From binoculars to big data: Citizen scientists use emerging technology in the wild

Curated on Posted on by Stefaan Verhulst

Interview by Rebecca Kondos: “For years, citizen scientists have trekked through local fields, rivers, and forests to observe, measure, and report on species and habitats with notebooks, binoculars, butterfly nets, and cameras in hand. It’s a slow process, and the gathered data isn’t easily shared. It’s a system that has worked to some degree, but one that’s in need of a technology and methodology overhaul.

Thanks to the team behind Wildme.org and their Wildbook software, both citizen and professional scientists are becoming active participants in using AI, computer vision, and big data. Wildbook is working to transform the data collection process, and citizen scientists who use the software have more transparency into conservation research and the impact it’s making. As a result, engagement levels have increased; scientists can more easily share their work; and, most important, endangered species like the whale shark benefit.

In this interview, Colin Kingen, a software engineer for WildBook, (with assistance from his colleagues Jason Holmberg and Jon Van Oast) discusses Wildbook’s work, explains classic problems in field observation science, and shares how Wildbook is working to solve some of the big problems that have plagued wildlife research. He also addresses something I’ve wondered about: why isn’t there an “uberdatabase” to share the work of scientists across all global efforts? The work Kingen and his team are doing exemplifies what can be accomplished when computer scientists with big hearts apply their talents to saving wildlife….(More)”.

Some Countries Like ‘Nudges’ More Than Others

Curated on Posted on by Stefaan Verhulst

Cass Sunstein at Bloomberg: “All over the world, private and public institutions have been adopting “nudges” — interventions that preserve freedom of choice, but that steer people in a particular direction.

A GPS device nudges you. So does a reminder from your doctor, informing you that you have an appointment next Wednesday; an automatic enrollment policy from your employer, defaulting you into a 401(k) plan; and a calorie label at fast-food restaurants, telling you that a cheeseburger won’t be great for your waistline.

Recent evidence demonstrates that nudges can be amazingly effective — far more so, per dollar spent, than other tools, such as economic incentives. But a big question remains: Across different nations, do nudges have the same impact? Here’s a cautionary note.

One of the most famous success stories in the annals of nudging comes from the U.K. To encourage delinquent taxpayers to pay up, British officials simply informed them, by letter, that the overwhelming majority of British taxpayers pay their taxes on time.

It worked. Within just a few weeks, the letters produced millions of dollars in additional revenue. Consistent with standard findings in behavioral science, recipients of the letters didn’t like learning that they were deviating from the social norm. Like most of us, they didn’t want to be creeps or shirkers, and so they paid up.

For other nations, including the U.S., that was an intriguing finding. So our Department of Treasury tried the same approach. It sent letters to delinquent taxpayers, informing them (accurately) that 91 percent of American taxpayers pay on time. On the basis of the British data, the expectation was that a lot of people would be ashamed, and pay their taxes.

Except they didn’t. The U.S. Treasury didn’t get any more money.

How come? It’s reasonable to speculate that in the U.S., delinquent taxpayers just don’t care about the social norm. If they learn about it, they still aren’t motivated to pay.

This finding demonstrates that different groups can react very differently to nudges. It’s well known that when people are told that they are using more energy than their neighbors, they scale back — so that information is an effective nudge. But a study in California suggests that things are a bit more complicated….

In general, we don’t yet have a lot of evidence of international differences on the impact of nudges. But it would be surprising if such evidence doesn’t start to accumulate. Wherever people begin with strong preferences, and don’t like the direction in which they are being nudged, nudges are going to have a weaker effect.

For many nudges, that’s just fine. Actually, it’s part of the point….(More)”.

Open data on universities – New fuel for transformation

Curated on Posted on by Stefaan Verhulst

François van Schalkwyk at University World News: “Accessible, usable and relevant open data on South African universities makes it possible for a wide range of stakeholders to monitor, advise and challenge the transformation of South Africa’s universities from an informed perspective.

Some describe data as the new oil while others suggest it is a new form of capital or compare it to electricity. Either way, there appears to be a groundswell of interest in the potential of data to fuel development.

Whether the proliferation of data is skewing development in favour of globally networked elites or disrupting existing asymmetries of information and power, is the subject of ongoing debate. Certainly, there are those who will claim that open data, from a development perspective, could catalyse disruption and redistribution.

Open data is data that is free to use without restriction. Governments and their agencies, universities and their researchers, non-governmental organisations and their donors, and even corporations, are all potential sources of open data.

Open government data, as a public rather than a private resource, embedded in principles of universal access, participation and transparency, is touted as being able to restore the deteriorating levels of trust between citizens and their governments.

Open data promises to do so by making the decisions and processes of the state more transparent and inclusive, empowering citizens to participate and to hold public institutions to account for the distribution of public services and resources.

Benefits of open data

Open data has other benefits over its more cloistered cousins (data in private networks, big data, etc). By democratising access, open data makes possible the use of data on, for example, health services, crime, the environment, procurement and education by a range of different users, each bringing their own perspective to bear on the data. This can expose bias in the data or may improve the quality of the data by surfacing data errors. Both are important when data is used to shape government policies.

By removing barriers to reusing data such as copyright or licence-fees, tech-savvy entrepreneurs can develop applications to assist the public to make more informed decisions by making available easy-to-understand information on medicine prices, crime hot-spots, air quality, beneficial ownership, school performance, etc. And access to open research data can improve quality and efficiency in science.

Scientists can check and confirm the data on which important discoveries are based if the data is open, and, in some cases, researchers can reuse open data from other studies, saving them the cost and effort of collecting the data themselves.

‘Open washing’

But access alone is not enough for open data to realise its potential. Open data must also be used. And data is used if it holds some value for the user. Governments have been known to publish server rooms full of data that no one is interested in to support claims of transparency and supporting the knowledge economy. That practice is called ‘open washing’. …(More)”

Formalised data citation practices would encourage more authors to make their data available for reuse

Curated on Posted on by Stefaan Verhulst

 Hyoungjoo Park and Dietmar Wolfram at the LSE Impact Blog: “Today’s researchers work in a heavily data-intensive and collaborative environment in order to further scientific discovery across and within fields. It is becoming routine for researchers (i.e. authors and data publishers) to submit their research data, such as datasets, biological samples in biomedical fields, and computer code, as supplementary information in order to comply with data sharing requirements of major funding agencies, high-profile journals, and data journals. This is part of open science, where data and any publication products are expected to be made available to anyone interested.

Given that researchers benefit from publicly shared data through data reuse in their own research, researchers who provide access to data should be acknowledged for their contributions, much in the same way that authors are recognised for their research publications through citation. Researchers who use shared data or other shared research products (e.g. open access software, tissue cultures) should also acknowledge the providers of these resources through formal citation. At present, data citation is not widely practised in most disciplines and as an object of study remains largely overlooked….

We found that data citations appear in the references section of an article less frequently than in the main text, making it difficult to identify the reward and credit for data authors (i.e. data sharers). Consistent data citation formats could not be found. Current data citation practices do not (yet) benefit data sharers. Also, data citation was sometimes located in the supplementary information, outside of the references. Data that had been reused was often not acknowledged in the reference lists, but was rather hidden in the representation of data (e.g. tables, figures, images, graphs, and other elements), which may be a consequence of the fact that data citation practices are not yet common in scholarly communications.

Ongoing challenges remain in identifying and documenting data citation. First, the practice of informal data citation presents a challenge for accurately documenting data citation. …

Second, data recitation by one or more co-authors of earlier studies (i.e. self-citation) is common, which reduces the broader impact of data sharing by limiting much of the reuse to the original authors..

Third, currently indexed data citations may not include rapidly advancing areas, such as in the hard sciences or computer engineering, because approximately 90% of indexed works were associated with journal articles…

Fourth, the number of authors associated with shared datasets raises questions of the ownership of and responsibility for a collective work, although some journals require one author to be responsible for the data used in the study…(More). (See also An examination of research data sharing and re-use: implications for data citation practice, published in Scientometrics)

Open Governance as a Service

Curated on Posted on by Stefaan Verhulst

Andrei Sambra and Lalana Kagal for the 2017 ACM on Web Science Conference: “This extended abstract discusses how public services can become more open and engage citizens more actively, by providing the local, public administration with the right tools. It calls for public services to think more creatively about how they can collaborate with the public to make better use of the energy and enthusiasm, as well as missing skills that people have and want to offer. It explores the challenges, both in terms of policy and technology, that public services face in mobilizing resources that are by nature voluntary. We intend to provide the governance tools that enable public services to leverage skills coming from the local community, and improve their autonomy, transparency and analytical tools required for true open governance….(More)”.

Civic Tech for Urban Collaborative Governance

Curated on Posted on by Stefaan Verhulst

Hollie Russon-Gilman in PS: Political Science & Politics: “This article aims to contribute to a burgeoning field of ‘civic technology’ to identify precise pathways through which multi-stakeholder partnerships can foster, embed, and encourage more collaborative governance, outlining a research agenda to guide next steps. Instead of looking at technology as a civic panacea or, at the other extreme, as an irrelevant force, this article takes seriously both the democratic potential and the political constraints of the use of technology for more collaborative governance. The article begins by delineating contours of a civic definition of technology focused on generating public good, provides case study examples of civic tech deployed in America’s cities, raises research questions to inform future multi-stakeholder partnerships, and concludes with implications for the public sector workforce and ecosystem.”…(More)”.

AI, people, and society

Curated on Posted on by Stefaan Verhulst

Eric Horvitz at Science: “In an essay about his science fiction, Isaac Asimov reflected that “it became very common…to picture robots as dangerous devices that invariably destroyed their creators.” He rejected this view and formulated the “laws of robotics,” aimed at ensuring the safety and benevolence of robotic systems. Asimov’s stories about the relationship between people and robots were only a few years old when the phrase “artificial intelligence” (AI) was used for the first time in a 1955 proposal for a study on using computers to “…solve kinds of problems now reserved for humans.” Over the half-century since that study, AI has matured into subdisciplines that have yielded a constellation of methods that enable perception, learning, reasoning, and natural language understanding.

Growing exuberance about AI has come in the wake of surprising jumps in the accuracy of machine pattern recognition using methods referred to as “deep learning.” The advances have put new capabilities in the hands of consumers, including speech-to-speech translation and semi-autonomous driving. Yet, many hard challenges persist—and AI scientists remain mystified by numerous capabilities of human intellect.

Excitement about AI has been tempered by concerns about potential downsides. Some fear the rise of superintelligences and the loss of control of AI systems, echoing themes from age-old stories. Others have focused on nearer-term issues, highlighting potential adverse outcomes. For example, data-fueled classifiers used to guide high-stakes decisions in health care and criminal justice may be influenced by biases buried deep in data sets, leading to unfair and inaccurate inferences. Other imminent concerns include legal and ethical issues regarding decisions made by autonomous systems, difficulties with explaining inferences, threats to civil liberties through new forms of surveillance, precision manipulation aimed at persuasion, criminal uses of AI, destabilizing influences in military applications, and the potential to displace workers from jobs and to amplify inequities in wealth.

As we push AI science forward, it will be critical to address the influences of AI on people and society, on short- and long-term scales. Valuable assessments and guidance can be developed through focused studies, monitoring, and analysis. The broad reach of AI’s influences requires engagement with interdisciplinary groups, including computer scientists, social scientists, psychologists, economists, and lawyers. On longer-term issues, conversations are needed to bridge differences of opinion about the possibilities of superintelligence and malevolent AI. Promising directions include working to specify trajectories and outcomes, and engaging computer scientists and engineers with expertise in software verification, security, and principles of failsafe design….Asimov concludes in his essay, “I could not bring myself to believe that if knowledge presented danger, the solution was ignorance. To me, it always seemed that the solution had to be wisdom. You did not refuse to look at danger, rather you learned how to handle it safely.” Indeed, the path forward for AI should be guided by intellectual curiosity, care, and collaboration….(More)”

NIH-funded team uses smartphone data in global study of physical activity

Curated on Posted on by Stefaan Verhulst

National Institutes of Health: “Using a larger dataset than for any previous human movement study, National Institutes of Health-funded researchers at Stanford University in Palo Alto, California, have tracked physical activity by population for more than 100 countries. Their research follows on a recent estimate that more than 5 million people die each year from causes associated with inactivity.

The large-scale study of daily step data from anonymous smartphone users dials in on how countries, genders, and community types fare in terms of physical activity and what results may mean for intervention efforts around physical activity and obesity. The study was published July 10, 2017, in the advance online edition of Nature.

“Big data is not just about big numbers, but also the patterns that can explain important health trends,” said Grace Peng, Ph.D., director of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) program in Computational Modeling, Simulation and Analysis.

“Data science and modeling can be immensely powerful tools. They can aid in harnessing and analyzing all the personalized data that we get from our phones and wearable devices.”

Almost three quarters of adults in developed countries and half of adults in developing economies carry a smartphone. The devices are equipped with tiny accelerometers, computer chip that maintains the orientation of the screen, and can also automatically record stepping motions. The users whose data contributed to this study subscribed to the Azumio Argus app, a free application for tracking physical activity and other health behaviors….

In addition to the step records, the researchers accessed age, gender, and height and weight status of users who registered the smartphone app. They used the same calculation that economists use for income inequality — called the Gini index — to calculate activity inequality by country.

“These results reveal how much of a population is activity-rich, and how much of a population is activity-poor,” Delp said. “In regions with high activity inequality there are many people who are activity poor, and activity inequality is a strong predictor of health outcomes.”…

The researchers investigated the idea that making improvements in a city’s walkability — creating an environment that is safe and enjoyable to walk — could reduce activity inequality and the activity gender gap.

“If you must cross major highways to get from point A to point B in a city, the walkability is low; people rely on cars,” Delp said. “In cities like New York and San Francisco, where you can get across town on foot safely, the city has high walkability.”

Data from 69 U.S. cities showed that higher walkability scores are associated with lower activity inequality. Higher walkability is associated with significantly more daily steps across all age, gender, and body-mass-index categories.  However, the researchers found that women recorded comparatively less activity than men in places that are less walkable.

The study exemplifies how smartphones can deliver new insights about key health behaviors, including what the authors categorize as the global pandemic of physical inactivity….(More)”.

Data and the City

Curated on Posted on by Stefaan Verhulst

Book edited by Rob Kitchin, Tracey P. Lauriault, and Gavin McArdle: “There is a long history of governments, businesses, science and citizens producing and utilizing data in order to monitor, regulate, profit from and make sense of the urban world. Recently, we have entered the age of big data, and now many aspects of everyday urban life are being captured as data and city management is mediated through data-driven technologies.

Data and the City is the first edited collection to provide an interdisciplinary analysis of how this new era of urban big data is reshaping how we come to know and govern cities, and the implications of such a transformation. This book looks at the creation of real-time cities and data-driven urbanism and considers the relationships at play. By taking a philosophical, political, practical and technical approach to urban data, the authors analyse the ways in which data is produced and framed within socio-technical systems. They then examine the constellation of existing and emerging urban data technologies. The volume concludes by considering the social and political ramifications of data-driven urbanism, questioning whom it serves and for what ends.

This book, the companion volume to 2016’s Code and the City, offers the first critical reflection on the relationship between data, data practices and the city, and how we come to know and understand cities through data. It will be crucial reading for those who wish to understand and conceptualize urban big data, data-driven urbanism and the development of smart cities….(More)”

Carnegie Mellon scientists use app to track foul odors in Pittsburgh

Curated on Posted on by Stefaan Verhulst

Ashley Murray at Pittsburgh Post-Gazette: “If you smell something, say something. Scientists at Carnegie Mellon University want Pittsburghers to put their collective noses to the task and report foul smells using a mobile reporting application called Smell PGH.

Since the app launched last year, more than 1,300 users have reported foul smells more than 4,300 times — most of which they’ve described as “industrial,” “sulfur” or “woodsmoke.”

The app was developed at CMU’s Community Robotics, Education and Technology Empowerment (CREATE) Lab.

“The app is really about the community,” said Beatrice Dias, project director at the CREATE Lab. “To show that you’re not alone in your negative experiences of pollution impact.”

Smartphone users can create a “smell report” within the app, which has the capability to alert the Allegheny County Health Department.

Health department spokeswoman Melissa Wade said the agency has received and followed-up on 3,000 reports generated from the app.

Users can also view a real-time map of all smell reports in and around the city. A new feature added last month allows users to go back in time and play a time-lapse animation of little colored triangles — green, yellow and red, symbolizing varying degrees of smell — that pop up and disappear as odors were reported….

“The goal is I’m trying to predict the smell in the next few hours, like a weather forecast,” Mr. Hsu said. “Let’s say today from 12 to 1 p.m. we have 10 smell reports. I can check not only the smell reports, but the data from other sensor stations around Pittsburgh, so I know during this hour what the reading is of all the air-quality related variables, like PM 2.5, like sulfur and nitrogen oxides, [and] the wind speed, the wind direction. There are a lot of parameters we need to consider.”…

Another goal of this citizen science initiative, Mr. Hsu said, is to improve communication between the public and governmental regulation agencies, like the health department.

“Before this technology if you smelled something bad, you might not be sure if this came from ambient air, your neighborhood or just traffic issues,” Mr. Hsu said. “But if you use the app, you can see a lot of your neighbors are reporting, too. And then maybe the government can use this to see the problems in a city.”…(More)”.