This study tested the claim that digital PCR (dPCR) can offer highly reproducible quantitative measurements in disparate labs. Twenty-one laboratories measured four blinded samples containing different quantities of a KRAS fragment encoding G12D, an important genetic marker for guiding therapy of certain cancers. This marker is challenging to quantify reproducibly using qPCR or NGS due to the presence of competing wild type sequences and the need for calibration. Using dPCR, eighteen laboratories were able to quantify the G12D marker within 12% of each other in all samples. Three laboratories appeared to measure consistently outlying results; however, proper application of a follow-up analysis recommendation rectified their data. Our findings show that dPCR has demonstrable reproducibility across a large number of laboratories without calibration and could enable the reproducible application of molecular stratification to guide therapy, and potentially for molecular diagnostics.
Reproducible Science Promoting Open Science
It seems like the most elementary of research principles: Make sure the cells and reagents in your experiment are what they claim to be and behave as expected. But when it comes to antibodies—the immune proteins used in all kinds of experiments to tag a molecule of interest in a sample—that validation process is not straightforward. Research antibodies from commercial vendors are often screened and optimized for narrow experimental conditions, which means they may not work as advertised for many scientists. Indeed, problems with antibodies are thought to have led many drug developers astray and generated a host of misleading or irreproducible scientific results.
Developing effective information retrieval models has been a long standing challenge in Information Retrieval (IR), and significant progresses have been made over the years. With the increasing number of developed retrieval functions and the release of new data collections, it becomes more difficult, if not impossible, to compare a new retrieval function with all existing retrieval functions over all available data collections. To tackle thisproblem, this paper describes our efforts on constructing a platform that aims to improve the reproducibility of IR researchand facilitate the evaluation and comparison of retrieval functions.
A high-quality search strategy is considered an essential component of systematic reviews but many do not contain reproducible search strategies. It is unclear if low reproducibility spans medical disciplines, is affected by librarian/search specialist involvement or has improved with increased awareness of reporting guidelines.
Reproducing palaeontological results depends on unrestricted access to fossils described in the literature, allowing others to re-examine or reinterpret them. Museums have policies and protocols for keeping materials in the public trust, but accessibility to privately owned fossil collections can be a problem.
There’s been a lot of discussion across many scientific fields about the "reproducibility crisis" in the past few years. Hundreds of psychologists attempted to redo 100 studies as part of the Reproducibility Project in Psychology, and claimed that fewer than half of the replication attempts succeeded. In Biomedicine, a study from the biotech firm Amgen tried to re-create results of 53 "landmark" preclinical cancer studies, and only got the same results for six of them. Amid a growing concern about research reliability, funders including the National Institutes of Health (NIH) have called for a greater effort to make research reproducible through transparent reporting of the methods researchers use to conduct their investigations.
The ongoing dialogue has included the role of improperly validated research reagents, such as antibodies, with blame falling at the feet of reagent vendors, researchers, and journals. This article will highlight how the lack of consistent research on antibody validation has contributed to the reproducibility crisis and the role of vendors from Cell Signaling Technology’s (CST) perspective in making research more robust and reproducible.
The lack of reproducibility of preclinical experimentation has implications for sustaining trust in and ensuring the viability and funding of the academic research enterprise. Here I identify problematic behaviors and practices and suggest solutions to enhance reproducibility in translational research.
Numerous variables can torpedo attempts to replicate cell experiments, from the batch of serum to the shape of growth plates. But there are ways to ensure reliability.
Many scientists worry over the reproducibility of wet-lab experiments, but data scientist Victoria Stodden's focus is on how to validate computational research: analyses that can involve thousands of lines of code and complex data sets. Beginning this month, Stodden — who works at the University of Illinois at Urbana-Champaign — becomes one of three ‘reproducibility editors’ appointed to look over code and data sets submitted by authors to the Applications and Case Studies (ACS) section of the Journal of the American Statistical Association (JASA). Other journals including Nature have established guidelines for accommodating data requests after publication, but they rarely consider the availability of code and data during the review of a manuscript. JASA ACS will now insist that — with a few exceptions for privacy — authors submit this information as a condition of publication.