scholarly journals Openness in Big Data and Data Repositories

2019 ◽  
Vol 11 (3) ◽  
pp. 255-273 ◽  
Author(s):  
Vicki Xafis ◽  
Markus K. Labude
Keyword(s):  
Big Data ◽  
Ethical Issues ◽  
Open Science ◽  
Research Data ◽  
Data Repositories ◽  
Ethics Framework ◽  
Full Explanation ◽  
Health Related Research ◽  
Health Related ◽  
Privacy And Confidentiality

Abstract There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

scholarly journals Cross-Sectoral Big Data

2019 ◽  
Vol 11 (3) ◽  
pp. 327-339 ◽  
Author(s):  
Graeme T. Laurie
Keyword(s):  
Big Data ◽  
Ethical Issues ◽  
Health Sector ◽  
Well Being ◽  
Biomedical Data ◽  
Ethical Values ◽  
Ethics Framework ◽  
Full Explanation ◽  
Health And Well Being

Abstract Discussion of uses of biomedical data often proceeds on the assumption that the data are generated and shared solely or largely within the health sector. However, this assumption must be challenged because increasingly large amounts of health and well-being data are being gathered and deployed in cross-sectoral contexts such as social media and through the internet of (medical) things and wearable devices. Cross-sectoral sharing of data thus refers to the generation, use and linkage of biomedical data beyond the health sector. This paper considers the challenges that arise from this phenomenon. If we are to benefit fully, it is important to consider which ethical values are at stake and to reflect on ways to resolve emerging ethical issues across ecosystems where values, laws and cultures might be quite distinct. In considering such issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of cross-sectoral big data. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

scholarly journals An Ethics Framework for Big Data in Health and Research

2019 ◽  
Vol 11 (3) ◽  
pp. 227-254 ◽  
Author(s):  
Vicki Xafis ◽  
G. Owen Schaefer ◽  
Markus K. Labude ◽  
Iain Brassington ◽  
Angela Ballantyne ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Ethical Decision ◽  
Ethical Decision Making ◽  
Ethical Issues ◽  
Real World Data ◽  
Data Repositories ◽  
Ethics Framework ◽  
Healthcare Interventions ◽  
Procedural Values

Abstract Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and Policy-relevant Ethics in Singapore (SHAPES) Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data.

scholarly journals Addressing disorder in scholarly communication: Strategies from NISO 2021

2021 ◽  
pp. 1-15
Author(s):  
Jodi Schneider ◽  
Michele Avissar-Whiting ◽  
Caitlin Bakker ◽  
Hannah Heckner ◽  
Sylvain Massip ◽  
...  
Keyword(s):  
Scientific Information ◽  
Scholarly Communication ◽  
Open Science ◽  
Fake News ◽  
Science Standards ◽  
Validation And Verification ◽  
Related Research ◽  
Health Related Research ◽  
Health Related ◽  
Conference Session

Open science and preprints have invited a larger audience of readers, especially during the pandemic. Consequently, communicating the limitations and uncertainties of research to a broader public has become important over the entire information lifecycle. This paper brings together reports from the NISO Plus 2021 conference session “Misinformation and truth: from fake news to retractions to preprints”. We discuss the validation and verification of scientific information at the preprint stage in order to support sound and open science standards, at the publication stage in order to limit the spread of retracted research, and after publication, to fight fake news about health-related research by mining open access content.

scholarly journals Comprehensive scoping review of health research using social media data

BMJ Open ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. e022931 ◽  
Author(s):  
Joanna Taylor ◽  
Claudia Pagliari
Keyword(s):  
Public Health ◽  
Social Media ◽  
Scoping Review ◽  
Ethical Issues ◽  
Social Media Data ◽  
Related Research ◽  
Health Related Research ◽  
Health Related ◽  
Google Search ◽  
Media Data

IntroductionThe rising popularity of social media, since their inception around 20 years ago, has been echoed in the growth of health-related research using data derived from them. This has created a demand for literature reviews to synthesise this emerging evidence base and inform future activities. Existing reviews tend to be narrow in scope, with limited consideration of the different types of data, analytical methods and ethical issues involved. There has also been a tendency for research to be siloed within different academic communities (eg, computer science, public health), hindering knowledge translation. To address these limitations, we will undertake a comprehensive scoping review, to systematically capture the broad corpus of published, health-related research based on social media data. Here, we present the review protocol and the pilot analyses used to inform it.MethodsA version of Arksey and O’Malley’s five-stage scoping review framework will be followed: (1) identifying the research question; (2) identifying the relevant literature; (3) selecting the studies; (4) charting the data and (5) collating, summarising and reporting the results. To inform the search strategy, we developed an inclusive list of keyword combinations related to social media, health and relevant methodologies. The frequency and variability of terms were charted over time and cross referenced with significant events, such as the advent of Twitter. Five leading health, informatics, business and cross-disciplinary databases will be searched: PubMed, Scopus, Association of Computer Machinery, Institute of Electrical and Electronics Engineers and Applied Social Sciences Index and Abstracts, alongside the Google search engine. There will be no restriction by date.Ethics and disseminationThe review focuses on published research in the public domain therefore no ethics approval is required. The completed review will be submitted for publication to a peer-reviewed, interdisciplinary open access journal, and conferences on public health and digital research.

scholarly journals AI-Assisted Decision-making in Healthcare

2019 ◽  
Vol 11 (3) ◽  
pp. 299-314 ◽  
Author(s):  
Tamra Lysaght ◽  
Hannah Yeefen Lim ◽  
Vicki Xafis ◽  
Kee Yuan Ngiam
Keyword(s):  
Ethical Issues ◽  
Healthcare Systems ◽  
Decision Makers ◽  
Computational Power ◽  
Special Issue ◽  
Public Benefit ◽  
Ethics Framework ◽  
Full Explanation ◽  
Algorithmic Bias

Abstract Artificial intelligence (AI) is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019). The analysis applies relevant values identified from the framework to demonstrate how decision-makers can draw on them to develop and implement AI-assisted support systems into healthcare and clinical practice ethically and responsibly. Please refer to Xafis et al. (2019) in this special issue of the Asian Bioethics Review for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end of this paper.

scholarly journals The State of Dissemination of Open Research Data in Ukraine and the World: Bibliometric Analysis

2021 ◽  
Vol 29 (4) ◽  
pp. 209-217
Author(s):  
Anton Boiko ◽  
Olha Kramarenko ◽  
Sardar Shabanov
Keyword(s):  
Bibliometric Analysis ◽  
Data Exchange ◽  
Open Science ◽  
Research Data ◽  
The State ◽  
Research Institutions ◽  
Data Repositories ◽  
Open Research ◽  
The World ◽  
Data Files

Purpose: To determine the current state of development of open science in the paradigm of open research data in Ukraine and the world, as well as to analyze the representation of Ukraine in the world research space, in terms of research data exchange. Design / Method / Research Approach: Methods of synthesis, logical and comparative analysis used to determine the dynamics of the number of research data journals and data files in the world, as well as to quantify the share of research data repositories in Ukraine and the world. Trend and bibliometric analysis were used to determine the share of publications with their open primary data; analysis of their thematic structures; identification of the main scientific clusters of such publications; research of geographic indicators and share of publications by research institutions. Findings: The study found a tendency to increase both the number of data logs and data files in Dryad (open data repository). The results of the analysis of the share of data repositories indexed in re3data (register of research data repositories) show that 51% of the total number are repositories of data from European countries, with Germany leading with 460 repositories, followed by the United Kingdom (302 repositories) and France (116 repositories). Ukraine has only 2 data repositories indexed in re3data. The trend of relevance of data exchange is confirmed by the increase of publications with datasets for the last 10 years (2011-2020) in 5 times. Research institutions and universities are the main sources of research data, which are mainly focused on the fields of knowledge in chemistry (23.3%); biochemistry, genetics and molecular biology (13.8%); medicine (12.9%). An analysis of the latest thematic groups formed on the basis of publications with datasets shows that there is a significant correlation between publications with open source data and COVID-19 studies. More than 50% of publications with datasets both in Ukraine and around the world are aimed at achieving the goal of SDG 3 Good Health. Theoretical Implications: It is substantiated that in Ukraine there is a need to implement specific tactical and strategic plans for open science and open access to research data. Practical Implications: The results of the study can be used to support decision-making in the management of research data at the macro and micro levels. Future Research: It should be noted that the righteous bibliometric analysis of the state of the dissemination of data underlying the research results did not include the assessment of quality indicators and compliance with the FAIR principles, because accessibility and reusability are fundamental components of open science, which may be an area for further research. Moreover, it is advisable to investigate the degree of influence of the disclosure of the data underlying the research result on economic indicators, as well as indicators of ratings of higher education, etc. Research Limitations: Since publications with datasets in Scopus-indexed journals became the information base of the analysis for our study, it can be assumed that the dataset did not include publications with datasets published in editions that the Scopus bibliographic database does not cover. Paper type: Theoretical

scholarly journals A survey of researchers' needs and priorities for data sharing

2021 ◽  
Author(s):  
Iain Hrynaszkiewicz ◽  
James Harney ◽  
Lauren Cadwallader
Keyword(s):  
Data Sharing ◽  
Research Impact ◽  
Open Science ◽  
Research Data ◽  
Data Reuse ◽  
Data Availability ◽  
Data Repositories ◽  
Use Of Data ◽  
Share Data ◽  
Do So

PLOS has long supported Open Science. One of the ways in which we do so is via our stringent data availability policy established in 2014. Despite this policy, and more data sharing policies being introduced by other organizations, best practices for data sharing are adopted by a minority of researchers in their publications. Problems with effective research data sharing persist and these problems have been quantified by previous research as a lack of time, resources, incentives, and/or skills to share data. In this study we built on this research by investigating the importance of tasks associated with data sharing, and researchers’ satisfaction with their ability to complete these tasks. By investigating these factors we aimed to better understand opportunities for new or improved solutions for sharing data. In May-June 2020 we surveyed researchers from Europe and North America to rate tasks associated with data sharing on (i) their importance and (ii) their satisfaction with their ability to complete them. We received 728 completed and 667 partial responses. We calculated mean importance and satisfaction scores to highlight potential opportunities for new solutions to and compare different cohorts.Tasks relating to research impact, funder compliance, and credit had the highest importance scores. 52% of respondents reuse research data but the average satisfaction score for obtaining data for reuse was relatively low. Tasks associated with sharing data were rated somewhat important and respondents were reasonably well satisfied in their ability to accomplish them. Notably, this included tasks associated with best data sharing practice, such as use of data repositories. However, the most common method for sharing data was in fact via supplemental files with articles, which is not considered to be best practice.We presume that researchers are unlikely to seek new solutions to a problem or task that they are satisfied in their ability to accomplish, even if many do not attempt this task. This implies there are few opportunities for new solutions or tools to meet these researcher needs. Publishers can likely meet these needs for data sharing by working to seamlessly integrate existing solutions that reduce the effort or behaviour change involved in some tasks, and focusing on advocacy and education around the benefits of sharing data. There may however be opportunities - unmet researcher needs - in relation to better supporting data reuse, which could be met in part by strengthening data sharing policies of journals and publishers, and improving the discoverability of data associated with published articles.

scholarly journals The establishment of a Norwegian node in RDA

2019 ◽  
Author(s):  
Trond Kvamme ◽  
Philipp Conzett
Keyword(s):  
Data Management ◽  
Data Exchange ◽  
The United States ◽  
Open Science ◽  
Research Data ◽  
Sensitive Data ◽  
Data Repositories ◽  
Management Plans ◽  
Inclusive Approach ◽  
Processing And Storage

Norway has been selected as a new national node in RDA (Research Data Alliance). Until the end of the project in May 2020, the node will be engaging with research communities, supporting national agendas, and contributing to the EU Open Science Strategy to ensure capillary uptake of RDA principles and outputs. Moreover, they will be working to increase the participation in RDA nationally. The Norwegian RDA node (NO-RDA) will be run by a consortium of seven partners, each of them with specific roles in the activities around the node, and led by NSD - Norwegian Centre for Research Data. NO-RDA will focus on supporting the implementation of RDA outputs and recommendations and on areas of strategic importance for the Nordic region, such as Data Management Plans, FAIR Data Stewardship and management of sensitive data in research within the framework of current international and statutory regulations. In addition to NSD the node consists of NTNU, UiB, UiO, UiT, Unit og Uninett/Sigma2. The Research Data Alliance (RDA) was launched as a community-driven initiative in 2013 by the European Commission, the United States Government's National Science Foundation and National Institute of Standards and Technology, and the Australian Government’s Department of Innovation with the goal of building the social and technical infrastructure to enable open sharing and re-use of data. RDA has a grass-roots, inclusive approach covering all data lifecycle stages, engaging data producers, users and stewards, addressing data exchange, processing, and storage. It has succeeded in creating the neutral social platform where international research data experts meet to exchange views and to agree on topics including social hurdles on data sharing, education and training challenges, data management plans and certification of data repositories, disciplinary and interdisciplinary interoperability, as well as technological aspects.

scholarly journals Österreichische Repositorien in OpenDOAR und re3data.org: Entwicklung und Status von Infrastrukturen für Green Open Access und Forschungsdaten

2018 ◽  
Vol 71 (1) ◽  
pp. 70-86
Author(s):  
Bruno Bauer ◽  
Andreas Ferus
Keyword(s):  
Open Access ◽  
Open Science ◽  
Research Data ◽  
Status Quo ◽  
Data Repositories

Der vorliegende Beitrag beleuchtet die Entwicklung und den Status Quo von Repositorien in Österreich. Diese haben mit dem Hochschulraumstrukturmittelprojekt e-Infrastructures Austria einen wichtigen Impuls bekommen. Während in den Jahren nach der „Berliner Erklärung über den offenen Zugang zu wissenschaftlichem Wissen“ (2003) vor allem die Bereiche Publikationen und Green Open Access bearbeitet wurden, rückten in jüngster Zeit insbesondere durch die European Open Science Cloud (EOSC) auch Forschungsdaten zunehmend in den Fokus des Interesses. Dieser Aufschwung spiegelt sich auch in der laufenden Steigerung der im Directory of Open Access Repositories (OpenDOAR) und im Registry of Research Data Repositories (re3data.org) erfassten österreichischen Repositorien wider. Mittels statistischer Auswertungen wurde erhoben, welche Dokumententypen in diesen Repositorien aufgenommen werden, welche Fachgebiete sie repräsentieren, welchen Umfang sie aufweisen, welche Software eingesetzt wird, ob bereits notwendige Schnittstellen (wie z.B. OAI) vorhanden sind und welche Policies für die jeweiligen Repositorien verfolgt werden.

scholarly journals Precision Medicine and Big Data

2019 ◽  
Vol 11 (3) ◽  
pp. 275-288 ◽  
Author(s):  
G. Owen Schaefer ◽  
E Shyong Tai ◽  
Shirley Sun
Keyword(s):  
Big Data ◽  
Precision Medicine ◽  
Ethical Issues ◽  
Disease Risk ◽  
Ethical Framework ◽  
Harm Minimization ◽  
Causal Pathways ◽  
Full Explanation ◽  
Effective Diagnosis

Abstract As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological (including genetic), medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in the Ethical Framework for Big Data in Health and Research (Xafis et al. 2019) to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner.

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