UMS Data Governance Charter & Framework

The University of Maine System Data Governance program launched in 2017, in order to emphasize the importance of data integrity, and to formalize processes related to data management and usage. The program embraces the following vision: Data Governance Vision: Data of the University of Maine System (UMS) are system-wide institutional assets that are leveraged to foster a culture of data-informed decisions to benefit all UMS institutions and stakeholders. This vision can be achieved through successful implementation of the following mission: Data Governance Mission: UMS Data Governance promotes data stewardship and communication to ensure that valid and reliable data are protected and readily accessible to university constituents, internal and external, for ethical use

Data Governance in International Neuroscience Research

open access articl

Responsible Data Governance of Neuroscience Big Data

Open access article.Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of “responsible data governance,” applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP)

Deconstructing Data Governance

Few institutional governance topics have garnered more attention and verbiage in the past two years as that of data (or information) governance. Thanks partly to the promotion of terms such as big data and eScience, more organizations — including academic institutions — are embracing the mantra that data are their second-most valuable asset (after people, of course). This suggests that beyond protection and security, data should be managed not as mere commodity but as a strategic asset. Data governance, then, is really about managing the behaviors of people, not bits. How can you derive an ROI — a Return On Information — that provides true strategic value to your institution? This presentation will stake out the broad landscape of considerations that encompass data governance, then focus on its key elements. Participants will take away a basic framework and approach for defining their own data governance initiatives.https://digitalrepository.unm.edu/hslic-posters-presentations/1010/thumbnail.jp

Financial Data Governance

Finance is one of the most digitalized, globalized, and regulated sectors of the global economy. Traditionally technology intensive, the financial industry has been at the forefront of digital transformation, starting with the dematerialization of financial assets in the 1960s and culminating in the post–2008 global financial crisis era with the fintech movement. Now, finance is data: financial transactions are transfers of data; financial infrastructures, such as stock exchanges and payment systems, are data networks; financial institutions are data processors, gathering, analyzing, and trading the data generated by their customers. Financial regulation has adapted to this fast-paced evolution both by implementing new regimes and by adapting existing ones. Concomitantly, general data governance frameworks to protect a broad spectrum of interests, from individual privacy to national security, have emerged. Though these areas of law intersect, their relationship often remains unclear. This Article sheds new light in this critical area, focusing on key challenges and providing viable solutions to address them. First, we define financial data governance as a heterogenous system of rules and principles concerned with financial data, digital finance, and related digital infrastructure. To explain how legal and regulatory regimes interact with the digitalization of finance, we consider the key emerging financial data governance styles in the European Union, People’s Republic of China, India, and the United States. Second, we examine the challenges affecting financial data governance. While finance is inextricably linked to data governance, the coalescence of financial regulation, new regulatory frameworks for digital finance, and general data governance regimes is not always harmonious. Conflicts arising from the intersection of different uncoordinated regimes threaten to frustrate core policy objectives of stability, integrity, and security, as well as the functioning of the global financial system. Addressing this requires a reconceptualization of the financial data centralization paradigm, both by regulators and by the financial industry

Tools for Data Governance

This article describes the challenges of data governance in terms of the broader framework of knowledge commons governance, an institutional approach to gov- erning shared knowledge, information, and data resources. Knowledge commons governance highlights the potential for effective community- and collective-based governance of knowledge resources. The article focuses on key concepts within the knowledge commons framework rather than on specific law and public pol- icy questions, directing the attention of researchers and policymakers to critical inquiry regarding relevant social groups and relevant data “things.” Both concepts are key tools for effective data governance

UAP 2580: Data Governance

University President; Provost and Executive Vice President for Academic Affairs; Executive Vice President for Administratio

A Framework to Analyze Data Governance of Swiss Population Registers

In June 2006 the Swiss Parliament adopted a new law on population registers' harmonization in order to simplify statistical data collection and data exchange from around 4'000 decentralized registers. Besides there are more than 2'000 administrative services delivered to Swiss citizens and businesses, of which hundreds could potentially use data from population registers. The law is rather vague about the implementation of this harmonization and even though many projects are currently being undertaken in this domain, most of them are quite technical. We believe there is a need for analysis tools and therefore in this paper we propose a conceptual framework to analyse data governance of these populations registers, with a strong focus on information requirements and identity management. In order to develop this framework we built on existing approaches to define its building blocks: data consumers, data sources, identity in a given context, requirements, and data sets.governance; data; identity; population registers; modelling; framework