Paper by André Grow et al: “Social scientists increasingly use Facebook’s advertising platform for research, either in the form of conducting digital censuses of the general population, or for recruiting participants for survey research. Both approaches depend on the accuracy of the data that Facebook provides about its users, but little is known about how accurate these data are. We address this gap in a large-scale, cross-national online survey (N = 137,224), in which we compare self-reported and Facebook-classified demographic information (sex, age and region of residence). Our results suggest that Facebook’s advertising platform can be fruitfully used for conducing social science research if additional steps are taken to assess the accuracy of the characteristics under consideration…(More)”.
Humanizing Science and Engineering for the Twenty-First Century
Essay by Kaye Husbands Fealing, Aubrey Deveny Incorvaia and Richard Utz: “Solving complex problems is never a purely technical or scientific matter. When science or technology advances, insights and innovations must be carefully communicated to policymakers and the public. Moreover, scientists, engineers, and technologists must draw on subject matter expertise in other domains to understand the full magnitude of the problems they seek to solve. And interdisciplinary awareness is essential to ensure that taxpayer-funded policy and research are efficient and equitable and are accountable to citizens at large—including members of traditionally marginalized communities…(More)”.
Science and the World Cup: how big data is transforming football
Essay by David Adam: “The scowl on Cristiano Ronaldo’s face made international headlines last month when the Portuguese superstar was pulled from a match between Manchester United and Newcastle with 18 minutes left to play. But he’s not alone in his sentiment. Few footballers agree with a manager’s decision to substitute them in favour of a fresh replacement.
During the upcoming football World Cup tournament in Qatar, players will have a more evidence-based way to argue for time on the pitch. Within minutes of the final whistle, tournament organizers will send each player a detailed breakdown of their performance. Strikers will be able to show how often they made a run and were ignored. Defenders will have data on how much they hassled and harried the opposing team when it had possession.
It’s the latest incursion of numbers into the beautiful game. Data analysis now helps to steer everything from player transfers and the intensity of training, to targeting opponents and recommending the best direction to kick the ball at any point on the pitch.
Meanwhile, footballers face the kind of data scrutiny more often associated with an astronaut. Wearable vests and straps can now sense motion, track position with GPS and count the number of shots taken with each foot. Cameras at multiple angles capture everything from headers won to how long players keep the ball. And to make sense of this information, most elite football teams now employ data analysts, including mathematicians, data scientists and physicists plucked from top companies and labs such as computing giant Microsoft and CERN, Europe’s particle-physics laboratory near Geneva, Switzerland….(More)”.
The network science of collective intelligence
Article by Damon Centola: “In the last few years, breakthroughs in computational and experimental techniques have produced several key discoveries in the science of networks and human collective intelligence. This review presents the latest scientific findings from two key fields of research: collective problem-solving and the wisdom of the crowd. I demonstrate the core theoretical tensions separating these research traditions and show how recent findings offer a new synthesis for understanding how network dynamics alter collective intelligence, both positively and negatively. I conclude by highlighting current theoretical problems at the forefront of research on networked collective intelligence, as well as vital public policy challenges that require new research efforts…(More)”.
Meaningful public engagement in the context of open science: reflections from early and mid-career academics
Paper by Wouter Boon et al: “How is public engagement perceived to contribute to open science? This commentary highlights common reflections on this question from interviews with 12 public engagement fellows in Utrecht University’s Open Science Programme in the Netherlands. We identify four reasons why public engagement is an essential enabler of open science. Interaction between academics and society can: (1) better align science with the needs of society; (2) secure a relationship of trust between science and society; (3) increase the quality and impact of science; and (4) support the impact of open access and FAIR data practices (data which meet principles of findability, accessibility, interoperability and reusability). To be successful and sustainable, such public engagement requires support in skills training and a form of institutionalisation in a university-wide system, but, most of all, the fellows express the importance of a formal and informal recognition and rewards system. Our findings suggest that in order to make public engagement an integral part of open science, universities should invest in institutional support, create awareness, and stimulate dialogue among staff members on how to ‘do’ good public engagement….(More)”.
Data Structures the Fun Way
Book by Jeremy Kubica: “This accessible and entertaining book provides an in-depth introduction to computational thinking through the lens of data structures — a critical component in any programming endeavor. Through diagrams, pseudocode, and humorous analogies, you’ll learn how the structure of data drives algorithmic operations, gaining insight into not just how to build data structures, but precisely how and when to use them.
This book will give you a strong background in implementing and working with more than 15 key data structures, from stacks, queues, and caches to bloom filters, skip lists, and graphs. Master linked lists by standing in line at a cafe, hash tables by cataloging the history of the summer Olympics, and Quadtrees by neatly organizing your kitchen cabinets. Along with basic computer science concepts like recursion and iteration, you’ll learn:
- The complexity and power of pointers
- The branching logic of tree-based data structures
- How different data structures insert and delete data in memory
- Why mathematical mappings and randomization are useful
- How to make tradeoffs between speed, flexibility, and memory usage
Data Structures the Fun Way shows how to efficiently apply these ideas to real-world problems—a surprising number of which focus on procuring a decent cup of coffee. At any level, fully understanding data structures will teach you core skills that apply across multiple programming languages, taking your career to the next level….(More)”.
Everything dies, including information
Article by Erik Sherman: “Everything dies: people, machines, civilizations. Perhaps we can find some solace in knowing that all the meaningful things we’ve learned along the way will survive. But even knowledge has a life span. Documents fade. Art goes missing. Entire libraries and collections can face quick and unexpected destruction.
Surely, we’re at a stage technologically where we might devise ways to make knowledge available and accessible forever. After all, the density of data storage is already incomprehensibly high. In the ever-growing museum of the internet, one can move smoothly from images from the James Webb Space Telescope through diagrams explaining Pythagoras’s philosophy on the music of the spheres to a YouTube tutorial on blues guitar soloing. What more could you want?
Quite a bit, according to the experts. For one thing, what we think is permanent isn’t. Digital storage systems can become unreadable in as little as three to five years. Librarians and archivists race to copy things over to newer formats. But entropy is always there, waiting in the wings. “Our professions and our people often try to extend the normal life span as far as possible through a variety of techniques, but it’s still holding back the tide,” says Joseph Janes, an associate professor at the University of Washington Information School.
To complicate matters, archivists are now grappling with an unprecedented deluge of information. In the past, materials were scarce and storage space limited. “Now we have the opposite problem,” Janes says. “Everything is being recorded all the time.”…(More)”.
Observing Many Researchers Using the Same Data and Hypothesis Reveals a Hidden Universe of Uncertainty
Paper by Nate Breznau et al: “This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to include conscious and unconscious decisions that researchers make during data analysis and that may lead to diverging results. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of research based on secondary data, we find that research teams reported widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predicted the wide variation in research outcomes. More than 90% of the total variance in numerical results remained unexplained even after accounting for research decisions identified via qualitative coding of each team’s workflow. This reveals a universe of uncertainty that is hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a new explanation for why many scientific hypotheses remain contested. It calls for greater humility and clarity in reporting scientific findings..(More)”.
Public Access to Advance Equity
Essay by Alondra Nelson, Christopher Marcum and Jedidah Isler: “Open science began in the scientific community as a movement committed to making all aspects of research freely available to all members of society.
As a member of the Organisation for Economic Co-operation and Development (OECD), the United States is committed to promoting open science, which the OECD defines as “unhindered access to scientific articles, access to data from public research, and collaborative research enabled by information and communication technology tools and incentives.”
At the White House Office of Science and Technology Policy (OSTP), we have been inspired by the movement to push for openness in research by community activists, researchers, publishers, higher-education leaders, policymakers, patient advocates, scholarly associations, librarians, open-government proponents, philanthropic organizations, and the public.
Open science is an essential part of the Biden-Harris administration’s broader commitment to providing public access to data, publications, and the other important products of the nation’s taxpayer-supported research and innovation enterprise. We look to the lessons, methods, and products of open science to deliver on this commitment to policy that advances equity, accelerates discovery and innovation, provides opportunities for all to participate in research, promotes public trust, and is evidence-based. Here, we detail some of the ways OSTP is working to expand the American public’s access to the federal research and development ecosystem, and to ensure it is open, equitable, and secure…(More)”.
Wicked Problems Might Inspire Greater Data Sharing
Paper by Susan Ariel Aaronson: “In 2021, the United Nations Development Program issued a plea in their 2021 Digital Economy Report. “ Global data-sharing can help address major global development challenges such as poverty, health, hunger and climate change. …Without global cooperation on data and information, research to develop the vaccine and actions to tackle the impact of the pandemic would have been a much more difficult task. Thus, in the same way as some data can be public goods, there is a case for some data to be considered as global public goods, which need to be addressed and provided through global governance.” (UNDP: 2021, 178). Global public goods are goods and services with benefits and costs that potentially extend to all countries, people, and generations. Global data sharing can also help solve what scholars call wicked problems—problems so complex that they require innovative, cost effective and global mitigating strategies. Wicked problems are problems that no one knows how to solve without
creating further problems. Hence, policymakers must find ways to encourage greater data sharing among entities that hold large troves of various types of data, while protecting that data from theft, manipulation etc. Many factors impede global data sharing for public good purposes; this analysis focuses on two.
First, policymakers generally don’t think about data as a global public good; they view data as a commercial asset that they should nurture and control. While they may understand that data can serve the public interest, they are more concerned with using data to serve their country’s economic interest. Secondly, many leaders of civil society and business see the data they have collected as proprietary data. So far many leaders of private entities with troves of data are not convinced that their organization will benefit from such sharing. At the same time, companies voluntarily share some data for social good purposes.
However, data cannot meet its public good purpose if data is not shared among societal entities. Moreover, if data as a sovereign asset, policymakers are unlikely to encourage data sharing across borders oriented towards addressing shared problems. Consequently, society will be less able to use data as both a commercial asset and as a resource to enhance human welfare. As the Bennet Institute and ODI have argued, “value comes from data being brought together, and that requires organizations to let others use the data they hold.” But that also means the entities that collected the data may not accrue all of the benefits from that data (Bennett Institute and ODI: 2020a: 4). In short, private entities are not sufficiently incentivized to share data in the global public good…(More)”.