Data science has been described as the fourth paradigm for scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges - our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI have sparked debates about the socio-technical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for "data science for social good" (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of socio-technical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the special issue. We hope that this article and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are garnering proportionately less attention with each passing day.

Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable and secure payments network, enabling individuals, businesses and economies to thrive. When you join Visa, you join a culture of purpose and belonging – where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world – helping unlock financial access to enable the future of money movement.

Using GitHub for data science research is not quite mainstream just yet for the data science industry, but it is growing in popularity in the field. From our point of view, there is a good reason for that growth. Data science has been a siloed field of research, but as technology continues to develop so does data science. It is now more common to see teams of data scientists all working towards similar goals rather than a single data scientist sifting through data on their own and pushing it forward to a team of developers or engineers. In this guide, we will explain how data science and GitHub are a perfect match, but also the "why" behind using GitHub at all for data science.

Victoria Stodden (vcs@stodden.net) is a statistician and associate professor at the University of Illinois at Urbana-Champaign, IL, USA. This material is based upon work supported by National Science Foundation Award #1941443.

The Coronavirus Disease 2019 (COVID-19) pandemic has caused extreme strains on health systems, public health infrastructure, and economies of many countries. As of April 2020, millions of people have been infected, and more than two billion globally are staying home to avoid coronavirus. This raging pandemic continues to disrupt our lives while the scientific community is rushing to find a cure for COVID-19. We can leverage artificial intelligence (AI) and big data to help combat the COVID-19 pandemic. For example, ongoing AI efforts aim to expedite the development of a safe and effective COVID-19 vaccine, use networks to repurpose drugs for COVID-19, predict antibacterial properties of new molecules, and design machine-readable datasets of scientific literature on the novel coronavirus.

Are you passionate about model strategy and research design? Do you want to learn from data scientists and have an immediate impact on our work? Civis Analytics is looking for an Data Science Research and Development intern to join our team! Civis Analytics was born on the campaign trail, with CEO Dan Wagner and our founding members spearheading the 2012 Obama for America analytics team. Since then, our DC and Chicago teams have been building software and growing rapidly among a steadily developing client base in education, energy, government, healthcare, media, nonprofits, and politics.