The Climatologist’s Almanac

Curated on Posted on by Stefaan Verhulst

Clara Chaisson at onEarth: “Forget your weather app with its five- or even ten-day forecasts—a supercomputer at NASA has just provided us with high-resolution climate projections through the end of the century. The massive new 11-terabyte data set combines historical daily temperatures and precipitation measurements with climate simulations under two greenhouse gas emissions scenarios. The project spans from 1950 to 2100, but users can easily zero in on daily timescales for their own locales—which is precisely the point.

The projections can be found on Amazon for free for all to see and plan by. The space agency hopes that developing nations and poorer communities that may not have any spare supercomputers lying around will use the info to predict and prepare for climate change. …(More)”

Field experimenting in economics: Lessons learned for public policy

Curated on Posted on by Stefaan Verhulst

Robert Metcalfe at OUP Blog: “Do neighbourhoods matter to outcomes? Which classroom interventions improve educational attainment? How should we raise money to provide important and valued public goods? Do energy prices affect energy demand? How can we motivate people to become healthier, greener, and more cooperative? These are some of the most challenging questions policy-makers face. Academics have been trying to understand and uncover these important relationships for decades.

Many of the empirical tools available to economists to answer these questions do not allow causal relationships to be detected. Field experiments represent a relatively new methodological approach capable of measuring the causal links between variables. By overlaying carefully designed experimental treatments on real people performing tasks common to their daily lives, economists are able to answer interesting and policy-relevant questions that were previously intractable. Manipulation of market environments allows these economists to uncover the hidden motivations behind economic behaviour more generally. A central tenet of field experiments in the policy world is that governments should understand the actual behavioural responses of their citizens to changes in policies or interventions.

Field experiments represent a departure from laboratory experiments. Traditionally, laboratory experiments create experimental settings with tight control over the decision environment of undergraduate students. While these studies also allow researchers to make causal statements, policy-makers are often concerned subjects in these experiments may behave differently in settings where they know they are being observed or when they are permitted to sort out of the market.

For example, you might expect a college student to contribute more to charity when she is scrutinized in a professor’s lab than when she can avoid the ask altogether. Field experiments allow researchers to make these causal statements in a setting that is more generalizable to the behaviour policy-makers are directly interested in.

To date, policy-makers traditionally gather relevant information and data by using focus groups, qualitative evidence, or observational data without a way to identify causal mechanisms. It is quite easy to elicit people’s intentions about how they behave with respect to a new policy or intervention, but there is increasing evidence that people’s intentions are a poor guide to predicting their behaviour.

However, we are starting to see a small change in how governments seek to answer pertinent questions. For instance, the UK tax office (Her Majesty’s Revenue and Customs) now uses field experiments across some of its services to improve the efficacy of scarce taxpayers money. In the US, there are movements toward gathering more evidence from field experiments.

In the corporate world, experimenting is not new. Many of the current large online companies—such as Amazon, Facebook, Google, and Microsoft—are constantly using field experiments matched with big data to improve their products and deliver better services to their customers. More and more companies will use field experiments over time to help them better set prices, tailor advertising, provide a better customer journey to increase welfare, and employ more productive workers…(More).

See also Field Experiments in the Developed World: An Introduction (Oxford Review of Economic Policy)

Signal: Understanding What Matters in a World of Noise,

Curated on Posted on by Stefaan Verhulst

Book by Stephen Few: “In this age of so-called Big Data, organizations are scrambling to implement new software and hardware to increase the amount of data they collect and store. However, in doing so they are unwittingly making it harder to find the needles of useful information in the rapidly growing mounds of hay. If you don’t know how to differentiate signals from noise, adding more noise only makes things worse. When we rely on data for making decisions, how do we tell what qualifies as a signal and what is merely noise? In and of itself, data is neither. Assuming that data is accurate, it is merely a collection of facts. When a fact is true and useful, only then is it a signal. When it’s not, it’s noise. It’s that simple. In Signal, Stephen Few provides the straightforward, practical instruction in everyday signal detection that has been lacking until now. Using data visualization methods, he teaches how to apply statistics to gain a comprehensive understanding of one’s data and adapts the techniques of Statistical Process Control in new ways to detect not just changes in the metrics but also changes in the patterns that characterize data…(More)”

5 cool ways connected data is being used

Curated on Posted on by Stefaan Verhulst

 at Wareable: “The real news behind the rise of wearable tech isn’t so much the gadgetry as the gigantic amount of personal data that it harnesses.

Concerns have already been raised over what companies may choose to do with such valuable information, with one US life insurance company already using Fitbits to track customers’ exercise and offer them discounts when they hit their activity goals.

Despite a mildly worrying potential dystopia in which our own data could be used against us, there are plenty of positive ways in which companies are using vast amounts of connected data to make the world a better place…

Parkinson’s disease research

Apple Health ResearchKit was recently unveiled as a platform for collecting collaborative data for medical studies, but Apple isn’t the first company to rely on crowdsourced data for medical research.

The Michael J. Fox Foundation for Parkinson’s Research recently unveiled a partnership with Intel to improve research and treatment for the neurodegenerative brain disease. Wearables are being used to unobtrusively gather real-time data from sufferers, which is then analysed by medical experts….

Saving the rhino

Connected data and wearable tech isn’t just limited to humans. In South Africa, the Madikwe Conservation Project is using wearable-based data to protect endangered rhinos from callous poachers.

A combination of ultra-strong Kevlar ankle collars powered by an Intel Galileo chip, along with an RFID chip implanted in each rhino’s horn allows the animals to be monitored. Any break in proximity between the anklet and horn results in anti-poaching teams being deployed to catch the bad guys….

Making public transport smart

A company called Snips is collecting huge amounts of urban data in order to improve infrastructure. In partnership with French national rail operator SNCF, Snips produced an app called Tranquilien to utilise location data from commuters’ phones and smartwatches to track which parts of the rail network were busy at which times.

Combining big data with crowdsourcing, the information helps passengers to pick a train where they can find a seat during peak times, while the data can also be useful to local businesses when serving the needs of commuters who are passing through.

Improving the sports fan experience

We’ve already written about how wearable tech is changing the NFL, but the collection of personal data is also set to benefit the fans.

Levi’s Stadium – the new home of the San Francisco 49ers – opened in 2014 and is one of the most technically advanced sports venues in the world. As well as a strong Wi-Fi signal throughout the stadium, fans also benefit from a dedicated app. This not only offers instant replays and real-time game information, but it also helps them find a parking space, order food and drinks directly to their seat and even check the lines at the toilets. As fans use the app, all of the data is collated to enhance the fan experience in future….

Creating interactive art

Don’t be put off by the words ‘interactive installation’. On Broadway is a cool work of art that “represents life in the 21st Century city through a compilation of images and data collected along the 13 miles of Broadway that span Manhattan”….(More)”

Tracking Employment Shocks Using Mobile Phone Data

Curated on Posted on by Stefaan Verhulst

Paper by Jameson L. Toole et al.: “Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them, and predict changes in aggregate unemployment rates using call detail records (CDRs) from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe a structural break model to correctly detect the date of a mass layoff and estimate its size. We then use a Bayesian classification model to identify affected individuals by observing changes in calling behavior following the plant’s closure. For these affected individuals, we observe significant declines in social behavior and mobility following job loss. Using the features identified at the micro level, we show that the same changes in these calling behaviors, aggregated at the regional level, can improve forecasts of macro unemployment rates. These methods and results highlight promise of new data resources to measure micro economic behavior and improve estimates of critical economic indicators….(More)”

Navigating the Health Data Ecosystem

Curated on Posted on by Stefaan Verhulst

New book on O’Reilly Media on “The “Six C’s”: Understanding the Health Data Terrain in the Era of Precision Medicine”: “Data-driven technologies are now being adopted, developed, funded, and deployed throughout the health care market at an unprecedented scale. But, as this O’Reilly report reveals, health care innovation contains more hurdles and requires more finesse than many tech startups expect. By paying attention to the lessons from the report’s findings, innovation teams can better anticipate what they’ll face, and plan accordingly.

Simply put, teams looking to apply collective intelligence and “big data” platforms to health and health care problems often don’t appreciate the messy details of using and making sense of data in the heavily regulated hospital IT environment. Download this report today and learn how it helps prepare startups in six areas:

  1. Complexity: An enormous domain with noisy data not designed for machine consumption
  2. Computing: Lack of standard, interoperable schema for documenting human health in a digital format
  3. Context: Lack of critical contextual metadata for interpreting health data
  4. Culture: Startup difficulties in hospital ecosystems: why innovation can be a two-edged sword
  5. Contracts: Navigating the IRB, HIPAA, and EULA frameworks
  6. Commerce: The problem of how digital health startups get paid

This report represents the initial findings of a study funded by a grant from the Robert Wood Johnson Foundation. Subsequent reports will explore the results of three deep-dive projects the team pursued during the study. (More)”

Big Data. Big Obstacles.

Curated on Posted on by Stefaan Verhulst

Dalton Conley et al. in the Chronicle of Higher Education: “After decades of fretting over declining response rates to traditional surveys (the mainstay of 20th-century social research), an exciting new era would appear to be dawning thanks to the rise of big data. Social contagion can be studied by scraping Twitter feeds; peer effects are tested on Facebook; long-term trends in inequality and mobility can be assessed by linking tax records across years and generations; social-psychology experiments can be run on Amazon’s Mechanical Turk service; and cultural change can be mapped by studying the rise and fall of specific Google search terms. In many ways there has been no better time to be a scholar in sociology, political science, economics, or related fields.

However, what should be an opportunity for social science is now threatened by a three-headed monster of privatization, amateurization, and Balkanization. A coordinated public effort is needed to overcome all of these obstacles.

While the availability of social-media data may obviate the problem of declining response rates, it introduces all sorts of problems with the level of access that researchers enjoy. Although some data can be culled from the web—Twitter feeds and Google searches—other data sit behind proprietary firewalls. And as individual users tune up their privacy settings, the typical university or independent researcher is increasingly locked out. Unlike federally funded studies, there is no mandate for Yahoo or Alibaba to make its data publicly available. The result, we fear, is a two-tiered system of research. Scientists working for or with big Internet companies will feast on humongous data sets—and even conduct experiments—and scholars who do not work in Silicon Valley (or Alley) will be left with proverbial scraps….

To address this triple threat of privatization, amateurization, and Balkanization, public social science needs to be bolstered for the 21st century. In the current political and economic climate, social scientists are not waiting for huge government investment like we saw during the Cold War. Instead, researchers have started to knit together disparate data sources by scraping, harmonizing, and geo­coding any and all information they can get their hands on.

Currently, many firms employ some well-trained social and behavioral scientists free to pursue their own research; likewise, some companies have programs by which scholars can apply to be in residence or work with their data extramurally. However, as Facebook states, its program is “by invitation only and requires an internal Facebook champion.” And while Google provides services like Ngram to the public, such limited efforts at data sharing are not enough for truly transparent and replicable science….(More)”

 

Selected Readings on Data Governance

Curated on Posted on by Jos Berens

Jos Berens (Centre for Innovation, Leiden University) and Stefaan G. Verhulst (GovLab)

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data governance was originally published in 2015.

Context
The field of Data Collaboratives is premised on the idea that sharing and opening-up private sector datasets has great – and yet untapped – potential for promoting social good. At the same time, the potential of data collaboratives depends on the level of societal trust in the exchange, analysis and use of the data exchanged. Strong data governance frameworks are essential to ensure responsible data use. Without such governance regimes, the emergent data ecosystem will be hampered and the (perceived) risks will dominate the (perceived) benefits. Further, without adopting a human-centered approach to the design of data governance frameworks, including iterative prototyping and careful consideration of the experience, the responses may fail to be flexible and targeted to real needs.

Selected Readings List (in alphabetical order)

Annotated Selected Readings List (in alphabetical order)

Better Place Lab, “Privacy, Transparency and Trust.” Mozilla, 2015. Available from: http://www.betterplace-lab.org/privacy-report.

  • This report looks specifically at the risks involved in the social sector having access to datasets, and the main risks development organizations should focus on to develop a responsible data use practice.
  • Focusing on five specific countries (Brazil, China, Germany, India and Indonesia), the report displays specific country profiles, followed by a comparative analysis centering around the topics of privacy, transparency, online behavior and trust.
  • Some of the key findings mentioned are:
    • A general concern on the importance of privacy, with cultural differences influencing conception of what privacy is.
    • Cultural differences determining how transparency is perceived, and how much value is attached to achieving it.
    • To build trust, individuals need to feel a personal connection or get a personal recommendation – it is hard to build trust regarding automated processes.

Montjoye, Yves Alexandre de; Kendall, Jake and; Kerry, Cameron F. “Enabling Humanitarian Use of Mobile Phone Data.” The Brookings Institution, 2015. Available from: http://www.brookings.edu/research/papers/2014/11/12-enabling-humanitarian-use-mobile-phone-data.

  • Focussing in particular on mobile phone data, this paper explores ways of mitigating privacy harms involved in using call detail records for social good.
  • Key takeaways are the following recommendations for using data for social good:
    • Engaging companies, NGOs, researchers, privacy experts, and governments to agree on a set of best practices for new privacy-conscientious metadata sharing models.
    • Accepting that no framework for maximizing data for the public good will offer perfect protection for privacy, but there must be a balanced application of privacy concerns against the potential for social good.
    • Establishing systems and processes for recognizing trusted third-parties and systems to manage datasets, enable detailed audits, and control the use of data so as to combat the potential for data abuse and re-identification of anonymous data.
    • Simplifying the process among developing governments in regards to the collection and use of mobile phone metadata data for research and public good purposes.

Centre for Democracy and Technology, “Health Big Data in the Commercial Context.” Centre for Democracy and Technology, 2015. Available from: https://cdt.org/insight/health-big-data-in-the-commercial-context/.

  • Focusing particularly on the privacy issues related to using data generated by individuals, this paper explores the overlap in privacy questions this field has with other data uses.
  • The authors note that although the Health Insurance Portability and Accountability Act (HIPAA) has proven a successful approach in ensuring accountability for health data, most of these standards do not apply to developers of the new technologies used to collect these new data sets.
  • For non-HIPAA covered, customer facing technologies, the paper bases an alternative framework for consideration of privacy issues. The framework is based on the Fair Information Practice Principles, and three rounds of stakeholder consultations.

Center for Information Policy Leadership, “A Risk-based Approach to Privacy: Improving Effectiveness in Practice.” Centre for Information Policy Leadership, Hunton & Williams LLP, 2015. Available from: https://www.informationpolicycentre.com/uploads/5/7/1/0/57104281/white_paper_1-a_risk_based_approach_to_privacy_improving_effectiveness_in_practice.pdf.

  • This white paper is part of a project aiming to explain what is often referred to as a new, risk-based approach to privacy, and the development of a privacy risk framework and methodology.
  • With the pace of technological progress often outstripping the capabilities of privacy officers to keep up, this method aims to offer the ability to approach privacy matters in a structured way, assessing privacy implications from the perspective of possible negative impact on individuals.
  • With the intended outcomes of the project being “materials to help policy-makers and legislators to identify desired outcomes and shape rules for the future which are more effective and less burdensome”, insights from this paper might also feed into the development of innovative governance mechanisms aimed specifically at preventing individual harm.

Centre for Information Policy Leadership, “Data Governance for the Evolving Digital Market Place”, Centre for Information Policy Leadership, Hunton & Williams LLP, 2011. Available from: http://www.huntonfiles.com/files/webupload/CIPL_Centre_Accountability_Data_Governance_Paper_2011.pdf.

  • This paper argues that as a result of the proliferation of large scale data analytics, new models governing data inferred from society will shift responsibility to the side of organizations deriving and creating value from that data.
  • It is noted that, with the reality of the challenge corporations face of enabling agile and innovative data use “In exchange for increased corporate responsibility, accountability [and the governance models it mandates, ed.] allows for more flexible use of data.”
  • Proposed as a means to shift responsibility to the side of data-users, the accountability principle has been researched by a worldwide group of policymakers. Tailing the history of the accountability principle, the paper argues that it “(…) requires that companies implement programs that foster compliance with data protection principles, and be able to describe how those programs provide the required protections for individuals.”
  • The following essential elements of accountability are listed:
    • Organisation commitment to accountability and adoption of internal policies consistent with external criteria
    • Mechanisms to put privacy policies into effect, including tools, training and education
    • Systems for internal, ongoing oversight and assurance reviews and external verification
    • Transparency and mechanisms for individual participation
    • Means of remediation and external enforcement

Crawford, Kate; Schulz, Jason. “Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harm.” NYU School of Law, 2014. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2325784&download=yes.

  • Considering the privacy implications of large-scale analysis of numerous data sources, this paper proposes the implementation of a ‘procedural data due process’ mechanism to arm data subjects against potential privacy intrusions.
  • The authors acknowledge that some privacy protection structures already know similar mechanisms. However, due to the “inherent analytical assumptions and methodological biases” of big data systems, the authors argue for a more rigorous framework.

Letouze, Emmanuel, and; Vinck, Patrick. “The Ethics and Politics of Call Data Analytics”, DataPop Alliance, 2015. Available from: http://static1.squarespace.com/static/531a2b4be4b009ca7e474c05/t/54b97f82e4b0ff9569874fe9/1421442946517/WhitePaperCDRsEthicFrameworkDec10-2014Draft-2.pdf.

  • Focusing on the use of Call Detail Records (CDRs) for social good in development contexts, this whitepaper explores both the potential of these datasets – in part by detailing recent successful efforts in the space – and political and ethical constraints to their use.
  • Drawing from the Menlo Report Ethical Principles Guiding ICT Research, the paper explores how these principles might be unpacked to inform an ethics framework for the analysis of CDRs.

Data for Development External Ethics Panel, “Report of the External Ethics Review Panel.” Orange, 2015. Available from: http://www.d4d.orange.com/fr/content/download/43823/426571/version/2/file/D4D_Challenge_DEEP_Report_IBE.pdf.

  • This report presents the findings of the external expert panel overseeing the Orange Data for Development Challenge.
  • Several types of issues faced by the panel are described, along with the various ways in which the panel dealt with those issues.

Federal Trade Commission Staff Report, “Mobile Privacy Disclosures: Building Trust Through Transparency.” Federal Trade Commission, 2013. Available from: www.ftc.gov/os/2013/02/130201mobileprivacyreport.pdf.

  • This report looks at ways to address privacy concerns regarding mobile phone data use. Specific advise is provided for the following actors:
    • Platforms, or operating systems providers
    • App developers
    • Advertising networks and other third parties
    • App developer trade associations, along with academics, usability experts and privacy researchers

Mirani, Leo. “How to use mobile phone data for good without invading anyone’s privacy.” Quartz, 2015. Available from: http://qz.com/398257/how-to-use-mobile-phone-data-for-good-without-invading-anyones-privacy/.

  • This paper considers the privacy implications of using call detail records for social good, and ways to mitigate risks of privacy intrusion.
  • Taking example of the Orange D4D challenge and the anonymization strategy that was employed there, the paper describes how classic ‘anonymization’ is often not enough. The paper then lists further measures that can be taken to ensure adequate privacy protection.

Bernholz, Lucy. “Several Examples of Digital Ethics and Proposed Practices” Stanford Ethics of Data conference, 2014, Available from: http://www.scribd.com/doc/237527226/Several-Examples-of-Digital-Ethics-and-Proposed-Practices.

  • This list of readings prepared for Stanford’s Ethics of Data conference lists some of the leading available literature regarding ethical data use.

Abrams, Martin. “A Unified Ethical Frame for Big Data Analysis.” The Information Accountability Foundation, 2014. Available from: http://www.privacyconference2014.org/media/17388/Plenary5-Martin-Abrams-Ethics-Fundamental-Rights-and-BigData.pdf.

  • Going beyond privacy, this paper discusses the following elements as central to developing a broad framework for data analysis:
    • Beneficial
    • Progressive
    • Sustainable
    • Respectful
    • Fair

Lane, Julia; Stodden, Victoria; Bender, Stefan, and; Nissenbaum, Helen, “Privacy, Big Data and the Public Good”, Cambridge University Press, 2014. Available from: http://www.dataprivacybook.org.

  • This book treats the privacy issues surrounding the use of big data for promoting the public good.
  • The questions being asked include the following:
    • What are the ethical and legal requirements for scientists and government officials seeking to serve the public good without harming individual citizens?
    • What are the rules of engagement?
    • What are the best ways to provide access while protecting confidentiality?
    • Are there reasonable mechanisms to compensate citizens for privacy loss?

Richards, Neil M, and; King, Jonathan H. “Big Data Ethics”. Wake Forest Law Review, 2014. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2384174.

  • This paper describes the growing impact of big data analytics on society, and argues that because of this impact, a set of ethical principles to guide data use is called for.
  • The four proposed themes are: privacy, confidentiality, transparency and identity.
  • Finally, the paper discusses how big data can be integrated into society, going into multiple facets of this integration, including the law, roles of institutions and ethical principles.

OECD, “OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data”. Available from: http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm.

  • A globally used set of principles to inform thought about handling personal data, the OECD privacy guidelines serve as one the leading standards for informing privacy policies and data governance structures.
  • The basic principles of national application are the following:
    • Collection Limitation Principle
    • Data Quality Principle
    • Purpose Specification Principle
    • Use Limitation Principle
    • Security Safeguards Principle
    • Openness Principle
    • Individual Participation Principle
    • Accountability Principle

The White House Big Data and Privacy Working Group, “Big Data: Seizing Opportunities, Preserving Values”, White House, 2015. Available from: https://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_5.1.14_final_print.pdf.

  • Documenting the findings of the White House big data and privacy working group, this report lists i.a. the following key recommendations regarding data governance:
    • Bringing greater transparency to the data services industry
    • Stimulating international conversation on big data, with multiple stakeholders
    • With regard to educational data: ensuring data is used for the purpose it is collected for
    • Paying attention to the potential for big data to facilitate discrimination, and expanding technical understanding to stop discrimination

William Hoffman, “Pathways for Progress” World Economic Forum, 2015. Available from: http://www3.weforum.org/docs/WEFUSA_DataDrivenDevelopment_Report2015.pdf.

  • This paper treats i.a. the lack of well-defined and balanced governance mechanisms as one of the key obstacles preventing particularly corporate sector data from being shared in a controlled space.
  • An approach that balances the benefits against the risks of large scale data usage in a development context, building trust among all stake holders in the data ecosystem, is viewed as key.
  • Furthermore, this whitepaper notes that new governance models are required not just by the growing amount of data and analytical capacity, and more refined methods for analysis. The current “super-structure” of information flows between institutions is also seen as one of the key reasons to develop alternatives to the current – outdated – approaches to data governance.

Chicago uses new technology to solve this very old urban problem

Curated on Posted on by Stefaan Verhulst

Mehboob Jeelani at Fortune: “Chicago has spent 12 years collecting data on resident complaints. Now the city is harnessing that data to control the rat population, stopping infestations before residents spot rats in the first place.

For the past three years, Chicago police have been analyzing 911 calls to better predict crime patterns across the city and, in one case, actually forecasted a shootout minutes before it occurred.

Now, the city government is turning its big data weapons on the city’s rat population.

The city has 12 years of data on the resident complaints, ranging from calls about rodent sitting to graffiti. Those clusters of data lead the engineers to where the rats can potentially breed. The report is shared with the city’s sanitation team, which later cleans up the rat-infested areas.

“We discovered really interesting relationship that led to developing an algorithm about rodent prediction,” says Brenna Berman, Chicago’s chief information officer. “It involved 31 variables related to calls about overflowing trash bins and food poisoning in restaurants.”

The results, Berman says, are 20% more efficient versus the old responsive model.

Governing cities in the 21st century is a difficult task. It needs a political and economic support. In America, it was only in the early 1990s—when young adults started moving from the suburbs back to the cities—that the academic and policy consensus shifted back toward urban centers. Since then, cities are facing an influx of new residents, overwhelming the service providing agencies. To meet that demand amid the recent budget sequestration, cities like New York, San Francisco, Philadelphia, and Chicago are constantly elevating the art of governance through innovative policies.

Due to this new model, in Chicago, you might not even spot a rat. The city’s Department of Innovation and Technology analyzes big chunks of data to an extent where the likelihood of a rodent infestation is thwarted seven days ahead of resident rat-sightings…(More)”

Apple Has Plans for Your DNA

Curated on Posted on by Stefaan Verhulst

Antonio Regalado at MIT Technology Review: “…Apple is collaborating with U.S. researchers to launch apps that would offer some iPhone owners the chance to get their DNA tested, many of them for the first time, according to people familiar with the plans.

The apps are based on ResearchKit, a software platform Apple introduced in March that helps hospitals or scientists run medical studies on iPhones by collecting data from the devices’ sensors or through surveys.

The first five ResearchKit apps, including one called mPower that tracks symptoms of Parkinson’s disease, quickly recruited thousands of participants in a few days, demonstrating the reach of Apple’s platform.

“Apple launched ResearchKit and got a fantastic response. The obvious next thing is to collect DNA,” says Gholson Lyon, a geneticist at Cold Spring Harbor Laboratory, who isn’t involved with the studies.

Nudging iPhone owners to submit DNA samples to researchers would thrust Apple’s devices into the center of a widening battle for genetic information. Universities, large technology companies like Google (see “Google Wants to Store Your Genome”), direct-to-consumer labs, and even the U.S. government (see “U.S. to Develop DNA Study of One Million People”) are all trying to amass mega-databases of gene information to uncover clues about the causes of disease (see “Internet of DNA”).

In two initial studies planned, Apple isn’t going to directly collect or test DNA itself. That will be done by academic partners. The data would be maintained by scientists in a computing cloud, but certain findings could appear directly on consumers’ iPhones as well. Eventually, it’s even possible consumers might swipe to share “my genes” as easily as they do their location….(More)”