eScience Infrastructures in Physical Chemistry.

As the volume of data associated with scientific research has exploded over recent years, the use of digital infrastructures to support this research and the data underpinning it has increased significantly. Physical chemists have been making use of eScience infrastructures since their conception, but in the last five years their usage has increased even more. While these infrastructures have not greatly affected the chemistry itself, they have in some cases had a significant impact on how the research is undertaken. The combination of the human effort of collaboration to create open source software tools and semantic resources, the increased availability of hardware for the laboratories, and the range of data management tools available has made the life of a physical chemist significantly easier. This review considers the different aspects of eScience infrastructures and explores how they have improved the way in which we can conduct physical chemistry research.

The AI for Scientific Discovery Network.

The Artificial Intelligence and Augmented Intelligence for Automated Investigation for Scientific Discovery Network+ (AI3SD) was established in response to the UK Engineering and Physical Sciences Research Council (EPSRC) late-2017 call for a Network+ to promote cutting-edge research in artificial intelligence to accelerate groundbreaking scientific discoveries. This article provides the philosophical, scientific, and technical underpinnings of the Network+, the history of the different domains represented in the Network+, and the specific focus of the Network+. The activities, collaborations, and research covered in the first year of the Network+ have highlighted the significant challenges in the chemistry and augmented and artificial intelligence space. These challenges are shaping the future directions of the Network+. The article concludes with a summary of the lessons learned in running this Network+ and introduces our plans for the future in a landscape redrawn by COVID-19, including rebranding into the AI 4 Scientific Discovery Network (www.ai4science.network).

A new wave of innovation in Semantic web tools for drug discovery.

INTRODUCTION: The use of semantic web technologies to aid drug discovery has gained momentum over recent years. Researchers in this domain have realized that semantic web technologies are key to dealing with the high levels of data for drug discovery. These technologies enable us to represent the data in a formal, structured, interoperable and comparable way, and to tease out undiscovered links between drug data (be it identifying new drug-targets or relevant compounds, or links between specific drugs and diseases). Areas covered: This review focuses on explaining how semantic web technologies are being used to aid advances in drug discovery. The main types of semantic web technologies are explained, outlining how they work and how they can be used in the drug discovery process, with a consideration of how the use of these technologies has progressed from their initial usage. Expert opinion: The increased availability of shared semantic resources (tools, data and importantly the communities) have enabled the application of semantic web technologies to facilitate semantic (context dependent) search across multiple data sources, which can be used by machine learning to produce better predictions by exploiting the semantic links in knowledge graphs and linked datasets.

Electronic lab notebooks: can they replace paper?

Despite the increasingly digital nature of society there are some areas of research that remain firmly rooted in the past; in this case the laboratory notebook, the last remaining paper component of an experiment. Countless electronic laboratory notebooks (ELNs) have been created in an attempt to digitise record keeping processes in the lab, but none of them have become a 'key player' in the ELN market, due to the many adoption barriers that have been identified in previous research and further explored in the user studies presented here. The main issues identified are the cost of the current available ELNs, their ease of use (or lack of it) and their accessibility issues across different devices and operating systems. Evidence suggests that whilst scientists willingly make use of generic notebooking software, spreadsheets and other general office and scientific tools to aid their work, current ELNs are lacking in the required functionality to meet the needs of the researchers. In this paper we present our extensive research and user study results to propose an ELN built upon a pre-existing cloud notebook platform that makes use of accessible popular scientific software and semantic web technologies to help overcome the identified barriers to adoption.