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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

P-values and confidence intervals: not fit for purpose?

DeclarationsCompeting interests: All authors have completed theunified competing interest form and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.\n\nThe lead author (the manuscripts guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted.\n\nEthical approval: not required\n\nDetails of funding: Not applicable\n\nStatement of independence of researchers from funders: Not applicable\n\nPatient involvement statement. Not applicable.\n\nData sharing statement: Not applicable.

scientific communication and education↗

bioRxiv2017

Findings of a retrospective, controlled cohort study of the impact of a change in Nature journals' editorial policy for life sciences research on the completeness of reporting study design and execution

ObjectiveTo determine whether a change in editorial policy, including the implementation of a checklist, has been associated with improved reporting of measures which might reduce the risk of bias.\n\nMethodsThe study protocol has been published at DOI: 10.1007/s11192-016-1964-8.\n\nDesignObservational cohort study.\n\nPopulationArticles describing research in the life sciences published in Nature journals, submitted after May 1st 2013.\n\nInterventionMandatory completion of a checklist at the point of manuscript revision.\n\nComparators(1) Articles describing research in the life sciences published in Nature journals, submitted before May 2013; (2) Similar articles in other journals matched for date and topic.\n\nPrimary OutcomeChange in proportion of Nature publications describing in vivo research published before and after May 2013 reporting the Landis 4 items (randomisation, blinding, sample size calculation, exclusions).\n\nWe included 448 NPG papers (223 published before May 2013, 225 after) identified by an individual hired by NPG for this specific task, working to a standard procedure; and an independent investigator used Pubmed Related Citations to identify 448 non-NPG papers with a similar topic and date of publication in other journals; and then redacted all publications for time sensitive information and journal name. Redacted manuscripts were assessed by 2 trained reviewers against a 74 item checklist, with discrepancies resolved by a third.\n\nResults394 NPG and 353 matching non-NPG publications described in vivo research. The number of NPG publications meeting all relevant Landis 4 criteria increased from 0/203 prior to May 2013 to 31/181 (16.4%) after (2-sample test for equality of proportions without continuity correction, X2 = 36.2, df = 1, p = 1.8 x 10-9). There was no change in the proportion of non- NPG publications meeting all relevant Landis 4 criteria (1/164 before, 1/189 after). There were more substantial improvements in the individual prevalences of reporting of randomisation, blinding, exclusions and sample size calculations for in vivo experiments, and less substantial improvements for in vitro experiments.\n\nConclusionsThere was a substantial improvement in the reporting of risks of bias in in vivo research in NPG journals following a change in editorial policy, to a level that to our knowledge has not been previously observed. However, there remain opportunities for further improvement.

scientific communication and education↗

bioRxiv2017

Enabling Precision Medicine via standard communication of NGS provenance, analysis, and results

A personalized approach based on a patients or pathogens unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to FAIR guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet lab procedures to computational methods. The BioCompute framework (https://osf.io/zm97b/) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCO) offer that standard, and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the \"Open-Stand.org principles for collaborative open standards development\". By communication of high-throughput sequencing studies using a BCO, regulatory agencies (e.g., FDA), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next generation sequencing workflow exchange, reporting, and regulatory reviews.

scientific communication and education↗

bioRxiv2017

Award or Reward? Which comes first, NIH funding or research impact?

Which comes first, funding or research impact? We use causal inference to determine the direction of influence in a record of 70,000 NIH-funded investigators, which was recently released. Contrary to the basic premise of many funding policies, we find that the number of citations of an investigator determine funding levels, but not the other way around.

scientific communication and education↗

bioRxiv2017

Peer-Led Team Learning Improves Minority Student Retention in STEM Majors

The Presidents Council of Advisors on Science and Technology issued a report in 2012 calling for a drastic increase in the number of STEM graduates produced in our country over the following decade if we are to remain economically competitive globally (PCAST, 2012). The report cited the disparity between the diversity among the general public versus that of the STEM professional community and recommended measures to ensure that the women and members of underrepresented racial groups, who together comprise 70% of college graduates but only 45% of college STEM graduates, would become better represented in those fields. This call for action echoed calls by the National Academy of Sciences to expand underrepresented minority participation in STEM at the college level (NAS, 2011). In the following study, we examined whether participation in the Peer-Led Team Learning (PLTL) model in introductory biology influenced the rates of recruitment into STEM and retention in STEM for underrepresented minority (URM) students and for non-URM students. Chi-square analyses reveal that there are significant gaps in STEM recruitment and retention rates between URM and non-URM students, but when these students participate in the PLTL model, no differences in STEM recruitment or retention rates were observed. Additionally, we found that STEM retention rates were significantly improved for URM students who engaged in PLTL.

scientific communication and education↗

bioRxiv2017

Using experimental data as a voucher for study pre-registration

Undisclosed exploitation of flexibility in data acquisition and analysis blurs the important distinction between exploratory and hypothesis-driven findings and inflates false-positive rates1-4. Indeed, recent replication attempts have revealed low levels of replicability, pointing to high rates of false-positives in the literature5-10. A contemporary solution to this problem is pre-registration: commitment to aspects of methods and analysis before data acquisition11. This solution is valid only to the extent that the commitment stage is time-locked to precede data collection. To date, time-locking can only be guaranteed by introducing a third party such as peer reviewers at an early stage, making this solution less appealing for many12. Here we adapt a cryptographic method13 to encode information of study protocol within random aspects of the data acquisition process. This way, the structure of variability in the data time-locks the commitment stage with respect to data acquisition. Being independent of any third party, this method fully preserves scientific autonomy and confidentiality. We provide code for easy implementation and a detailed example from the field of functional Magnetic Resonance Imaging (fMRI).

scientific communication and education↗

bioRxiv2017

A systematic review of sample size and power in leading neuroscience journals

Adequate sample size is key to reproducible research findings: low statistical power can increase the probability that a statistically significant result is a false positive. Journals are increasingly adopting methods to tackle issues of reproducibility, such as by introducing reporting checklists. We conducted a systematic review comparing articles submitted to Nature Neuroscience in the 3 months prior to checklists (n=36) that were subsequently published with articles submitted to Nature Neuroscience in the 3 months immediately after checklists (n=45), along with a comparison journal Neuroscience in this same 3-month period (n=123). We found that although the proportion of studies commenting on sample sizes increased after checklists (22% vs 53%), the proportion reporting formal power calculations decreased (14% vs 9%). Using sample size calculations for 80% power and a significance level of 5%, we found little evidence that sample sizes were adequate to achieve this level of statistical power, even for large effect sizes. Our analysis suggests that reporting checklists may not improve the use and reporting of formal power calculations.

scientific communication and education↗

bioRxiv2017

Explanation implies causation?

Most researchers do not deliberately claim causal results in an observational study. But do we lead our readers to draw a causal conclusion unintentionally by explaining why significant correlations and relationships may exist? Here we perform a randomized study in a data analysis massive online open course to test the hypothesis that explaining an analysis will lead readers to interpret an inferential analysis as causal. We show that adding an explanation to the description of an inferential analysis leads to a 15.2% increase in readers interpreting the analysis as causal (95% CI 12.8% - 17.5%). We then replicate this finding in a second large scale massive online open course. Nearly every scientific study, regardless of the study design, includes explanation for observed effects. Our results suggest that these explanations may be misleading to the audience of these data analyses.

scientific communication and education↗

bioRxiv2017

How Changes in Common Practice Can Improve the Quality of Biomedical Science

Assessing the impact of recommendations rectifying the reproducibility crisis (publishing both positive and \"negative\" results and increasing statistical power) on competing objectives, such as discovering causal relationships, avoiding publishing false positive results, and reducing resource consumption, we developed a new probabilistic model. Our model quantifies the impact of each single suggestion for an individual study and their relation and consequences for the entire research process. We can prove that higher-powered experiments can save resources in the overall research process without generating excess false positives. The better the quality of the pre-study information and its exploitation, the more likely this beneficial effect is to occur. Additionally, we quantify the adverse effects of neglecting good practices in the design and conduct of hypotheses-based research, and the responsibility to publish \"negative\" findings. Our contribution is a plea for adherence to or reinforcement of the good scientific practice and publication of \"negative\" findings.

scientific communication and education↗

bioRxiv2017

Bin-Free Fitting of Probability Distributions Emphasizing DNA Histograms

It is often challenging to find the right bin size when constructing a histogram to represent a noisy experimental data set. This problem is frequently faced when assessing whether a cell synchronization experiment was successful or not. In this case the goal is to determine whether the DNA content is best represented by a unimodal, indicating successful synchronization, or bimodal, indicating unsuccessful synchronization, distribution. This choice of bin size can greatly affect the interpretation of the results; however, it can be avoided by fitting the data to a cumulative distribution function (CDF). Fitting data to a CDF removes the need for bin size selection. The sorted data can also be used to reconstruct an approximate probability density function (PDF) without selecting a bin size. A simple CDF-based approach is presented and the benefits and drawbacks relative to usual methods are discussed.

scientific communication and education↗

bioRxiv2017

A design framework and exemplar metrics for FAIRness

\"FAIRness\" - the degree to which a digital resource is Findable, Accessible, Interoperable, and Reusable - is aspirational, yet the means of reaching it may be defined by increased adherence to measurable indicators. We report on the production of a core set of semi-quantitative metrics having universal applicability for the evaluation of FAIRness, and a rubric within which additional metrics can be generated by the community. This effort is the output from a stakeholder-representative group, founded by a core of FAIR principles co-authors and drivers. We now seek input from the community to more broadly discuss their merit.

scientific communication and education↗

bioRxiv2017

Assessing the Landscape of U.S. Postdoctoral Salaries

PurposePostdocs make up a significant portion of the biomedical workforce. However, data about the postdoctoral position are generally scarce, including salary data. The purpose of this study was to request, obtain and interpret actual salaries, and the associated job titles, for postdocs at U.S. public institutions.\n\nMethodologyFreedom of Information Act Requests were submitted to U.S. public institutions estimated to have at least 300 postdocs according to the National Science Foundations Survey of Graduate Students and Postdocs. Salaries and job titles of postdoctoral employees as of December 1st, 2016 were requested.\n\nFindingsSalaries and job titles for over 13,000 postdocs at 52 public U.S. institutions and 1 private institution around the date of December 1st, 2016 were received, and individual postdoc names were also received for approximately 7,000 postdocs. This study shows evidence of gender-related salary discrepancies, a significant influence of job title description on postdoc salary, and a complex relationship between salaries and the level of institutional NIH funding.\n\nValueThese results provide insights into the ability of institutions to collate actual payroll-type data related to their postdocs, highlighting difficulties faced in tracking, and reporting data on this population. Ultimately, these types of efforts, aimed at increasing transparency, may lead to improved tracking and support for postdocs at all U.S. institutions.

scientific communication and education↗

bioRxiv2017

Understanding Research Development Needs in Capacity Building for NCD Research: The Case of Thailand

Chronic non-communicable diseases (NCD) are the most significant causes of death globally. In Thailand, NCDs have increased 10.4% and 11%, respectively, since 2002. Thus, there is a compelling need in Thailand to enhance the capacity for research aimed at improving both NCD prevention and care. A survey was conducted of current multi-disciplinary doctorally prepared faculty (n=115) to determine perceived NCD research training needs. The results of this survey showed that the greatest exposure to NCD was in clinical practice, followed by teaching NCD content, and then research. Few researchers published their findings in journals. All responders reported needing significant support in research design, methods and statistical analysis procedures. These results were used to guide the development of a post-doctoral training program for NCD research in Thailand. After three years of the training program, we found that trainee applicants preferences and choices were aligned with the original survey-based planning.

scientific communication and education↗

bioRxiv2017

Teaching data science fundamentals through realistic synthetic clinical cardiovascular data

ObjectiveOur goal was to create a synthetic dataset and curricular materials to assist in teaching fundamentals of translational data science.\n\nMaterials and MethodsA literature review was conducted to extract current cardiovascular risk score logic, data elements, and population characteristics. Then, clinical data elements in the models were pulled from clinical data and transformed to the Observational Medical Outcomes Partnership (OMOP) common data model; genetic data elements were added based on population rates. A hybrid Bayesian network was used to create synthetic data from the logical elements of the risk scores and the underlying population frequencies of the clinical data.\n\nResultsA synthetic dataset of 446,000 patients was created. A two-day curriculum was created based on this synthetic data with exploratory data analysis and machine learning components. The curriculum was offered on two separate occasions; the two groups of learners were given the curriculum and data, and results were tallied, summarized, and compared. Students ability to complete the challenge was mixed; more experienced students achieved a range of 70%-85% in balanced accuracy, but many others did not perform better than the baseline model.\n\nDiscussionOverall, students enjoyed the course and dataset, but some struggled to consistently apply machine learning techniques. The curriculum, data set, techniques for generation, and results are available for others to use for their own training.\n\nConclusionA realistic synthetic data with clinical and genetic components helps students learn issues in cardiovascular risk scoring, practice data science skills, and compete in a challenge to improve identification of risk.

scientific communication and education↗

bioRxiv2017

Female grant applicants are equally successful when peer reviewers assess the science, but not when they assess the scientist

BackgroundPrevious research shows that men often receive more research funding than women, but does not provide empirical evidence as to why this occurs. In 2014, the Canadian Institutes of Health Research (CIHR) created a natural experiment by dividing all investigator-initiated funding into two new grant programs: one with and one without an explicit review focus on the caliber of the principal investigator.\n\nMethodsWe analyzed application success among 23,918 grant applications from 7,093 unique principal investigators in a 5-year natural experiment across all investigator-initiated CIHR grant programs in 2011-2016. We used Generalized Estimating Equations to account for multiple applications by the same applicant and an interaction term between each principal investigators self-reported sex and grant programs to compare success rates between male and female applicants under different review criteria.\n\nResultsThe overall grant success rate across all competitions was 15.8%. After adjusting for age and research domain, the predicted probability of funding success in traditional programs was 0.9 percentage points higher for male than for female principal investigators (OR 0.934, 95% CI 0.854-1.022). In the new program focused on the proposed science, the gap was 0.9 percentage points in favour of male principal investigators (OR 0.998, 95% CI 0.794-1.229). In the new program with an explicit review focus on the caliber of the principal investigator, the gap was 4.0 percentage points in favour of male principal investigators (OR 0.705, 95% CI 0.519- 0.960).\n\nInterpretationThis study suggests gender gaps in grant funding are attributable to less favourable assessments of women as principal investigators, not differences in assessments of the quality of science led by women. We propose ways for funders to avoid allowing gender bias to influence research funding.\n\nFundingThis study was unfunded.

scientific communication and education↗

bioRxiv2017

Disequilibrium in Gender Ratios among Authors who Contributed Equally

In recent decades, the biomedical literature has witnessed an increasing number of authors per article together with a concomitant increase of authors claiming to have contributed equally. In this study, we analyzed over 3000 publications from 1995-2017 claiming equal contributions for authors sharing the first author position for author number, gender, and gender position. The frequency of dual pairings contributing equally was male-male > mixed gender > female-female. For mixed gender pairs males were more often at the first position although the disparity has lessened in the past decade. Among author associations claiming equal contribution and containing three or more individuals, males predominated in both the first position and number of gender exclusive groupings. Our results show a disequilibrium in gender ratios among authors who contributed equally from expected ratios had the ordering been done randomly or alphabetical. Given the importance of the first author position in assigning credit for a publication, the finding of fewer than expected females in associations involving shared contributions raises concerns about women not receiving their fair share of expected credit. The results suggest a need for journals to request clarity on the method used to decide author order among individuals claiming to have made equal contributions to a scientific publication.

scientific communication and education↗

bioRxiv2018

Edge Factors: Scientific Frontier Positions of Nations

A key decision in scientific work is whether to build on novel or well-established ideas. Because exploiting new ideas is often harder than more conventional science, novel work can be especially dependent on interactions with colleagues, the training environment, and ready access to potential collaborators. Location may thus influence the tendency to pursue work that is close to the edge of the scientific frontier in the sense that it builds on recent ideas. We calculate for each nation its position relative to the edge of the scientific frontier by measuring its propensity to build on relatively new ideas in biomedical research. Text analysis of 20+ million publications shows that the United States and South Korea have the highest tendencies for novel science. China has become a leader in favoring newer ideas when working with basic science ideas and research tools, but is still slow to adopt new clinical ideas. Many locations remain far behind the leaders in terms of their tendency to work with novel ideas, indicating that the world is far from flat in this regard.

scientific communication and education↗

bioRxiv2018

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.

scientific communication and education↗