Biology subjects

Riedel, N.

Publications and source records attributed to Riedel, N..

5 recordsLinked to original sources

bioRxiv2022

Blind spots on western blots: a meta-research study highlighting opportunities to improve figures and methods reporting

Western blotting is a standard laboratory method used to detect proteins and assess their expression levels. Unfortunately, poor western blot image display practices and a lack of detailed methods reporting can limit a readers ability to evaluate or reproduce western blot results. While several groups have studied the prevalence of image manipulation or provided recommendations for improving western blotting, data on the prevalence of common publication practices are scarce. We systematically examined 551 articles published in the top 25% of journals in neurosciences (n=151) and cell biology (n=400) that contained western blot images, focusing on practices that may omit important information. Our data show that most published western blots are cropped and blot source data are not made available to readers in the supplement. Publishing blots with visible molecular weight markers is rare, and many blots additionally lack molecular weight labels. Western blot methods sections often lack information on the amount of protein loaded on the gel, blocking steps and antibody labeling protocol. Important antibody identifiers like source, catalog number or RRID were omitted frequently for primary antibodies, and regularly for secondary antibodies. We present detailed descriptions and visual examples to help scientists, peer reviewers and editors to publish more informative western blot figures and methods. Additional resources include a toolbox to help scientists produce more reproducible western blot data, teaching slides in English and Spanish and an antibody reporting template.

cell biology↗

bioRxiv2022

Replacing bar graphs of continuous data with more informative graphics: Are we making progress?

Recent work has raised awareness about the need to replace bar graphs of continuous data with informative graphs showing the data distribution. The impact of these efforts is not known. This observational meta-research study examined how often scientists in different fields use various graph types, and assessed whether visualization practices have changed between 2010 and 2020. We developed and validated an automated screening tool, designed to identify bar graphs of counts or proportions, bar graphs of continuous data, bar graphs with dot plots, dot plots, box plots, violin plots, histograms, pie charts, and flow charts. Papers from 23 fields (approximately 1,000 papers/field/year) were randomly selected from PubMed Central and screened (n=227,998). F1 scores for different graphs ranged between 0.83 and 0.95 in the internal validation set. While the tool also performed well in external validation sets, F1 scores were lower for uncommon graphs. Bar graphs are more often used incorrectly to display continuous data than they are used correctly to display counts or proportions. The proportion of papers that use bar graphs of continuous data varies markedly across fields (range in 2020: 4%-58%), with high rates in biochemistry and cell biology, complementary and alternative medicine, physiology, genetics, oncology and carcinogenesis, pharmacology, microbiology and immunology. Visualization practices have improved in some fields in recent years. Fewer than 25% of papers use flow charts, which provide information about attrition and the risk of bias. This study highlights the need for continued interventions to improve visualization and identifies fields that would benefit most.

scientific communication and education↗

bioRxiv2020

Assessment of transparency indicators across the biomedical literature: how open is open?

Recent concerns about the reproducibility of science have led to several calls for more open and transparent research practices and for the monitoring of potential improvements over time. However, with tens of thousands of new biomedical articles published per week, manually mapping and monitoring changes in transparency is unrealistic. We present an open-source, automated approach to identify five indicators of transparency (data sharing, code sharing, conflicts of interest disclosures, funding disclosures and protocol registration) and apply it across the entire open access biomedical literature of 2.75 million articles on PubMed Central. Our results indicate remarkable improvements in some (e.g. conflict of interest disclosures, funding disclosures), but not other (e.g. protocol registration, code sharing) areas of transparency over time, and map transparency across fields of science, countries, journals and publishers. This work has enabled the creation of a large, integrated, and openly available database to expedite further efforts to monitor, understand and promote transparency and reproducibility in science.

scientific communication and education↗

bioRxiv2020

ODDPub - a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications

Open research data are increasingly recognized as a quality indicator and an important resource to increase transparency, robustness and collaboration in science. However, no standardized way of reporting Open Data in publications exists, making it difficult to find shared datasets and assess the prevalence of Open Data in an automated fashion.We developed ODDPub (Open Data Detection in Publications), a text-mining algorithm that screens biomedical publications and detects cases of Open Data. Using English-language original research publications from a single biomedical research institution (n=8689) and randomly selected from PubMed (n=1500) we iteratively developed a set of derived keyword categories. ODDPub can detect data sharing through field-specific repositories, general-purpose repositories or the supplement. Additionally, it can detect shared analysis code (Open Code).To validate ODDPub, we manually screened 792 publications randomly selected from PubMed. On this validation dataset, our algorithm detected Open Data publications with a sensitivity of 0.74 and specificity of 0.97. Open Data was detected for 11.5% (n=91) of publications. Open Code was detected for 1.4% (n=11) of publications with a sensitivity of 0.73 and specificity of 1.00. We compared our results to the linked datasets found in the databases PubMed and Web of Science.Our algorithm can automatically screen large numbers of publications for Open Data. It can thus be used to assess Open Data sharing rates on the level of subject areas, journals, or institutions. It can also identify individual Open Data publications in a larger publication corpus. ODDPub is published as an R package on GitHub.Competing Interest StatementThe authors have declared no competing interest.View Full Text

scientific communication and education↗

bioRxiv2018

Benchmarking university medical centres for responsible metrics. A cross sectional study on timely results dissemination across 36 German centres

ObjectiveTimely and comprehensive reporting of clinical trial results build the backbone of evidence-based medicine and responsible research. The proportion of timely disseminated trial results can inform alternative national and international benchmarking of university medical centers (UMCs).\n\nStudy Design and SettingFor all German UMCs we tracked all registered trials completed between 2009 and 2013. The results and an interactive website benchmark German UMCs regarding their performance in results dissemination.\n\nResultsWe identified and tracked 2,132 clinical trials. For 1,509 trials, one of the German UMCs took the academic lead. Of these 1,509 \"lead trials\", 39% published their results (mostly via journal publications) in a timely manner (<24 months after completion). More than six years after study completion, 26% of all eligible lead trials still had not disseminated results.\n\nConclusionDespite substantial attention from many stakeholders to the topic, there is still a strong delay or even absence of results dissemination for many trials. German UMCs have several opportunities to improve this situation. Further research should evaluate whether and how a transparent benchmarking of UMC performance in results dissemination helps to increase value and reduce waste in medical research.

scientific communication and education↗