Enhancing data governance in collaborative research: Introducing SA DTA 1.1

The circular economy (‘CE’) is a political goal to shift the economy towards a more circular and sustainable one. Data sharing shall close information gaps in the predominant economy and, hence, form the backbone of the transition to a CE. As part of the recent legislative proposals from the EC, “digital product passports” (‘DPPs’) are expected to constitute one-stop shops for such data and information delivery. The principal idea of DPPs is to provide product information to interested stakeholders within and outside the product value chain. However, many challenges arise with DPPs, in particular how to reconcile a broad (if no ‘open’) access to data while preserving the (legally protected) interests of certain actors, and the lack of trust concerning data use and quality between the actors. Our hypothesis are (i.) that such challenges are related to the absence of an appropriate (data) governance model for DPPs and (ii.) that data governance as a main concern within the data economy agenda could inform the regulation and operationalisation of DPPs. We analyse the – lack of - (data) governance of DPPs, both in the literature and in the recent legislative proposals from the EC. We find that data governance is significantly overlooked or considered only from a pure technological perspective (techno-solutionism), which is likely to jeopardise DPPs. As both a confirmation and a way forward, we establish connections to data governance as per the data economy agenda. In turn, the analysis of DPPs governance informs the general discussion on data governance.

Jeannette Colyvas • Michael Crow • Annetine Gelijns • Roberto Mazzoleni Richard R. Nelson • Nathan Rosenberg • Bhaven N. Sampat Institute for International Studies, Stanford University, Stanford, California 94305 Office of the Executive Vice Provost, Columbia University, New York, New York 10027 INCHOIR, Columbia University, New York, New York 10027 Department of Economics and Geography, Hofstra University, Hempstead, New York 11594 School of International and Public Affairs, Columbia University, New York, New York 10027 Department of Economics, Stanford University, Stanford, California 94305 School of Public Policy, Georgia Institute of Technology, Atlanta, Georgia 30332 colyvas@leland.stanford.edu • mc71@columbia.edu • acp10@columbia.edu • ecorzm@mail1.hofstra.edu rrn2@columbia.edu • nate@leland.stanford.edu • bhaven.sampat@pubpolicy.gatech.edu

Policy‐makers and scientists are increasingly worried about the declining productivity of the pharmaceutical industry. Despite growing levels of investment from governments and industry, the number of new medicines that are approved each year has declined slowly during the past three decades (Betz, 2005; Booth & Zemmel, 2004; Cuatrecasas, 2006). The increased costs of drug discovery and development can no longer be supported by healthcare systems, and regulators are implementing control mechanisms such as price control schemes and increased regulatory demands, which could ultimately affect the drug discovery process by altering the financial and regulatory landscapes for new research programmes (Ess et al , 2003; Miller & Henderson, 2007). > The increased costs of drug discovery and development can no longer be supported by healthcare systems… Over the years, the industry has relied on various technologies to improve drug discovery. However, despite the use of structure‐based drug design, high‐throughput screening, combinatorial chemistry and the various ‘‐omics’ technologies, productivity continues to decline. The various explanations for this lack of success include increasing regulatory requirements, the depletion of therapeutic opportunities and ‘druggable’ targets, competition and organizational inefficiencies (Booth & Zemmel, 2004; Cuatrecasas, 2006; Dickson & Gagnon, 2004; Garnier, 2008). Although each of these explanations rings true, at the heart of the problem is a lack of proper scientific understanding of human biology and disease mechanisms. This is ultimately manifested in the high fraction of clinical programmes that fail owing to a lack of efficacy or safety, often in spite of excellent pre‐clinical data (Fingleton, 2008; Kola & Landis, 2004). Without a doubt, improved drug‐target validation would improve the success rate of drug discovery programmes, but this necessitates a better understanding of fundamental biological processes and the biological role of the drug target—usually a protein—under investigation. Paradoxically, …

DE GRUYTER Abstract: Since 2005 at least fifteen community and pub - lic libraries have been deliberately set alight in South African townships and informal settlements, reportedly by individuals or groups from the communities which these libraries were intended to serve. This has given rise to dismay, horror and outrage among librarians. This ar- ticle seeks to situate the deliberate destruction of librar- ies in a broader international context before focusing on the South African context of what are commonly called service delivery protests. An overview is given of some recent scholarly analyses of violent protests in South Afri- can communities in an attempt to answer four questions: (1) what were the circumstances in which libraries were set alight? (2) who did this? (3) were libraries deliberately tar- geted or were they simply collateral damage? and (4) if li- braries were deliberately targeted, what motivated this? A fifth question concerns how the South African library pro - fession responded to these incidents. Using the burning of the Ratanda Library on 20 March 2012 as a case study, the article explores the response of the South African library profession to the incident. In an analysis of the content of contributions posted on the discussion list and website of the Library and Information Association of South Africa (LIASA), four main groups of themes are identified. These concern expressions of revulsion, the impact of the inci- dent, professional action, and underlying societal issues. The article concludes with some observations on the re- sponses of the South African library community.

The problem this study examined was lack of interest among African youth in pursuing artistry disciplines due to minimal promotion of the discipline, its minimal role in self- and talent discovery initiatives. The artistry works of the South African artists such as Lesego Rampolokeng, who promotes self-awareness and Black consciousness are not promoted in the South African school curriculum and communities, and media is also silent about them. The primary objective of the study was to examine the views of student teachers and lectures on the works of Lesego Rampolokeng, and its contribution towards youth’s self-awareness and talent discovery; and how African artistry can be used to promote youth that is self-propelled and self- actualised in their career life. The key findings of the study indicate that black consciousness lessons of Rampolokeng evoke student teachers’ self -awareness and become self-propelled towards self- actualised career life. Furthermore, Rampolokeng is regarded as a role model whose works can be used to promote self- and talent discovery among South African youth. This study therefore, recommends that performing arts should be promoted in African communities and become a compulsory subject in the basic education. The study further proposes an increased exposure of African Performing Arts works in the South African school curriculum; and school Arts competition, as well as through increased number of Arts festival in South African communities.

An Early‐Career Scientist's Guide to Delving Into Data Synthesis

Data management solutions on their own are becoming very expensive and not able to cope with the reality of everlasting data complexity. Businesses have grown more sophisticated in their use of data, which drives new demands that require different ways to handle this data. Forward-thinking organizations believe that the only way to solve the data problem will be the implementation of an effective data governance. Attempts in governing data failed before, as they were driven by IT, and affected by rigid processes and fragmented activities carried out on system by system basis. Up to very recently governance is mostly informal with very ambiguous and generic regulations, in siloes around specific enterprise repositories, lacking structure and the wider support of the organization. Despite its highly recognized importance, the area of data governance is still under developed and under researched. Since data governance is still under researched, there is need to advance research in data governance in order to deepen practice. Currently, what exist are mostly descriptive literature reviews in the area of data governance. In this paper, a systematic literature review (SLR), which offers a structured, methodical, and rigorous approach to the understanding of the state-of-the-art of research in data governance. The objective of the study is to provide a credible intellectual guide for upcoming researchers in data governance to help them identify areas in data governance research where they can make the most impact. The systematic literature review covered published contributions from both academia and industry. The presented SLR searches and examines most relevant published work since year 2000 to-date for data governance for non-cloud, and for cloud computing since 2007. Only 52 studies met the inclusion criteria, which are critically reviewed.

Traditionally, clinical research has been conducted via either industry sponsored studies or non-industry investigator sponsored studies. Collaborative Research provides a relatively new mechanism for industry and non-industry partners to work together in the pursuit of effective and safe treatments for the patient. The aims of this article are to provide both industry and non-industry investigators with a greater insight into the complex processes that are currently employed by industry when entering into Collaborative Research agreements, and to encourage consistency and transparency in approach across companies. In Collaborative Research, instead of being limited to providing funding and/or product, the industry partner contributes expertise complementary to that of the non-industry partner, who is the sponsor of the study. Collaborative Research may be conducted before, during or after regulatory approval of a drug or medical device, and may be interventional, observational or preclinical. A collaboration requires appropriate process and governance frameworks to be established in order to be successful. Important considerations include the routes for submitting a request, the review and approval process, due diligence criteria, budgeting and contracting processes, permissible interactions during the execution of the research, the closing out of the research, and dispute resolution. It is also necessary to have in place an agreed communication strategy and a risk control framework. Clear and specific contract language around roles and responsibilities, intellectual property, rights to data, registration and disclosure of publications, and an understanding of adverse event reporting procedures are other critical facets of Collaborative Research that are essential to avoid delays and disputes. With no global standards for Collaborative Research, it is important that partners establish practical procedures, good ongoing communication, alignment of goals, and transparent interactions and disclosure to jointly advance the science of new, safe and effective therapies.

The author concentrates on data governance in research and practice. The author has developed a strong knowledge of data governance through numerous case studies of electricity, retail, telecom, banks, or insurance companies. With more than nine years of experience from both technical and process point of view, the author has become a Data Governance specialist with focus on design and implementation of governance frameworks. The author has given numerous training courses to improve knowledge of the data to derive maximum value from it. Data governance in financial institutions has been significantly driven by regulatory requirements. Data governance standards are complex and lack implementation details. This paper proposes Data Governance Model used in a financial institution. Data Governance Model is based on BCBS 239 and DAMA methodology. The Model enhances practices of DAMA and tailors them to the needs of financial institution. The scope of the Data Governance Model is limited to data governance, data quality, and metadata management.

An essential component of an active knowledge economy, is successful research and development (R&D) collaboration between entities in the private and public sector. This is particularly important in technology research, especially with software technologies where research results need to rapidly move into the market to have any chance of uptake in a normally shorter commercial window of opportunity. The Intellectual Property Rights from Publicly Financed Research and Development Act of 2008 (the IPR Act) seeks to provide measures to more effectively use intellectual property (IP) generated in whole or in part by the use of public funds. However, the requirements of the IPR Act have placed additional constraints on structuring industry collaboration with public research institutions. In particular, the Act requires that, should an industry partner wish to take ownership of IP generated at a public research institution, it should fund the research on a full cost basis, contribute materially to the development of the IP, or face a complicated and still mostly untested bureaucratic process of seeking assignment of IP. In this research paper, we consider a number of case studies of industry-academic research collaboration projects in South Africa undertaken within the context of the new IPR Act. We highlight certain benefits and detractions of each model, revealing practical implications and sometimes limitations of each.

Data governance and management solutions are experiencing a continuous data complexity, which make the process very expensive. The sophisticated data usage in many businesses has been a driving factor for new demands in the data handling, which has led to business requiring different ways of managing their data. Many businesses have resolved to the implementation of effective data governance as the most possible way to solve the data issue. Initially, many businesses attempted governing data but the process was not successful due to IT drive and the rigid processes that affected the activities that were carried out on system by system basis. The process has also suffered inadequate support from the organizations and structures for implementation. Data governance has highly recognized significances, but still no advanced research and development directed toward the achievement of the process. Due to the minimum research, this provides a room to advance research in the field in order to widen practice. In this paper, a mini literature review has been conducted to provide a structured and methodical approach for understanding aspects of research in data governance. The main objective is to provide an intellectual foundation for emerging scholars in the field of data governance—principles, models, challenges of data governance and digital transformation, and key dimensions of data governance strategy in Saudi Arabia. The study will incorporate IBM and agility data governance tools and model in integrating the useful information and for establishing and implementing the high level policies and procedures in the study. The mini literature review covered the importance of data governance, data governance principles, data governance models, the challenges facing data governance and digital transformation and the key dimensions in data governance strategy.

In the biomedical fi eld multiple stakeholders may collaboratively develop, package and build transactions around technology. This is often referred to as open innovation, open biotechnology or open source R&D. Intellectual Property (IP) and IP rights play important roles in collaborative platforms but little research has previously assessed the impact that such platforms exert on corporate IP-strategies. A case study was therefore conducted of the Innovative Medicines Initiative (IMI), CAMBIA Biological Open Source (BiOS) and the Medicines for Malaria Venture (MMV). Our results impact managerial decision-makers at pharmaceutical, biotech and agriscience companies intending to implement corporate open innovation and IP strategies.

In today’s data-driven world, ensuring high data quality and robust governance is essential for organizations striving to remain competitive and compliant. This paper examines the integral role of data quality and governance in establishing a trustworthy and compliant data ecosystem. By implementing rigorous quality assurance processes, organizations can ensure that data remains accurate, consistent, and reliable across all operations. Effective data governance frameworks provide the necessary policies, procedures, and accountability structures to oversee data management, reducing risks associated with inaccuracies and non-compliance. The discussion highlights the importance of integrating automated monitoring tools and regular audits to swiftly detect and address anomalies, thereby enhancing data integrity. Emphasis is placed on the need for cross-functional collaboration and clear leadership commitment to foster a culture where data is viewed as a strategic asset. Additionally, the paper explores how aligning data practices with regulatory requirements not only protects against legal and financial penalties but also improves operational efficiency and decision-making capabilities. Through the synthesis of best practices and case studies, the framework presented demonstrates how structured data governance and continuous quality improvement initiatives can drive business success. Ultimately, this research provides a comprehensive roadmap for organizations seeking to build resilient data infrastructures that support both innovation and compliance in a rapidly evolving digital landscape

Background: Real world data provides unique insights into the natural history of MM, resource utilisation, adherence to standards and outcomes of treatments outside clinical trials. However, the quality of such initiatives can be poor due to the need for manual data entry, inability to integrate multiple data sources, lack of harmonisation of data across centres and limited generalisability due to centre bias. Exemplar registries collate and transform multidisciplinary routinely collected data from Electronic Health Records (EHRs) to a common data language allowing integration, analysis and expansion. Aims: To develop a secure national MM registry beginning with a three site pilot. Methods: Potential registry providers were evaluated against the following criteria: previous experience, infrastructure and technical capability, data governance, compliance with legal standards and cost effectiveness. The platform required ethical and data sharing approvals. A common data model was investigated and a myeloma focused multidisciplinary dataset was designed and mapped to source data. Data extraction, transformation and analysis is planned. Results: 8 providers were assessed for suitability. The NIHR HIC was chosen due to its established data sharing network across 30 UK sites and overarching governance framework. This platform permitted the establishment of harmonised data flow, comparable across sites and retaining clinical interpretability. A minimal myeloma dataset of routinely collected information was agreed by clinicians and data scientists to contain the following fields: baseline and disease characteristics including imaging findings eg: extramedullary/paramedullary disease, skeletal/spinal disease, comorbidities, mode of presentation, cytogenetics, treatments including chemotherapy, stem cell transplants/CAR-T cells, spinal interventions, anti-infectives, bisphosphonates and other supportive care. Each variable was mapped to identify the location of raw data in the EHR, its format and suitability for extraction. For unstructured data, eg: cytogenetics and imaging reports, natural language processing is being developed to extract key fields. To enable harmonisation of data from different systems, data is transformed into a common data model. The Observational Medical Outcomes Partnership common data model (OMOP CDM) was chosen as it provided a framework for systematic analysis of data from disparate data sources, typical of those held within health care information systems. This model allows for international benchmarking and collaboration for research. OMOP CDM maps commonly used coding systems and vocabularies, eg ICD-10, SNOMED-CT into standard concepts permitting raw data entering the registry to be standardised into a research ready format that is person-centric, disease agnostic and syntactically and semantically defined. Anonymised data from multiple sites will be electronically transferred to a ISO27001 certified, GDPR compliant UCL data safe haven for analysis. Oversight is provided by the Steering Committee of clinicians, data scientists, myeloma patients and Myeloma UK with patient involvement at every step. Data will be analysed to answer 3 initial themes (Image 1), and extended further to include control data for Health Technology Appraisals. To date we have successfully identified an initial cohort of over 4000 myeloma cases since 2015. Image:Summary/Conclusion: The NIHR HIC Myeloma registry represents a research ready platform to integrate and analyse UK wide real word data. Detailed data analysis and progress in answering the initial 3 themes is ongoing. Funding: Myeloma UK & NIHR

Business Process Management (BPM) is a collection of methods, techniques, and tools to optimize organizational performance. In the case of higher education institutions (HEIs), BPM is usually implemented to provide a standard business process. Implementation of Data Governance (DG) is often based on the desire for good Data Quality (DQ), to help an organization carry out its strategies and achieve its objectives. Previous research has proven that DG would improve data quality and safety and trigger a better standard. While data and information are essentially connected, BPM and DG have been addressed separately in the process of obtaining high quality data and information. This study conducts a literature review to investigate the relationship of BPM in the development of DG in HEIs. Articles related to BPM, DG, and DQ are collected from relevant databases. The articles are filtered and perused further to obtain 23 articles that are analyzed and synthesized to answer the research question. Based on these articles, a conceptual model is developed, depicting relationship in the application of BPM and DG. The first relationship identified from previous work is that the successful implementation of BPM influences DG planning and implementation success. Second, a good BPM implementation influences the process of obtaining a good DQ. Finally, implementing a good DG influences the acquirement of a good DQ. This study also found that BPM, DG, and DQ implementation at HEIs was fewer than that at other sectors or case studies, which open up opportunities for further studies.