ArticleFrontiers in computational neuroscience2012
Designing next-generation platforms for evaluating scientific output: what scientists can learn from the social web.
Article in Frontiers in computational neuroscience, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Beyond advertising: New infrastructures for publishing integrated research objects.PLoS computational biology · 2022Article
- Peer Review in Law Journals.Frontiers in research metrics and analytics · 2021Article
- Evidence appraisal: a scoping review, conceptual framework, and research agenda.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- A multi-disciplinary perspective on emergent and future innovations in peer review.F1000Research · 2017Review
- Using science and psychology to improve the dissemination and evaluation of scientific work.Frontiers in computational neuroscience · 2014Article
- Impact factor 2012 for cardiovascular journals: true impact?Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2013Article
- Deep impact: unintended consequences of journal rank.Frontiers in human neuroscience · 2013Article
- An emerging consensus for open evaluation: 18 visions for the future of scientific publishing.Frontiers in computational neuroscience · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional pre-publication peer review of scientific output is a slow, inefficient, and unreliable process. Efforts to replace or supplement traditional evaluation models with open evaluation platforms that leverage advances in information technology are slowly gaining traction, but remain in the early stages of design and implementation. Here I discuss a number of considerations relevant to the development of such platforms. I focus particular attention on three core elements that next-generation evaluation platforms should strive to emphasize, including (1) open and transparent access to accumulated evaluation data, (2) personalized and highly customizable performance metrics, and (3) appropriate short-term incentivization of the userbase. Because all of these elements have already been successfully implemented on a large scale in hundreds of existing social web applications, I argue that development of new scientific evaluation platforms should proceed largely by adapting existing techniques rather than engineering entirely new evaluation mechanisms. Successful implementation of open evaluation platforms has the potential to substantially advance both the pace and the quality of scientific publication and evaluation, and the scientific community has a vested interest in shifting toward such models as soon as possible.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.