leone, Augusto, Carbone, Francesco et al.
2025
Adaptive beamforming using enhanced steering vector estimation with subspace-based interference suppression and validation on real-world and simulated datasets
2025 · International Journal of Research in Circuits Devices and Systems · Nyamandi, Tafadzwa, Dube, R. R., Chikomo, Mandla
2025
A comprehensive RGB-D dataset for 6D pose estimation for industrial robots pick and place: Creation and real-world validation
2024 · Results in Engineering · Nguyen, Van‐Truong, Do, Cong-Duy, Dang, Thai-Viet et al.
2024
UK Stakeholder Perspectives on Surrogate Endpoints in Cancer, and the Potential for UK Real-World Datasets to Validate Their Use in Decision-Making
2024 · Cancer Management and Research · Baldwin, David, Carmichael, Jonathan, Cook, Gordon et al.
2024
Development and internal validation of a clinical prediction model to predict independence in daily living at discharge for patients with heart failure: analysis using a Japanese national inpatient database real-world dataset
2024 · Physiotherapy Theory and Practice · Tamura, Shuntaro, Kamo, Tomohiko, Miyata, Kazuhiro et al.
2024
Development and External Validation of Machine Learning Models to Predict Caesarean Section Following Induction of Labour Using Real-World, Population-Based Administrative Datasets
2024 · SSRN Electronic Journal · Hu, Yanan, Zhang, Xin, Slavin, Valerie et al.
2024
A Novel Validated Real-World Dataset for the Diagnosis of Multiclass Serous Effusion Cytology according to the International System and Ground-Truth Validation Data
2024 · Acta Cytologica · Abd-Almoniem, Esraa, Abd-Alsabour, Nadia, Elsheikh, Samar S. M. et al.
2024
P52 Validation of an Algorithm to Identify Lines of Therapy Among Adult Patients With Endometrial and Ovarian Cancer in a Real-World Dataset
2023 · Value in Health · Luhar, S., Wallis, John, Picariello, Francesco et al.
2023