Open Data: What’s new in 2017?

I am now in Montréal, where I participated, last Friday, in a panel on Open Data at “Science & You” international conference. It was interesting for me to reflect on how the picture has changed since my previous panel on the same topic – in Kiev in 2012. Back then, we were busy trying to convince public administrations that data opening was good for transparency and could help improve services to communities. Since then, a lot of attempts have been made in numerous countries – local authorities often pioneering the process, followed only later by central governments (one example cited in my panel was Québec City). What is made open is typically information from public registers (first names of newborns, records of road accidents) and increasingly, from technological devices and sensors (bus traffic information).

There are some conditions to be met for a dataset to be said “open”:

  • Technically, it needs to be “raw”, detailed, digital and reusable. The French Interior Ministry released results of the first round of the recent presidential elections within a few days, at polling station level. This is sufficiently detailed (with over 69,000 polling stations throughout the country), raw (allowing aggregations, comparisons etc.), and digital/reusable (so much so that the newspaper Le Monde could develop a user-friendly application to let readers easily check results in their neighborhoods). Some would also insist that “open” data should be released in non-proprietary formats (better .csv than .xls, for example).
  • Legally, the data must come with a license that allows re-use by third parties (typically within the Creative Commons family). Ideally, no type of reuse should be ruled out (including somewhat controversially, commercial / for-profit reuse).
  • Economically, the data should be available to all for free (or at least with minimal charges if data preparation requires extra work or expenses).

If in the past few years, a lot of thought has been devoted to the “ideal” conditions for data opening and how this would positively affect public service, the data landscape has now significantly changed.

Continue reading

Visualisation, mixed methods and social networks: what’s new

This morning, we had a plenary on “Visualisation and social networks in mixed-methods sociological research” at the British Sociological Association conference now going on in Manchester. This session, organized by the BSA study group on social networks that I convene with Alessio D’Angelo (BSA SNAG), builds on a special section of Sociological Research Online that we edited in 2016. Alessio and I chaired and had four top-flying speakers: Nick Crossley, Gemma Edwards (both at the University of Manchester), Bernie Hogan (Oxford Internet Institute) and Louise Ryan (University of Sheffield).

Each speaker briefly presented a case study that involved visualization, and all were great in conveying exciting albeit complex ideas in a short time span. What follows is a short summary of the main insight (as I saw it).

Continue reading

Science XXL: digital data and social science

I attended last week (unfortunately only part of) an interesting workshop on the effects of today’s abundance and diversity of digital data on social science practices, aptly called “Science XXL“. A variety of topics were discussed and different research experiences were shared, but I’ll just summarize here a few lessons learned that I find interesting.

  • Digital data are archive data. Data retrieved automatically from the digital traces of individual actions, such as those mined from the APIs of platforms such as Twitter, are unlike survey data in that they were not originally recorded for research purposes. The researcher must select relevant records on the basis of some understanding of the conditions under which these data were produced. Perhaps ironically, digital data share these characteristic with data from historical or literary archives.
  • Digital data are not necessarily “big”, in the sense that their volume is often small (at least in social science research so far!), even though they may share other characteristics of big data such as velocity (being generated on the fly as people use digital platforms) or variety (being little or not structured).
  • Digital data can help fill gaps in survey data, for example when survey sampling is not statistically representative: detail and volume can provide extra information that supports general conclusions.
  • Non-clean data, outliers and aberrant observations may be very informative, revealing details that would escape attention if researchers focused only on the average or center of the distribution (the normal law cherished in classical statistical approaches). Special cases are no longer a prerogative of qualitative research.
  • Data analysis is a key ingredient of “computational social science” a field that is growing in importance after an initial phase in which it was largely confined to agent-based simulation and complexity theory.

A cooperative approach to platforms

I was yesterday at a nice and interesting conference in Brussels on “How to coop the collaborative economy“, organized by major actors of the Belgian cooperative movement and building on the experience of a growing network of persons and organizations to enhance a cooperative view of the internet. Several themes in connection with my studies of the collaborative economy emerged, and I’d like to summarize here what were, in my view, the main lessons learned of the day.

Continue reading

New: Paris Seminar on the Analysis of Social Processes and Structures (SPS)

Together with colleagues Gianluca Manzo, Etienne Ollion, Ivan Ermakoff, and Ivaylo Petev, I organize a new inter-institutional seminar series in sociology.

This new Social Processes and Structures (SPS) Seminar aims to take stock of the debates within the international scientific community that have repercussions for the practice of contemporary sociology, and that renew the ways in which we construct research designs, i.e., the ways in which we connect theoretical claims, data collection and methods to assess the link between data and theory. Several observations motivate this endeavor. Increasing interactions between social sciences and disciplines such as computer science, physics and biology outline new conceptual and methodological perspectives on social realities. The availability of massive data sets raises the question of the tools required to describe, visualize and model these data sets. Simulation techniques, experimental methods and counterfactual analyses modify our conceptions of causality. Crossing sociology’s disciplinary frontiers, network analysis expands its range of scales. In addition, the development of mixed methods redraws the distinction between qualitative and quantitative approaches. In light of these challenges, the SPS seminar discusses studies that, no matter their subject and disciplinary background, provide the opportunity to deepen our understanding of the relations between theory, data and methods in social sciences.

The inaugural session took place on 20 November 2016; the “regular” series starts this Friday, 27 January, and will continue until June, with one meeting per month.

All sessions take place at Maison de la Recherche, 28 rue Serpente, 75006 Paris, room D040, 5pm-7pm. All interested students and scholars are welcome, and there is no need to register in advance.

Continue reading

Big data, big money: how companies thrive on informational resources

Information oils the economy – as we know since the path-breaking research of George Akerlof, Michael Spence and Joseph Stiglitz in the 1970s – and information can be extracted from data. Today, increased availability of “big” data creates the opportunity to access ever more information – for the good of the economy, then.

But in practice, how do companies extract value from this increasingly available information? In a nutshell, there are three ways in which they can do so: matching, targeted advertising, and market segmentation.

Matching is the key business idea of many recently-created companies and start-ups, and consists in helping potential parties to a transaction to find each other: driver and passenger (Uber), host and guest (Airbnb), buyer and seller (eBay), and so on. It is by processing users’ data with suitable algorithms that matching can be done, and the more detailed are the data, the more satisfactory the matching. Firms’ business model is usually based on taking a fee for each successful transaction (each realized match).

Targeted advertising is the practice of selecting, for each user, only the ads that correspond at best to their tastes or practices. Publicizing diapers to the general population will be largely ineffective as many people do not have young children; but targeting only those with young children is likely to produce better results. Here, the function of data is to help decide what to advertise to whom; useful data are people’s socio-demographic situation (age, marriage, children…), their current or past practices (if you bought diapers last week, you might do that again next week), and any declared tastes (for example as a post on Facebook or Twitter). How this produces a gain is obvious: if targeted adverts are more effective, sales will go up.

Continue reading

Special RFS issue on Big Data

Revue Française de Sociologie invites article proposals for a special issue on “Big Data, Societies and Social Sciences”, edited by Gilles Bastin (PACTE, Sciences Po Grenoble) and myself.

Focus is on two inextricably interwoven questions: how do big data transform society? How do big data affect social science practices?

Substantive as well as epistemological / methodological contributions are welcome. We are particularly interested in proposals that examine the social effects and/or the scientific implications of big data based on first-hand experience in the field.

The deadline for submission of extended abstracts is 28 February 2017; for full contributions, it is 15 September 2017. Revue Française de Sociologie accepts articles in French or English.

Further details and guidelines for submission are in the call for papers.