The First Piece of the Pattern

It is our great honor and privilege to welcome you to the very first issue of Patterns, Cell Press’s new, open access journal of data science. The past 11 months have been an amazing time of work, talking to people, learning, and making decisions, and it’s been thrilling. At this stage, one could ask: why a new journal of data science? And why have you launched something that’s so broad scope and, frankly, ambitious? We’re very glad you asked. Data science can often feel like a new topic, a new domain springing out of computer science or mathematics, but in reality, it is a fundamental part of what it means to do research. Science has always been about data—after all, how can you test a hypothesis without making measurements, and how can you support a conclusion without showing your results? Only a few short decades ago, it took a great deal of effort to create data. Researchers would send out surveys, carefully design experiments, and spend long hours taking repeated and precious measurements. How quickly things have changed. Now the average person in the street is generating data, often without being aware of it, simply by walking down the street with a smartphone and their geolocation tagged. For researchers, the ability to collect greater volumes of more detailed data about their subject of interest has come as a great blessing but has also caused problems. As our ability to collect data grows, the ways we need to deal with it get more complicated, and the tools we need to understand it also become more detailed. If we are to make good decisions about the problems that we are facing as a species, whether that’s climate change, pandemics, or even how we decide who gets a job or not, then we need good data, and we need to properly understand those data. On the other side of all this new detail are the tremendous possibilities we have as a result of this data deluge. We can learn so much more about the structure of the universe, our environment, and also how our own bodies work, because we have access to new, better, and more detailed sources of data. Patterns is about understanding—not only communicating between authors and readers but also breaking down those disciplinary boundaries to share the data science technologies that can be used to solve the problems that span domains. Patterns is about discussion, sharing opinions and ideas, and developing new standards of practice. Patterns is about community, bringing together people with similar interests, regardless of what their original domain is. That is why, in this first issue, we bring together a wide range of authors to discuss an even wider range of topics from data-intensive research, computer science, and data curation. The thing they all have in common is data: how to use them, how to manage them, and how to use new technologies to learn from them. The Research Articles are the core focus of Patterns—they take data science techniques and apply them to real-world problems to discover new knowledge. They span the entirety of the data-intensive research and data science space and cover topics as diverse as sentiment analysis of conservation studies (Van Houtan), intelligent electromagnetic sensing (Li), and how to identify data sharing and reuse (Khan). Our Perspective pieces are a useful way of explaining, especially in such a fast-growing field as data science, the current situation with a given topic and what the authors think should be done to address the situation. In this issue, we deal with the thorny problem of information entropy (Habermann) and look at the potentials, possibilities, and complications of interdependent networks (Amini). Because everyone creates and uses data, there are almost as many opinions about data as there are people. Opinion pieces are a way for the community to start discussions, to outline ideas and find collaborators, and to generally share information in a quick and easily understood way. This issue features a wide variety of opinions, ranging from top tips on how to use games to teach important data management concepts (McCutcheon) to how to tell if the next big technology is really suitable for the problem you want to solve (Crowcroft). With Opinions, we also get into the philosophical aspects of data science, giving an example of how researchers can work across disciplinary boundaries to enrich a valuable (and irreplaceable) climate science database (Slonosky). Fundamentally, Patterns is all about the people as well as the data. Data ethics is a topic we take very seriously, hence the Opinion piece “Who Should Do Data Ethics?” (Wylie). We are firm believers that everyone should do data ethics. Data have the potential to improve life for everyone, when they’re used to empower and support communities (Cerit) or to help mitigate and recover from the effects of global pandemics (Perakslis). Last, but by no means least, this issue looks even closer at the people at the cutting edge of data science, and how they are and were impacted by it. We take both a historical and contemporary perspective, identifying those who have come before us to lay the foundations of the data science field (Inman) and those who are working on the front lines of data science today (Gordon). There are so many people we need to thank for their efforts and support in launching a new journal that is this broad and ambitious. From our wonderful, supportive colleagues at Cell Press to our amazing advisory board and the reviewers who kindly gave so much of their time and expertise to review for a journal that they didn’t know. And of course, to the authors, whose desire to tell their data stories has meant that we have stories to tell and a beautiful array of different articles to publish. A pattern is something that is repeated. We’ve laid the first piece with this first issue, and now it’s time to see how Patterns will grow. Thank you for sharing these data stories with us. How to Tell When a Digital Technology Is Not Ready for YouJon CrowcroftPatternsApril 10, 2020In BriefThe stages of digital technology readiness are viewed through the lens of three contemporary and widely discussed examples, namely distributed ledger technology, machine learning, and the internet of things. I use these examples to clarify when there is really just an old technology being re-branded, when there is something genuinely new and useful, and whether there may be over-claiming. Full-Text PDF Open AccessInterdependent Networks: A Data Science PerspectiveAmini et al.PatternsMarch 19, 2020In BriefReal-world complex networks are coupled through shared entities and decision makers; e.g., there is a transition toward coupled smart cities infrastructures by integrating intelligent technologies. In this study, we provide a holistic overview of coupled societal-water-energy-economical-transportation (SWEET) networks as critical components of smart cities. We explore required methods to ensure optimal decision making and data analytics considering the increasing coupling of these human-centered networks. We finally provide future directions for researchers who work at the intersection of data science and network science. Full-Text PDF Open AccessIntelligent Electromagnetic Sensing with Learnable Data Acquisition and ProcessingLi et al.PatternsApril 10, 2020In Brief“Smart” devices must “see” and “recognize” objects and gestures in their surroundings as quickly as possible. We consider a contactless sensor that illuminates its surroundings with microwave illumination shaped by a programmable metasurface. By integrating the measurement process directly into the machine-learning pipeline that processes the data, we learn optimal illumination patterns that efficiently extract task-relevant information. Our experimental demonstration of low-latency intelligent electromagnetic sensing will influence human-computer interaction, health care, automotive radar, and security screening. Full-Text PDF Open AccessWhat Have Games Got to Do with Me?McCutcheon et al.PatternsMarch 19, 2020In BriefIs the proliferation of work-based games just a distraction, or can they actually help us to acquire work-specific knowledge? This Opinion explains why we can see the benefits of such games, despite initial skepticism. Players learn from listening to and observing others, and some people even enjoy the games. Full-Text PDF Open AccessIdentifying Data Sharing and Reuse with Scholix: Potentials and LimitationsKhan et al.PatternsApril 10, 2020In BriefIdentifying links between articles and supporting data is vital for demonstrating reuse and impact of published data. Scholix creates these links, and we find that the Scholexplorer API can locate more article-dataset links than was previously possible in practice. Our study finds evidence of data reuse, but we suggest that further enhancement of the Scholix schema and enrichment of Scholexplorer metadata through controlled vocabulary and inclusion of persistent identifiers would recover more cases of secondary data use. Full-Text PDF Open AccessRejewski & EnigmaDavid InmanPatternsApril 10, 2020In BriefAlan Turing and Bletchley Park are rightly recognized for their work on breaking the Enigma code. However, this was built on a foundation of work during the 1930s by the Polish cryptographer, Marian Rejewski. Often working alone, and with limited resources, he found ways to break early Enigma code. This article attempts to highlight the man and his invaluable contribution. Full-Text PDF Open AccessAdministrative Data Research UKEmma GordonPatternsApril 10, 2020In BriefADR UK is helping to transform the way researchers access the UK’s wealth of administrative data, enabling government policy to be informed by the best evidence available. Emma shares her insights into the ADR UK approach to making this happen, explaining why building trust is central to the ADR UK mission. Full-Text PDF Open AccessBuilding a Traceable and Sustainable Historical Climate Database: Interdisciplinarity and DRAWSlonosky et al.PatternsApril 10, 2020In BriefTurning historical meteorological observations into usable data is a challenging process that is immeasurably enriched when it encompasses interdisciplinarity. Here, the McGill DRAW (Data Rescue: Archives and Weather) project shows how climatologists, geographers, archivists, data scientists, and coders together built a citizen-science-based transcription platform to transform the McGill Observatory paper records into a traceable and sustainable database. Full-Text PDF Open AccessA Primer on Biodefense Data Science for Pandemic PreparednessEric PerakslisPatternsMarch 26, 2020In BriefThe coronavirus outbreak is sweeping the globe with outbreaks reported on every continent except Antarctica as of March 2020. Data scientists are uniquely and diversely skilled in ways that can be highly effective in minimizing, combatting, and recovering from the impacts of the COVID-19 outbreak. In this Opinion, the basics of biodefense as well as specific opportunities for the data science community to contribute are discussed. Full-Text PDF Open AccessWomen, AI, and the Power of Supporting Communities: A Digital Gender-Support PartnershipCerit et al.PatternsApril 10, 2020In BriefWith the rapid development of the fields of data science and artificial intelligence, a dichotomy presents itself: more professionals are needed to fulfill the growing workfoce demand, and women continue to be underrepresented in all computer science-related jobs. Women AI Academy addresses both issues by inspiring, enabling, and targeting the employment of women in data science and artificial intelligence. Full-Text PDF Open AccessMetadata and Reuse: Antidotes to Information EntropyTed HabermannPatternsApril 10, 2020In BriefAre data from your published work suffering from information entropy since they were published? Are they experiencing a slow decrease in impact because other researchers cannot understand or trust them? High-quality metadata, persistent identifiers, and active repository partnerships might help revive them and ensure their role in building community understanding and wisdom. Full-Text PDF Open AccessWho Should Do Data Ethics?Caitlin D. WyliePatternsApril 10, 2020In BriefWho decides what good data science looks like? And who gets to decide what “data ethics” means? The answer is all of us. Good data science should incorporate the perspectives of people who create and work with data, people who study the interactions between science and society, and people whose lives are affected by data science. Full-Text PDF Open AccessSentiment Analysis of Conservation Studies Captures Successes of Species ReintroductionsVan Houtan et al.PatternsMarch 19, 2020In BriefScience and technology are increasingly integrated into our everyday lives. A key aspect of science is that the community learns through verified, published findings. Online archives and publications have vastly increased the volume of published science, affording greater access to research results while also presenting new challenges. This study uses established methods in artificial intelligence to assess whether reading scientific papers can be automated. The results are promising, although technical disciplines with specific vocabulary will require special considerations. Full-Text PDF Open Access

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