ABSTRACT The exponential increase of published data and the diversity of systems require the adoption of good practices to achieve quality indexes that enable discovery, access, and reuse. To identify good practices, an integrative review was used, as well as procedures from the ProKnow-C methodology. After applying the ProKnow-C procedures to the documents retrieved from the Web of Science, Scopus and Library, Information Science & Technology Abstracts databases, an analysis of 31 items was performed. This analysis allowed observing that in the last 20 years the guidelines for publishing open government data had a great impact on the Linked Data model implementation in several domains and currently the FAIR principles and the Data on the Web Best Practices are the most highlighted in the literature. These guidelines presents orientations in relation to various aspects for the publication of data in order to contribute to the optimization of quality, independent of the context in which they are applied. The CARE and FACT principles, on the other hand, although they were not formulated with the same objective as FAIR and the Best Practices, represent great challenges for information and technology scientists regarding ethics, responsibility, confidentiality, impartiality, security, and transparency of data.

The EOSC-A FAIR Metrics and Data Quality Task Force (TF) supported the European Open Science Cloud Association (EOSC-A) by providing strategic directions on FAIRness (Findable, Accessible, Interoperable, and Reusable) and data quality. The Task Force conducted a survey using the EUsurvey tool between 15.11.2022 and 18.01.2023, targeting both developers and users of FAIR assessment tools. The survey aimed at supporting the harmonisation of FAIR assessments, in terms of what it evaluated and how, across existing (and future) tools and services, as well as explore if and how a community-driven governance on these FAIR assessments would look like. The survey received 78 responses, mainly from academia, representing various domains and organisational roles. This is the anonymised survey dataset in csv format; most open-ended answers have been dropped. The codebook contains variable names, labels, and frequencies.

There is no description available for this dataset.

This dataset contains open access publications in EPMC dental journals from 2016 to 2021 and 500 non-open access dental publications. We evaluated the level of compliance with the FAIR principles. The original dataset and codebook are attached.

The FAIR principles as a framework for evaluating and improving open science and research data management have gained much attention over the last years. By defining a set of properties that indicates good practice for making data findable, accessible, interoperable, and reusable (FAIR), a quality measurement is created, which can be applied to diverse research outputs, including research data. There are some software tools available to help with the assessment, with the F-UJI tool being the most prominent of them. It uses a set of metrics which defines tests for each of the FAIR components, and it creates an overall assessment score.

The article examines differences between manually and automatically assessing FAIR principles, shows that there are significantly different results by using national election studies as examples. An evaluation of progress is done by comparing the automatically assessed FAIRness scores of the datasets from 2018 with those of 2024, showing that there is only a very slight yet not significant difference. Specific measures which have improved the FAIRness scores are described by the example of the Politbarometer 2022 dataset at the GESIS Data Archive. The article highlights the role of archives in securing a high level of data and metadata quality and technically sound implementation of the FAIR principles to help researchers benefit from getting the most of their valuable research data.

The replication data contains the manual and automatic coded values for FAIR criteria and the complete code to re-produce the results for the article.

LODS, the Hawaii Longline Observer Data System, is a complete suite of tools designed to collect, process, and manage quality fisheries data and information. Guided by the principles of the NOAA Data Quality Act, LODS is the result of the collaboration and cooperation of scientists, data collectors and information management experts across the NOAA Fisheries Pacific Islands Region. LODS is an end-to-end data management solution, articulating the four major data management areas of data collection management, data resource development, data maintenance and data dissemination. Every effort was made to eliminate redundant or unnecessary data items and to have the core data collection items be formally adopted by data stewards that would assume the responsibility of maintaining complete documentation and regular review of the quality, objectivity and suitability of the data item.

The increasing availability of digitized biodiversity data worldwide, provided by an increasing number of institutions and researchers, and the growing use of those data for a variety of purposes have raised concerns related to the "fitness for use" of such data and the impact of data quality (DQ) on the outcomes of analyses, reports, and decisions. A consistent approach to assess and manage data quality is currently critical for biodiversity data users. However, achieving this goal has been particularly challenging because of idiosyncrasies inherent in the concept of quality. DQ assessment and management cannot be performed if we have not clearly established the quality needs from a data user’s standpoint. This paper defines a formal conceptual framework to support the biodiversity informatics community allowing for the description of the meaning of "fitness for use" from a data user’s perspective in a common and standardized manner. This proposed framework defines nine concepts organized into three classes: DQ Needs, DQ Solutions and DQ Report. The framework is intended to formalize human thinking into well-defined components to make it possible to share and reuse concepts of DQ needs, solutions and reports in a common way among user communities. With this framework, we establish a common ground for the collaborative development of solutions for DQ assessment and management based on data fitness for use principles. To validate the framework, we present a proof of concept based on a case study at the Museum of Comparative Zoology of Harvard University. In future work, we will use the framework to engage the biodiversity informatics community to formalize and share DQ profiles related to DQ needs across the community.

Science journalists, traditionally, play a key role in delivering science information to a wider audience. However, changes in the media ecosystem and the science-media relationship are posing challenges to reliable news production. Additionally, recent developments such as ChatGPT and Artificial Intelligence (AI) more generally, may have further consequences for the work of (science) journalists. Through a mixed-methodology, the quality of news reporting was studied within the context of AI. A content analysis of media output about AI (news articles published within the time frame 1 September 2022–28 February 2023) explored the adherence to quality indicators, while interviews shed light on journalism practices regarding quality reporting on and with AI. Perspectives from understudied areas in four European countries (Belgium, Italy, Portugal, and Spain) were included and compared. The findings show that AI received continuous media attention in the four countries. Furthermore, despite four different media landscapes, the reporting in the news articles adhered to the same quality criteria such as applying rigour, including sources of information, accessibility, and relevance. Thematic analysis of the interview findings revealed that impact of AI and ChatGPT on the journalism profession is still in its infancy. Expected benefits of AI related to helping with repetitive tasks (e.g. translations), and positively influencing journalistic principles of accessibility, engagement, and impact, while concerns showed fear for lower adherence to principles of rigour, integrity and transparency of sources of information. More generally, the interviewees expressed concerns about the state of science journalism, including a lack of funding influencing the quality of reporting. Journalists who were employed as staff as well as those who worked as freelancers put efforts in ensuring quality output, for example, via editorial oversight, discussions, or memberships of associations. Further research into the science-media relationship is recommended.

Robot Data Quality Monitoring Platforms Market Outlook

As per our latest research, the global Robot Data Quality Monitoring Platforms market size reached USD 1.92 billion in 2024, reflecting robust adoption across industries striving for improved automation and data integrity. The market is expected to grow at a CAGR of 17.8% during the forecast period, with the value projected to reach USD 9.21 billion by 2033. This strong growth trajectory is primarily driven by the increasing integration of robotics in industrial processes, a heightened focus on data-driven decision-making, and the need for real-time monitoring and error reduction in automated environments.

The rapid expansion of robotics across multiple sectors has created an urgent demand for platforms that ensure the accuracy, consistency, and reliability of the data generated and utilized by robots. As robots become more prevalent in manufacturing, healthcare, logistics, and other industries, the volume of data they generate has grown exponentially. This surge in data has highlighted the importance of robust data quality monitoring solutions, as poor data quality can lead to operational inefficiencies, safety risks, and suboptimal decision-making. Organizations are increasingly investing in advanced Robot Data Quality Monitoring Platforms to address these challenges, leveraging AI-powered analytics, real-time anomaly detection, and automated data cleansing to maintain high standards of data integrity.

A key growth factor for the Robot Data Quality Monitoring Platforms market is the rising complexity of robotic systems and their integration with enterprise IT infrastructures. As businesses deploy more sophisticated robots, often working collaboratively with human operators and other machines, the potential for data inconsistencies, duplication, and errors increases. This complexity necessitates advanced monitoring platforms capable of handling diverse data sources, formats, and communication protocols. Furthermore, the adoption of Industry 4.0 principles and the proliferation of Industrial Internet of Things (IIoT) devices have amplified the need for seamless data quality management, as real-time insights are essential for predictive maintenance, process optimization, and compliance with stringent regulatory standards.

Another significant driver is the growing emphasis on regulatory compliance and risk management, particularly in sectors such as healthcare, automotive, and manufacturing. Regulatory bodies are imposing stricter requirements on data accuracy, traceability, and auditability, making it imperative for organizations to implement comprehensive data quality monitoring frameworks. Robot Data Quality Monitoring Platforms offer automated compliance checks, audit trails, and reporting capabilities, enabling businesses to meet regulatory demands while minimizing the risk of costly errors and reputational damage. The convergence of these factors is expected to sustain the market’s momentum over the coming years.

From a regional perspective, North America currently leads the global market, accounting for a significant share of total revenue in 2024, followed closely by Europe and Asia Pacific. The strong presence of advanced manufacturing hubs, early adoption of automation technologies, and the concentration of leading robotics and software companies have contributed to North America’s dominance. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid industrialization, increasing investments in smart factories, and the expanding footprint of multinational corporations in countries such as China, Japan, and South Korea. These regional trends are expected to shape the competitive landscape and innovation trajectory of the Robot Data Quality Monitoring Platforms market through 2033.

Component Analysis

The Robot Data Quality Monitoring Platforms market is segmented by component into Software, Hardware, and Services. The software segment holds the largest market share, as organizations

The Sandia Fire Protection program activities are compared with the technical functions required of an assurance program that has been tailored for ES and H assurance. Parts of all of the required assurance functions are present. The focus is on facilities, as they are occupied at any given point in time, and on the generic types of hazards associated with them. The facilities list is formalized. Major fire hazards are known to the Fire Protection staff, although no single formal list is maintained. Risk is used as a measure of performance for insurance-related evaluations and also as a basis for ranking. Risk is consciously considered, as are risk factors, although the relationship between them is understood only qualitatively. Formal efforts to identify nontechnical risk factors are made, but specific risk and risk factor activities have an informal character.

"The Principles for Open Government Data Initiatives by the Executive Yuan and its subordinate agencies aim to promote the openness and sharing of government data, enhance administrative transparency, and improve public service effectiveness. The principles regulate the scope, format, and management mechanisms of open data, emphasizing data quality, usability, and personal data protection, thereby promoting interagency cooperation and innovative social applications."

Due to the complexity and volume of data generated through non-target screening (NTS) using chromatographic couplings with high-resolution mass spectrometry, automized processing routines are necessary. The processing routines usually consist of many individual steps that are user-parameter-dependent and, thus, require labor-intensive optimization. Additionally, the effect of variations in raw data quality on the processing results is unclear and not fully understood. Within this work, we present qBinning, a novel algorithm for constructing extracted ion chromatograms (EICs) based on statistical principles and, thus, without the need to set user parameters. Furthermore, we give the user feedback on the specific qualities of the generated EICs using a scoring system (DQSbin). The DQSbin measures reliability as it correlates with the probability of correct classification of masses into EICs and the degree of overlap between different EIC construction algorithms. This work is a big step forward in understanding the behavior of NTS data and increasing the overall transparency in the results of NTS.

Dataset from the Online Survey of the Research Data Alliance's Discipline-Specific Guidance for Data Management Plans Working Group.

The data was collected from November 8, 2021 to January 14, 2022.

The survey was divided into the following areas after a brief introduction on "Purpose of this survey" and "Use of the information you provide."

• Demographics
• Data Description and Collection
• Data Documentation & Quality
• Data Archiving, Publishing & Sharing After the Project
• Guidelines, Principles & Best Practices
• Follow-up interviews and group discussions

The analysis of the online survey was focused on the four areas: Natural Sciences, Life Sciences, Humanities & Social Sciences, and Engineering. The results of the evaluation will be presented in a separate publication.

In addition to the data, the variables and values are also published here.

The online survey questions can be accessed here: https://doi.org/10.5281/zenodo.7443373

A more detailed analysis and description can be found in the paper "Discipline-specific Aspects in Data Management Planning" submitted to Data Science Journal (2022-12-15).

Data Sharing Governance Market Outlook

According to our latest research, the global Data Sharing Governance market size reached USD 2.4 billion in 2024. The market is poised for substantial growth, registering a robust CAGR of 15.2% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a value of USD 7.5 billion. The rapid expansion is primarily driven by the increasing demand for secure, compliant, and efficient data sharing practices across industries, as organizations recognize the strategic importance of robust data governance in the digital era.

One of the principal growth factors propelling the Data Sharing Governance market is the exponential rise in data volumes and the growing complexity of data ecosystems. As organizations across sectors embrace digital transformation, they generate and exchange massive amounts of sensitive data. This surge in data flow necessitates advanced governance frameworks to ensure data quality, integrity, and security. The proliferation of cloud computing, big data analytics, and IoT devices further amplifies the need for comprehensive data sharing governance solutions. These frameworks help organizations maintain control over data access, usage, and sharing, thereby reducing the risk of data breaches, unauthorized access, and regulatory non-compliance. The imperative for organizations to build trust with stakeholders by demonstrating responsible data stewardship further accentuates the demand for sophisticated data governance mechanisms.

Another significant driver is the tightening regulatory landscape surrounding data privacy and protection. Regulations such as GDPR in Europe, CCPA in the United States, and similar frameworks globally have mandated stricter controls over data sharing and processing. Organizations are now compelled to implement robust data governance policies that ensure transparency, accountability, and compliance throughout the data lifecycle. Non-compliance can result in severe financial penalties and reputational damage, making regulatory adherence a critical business priority. As a result, enterprises are increasingly investing in advanced data sharing governance solutions that facilitate automated policy enforcement, audit trails, and real-time monitoring, enabling them to navigate complex regulatory requirements efficiently while maintaining operational agility.

The surge in collaborative business models and ecosystem partnerships is also fueling the expansion of the Data Sharing Governance market. In today’s interconnected business environment, organizations frequently share data with partners, suppliers, customers, and third-party vendors to drive innovation, enhance customer experiences, and optimize operations. However, this increased inter-organizational data exchange introduces new risks related to data privacy, security, and ownership. Effective data sharing governance frameworks address these challenges by establishing clear protocols, roles, and responsibilities for data access and usage. They also enable organizations to manage consent, monitor data flows, and enforce data minimization principles, thereby mitigating risks and fostering secure, value-driven data collaborations. The growing recognition of data as a strategic asset further underscores the importance of robust governance structures in maximizing data utility while safeguarding against misuse.

From a regional perspective, North America currently dominates the Data Sharing Governance market, accounting for the largest revenue share in 2024. The region’s leadership is attributed to the early adoption of advanced data governance technologies, a mature regulatory environment, and the presence of major technology providers. Europe follows closely, driven by stringent data protection regulations and a strong focus on privacy and compliance. The Asia Pacific region is anticipated to exhibit the fastest growth over the forecast period, propelled by rapid digitalization, expanding IT infrastructure, and increasing awareness of data governance best practices among enterprises. Latin America and the Middle East & Africa are also witnessing steady growth, supported by evolving regulatory frameworks and rising investments in digital transformation initiatives.

Component Analysis

The Data Sharing Governance market is segmented by component into solutions and services, each playing a pivotal role in the overall ecosystem. Solutions encompass software platforms and tools that prov

BCBS 239 Data Governance Programs Market Outlook

As per our latest research, the global BCBS 239 Data Governance Programs market size reached USD 3.61 billion in 2024, reflecting the growing imperative for robust data governance across financial institutions. The market is expected to expand at a CAGR of 15.7% from 2025 to 2033, reaching a forecasted value of USD 13.89 billion by 2033. This remarkable growth trajectory is primarily driven by the rising regulatory scrutiny, increasing complexity of data ecosystems, and the need for enhanced risk management and compliance in the global financial sector.

One of the most significant growth factors for the BCBS 239 Data Governance Programs market is the intensification of regulatory mandates worldwide. Financial institutions are under constant pressure to comply with the Basel Committee on Banking Supervision’s BCBS 239 principles, which emphasize risk data aggregation and reporting. These regulations require banks and financial institutions to establish robust data governance frameworks that ensure data accuracy, completeness, and timeliness. The demand for comprehensive solutions that facilitate compliance, streamline reporting, and reduce the risk of regulatory penalties is surging. Moreover, the increasing frequency of regulatory updates and the global harmonization of banking standards are compelling organizations to invest in advanced data governance programs, thereby fueling market growth.

Another critical driver propelling the BCBS 239 Data Governance Programs market is the exponential growth of data volumes and the complexity of financial products and services. As digital transformation accelerates, financial institutions are generating and managing vast amounts of structured and unstructured data. The need to maintain data integrity, ensure data lineage, and provide real-time analytics necessitates the deployment of sophisticated data governance solutions. These programs enable organizations to establish standardized data management practices, automate data quality checks, and facilitate seamless data integration across disparate systems. The integration of artificial intelligence and machine learning within data governance frameworks is further enhancing the ability of financial institutions to extract actionable insights, mitigate risks, and drive strategic decision-making.

Additionally, the growing emphasis on operational efficiency and cost optimization is shaping the BCBS 239 Data Governance Programs market. Financial institutions are increasingly recognizing that robust data governance not only ensures regulatory compliance but also delivers significant business value by improving data quality, reducing operational risks, and enabling faster, more accurate reporting. The adoption of cloud-based solutions is making data governance more scalable, flexible, and cost-effective, especially for small and medium-sized enterprises (SMEs). The shift towards digital banking, coupled with the proliferation of fintech innovations, is further accelerating the need for agile and resilient data governance frameworks that can adapt to evolving business and regulatory requirements.

From a regional perspective, North America continues to lead the BCBS 239 Data Governance Programs market, driven by the presence of large financial institutions, stringent regulatory frameworks, and early adoption of advanced technologies. Europe follows closely, with significant investments in data governance initiatives across major banking hubs such as the United Kingdom, Germany, and France. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, expanding financial services sector, and increasing regulatory oversight in countries like China, Japan, and Australia. Latin America and the Middle East & Africa are also witnessing steady growth, supported by regulatory reforms and the modernization of legacy banking systems.

Component Analysis

The component segment&

Election studies are an important data pillar in political and social science, as most political research investigations involve secondary use of existing datasets. Researchers depend on high-quality data because data quality determines the accuracy of the conclusions drawn from statistical analyses. We outline data reuse quality criteria pertaining to data accessibility, metadata provision, and data documentation using the FAIR Principles of research data management as a framework. We then investigate the extent to which a selection of election studies fulfils these criteria using studies from Western democracies. Our results reveal that although most election studies are easily accessible and well documented and that the overall level of data processing is satisfactory, some important deficits remain. Further analyses of technical documentation indicate that while a majority of election studies provide the necessary documents, there is still room for improvement. Inhaltscodierung Content Analysis Large-scale election studies from Western democracies Non-probability: Purposive

FAIR Data Management Platforms for Life Sciences Market Outlook

According to our latest research, the global market size for FAIR Data Management Platforms for Life Sciences reached USD 1.35 billion in 2024, with a robust compound annual growth rate (CAGR) of 14.2% projected through the forecast period. By 2033, the market is expected to achieve a value of USD 4.27 billion. The primary growth driver is the increasing adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles in data management to enhance data quality, compliance, and collaborative research across the life sciences sector.

The growth of the FAIR Data Management Platforms for Life Sciences market is predominantly fueled by the exponential rise in data generation within the life sciences industry. With the proliferation of high-throughput technologies such as next-generation sequencing, proteomics, and advanced imaging, organizations are generating vast volumes of complex and heterogeneous data. This surge has created an urgent need for robust data management solutions that can ensure data is not only stored securely but also remains accessible and reusable over time. The implementation of FAIR principles is becoming a strategic imperative for pharmaceutical companies, research institutes, and contract research organizations (CROs), as it directly impacts the efficiency and reproducibility of scientific research. Furthermore, the growing focus on collaborative research, open science initiatives, and regulatory compliance is compelling organizations to invest in advanced FAIR data management platforms.

Another significant growth factor is the increasing regulatory pressure and industry standards related to data integrity and transparency. Regulatory agencies such as the FDA, EMA, and other global bodies are mandating stringent data governance and traceability requirements for clinical trials, drug development, and biomedical research. This has led to a paradigm shift in how organizations approach data stewardship, with a strong emphasis on ensuring data is well-documented, interoperable, and auditable. FAIR data management platforms are uniquely positioned to address these regulatory demands by offering comprehensive solutions that facilitate metadata management, data harmonization, and secure sharing while maintaining data privacy and compliance. As a result, life sciences organizations are allocating larger budgets toward the adoption and integration of FAIR-compliant platforms, further accelerating market growth.

The rapid advancement of digital transformation initiatives within the life sciences sector is also propelling the market forward. The adoption of cloud computing, artificial intelligence, and machine learning is enabling organizations to derive actionable insights from vast datasets, thereby driving innovation in drug discovery, clinical research, and precision medicine. FAIR data management platforms are increasingly integrating with these advanced technologies to provide scalable, flexible, and intelligent data solutions. This integration not only enhances the efficiency of data curation and retrieval but also supports advanced analytics and predictive modeling. The growing recognition of data as a strategic asset, coupled with the need for interoperable and reusable datasets, is prompting both established players and startups to innovate and expand their offerings in the FAIR data management ecosystem.

Regionally, North America continues to dominate the FAIR Data Management Platforms for Life Sciences market, accounting for over 38% of the global revenue in 2024. This leadership is attributed to the presence of major pharmaceutical companies, advanced research infrastructure, and strong regulatory frameworks supporting data standardization and interoperability. Europe follows closely, driven by robust funding for biomedical research and proactive adoption of FAIR principles through initiatives such as the European Open Science Cloud. Meanwhile, the Asia Pacific region is witnessing the fastest growth, with a CAGR of 17.8%, fueled by increasing investments in life sciences R&D, expanding biobanking activities, and government support for digital health initiatives. Latin America and the Middle East & Africa are also gradually embracing FAIR data management, although adoption rates remain comparatively lower due to infrastructural and regulatory challenges.

The AI Data Management market is experiencing exponential growth, fundamentally driven by the escalating adoption of Artificial Intelligence and Machine Learning across diverse industries. As organizations increasingly rely on data-driven insights, the need for robust solutions to manage, prepare, and govern vast datasets becomes paramount for successful AI model development and deployment. This market encompasses a range of tools and platforms for data ingestion, preparation, labeling, storage, and governance, all tailored for AI-specific workloads. The proliferation of big data, coupled with advancements in cloud computing, is creating a fertile ground for innovation. Key players are focusing on automation, data quality, and ethical AI principles to address the complexities and challenges inherent in managing data for sophisticated AI applications, ensuring the market's upward trajectory.

Key strategic insights from our comprehensive analysis reveal:

The paradigm is shifting from model-centric to data-centric AI, placing immense value on high-quality, well-managed, and properly labeled training data, which is now considered a primary driver of competitive advantage.
There is a growing convergence of DataOps and MLOps, leading to the adoption of integrated platforms that automate the entire data lifecycle for AI, from preparation and training to model deployment and monitoring.
Synthetic data generation is emerging as a critical trend to overcome challenges related to data scarcity, privacy regulations (like GDPR and CCPA), and bias in AI models, offering a scalable and compliant alternative to real-world data.

Global Market Overview & Dynamics of AI Data Management Market Analysis The global AI Data Management market is on a rapid growth trajectory, propelled by the enterprise-wide integration of AI technologies. This market provides the foundational layer for successful AI implementation, offering solutions that streamline the complex process of preparing data for machine learning models. The increasing volume, variety, and velocity of data generated by businesses necessitate specialized management tools to ensure data quality, accessibility, and governance. As AI moves from experimental phases to core business operations, the demand for scalable and automated data management solutions is surging, creating significant opportunities for vendors specializing in data labeling, quality control, and feature engineering.

Global AI Data Management Market Drivers

Proliferation of AI and ML Adoption: The widespread integration of AI/ML technologies across sectors like healthcare, finance, and retail to enhance decision-making and automate processes is the primary driver demanding sophisticated data management solutions.
Explosion of Big Data: The exponential growth of structured and unstructured data from IoT devices, social media, and business operations creates a critical need for efficient tools to process, store, and manage these massive datasets for AI training.
Demand for High-Quality Training Data: The performance and accuracy of AI models are directly dependent on the quality of the training data. This fuels the demand for advanced data preparation, annotation, and quality assurance tools to reduce bias and improve model outcomes.

Global AI Data Management Market Trends

Rise of Data-Centric AI: A significant trend is the shift in focus from tweaking model algorithms to systematically improving data quality. This involves investing in tools for data labeling, augmentation, and error analysis to build more robust AI systems.
Automation in Data Preparation: AI-powered automation is being increasingly used within data management itself. Tools that automate tasks like data cleaning, labeling, and feature engineering are gaining traction as they reduce manual effort and accelerate AI development cycles.
Adoption of Cloud-Native Data Management Platforms: Businesses are migrating their AI workloads to the cloud to leverage its scalability and flexibility. This trend drives the adoption of cloud-native data management solutions that are optimized for distributed computing environments.

Global AI Data Management Market Restraints

Data Privacy and Security Concerns: Stringent regulations like GDPR and CCPA impose strict rules on data handling and usage. Ensuring compliance while managing sensitive data for AI training presents a significant challenge and potential restraint...

Successfully implementing care technology to enhance people's health-related quality of life poses several challenges. Although many technological tools are available, we lack consensus on values and principles, regulatory systems, and quality labels. This article describes the FIDE process of the Belgian ‘Teckno 2030' project: Future-thinking Interdisciplinary workshops for the Development of Effectiveness principles, resulting in a framework of eight Caring Technology principles. These principles are built on three overarching values: autonomy, justice, and trust. The framework enables responsible health technology innovation by focusing on the needs of users and society, data security, equity, participatory governance, and quality control. A learning community was established to support the framework’s implementation in projects, organizations, and the broader innovation community. We also discuss the barriers, facilitators, and practical tools developed within this learning community. The FIDE process, caring technology principles, and learning community provide a case study for responsible innovation in care technology.

Quality metrics can be (in principle) calculated on various forms of data (such as datasets, graphs, set of triples etc...). This vocabulary allow the owner/user of such RDF data to calculate metrics on multiple (and different) resources. @en