Modality and uncertainty in data visualizations : A corpus approach to the use of connecting lines

publishedVersionPaid Open Acces

REFORMS: Reporting Standards for Machine Learning Based Science

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear reporting standards for ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist ($\textbf{Re}$porting Standards $\textbf{For}$ $\textbf{M}$achine Learning Based $\textbf{S}$cience). It consists of 32 questions and a paired set of guidelines. REFORMS was developed based on a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility

Towards a Conceptual Model for Data Narratives

International audienc

Modality and uncertainty in data visualizations: A corpus approach to the use of connecting lines

publishedVersionPaid Open Acces

Gibraltar from the new mole fort looking north

1 mapa.19 x 25 cm, full 22 x 28 c

BarCamp: Technology Foresight and Statistics for the Future

In the last two decades a drastic renewal has occurred in Statistics and in all the fields that involve Statistics. New requirements have arisen from new kinds of data, while technology has increased the ability of exploration and computation using massive amounts of information. As a result, more and more disciplines have started making an intensive use of statistical methods, driving the development of novel tools and providing new questions to be answered in a wide range of new application settings. In general, the production and the communication of results have changed in an extremely rapid way. The aim of this paper is to introduce the BarCamp as an innovative way of producing and communicating statistical knowledge. For this purpose, we propose an algorithm to organize a scientific BarCamp and we describe it in detail in Sect. 2. In Sect. 3 we describe the BarCamp held at Politecnico di Milano and we discuss the vision of Statistics for the next 25 years emerged during the event. Finally, some conclusive observations are drawn in the section “Conclusions”