Application of Newtonian physics to predict the speed of a gravity racer

Gravity racing can be studied using numerical solutions to the equations of motion derived from Newton’s second law. This allows students to explore the physics of gravity racing and to understand how design and course selection influences vehicle speed. Using Euler’s method, we have developed a spreadsheet application that can be used to predict the speed of a gravity powered vehicle. The application includes the effects of air and rolling resistance. Examples of the use of the application for designing a gravity racer are presented and discussed. Predicted speeds are compared to the results of an official world record attempt

Data Sharing and Research on Peer Review: A Call to Action

While recent surveys show that most stakeholders recognise the importance of peer review to the publication process, there is a lack of systematic research on the topic. In a period of hyper-competition for resources, with perverse incentives that lead to academic capitalism and a \u201cpublish or perish\u201d mentality, the lack of robust and cumulative research on approaches, models and practices of peer review can slow down efforts towards fostering research integrity and the credibility of scholarly communication. A major challenge in studying peer review systematically is the lack of available data. While data sharing in scientific research has made relevant progress in certain fields, the lack of infrastructures to promote the sharing of peer review data among publishers, journals and academic scholars, the challenges posed by privacy and data protection legislation, and the perceived lack of incentives for publishers, learned societies and journals to share data, have all hampered efforts in this important domain. While public authorities, learned societies and publishers may face different priorities, incentives and obstacles regarding data sharing, the time has come to call to action all stakeholders who play a part in this field. In this paper, we argue that an infrastructure for data sharing is needed to stimulate independent, collaborative, public research on peer review and we suggest measures and initiatives to set up a collaborative effort towards this goal

Unlock ways to share data on peer review

Peer review is the defining feature of scholarly communication. In a 2018 survey of more than 11,000 researchers, 98% said that they considered peer review important or extremely important for ensuring the quality and integrity of scholarly communication. Indeed, now that the Internet and social media have assumed journals\u2019 original role of dissemination, a journal\u2019s main function is curation. Both the public and the scientific community trust peer review to uphold shared values of rigour, ethics, originality and analysis by improving publications and filtering out weak or errant ones. Scholarly communities rely on peer review to establish common knowledge and credit

Autonomous discovery in the chemical sciences part II: Outlook

This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to ``discover'' despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this article defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress toward the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.Comment: Revised version available at 10.1002/anie.20190998