Background The Multicentre Project for Tuberculosis Research (MPTR) was a clinical-epidemiological study on tuberculosis carried out in Spain from 1996 to 1998. In total, 96 centres scattered all over the country participated in the project, 19935 "possible cases" of tuberculosis were examined and 10053 finally included. Data-handling and quality control procedures implemented in the MPTR are described. Methods The study was divided in three phases: 1) preliminary phase, 2) field work 3) final phase. Quality control procedures during the three phases are described. Results: Preliminary phase: a) organisation of the research team; b) design of epidemiological tools; training of researchers. Field work: a) data collection; b) data computerisation; c) data transmission; d) data cleaning; e) quality control audits; f) confidentiality. Final phase: a) final data cleaning; b) final analysis. Conclusion The undertaking of a multicentre project implies the need to work with a heterogeneous research team and yet at the same time attain a common goal by following a homogeneous methodology. This demands an additional effort on quality control.

This dataset contains Tags code and algorithm traces that are referred to in the paper. They were not included in the paper, as it could also be understood without them. Thus, this dataset could be viewed as appendix for those who would like to deepen their understanding of the paper.

Learning Goals: • explain importance of data management • identify elements of an organized data sheet • create & manipulate data in a spreadsheet • calculate vital statistics using life tables • collect, manage and analyze data to test hypotheses

ML Data Management Platform Market Outlook

According to our latest research, the global ML Data Management Platform market size reached USD 6.4 billion in 2024, reflecting the rapid adoption of machine learning-driven data management solutions across diverse industries. The market is expected to register a robust CAGR of 17.2% during the forecast period, reaching approximately USD 29.9 billion by 2033. This significant growth trajectory is primarily fueled by the increasing demand for efficient data handling, real-time analytics, and the integration of artificial intelligence (AI) and machine learning (ML) technologies within enterprise data ecosystems, as per our latest research findings.

A major growth factor for the ML Data Management Platform market is the exponential surge in data volumes generated by businesses globally. Organizations across sectors such as BFSI, healthcare, retail, and manufacturing are accumulating vast amounts of structured and unstructured data. The need to extract actionable insights from this data in real time has led to the widespread adoption of advanced ML-powered data management platforms. These platforms enable automated data integration, cleansing, and governance, thereby enhancing decision-making processes and operational efficiency. Furthermore, the proliferation of IoT devices and the increasing reliance on cloud-based solutions have amplified the necessity for scalable and intelligent data management systems, further propelling market growth.

Another pivotal driver is the growing emphasis on data privacy, compliance, and security. With stringent regulatory frameworks such as GDPR, HIPAA, and CCPA coming into play, enterprises are under immense pressure to ensure robust data governance and security protocols. ML Data Management Platforms are equipped with advanced features like automated data lineage, metadata management, and anomaly detection, which help organizations maintain compliance and safeguard sensitive information. The integration of AI and ML capabilities enables proactive threat detection and mitigation, reducing the risk of data breaches and ensuring regulatory adherence. This heightened focus on data security is compelling organizations to invest in sophisticated data management solutions, thereby accelerating market expansion.

The increasing adoption of cloud computing and hybrid data architectures is also catalyzing the ML Data Management Platform market. Enterprises are transitioning from traditional on-premises infrastructure to cloud-based and hybrid environments to achieve greater agility, scalability, and cost-efficiency. ML Data Management Platforms facilitate seamless data movement, integration, and synchronization across multiple environments, ensuring data consistency and accessibility. This trend is particularly pronounced among large enterprises and digitally native businesses that require real-time analytics and AI-driven insights to maintain a competitive edge. As the digital transformation wave continues to sweep across industries, the demand for intelligent data management solutions is set to surge further.

From a regional perspective, North America currently dominates the ML Data Management Platform market, accounting for the largest revenue share in 2024. The presence of leading technology providers, early adoption of advanced analytics solutions, and a mature digital infrastructure are key factors driving market growth in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, fueled by rapid digitalization, increasing investments in AI and ML technologies, and the expanding presence of global enterprises. Europe is also emerging as a significant market, driven by stringent data privacy regulations and a strong focus on data-driven innovation. Overall, the global outlook for the ML Data Management Platform market remains highly promising, with robust growth anticipated across all major regions.

Component Analysis

The ML Data Management Platform market is segmented by component into software and services, each playing a critical role in enabling organizations to manage their data efficiently. The software segment encompasses a wide array of tools and platforms designed to automate data integration, quality assessment, governance, and security using machine learning algorithms. These software solutions are the backbone of modern data management strategies, empowering enterprises to handle vast and comple

The Global AI Data Management Market size was valued at around USD 23.8 billion in 2023 & is estimated to grow at a CAGR of around 24% during the forecast period 2024-30.

The Oil & Gas Data Management market is experiencing robust growth, driven by the increasing need for efficient data handling in an increasingly complex and data-rich industry. The market's expansion is fueled by several key factors. Firstly, the digital transformation sweeping across the oil and gas sector is demanding sophisticated data management solutions to optimize operations, enhance safety, and improve decision-making. This involves integrating data from various sources, including exploration, production, and refining, necessitating robust platforms capable of handling large volumes of diverse data types. Secondly, the growing adoption of advanced analytics and machine learning technologies within the industry is creating a higher demand for effective data management to unlock valuable insights from this data. Predictive maintenance, reservoir optimization, and risk management are just a few applications driving this growth. Lastly, stringent regulatory compliance requirements regarding data security and reporting are further fueling market expansion, as companies invest in solutions ensuring adherence to these regulations. We estimate the market size in 2025 to be $15 Billion based on reasonable extrapolations of industry growth patterns. However, the market faces certain challenges. High initial investment costs associated with implementing advanced data management systems can be a barrier to entry for smaller companies. Furthermore, integrating legacy systems with new technologies can present significant technical complexities and require substantial effort and expertise. Data security and privacy concerns, especially given the sensitive nature of operational and financial data in the oil and gas industry, also represent significant hurdles. Despite these restraints, the long-term outlook for the Oil & Gas Data Management market remains positive, with continued technological advancements and growing industry demand expected to drive substantial growth over the forecast period. The presence of major players like Accenture, Cisco, and IBM signifies the industry’s maturity and the significant investments being made in this space. A moderate CAGR of 8% is projected for the coming years, demonstrating a sustainable and expanding market.

Corrections to SLR tracking data collected from various tables on CDDIS, resolutions from the ILRS/ASC (AWG) meetings, the T2L2 @ Jason-2 project (July 2008 to December 2017), the final results of the ILRS Station Systematic Error Monitoring--SSEM project, amended with results from its 2023 extension as an ongoing project, SSEM-X.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for data processing in the U.S.