Here published artikel about The FAIR Guiding Principles for scientific data management and stewardship

The Tri-Agency Statement of Principles on Digital Data Management was jointly released by Canada’s three major federal funding agencies – the Social Sciences and Humanities Research Council (SSHRC), the Natural Sciences and Engineering Research Council (NSERC), and the Canadian Institutes of Health Research (CIHR) – in 2016. The statement of principles lays the groundwork for research data management mandates that are expected to come into effect in 2018. Canadian funders are following a larger global trend towards improving research data management. The Research Councils UK published their Common Principles on Data Policy in 2011. The US National Science Foundation has required all applicants to submit Data Management Plans since 2011.

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.

This data publication contains an overview to the Top-Level Architecture of the proposed HZDR Data Management Strategy with additional description of the various systems and services.

The Helmholtz-Zentrum Dresden-Rossendorf (HZDR) pursues a comprehensive data management strategy that is designed as an architecture of services to describe and manage scientific experiments in a sustainable manner. This strategy is based on the FAIR principles and aims to ensure the findability, accessibility, interoperability and reusability of research data.
The HZDR's comprehensive data lifecycle covers all phases of the data lifecycle: from planning and collection to analysis, storage, publication and archiving. Each phase is supported by specialised services and tools that help scientists to efficiently collect, store and share their data. These services include:

• Electronic lab notebook: for the digital recording and management of lab experiments and data.
• Data management plans (RDMO): For planning and organising data management during a research project.
• (Time Series) Databases: For structured storage and retrieval of research data.
• File systems: For storing and managing files in a controlled environment.
• Publication systems (ROBIS, RODARE): For the publication and accessibility of research data and results.
• Metadata catalogue (SciCat): For describing data in a wide variety of subsystems using searchable metadata
• Repositories (Helmholtz Codebase): For archiving, version control and provision of software, special data sets and workflows.
• Proposal Management System (GATE): For the administration of project proposals and approvals.

The superordinate web service HELIPORT plays a central role here. HELIPORT acts as a gateway and connecting service that links all components of the Data Management Strategy and describes them in a sustainable manner. HELIPORT ensures standardised access to the various services and tools, which considerably simplifies collaboration and the exchange of data.

The size of the Data Management Platform Market was valued at USD 3.4 billion in 2023 and is projected to reach USD 8.25 billion by 2032, with an expected CAGR of 13.50% during the forecast period. Recent developments include: March 2022 Oracle Corporation announced that Oracle Unity Customer Data Platform- which is an enterprise grade data platform that powers next generation adtech strategies and enables marketers to unify customer data for segmentation. It is also used for providing hyper personalized experience. Thus oracle has unified AdTech and martech into one unit. The company has done so by using design principles of marketing and advertising products around first party data. Thus improved data management capabilities are used to compliment systems of customer record and help marketers gain cost efficiencies., September 2019 Oracle Corporation has announced that they have integrated Bluekai and ID graph with CX unity. The company has integrated Bluekai data management platform DMP and ID graph with its customer data platform. This step is aimed to help marketers tie device level data about unknown prospects to their customers data and receive insights about marketing techniques and ad techniques. this step is going to allow customers to deliver personalization at a whole new level., March 2023 Adobe Corporation at Adobe Summit in New Delhi announced that they have launched Adobe product analytics in adobe experience cloud. The tool unifies customer journey insights across marketing and products. Using the tool, customer experience teams can now look deeply across marketing and products insights for a single customer view., March 2023 Adobe Corporation announced at Adobe Summit in New Delhi announced that they have launched new innovations in Adobe Experience manager which is a leading Data Management Platform DMP. The new release will deliver next generation features that bring speed and makes it easy for content developments and publishing higher quality web experiences and AI powered data insights that help organizations to optimize new content for the targeted audiences.. Key drivers for this market are: Increasing data volumes and complexity Growing importance of customer data and personalization Adoption of digital marketing channels Need for data-driven decision-making Government regulations. Potential restraints include: Data privacy concerns Cost and complexity of implementation Lack of skilled data professionals Data quality issues Integration challenges with other systems. Notable trends are: Rise of the Identity Graph Adoption of Cloud-Native Platforms Real-Time Data Management Multi-Vendor Integration Ethical and Sustainable Data Use.

Questions and Answers are organised in a tabular fashion. The questions act as titles of the columns. Recorded answers are text-based, nominal, and ordinal. The theme of the questions enable the assessment awareness of FAIR principles and data management practices for early career scientists (PhDs) in geography. Some questions were reused from related surveys with permission. No data was collected to identify the individual. Contact details for follow up interviews were removed from the questionnaire results prior to the upload of the dataset.

Data management is a critical aspect of empirical research. Unfortunately, principles of good data management are rarely taught to social scientists in a systematic way as part of their methods training. As a result, researchers often do things in an ad hoc fashion and have to learn from their mistakes.

The Qualitative Data Repository (QDR, www.qdr.org) presented a webinar on social science data management, with a special focus on keeping qualitative data safe and secure. The webinar will emphasize best practices with the aim of helping participants to save time and minimize frustration in their future research endeavors. We will cover the following topics:

1) The value of planning and Data Management Plans (DMPs)

2) Transparency and data documentation

3) Ethical, legal, and logistical challenges to sharing qualitative data and best practices to address them

4) Keeping data safe and secure.

Attribution: Parts of this presentation are based on slides used in a course co-taught by personnel from QDR and the UK Data Service. All materials provided under a CC-BY license.

A comprehensive workbook created to facilitate and document the process of decomposing the FAIR Guiding Principles and mapping them to key categories, core concepts, focus elements, and harmonized indicators. It also contains a complete list of the indicators.

The Tri-Agency outlines expectations of best practice with regards to data management, and responsibilities of researchers, research communities, research institutions and research funders.

A newer version of the workbook was released to correct several typos, which can be accessed at: https://doi.org/10.5281/zenodo.8057317

A comprehensive workbook created to facilitate and document the process of decomposing the FAIR Guiding Principles and mapping them to key categories, core concepts, focus elements, and harmonized indicators. It also contains a complete list of the indicators.

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).

This dataset is about books. It has 2 rows and is filtered where the book is Modern management : principles and practices. It features 7 columns including author, publication date, language, and book publisher.

The FAIR principles were published in 2016 in a Scientific Data article titled ‘FAIR Guiding Principles for scientific data management and stewardship’. These were developed to aid in the discovery and reuse of research data.FAIR stands for Findable, Accessible, Interoperable, and Reusable. Data that meet these principles are more optimal for reuse and discoverability and in turn increase your research’s exposure.Here’s how your data is more FAIR when it’s on Figshare.Illustration by Jason McDermott of RedPenBlackPen.

A short introduction about data management along the FAIR principles with outlook to DMP (data management plans)

In May 2018, the Government of Canada released a draft Tri-Agency Research Data Management Policy for Consultation (RDM Policy). The draft policy is part of a Tri-Agency strategy for encouraging and supporting research data management (RDM), which also includes the Tri-Agency Open Access Policy on Publications (2015) and the Tri-Agency Statement of Principles on Digital Data Management (2016).

This dataset is extended data to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1

This extended data includes:

- Data Analysis Sheet and results table

Note: The data is anonymized (i.e., all IP addresses as well as personal comments were deleted)

The revised version was published after the peer-review process of the original article on zenodo.org

This chapter described at a very high level some of the considerations that need to be made when designing algorithms for a vehicle health management application. The choices made here affect the quality of the diagnosis and prognosis (covered in Chapter 7). Therefore, the algorithmic design choices are made in conjunction with the design choices for diagnostics and prognostics to optimally support these tasks. Furthermore, additional considerations imposed by computational constraints, resource availability, algorithm maintenance, need for algorithm re-tuning, etc. will impact the solutions. It should also be noted that technological advances, both in hardware and software, impose the need for new solutions. For example, as new materials and new sensors are being developed, the algorithmic solutions will need to follow suit. In general, there seems to be a trend to have more sensor data available. While this is potentially a good thing, sensor data provides value only when it is being processed and interpreted properly, in part by the techniques described here. Testing of the methods, however, requires the “right” kind of data. Generally, there is a lack of seeded fault data which are required to train and validate algorithms. It is also important to migrate information from the component to the subsystem to the system levels so that health management technologies can be applied effectively and efficiently at the vehicle level. It may be required to perform elements described in this chapter between different levels of the vehicle architecture.

Filtered dataset containing results of a discrete-choice stated-preference survey, conducted amongst academic researchers and PhD-candidates of the faculty of Technology, Policy and Management of the Delft University of Technology.

Choices concern packages of stimuli and support measures to incentivize researchers to publish metadata and research data (where possible) in 'accessible' format, according to the FAIR guidelines for data management.

FAIR Data Management Platforms for Life Sciences Market Outlook

According to our latest research, the global FAIR Data Management Platforms for Life Sciences market size has reached USD 1.25 billion in 2024, with a robust year-on-year growth trajectory. The market is experiencing a significant push due to the increasing demand for data-driven decision-making in life sciences, and it is expected to expand at a CAGR of 14.8% during the forecast period. By 2033, the market is forecasted to reach USD 4.05 billion, highlighting the accelerating adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles across the sector. This remarkable growth is fueled by the need for better research reproducibility, regulatory compliance, and efficient data sharing within the life sciences industry, as organizations strive to leverage large-scale data for innovation and competitive advantage.

One of the primary growth factors driving the FAIR Data Management Platforms for Life Sciences market is the increasing complexity and volume of biomedical data. With advancements in genomics, proteomics, and medical imaging, life sciences organizations are generating unprecedented amounts of data that require robust management solutions. The adoption of FAIR data principles ensures that this data is not only stored securely but is also easily discoverable and usable by both humans and machines. As a result, pharmaceutical and biotechnology companies, as well as academic institutions, are prioritizing investments in platforms that enable seamless data integration, annotation, and sharing, thereby accelerating research and development timelines and reducing costs associated with data silos and duplication.

Another significant driver is the stringent regulatory landscape governing data management in the life sciences sector. Regulatory authorities across North America, Europe, and other regions are increasingly mandating transparent, auditable, and reproducible data practices for clinical trials and drug development processes. FAIR Data Management Platforms are uniquely positioned to address these requirements by providing traceability, provenance tracking, and standardized metadata frameworks. This not only enhances compliance with frameworks such as GDPR, HIPAA, and FDA 21 CFR Part 11 but also fosters greater collaboration among stakeholders, including contract research organizations (CROs), healthcare providers, and governmental agencies. The ability to demonstrate data integrity and lineage is becoming a key differentiator for organizations seeking regulatory approvals and public trust.

Furthermore, the growing emphasis on collaborative research and open science initiatives is propelling the adoption of FAIR Data Management Platforms. Life sciences research is increasingly conducted in multi-institutional and cross-border settings, necessitating interoperable data infrastructures that support seamless data exchange and joint analysis. FAIR-aligned platforms enable researchers to contribute, access, and reuse datasets efficiently, thereby driving scientific discovery and innovation. The proliferation of artificial intelligence and machine learning applications in drug discovery, genomics, and precision medicine also relies heavily on high-quality, well-annotated data, further underlining the importance of robust data management solutions. As more organizations recognize the strategic value of FAIR data, market growth is expected to accelerate.

From a regional perspective, North America currently dominates the FAIR Data Management Platforms for Life Sciences market, accounting for the largest share in 2024. This leadership position is attributed to the presence of leading pharmaceutical and biotechnology companies, advanced healthcare infrastructure, and a proactive regulatory environment. Europe follows closely, driven by strong public and private investments in life sciences research and a well-established culture of data stewardship. The Asia Pacific region is emerging as a high-growth market, supported by expanding biomedical research capabilities, increasing government initiatives to promote data standardization, and rising adoption of digital health technologies. Latin America and the Middle East & Africa are also witnessing gradual uptake, albeit at a slower pace, as local stakeholders recognize the long-term benefits of FAIR data principles for research efficiency and innovation.

Component Analysis

The FAIR Data Manage

This chapter described at a very high level some of the considerations that need to be made when designing algorithms for a vehicle health management application. The choices made here affect the quality of the diagnosis and prognosis (covered in Chapter 7). Therefore, the algorithmic design choices are made in conjunction with the design choices for diagnostics and prognostics to optimally support these tasks. Furthermore, additional considerations imposed by computational constraints, resource availability, algorithm maintenance, need for algorithm re-tuning, etc. will impact the solutions. It should also be noted that technological advances, both in hardware and software, impose the need for new solutions. For example, as new materials and new sensors are being developed, the algorithmic solutions will need to follow suit. In general, there seems to be a trend to have more sensor data available. While this is potentially a good thing, sensor data provides value only when it is being processed and interpreted properly, in part by the techniques described here. Testing of the methods, however, requires the “right” kind of data. Generally, there is a lack of seeded fault data which are required to train and validate algorithms. It is also important to migrate information from the component to the subsystem to the system levels so that health management technologies can be applied effectively and efficiently at the vehicle level. It may be required to perform elements described in this chapter between different levels of the vehicle architecture.