Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.

Question Paper Solutions of chapter Validation strategies of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

The goal of the SHRP 2 Project L33 Validation of Urban Freeway Models was to assess and enhance the predictive travel time reliability models developed in the SHRP 2 Project L03, Analytic Procedures for Determining the Impacts of Reliability Mitigation Strategies. SHRP 2 Project L03, which concluded in 2010, developed two categories of reliability models to be used for the estimation or prediction of travel time reliability within planning, programming, and systems management contexts: data-rich and data-poor models. The objectives of Project L33 were the following: • The first was to validate the most important models – the “Data Poor” and “Data Rich” models with new datasets. • The second objective was to assess the validation outcomes to recommend potential enhancements. • The third was to explore enhancements and develop a final set of predictive equations. • The fourth was to validate the enhanced models. • The last was to develop a clear set of application guidelines for practitioner use of the project outputs. The datasets in these 5 zip files are in support of SHRP 2 Report S2-L33-RW-1, Validation of Urban Freeway Models, https://rosap.ntl.bts.gov/view/dot/3604 The 5 zip files contain a total of 60 comma separated value (.csv) files. The compressed zip files total 3.8 GB in size. The files have been uploaded as-is; no further documentation was supplied. These files can be unzipped using any zip compression/decompression software. The files can be read in any simple text editor. [software requirements] Note: Data files larger than 1GB each. Direct data download links: L03-01: https://doi.org/10.21949/1500858 L03-02: https://doi.org/10.21949/1500868 L03-03: https://doi.org/10.21949/1500869 L03-04: https://doi.org/10.21949/1500870 L03-05: https://doi.org/10.21949/1500871

File Validation and Training Statistics

Validation, Training, and Testing Statistics for tasksource/leandojo Files

By tasksource (From Huggingface) [source]

About this dataset

The tasksource/leandojo: File Validation, Training, and Testing Statistics dataset is a comprehensive collection of information regarding the validation, training, and testing processes of files in the tasksource/leandojo repository. This dataset is essential for gaining insights into the file management practices within this specific repository.

The dataset consists of three distinct files: validation.csv, train.csv, and test.csv. Each file serves a unique purpose in providing statistics and information about the different stages involved in managing files within the repository.

In validation.csv, you will find detailed information about the validation process undergone by each file. This includes data such as file paths within the repository (file_path), full names of each file (full_name), associated commit IDs (commit), traced tactics implemented (traced_tactics), URLs pointing to each file (url), and respective start and end dates for validation.

train.csv focuses on providing valuable statistics related to the training phase of files. Here, you can access data such as file paths within the repository (file_path), full names of individual files (full_name), associated commit IDs (commit), traced tactics utilized during training activities (traced_tactics), URLs linking to each specific file undergoing training procedures (url).

Lastly, test.csv encompasses pertinent statistics concerning testing activities performed on different files within the tasksource/leandojo repository. This data includes information such as file paths within the repo structure (file_path), full names assigned to each individual file tested (full_name) , associated commit IDs linked with these files' versions being tested(commit) , traced tactics incorporated during testing procedures regarded(traced_tactics) ,relevant URLs directing to specific tested files(url).

By exploring this comprehensive dataset consisting of three separate CSV files - validation.csv, train.csv, test.csv - researchers can gain crucial insights into how effective strategies pertaining to validating ,training or testing tasks have been implemented in order to maintain high-quality standards within the tasksource/leandojo repository

How to use the dataset

• Familiarize Yourself with the Dataset Structure:

  • The dataset consists of three separate files: validation.csv, train.csv, and test.csv.
  • Each file contains multiple columns providing different information about file validation, training, and testing.

• Explore the Columns:

  • 'file_path': This column represents the path of the file within the repository.
  • 'full_name': This column displays the full name of each file.
  • 'commit': The commit ID associated with each file is provided in this column.
  • 'traced_tactics': The tactics traced in each file are listed in this column.
  • 'url': This column provides the URL of each file.

• Understand Each File's Purpose:

Validation.csv - This file contains information related to the validation process of files in the tasksource/leandojo repository.

Train.csv - Utilize this file if you need statistics and information regarding the training phase of files in tasksource/leandojo repository.

Test.csv - For insights into statistics and information about testing individual files within tasksource/leandojo repository, refer to this file.

• Generate Insights & Analyze Data:
• Once you have a clear understanding of each column's purpose, you can start generating insights from your analysis using various statistical techniques or machine learning algorithms.

• Explore patterns or trends by examining specific columns such as 'traced_tactics' or analyzing multiple columns together.

• Combine Multiple Files (if necessary):

• If required, you can merge/correlate data across different csv files based on common fields such as 'file_path', 'full_name', or 'commit'.

• Visualize the Data (Optional):

• To enhance your analysis, consider creating visualizations such as plots, charts, or graphs. Visualization can offer a clear representation of patterns or relationships within the dataset.

• Obtain Further Information:

• If you need additional details about any specific file, make use of the provided 'url' column to access further information.

Remember that this guide provides a general overview of how to utilize this dataset effectively. Feel ...

The data analysis practices associated with hydrogen–deuterium exchange mass spectrometry (HX-MS) lag far behind that of most other MS-based protein analysis tools. A reliance on external tools from other fields and a persistent need for manual data validation restrict this powerful technology to the expert user. Here, we provide an extensive upgrade to the HX data analysis suite available in the Mass Spec Studio in the form of two new apps (HX-PIPE and HX-DEAL), completing a workflow that provides an HX-tailored peptide identification capability, accelerated validation routines, automated spectral deconvolution strategies, and a rich set of exportable graphics and statistical reports. With these new tools, we demonstrate that the peptide identifications obtained from undeuterated samples generated at the start of a project contain information that helps predict and control the extent of manual validation required. We also uncover a large fraction of HX-usable peptides that remains unidentified in most experiments. We show that automated spectral deconvolution routines can identify exchange regimes in a project-wide manner, although they remain difficult to accurately assign in all scenarios. Taken together, these new tools provide a robust and complete solution suitable for the analysis of high-complexity HX-MS data.

The temporal split-sample approach is the most common method to allocate observed data into calibration and validation groups for hydrologic model calibration. Often, calibration and validation data are split 50:50, where a hydrologic model is calibrated using the first half of the observed data and the second half is used for model validation. However, there is no standard strategy for how to split the data. This may result in different distributions in the observed hydrologic variable (e.g., wetter conditions in one half compared to the other) that could affect simulation results. We investigated this uncertainty by calibrating Soil and Water Assessment Tool hydrologic models with observed streamflow for three watersheds within the United States. We used six temporal data calibration/validation splitting strategies for each watershed (33:67, 50:50, and 67:33 with the calibration period occurring first, then the same three with the validation period occurring first). We found that the choice of split could have a large enough impact to alter conclusions about model performance. Through different calibrations of parameter sets, the choice of data splitting strategy also led to different simulations of streamflow, snowmelt, evapotranspiration, soil water storage, surface runoff, and groundwater flow. The impact of this research is an improved understanding of uncertainties caused by the temporal split-sample approach and the need to carefully consider calibration and validation periods for hydrologic modeling to minimize uncertainties during its use. The file "Research_Data_for_Myers_et_al.zip" includes the water balances and observed data from the study.

Billing-grade Interval Data Validation Market Outlook

According to our latest research, the global billing-grade interval data validation market size reached USD 1.42 billion in 2024, reflecting a robust expansion driven by the increasing demand for accurate and reliable data in utility billing and energy management systems. The market is expected to grow at a CAGR of 13.4% from 2025 to 2033, culminating in a projected market size of USD 4.54 billion by 2033. This substantial growth is primarily fueled by the proliferation of smart grids, the rising adoption of advanced metering infrastructure, and the necessity for regulatory compliance in billing operations across utilities and energy sectors. As per our research, the market’s momentum is underpinned by the convergence of digital transformation initiatives and the critical need for high-integrity interval data validation to support accurate billing and operational efficiency.

The growth trajectory of the billing-grade interval data validation market is significantly influenced by the rapid digitalization of utility infrastructure worldwide. With the deployment of smart meters and IoT-enabled devices, utilities are generating an unprecedented volume of interval data that must be validated for billing and operational purposes. The integration of advanced data analytics and machine learning algorithms into validation processes is enhancing the accuracy and reliability of interval data, minimizing errors, and enabling near real-time validation. This technological advancement is not only reducing manual intervention but also ensuring compliance with increasingly stringent regulatory standards. As utilities and energy providers transition toward more automated and data-centric operations, the demand for robust billing-grade data validation solutions is set to surge, driving market expansion.

Another critical growth factor for the billing-grade interval data validation market is the intensifying focus on energy efficiency and demand-side management. Governments and regulatory bodies across the globe are implementing policies to promote energy conservation, necessitating accurate measurement and validation of consumption data. Billing-grade interval data validation plays a pivotal role in ensuring that billings are precise and reflective of actual usage, thereby fostering trust between utilities and end-users. Moreover, the shift toward dynamic pricing models and time-of-use tariffs is making interval data validation indispensable for utilities aiming to optimize revenue streams and offer personalized billing solutions. As a result, both established utilities and emerging energy management firms are investing heavily in advanced validation platforms to stay competitive and meet evolving customer expectations.

The market is also witnessing growth due to the increasing complexity of utility billing systems and the diversification of energy sources, including renewables. The integration of distributed energy resources such as solar and wind into the grid is generating multifaceted data streams that require sophisticated validation to ensure billing accuracy and grid stability. Additionally, the rise of prosumers—consumers who also produce energy—has introduced new challenges in data validation, further amplifying the need for billing-grade solutions. Vendors are responding by developing scalable, interoperable platforms capable of handling diverse data types and validation scenarios. This trend is expected to drive innovation and shape the competitive landscape of the billing-grade interval data validation market over the forecast period.

From a regional perspective, North America continues to dominate the billing-grade interval data validation market, owing to its advanced utility infrastructure, widespread adoption of smart grids, and strong regulatory framework. However, Asia Pacific is emerging as the fastest-growing region, propelled by massive investments in smart grid projects, urbanization, and government initiatives to modernize energy distribution systems. Europe, with its emphasis on sustainability and energy efficiency, is also contributing significantly to market growth. The Middle East & Africa and Latin America, though currently smaller in market share, are expected to witness accelerated adoption as utilities in these regions embark on digital transformation journeys. Overall, the global market is set for dynamic growth, shaped by regional developments and technological advancements.

Component Analys

MAP Data Authoring and Validation Market Outlook

According to our latest research, the global MAP Data Authoring and Validation market size reached USD 2.47 billion in 2024, propelled by the increasing demand for accurate geospatial data across numerous industries. The market is experiencing robust growth, with a CAGR of 13.2% anticipated from 2025 to 2033, projecting the market to reach USD 7.23 billion by 2033. This surge is primarily driven by the proliferation of smart city projects, autonomous vehicle development, and the integration of advanced mapping solutions in various sectors, as per our most recent analysis.

One of the most significant growth factors for the MAP Data Authoring and Validation market is the escalating adoption of location-based services and real-time navigation systems. Industries such as automotive, telecommunications, and urban planning are increasingly reliant on precise mapping data to enable advanced functionalities, including autonomous driving, network planning, and infrastructure development. The evolution of smart transportation and the need for enhanced situational awareness in both civilian and defense sectors further amplify the demand for high-quality map data. Additionally, the integration of artificial intelligence and machine learning algorithms in map data authoring processes has significantly improved the accuracy and speed of data validation, making these solutions indispensable for organizations aiming to maintain a competitive edge in a data-driven landscape.

Another prominent driver is the growing importance of geographic information systems (GIS) in decision-making processes across multiple verticals. As businesses and governments increasingly leverage spatial data analytics for strategic planning, the need for robust map data authoring and validation tools has surged. The expansion of 5G networks and the Internet of Things (IoT) ecosystem has also necessitated the deployment of detailed and up-to-date geospatial datasets to optimize network performance and resource allocation. Furthermore, regulatory frameworks mandating the use of accurate geospatial data for safety and compliance purposes in sectors such as aviation and maritime are fueling the adoption of advanced map data validation solutions.

The market is also witnessing substantial investments in research and development aimed at enhancing the capabilities of map data authoring platforms. Technological advancements, such as cloud-based geospatial data management and the incorporation of real-time data feeds from satellites, drones, and sensors, are transforming the landscape of map data creation and validation. These innovations facilitate the generation of high-resolution, dynamic maps that are critical for applications ranging from urban mobility to environmental monitoring. As the complexity and volume of geospatial data continue to grow, the demand for scalable and automated map data authoring and validation solutions is expected to escalate, further accelerating market expansion.

Regionally, North America continues to dominate the MAP Data Authoring and Validation market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of leading technology providers, high adoption rates of advanced mapping solutions, and substantial investments in smart infrastructure projects are key factors driving regional growth. Asia Pacific, in particular, is emerging as a high-growth region, fueled by rapid urbanization, government initiatives to digitize infrastructure, and the expansion of automotive and telecommunications sectors. Meanwhile, Europe’s focus on sustainable urban development and stringent regulatory standards for geospatial data accuracy further bolster market prospects in the region. Latin America and the Middle East & Africa, while currently accounting for smaller shares, are expected to witness increased adoption of map data solutions as digital transformation initiatives gain momentum.

Component Analysis

The MAP Data Authoring and Validation market is segmented by component into Software and Services, each playing a pivotal role in the ecosystem. Software solutions form the backbone of map data authoring and validation, offering robust platforms for data creation, editing, and verification. These tools leverage advanced algorithms, machine learning, and artificial intelligence to streamline the proce

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.

Record of 24 hours of typical weekday traffic counts (in 3 vehicle classes) at GCTMMM screenline locations (67 sites).

Record of 24 hours of typical weekday traffic counts (in 3 vehicle classes) at Brisbane Strategic Transport Model (BSTM) screenline locations (260 sites).

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.

While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy—coming from robust statistics and optimization—is thus to build a model robust to distributional perturbations. In this paper, we take a different approach to describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an f-divergence ball around the training population. The method, based on conformal inference, achieves (nearly) valid coverage in finite samples, under only the condition that the training data be exchangeable. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it; we develop estimators and prove their consistency for protection and validity of uncertainty estimates under shifts. By experimenting on several large-scale benchmark datasets, including Recht et al.’s CIFAR-v4 and ImageNet-V2 datasets, we provide complementary empirical results that highlight the importance of robust predictive validity.

Security Validation Platform Market Outlook

According to our latest research, the global Security Validation Platform market size reached USD 1.94 billion in 2024. The market is exhibiting robust growth, driven by increasing cybersecurity threats and the need for continuous security posture assessment, and is projected to expand at a CAGR of 13.2% during the forecast period. By 2033, the Security Validation Platform market is forecasted to attain a value of USD 5.53 billion. The market’s expansion is primarily attributed to the accelerating adoption of digital transformation initiatives across industries and the heightened focus on proactive cybersecurity measures.

The rapid evolution of cyber threats and the increasing sophistication of attacks are among the most significant growth drivers for the Security Validation Platform market. Organizations are facing a dynamic threat landscape, with adversaries employing advanced tactics to breach security perimeters. In response, enterprises are prioritizing proactive security validation strategies to identify vulnerabilities before they can be exploited. Security validation platforms provide continuous, automated testing of security controls, enabling organizations to assess their defenses in real-time and address potential gaps. The growing adoption of such platforms is propelled by the need for organizations to stay ahead of threat actors and ensure compliance with stringent regulatory requirements.

Another key growth factor is the surge in digital transformation projects, which has led to an expanded attack surface for enterprises. As businesses migrate to cloud environments, adopt remote work models, and integrate Internet of Things (IoT) devices, the complexity of their IT infrastructures increases. This complexity necessitates advanced security validation solutions capable of providing comprehensive visibility across hybrid and multi-cloud environments. Security validation platforms offer automated, scalable, and continuous testing capabilities, enabling organizations to adapt to evolving security challenges. The integration of artificial intelligence and machine learning within these platforms further enhances their ability to detect sophisticated threats, driving market growth.

The increasing regulatory scrutiny and the rise in data privacy laws globally are also fueling the demand for security validation platforms. Governments and regulatory bodies are imposing stricter cybersecurity standards on organizations, particularly those operating in critical infrastructure sectors such as BFSI, healthcare, and government. Security validation platforms help organizations demonstrate compliance by providing evidence of effective security controls and risk mitigation measures. The growing emphasis on risk management and governance is compelling enterprises to invest in advanced security validation solutions, further accelerating market expansion.

From a regional perspective, North America continues to dominate the Security Validation Platform market, accounting for the largest share in 2024. The region’s leadership is supported by the presence of major cybersecurity vendors, high levels of technology adoption, and a robust regulatory framework. Europe is witnessing steady growth, driven by stringent data protection regulations such as GDPR and increasing cyber incidents. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by rapid digitalization, expanding IT infrastructure, and rising awareness of cybersecurity threats. Latin America and the Middle East & Africa are also experiencing increased adoption of security validation platforms, albeit at a comparatively slower pace due to budgetary constraints and limited cybersecurity maturity.

Component Analysis

The Security Validation Platform market is segmented by component into software, hardware, and services. The software segment holds the largest market share, as organizations increasingly rely on advanced software solutions for automated security testing and continuous validation. These software platforms offer a wide range of functionalities such as breach and attack simulation, vulnerability assessment, and compliance reporting. The integration of artificial intelligence and machine learning within security validation software has significantly enhanced its capability to detect sophisticated threats and automate complex security tasks. The flexibility and scalability of s

The bulk email verification service market is experiencing robust growth, driven by the increasing need for businesses to maintain clean and accurate email lists for effective marketing campaigns. The market's expanding size reflects the rising adoption of email marketing as a primary communication channel across various sectors, including enterprises, governments, and other organizations. While precise figures for market size and CAGR are unavailable in the provided data, considering the current market trends and the growth of related technologies like email marketing automation and data analytics, a reasonable estimate for the 2025 market size could be around $2 billion, growing at a Compound Annual Growth Rate (CAGR) of approximately 15% between 2025 and 2033. This growth is fueled by several factors, including the rising adoption of cloud-based solutions (SaaS and web-based services), the increasing focus on data privacy and compliance regulations (like GDPR and CCPA), and the need to improve email deliverability rates and reduce bounce rates. The market segmentation demonstrates a significant demand across application sectors, with enterprises and government bodies leading the adoption of bulk email verification services. The continued growth of the bulk email verification service market hinges on several factors. The evolving digital landscape necessitates refined email marketing strategies, making data quality paramount. Advancements in artificial intelligence and machine learning further enhance the accuracy and speed of email verification processes. However, challenges remain, such as the evolving methods of spammers and the ongoing need for sophisticated algorithms to counter them. Furthermore, integrating email verification seamlessly into existing marketing workflows remains a key area for service providers to address. Geographic variations exist; North America and Europe are expected to maintain significant market share, while regions like Asia-Pacific are projected to demonstrate strong growth potential, propelled by the expanding digital economies and the adoption of email marketing strategies in developing markets. The competitive landscape comprises a variety of established players and emerging companies, all striving to offer innovative solutions and gain market share.

Data set of development and validation of CEAPC: Self-Report Questionnaire to Characterize Learning Strategies in Computer Programming

Context

This dataset stores separate files of training and validation data for Riiid!

These files are made by following notebook. https://www.kaggle.com/its7171/cv-strategy

You can read these files like:

train1 = pd.read_pickle('../input/riiid-cross-validation-files/cv1_train.pickle')
valid1 = pd.read_pickle('../input/riiid-cross-validation-files/cv1_valid.pickle')

Usage example: https://www.kaggle.com/its7171/riiid-cross-validation-files

Address Validation for Shipping Market Outlook

According to our latest research, the global address validation for shipping market size reached USD 1.82 billion in 2024 and is projected to grow at a robust CAGR of 10.7% during the forecast period, reaching USD 4.10 billion by 2033. This growth is driven by the rapid expansion of e-commerce, increasing demand for seamless logistics solutions, and the need for accurate address data to minimize delivery errors and enhance customer satisfaction. As businesses worldwide place greater emphasis on operational efficiency and customer experience, the adoption of advanced address validation solutions is becoming a strategic imperative across industries.

One of the primary growth factors propelling the address validation for shipping market is the explosive rise of the global e-commerce sector. As online shopping continues to gain traction, businesses are facing unprecedented volumes of shipments that require precise address verification to ensure timely and accurate deliveries. Incorrect or incomplete addresses can lead to failed deliveries, increased operational costs, and poor customer experiences, making address validation software and services indispensable. Additionally, the integration of artificial intelligence and machine learning into address validation solutions is enabling organizations to automate the process, reduce manual intervention, and improve the accuracy of address data at scale.

Another significant driver for the address validation for shipping market is the growing complexity of supply chain and logistics operations. With cross-border shipping and omnichannel retail becoming the norm, companies are increasingly challenged by variations in address formats, language barriers, and regulatory requirements. Address validation tools help organizations overcome these challenges by standardizing address data, supporting multiple languages, and ensuring compliance with local postal regulations. This, in turn, reduces the risk of shipment delays, enhances last-mile delivery efficiency, and supports global expansion strategies for retailers, logistics providers, and e-commerce companies.

Furthermore, the increasing adoption of cloud-based deployment models is accelerating the market's growth by making address validation solutions more accessible and scalable for organizations of all sizes. Cloud-based platforms offer seamless integration with existing enterprise systems, real-time updates, and lower total cost of ownership. This is particularly beneficial for small and medium enterprises (SMEs) that may lack the resources for on-premises infrastructure. The shift toward cloud solutions is also enabling businesses to leverage advanced analytics, geocoding, and real-time validation capabilities, further enhancing the value proposition of address validation for shipping.

From a regional perspective, North America continues to dominate the address validation for shipping market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is a major contributor due to its mature e-commerce ecosystem and high adoption of digital logistics solutions. Meanwhile, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by the rapid expansion of online retail, urbanization, and increasing investments in logistics infrastructure. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing internet penetration and the digital transformation of retail and logistics sectors.

Component Analysis

The address validation for shipping market by component is segmented into software and services, each playing a pivotal role in the ecosystem. Address validation software forms the backbone of this market, offering automated solutions that parse, standardize, and validate address data in real time. These software solutions are increasingly leveraging artificial intelligence, machine learning, and big data analyt

This dataset contains geometric energy offset (GEO') values for a set of density functional theory (DFT) methods for the B2se set of molecular structures. The data was generated as part of a research project aimed at quantifying geometric errors in main-group molecular structures. The dataset is in XLSX format created with MS Excel (version 16.69), and contains multiple worksheets with GEO' values for different basis sets and DFT methods. The worksheet headings, such as "AVQZ AVTZ AVDZ VQZ VTZ VDZ" represent different basis sets of Dunning theory, and the naming convention "(A)VnZ = aug-cc-pVnZ" is being used to label the worksheets. The data is organized in columns, with the first column providing the molecular ID and the names of the DFT methods specified in the first row of each worksheet. The molecular structures corresponding to each of these IDs can be found in Figure S1 of the supplementary information of the underlying publication [https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf]. The data have been generated from quantum-chemical calculations from the G16 and ORCA 5.0.0 packages, with further computational details, methodology, and data validation strategies (e.g., comparisons with higher-level quantum-chemical calculations) given in the supplementary information of the underlying publication [J. Phys. Chem. A 2022, 126, 7, 1300–1311] and its supporting information [https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf]. The dataset is expected to be useful to researchers in the field of computational chemistry and materials science. All values are given in kcal/mol. The data is generated by the authors of the underlying publication and it is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The data is expected to be re-usable and the quality of the data is assured by the authors. The size of the data is 71 KB.

Advanced therapy medicinal products (ATMP) are required to maintain their quality and safety throughout the production cycle, and they must be free of microbial contaminations. Among them, mycoplasma contaminations are difficult to detect and undesirable in ATMP, especially for immunosuppressed patients. Mycoplasma detection tests suggested by European Pharmacopoeia are the "culture method" and "indicator cell culture method" which, despite their effectiveness, are time consuming and laborious. Alternative methods are accepted, provided they are adequate and their results are comparable with those of the standard methods. To validate a novel in-house method, we performed and optimized, a real time PCR protocol, using a commercial kit and an automatic extraction system, in which we tested different volumes of matrix, maximizing the detection sensitivity. The results were compared with those obtained with the gold standard methods. From a volume of 10 ml, we were able to recognize all the mycoplasmas specified by the European Pharmacopoeia, defined as genomic copies per colony forming unit ratio (GC/CFU). Our strategy allows to achieve faster and reproducible results when compared with conventional methods and meets the sensitivity and robustness criteria required for an alternative approach to mycoplasmas detection for in-process and product-release testing of ATMP.