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Discover the most talked about and latest scientific content & concepts.

Concept: Open research

505

Open access, open data, open source, and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities, and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices.

Concepts: Scientific method, Academic publishing, Research, Open source, Open content, Open Notebook Science, Open research, Open Data

58

The development of new antimalarial compounds remains a pivotal part of the strategy for malaria elimination. Recent large-scale phenotypic screens have provided a wealth of potential starting points for hit-to-lead campaigns. One such public set is explored, employing an open source research mechanism in which all data and ideas were shared in real time, anyone was able to participate, and patents were not sought. One chemical subseries was found to exhibit oral activity but contained a labile ester that could not be replaced without loss of activity, and the original hit exhibited remarkable sensitivity to minor structural change. A second subseries displayed high potency, including activity within gametocyte and liver stage assays, but at the cost of low solubility. As an open source research project, unexplored avenues are clearly identified and may be explored further by the community; new findings may be cumulatively added to the present work.

Concepts: Present, Time, Pharmacology, Solubility, Research, Source, Open source, Open research

39

Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility.

Concepts: Scientific method, Improve, Research, Philosophy of science, Falsifiability, Reproducibility, Pseudoscience, Open research

20

Research studies that leverage emerging technologies, such as passive sensing devices and mobile apps, have demonstrated encouraging potential with respect to favorably influencing the human condition. As a result, the nascent fields of mHealth and digital medicine have gained traction over the past decade as demonstrated in the United States by increased federal funding for research that cuts across a broad spectrum of health conditions. The existence of mHealth and digital medicine also introduced new ethical and regulatory challenges that both institutional review boards (IRBs) and researchers are struggling to navigate. In response, the Connected and Open Research Ethics (CORE) initiative was launched. The CORE initiative has employed a participatory research approach, whereby researchers and IRB affiliates are involved in identifying the priorities and functionality of a shared resource. The overarching goal of CORE is to develop dynamic and relevant ethical practices to guide mHealth and digital medicine research. In this Viewpoint paper, we describe the CORE initiative and call for readers to join the CORE Network and contribute to the bigger conversation on ethics in the digital age.

Concepts: United States, Research, Philosophy of life, Bioethics, Institutional review board, Participatory action research, Transhumanism, Open research

16

Despite the clear demand for open data sharing, its implementation within plant science is still limited. This is, at least in part, because open data-sharing raises several unanswered questions and challenges to current research practices. In this commentary, some of the challenges encountered by plant researchers at the bench when generating, interpreting, and attempting to disseminate their data have been highlighted. The difficulties involved in sharing sequencing, transcriptomics, proteomics, and metabolomics data are reviewed. The benefits and drawbacks of three data-sharing venues currently available to plant scientists are identified and assessed: (i) journal publication; (ii) university repositories; and (iii) community and project-specific databases. It is concluded that community and project-specific databases are the most useful to researchers interested in effective data sharing, since these databases are explicitly created to meet the researchers' needs, support extensive curation, and embody a heightened awareness of what it takes to make data reuseable by others. Such bottom-up and community-driven approaches need to be valued by the research community, supported by publishers, and provided with long-term sustainable support by funding bodies and government. At the same time, these databases need to be linked to generic databases where possible, in order to be discoverable to the majority of researchers and thus promote effective and efficient data sharing. As we look forward to a future that embraces open access to data and publications, it is essential that data policies, data curation, data integration, data infrastructure, and data funding are linked together so as to foster data access and research productivity.

Concepts: Scientific method, Academic publishing, Research, Botany, Bench, Open content, Data management, Open research

3

There is an emergent and intensive dialogue in the United States with regard to the accessibility, reproducibility, and rigor of health research. This discussion is also closely aligned with the need to identify sustainable ways to expand the national research enterprise and to generate actionable results that can be applied to improve the nation’s health. The principles and practices of Open Science offer a promising path to address both goals by facilitating (1) increased transparency of data and methods, which promotes research reproducibility and rigor; and (2) cumulative efficiencies wherein research tools and the output of research are combined to accelerate the delivery of new knowledge in proximal domains, thereby resulting in greater productivity and a reduction in redundant research investments.

Concepts: Scientific method, United States, Science, Data, Research, Collaboration, The Delivery, Open research

2

There is a growing consensus that drug discovery thrives in an open environment. Here, we describe how the malaria community has embraced four levels of open data - open science, open innovation, open access and open source - to catalyse the development of new medicines, and consider principles that could enable open data approaches to be applied to other disease areas.

Concepts: Pharmacology, Infectious disease, Research, Peer review, Innovation, Open source, Open content, Open research

2

An individual’s health, genetic, or environmental-exposure data, placed in an online repository, creates a valuable shared resource that can accelerate biomedical research and even open opportunities for crowd-sourcing discoveries by members of the public. But these data become “immortalized” in ways that may create lasting risk as well as benefit. Once shared on the Internet, the data are difficult or impossible to redact, and identities may be revealed by a process called data linkage, in which online data sets are matched to each other. Reidentification (re-ID), the process of associating an individual’s name with data that were considered deidentified, poses risks such as insurance or employment discrimination, social stigma, and breach of the promises often made in informed-consent documents. At the same time, re-ID poses risks to researchers and indeed to the future of science, should re-ID end up undermining the trust and participation of potential research participants. The ethical challenges of online data sharing are heightened as so-called big data becomes an increasingly important research tool and driver of new research structures. Big data is shifting research to include large numbers of researchers and institutions as well as large numbers of participants providing diverse types of data, so the participants' consent relationship is no longer with a person or even a research institution. In addition, consent is further transformed because big data analysis often begins with descriptive inquiry and generation of a hypothesis, and the research questions cannot be clearly defined at the outset and may be unforeseeable over the long term. In this article, we consider how expanded data sharing poses new challenges, illustrated by genomics and the transition to new models of consent. We draw on the experiences of participants in an open data platform-the Personal Genome Project-to allow study participants to contribute their voices to inform ethical consent practices and protocol reviews for big-data research.

Concepts: Scientific method, Human Genome Project, Data, Research, Research and development, Personal genomics, Personal Genome Project, Open research

1

To the Editor: For all the understandable uproar over the term “research parasites” - an inflammatory term that gives short shrift to how open data changed our understanding of Tamiflu, Paxil, and other treatments - those of us who support increased data sharing should realize that Drazen and Longo(1),(2) were giving voice to an opinion that many researchers privately hold. After all, it is only human nature that some feel wary of a policy that seems to require them to do extra work that other people will then use for their own academic advancement. The best way to create . . .

Concepts: Scientific method, Understanding, Science, Research, Perception, Knowledge, Human nature, Open research

0

Several researchers recently outlined unacknowledged costs of open science practices, arguing these costs may outweigh benefits and stifle discovery of novel findings. We scrutinize these researchers' (a) statistical concern that heightened stringency with respect to false-positives will increase false-negatives and (b) metascientific concern that larger samples and executing direct replications engender opportunity costs that will decrease the rate of making novel discoveries. We argue their statistical concern is unwarranted given open science proponents recommend such practices to reduce the inflated Type I error rate from .35 down to .05 and simultaneously call for high-powered research to reduce the inflated Type II error rate. Regarding their metaconcern, we demonstrate that incurring some costs is required to increase the rate (and frequency) of making true discoveries because distinguishing true from false hypotheses requires a low Type I error rate, high statistical power, and independent direct replications. We also examine pragmatic concerns raised regarding adopting open science practices for relationship science (preregistration, open materials, open data, direct replications, sample size); while acknowledging these concerns, we argue they are overstated given available solutions. We conclude benefits of open science practices outweigh costs for both individual researchers and the collective field in the long run, but that short term costs may exist for researchers because of the currently dysfunctional academic incentive structure. Our analysis implies our field’s incentive structure needs to change whereby better alignment exists between researcher’s career interests and the field’s cumulative progress. We delineate recent proposals aimed at such incentive structure realignment. (PsycINFO Database Record

Concepts: Scientific method, Mathematics, Type I and type II errors, Economics, Science, Research, Open content, Open research