Open science: The open clinical trials data journey

Open data sharing and access has the potential to promote transparency and reproducibility in research, contribute to education and training, and prompt innovative secondary research. Yet, there are many reasons why researchers don’t share their data. These include, among others, time and resource constraints, patient data privacy issues, lack of access to appropriate funding, insufficient recognition of the data originators’ contribution, and the concern that commercial or academic competitors may benefit from analyses based on shared data. Nevertheless, there is a positive interest within and across the research and patient communities to create shared data resources. In this perspective, we will try to highlight the spectrum of “openness” and “data access” that exists at present and highlight the strengths and weakness of current data access platforms, present current examples of data sharing platforms, and propose guidelines to revise current data sharing practices going forward.

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Data Reuse and the Social Capital of Open Science

10.1101/093518 ◽

2016 ◽

Author(s):

Bradly Alicea

Keyword(s):

Game Theory ◽

Social Capital ◽

Data Sharing ◽

Open Data ◽

Open Science ◽

Payoff Matrix ◽

Reciprocal Relationship ◽

Data Reuse ◽

Data Set ◽

Shared Data

ABSTRACTParticipation in open data initiatives require two semi-independent actions: the sharing of data produced by a researcher or group, and a consumer of shared data. Consumers of shared data range from people interested in validating the results of a given study to people who actively transform the available data. These data transformers are of particular interest because they add value to the shared data set through the discovery of new relationships and information which can in turn be shared with the same community. The complex and often reciprocal relationship between producers and consumers can be better understood using game theory, namely by using three variations of the Prisoners’ Dilemma (PD): a classical PD payoff matrix, a simulation of the PD n-person iterative model that tests three hypotheses, and an Ideological Game Theory (IGT) model used to formulate how sharing strategies might be implemented in a specific institutional culture. To motivate these analyses, data sharing is presented as a trade-off between economic and social payoffs. This is demonstrated as a series of payoff matrices describing situations ranging from ubiquitous acceptance of Open Science principles to a community standard of complete non-cooperation. Further context is provided through the IGT model, which allows from the modeling of cultural biases and beliefs that influence open science decision-making. A vision for building a CC-BY economy are then discussed using an approach called econosemantics, which complements the treatment of data sharing as a complex system of transactions enabled by social capital.

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Bringing Code to Data: Do Not Forget Governance

Journal of Medical Internet Research ◽

10.2196/18087 ◽

2020 ◽

Vol 22 (7) ◽

pp. e18087

Author(s):

Christine Suver ◽

Adrian Thorogood ◽

Megan Doerr ◽

John Wilbanks ◽

Bartha Knoppers

Keyword(s):

Data Sharing ◽

Clinical Data ◽

Local Control ◽

Data Privacy ◽

High Performance ◽

Data Access ◽

Open Science ◽

Data Sets ◽

Regulatory Frameworks ◽

Legal Constraints

Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing. For example, researchers can access data in high performance, secure, and auditable cloud computing environments without the need for copying or downloading. An alternative path to accessing data sets requiring additional protection is the model-to-data approach. In model-to-data, researchers submit algorithms to run on secure data sets that remain hidden. Model-to-data is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. Model-to-data has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that model-to-data can make a valuable addition to our data sharing arsenal, with 2 caveats. First, model-to-data should only be adopted where necessary to supplement rather than replace existing data-sharing approaches given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, although model-to-data reduces concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt model-to-data approaches without guidance on how to do so responsibly. To address this gap, we explored how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in a model-to-data context.

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Bringing Code to Data: Do Not Forget Governance (Preprint)

10.2196/preprints.18087 ◽

2020 ◽

Author(s):

Christine Suver ◽

Adrian Thorogood ◽

Megan Doerr ◽

John Wilbanks ◽

Bartha Knoppers

Keyword(s):

Data Sharing ◽

Clinical Data ◽

Local Control ◽

Data Privacy ◽

High Performance ◽

Data Access ◽

Open Science ◽

Data Sets ◽

Regulatory Frameworks ◽

Legal Constraints

UNSTRUCTURED Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing. For example, researchers can access data in high performance, secure, and auditable cloud computing environments without the need for copying or downloading. An alternative path to accessing data sets requiring additional protection is the model-to-data approach. In model-to-data, researchers submit algorithms to run on secure data sets that remain hidden. Model-to-data is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. Model-to-data has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that model-to-data can make a valuable addition to our data sharing arsenal, with 2 caveats. First, model-to-data should only be adopted where necessary to supplement rather than replace existing data-sharing approaches given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, although model-to-data reduces concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt model-to-data approaches without guidance on how to do so responsibly. To address this gap, we explored how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in a model-to-data context.

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Organising RDM and Open Science Services

International Journal of Digital Curation ◽

10.2218/ijdc.v14i1.641 ◽

2019 ◽

Vol 14 (1) ◽

pp. 180-193

Author(s):

Anne Sunikka

Keyword(s):

Open Data ◽

Open Science ◽

Open Access Publishing ◽

Research Needs ◽

Ministry Of Education ◽

Training Material ◽

Research Data Management ◽

Changing Roles ◽

Academic Personnel ◽

And Training

This paper describes how the Finnish Ministry of Education and Culture launched an initiative on research data management and open data, open access publishing, and open and collaborative ways of working in 2014. Most of the universities and research institutions took part in the collaborative initiative building new tools and training material for the Finnish research needs. Measures taken by one university, Aalto University, are described in detail and analysed, and compared with the activities taking place in other universities. The focus of this paper is in the changing roles of experts at Aalto University, and organisational transformation that offers possibilities to serve academic personnel better. Various ways of building collaboration and arranging services are described, and their benefits and drawbacks are discussed.

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Teaching Open Science: Published Data and Digital Literacy in Archaeology Classrooms

Advances in Archaeological Practice ◽

10.1017/aap.2018.5 ◽

2018 ◽

Vol 6 (2) ◽

pp. 144-156 ◽

Cited By ~ 3

Author(s):

Katherine Cook ◽

Canan Çakirlar ◽

Timothy Goddard ◽

Robert Carl DeMuth ◽

Joshua Wells

Keyword(s):

Digital Literacy ◽

Open Data ◽

Skills Training ◽

The United States ◽

Open Science ◽

Data Reuse ◽

Published Data ◽

Skill Levels ◽

And Training ◽

Different Levels

ABSTRACTDigital literacy has been cited as one of the primary challenges to ensuring data reuse and increasing the value placed on open science. Incorporating published data into classrooms and training is at the core of tackling this issue. This article presents case studies in teaching with different published data platforms, in three different countries (the Netherlands, Canada, and the United States), to students at different levels and with differing skill levels. In outlining their approaches, successes, and failures in teaching with open data, it is argued that collaboration with data publishers is critical to improving data reuse and education. Moreover, increased opportunities for digital skills training and scaffolding across program curriculum are necessary for managing the learning curve and teaching students the values of open science.

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Automating Privacy Compliance Using Policy Integrated Blockchain

Cryptography ◽

10.3390/cryptography3010007 ◽

2019 ◽

Vol 3 (1) ◽

pp. 7 ◽

Cited By ~ 1

Author(s):

Karuna Pande Joshi ◽

Agniva Banerjee

Keyword(s):

Data Sharing ◽

Data Privacy ◽

Personal Data ◽

Data Access ◽

Information Management System ◽

Knowledge Graph ◽

Efficient System ◽

Essential Requirement ◽

Blockchain Technology ◽

Rich Data

An essential requirement of any information management system is to protect data and resources against breach or improper modifications, while at the same time ensuring data access to legitimate users. Systems handling personal data are mandated to track its flow to comply with data protection regulations. We have built a novel framework that integrates semantically rich data privacy knowledge graph with Hyperledger Fabric blockchain technology, to develop an automated access-control and audit mechanism that enforces users' data privacy policies while sharing their data with third parties. Our blockchain based data-sharing solution addresses two of the most critical challenges: transaction verification and permissioned data obfuscation. Our solution ensures accountability for data sharing in the cloud by incorporating a secure and efficient system for End-to-End provenance. In this paper, we describe this framework along with the comprehensive semantically rich knowledge graph that we have developed to capture rules embedded in data privacy policy documents. Our framework can be used by organizations to automate compliance of their Cloud datasets.

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The Yale Open Data Access (YODA) Project — A Mechanism for Data Sharing

New England Journal of Medicine ◽

10.1056/nejmp1607342 ◽

2016 ◽

Vol 375 (5) ◽

pp. 403-405 ◽

Cited By ~ 66

Author(s):

Harlan M. Krumholz ◽

Joanne Waldstreicher

Keyword(s):

Data Sharing ◽

Open Data ◽

Data Access

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Publishing descriptions of non-public clinical datasets: guidance for researchers, repositories, editors and funding organisations

10.1101/021667 ◽

2015 ◽

Cited By ~ 3

Author(s):

Iain Hrynaszkiewicz ◽

Varsha Khodiyar ◽

Andrew L Hufton ◽

Susanna-Assunta Sansone

Keyword(s):

Clinical Research ◽

Data Sharing ◽

Peer Review Process ◽

Open Data ◽

Data Access ◽

Research Data ◽

Future Research ◽

Patient Privacy ◽

Journal Articles ◽

Data Repositories

AbstractSharing of experimental clinical research data usually happens between individuals or research groups rather than via public repositories, in part due to the need to protect research participant privacy. This approach to data sharing makes it difficult to connect journal articles with their underlying datasets and is often insufficient for ensuring access to data in the long term. Voluntary data sharing services such as the Yale Open Data Access (YODA) and Clinical Study Data Request (CSDR) projects have increased accessibility to clinical datasets for secondary uses while protecting patient privacy and the legitimacy of secondary analyses but these resources are generally disconnected from journal articles – where researchers typically search for reliable information to inform future research. New scholarly journal and article types dedicated to increasing accessibility of research data have emerged in recent years and, in general, journals are developing stronger links with data repositories. There is a need for increased collaboration between journals, data repositories, researchers, funders, and voluntary data sharing services to increase the visibility and reliability of clinical research. We propose changes to the format and peer-review process for journal articles to more robustly link them to data that are only available on request. We also propose additional features for data repositories to better accommodate non-public clinical datasets, including Data Use Agreements (DUAs).

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Regulating Statistics in the Age of Data Abundance

International Journal for Population Data Science ◽

10.23889/ijpds.v4i3.1303 ◽

2019 ◽

Vol 4 (3) ◽

Author(s):

Catherine Bromley

Keyword(s):

Data Sharing ◽

Data Linkage ◽

Resource Constraints ◽

Data Access ◽

Data Governance ◽

Code Of Practice ◽

Regulatory Guidance ◽

Use Of Data ◽

Regulatory Approach ◽

Major Review

Background with rationaleThe Office for Statistics Regulation is the UK’s independent regulator of official statistics produced by public sector bodies. The Code of Practice for Statistics sets out our expectations for statistics to be produced in a trustworthy way, be of high quality, and to serve the public good by informing answers to society’s important questions. We now live in a world of increasingly abundant data. Statistics producers need to adapt to this environment, and so do we as regulators. ApproachThe Code of Practice was updated in 2018 with new provisions to maximise the potential use of data for both citizens and organisations, and to make data available for wider reuse with appropriate safeguards. We have supplemented our commitment to these provisions with a review of data sharing and linking in government, new regulatory guidance on data governance, an increased focus on data access challenges (particularly users of English health data), and by putting data at the heart of our regulatory vision (published in summer 2019). These steps build on our existing work around admin data quality. OverviewThe National Statistician’s response to our data sharing and linkage review included many welcome commitments and a major review of data linkage methodology is now underway. A data linkage community is developing across government. However, we have raised concerns about ongoing difficulties with admin data sharing between departments, resource constraints, and the limited extent of public engagement about data sharing and use. ConclusionsOur regulatory approach to data is evolving and we are building new relationships with organisations with an interest in data beyond the statistics world. Our work to support users to access admin data may yet require more direct interventions to bring about the outcomes we desire. We are keen to share our experiences with admin data users.

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The Spectrum of Data sharing Policies in Neuroimaging Data Repositories

10.31234/osf.io/cnuy7 ◽

2021 ◽

Author(s):

Anita Jwa ◽

Russell Poldrack

Keyword(s):

Data Sharing ◽

Data Privacy ◽

Public Investment ◽

Science Inquiry ◽

Open Science ◽

Machine Learning Techniques ◽

Data Repositories ◽

Learning Techniques ◽

Neuroimaging Data ◽

Regulatory Analysis

Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. Considering these benefits, data sharing has been widely promoted in diverse fields and neuroscience has been no exception to this movement. For all its promise, however, the sharing of human neuroimaging data raises critical ethical and legal issues, such as data privacy. Recently, the heightened risks to data privacy posed by the exponential development in artificial intelligence and machine learning techniques has made data sharing more challenging; the regulatory landscape around data sharing has also been evolving rapidly. Here we present an in-depth ethical and regulatory analysis that will examine how neuroimaging data are currently shared against the backdrop of the relevant regulations and policies and how advanced software tools and algorithms might undermine subjects’ privacy in neuroimaging data sharing. This analysis will inform researchers on responsible practice of neuroimaging data sharing and shed light on a regulatory framework to provide adequate protection of neuroimaging data while maximizing the benefits of data sharing.

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