The scientific community is increasingly concerned with cases of published "discoveries" that are not replicated in further studies. The field of mouse 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 the field 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 view of identifying specific problems, as well as 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.
The solution to science's replication crisis is a new ecosystem in which scientists sell what they learn from their research. In each pairwise transaction, the information seller makes (loses) money if he turns out to be correct (incorrect). Responsibility for the determination of correctness is delegated, with appropriate incentives, to the information purchaser. Each transaction is brokered by a central exchange, which holds money from the anonymous information buyer and anonymous information seller in escrow, and which enforces a set of incentives facilitating the transfer of useful, bluntly honest information from the seller to the buyer. This new ecosystem, capitalist science, directly addresses socialist science's replication crisis by explicitly rewarding accuracy and penalizing inaccuracy.
Wireless networks are the key enabling technology of the mobile revolution. However, experimental mobile and wireless research is still hindered by the lack of a solid framework to adequately evaluate the performance of a wide variety of techniques and protocols proposed by the community. In this talk, I will motivate the need for experimental reproducibility as a necessary aspect for healthy progress as accepted by other communities. I will illustrate how other research communities went through similar processes. I will then present the unique challenges of mobile and wireless experimentation, and discuss approaches, past, current, and future to address these challenges. Finally, I will discuss how reproducibility extends to mobile and wireless security research.
A number of proactive steps are underway to improve the rigor and reproducibility of the data reported in the Journal of Neurophysiology. The American Physiological Society's Publications Committee is currently devising implementation plans for the following recommendations from editors of the Society's journals.
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.
Crowdsourcing is a multidisciplinary research area in-cluding disciplines like artificial intelligence, human-computer interaction, database, and social science. One of the main objectives of AAAI HCOMP conferences is to bring together researchers from different fields and provide them opportunities to exchange ideas and share new research results. To facilitate cooperation across disciplines,repro-ducibilityis a crucial factor, but unfortunately it has not got-ten enough attention in the HCOMP community.