Reproducibility will only come with data liberation

Accuracy comparison between statistical and computational classifiers applied for predicting student performance in online higher education

Educational institutions abruptly implemented online higher education to cope with sanitary distance restrictions in 2020, causing an increment in student failure. This negative impact attracts the analyses of online higher education as a critical issue for educational systems. The early identification of students at risk is a strategy to cope with this issue by predicting their performance. Computational techniques are projected helpful in performing this task. However, the accurateness of predictions and the best model selection are goals in progress. This work objective is to describe two experiments using student grades of an online higher education program to build and apply three classifiers to predict student performance. In the literature, the three classifiers, a Probabilistic Neural Network, a Support Vector Machine, and a Discriminant Analysis, have proved efficient. I applied the leave-one-out cross-validation method, tested their performances by five criteria, and compared their results through statistical analysis. The analyses of the five performance criteria support the decision on which model applies given particular prediction goals. The results allow timely identification of students at risk of failure for early intervention and predict which students will succeed.

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines

Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays.

Factors that impact the reproducibility of experimental data are poorly understood. Five NIH-LINCS centers performed the same set of drug-response measurements and compared results. Technical and biological variables that impact precision and reproducibility and are also sensitive to biological context were the most problematic.

Scientific rigor and credibility in the nutrition research landscape

Scientific progress depends on the quality and credibility of research methods. As discourse on rigor, transparency, and reproducibility joins the cacophony of nutrition information and misinformation in mass media, buttressing the real and perceived reliability of nutrition science is more important than ever. This broad topic was the focus of a 2016 plenary session, "Scientific Rigor and Competing Interests in the Nutrition Research Landscape." This article summarizes and expands on this session in an effort to increase understanding and dialogue with regard to factors that limit the real and perceived reliability of nutrition science and steps that can be taken to mitigate those factors. The end goal is to both earn and merit greater trust in nutrition science by both the scientific community and the general public. The authors offer suggestions in each of the domains of education and training, communications, research conduct, and procedures and policies to help achieve this goal. The authors emphasize the need for adequate funding to support these efforts toward greater rigor and transparency, which will be resource demanding and may require either increased research funding or the recognition that a greater proportion of research funding may need to be allocated to these tasks.

Increasing the Impact of Materials in and beyond Bio-Nano Science

This is an exciting time for the field of bio-nano science: enormous progress has been made in recent years, especially in academic research, and materials developed and studied in this area are poised to make a substantial impact in real-world applications. Herein, we discuss ways to leverage the strengths of the field, current limitations, and valuable lessons learned from neighboring fields that can be adopted to accelerate scientific discovery and translational research in bio-nano science. We identify and discuss five interconnected topics: (i) the advantages of cumulative research; (ii) the necessity of aligning projects with research priorities; (iii) the value of transparent science; (iv) the opportunities presented by "dark data"; and (v) the importance of establishing bio-nano standards.