The regional networking strategy is widely implemented in China as a normative policy aimed at fostering cohesion and enhancing competitiveness. However, the empirical basis for this strategy remains relatively weak due to limitations in measurement methods and data availability. This paper establishes the urban networks by the enterprise investment data, and then accurately measures the network’s external effects of each city by the method of MGWR model. The results show that: (1) Regional networking plays a significant role in urban development, although it is not the dominant factor. (2) The benefits of network connections may vary depending on the location and level of cities. (3) The major cities assume a pivotal role in the urban network. Based upon the aforementioned research conclusions, this paper presents strategic measures to enhance the network’s external impacts, aiming to offer insights for other regions in formulating regional development strategies and establishing regional urban networks.

Prognostics and health management (PHM) is a maturing system engineering discipline. As with most maturing disciplines, PHM does not yet have a universally accepted research methodology. As a result, most component life estimation efforts are based on ad-hoc experimental methods that lack statistical rigor. In this paper, we provide a critical review of current research methods in PHM and contrast these methods with standard research approaches in a more established discipline (medicine). We summarize the developmental steps required for PHM to reach full maturity and to generate actionable results with true business impact.

Number of deaths, crude mortality rates and age standardized mortality rates (based on 2021 estimated population) for selected grouped causes, by sex, 2000 to most recent year.

BackgroundMethods for comparing hospitals regarding cardiac arrest (CA) outcomes, vital for improving resuscitation performance, rely on data collected by cardiac arrest registries. However, most CA patients are treated at hospitals that do not participate in such registries. This study aimed to determine whether CA risk standardization modeling based on administrative data could perform as well as that based on registry data.Methods and resultsTwo risk standardization logistic regression models were developed using 2453 patients treated from 2000–2015 at three hospitals in an academic health system. Registry and administrative data were accessed for all patients. The outcome was death at hospital discharge. The registry model was considered the “gold standard” with which to compare the administrative model, using metrics including comparing areas under the curve, calibration curves, and Bland-Altman plots. The administrative risk standardization model had a c-statistic of 0.891 (95% CI: 0.876–0.905) compared to a registry c-statistic of 0.907 (95% CI: 0.895–0.919). When limited to only non-modifiable factors, the administrative model had a c-statistic of 0.818 (95% CI: 0.799–0.838) compared to a registry c-statistic of 0.810 (95% CI: 0.788–0.831). All models were well-calibrated. There was no significant difference between c-statistics of the models, providing evidence that valid risk standardization can be performed using administrative data.ConclusionsRisk standardization using administrative data performs comparably to standardization using registry data. This methodology represents a new tool that can enable opportunities to compare hospital performance in specific hospital systems or across the entire US in terms of survival after CA.

Mortgage Data Standardization Market Outlook

According to our latest research, the Global Mortgage Data Standardization market size was valued at $1.8 billion in 2024 and is projected to reach $5.1 billion by 2033, expanding at a robust CAGR of 12.3% during the forecast period of 2025–2033. One of the primary factors fueling this growth is the increasing regulatory scrutiny and compliance requirements across financial institutions, which has made standardized mortgage data essential for transparency, risk management, and operational efficiency. As the mortgage industry continues to digitize and expand globally, the demand for seamless, interoperable data frameworks is accelerating, enabling lenders, servicers, and regulators to achieve higher levels of accuracy, security, and speed in mortgage processing.

Regional Outlook

North America currently holds the largest share in the global Mortgage Data Standardization market, accounting for approximately 38% of the total market value in 2024. The region’s dominance is attributed to its mature financial ecosystem, rapid adoption of advanced technologies, and stringent regulatory mandates such as the Home Mortgage Disclosure Act (HMDA) and the Dodd-Frank Act. Major U.S. and Canadian banks have been early adopters of digital mortgage platforms and data standardization tools, driving significant investments in software, services, and platforms. The presence of leading technology vendors and a highly competitive lending environment further accelerates innovation and implementation of standardized data solutions. Additionally, North America benefits from a robust ecosystem of fintech startups and established players collaborating to streamline mortgage data processes, ensuring compliance and operational efficiency.

The Asia Pacific region is emerging as the fastest-growing market, with a projected CAGR of 15.2% from 2025 to 2033. This rapid growth is driven by increasing urbanization, rising home ownership rates, and significant investments in digital banking infrastructure across countries like China, India, and Australia. Governments and regulatory bodies in the region are actively promoting digital transformation in the financial sector, including the adoption of standardized mortgage data frameworks to enhance transparency and reduce fraud. Furthermore, the influx of global fintech companies and the expansion of local mortgage lenders are creating a fertile environment for innovative data standardization solutions. As regional players seek to improve customer experience and comply with evolving regulations, demand for cloud-based and automated mortgage data platforms is set to surge.

Emerging economies in Latin America, the Middle East, and Africa are witnessing gradual adoption of mortgage data standardization, albeit at a slower pace. These regions face unique challenges, such as fragmented regulatory frameworks, limited digital infrastructure, and varying levels of financial literacy. However, localized demand for affordable housing and government-led initiatives to modernize the mortgage sector are opening new opportunities for market entrants. In particular, pilot projects and partnerships with global technology providers are helping to bridge the gap, enabling financial institutions to experiment with scalable, standardized data solutions tailored to local market needs. Despite these advancements, widespread adoption remains constrained by budgetary limitations and the need for customized regulatory compliance frameworks.

Report Scope

Additional file 1. Sample Dataset for Application of Proposed Methodology (data.csv). To protect patient confidentiality, the hospitals providing the example data used in this paper have not given permission for the data to be made publicly available. We have, however, included a limited “fake” version of the dataset. This dataset contains 3 variables - dlp.over indicates whether an exam is “high dose,” sizeC is an ID indicating the combination of anatomic area examined and patient size category, while fac is an ID indicating the hospital the exam was performed in. Information on which ID values are associated with which anatomic areas, patient sizes, and hospital will not be provided, as they are not necessary for the illustration of statistical methods described in the paper. Note that since the dataset made available is different from the dataset used in the paper, the results should be expected to be comparable, but not identical. The software implementing the methods described in this article is available on request from the author.

The dealer management system (DMS) is a data warehouse that stores federal and state dealer permit and license information. The issuing agency, whether it is a state or federal agency, provides information on the seafood businesses, i.e., company name, mailing/physical address, telephone and license/permit number, to the Southeast Fisheries Science Center and these data are stored in the DMS. The system is developed to identify multiple records for the same dealer, i.e., dealers that have licenses or permits that are issued by different agencies. These separate dealer codes are linked such that the separate licenses/permits can be identified for the same dealer. Although the DMS was developed in 2004, the data in the warehouse begin in 1999.

ITVC system - Terms and metadata for standardization of data collection

This analysis provides a closer look on the future sustainability of the more than 35 years old co-regulation regime ‘New Approach’. We understand our work as an update of Governing Standards: The Rise of Standardization Processes in France and in the EU (Borraz 2007), one of the rare contributions studying the European co-regulation regime. We therefore widen the perspective by asking “How efficient is the New Approach regarding a growing product complexity and the technological and industrial change?”, a key question which has not yet been answered. Based on a literature review and a document analysis, this paper first highlights the role of standardization in the regulation regime. We then present an in-depth case study of selected New Approach processes based on expert interviews and standard data analyses. Additionally, we deliver a brief German perspective of co-regulation with standards. The overall results show that co-regulation regimes are resilient enough to face the challenges of the technical progress on both, a European and national level.

Standardized data from Mobilise-D participants (YAR dataset) and pre-existing datasets (ICICLE, MSIPC2, Gait in Lab and real-life settings, MS project, UNISS-UNIGE) are provided in the shared folder, as an example of the procedures proposed in the publication "Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization" that is currently under review in Scientific data. Please refer to that publication for further information. Please cite that publication if using these data.

The code to standardize an example subject (for the ICICLE dataset) and to open the standardized Matlab files in other languages (Python, R) is available in github (https://github.com/luca-palmerini/Procedure-wearable-data-standardization-Mobilise-D).

The ‘ODD’ (Overview, Design concepts, and Details) protocol was published in 2006 to standardize the published descriptions of individual-based and agent-based models (ABMs).

The table displays weekly age standardized mortality rates for every province in Canada (excluding territories), by sex, since 2019. The standardization is done using the 2011 Canadian population.

WAIS Standardization Data Generalized to Ages 55–69.

R and Python libraries for the standardization of data extraction and analysis from NHANES.

The Unified Infrastructure for Canadian Census Research, or UNI·CEN, is a comprehensive database of historical and contemporary Canadian aggregate Census data, digital boundary files, and ancillary material, all provided in modern data formats. The goal of the project is to liberate Canadian Census data so that it can be easily used by academic researchers, students, and the public. The UNI·CEN Standardized Census Data Tables series contains reformatted versions of all publicly available digital Census data. This documentation report describes the data sources, tabular formats, and file types used. Citation: Taylor, Zack. 2022. "UNI·CEN Documentation Report 2: Standardized Census Data Tables.” London, Canada: Network for Economic and Social Trends, Western University. https://ir.lib.uwo.ca/nest_observatory_docs/3 Available at: https://ir.lib.uwo.ca/nest_observatory_docs/3

Rank, number of deaths, percentage of deaths and age standardized mortality rates (based on 2021 estimated population) for leading causes of death, by sex, 2000 to most recent year.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

Number of deaths, crude mortality rates and age standardized mortality rates (based on 2011 population) for selected grouped causes, by sex, 2000 to most recent year.

Global Healthcare Data Standardization Tools Market is segmented by Application (Hospitals_Health IT Vendors_Laboratories_Research Institutes_Payers), Type (Terminology Mapping Tools_API Integration Platforms_HL7/FHIR Converters_Ontology Management Systems_Data Cleansing Engines), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.