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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Eyeballs On Science: Impact Is Not Just Citations, But How Big Is Readership?

A full evaluation of the impact of scientific publications needs to count readers who dont generate citations. But this is very difficult to do. I set up a scholarly foraging experiment to try to estimate readership for my own body of work: a personal web site with reprint pdf files available for downloading and a web statistics program engaged to count these downloads. (I made the site topically rather than author-oriented to increase potential audience size.) Despite this, human users are difficult to count. There are a lot of bots, spiders, and other web programs that increasingly mimic human behavior (e.g., with IP- and chrono-camouflage), and humans own behaviors are changing (e.g., reading papers online many times). From four years of data, after culling both automated activity (a fascinating ecosystem) and humans reading repeatedly online, my papers receive [~]4000-8000 downloads per year, about an order of magnitude higher than the number of citations they receive annually. This is a conservative minimum, because these publications can be obtained from other sources as well. On average, this body of work may be being read at an approximately 10:1 download-to-citation ratio. At a more granular scale, downloads do not correlate with citations; there are some papers being downloaded at about a 100:1 ratio, and it turns out these are exactly how they were meant to be used (e.g., an instruction manual). How intensively someone reads a paper is of course highly variable, but this exercise gives us an idea of how broad our publications impacts actually are. Citations alone dont come close to measuring it.

scientific communication and education↗

bioRxiv2017

A tale of two cores: a quantitative comparative analysis of the institutional and the open access models for flow research technology platforms

Quinquennium of sorting for Cambridge Quinquennium of 'sorting for... Emerging divergence: origins Shortage of capacity: perception... Current and projected capacity... Conclusions References Biomedical specialist core facilities are major part of institutional infrastructure in academia and play important role in research by creating access to well established and emerging technologies and being a source of valuable expertise. In each particular case, the extent, range and content of services they provide are determined by the technology specialism, demands of the users and institutional/overarching policies.\n\nIn 2012, two flow cytometry core facilities embarked on a new journey in development of their services to research community in Cambridge. Both facilities started from the same initial level: ...

scientific communication and education↗

bioRxiv2017

Percentage-Based Author Contribution Index. A Universal Measure Of Author Contribution To Scientific Articles

Deciphering the amount of work provided by different co-authors of a scientific paper has been a recurrent problem in science. Despite the myriad of metrics available, the scientific community still largely relies on the position in the list of authors to evaluate contributions, a metric that attributes subjective and unfounded credit to co-authors. We propose an easy to apply, fair and universally comparable metric to measure and report co-authors contribution in the scientific literature. The proposed Author Contribution Index (ACI) is based on contribution percentages provided by the authors, preferably at the time of submission. Researchers can use ACI for a number of purposes, including comparing the contributions of different authors, describing the contribution profile of a researcher or analysing how contribution changes through time. We provide an example analysis based on data collected from 97 scientists from the field of ecology who voluntarily responded to an online anonymous survey.

scientific communication and education↗

bioRxiv2017

Modeling Science Trustworthiness Under Publish Or Perish Pressure

The scientific endeavor pivots on the accurate reporting of experimental and theoretical findings, and consequently scientific publication is immensely important. As the number of active scientists continues to increase, there is concern that rewarding scientists chiefly on publication creates a perverse incentive where careless and fraudulent research can thrive. This is compounded by the predisposition of top-tier journals towards novel or positive findings rather than negative results or investigations that merely confirm a null hypothesis, despite their intrinsic value, potentially compounding a reproducibility crisis in several fields. This is a serious problem for both science and public trust in scientific findings. To date, there has been comparatively little mathematical modeling on the factors that influence science trustworthiness, despite the importance of quantifying the problem. In this work, we present a simple phenomenological model with cohorts of diligent, careless and unethical scientists with funding allocated based on published outputs. The results of this analysis suggest that trustworthiness of published science in a given field is strongly influenced by the false positive rate and the pressures from journals for positive results, and that decreasing available funding has negative consequences for the resulting trustworthiness. We also examine strategies to combat propagation of irreproducible science, including increasing fraud detection and awarding diligence, discussing the implications of these findings.

scientific communication and education↗

bioRxiv2017

Cooperation And Liaison Between Universities And Editors (CLUE): Recommendations On Best Practice

Journals and research institutions have common interests regarding the trustworthiness of research publications but their specific roles and responsibilities differ. These draft recommendations aim to address issues surrounding cooperation and liaison between journals and institutions about possible and actual problems with reported research. The proposals will be discussed at various meetings including the World Conference on Research Integrity in May 2017. We will also consider comments and suggestions posted on this preprint.\n\nThe main recommendations are that: O_LINational registers of individuals or departments responsible for research integrity at institutions should be created.\nC_LIO_LIInstitutions should develop mechanisms for assessing the validity of research reports that are independent from processes to determine whether individual researchers have committed misconduct.\nC_LIO_LIEssential research data and peer review records should be retained for at least 10 years.\nC_LIO_LIWhile journals should normally raise concerns with authors in the first instance, they also need criteria to determine when to contact the institution before, or at the same time as, alerting the authors in cases of suspected data fabrication or falsification to prevent the destruction of evidence.\nC_LIO_LIAnonymous or pseudonymous allegations made to journals or institutions should be judged on their merit and not dismissed automatically.\nC_LIO_LIInstitutions should release relevant sections of reports of research trustworthiness or misconduct investigations to all journals that have published research that was the subject of the investigation.\nC_LI

scientific communication and education↗

bioRxiv2017

Manuscript 101: A Data-Driven Writing Exercise For Beginning Scientists

Learning to write a scientific manuscript is one of the most important and rewarding scientific training experiences, yet most young scientists only embark on this experience relatively late in graduate school, after gathering sufficient data in the lab. Yet, familiarity with the process of writing a scientific manuscript and receiving peer reviews, often leads to a more focused and driven experimental approach. To jump-start this training, we developed a protocol for teaching manuscript writing and reviewing in the classroom, appropriate for new graduate or upper-level undergraduate students of developmental biology. First, students are provided one of four cartoon data sets, which are focused on genetic models of animal development. Students are instructed to use their creativity to convert evidence into argument, and then to integrate their interpretations into a manuscript, including an illustrated, mechanistic model figure. After student manuscripts are submitted, manuscripts are redacted and distributed to classmates for peer review. Here, we present our cartoon datasets, homework instructions, and grading rubrics as a new resource for the scientific community. We also describe methods for developing new datasets so that instructors can adapt this activity to other disciplines. Our data-driven manuscript writing exercise, as well as the formative and summative assessments resulting from the peer review, enables students to learn fundamental concepts in developmental genetics. In addition, students practice essential skills of scientific communication, including arguing from evidence, developing and testing models, the unique conventions of scientific writing, and the joys of scientific story telling.

scientific communication and education↗

bioRxiv2017

Transparency in Authors’ Contributions and Responsibilities to Promote Integrity in Scientific Publication

In keeping with the growing movement in scientific publishing toward transparency in data and methods, we argue that the names of authors accompanying journal articles should provide insight into who is responsible for which contributions, a process should exist to confirm that the list is complete, clearly articulated standards should establish whether and when the contributions of an individual justify authorship credit, and those involved in the generation of scientific knowledge should follow these best practices.\n\nTo accomplish these goals, we recommend that journals adopt common and transparent standards for authorship, outline responsibilities for corresponding authors, adopt the CRediT (Contributor Roles Taxonomy)1 methodology for attributing contributions, include this information in article metadata, and encourage authors to use the digital persistent identifier ORCID.2 Furthermore, we suggest that research institutions have regular open conversations on authorship criteria and ethics and that funding agencies adopt ORCID and accept CRediT. Scientific societies should further authorship transparency by promoting these recommendations through their meetings and publications programs.

scientific communication and education↗

bioRxiv2017

Marginal Returns And Levels Of Research Grant Support Among Scientists Supported By The National Institutes Of Health

The current era of worsening hypercompetition in biomedical research has drawn attention to the possibility of decreasing marginal returns from research funding. Recent work has described decreasing marginal returns as a function of annual dollars granted to individual scientists. However, different fields of research incur varying cost structures. Therefore, we developed a Grant Support Index (GSI) that focuses on grant activity code, as opposed to field of study or cost. In a cohort of over 71,000 unique scientists funded by NIH between 1996 and 2014 we analyzed the association of grant support (as measured by annual GSI) with 3 bibliometric outcomes, maximum Relative Citation Ratio (which arguably reflects a scientists most influential work), median Relative Citation Ratio, and annual weighted Relative Citation Ratio (which is more dependent on publication counts). We found that for all 3 measures marginal returns decline as annual GSI increases. Thus, we confirm prior findings of decreasing marginal returns with higher levels of research funding support.

scientific communication and education↗

bioRxiv2017

BioSharing: Harnessing Metadata Standards For The Data Commons

The use of community-driven metadata standards, such as minimal information guidelines, terminologies, formats/models, is essential to ensure that data and other digital research outputs are Findable, Accessible, Interoperable, and Reusable, according to the FAIR principles. As with other types of digital assets, metadata standards also need be FAIR. Their discoverability and accessibility is ensured by BioSharing, the most comprehensive resource of metadata standards, interlinked to data repositories and policies, available in the life, environmental and biomedical sciences. With its growing content, endorsements, and collaborative network, BioSharing is part of a larger ecosystem of interoperable resources. Here we describe some of the activities under the USA National Institutes of Health (NIH)s Big Data to Knowledge (BD2K) Initiative, illustrating how we track the evolution and use of metadata standards and work to connect them to indexes and annotation tools.

scientific communication and education↗

bioRxiv2017

The Reproducibility Of Research And The Misinterpretation Of P Values

We wish to answer this question If you observe a \"significant\" P value after doing a single unbiased experiment, what is the probability that your result is a false positive?. The weak evidence provided by P values between 0.01 and 0.05 is explored by exact calculations of false positive rates.\n\nWhen you observe P = 0.05, the odds in favour of there being a real effect (given by the likelihood ratio) are about 3. This is far weaker evidence than the odds of 19 to 1 that might, wrongly, be inferred from the P value. And if you want to limit the false positive rate to 5 percent, you would have to assume that you were 87% sure that there was a real effect before the experiment was done.\n\nIf you observe P = 0.001, which gives likelihood ratio of 100:1 odds on there being a real effect, that would usually be regarded as conclusive, But the false positive rate would still be 8% if the prior probability of a real effect was only 0.1\n\nDespite decades of warnings, many areas of science still insist on labelling a result of P < 0.05 as \"significant\". This must account for a substantial part of the lack of reproducibility in some areas of science. And this is before you get to the many other well-known problems, like multiple comparisons, lack of randomisation and P-hacking. Science is endangered by statistical misunderstanding, and by people who impose perverse incentives on scientists.

scientific communication and education↗

bioRxiv2017

Research Synergy and Drug Development: Bright Stars in Neighboring Constellations

Drug discovery and subsequent availability of a new breakthrough therapeutic or cure is a compelling example of societal benefit from research advances. These advances are invariably collaborative, involving the contributions of many scientists to a discovery network in which theory and experiment are built upon. To understand such scientific advances, data mining of public and commercial data sources coupled with network analysis can be used as a digital methodology to assemble and analyze component events in the history of a therapeutic. This methodology is extensible beyond the history of therapeutics and its use more generally supports (i) efficiency in exploring the scientific history of a research advance (ii) documenting and understanding collaboration (iii) portfolio analysis, planning and optimization (iv) communication of the societal value of research. As a proof of principle, we have conducted a case study of five anti-cancer therapeutics. We have linked the work of roughly 237,000 authors in 106,000 scientific publications that capture the research crucial for the development of these five therapeutics. We have enriched the content of networks of these therapeutics by annotating them with information on research awards as well as peer review that preceded these awards. Applying retrospective citation discovery, we have identified a core set of publications cited in the networks of all five therapeutics and additional intersections in combinations of networks as well as awards from the National Institutes of Health that supported this research. Lastly, we have mapped these awards to their cognate peer review panels, identifying another layer of collaborative scientific activity that influenced the research represented in these networks.

scientific communication and education↗

bioRxiv2017

Online, Interactive Tutorials Provide Effective Instruction in Science Process Skills

Explicit emphasis on teaching science process skills leads to both gains in the skills themselves and, strikingly, deeper understanding of content. Here, we created and tested a series of online, interactive tutorials with the goal of helping undergraduate students develop science process skills. We designed the tutorials in accordance with evidence-based multimedia design principles and student feedback from usability testing. We then tested the efficacy of the tutorials in an introductory undergraduate biology class. Based on a multivariate ordinary least squares regression model, students that received the tutorials are predicted to score 0.824 points higher on a 15 point science process skill assessment than their peers that received traditional textbook instruction on the same topic. This moderate but significant impact indicates that well designed online tutorials can be more effective than traditional ways of teaching science process skills to undergraduate students. We also found trends that suggest the tutorials are especially effective for non-native English speaking students. However, due to a limited sample size, we were unable to confirm that these trends occurred due to more than just variation in the sampled student group.

scientific communication and education↗

bioRxiv2017

Structural brain development: a review of methodological approaches and best practices

Continued advances in neuroimaging technologies and statistical modelling capabilities have improved our knowledge of structural brain development in children and adolescents. While this has provided an increasingly nuanced understanding of brain development, the field is still plagued by inconsistent findings. This review highlights the methodological diversity in existing longitudinal magnetic resonance imaging (MRI) studies on structural brain development during childhood and adolescence, and addresses how such variation might contribute to inconsistencies in the literature. We discuss the impact of method choices at multiple decision points across the research process, from study design and sample selection, to image processing and statistical analysis. We also highlight the extent to which different methodological considerations have been empirically examined, drawing attention to specific areas that would benefit from future investigation. Where appropriate, we recommend certain best practices that would be beneficial for the field to adopt, including greater completeness and transparency in reporting methods, in order to ultimately develop an accurate and detailed understanding of normative child and adolescent brain development.

scientific communication and education↗

bioRxiv2017

Scientific Theories and Artificial Intelligence

The grand ambition of theorists studying ecology and evolution is to discover the logical and mathematical rules driving the worlds biodiversity at every level from genetic diversity within species to differences between populations, communities, and ecosystems. This ambition has been difficult to realize in great part because of the complexity of biodiversity. Theoretical work has led to a complex web of theories, each having non-obvious consequences for other theories. Case in point, the recent realization that genetic diversity involves a great deal of temporal and spatial stochasticity forces theoretical population genetics to consider abiotic and biotic factors generally reserved to ecosystem ecology. This interconnectedness may require theoretical scientists to adopt new techniques adapted to reason about large sets of theories. Mathematicians have solved this problem by using formal languages based on logic to manage theorems. However, theories in ecology and evolution are not mathematical theorems, they involve uncertainty. Recent work in Artificial Intelligence in bridging logic and probability theory offers the opportunity to build rich knowledge bases that combine logics ability to represent complex mathematical ideas with probability theorys ability to model uncertainty. We describe these hybrid languages and explore how they could be used to build a unified knowledge base of theories for ecology and evolution.\n\ncase study you explore using the Salix tritrophic system.

scientific communication and education↗

bioRxiv2017

Bringing plants and soils to life through a simple role-playing activity

Interactions are at the core of many ecological and evolutionary forces in nature. Plant-soil interactions provide a rich example of the interconnectedness of living systems, but are hidden from everyday view and overshadowed in the classroom by more popular teaching examples involving animals, reptiles, or invertebrates. To highlight the importance and relevance of plant-soil relationships, we devised a simple role-playing activity suitable for college students. Specifically, the activity simulates how feedbacks between plants and soil environments influence plant species abundance and community richness. With this activity, students will gain a better understanding of these prolific, but overlooked, forms of biological interactions that impact the diversity and functioning of ecosystems.

scientific communication and education↗

bioRxiv2017

The GSS is an unreliable indicator of biological sciences postdoc population trends

The postdoctoral research position is an essential step on the academic career track, and the biomedical research enterprise has become heavily dependent on postdoctoral scholars to conduct experimental research. Monitoring postdoc population trends is important for crafting and evaluating policies that affect this critical population. The tool most use for understanding the trends of the biological sciences postdoc population is the Survey of Graduate Students and Postdoctorates in Science and Engineering (GSS) administered by the National Center for Science and Engineering Statistics. To determine how well institutions tracked their postdocs, we analyzed the yearly changes in the biological sciences postdoc population at institutions surveyed by the GSS from 1980 to 2015. We find examples of large changes in the biological sciences postdoc population at one or a few institutions most years from 1980 to 2015. Most universities could not explain the data presented in the GSS, and for those that provided an explanation, the most common causes were improved institutional policies and more robust tracking of their postdocs. These large changes, unrelated to hiring or layoffs, sometimes masked population trends in the broader biological sciences postdoc population. We propose the adoption of a unified definition and titles for postdocs and the creation of an index to better assess biological sciences postdoc population trends.\n\nAbbreviations

scientific communication and education↗

bioRxiv2017

Science with no fiction: measuring the veracity of scientific reports by citation analysis

The current crisis of veracity in biomedical research is enabled by the lack of publicly accessible information on whether the reported scientific claims are valid. One approach to solve this problem is to replicate previous studies by specialized reproducibility centers. However, this approach is costly or unaffordable and raises a number of yet to be resolved concerns that question its effectiveness and validity. We propose to use an approach that yields a simple numerical measure of veracity, the R-factor, by summarizing the outcomes of already published studies that have attempted to test a claim. The R-factor of an investigator, a journal, or an institution would be the average of the R-factors of the claims they reported. We illustrate this approach using three studies recently tested by a replication initiative, compare the results, and discuss how using the R-factor can help improve the veracity of scientific research.

scientific communication and education↗

bioRxiv2017

Issues in the statistical detection of data fabrication and data errors in the scientific literature: simulation study and reanalysis of Carlisle, 2017

BackgroundThe detection of fabrication or error within the scientific literature is an important and underappreciated problem. Retraction of scientific articles is rare, but retraction may also be conservative, leaving open the possiblity that many fabricated or erroneous findings remain in the literature as a result of lack of scrutiny. A recently statistical analysis of randomized controlled trials [1] has suggested that the reported statistics form these trials deviate substantially from expectation under truely random assignment, raising the possiblity of fraud or error. It has also been proposed that the method used could be implemented to prospectively screen research, for example by applying the method prior to publication.\n\nMethods and FindingsTo assess the properties of the method proposed in [1], I carry out both theoretical and empirical evaluations of the method. Simulations suggest that the method is sensitive to assumptions that could reasonably be violated in real randomized controlled trials. This suggests that deviation for expectation under this method can not be used to measure the extent of fraud or error within the literature, and raises questions about the utlity of the method for propsective screening. Empirically analysis of the results of the method on a large set of randomized trials suggests that important assumptions may plausibly be violated within this sample. Using retraction as a proxy for fraud or serious error, I show that the method faces serious challenges in terms of precision and sensitivity for the purposes of screening, and that the performance of the method as a screening tool may vary across journals and classes of retractions.\n\nConclusionsThe results in [1] should not be interpreted as indicating large amount of fraud or error within the literature. The use of this method for screening of the literature should be undertaken with great caution, and should recognize critical challenges in interpreting the results of this method.

scientific communication and education↗