Can data repositories help find effective treatments for complex diseases?

Farber, Gregory K.

doi:10.1016/j.pneurobio.2016.03.008

Cited by 12 publications

(7 citation statements)

References 77 publications

(88 reference statements)

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“…Full anonymization may not be possible for projects requiring phenotypic details [ 41 ]; other protections such as limited access and firewalls must therefore be considered so as not to lose “social licence” for these types of projects [ 42 – 44 ]. Both researchers and patients should have input to the next generation of data repositories and projects to shape them towards the kinds of questions that remain outstanding, such as capturing traits as well as diseases for research compatible with the USA’s National Institute of Mental Health Research Domain Criteria (RDoC) paradigm [ 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

‘Big data’ in mental health research: current status and emerging possibilities

Stewart

Davis

2016

Soc Psychiatry Psychiatr Epidemiol

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Purpose‘Big data’ are accumulating in a multitude of domains and offer novel opportunities for research. The role of these resources in mental health investigations remains relatively unexplored, although a number of datasets are in use and supporting a range of projects. We sought to review big data resources and their use in mental health research to characterise applications to date and consider directions for innovation in future.MethodsA narrative review.Results Clear disparities were evident in geographic regions covered and in the disorders and interventions receiving most attention.Discussion We discuss the strengths and weaknesses of the use of different types of data and the challenges of big data in general. Current research output from big data is still predominantly determined by the information and resources available and there is a need to reverse the situation so that big data platforms are more driven by the needs of clinical services and service users.

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Section: Discussionmentioning

confidence: 99%

‘Big data’ in mental health research: current status and emerging possibilities

Stewart

Davis

2016

Soc Psychiatry Psychiatr Epidemiol

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“…; Michener ); medical research, neuroimaging, and genomics (Walport and Brest ; Warren ; Poline et al. ; Kaye and Hawkins ; Farber ); and materials science (Graulis et al. ; Lafuente et al.…”

Section: Current Trends In Data Opennessmentioning

confidence: 99%

“…Without sufficient access to data, scientific analyses cannot be replicated and subsequent research cannot build upon previous results, both of which undermine the foundation of scientific progress. As a consequence, data archiving and sharing have become a cross-cutting issue across all scientific fields, including: r physics (Hey and Payne 2015); r political science (Gherghina and Katsanidou 2013); r bioinformatics (Hothorn and Leisch 2011); r ecology and biodiversity research (Costello et al 2013;Michener 2015); r medical research, neuroimaging, and genomics (Walport and Brest 2011;Warren 2016;Poline et al 2012;Kaye and Hawkins 2014;Farber 2017); and r materials science (Graulis et al 2009;Lafuente et al 2016).…”

Section: Trends Across Scientific Fieldsmentioning

confidence: 99%

Nullius in Verba1: Advancing Data Transparency in Industrial Ecology

Hertwich

Heeren

Kuczenski

et al. 2018

J of Industrial Ecology

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SummaryWith the growth of the field of industrial ecology (IE), research and results have increased significantly leading to a desire for better utilization of the accumulated data in more sophisticated analyses. This implies the need for greater transparency, accessibility, and reusability of IE data, paralleling the considerable momentum throughout the sciences. The Data Transparency Task Force (DTTF) was convened by the governing council of the International Society for Industrial Ecology in late 2016 to propose best-practice guidelines and incentives for sharing data. In this article, the members of the DTTF present an overview of developments toward transparent and accessible data within the IE community and more broadly. We argue that increased transparency, accessibility, and reusability of IE data will enhance IE research by enabling more detailed and reproducible research, and also facilitate meta-analyses. These benefits will make the results of IE work more timely. They will enable independent verification of results, thus increasing their credibility and quality. They will also make the uptake of IE research results easier within IE and in other fields as well as by decision makers and sustainability practitioners, thus increasing the overall relevance and impact of the field. Here, we present two initial actions intended to advance these goals: (1) a minimum publication requirement for IE research to be adopted by the Journal of Industrial Ecology; and (2) a system of optional data openness badges rewarding journal articles that contain transparent and accessible data. These actions will help the IE community to move toward data transparency and accessibility. We close with a discussion of potential future initiatives that could build on the minimum requirements and the data openness badge system.

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“…There is an apparent need to allow pluralism of investigation, discovery and application, while maintaining standards of the scientific method. Consensus standards often emerge as a result of data-sharing [ 44 ], which may become a practical challenge of its own.…”

Section: Methodological Challengesmentioning

confidence: 99%

Recognizing Degenerative Aging as a Treatable Medical Condition: Methodology and Policy

Stambler¹

2017

Aging and disease

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It is becoming increasingly clear that in order to accomplish healthy longevity for the population, there is an urgent need for the research and development of effective therapies against degenerative aging processes underlying major aging-related diseases, including heart disease, neurodegenerative diseases, type 2 diabetes, cancer, pulmonary obstructive diseases, as well as aging-related complications and susceptibilities of infectious communicable diseases. Yet, an important incentive for the research and development of such therapies appears to be the development of clinically applicable and scientifically grounded definitions and criteria for the multifactorial degenerative aging process (or “senility” using the existing ICD category), underlying those diseases, as well as for the safety and effectiveness of interventions against it. Such generally agreed definitions and criteria are currently absent. The devising of such criteria is important not only for the sake of their scientific value and their utility for the development of therapeutic solutions for the aging population, but also to comply with and implement major existing national and international programmatic and regulatory requirements. Some methodological suggestions and potential pitfalls for the development of such criteria are examined.

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Can data repositories help find effective treatments for complex diseases?

Cited by 12 publications

References 77 publications

‘Big data’ in mental health research: current status and emerging possibilities

‘Big data’ in mental health research: current status and emerging possibilities

Nullius in Verba1: Advancing Data Transparency in Industrial Ecology

Recognizing Degenerative Aging as a Treatable Medical Condition: Methodology and Policy

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