This data contains Text Function, Date, Data Validation.

The construction of a robust healthcare information system is fundamental to enhancing countries’ capabilities in the surveillance and control of hepatitis B virus (HBV). Making use of China’s rapidly expanding primary healthcare system, this innovative approach using big data and machine learning (ML) could help towards the World Health Organization’s (WHO) HBV infection elimination goals of reaching 90% diagnosis and treatment rates by 2030. We aimed to develop and validate HBV detection models using routine clinical data to improve the detection of HBV and support the development of effective interventions to mitigate the impact of this disease in China. Relevant data records extracted from the Family Medicine Clinic of the University of Hong Kong-Shenzhen Hospital’s Hospital Information System were structuralized using state-of-the-art Natural Language Processing techniques. Several ML models have been used to develop HBV risk assessment models. The performance of the ML model was then interpreted using the Shapley value (SHAP) and validated using cohort data randomly divided at a ratio of 2:1 using a five-fold cross-validation framework. The patterns of physical complaints of patients with and without HBV infection were identified by processing 158,988 clinic attendance records. After removing cases without any clinical parameters from the derivation sample (n = 105,992), 27,392 cases were analysed using six modelling methods. A simplified model for HBV using patients’ physical complaints and parameters was developed with good discrimination (AUC = 0.78) and calibration (goodness of fit test p-value >0.05). Suspected case detection models of HBV, showing potential for clinical deployment, have been developed to improve HBV surveillance in primary care setting in China. (Word count: 264) This study has developed a suspected case detection model for HBV, which can facilitate early identification and treatment of HBV in the primary care setting in China, contributing towards the achievement of WHO’s elimination goals of HBV infections.We utilized the state-of-art natural language processing techniques to structure the data records, leading to the development of a robust healthcare information system which enhances the surveillance and control of HBV in China. This study has developed a suspected case detection model for HBV, which can facilitate early identification and treatment of HBV in the primary care setting in China, contributing towards the achievement of WHO’s elimination goals of HBV infections. We utilized the state-of-art natural language processing techniques to structure the data records, leading to the development of a robust healthcare information system which enhances the surveillance and control of HBV in China.

This Data contains the PEN-Predictor-Keras-Model as well as the 100 validation data sets.

Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participants. High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting. Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size. We also show that feature selection if performed on pooled training and testing data is contributing to bias considerably more than parameter tuning. In addition, the contribution to bias by data dimensionality, hyper-parameter space and number of CV folds was explored, and validation methods were compared with discriminable data. The results suggest how to design robust testing methodologies when working with small datasets and how to interpret the results of other studies based on what validation method was used.

One of NASA’s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation techniques address this problem: given a vector of sensor readings, decide whether sensors have failed, therefore producing bad data. We take in this paper a probabilistic approach, using Bayesian networks, to diagnosis and sensor validation, and investigate several relevant but slightly different Bayesian network queries. We emphasize that on-board inference can be performed on a compiled model, giving fast and predictable execution times. Our results are illustrated using an electrical power system, and we show that a Bayesian network with over 400 nodes can be compiled into an arithmetic circuit that can correctly answer queries in less than 500 microseconds on average. Reference: O. J. Mengshoel, A. Darwiche, and S. Uckun, "Sensor Validation using Bayesian Networks." In Proc. of the 9th International Symposium on Artificial Intelligence, Robotics, and Automation in Space (iSAIRAS-08), Los Angeles, CA, 2008. BibTex Reference: @inproceedings{mengshoel08sensor, author = {Mengshoel, O. J. and Darwiche, A. and Uckun, S.}, title = {Sensor Validation using {Bayesian} Networks}, booktitle = {Proceedings of the 9th International Symposium on Artificial Intelligence, Robotics, and Automation in Space (iSAIRAS-08)}, year = {2008} }

This brief literature survey groups the (numerical) validation methods and emphasizes the contradictions and confusion considering bias, variance and predictive performance. A multicriteria decision-making analysis has been made using the sum of absolute ranking differences (SRD), illustrated with five case studies (seven examples). SRD was applied to compare external and cross-validation techniques, indicators of predictive performance, and to select optimal methods to determine the applicability domain (AD). The ordering of model validation methods was in accordance with the sayings of original authors, but they are contradictory within each other, suggesting that any variant of cross-validation can be superior or inferior to other variants depending on the algorithm, data structure and circumstances applied. A simple fivefold cross-validation proved to be superior to the Bayesian Information Criterion in the vast majority of situations. It is simply not sufficient to test a numerical validation method in one situation only, even if it is a well defined one. SRD as a preferable multicriteria decision-making algorithm is suitable for tailoring the techniques for validation, and for the optimal determination of the applicability domain according to the dataset in question.

This data package contains information on Structured Product Labeling (SPL) Terminology for SPL validation procedures and information on performing SPL validations.

The email validation tools market is experiencing robust growth, driven by the increasing need for businesses to maintain clean and accurate email lists for effective marketing campaigns. The rising adoption of email marketing as a primary communication channel, coupled with stricter data privacy regulations like GDPR and CCPA, necessitates the use of tools that ensure email deliverability and prevent bounces. This market, estimated at $500 million in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $1.5 billion by 2033. This expansion is fueled by the growing sophistication of email validation techniques, including real-time verification, syntax checks, and mailbox monitoring, offering businesses more robust solutions to improve their email marketing ROI. Key market segments include small and medium-sized businesses (SMBs), large enterprises, and email marketing agencies, each exhibiting varying levels of adoption and spending based on their specific needs and email marketing strategies. The competitive landscape is characterized by a mix of established players and emerging startups, offering a range of features and pricing models to cater to diverse customer requirements. The market's growth is, however, subject to factors like increasing costs associated with maintaining data accuracy and the potential for false positives in email verification. The key players in this dynamic market, such as Mailgun, BriteVerify, and similar companies, are continuously innovating to improve accuracy, speed, and integration with other marketing automation platforms. The market's geographical distribution is diverse, with North America and Europe currently holding significant market share due to higher email marketing adoption rates and a robust technological infrastructure. However, Asia-Pacific and other emerging markets are poised for considerable growth in the coming years due to increasing internet penetration and rising adoption of digital marketing techniques. The ongoing evolution of email marketing strategies, the increasing emphasis on data hygiene, and the rise of artificial intelligence in email verification are likely to further shape the trajectory of this market in the years to come, leading to further innovation and growth.

The objective of the fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to Artificial Intelligence for automatization and improving reliability of control processes.

One table with data used to validate aerial fish surveys in Prince William Sound, Alaska. Data includes: date, location, latitude, longitude, aerial ID, validation ID, total length and validation method. Various catch methods were used to obtain fish samples for aerial validations, including: cast net, GoPro, hydroacoustics, jig, dip net, gillnet, purse seine, photo and visual identification.

As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.

Data for method validation on detecting pmp-glucose by HPLC

Background and ObjectiveThe use of valid surrogate endpoints can accelerate the development of phase III trials. Numerous validation methods have been proposed with the most popular used in a context of meta-analyses, based on a two-step analysis strategy. For two failure time endpoints, two association measures are usually considered, Kendall’s τ at individual level and adjusted R2 () at trial level. However, is not always available mainly due to model estimation constraints. More recently, we proposed a one-step validation method based on a joint frailty model, with the aim of reducing estimation issues and estimation bias on the surrogacy evaluation criteria. The model was quite robust with satisfactory results obtained in simulation studies. This study seeks to popularize this new surrogate endpoints validation approach by making the method available in a user-friendly R package.MethodsWe provide numerous tools in the frailtypack R package, including more flexible functions, for the validation of candidate surrogate endpoints using data from multiple randomized clinical trials.ResultsWe implemented the surrogate threshold effect which is used in combination with to make decisions concerning the validity of the surrogate endpoints. It is also possible thanks to frailtypack to predict the treatment effect on the true endpoint in a new trial using the treatment effect observed on the surrogate endpoint. The leave-one-out cross-validation is available for assessing the accuracy of the prediction using the joint surrogate model. Other tools include data generation, simulation study and graphic representations. We illustrate the use of the new functions with both real data and simulated data.ConclusionThis article proposes new attractive and well developed tools for validating failure time surrogate endpoints.

VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer.

Validation Data from VQA v2 Dataset - https://visualqa.org/index.html Validation images - 40,504 images Validation questions 2017 v2.0 - 214,354 questions Validation annotations 2017 v2.0 - 2,143,540 answers

This dataset is only for ease of use in kaggle environments. I do not take any credit for the creation of the dataset. The annotations in this dataset belong to the VQA Consortium and are licensed under a Commons Attribution 4.0 International License.

Copyright © 2015, VQA Consortium. All rights reserved. Redistribution and use software in source and binary form, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the VQA Consortium nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE AND ANNOTATIONS ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

The global thermal validation system market is experiencing robust growth, driven by increasing regulatory scrutiny across pharmaceutical, biotechnology, and food processing industries. Stringent quality control standards and the need for accurate temperature monitoring throughout the manufacturing and storage processes are key factors fueling market expansion. The market is segmented by system type (e.g., autoclaves, ovens, incubators), application (pharmaceutical, food & beverage, etc.), and end-user (contract research organizations, pharmaceutical manufacturers, etc.). Technological advancements, such as the integration of IoT sensors and cloud-based data analysis, are enhancing the capabilities of thermal validation systems, leading to improved efficiency and data management. Furthermore, the rising demand for sophisticated validation techniques to comply with international regulations like GMP and FDA guidelines is further bolstering market growth. We estimate the 2025 market size to be approximately $850 million, growing at a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth reflects the increasing adoption of advanced technologies and the expanding regulatory landscape in key regions like North America and Europe. Competition in the thermal validation system market is intense, with several established players and emerging companies vying for market share. Key players like Kaye, Ellab, and Thermo Fisher Scientific are leveraging their strong brand reputation and technological expertise to maintain market leadership. However, smaller, specialized firms are also gaining traction by offering niche solutions and innovative technologies. The market is expected to witness further consolidation in the coming years, with strategic acquisitions and partnerships playing a crucial role in shaping the competitive landscape. Geographic expansion, particularly in emerging markets in Asia-Pacific and Latin America, represents a significant growth opportunity for market participants. The restraints to growth include the high initial investment cost associated with implementing thermal validation systems and the need for skilled personnel to operate and maintain these systems.

The GPM Ground Validation NOAA CPC Morphing Technique (CMORPH) IFloodS dataset consists of global precipitation analyses data produced by the NOAA Climate Prediction Center (CPC). The Iowa Flood Studies (IFloodS) campaign was a ground measurement campaign that took place in eastern Iowa from May 1 to June 15, 2013. The goals of the campaign were to collect detailed measurements of precipitation at the Earth'ssurface using ground instruments and advanced weather radars and, simultaneously, collect data from satellites passing overhead. The CPC morphing technique uses precipitation estimates from low orbiter satellite microwave observations to produce global precipitation analyses at a high temporal and spatial resolution. Data has been selected for the Iowa Flood Studies (IFloodS) field campaign which took place from April 1, 2013 to June 30, 2013. The dataset includes both the near real-time raw data and bias corrected data from NOAA in binary and netCDF format.

We searched PubMed, EMBASE and the Cochrane Library for reviews that described the tools and methods applied to define cohorts used for patient stratification or validation of patient clustering. We focused on cancer, stroke, and Alzheimer’s disease (AD) and limited the searches to reports in English, French, German, Italian and Spanish, published from 2005 to April 2020. Two authors screened the records, and one extracted the key information from each included review. The result of the screening process was reported through a PRISMA flowchart.

Policy search methods provide a heuristic mapping between observations and decisions and have been widely used in reservoir control studies. However, recent studies have observed a tendency for policy search methods to overfit to the hydrologic data used in training, particularly the sequence of flood and drought events. This technical note develops an extension of bootstrap aggregation (bagging) and cross-validation techniques, inspired by the machine learning literature, to improve control policy performance on out-of-sample hydrology. We explore these methods using a case study of Folsom Reservoir, California using control policies structured as binary trees and daily streamflow resampling based on the paleo-inflow record. Results show that calibration-validation strategies for policy selection and certain ensemble aggregation methods can improve out-of-sample tradeoffs between water supply and flood risk objectives over baseline performance given fixed computational costs. These results highlight the potential to improve policy search methodologies by leveraging well-established model training strategies from machine learning.

HD Map Validation Market Outlook

According to our latest research, the global HD Map Validation market size in 2024 stands at USD 1.22 billion, reflecting the sector’s rapid evolution in response to the surging demand for high-precision mapping solutions across autonomous systems. The market is anticipated to witness a robust compound annual growth rate (CAGR) of 16.3% during the forecast period, reaching an estimated USD 4.06 billion by 2033. This remarkable growth is primarily fueled by increasing investments in autonomous vehicle technology, the proliferation of advanced driver assistance systems (ADAS), and the critical need for real-time, centimeter-level accuracy in digital mapping for next-generation mobility platforms.

One of the key growth drivers propelling the HD Map Validation market is the accelerated adoption of autonomous vehicles and smart mobility solutions. As automotive manufacturers and technology companies race to bring fully autonomous cars to market, the demand for highly accurate and frequently updated HD maps has soared. These maps serve as the digital backbone for autonomous navigation, enabling vehicles to interpret their surroundings with unmatched precision. The validation of these maps is essential to ensure safety, regulatory compliance, and operational efficiency, especially as vehicles transition from controlled environments to complex urban landscapes. The integration of AI-driven validation techniques and real-time data processing capabilities has further enhanced the reliability and scalability of HD map validation processes, making them indispensable to the future of mobility.

Moreover, the expansion of advanced driver assistance systems (ADAS) and the growing prevalence of robotics and unmanned aerial vehicles (drones) in industrial and commercial applications are contributing significantly to market growth. ADAS technologies rely heavily on HD maps for features like lane keeping, adaptive cruise control, and automated parking. Likewise, robotics and drones require high-definition spatial awareness for navigation and task execution in dynamic environments. The increasing complexity of these applications necessitates robust validation methodologies, including simulation, real-world testing, and crowdsourced validation, to maintain map accuracy and integrity. As a result, stakeholders across the automotive, logistics, and robotics sectors are investing heavily in HD map validation solutions to support their innovation agendas.

From a regional perspective, the HD Map Validation market exhibits strong momentum in North America, Europe, and Asia Pacific, with each region presenting unique opportunities and challenges. North America leads in terms of technological advancements and early adoption of autonomous vehicles, supported by substantial investments from industry giants and startups alike. Europe follows closely, driven by stringent regulatory frameworks and a robust automotive sector. Asia Pacific, particularly China, Japan, and South Korea, is emerging as a high-growth market, fueled by rapid urbanization, government initiatives, and a burgeoning electric and autonomous vehicle ecosystem. These regions collectively account for the lion’s share of global revenue, with ongoing R&D activities and strategic partnerships further accelerating market expansion.

Component Analysis

The Component segment of the HD Map Validation market is broadly categorized into software, services, and hardware, each playing a pivotal role in the value chain. Software solutions are at the heart of map validation, offering advanced algorithms for data processing, feature extraction, and error detection. These platforms leverage machine learning and artificial intelligence to automate the validation process, significantly reducing manual intervention and enhancing accuracy. The software segment also encompasses cloud-based platforms that facilitate real-time map updates and remote validation, catering to the growing demand for scalable and flexible solutions. As the com

This research effort represents our initial method development research for soil gas, sewer gas, and indoor air samples for PFAS analyses as related to vapor intrusion. The product and associated subsequent products will be consistently updated leading to a constantly expanding database incorporating additional experiments. This dataset is associated with the following publication: Hayes, H., C. Lutes, N. Watson, D. Benton, D. Hanigan, S. McCoy, C. Holton, K. Bronstein, B. Schumacher, J. Zimmerman, and A. Williams. Laboratory development and validation of vapor phase PFAS methods for soil gas, sewer gas, and indoor air. Environmental Science: Atmospheres. Royal Society of Chemistry, Cambridge, UK, 5: 94-109, (2025).