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We exemplify how community driven paid reviews could work in conjunction with a feed-back loop to young scientists. This would remove pressure from the conventional peer-review system and promote the integration of reviews into an academic career. This concept may eventually revive the community based journal type.
ContextThe statistical analysis is an important part of the process of assessing the quality of randomized controlled trials, unfortunately it tends to be underestimated or even omitted by editors, in this sense, scales that cover this gap becomes necessary.\n\nObjectiveTo build a definition and a scale for assessing the quality of statistical analysis in randomized controlled trials.\n\nMethodsA content analysis on 16 biostatistics texts was considered in the building of the definition and of the scale. The indicators of quality were based on the description and presentation of results of articles.\n\nResultsWe identify 32 quality indicators grouping in six dimensions: randomization and management of lost; sample size computation; use of mean and median; statistical test; use and interpretation of confidence interval and P-values. The scale range from 0 to 6 and score greater than three identify article with appropriate statistical analysis.\n\nConclusionsThe scale presented three dimensions linked to quality of description and three linked to quality of used strategy.
Increasingly, preprints are at the center of conversations across the research ecosystem. But disagreements remain about the role they play. Do they \"count\" for research assessment? Is it ok to post preprints in more than one place? In this paper, we argue that these discussions often conflate two separate issues, the history of the manuscript and the status granted it by different communities. In this paper, we propose a new model that distinguishes the characteristics of the object, its \"state\", from the subjective \"standing\" granted to it by different communities. This provides a way to discuss the difference in practices between communities, which will deliver more productive conversations and facilitate negotiation on how to collectively improve the process of scholarly communications not only for preprints but other forms of scholarly contributions.
Participation 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.
This article presents a practical roadmap for scholarly data repositories to implement data citation in accordance with the Joint Declaration of Data Citation Principles, a synopsis and harmonization of the recommendations of major science policy bodies. The roadmap was developed by the Repositories Expert Group, as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH BioCADDIE (https://biocaddie.org) program. The roadmap makes 11 specific recommendations, grouped into three phases of implementation: a) required steps needed to support the Joint Declaration of Data Citation Principles, b) recommended steps that facilitate article/data publication workflows, and c) optional steps that further improve data citation support provided by data repositories.
The quality of evidence in meta-analysis of randomized controlled trials is the degree to which the estimated effect represents the \"truth.\" Current approaches to assessing the quality of evidence focus on trial design and methods. I describe a new quality of evidence index composed of four sub-indexes that measure pre-registration, independent replication, data availability, and trial design and methods, respectively. This index is systematic, objective, and quantitative. I illustrate the index with an empirical example and provide a spreadsheet for easy calculation.\n\nO_QD\"...when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind.\"\n\n-Lord Kelvin (William Thompson), 1824-1907\n\nC_QD
This article presents a practical roadmap for scholarly publishers to implement data citation in accordance with the Joint Declaration of Data Citation Principles (JDDCP), a synopsis and harmonization of the recommendations of major science policy bodies. It was developed by the Publishers Early Adopters Expert Group as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH BioCADDIE program. The structure of the roadmap presented here follows the \"life of a paper\" workflow and includes the categories Pre-submission, Submission, Production, and Publication. The roadmap is intended to be publisher-agnostic so that all publishers can use this as a starting point when implementing JDDCP-compliant data citation. Authors reading this roadmap will also better know what to expect from publishers and how to enable their own data citations to gain maximum impact, as well as complying with what will become increasingly common funder mandates on data transparency.
ABSTACTThe dynamic intersection of the emerging field of Data Science with the established academic communities of Statistics and Biostatistics continues to generate lively debate, often with the two fields playing the role of an upstart (but brilliant), tech-savvy prodigy and an established (but brilliant), curmudgeonly expert, respectively. Like any new discipline, Data Science brings new perspectives and new tools to address new questions requiring new perspectives on traditionally established concepts. In this paper, we explore a specific component of this discussion, namely the documentation and evaluation of Data Science-related research, teaching, and service contributions for faculty members seeking promotion and tenure within traditional departments of statistics and Biostatistics.
BackgroundAn editorial expression of concern (EEoC) is issued by editors or publishers to draw attention to potential problems in a publication, without itself constituting a retraction or correction.\n\nMethodsWe searched PubMed, PubMed Central (PMC), and Google Scholar to identify EEoCs issued for publications in PubMed and PMC up to 22 August 2016. We also searched the archives of the Retraction Watch blog, some journal and publisher websites, and studies of EEoCs. In addition, we searched for retractions of EEoCs and affected articles in PubMed up to 8 December 2016. We analyzed overall historical trends, as well as reported reasons and subsequent editorial actions related to EEoCs issued between August 2014 and August 2016.\n\nResultsAfter screening 5,076 records, we identified 230 EEoCs that affect 300 publications indexed in PubMed, the earliest issued in 1985. Half of the primary EEoCs were issued between 2014 and 2016 (52%). We found evidence of some EEoCs that had been removed by the publisher without leaving a record and some were not submitted for PubMed or PMC indexing. A minority of publications affected by EEoCs had been retracted by early December 2016 (25%). For the subset of 92 EEoCs issued between August 2014 and August 2016, affecting 99 publications, the rate of retraction was similar (29%). The majority of EEoCs were issued because of concerns with validity of data, methods, or interpretation of the publication (68%), and 31% of cases remained open. Issues with images were raised in 40% of affected publications. Ongoing monitoring after the study identified another 17 EEoCs to years end in 2016, increasing the number of EEoCs to 247 and publications in PubMed known to be affected by EEoCs to 320 at the end of 2016.\n\nConclusionsEEoCs have been rare publishing events in the biomedical literature, but their use has been increasing. Most have not led to retractions, and many remain unresolved. Lack of prominence and inconsistencies in management of EEoCs reduce the ability of these notices to alert the scientific community to potentially serious problems in publications. EEoCs will be made identifiable in PubMed in 2017.
The use of the journal impact factor (JIF) as a measure for the quality of individual manuscripts and the merits of scientists has faced significant criticism in recent years. We add to the current criticism in arguing that such an application of the JIF in policy and decision making in academia is based on false beliefs and unwarranted inferences. To approach the problem, we use principles of deductive and inductive reasoning to illustrate the fallacies that are inherent to using journal based metrics for evaluating the work of scientists. In doing so, we elaborate that if we judge scientific quality based on the JIF or other journal based metrics we are either guided by invalid or weak arguments or in fact consider our uncertainty about the quality of the work and not the quality itself.
Contemporary biomedical research is performed by increasingly large teams. As a consequence, an increasingly large number of individuals are being listed as authors in the byline of biomedical articles, which complicates the proper attribution of credit and responsibility to individual authors for their work. Typically, more importance is given to the first and last authors of biomedical papers, and the others (the middle authors) are considered to have made smaller contributions. However, we argue that this distinction between first, middle and last authors does not properly reflect the actual division of labor and does not allow a fair allocation of credit among the members of the research teams. In this paper, we use partial alphabetical authorship to divide the authors of all biomedical articles in the Web of Science published over the 1980-2015 period in three groups: primary authors, middle authors, and supervisory authors. We show that alphabetical ordering of middle authors is frequent in biomedical research, and that the prevalence of this practice is positively correlated with the number of authors in the bylines. We also find that, for articles with 7 or more authors, the average proportion of team members in each group is independent of the team size, more than half of the authors being middle authors. This suggests that growth in authors lists are not due to an increase in secondary contributions but, rather, in equivalent increases of all types of roles and contributions. Nevertheless, we show that the relative contribution of middle authors to the overall production of knowledge in the biomedical field as increased dramatically over the last 35 years.
An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted, due to problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the end result of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part towards enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload, and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can add checks and balances that promote responsible research practices and can provide significant benefits for the biomedical research enterprise.\n\nSupplementary informationSupplementary material is available at BioRxiv.
In a 2016 survey of 704 National Science Foundation (NSF) Biological Sciences Directorate principal investigators (BIO PIs), nearly 90% indicated they are currently or will soon be analyzing large data sets. BIO PIs considered a range of computational needs important to their work--including high performance computing (HPC), bioinformatics support, multi-step workflows, updated analysis software, and the ability to store, share, and publish data. Previous studies in the United States and Canada emphasized infrastructure needs. However, BIO PIs said the most pressing unmet needs are training in data integration, data management, and scaling analyses for HPC--acknowledging that data science skills will be required to build a deeper understanding of life. This portends a growing data knowledge gap in biology and challenges institutions and funding agencies to redouble their support for computational training in biology.
The preclinical research process is a cycle of idea generation, experimentation, and reporting of results. The biomedical research community relies on the reproducibility of published discoveries to create new lines of research and to translate research findings into therapeutic applications. Since 2012, when scientists from Amgen reported that they were able to reproduce only 6 of 53 \"landmark\" preclinical studies, the biomedical research community began discussing the scale of the reproducibility problem and developing initiatives to address critical challenges. GBSI released the \"Case for Standards\" in 2013, one of the first comprehensive reports to address the rising concern of irreproducible biomedical research. Further attention was drawn to issues that limit scientific self-correction including reporting and publication bias, underpowered studies, lack of open access to methods and data, and lack of clearly defined standards and guidelines in areas such as reagent validation. To evaluate the progress made towards reproducibility since 2013, GBSI identified and examined initiatives designed to advance quality and reproducibility. Through this process, we identified key roles for funders, journals, researchers and other stakeholders and recommended actions for future progress. This paper describes our findings and conclusions.
The creation of datasets that are findable, accessible, interoperable and reproducible (the FAIR standard) requires that data provenance be maintained1. Provenance is particularly important for microscopy data, whose interpretation is dependent on the biological context (e.g. cell state) and detection reagent (e.g. antibody.) This paper describes a new software tool, OMERO.forms, that extends the OMERO microscopy data management system2 to simplify and enhance metadata entry and provenance tracking.
On November 18-19, 2016, the Human Frontier Science Program Organization (HFSPO) hosted a meeting of senior managers of key data resources and leaders of several major funding organizations to discuss the challenges associated with sustaining biological and biomedical (i.e., life sciences) data resources and associated infrastructure. A strong consensus emerged from the group that core data resources for the life sciences should be supported through a coordinated international effort(s) that better ensure long-term sustainability and that appropriately align funding with scientific impact. Ideally, funding for such data resources should allow for access at no charge, as is presently the usual (and preferred) mechanism. Below, the rationale for this vision is described, and some important considerations for developing a new international funding model to support core data resources for the life sciences are presented.
To ensure the clinical value of medical interventions, randomized controlled trials (RCTs) are necessary. However, the results of conventional RCTs cannot show individual therapeutic efficacy and safety for medical intervention to a targeted patient. It is the most important weak point of conventional RCTs. Here I show that the new clinical research method by using human induced pluripotent stem (iPS) cells technology will be able to complement the most important weak point of conventional RCTs.\n\nAs the representative examples, I show the new clinical values of statins (inhibitors of 3-hydroxy-3-methylglutaryl-coenzyme A reductase) found by using human iPS cells technology in achondroplasia or hanatophoric dysplasia (type 1) case and hepatitis C virus (HCV) infection case. Furthermore, they are also important examples for drug repositioning.\n\nTherefore, my article would be valuable as a scientific communication for physicians and/or scientists.
Infrastructures for data, such as repositories, curation systems, aggregators, indexes and standards are public goods. This means that finding sustainable economic models to support them is a challenge. This is due to free-loading, where someone who does not contribute to the support of the infrastructure nonetheless gains the benefit of it. The work of Mancur Olson (1974) suggests there are only three ways to address this for large groups: compulsion (often as some form of taxation) to support the infrastructure; the provision of non-collective (club) goods limited to those who contribute as a side-effect of providing the collective good; or mechanisms that lower the effective number of participants in the negotiation (oligopoly).\n\nIn this paper I use Olsons framework to analyze existing scholarly infrastructures and proposals for the sustainability of new infrastructures. I argue that the focus on sustainability models prior to seeking a set of agreed governance principles is the wrong approach. Rather we need to understand how to navigate from club-like to public-like goods. We need to define the communities that contribute and identify club-like benefits for those contributors. We need interoperable principles of governance and resourcing to provide public-like goods and we should draw on the political economics of taxation to develop this.