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Network-based statistics for a community driven transparent publication process

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

The current publishing system with its merits and pitfalls is a mending topic for debate among scientists of various disciplines. Editors and reviewers alike, both face difficult decisions about the judgment of new scientific findings. Increasing interdisciplinary themes and rapidly changing dynamics in method development of each field make it difficult to be an “expert” with regard to all issues of a certain paper. Although unintended, it is likely that misunderstandings, human biases, and even outright mistakes can play an unfortunate role in final verdicts. We propose a new community-driven publication process that is based on network statistics to make the review, publication, and scientific evaluation process more transparent.

From an idealistic point of view, scientists aim to publish their work in order to communicate relevant findings. If we could rely on our own and individual judgment, review processes would not be needed. We obviously do not rely on our own judgment since more eyes see more and hence relevance and validity can be specified in a more objective way. Therefore, a system of peer review has been established as the method of choice to control for scientific relevance and methodological correctness/appropriateness. In fact, journal editors decide via the peer review process what is relevant and what in turn is communicated to other scientists via publication. Peer review has been the method of choice for many years, but scientists are concerned about the state of the current publishing system. Editorial as well as review decisions are not always fully transparent and vary between journals. The quality of a review depends on the expertise of the reviewer and the editorial office sometimes arbitrarily selects this expertise. The arbitrary element is a natural consequence of the task of the office and its realization in times of fast increase in submissions, the increase of interdisciplinary topics, and the lack of individual review expertise necessary to cover all issues of a modern science paper.

This discussion is not new at all. It has been stated before that the metrics by which the possible impact of an article is measured in the editorial handling phase are not well defined and leave a large degree of uncertainty about how decisions are made (Kreiman and Maunsell, 2011). The system is amenable to political as well as opportunistic biases playing a role in whether a paper is accepted or rejected (Akst, 2010). Public communication about an article and the review process to which it was subjected is very limited, if possible at all. In addition, there is growing pressure from grant agencies and local institutions to publish a high number of articles, thereby potentially compromising the scientific quality of submitted papers, while the review process itself might be compromised by increased load due to the increasing number of submissions. Hence, we fear that the large increase in the number of publications in the field of neuroscience and other fields may be accompanied by a decrease in overall quality. Moreover, the explosion in numbers of publications makes it difficult to follow the evolution of a specific topic even for experts of that field. In the light of increasing financial pressure and importance of external funds, the reform of the publishing system cannot be viewed in isolation but has to take into account other parameters, which interact with the publishing system. Here, we provide an alternative to the current review and publishing system, which is meant to be implemented in two steps. The idea we propose is inspired by the development of social media. In the first step it would function as an add-on to the existing scientific publishing system, but in the second step may evolve to completely replace it. It involves the quantification of interactions among scientists using Network-Based Statistics (NBS), as done in social media, in combination with search tools, as used by Google. The proposal laid out below should act as an inspiration to where the future of publishing might lead, and is not intended to be a fully detailed roadmap.

    Research areas

  • network-based statistics, publishing system, scientific evaluation, peer review
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Details

Original languageEnglish
Article number11
Number of pages5
JournalFrontiers in Computational Neuroscience
Volume6
Early online date27 Dec 2011
DOIs
Publication statusPublished - 5 Mar 2012