scholarly journals The opportunities and ethics of big data: practical priorities for a national Council of Data Ethics

2016 ◽  
Vol 374 (2083) ◽  
pp. 20160116 ◽  
Author(s):  
Olivia Varley-Winter ◽  
Hetan Shah
Keyword(s):  
Big Data ◽  
Data Science ◽  
Professional Competence ◽  
Data Access ◽  
National Council ◽  
Data Infrastructure ◽  
Ethical Frameworks ◽  
Data Ethics ◽  
Address Data ◽  
The Uk

In order to generate the gains that can come from analysing and linking big datasets, data holders need to consider the ethical frameworks, principles and applications that help to maintain public trust. In the USA, the National Science Foundation helped to set up a Council for Big Data, Ethics and Society, of which there is no equivalent in the UK. In November 2015, the Royal Statistical Society convened a workshop of 28 participants from government, academia and the private sector, and discussed the practical priorities that might be assisted by a new Council of Data Ethics in the UK. This article draws together the views from that meeting. Priorities for policy-makers and others include seeking a public mandate and informing the terms of the social contract for use of data; building professional competence and due diligence on data protection; appointment of champions who are competent to address public concerns; and transparency, across all dimensions. For government data, further priorities include improvements to data access, and development of data infrastructure. In conclusion, we support the establishment of a national Data Ethics Council, alongside wider and deeper engagement of the public to address data ethics dilemmas. This article is part of the themed issue ‘The ethical impact of data science’.

scholarly journals Learning health systems need to bridge the ‘two cultures’ of clinical informatics and data science

2018 ◽  
Vol 25 (2) ◽  
pp. 126-131 ◽  
Author(s):  
Philip J. Scott ◽  
Rachel Dunscombe ◽  
David Evans ◽  
Mome Mukherjee ◽  
Jeremy C. Wyatt
Keyword(s):  
Big Data ◽  
System Design ◽  
Data Science ◽  
Digital Health ◽  
Evidence Base ◽  
Clinical Informatics ◽  
Two Cultures ◽  
Design And Implementation ◽  
The Two Cultures ◽  
The Uk

BackgroundUK health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational ‘Big Data’. Learning health systems require not only data, but feedback loops of knowledge into changed practice. This depends on knowledge management and application, which in turn depends upon effective system design and implementation. Biomedical informatics is the interdisciplinary field at the intersection of health science, social science and information science and technology that spans this entire scope.IssuesIn the UK, the separate worlds of health data science (bioinformatics, ‘Big Data’) and effective healthcare system design and implementation (clinical informatics, ‘Digital Health’) have operated as ‘two cultures’. Much National Health Service and social care data is of very poor quality. Substantial research funding is wasted on ‘data cleansing’ or by producing very weak evidence. There is not yet a sufficiently powerful professional community or evidence base of best practice to influence the practitioner community or the digital health industry.RecommendationThe UK needs increased clinical informatics research and education capacity and capability at much greater scale and ambition to be able to meet policy expectations, address the fundamental gaps in the discipline’s evidence base and mitigate the absence of regulation. Independent evaluation of digital health interventions should be the norm, not the exception.ConclusionsPolicy makers and research funders need to acknowledge the existing gap between the ‘two cultures’ and recognise that the full social and economic benefits of digital health and data science can only be realised by accepting the interdisciplinary nature of biomedical informatics and supporting a significant expansion of clinical informatics capacity and capability.

scholarly journals Data Science: Trends, Perspectives, and Prospects

2021 ◽  
Author(s):  
Chaolemen Borjigin ◽  
Chen Zhang
Keyword(s):  
Big Data ◽  
Data Science ◽  
Scientific Discovery ◽  
Big Data Analytics ◽  
Theoretical Studies ◽  
Data Intensive ◽  
Data Ethics ◽  
Big Data Visualization ◽  
Data Products ◽  
And Performance

Abstract Data Science is one of today’s most rapidly growing academic fields and has significant implications for all conventional scientific studies. However, most of the relevant studies so far have been limited to one or several facets of Data Science from a specific application domain perspective and fail to discuss its theoretical framework. Data Science is a novel science in that its research goals, perspectives, and body of knowledge is distinct from other sciences. The core theories of Data Science are the DIKW pyramid, data-intensive scientific discovery, data science lifecycle, data wrangling or munging, big data analytics, data management and governance, data products development, and big data visualization. Six main trends characterize the recent theoretical studies on Data Science: growing significance of DataOps, the rise of citizen data scientists, enabling augmented data science, diversity of domain-specific data science, and implementing data stories as data products. The further development of Data Science should prioritize four ways to turning challenges into opportunities: accelerating theoretical studies of data science, the trade-off between explainability and performance, achieving data ethics, privacy and trust, and aligning academic curricula to industrial needs.

scholarly journals Introducing Health Data Research Network Canada (HDRN Canada): A New Organization to Advance Canadian And International Population Data Science

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Kimberlyn McGrail ◽  
Brent Diverty ◽  
Lisa Lix
Keyword(s):  
Data Science ◽  
Data Access ◽  
Indigenous Communities ◽  
Health Data ◽  
Research Strategy ◽  
Research Network ◽  
Case Definitions ◽  
Extensive Experience ◽  
Data Infrastructure ◽  
Metadata Standards

IntroductionNotwithstanding Canada’s exceptional longitudinal health data and research centres with extensive experience transforming data into knowledge, many Canadian studies based on linked administrative data have focused on a single province or territory. Health Data Research Network Canada (HDRN Canada), a new not-for-profit corporation, will bring together major national, provincial and territorial health data stewards from across Canada. HDRN Canada’s first initiative is the $81 million SPOR Canadian Data Platform funded under the Canadian Institutes of Health Research Strategy for Patient-Oriented Research (SPOR). Objectives and ApproachHDRN Canada is a distributed network through which individual data-holding centres work together to (i) create a single portal and support system for researchers requesting multi-jurisdictional data, (ii) harmonize and validate case definitions and key analytic variables across jurisdictions, (iii) expand the sources and types of data linkages, (iv) develop technological infrastructure to improve data access and collection, (v) create supports for advanced analytics and (vi) establish strong partnerships with patients, the public and with Indigenous communities. We will share our experiences and gather international feedback on our network and its goals from symposium participants. ResultsIn January 2020, HDRN Canada launched its Data Access Support Hub (DASH) which includes an inventory listing over 380 datasets, information about more than 120 algorithms and a repository of requirements and processes for accessing data. HDRN Canada is receiving requests for multi-province research studies that would be challenging to conduct without HDRN Canada. Conclusion / ImplicationsThus far, HDRN Canada services and tools have been developed primarily for Canadian researchers but HDRN Canada can also serve as a prompt for an international discussion about what has/has not worked in terms of multi-jurisdictional research data infrastructure. It can also present an opportunity for the development of metadata, standards and common approaches that support more multi-country research.

scholarly journals Unifying the UK’S Data for Respiratory Science: BREATHE, The Health Data Research UK (HDR UK) Respiratory Health Data Research Hub

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Chris Orton ◽  
David Ford ◽  
Aziz Sheikh ◽  
John Norrie ◽  
Monica Fletcher ◽  
...  
Keyword(s):  
Data Sharing ◽  
Data Science ◽  
Respiratory Health ◽  
Tertiary Care ◽  
Data Access ◽  
Health Data ◽  
Quality Improvement Effort ◽  
Domain Expertise ◽  
The Uk ◽  
Collaborative Efforts

IntroductionThe BREATHE Health Data Research Hub is a consortium of five academic institutions and several industry partners seeking to facilitate and accelerate respiratory science initiatives and outcomes. Unlocking organisational, jurisdictional, and scientific challenges, such as differing and inherent complexities with data standards, incongruous governance, and disparate data access mechanisms for over 100 diverse UK datasets are key aims. Objectives and ApproachCentral to the data effort is the UK Secure eResearch Platform (SeRP UK), and its flagship tenancy, the SAIL Databank. Onboarding datasets, making them remotely available to the respiratory research community, is a key approach. Datasets targeted range from population cohort studies, to respiratory trials data, routine healthcare datasets, and specialist ‘omics data. Partnerships with national safe havens and providers such as eDRIS and NHS Digital will enable BREATHE to expedite and improve wider sharing of datasets for the respiratory science. Data improvements focus on datasets from primary, secondary, and tertiary care from national healthcare systems, ‘respiratorising’ these datasets and increasing utility for academic and industry respiratory scientists. Incorporating dataset metadata and access permutations into national cataloguing systems at HDR UK, standardising metadata, and interoperability for in-scope datasets form a concerted data quality improvement effort. ResultsFacilitating data sharing through initiatives such as BREATHE will increase visibility and accessibility for datasets within respiratory science, whilst addressing national cultural and governance issues to data sharing. BREATHE data sharing processes will allow for team science to be undertaken in a highly collaborative manner and allow for best practise in data collection and sharing to flow to nationwide datasets in respiratory science. Conclusion / ImplicationsCollaborative hubs with scientific domain expertise can be created and leveraged to accelerate data sharing and data science within the scientific area. These collaborative efforts can however be translated to other disease-specific efforts, and indeed disease agnostic platform solutions.

scholarly journals Iinterdisciplinarity in Data Science over Big Data: findings for mining industry

2019 ◽  
Vol 29 (4) ◽  
pp. 61-74
Author(s):  
Vitor Afonso Pinto ◽  
Ana Maria Pereira Cardoso ◽  
Marta Macedo Kerr Pinheiro ◽  
Fernando Silva Parreiras
Keyword(s):  
Big Data ◽  
Iron Ore ◽  
Data Science ◽  
Mining Industry ◽  
Interdisciplinary Approach ◽  
Research Field ◽  
Address Data ◽  
Knowledge Area ◽  
Narrative Literature ◽  
Narrative Literature Review

Data Science and Big Data are leveraged by businesses in many ways to improve operational and strategic capabilities, and ultimately, to positively impact corporate financial performance. However, there are challenges related to Big Data, such as modelling, new paradigms and novel architectures that require original approaches to address data complexities. In the specific case of iron ore mining industry, there is a considerable pressure at present to reduce costs due to the recent major fall in iron ore prices. This study discusses if an interdisciplinary approach could help mining industries to extract the most of data science initiatives over big data. In this study we applied a narrative literature review method to briefly present a chronological review of disciplines and interdisciplinarity as well as the evolution of data science over big data. Then we discussed: 1) the importance of involving people from different profiles; 2) the relevance of technology transfer inside computing research field; 3) the requirements for integrating so many different people and technologies in such initiative. We concluded that achieving results with Data Science initiative over big data is not related to a single knowledge area, especially in mining industries.

scholarly journals Design for data ethics: using service design approaches to operationalize ethical principles on four projects

2018 ◽  
Vol 376 (2128) ◽  
pp. 20170353 ◽  
Author(s):  
Cat Drew
Keyword(s):  
Data Science ◽  
Service Design ◽  
Initial Development ◽  
Social Good ◽  
Ethics Framework ◽  
Ethical Frameworks ◽  
Data Ethics ◽  
Use Of Data ◽  
Government Data ◽  
Guidance Needs

Ethical frameworks provide helpful guidance about what you should—and should not—do in relation to data projects. But they do not provide definitive yes/no answers about what an ethical data project is or is not. Indeed, research (Ipsos-MORI 2015 Public dialogue into the ethics of data science in government) conducted for the initial development of the Government's Data Ethics Framework shows that the public does not hold any clear red lines; rather, they make nuanced assessments based on a number of variables, including public good and privacy. Ethical frameworks provide a list of these variables to consider in shaping the form of the work. Some are now starting to provide more practical tools and guidance to reshape data projects and push it along those variables into a more ethical space. Alongside technical tools, service design approaches can help enhance the degree to which a data project is ethical, and provides a toolkit for data scientists, analysts and policymakers to take projects from ‘what should we do’ to ‘how can we do it’. This paper sets out the emergence of data science ethical frameworks within the context of the use of data for social good, and—with the recent release of the updated UK Government Data Ethics Framework—shows the recognition more practical guidance needs to be provided. The author then argues that service design approaches provide a helpful ‘wrap around’ for data projects, and draws on experience in using service design tools on four projects, as well as wider examples. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.

scholarly journals The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study

2021 ◽  
pp. 155626462110535
Author(s):  
Agata Ferretti ◽  
Marcello Ienca ◽  
Minerva Rivas Velarde ◽  
Samia Hurst ◽  
Effy Vayena
Keyword(s):  
Big Data ◽  
Research Ethics ◽  
Data Science ◽  
Ad Hoc ◽  
Qualitative Interviews ◽  
Ethics Committees ◽  
Research Quality ◽  
Research Ethics Committees ◽  
Data Ethics ◽  
Research Risks

Big data trends in health research challenge the oversight mechanism of the Research Ethics Committees (RECs). The traditional standards of research quality and the mandate of RECs illuminate deficits in facing the computational complexity, methodological novelty, and limited auditability of these approaches. To better understand the challenges facing RECs, we explored the perspectives and attitudes of the members of the seven Swiss Cantonal RECs via semi-structured qualitative interviews. Our interviews reveal limited experience among REC members with the review of big data research, insufficient expertise in data science, and uncertainty about how to mitigate big data research risks. Nonetheless, RECs could strengthen their oversight by training in data science and big data ethics, complementing their role with external experts and ad hoc boards, and introducing precise shared practices.

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

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