Open Data Portals: News

There are new or refurbished open data portals to be announced.

Switzerland just published in a new look for a better presentation of data. See the press release.



The European Commission published some months ago the European Data Portal.

snip_EuropeanDataPortal is much more than a collection of open data. It is an ecosystem with lots of documents explaining and promoting open data.


SPARQL inside!

The portal offers metadata as linked open data with an SPARQL endpoint for powerful searching.


select ?theme (count(?theme) as ?count) where {?s a dcat:Dataset . ?s dcat:theme ?theme} GROUP BY ?theme LIMIT 100  gives all  data categories/themes and their number of datasets .

Impact studies

Most of all these data are already published on other websites. The advantage of such open data portals are a centralized access and clear licence information, A main intention is to attract developers, to foster data usage and with this economic growth.

A Swiss study (January 2014) assesses the economic impact of Open Government Data: ´The report determined that the economic benefits from OGD for Switzerland lie most likely between CHF0.9 B and CHF1.2 B´.

snip_ogdstudie       All the details >>> here  (look for the extended executive summary).

European Study (November 2015) within the context of the launch of the European Data Portal got these results: “The aim of this study is to collect, assess and aggregate economic evidence to forecast the benefits of the re-use of Open Data for the EU28+. Four key indicators are measured: direct market size, number of jobs created, cost savings, and efficiency gains. Between 2016 and 2020, the market size of Open Data is expected to increase by 36.9%, to a value of 75.7 bn EUR in 2020. The forecasted number of direct Open Data jobs in 2016 is 75,000 jobs. From 2016 to 2020, almost 25,000 extra direct Open Data jobs are created. The forecasted public sector cost savings for the EU28+ in 2020 are 1.7 bn EUR. Efficiency gains are measured in a qualitative approach. ”

snip_EUimpactSee the details >>> here

Next: LOD

Open and machine-readable formats help to access data and foster the economic impact. Even better when the data have metadata in a standardized description. Linked Open Data (LOD) in RDF format provide this; uses this format describing the harvested datasets (metadata). The next step will and must be data in this format in order to link masses of data in the linked data cloud.

With a first step is been made in Switzerland.


Linked Data? In’s ecosystem well made videos present explanations:




LOD Cloud Growing

Linked Open Data Cloud is growing. The new diagram as of April 2014 shows this development, compared to 2011 (diagram below).

Linked Data Cloud 2014


In the government sector growth is especially visible with the geospatial reference portal provided by the Office for National Statistics ONS.

‘The ONS linked data portal is the access point for information on statistical geographies required to support the use of official statistics. It is designed to allow users to discover, view and use geospatial data.’

Other statistical data portals are now visible too. Linked data become a new standard. So by the Swiss Federal Statistical Office (BFS), IMF, FAO, Worldbank or Eurostat.




Linked Data Cloud 2011



And now: Semantic Statistics (SemStats)

Official Statistics has a long tradition in creating and providing high-quality metadata. And the Semantic Web needs just this: metadata!

So it’s not surprising that these two find together, more and more.
A special workshop will be organized during the The 12th International Semantic Web Conference ISWC, 21-25 October 2013, Sydney, Australia.

It is the 1st International Workshop on Semantic Statistics (SemStats 2013) organized by Raphaël Troncy (EURECOM), Franck Cotton (INSEE), Richard Cyganiak (DERI), Armin Haller(CSIRO) and Alistair Hamilton (ABS).

ISWC 2013 is the premier international forum for the Semantic Web / Linked Data Community. Here, scientists, industry specialists, and practitioners meet to discuss the future of practical, scalable, user-friendly, and game changing solutions.’

The workshop summary

How to publish linked statistics? And: How to use linked data for statistics? These are the key questions of this workshop.

‘The goal of this workshop is to explore and strengthen the relationship between the Semantic Web and statistical communities, to provide better access to the data held by statistical offices. It will focus on ways in which statisticians can use Semantic Web technologies and standards in order to formalize, publish, document and link their data and metadata.

The statistics community faces sometimes challenges when trying to adopt Semantic Web technologies, in particular:

  • difficulty to create and publish linked data: this can be alleviated by providing methods, tools, lessons learned and best practices, by publicizing successful examples and by providing support.
  • difficulty to see the purpose of publishing linked data: we must develop end-user tools leveraging statistical linked data, provide convincing examples of real use in applications or mashups, so that the end-user value of statistical linked data and metadata appears more clearly.
  • difficulty to use external linked data in their daily activity: it is important do develop statistical methods and tools especially tailored for linked data, so that statisticians can get accustomed to using them and get convinced of their specific utility.’

A tradition

RDF, Triples, Linked Data … these are topics statisticians already treated and adapted. But rather on an individual track and not as an organization.

This blog has a lot of information about Semantic Web and Official Statistics, about 40 posts since 2007.

See this post (2012) with a recent paper from Statistics Switzerland (where a study on publishing linked data has just been finished in collaboration with the Bern University of Applied Sciences):

Or this (2009) about SDMX and RDF or about LOD activities in 2009:

Big Data, Open Data and Official Statistics

There are (at least) two big challenges official statistics will be faced with in the  next few years and which will possibly change its quasi-monoplistic position.


On the input side it’s Big Data

‘“Big Data” is a term used to describe massive information stores – generally measured in petabytes and exabytes – and also refers to the methods and technologies used to analyze these large data volumes.  The core principles of Big Data (data mining, analytics) have been around for some time, but recent technology has enabled the collection and analysis of previously unimaginable data volumes at extremely high speeds.’ So says for example SAP and gives some examples how  Big Data will change your life (big words and they show how big software and hardware players begin to occupy the field).

Official Statistics has already put this on the agenda! And so has the in United Nations Statistics Division’s (UNSD) Friday Seminar on Emerging Issues, 22 February 2013.

Some papers from this Seminar:

Gosse van der Veen Statistics Netherlands. High Level Group for the Modernization of Statistical Products and Services. Big Data: Big Opportunity!


The High-Level Group for the Modernisation of Statistical Production and Services (HLG) established an informal Task Team of national and international experts, coordinated by the UNECE Secretariat. The Paper of this group gives an excellent overview of the topic: What Does “Big Data” mean for Official Statistics.



Andrew Wyckoff, Big Data for Policy,Development and Official Statistics, Directorate for Science, Technology & Industry. Organisation for Economic Co-operation and Development OECD (personal opinion).



Aspects of Big Data and real-time analytics are provided in another paper by Global Pulse (an innovation initiative launched by the Executive Office of the United Nations Secretary-General): Big Data for Development: Opportunities & Challenges



The discussion is launched and as mentions the HLG  paper: ‘To use Big data, statisticians are needed with a different mind-set and new skills. The processing of more and more data for official statistics requires statistically aware people with an analytical mind-set, an affinity for IT (e.g. programming skills) and a determination to extract valuable ‘knowledge’ from data. These so-called “data scientists” can be derived from various scientific disciplines.’


On the output side it’s (Linked) Open Data in combination with APIs

Open Data is not at all a new topic for Official Statistics. National Statistical Institutes were forerunners in openly providing data; organizations like UN or EUROSTAT went this way as well.

Several Open Data initiatives (USA, UK, France, EU …) consist mostly of data catalogues, and are in that sense also public relations initiatives. A large part of the data so provided consists of statistical data already available, often, on the website of the National Statistical Institute concerned. The EU portal, for instance, offers 5716 datasets  of statistical data from a total of 5893 (as of April 2013).

Further central questions are the licensing of data, 2013-04-20_CCBYas well as their availability in machine-readable formats.

Machine-readable statistical data, Application Programming Interfaces (APIs) to the data and especially Linked Open Data LOD (–> essentials, –>tutorial) open the way to creative applications and new models of presenting information.

2015-01-25_berners lee

An Europe-wide Linked Open Data (LOD2) project ‘was launched in September 2010 and will run for four years. It addresses exploitation of the web as a platform for data and information integration, and the use of semantic technologies to make government data more useable.’

Looking for third-party APPs

Data Providers are looking at applications or mashups made with their data  with much interest, and they are even sponsoring competitions and hack days (like Apps4EU) to stimulate the reuse of open data, especially from the public sector.

The most popular APP creator and statistical storyteller is Hans Roslings  with Gapminder. Rosling himself is a pioneer in fighting for open data.

Changing paradigms

Open Data, Linked Open Data and APIs are changing the dissemination paradigm of statistical agencies. More people with new skills will do new things. Coding is becoming the new literacy, says i.e. Garrett Heath in his advice for his unborn daughter: ‘I was blown away that the buzz is not around mobile apps, but rather around using APIs. Ten years ago saw the creation of the social networking platforms. The past five years has been about accumulating the data. The next five years and beyond will be about interpreting that data. [My daughter will have access to] a boatload of interesting data sitting in accessible databases that is waiting to be exposed and interpreted with her [the programmer’s]) creativity.’

Storytelling with data

Storytelling based on data is less and less the domain of statistical agencies. Storytelling can access multiple (new) resources and take on new forms.  To satisfy the basic idea of an easily understandable and appealing presentation of statistical content, statistical institutions cannot avoid taking certain measures to improve their content and presentation. The “composer” must know how the music is to be played, that is as a quick, competent, qualitatively unique, reliable and indispensable data source.
But this presentation job can no longer be done on one’s own: cooperative partnerships are necessary and have already begun to some extent, both with partners outside statistical institutions and between such institutions. This discussion has been launched.

Statistical Storytelling revisited! More in a paper from IMAODBC Vilnius 2010:


And this: Many small open data give big data insights

FORGET BIG DATA, SMALL DATA IS THE REAL REVOLUTION says Rufus Pollock co-Director of the Open Knowledge Foundation : ‘… the discussions around big data miss a much bigger and more important picture: the real opportunity is not big data, but small data. Not centralized “big iron”, but decentralized data wrangling. Not “one ring to rule them all” but “small pieces loosely joined”.’


IMAODBC 2012: And the winner is …

The Bo Sundgren Award of the International Marketing and Output Database Conference IMAODBC 2012 in Pruhonice near Prag goes to Alain Nadeau from the  Swiss Federal Statistical Office FSO.

In his contribution Alain showed how the renovation of the FSO website can go together with a more open data-oriented publishing. This by separating the three layers of the application.

One of the databases in the data layer is planned to be in the linked open data format, the 5star format described by Tim Berners-Lee. A prototype is under way and first experiences will show up beginning 2013.


Semantic Web, RDF and APIs

This is one, ambitious RDF-based way providing open data. It’s not the only one because data can also be offered i.e. via specific APIs. Such an API has been developped at FSO. It uses data from PX-Cubes and displays HTML-tables  (for the moment only internal access to the API).


The presentation

See the full presentation here.


Curious about abbreviations? Here’s (a new) one: Linked Open Government Statistical Data LOGSD.

LOGSD are statistical data official statistics agencies provide in a LOD format for reuse. And such reuse may combine (mash up) statistical LOD with other sources in the LOD Cloud.

For example: ONS

The Office of National Statistics ONS and others in UK are very active in this field. So for better accessing geographical metadata which are essential in presenting statistics:

‘The solution is to use as a single access point for discovery of geographic data, and to link from there to a geoportal (that is currently in development) where users could download the geographic products online. This goes most of the way to delivering the tools that users need to work with statistical data but there is also an opportunity to go further and provide geographic data as linked data, using the GSS codes that uniquely identify each geography to link the attributes from the different geographic products. Now, instead of a 9 character GSS identifier, each geography is given a URI that allows it to not only be uniquely identified but also makes it available online. We therefore end up with identifiers such as that only require users to change the GSS code at the end to get to the geographic information that they need.’


And here an example how LOD and statistical (not yet LOGSD) data could work together. It’s an experimental proof of concept using data from Mercer quality of living survey and Transparency International, enriching these data with more information from DBpedia and calculating correlations that lead to hypotheses about the data.

Heiko Paulheim from Technische Universität Darmstadt made this interesting experiment which illustrates how linking data works. Abstract of Paulheim’s study “Generating Possible Interpretations for Statistics from Linked Open Data’ :

Statistics are very present in our daily lives. Every day, new statistics are published, showing the perceived quality of living in different cities, the corruption index of different countries, and so on. Interpreting those statistics, on the other hand, is a difficult task. Often, statistics collect only very few attributes, and it is difficult to come up with hypotheses that explain, e.g., why the perceived quality of living in one city is higher than in another. In this paper, we introduce Explain-a-LOD, an approach which uses data from Linked Open Data for generating hypotheses that explain statistics. We show an implemented prototype and compare different approaches for generating hypotheses by analyzing the perceived quality of those hypotheses in a user study.’