Data Stewardship Research Articles

"I inherently just trust that it works": Investigating Mental Models of Open-Source Libraries for Differential Privacy

Differential privacy (DP) is a promising framework for privacy-preserving data science, but recent studies have exposed challenges in bringing this theoretical framework for privacy into practice. These tensions are particularly salient in the context of open-source software libraries for DP data analysis, which are emerging tools to help data stewards and analysts build privacy-preserving data pipelines for their applications. While there has been significant investment into such libraries, we need further inquiry into the role of these libraries in promoting understanding of and trust in DP, and in turn, the ways in which design of these open-source libraries can shed light on the challenges of creating trustworthy data infrastructures in practice. In this study, we use qualitative methods and mental models approaches to analyze the differences between conceptual models used to design open-source DP libraries and mental models of DP held by users. Through a two-stage study design involving formative interviews with 5 developers of open-source DP libraries and user studies with 17 data analysts, we find that DP libraries often struggle to bridge the gaps between developer and user mental models. In particular, we highlight the tension DP libraries face in maintaining rigorous DP implementations and facilitating user interaction. We conclude by offering practical recommendations for further development of DP libraries.

The role of data governance in enhancing cybersecurity resilience for global enterprises

Data governance plays a critical role in enhancing cybersecurity resilience for global enterprises. In the face of increasingly sophisticated cyber threats, coupled with rising regulatory demands such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), enterprises must implement robust data governance frameworks to safeguard data integrity, availability, and confidentiality (Ramirez et al., 2008). Effective data governance involves a combination of policies, procedures, and technologies that align with an organization’s overall cybersecurity strategy (Khatri & Brown, 2010). This paper provides a comprehensive analysis of the critical intersection between data governance and cybersecurity, emphasizing the link between governance structures, risk management processes, and technological innovations in mitigating evolving cyber threats (Weber et al., 2009). Additionally, we explore the essential role of data stewardship, stakeholder responsibilities, and the deployment of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), in fostering organizational resilience (Arora & Pedersen, 2017). Through case studies and best practices, the study presents a conceptual framework that enables global enterprises to adapt to the fast-changing cybersecurity landscape while maintaining compliance with international data regulations. By addressing key elements such as access control, data classification, and real-time monitoring, this research underscores how comprehensive data governance frameworks serve as a foundation for enhanced cybersecurity resilience in today’s digital economy (Da Veiga & Eloff, 2007).

Stewarding Contextual Integrity in Data Services for Indigenous Scholarship

ABSTRACTThe CARE Principles for Indigenous Data Governance provide essential guideposts for the stewardship of Indigenous data. To put CARE into practice in libraries and repositories, resources are needed to support implementation and integration into current research data services (RDS). Based on analysis of case studies with scholars of Indigenous language and culture, this paper articulates specific Indigenous research and data practices to guide metadata work and other areas of responsibility in RDS. The cases surface the richness of relationships and the significance of accountability in the research process—demonstrating the “relational accountability” inherent in Indigenous research methods. Robust representation of relationality is essential to retaining integrity of context in metadata for Indigenous research data. We consider the practical implications of documenting relational context with current descriptive metadata approaches and challenges in achieving CARE adherent metadata, which we argue is the backbone for broader application of CARE for Indigenous RDS.

Data stewardship: case studies from North American, Dutch and Finnish universities

PurposeAs national legislation, federated national services, institutional policies and institutional research service arrangements may differ, data stewardship programs may be organized differently in higher education institutions across the world. This work seeks to elaborate the picture of different data stewardship programs running in different institutional and national research environments.Design/methodology/approachUtilizing a case study design, this study described three distinct data stewardship programs from Purdue University (United States), Delft Technical University (Netherlands) and Aalto University (Finland). In addition, this work investigated the institutional and national research environments of the programs. The focus was on initiatives led by academic libraries or similar services.FindingsThis work demonstrates that data stewardship programs may be organized differently within varying national and institutional contexts. The data stewardship programs varied in terms of roles, organization and funding structures. Furthermore, policies and legislation, organizational structures and national infrastructures differed.Research limitations/implicationsThe data stewardship programs and their contexts develop, and the descriptions presented in this work should be considered as snapshots.Originality/valueThis work broadens the current literature on data stewardship by not only providing detailed descriptions of three distinct data stewardship programs but also highlighting how research environments may affect their organization. We present a summary of key factors in the organization of data stewardship programs.

Current state of data stewardship tools in life science.

In today's data-centric landscape, effective data stewardship is critical for facilitating scientific research and innovation. This article provides an overview of essential tools and frameworks for modern data stewardship practices. Over 300 tools were analyzed in this study, assessing their utility, relevance to data stewardship, and applicability within the life sciences domain.

Practical approaches for engaging the public in a population-level data analysis project.

ObjectiveStewards of population-level data have an obligation to serve the public interest. As one such publicly funded data steward, our Public Advisory Council (PAC) co-led and determined the focus of an analysis project using population-level administrative health data. We describe our engagement strategies herein. ApproachThe 20-member PAC was involved in each stage of the project, from research question formulation to knowledge translation planning over 18 months, consisting of 12 meetings with both large and smaller groups. Our approach to engagement applied the International Association for Public Participation’s Framework as the foundation for an ‘empowerment’ level of engagement and adapted approaches from the James Lind Alliance and the ‘Plan-Do-Study-Act’ cycle. ResultsThe PAC chose to focus their analysis project on factors related to mental health and addiction service use. Four strategies were used and co-designed by members to foster engagement throughout: providing education and guidance, shared and guided brainstorming, building consensus, and responsiveness to feedback and evaluation. PAC members directed how and when these strategies were used, with challenges and lessons learned currently being co-developed into a publicly accessible report. ConclusionsOur work demonstrates the importance and value of public-driven research outputs and the feasibility of integrating public members in the work of data stewards. ImplicationsThe insights and practical strategies generated from this project will be used to guide effective engagement of the public for future analysis projects and to improve trust and social license for other initiatives using population-level data.

The FAIRification process for data stewardship: A comprehensive discourse on the implementation of the FAIR principles for data visibility, interoperability and management

This article discusses the importance of good data management for knowledge discovery and innovation. It outlines the FAIR principles, which provide guidelines for publishing digital resources in a way that makes the data findable, accessible, interoperable and reusable. Using a systematic literature review, the study focuses on the implementation of these principles in research data management and their applicability in data repositories and data centres. It highlights the importance of implementing these principles systematically, allowing stakeholders to choose the minimum requirements and provide a vision for implementing them in data repositories and data centres. The article also highlights the steps in the FAIRification process, which can enhance data interoperability, discovery and reusability. The paper is unique in that it explores how implementing FAIR principles impacts data management for data hosting platforms on a global scale.

Perspective: Better Privacy Theorists for Better Data Stewards

The U.S. Census Bureau's use of differential privacy (DP) fundamentally changed how academic DP researchers perform outreach with official statistics stakeholders. In this perspectives piece, I propose ways for us in this community to improve those processes by being more receptive to the practical concerns raised by building DP systems. First, I discuss how academic DP work fundamentally differs from the policy decisions needed to implement DP systems and why this distinction has political consequences. Through examples and discussions from workshops, I show how the DP community largely asked applied stakeholders to communicate on DP's theoretical terms, when such an ask foreclosed important considerations relevant for the Census Bureau's policy problems. Second, I discuss how existing polarization between theoretical and empirical privacy researchers unintentionally seeped into the ways we communicated about DP, pointing to why both perspectives are necessary in different ways for policy conversations. Finally, I conclude by discussing how these issues are not unique to data privacy work, but instead reflect structural problems in translating theoretical science into practice. These ideas are presented in service of a single goal: to ensure DP theory supports substantive, privacy-aware data processing and dissemination in practice for essential data curators.

Health data stewardship: achieving trust through accountability in health data sharing for research

ABSTRACT Attempts in the UK to regulate the sharing and use of health data for secondary purposes such as research have been dogged by controversy and failure. This article considers health data stewardship as a response to these challenges. It develops a normative understanding of the concept through an examination of stewardship in a variety of contexts. This leads to a definition of health data stewardship which encapsulates the necessary constituent elements that focus group participants viewed as crucial to an accountable health data sharing scheme in which they could place their trust. It examines two potential mechanisms for facilitating health data stewardship, the data trust and the data cooperative, arguing that whichever regulatory mechanism is adopted must incorporate mechanisms responsive to citizens’ requirements for accountable stewardship of their data. Without this, their wishes, and societal aims of improving health through research using comprehensive data sets may be unachievable.

Assessing Opportunities and Barriers to Improving the Secondary Use of Health Care Data at the National Level: Multicase Study in the Kingdom of Saudi Arabia and Estonia.

Digitization shall improve the secondary use of health care data. The Government of the Kingdom of Saudi Arabia ordered a project to compile the National Master Plan for Health Data Analytics, while the Government of Estonia ordered a project to compile the Person-Centered Integrated Hospital Master Plan. This study aims to map these 2 distinct projects' problems, approaches, and outcomes to find the matching elements for reuse in similar cases. We assessed both health care systems' abilities for secondary use of health data by exploratory case studies with purposive sampling and data collection via semistructured interviews and documentation review. The collected content was analyzed qualitatively and coded according to a predefined framework. The analytical framework consisted of data purpose, flow, and sharing. The Estonian project used the Health Information Sharing Maturity Model from the Mitre Corporation as an additional analytical framework. The data collection and analysis in the Kingdom of Saudi Arabia took place in 2019 and covered health care facilities, public health institutions, and health care policy. The project in Estonia collected its inputs in 2020 and covered health care facilities, patient engagement, public health institutions, health care financing, health care policy, and health technology innovations. In both cases, the assessments resulted in a set of recommendations focusing on the governance of health care data. In the Kingdom of Saudi Arabia, the health care system consists of multiple isolated sectors, and there is a need for an overarching body coordinating data sets, indicators, and reports at the national level. The National Master Plan of Health Data Analytics proposed a set of organizational agreements for proper stewardship. Despite Estonia's national Digital Health Platform, the requirements remain uncoordinated between various data consumers. We recommended reconfiguring the stewardship of the national health data to include multipurpose data use into the scope of interoperability standardization. Proper data governance is the key to improving the secondary use of health data at the national level. The data flows from data providers to data consumers shall be coordinated by overarching stewardship structures and supported by interoperable data custodians.

Veterinary Services'use of the Global Burden of Animal Diseases to prioritise interventions, monitor impact and develop critical competencies.

Attracting and sustaining investment in Veterinary Services and animal health programmes from national government budgets, development aid and grants, and philanthropic donors requires economic rationale using relevant, reliable and validated analytical approaches. The complex interwoven relationships between animal health, livestock husbandry systems, national food security, global health security and environmental sustainability emphasise the importance of improving data governance and stewardship and applying economic analysis to understand animal disease burdens. These efforts should enable prioritised investment of limited resources and effective monitoring of the impact of programmes over time. Data governance and stewardship capacities are fundamental to development, implementation and performance monitoring of evidence-based policies in animal health. There are challenges in data availability for national and subnational livestock populations in different sectors, for disease incidence and prevalence, and for animal health expenditure in support of optimised allocation of scarce resources, be they finance, land, labour, or management attention and policy focus. Animal health data systems governance and stewardship and economic analysis are core skills for Veterinary Services in developing and applying evidence-based policy, but capability probably varies among World Organisation for Animal Health (WOAH) Members. The WOAH Performance of Veterinary Services programme has several critical competencies that are relevant to economics of animal health and to data governance and stewardship, but these have not yet been targeted for coordinated capacity development. Implementation of publicâ€"private partnership approaches for animal health programmes creates increasing expectations of robust data and methods for prioritisation, options analysis, and assessing impacts and costs. Experience and examples from national systems in New Zealand, Australia, Ethiopia and Indonesia illustrate current challenges associated with prioritisation of animal health programmes using economic analysis. The Global Burden of Animal Diseases programme intends to support WOAH Members and partners to develop capacities for and standardise approaches to economic analysis and prioritisation in animal health programmes.

Accelerating AI for science: open data science for science.

Aspirations for artificial intelligence (AI) as a catalyst for scientific discovery are growing. High-profile successes deploying AI in domains such as protein folding have highlighted AI's potential to unlock new frontiers of scientific knowledge. However, the pathway from AI innovation to deployment in research is not linear. Those seeking to drive a new wave of scientific progress through the application of AI require a diffusion engine that can enhance AI adoption across disciplines. Lessons from previous waves of technology change, experiences of deploying AI in real-world contexts and an emerging research agenda from the AI for science community suggest a framework for accelerating AI adoption. This framework requires action to build supply chains of ideas between disciplines; rapidly transfer technological capabilities through open research; create AI tools that empower researchers; and embed effective data stewardship. Together, these interventions can cultivate an environment of open data science that deliver the benefits of AI across the sciences.

AI integration in business development: Ethical considerations and practical solutions

The winds of change are sweeping through the business landscape, propelled by the transformative power of artificial intelligence (AI). This integration isn't merely a technological upgrade; it's a paradigm shift, unlocking a treasure trove of opportunities for companies to achieve unprecedented growth, efficiency, and innovation. Imagine AI-powered systems that analyze vast amounts of data, identify new market opportunities, predict customer behavior with uncanny accuracy, and even automating tedious tasks associated with lead generation and qualification. This is the future that AI promises, a future brimming with potential. However, the path to this future is not without its challenges. The immense power of AI comes with a critical responsibility: ensuring its ethical and responsible use. This review paper delves into the ethical considerations that businesses must navigate as they integrate AI into their business development strategies, exploring potential pitfalls like bias in algorithms, data privacy concerns, and job displacement. But fear not, for this paper doesn't just identify challenges; it also proposes practical solutions to navigate these complexities effectively. By acknowledging the potential for bias in AI algorithms and taking proactive steps to mitigate it, businesses can ensure fairness and inclusivity in their AI-driven decision-making. Imagine a team of data scientists meticulously scrutinizing training data, removing skewed information that could lead to discriminatory outcomes in lead generation or customer targeting. This commitment to responsible data practices fosters trust and ensures AI is used for good. Data privacy is another paramount concern in the age of AI. As AI systems collect and analyze vast amounts of customer data, businesses have a responsibility to safeguard this information. Imagine robust data security measures in place, protecting sensitive customer data with encryption protocols and access controls. Furthermore, fostering transparency about data collection practices and obtaining explicit consent from customers builds trust and demonstrates a commitment to responsible data stewardship. The specter of job displacement also looms large in the discussion of AI. While automation powered by AI might replace some repetitive tasks, it's important to remember that it also creates new opportunities. Imagine a world where AI handles lead scoring and qualification, freeing up human business developers to focus on high-value activities like strategic relationship building and closing deals. By investing in retraining programs and fostering a culture of continuous learning, businesses can empower their workforce to adapt and thrive in this evolving landscape.

What Is Qualitative Research? An Overview and Guidelines

This guide explains the focus, rigor, and relevance of qualitative research, highlighting its role in dissecting complex social phenomena and providing in-depth, human-centered insights. The guide also examines the rationale for employing qualitative methods, underscoring their critical importance. An exploration of the methodology’s strengths, such as its ability to offer contextual depth and holistic perspectives, is juxtaposed against challenges like constrained generalizability and potential researcher bias, accompanied by practical mitigation strategies. Covering a spectrum of qualitative approaches, including grounded theory, phenomenology, ethnography, action research, and general inquiry, the guide sheds light on diverse data collection and analysis techniques. Noteworthily, the guide underscores the crucial aspect of trustworthiness in qualitative research, detailing methods to establish credibility, dependability, confirmability, and transferability. The integration of technologies like recording and transcribing tools with data analysis software and the growing influence of artificial intelligence, machine learning, and deep learning are also explored. Concluding with a discussion on ethical considerations, from participant recruitment to data stewardship, this guide serves as an essential resource that offers insightful, actionable guidance for conducting effective and impactful qualitative research.

Metrology for sensor networks: metrological traceability and measurement uncertainties for air quality monitoring

Abstract In situ calibration of sensors delivering SI traceable measurement results still provides an open question to the design and operation of sensor networks. Particularly when addressing low-cost sensors, currently, the use of sensor networks for air quality monitoring is limited by the low or unknown accuracy of measurements that they can achieve, while the data quality of individual sensor networks is mainly derived by algorithms. Standardization bodies like DIN and CEN therefore announced the need for investigations of validation methods on gas phase species and particulate matter on the one hand side, and for the development of fully digitized quality assurance/quality control and calibration techniques for sensor networks on the other (CEN/CENELEC, Opportunity for Standardisation to Contribute to the European Partnership on Metrology EPM under Horizon Europe). This contribution concentrates on the metrological traceability of sensor networks for air quality monitoring to the international system of units (SI) based on FAIRified intra-network communications (M. Wilkinson, et al., “The FAIR guiding principles for scientific data management and stewardship,” Sci. Data, vol. 3, 2016, Art. no. 160018) and including delocalized Optical Gas Standards operated according to the digital TILSAM method (O. Werhahn, et al., The TILSAM Method Adapted into Optical Gas Standards – Complementing Gaseous Reference Materials, PTB Open Access Repository, 2021). Informed by related activities in EURAMET (Partnership project FunSNM, EMNs COO & POLMO, TC-IM 1551) (European Metrology Network Climate and Ocean Observation (COO), European Metrology Network Pollution Monitoring (POLMO), EURAMET Project TC-IM 1551, Project Database) this contribution discusses the importance of measurement uncertainties in the context of sensor networks, comprising different sensor principles and promoting an efficient uptake of state-of-the-art methods. We discuss how the sensor network case can be addressed with sensors individually using the GUM principles (Joint Committee for Guides in Metrology, Guide to the Expression of Uncertainty in Measurement (GUM), JCGM 100: 2008 (E)). For sensor network measurements becoming metrologically traceable to the SI, documented and unbroken chains of calibrations need to be implemented each contributing to the measurement uncertainty. This applies to each individual sensor of the network including the potential gold standard among them, but also to the network’s output viewed as a single entity. The contribution provides first approaches to be tested and validated that are underpinned by fundamental design strategies for sensor networks. It follows on practical applications in real world scenarios aside from model uncertainties discussed in artificial intelligence prospects.

Get Started in Data Governance and Data Stewardship

Data is growing rapidly in this new era where almost all organizations need to handle data with care, as data is an asset for organizations. A data governance program is essential for data quality management to improve data-driven insights that enable an organization to make better decisions. To implement an effective data governance program, data stewardship responsible for data management plays a vital role in it. Therefore, this study aims to provide fundamental information on successfully getting started in the data governance and data stewardship program. The literature reviews, principles, roles and responsibilities, framework, implementation, and maturity model assessment will be discussed in detail.

Indigenous Fire Data Sovereignty: Applying Indigenous Data Sovereignty Principles to Fire Research

Indigenous Peoples have been stewarding lands with fire for ecosystem improvement since time immemorial. These stewardship practices are part and parcel of the ways in which Indigenous Peoples have long recorded and protected knowledge through our cultural transmission practices, such as oral histories. In short, our Peoples have always been data gatherers, and as this article presents, we are also fire data gatherers and stewards. Given the growing interest in fire research with Indigenous communities, there is an opportunity for guidance on data collection conducted equitably and responsibly with Indigenous Peoples. This Special Issue of Fire presents fire research approaches and data harvesting practices with Indigenous communities as we “Reimagine the Future of Living and Working with Fire”. Specifically, the article provides future-thinking practices that can achieve equitable, sustainable, and just outcomes with and for stakeholders and rightholders (the preferred term Indigenous Peoples use in partnerships with academics, agencies, and NGOs). This research takes from the following key documents to propose an “Indigenous fire data sovereignty” (IFDS) framework: (1) Articles declared in the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) as identified by the author and specified in Indigenous-led and allied Indigenous fire research in Australia, Canada, and the U.S.; (2) recommendations specific to cultural fire policy and calls for research in the 2023 Wildland Fire Mitigation and Management Commission report; (3) research and data barriers and opportunities produced in the 2024 Good Fire II report; and threads from (4) the Indigenous Fire Management conceptual model. This paper brings together recommendations on Indigenous data sovereignty, which are principles developed by Indigenous researchers for the protection, dissemination, and stewardship of data collected from Tribal/Nation/Aboriginal/First Nations Indigenous communities. The proposed IFDS framework also identifies potential challenges to Indigenous fire data sovereignty. By doing so, the framework serves as an apparatus to deploy fire research and data harvesting practices that are culturally informed, responsible, and ethically demonstrated. The article concludes with specific calls to action for academics and researchers, allies, fire managers, policymakers, and Indigenous Peoples to consider in exercising Indigenous fire data sovereignty and applying Indigenous data sovereignty principles to fire research.

Data Management Plan for a rapid response sub-study of an existing cohort. The Irish Longitudinal Study on Ageing (TILDA) COVID-19 sub-study

A Data Management Plan (DMP) is a formal document that outlines the management and stewardship of data generated over the lifecycle of a research project from data collection, and governance structures, to the long-term preservation of data outputs. DMPs are an important feature of good research practice. Our aim is to provide details of the development of a DMP that others can learn from and adapt to their specific needs. Our DMP was developed as part of a COVID-19 sub-study of The Irish Longitudinal Study on Ageing (TILDA), titled “Altered lives in a time of crisis: preparing for recovery from the impact of the COVID-19 pandemic on the lives of older adults”. TILDA is a longitudinal study of community-dwelling older adults. In 2009/2010, an initial nationally representative sample of 8,500 adults aged 50 years and older were selected. The sample for the COVID-19 study were recruited from this existing sample. The objective of the sub-study was to document the lives and experiences of older adults during the COVID-19 pandemic to better understand the effect of the pandemic and public health responses on their well-being. This DMP describes the study design and objectives; data collection tools and procedures; data preparation; data storage and security; data sharing and preservation; and ethical and legal considerations within the European Union and Irish Health Research legislative context. Responsible data governance in Ireland is complex, requiring adherence to both European and Irish legislation. Implementation of the Health Information Bill (2023) may bring further complexities to this context. It is therefore crucial that researchers, data stewards, and other practitioners, share their expertise freely, as we have done here, so that others can learn from their experiences and the health research community can develop standards of best practice.

DMPs as Management Tool for Intellectual Assets by SMART-metrics

Data Management Plans (DMPs) are vital components of effective research data management (RDM). They serve not only as organisational tools but also as a structured framework dictating the collection, processing, sharing/publishing, and management of data throughout the research data life cycle. This can include existing data curation standards, the establishment of data handling protocols, and the creation, when necessary, of community curation policies. Therefore, DMPs present a unique opportunity to harmonise project management efforts for optimising the formulation and execution of project objectives. To harness the full potential of DMPs as project management tools, the SMART approach (i.e., Specific, Measurable, Achievable, Relevant, and Time-bound) emerges as a compelling methodology. During the initial stage of the project proposal, drafted SMART metrics can offer a systematic approach to map work packages (WPs) and deliverables to the overarching project objectives. Then, the Principal Investigators (PIs) can ensure the consortia that all the project potential intellectual assets (i.e., expected research results) were considered properly, as well as their necessary timelines, resources, and execution. It becomes imperative for data stewards (DSs) and governance policymakers to educate and provide guidelines to researchers on the advantages of developing well-curated DMPs that align results with SMART metrics. This alignment ensures that every intellectual asset intended as a research result (e.g., intellectual properties, publications, datasets, and software) within the project is subject to rigorous drafted planning, execution, and accountability. Consequently, the risk of unforeseen setbacks and/or deviations from the original objectives is minimised, increasing the traceability and transparency of the research data life cycle. In addition, the integration of Technology Readiness Levels (TRLs) into this proposed enhanced DMP provides a systematic method to evaluate the maturity and readiness of technologies across scientific disciplines. Regular TRL assessments will allow PIs: (1) to monitor the WP progress, (2) to adapt research strategies if required, and (3) to ensure the projects remain in line with the drafted SMART metrics in the enhanced DMP before the project started. The TRLs can also help PIs maintain their focus on project milestones and specific tasks aligned with the original objectives, contributing to the overall success of their endeavours, while improving the transparency for the reporting and divulgation of the research results. The paper presents the overall framework for enhancing DMPs as project management tools for any intellectual assets using SMART metrics and TRLs, as well as introducing suggested support services for data stewardship teams to assist PIs when implementing this novel framework effectively.

Centering Relationality and CARE for Stewardship of Indigenous Research Data

Centering Relationality and CARE for Stewardship of Indigenous Research Data