Age of Discovery

Summary

The Age of Discovery or the Age of Exploration, part of the early modern period and largely overlapping with the Age of Sail, was a period from approximately the 15th century to the 17th century, during which seafarers from a number of European countries explored, colonized, and conquered regions across the globe. The extensive overseas exploration, particularly the European colonisation of the Americas, with the Spanish and Portuguese at the forefront, later joined by the Dutch, English, and French, marked an increased adoption of colonialism as a government policy in several European states. As such, it is sometimes synonymous with the first wave of European colonization. European exploration outside the Mediterranean started with the maritime expeditions of Portugal to the Canary Islands in 1336, and later with the Portuguese discoveries of the Atlantic archipelagos of Madeira and Azores, the coast of West Africa in 1434, and the establishment of the sea route to India in 1498

Official source

https://en.wikipedia.org/wiki/Age_of_Discovery

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Data-driven approaches based on high-throughput capabilities and machine learning hold promise in revolutionizing human-centred materials discovery for sustainability and decarbonization. This Review examines the strengths and limitations of different traditional and emerging approaches to demonstrate their inherent connection and highlight the evolving paradigms of materials design. Breakthroughs in molecular and materials discovery require meaningful outliers to be identified in existing trends. As knowledge accumulates, the inherent bias of human intuition makes it harder to elucidate increasingly opaque chemical and physical principles. Moreover, given the limited manual and intellectual throughput of investigators, these principles cannot be efficiently applied to design new materials across a vast chemical space. Many data-driven approaches, following advances in high-throughput capabilities and machine learning, have tackled these limitations. In this Review, we compare traditional, human-centred methods with state-of-the-art, data-driven approaches to molecular and materials discovery. We first introduce the limitations of human-centred Edisonian, model-system and descriptor-based approaches. We then discuss how data-driven approaches can address these limitations by promoting throughput, reducing cognitive overload and biases, and establishing atomistic understanding that is transferable across a broad chemical space. We examine how high-throughput capabilities can be combined with active learning and inverse design to efficiently optimize materials out of millions or an intractable number of candidates. Lastly, we pinpoint challenges to accelerate future workflows and ultimately enable self-driving platforms, which automate and streamline the optimization of molecules and materials in iterative cycles.

NATURE PORTFOLIO2022

Confronting false discoveries in single-cell differential expression

Mark Andrew Anderson, Quentin Barraud, Grégoire Courtine, Matthieu Pierre Gautier, Rémi Bernard René Hudelle, Thomas Haynes Hutson, Nicholas David James, Claudia Kathe, Gioele La Manno, Michael Alexander Skinnider, Jordan Squair

Differential expression analysis of single-cell transcriptomics allows scientists to dissect cell-type-specific responses to biological perturbations. Here, the authors show that many commonly used methods are biased and can produce false discoveries. Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. To exemplify these principles, we exposed true and false discoveries of differentially expressed genes in the injured mouse spinal cord.

NATURE PORTFOLIO2021

Phantom Curves: Scientific Discovery through Interactive Music Visualization

Daniel Harasim, Fabian Claude Moss

We introduce phantom curves, a novel music-theoretical concept based on the discrete Fourier transform (DFT), and document the creative process that led to their discovery. In particular, we emphasize the importance of interactive web applications for music visualization and analysis. This is demonstrated using the example of the application midiVERTO which affords interactions with the pitch-class content of musical pieces encoded in MIDI format without requiring in-depth understanding of the underlying mathematics. We illustrate the analytical value of studying families of phantom curves by applying the concept to music from a Broadway musical, a video game, and a Hollywood movie. This process of discovery thus testifies to the fact that digital tools can bridge disciplinary boundaries between music theory and mathematics, and this interaction can generate new scientific knowledge.

ACM2022