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Results for “Scientific Communication and Education”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 91 records · Page 5Linked to original sources
bioRxiv2016

Identification of Successful Mentoring Communities using Network-based Analysis of Mentor-Mentee Relationships across Nobel Laureates

Skills underlying scientific innovation and discovery generally develop within an academic community, often beginning with a graduate mentors laboratory. In this paper, a network analysis of doctoral student-dissertation advisor relationships in The Academic Tree is used to identify successful mentoring communities in high-level science, as measured by number of Nobel laureates within the community. Nobel laureates form a distinct group in the network with greater numbers of Nobel laureate ancestors, descendants, mentees/grandmentees, and local academic family. Subnetworks composed entirely of Nobel laureates extend across as many as four generations. Successful historical mentoring communities were identified centering around Cambridge University in the latter 19th century and Columbia University in the early 20th century. The current practice of building web-based academic networks, extended to include a wider variety of measures of academic success, would allow for the identification of modern successful scientific communities and should be promoted.

Scientific Communication and Education↗
bioRxiv2016

Reproducibility and replicability of rodent phenotyping in preclinical studies

The scientific community is increasingly concerned with cases of published \"discoveries\" that are not replicated in further studies. The field of mouse behavioral phenotyping was one of the first to raise this concern, and to relate it to other complicated methodological issues: the complex interaction between genotype and environment; the definitions of behavioral constructs; and the use of the mouse as a model animal for human health and disease mechanisms. In January 2015, researchers from various disciplines including genetics, behavior genetics, neuroscience, ethology, statistics and bioinformatics gathered in Tel Aviv University to discuss these issues. The general consent presented here was that the issue is prevalent and of concern, and should be addressed at the statistical, methodological and policy levels, but is not so severe as to call into question the validity and the usefulness of model organisms as a whole. Well-organized community efforts, coupled with improved data and metadata sharing, were agreed by all to have a key role to play in identifying specific problems and promoting effective solutions. As replicability is related to validity and may also affect generalizability and translation of findings, the implications of the present discussion reach far beyond the issue of replicability of mouse phenotypes but may be highly relevant throughout biomedical research.

Scientific Communication and Education↗
bioRxiv2016

Bayesian Analysis of High Throughput Data

Duplicate or triplicate experimental replicates are commonplace in the high throughput literature. However, it has not been tested whether this is statistically defensible or not. To address this issue, we use probabilistic programming to develop a simple hierarchical model for analyzing high throughput measurement data. With the model and simulated data, we show that a small increase in replicate experiments can quantitatively improve accuracy in measurement. We also provide posterior densities for statistical parameters used in the evaluation of HT data. Finally, we provide an extensible open source implementation that ingests data structured in a simple format and produces posterior densities of estimated measurement and assay evaluation parameters.

Scientific Communication and Education↗
bioRxiv2016

All or Nothing: the False Promise of Anonymity

In early 2016, the International Committee of Medical Journal Editors (ICMJE) proposed that responsible sharing of de-identified individual-level data be required for clinical trials published in their affiliated journals. There would be a delay in implementing this policy to allow for the necessary informed consents to work their way through ethical review. Meanwhile, some researchers and policy makers have conflated the notions of de-identification and anonymity. The former is a process that seeks to mitigate disclosure risk though careful application of rules and statistical analysis, while the latter is an absolute state. The consequence of confusing the process and the state is profound. Extensions to the ICMJE proposal based on the presumed anonymity of data include: sharing unconsented data; sharing data without managing access, as Open Data; and proposals to sell data. This essay aims to show that anonymity (the state) cannot be guaranteed by de-identification (the process), and so these extensions to the ICMJE proposal should be rejected on governance grounds, if no other. This is not as negative a position as it might seem, as other disciplines have been aware of these limitations and concomitant responsibilities for many years. The essay concludes with an example from social science of managed access strategies that could be adopted by the medical field.

scientific communication and education↗
bioRxiv2016

Starting from the end: what to do when restricted data is released

Repository managers can never be one hundred percent sure of the security ofhosted research data. Even assuming that human errors and technical faults will never happen, repositories can be subject to hacking attacks. Therefore, repositories accepting personal/sensitive data (or other forms of restricted data) should have workflows in place with defined procedures to be followed should things go wrong and restricted data is inappropriately released. In this paper wewill report on our considerations and procedures when restricted data from ourinstitution was inappropriately released.

scientific communication and education↗
bioRxiv2016

Copyright and the Use of Images as Biodiversity Data

1.Taxonomy is the discipline responsible for charting the worlds organismic diversity, understanding ancestor/descendant relationships, and organizing all species according to a unified taxonomic classification system. Taxonomists document the attributes (characters) of organisms, with emphasis on those can be used to distinguish species from each other. Character information is compiled in the scientific literature as text, tables, and images. The information is presented according to conventions that vary among taxonomic domains; such conventions facilitate comparison among similar species, even when descriptions are published by different authors.\n\nThere is considerable uncertainty within the taxonomic community as to how to re-use images that were included in taxonomic publications, especially in regard to whether copyright applies. This article deals with the principles and application of copyright law, database protection, and protection against unfair competition, as applied to images. We conclude that copyright does not apply to most images in taxonomic literature because they are presented in a standardized way and lack the creativity that is required to qualify as 'copyrightable works'. There are exceptions, such as wildlife photographs, drawings and artwork produced in a distinctive individual form and intended for other than comparative purposes (such as visual art). Further exceptions may apply to collections of images that qualify as a database in the sense of European database protection law. In a few European countries, there is legal protection for photographs that do not qualify as works in the usual sense of copyright. It follows that most images found in taxonomic literature can be re-used for research or many other purposes without seeking permission, regardless of any copyright declaration. In observance of ethical and scholarly standards, re-users are expected to cite the author and original source of any image that they use.

scientific communication and education↗
bioRxiv2016

Ten simple rules for structuring papers

Good scientific writing is essential to career development and to the progress of science. A well-structured manuscript allows readers and reviewers to get excited about the subject matter, to understand and verify the papers contributions, and to integrate these contributions into a broader context. However, many scientists struggle with producing high-quality manuscripts and typically get little training in paper writing. Focusing on how readers consume information, we present a set of 10 simple rules to help you get across the main idea of your paper. These rules are designed to make your paper more influential and the process of writing more efficient and pleasurable.

scientific communication and education↗
bioRxiv2016

Statistical Quality Scale 6 (SQS-6)

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.

scientific communication and education↗
bioRxiv2016

On the origin of nonequivalent states: how we can talk about preprints

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.

scientific communication and education↗
bioRxiv2016

The sharing of open data: a game-theoretic approach

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.

scientific communication and education↗
bioRxiv2016

A Data Citation Roadmap for Scholarly Data Repositories

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.

scientific communication and education↗
bioRxiv2017

An index of the quality of evidence in meta-analysis of randomized controlled trials

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

scientific communication and education↗
bioRxiv2017

A Data Citation Roadmap for Scientific Publishers

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.

scientific communication and education↗
bioRxiv2017

Documenting and Evaluating Data Science Contributions in Academic Promotion in Departments of Statistics and Biostatistics

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.

scientific communication and education↗
bioRxiv2017

Concern noted: A descriptive study of editorial expressions of concern in PubMed and PubMed Central.

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.

scientific communication and education↗
bioRxiv2017

The impact factor fallacy

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.

scientific communication and education↗
bioRxiv2017

The Rise of the Middle Author: Investigating Collaboration and Division of Labor in Biomedical Research using Partial Alphabetical Authorship

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.

scientific communication and education↗