In this way, for example, population-representative 16 data on social enterprises in Hungary Romania, Spain, Sweden,
and reflections and this data is analysed at a local and regional level by project staff to identify any patterns
Others use assets such as property to 41 Recent data tobe found on CIRIEC 2012.42 http://www. euclidnetwork. eu/news
and Mainstreaming. http//ec. europa. eu/employment social/equal/data/document/200606-reflectionnote-inno en. pdf) 56 2. Social innovation interventions can also take the form of a dedicated priority
110 3. 3. 1. 2. Database of labels and certifications of social enterprises...110 3. 3. 1. 3. Social innovation prizes...
privacy-aware architectures for the social good (including open data and public federated identity management) The internet ecosystem currently faces two major and urgent problems:
a handful of non-European companies continue to consolidate their leading positions in data aggregation
security, data, and collective governance, based on democratic and participatory processes. The only practical response is the development of distributed
this includes the need for open data distributed repositories, distributed cloud, distributed search and distributed social networking.
and 3. New governance modalities for big data (main question around collective ownership of data, data portability and data as knowledge commons:
the question is how to ensure user control over personal information in an ocean of commercially valuable big data.
Defining sensible governance modalities for big data will require substantial collaboration between the public and private sectors, based on a multi-stakeholder model,
in order to define the minimum level of sensible regulation allowing fair competition in the emerging areas of big data.
and insufficiently precise and systematic use of measurement and data. There are efforts underway to address these barriers, both in the European union (e g.
yy Embracing creative disruption from technology (the pervasive use of social media, mobility, big data, cloud computing packaged in new digital government offerings;
Measurement, characterisation and mapping of social enterprise to collect (through primary and secondary research) and analyse data on the scale, characteristics and patterns of development of social enterprise in each country studied;
supporting the Europe 2020 priorities in areas like innovation, the digital economy, employment, youth, industrial policy, poverty,
It also manages to give the digital economy the necessary political attention. It gave rise to the cooperation
yy Improve their visibility (mapping of social enterprises, database of labels, support for local and national authorities to build integrated strategies for social enterprises, information and exchange platform.
the obligation to ensure accuracy and frequency of billing based on actual consumption, and the obligation to provide appropriate information with the bill providing a comprehensive account of the current energy costs,
in order to deliver better social outcomes means in the specific case of social services improving the quality, access, coverage, and affordability.
Easi integrates and extends, in the three axes of the programme, the coverage of three existing programmes:
and analyse the data necessary to measure the efficacy of the programme. 3. 1. 4. New practices for making policy As mentioned in the first BEPA report on social innovation in the EU,
A study has also been launched to crowdsource policy insights for new sources of growth and jobs in the digital economy through an online platform.
More information and data collection concerning the use and volume of dormant accounts in each Member State is necessary
and policymakers to operate across Europe. 3. 3. 1. 2. Database of labels and certifications of social enterprises Key Action 6 of the SBI has to be implemented by the European commission after the completion of the‘Mapping of social enterprises'action.
The two networks have a broad geographical coverage across the EU and are designed to assess, provide support
The platform does this through a searchable database, which has been used to collect data ranging from organisation listings, news events,
and interviews to articles and an editorial. Moreover, the website features case studies of the most successful social innovations, profiles of leading social innovators,
distributed knowledge creation and data from real environments(‘Internet of things')in order to create new forms of social innovation.
National Oceanic and Atmospheric Association and the World bank will provide a rich stream of input data
and nonofficial statistics that shape the way information and data is collected and produced. yy Increasing awareness of the potential of the network effect (CAP2020):
access and engagement with statistics to life so that we can enjoy easier access to data
production and visualisation of data related to societal progress and wellbeing*facilitate access, uploading and use of data produced by grassroots civil society organisations*promote the use of a broader range of statistics to inform the development of new indicators The project plans to improve citizen access
and use of statistics beyond GDP by:**mapping existing measurement initiatives in Europe and around the world*involving communities to foster the use of locally generated grassroots data (bottom up)* distilling best practice from civil society initiatives supporting the need for official and nonofficial statistics in debating policy issues
*investigating the experiences of social entrepreneurs; highlighting their involvement in measuring wellbeing and progress to steer socially sustainable and innovative initiatives.
The CAPS participants share data and collaborate to reach collective sustainability goals on open source platforms (open in terms of software but also hardware,
and an initiative to improve the availability of data on European higher education learning mobility and employment in cooperation with Eurostat.
this strand could contribute to capacity-building measures to develop a good practice database and (potentially) to support networks of social innovation incubators.
Against the update of structural data, the project will test these hypotheses on the qualitative impacts of the Third Sector in terms of capital building (e g. social networks,
intersectional and gender sensitive approach to the issue of Early School Leaving (ESL) aimed at in depth analyses of existing data
and the collection of new empirical data in order to innovate educational systems at the European, national and regional level. 3. 4. 3. Digital social innovation A large study launched by the Net Innovation unit of DG Connect in May 2013 explores what potential gains can be achieved in Europe
The study analyses social innovation as enabled by the‘network effect'(internet connectivity) as well as by new economic models for co-production and data sharing, the internet of things,
although this could be an underestimation because of lack of empirical data. P A r T I I M A i N d E V E L O P m E N t S i N e U P O L
and the open data movement. 3. Broad communication with the general public and citizens, reach out
social movements, business models, laws and regulations, data and infrastructures, and entirely new ways of thinking
research, mapping and data collection are used to uncover problems, as a first step to identifying solutions.
Artificial intelligence, for example, 1 16 THE OPEN BOOK OF SOCIAL INNOVATION has been used in family law in Australia
Research and mapping Many innovations are triggered by new data and research. In recent years, there has been a rise in the use of mapping techniques to reveal hidden needs and unused assets.
today policy and provision is interested much more in disaggregating data. There are also a range of tools for combining
and mining data to reveal new needs and patterns. 1 18 THE OPEN BOOK OF SOCIAL INNOVATION These sites show how to run competitions for‘mash up'ideas from citizens using government data, such as Sunlight Labs and Show Us a Better Way
. 9) Mapping physical assets. Within the social economy, especially amongst artists, entrepreneurs and community groups, there is a long tradition of taking advantage of empty, abandoned or derelict buildings and spaces.
Service users are responsible for all stages of the research process from design, recruitment, ethics and data collection to data analysis, writing up, and dissemination.
action research is geared normatively toward prescriptions emerging out of the data which can be employed for the improvement of future action. 16) Literature surveys
and analyse large quantities of data has been the basis for remarkable changes for example: in flexible manufacturing,
In Japanese factories data is collected by front 1 PROMPTS, INSPIRATIONS AND DIAGNOSES 21 line workers, and then discussed in quality circles that include technicians.
embedded with GPS data pinpointing the exact location of the problem. These complaints will then get forwarded to the relevant city department. 18) Integrated user-centred data such as Electronic Patient Records in the UK,
which, when linked through grid and cloud computing models provide the capacity to spot emerging patterns.
A contrasting integrated system for monitoring renal patients has led to dramatic improvements in survival rates and cost reductions in the United states. 9 19) Citizen-controlled data,
and chart their own behaviour and actions. 20) Holistic services include phone based services such as New york's 311 service which provide a database that can be analysed for patterns of recurring problems and requests. 21) Tools
The gathering and presentation of data requires a process of interpretation. This should ideally include those involved in the implementation of ideas and those affected by the proposals.
In analysing an issue or a set of data, it is useful to have the perspectives of a variety of professional disciplines,
and experiences that has a database of 4, 000 ideas online, receives a quarter of a million visitors a year,
and research data to demonstrate effectiveness and value for money (see list of metrics below) as well as adapting models to reduce costs
Variations will include toolkits, oral histories, databases, and manuals. One new initiative by Open Business is the creation of a database of open business models. 199) Barefoot consultants.
There is an important role for consultants and those with specialist knowledge who can act as knowledge brokers and advisers in the new systems.
to 5 102 THE OPEN BOOK OF SOCIAL INNOVATION provide funders or investors with data on impact;
methods using artificial neural networks and‘hedonic'price models (which attempt to define the various characteristics of a product or service), spatial analysis methods, fuzzy logic methods;‘
‘auto-regressive integrated moving averages methods';'and‘triple bottom line property appraisal methods'.'10 5 SCALING AND DIFFUSION 105 223) Operational metrics,
For example, a study of the operational data of public housing repairs found that the time taken to do repairs varied from a few minutes to 85 days,
and user-generated metrics such as the‘sousveys'surveys undertaken by citizens on services provided by the state used to gather chronic disease data in Sheffield
which provides support for every aspect of school management. 237) Personalized support services such as personal health and fitness coaches, increasingly backed up by shared data services and networks.
& How-Tos Get the Data Economic Empowerment-A year-round conversation (Forums, media spokespeople, & branding) Global Health-A year-round conversation (Forums, media spokespeople,
and girls expertise Pilot Tool kit Development Finalize summit definition Validate Indicators Test Acceptance Our Work Portfolio M&e The Work of Others Global Health Agenda Girls Database
and more formal legal devices (like public databases). With the increasing mixing of voluntary and professional roles (for example around care for the elderly,
2 244) Data infrastructures. A different, and controversial, infrastructure is the creation of a single database of children deemed‘at risk'in the UK.
This was seen as crucial to creating a holistic set of services to deal with children's needs,
that combines rich data feedback with support structures which help patients understand and treat their own conditions more effectively. 6 SYSTEMIC CHANGE 117 Strategic moves that accelerate systems change Every story of systemic innovation involves key moments
So while familiar data on income, employment, diseases or educational achievement continues to be gathered, there is growing interest in other types of measurement that may give more insights into
Requiring public agencies to publish data on their balance sheets, or to show disaggregated spending patterns,
as can the consolidation of spending data for particular areas or groups of people. Too often, public accounting has been structured around the issues of targets, control,
In 2009 it launched Wikiprogress, bringing together data and analysis on progress. The same year President Sarkozy commissioned Joseph Stiglitz to chair an inquiry into new measures of GDP.
This includes file sharing services such as Napster, and open-source software such as the Linux operating system, the Mozilla Firefox browser,
using transparent access to public financial and other data. 342) Audit and inspection regimes which overtly assess
and the coverage of core costs. 4 403) Direct funding for individuals, including the grants given by Unltd, The Skoll Foundation,
which allow recipients to rate philanthropic foundations. 427) Providing extensive information on NGO performance, such as Guidestar's services and databases in many countries worldwide,
Everyblock in Chicago provides a useful platform for aggregating ultra local data. Prosumption There has been marked a development of users becoming more engaged in the production of services.
Image courtesy of San Patrignano. 4 SUPPORT IN THE INFORMAL OR HOUSEHOLD ECONOMY 207 This could include educational coaching services, relief and backup for home carers, health coaches, birthing
146 Data 17-18; 21-2; 101-105; 112; 114; 116; 119-120; 204; De Bono, Edward 32 Demand 13;
53 5. 3 Emerging digital innovation policy issues 55 5. 4 Preliminary Recommendations on innovation policies 56 6. Analysing network data:
data and open knowledge community,(5) smart citizens, and (6) the open democracy community, including civil society and new social movements.
The project's most substantial challenge is to develop a crowdmapping facility based on open and linked data with visual identity functionalities that the that attract the DSI community
Tanks to the open data mapping facility, in combination with our hybrid iterative strategy of case study interviews, workshops,
In order to analyse the relationship data from the mapping, we are adopting social network analysis to detect patterns of relations
which has been used to capture data on DSI organisation via www. digitalsocial. eu. We highlighted 6 areas that capture key dimensions of the phenomenon under investigation:(
Data is aslo categorised by:(e g. Government and public sector organisations, businesses, academia and research organisations, social enterprises, charities and foundations;
the organisations and their activities fit under (open data, open networks, open knowledge, open hardware;
based on the open data set on organisations captured on www. digitalsocial. eu 5 Mapping and Engaging the DSI community As outline in more details in the engagement summary an ongoing focus has been to engage with
such as (including the need for open data distributed repositories, distributed cloud, distributed search, and distributed social networking);
by defining sensible governance modalities for big data thorugh a large collaboration between public and private actors;
The development of open data infrastructures knowledge co-creation platforms, wireless sensor networks, decentralised social networking,
1) the open hardware and free software communities,(2) the community of developers,(3) innovation labs,(4) the open data and open knowledge community,(5) smart citizens,
A description of the latest development of the DSI open data mapping website an overview of the engagement strategies to involve the DSI community, outreach and communitcation activities.
the open source community, the developers'community, the innovation labs community, the open/big data community, the smart citizen/civic society community,
A emergent analysis of the network data, looking at the type of DSI communities, the distribution of DSI in Europe,
In the DSI Network Data-Set, there are a total of 590 organisations with 645 projects as of August 2014.
open hardware and open data infrastructures. The new front page has been redesigned to inspire visitors to learn about DSI and join the map..
In time, the site will be an open database of relational links between DSI organisations and projects,
We also created statistical visualisations showing all the relevant dimensions in the data, such as EU countries with most DSI projects;
or Spanish organisation using the survey data. Figure 1. A view of the European section of the map.
such as the Open Data community at the Open Knowledge Conference and the Maker community at the Fab10, has helped us test our research findings
when it comes to open data and also in encouraging more women to participate in learning to code through open workshops and support networks.
and findings from the DSI research and how data analysts from Lodz University of Technology could access
and analyse the open data set on US DSI organisations and projects hosted on www. digitalsocial. eu Workshop at the Fablab community 10th anniversary gathering in Barcelona. 30 participants engaged in mapping out social action applications for makerspaces and Fab-Lab
and access the data they have captured. This includes: concertation meeting. The CAPS project representatives collaboratively mapped the synergies between the CAPS projects including involving CAPS projects in mapping their projects on www. digitalsocial. eu. Chest is considered to be the CAPS project with the strongest links to DSI.
where we with practitioners will explore what the big challenges organisations working on Open Data, Open Networks,
The recently launched Open Data Strategy for Europe9 established a level playing field for open data across the EU10 that should encourage disruptive innovation by unlocking the value of public data.
Furthermore, a EU Big data strategy is becoming a priority for the competitiveness of European industries,
and it presents a strong focus on fostering a European Data-driven Economy26. In this framework the EC is promising to launch a launch a multi-million euro Public Private Partnership on big data with industry towards the end of this year.
The focus is driven business, with little attention to societal challenges or to the inclusion of civil society actors and bottom-up approaches.
However, the call for the creation of an open data incubator within Horizon 2020 aims to help SMES set up supply chains,
Furthermore, by re-centralising computing, data storage and service provision according to the cloud paradigm there is a risk of closing the innovation ecosystem in favour of incumbents or dominant players,
and exploring the potential of open data, open Access, and the digital commons. In particular it is the forthcoming research area in DG CONNECT that addresses the need to facilitate SI processes and collective decision making through 30.
and data-driven Network structure Centralized and hierarchical Decentralized and digitally connected Table 4: Closed versus open public policy innovation processes Several policies may benefit from open public policy innovation.
This typology of communities matches the main technology trends emerging in the grassroots innovation space (e g. open data, open knowledge, open hardware, open networks),
The open data and open knowledge community Torkington (2010) suggests five types of people that are interested in open data:
1) governments who want to see a win from opening their data, 2) transparency advocates who want a more efficient and honest government,
therefore, government data should be available for free to the people, and 5) people who are hoping that releasing datasets will deliver economic benefits to the 27 country.
In this report, the open/big data community refers to the set of governments, usually at the local level,
that decide to open their data. Their goal is usually twofold: on one hand, they aim at being more transparent;
The commonly accepted premise underlying these objectives is that the publishing of government data in a reusable format can strengthen citizen engagement and participation and yield new innovative businesses.
There are many examples of cities that have opened their data. One of the most interesting is Helsinki,
which has become the most successful open data city in the world. Through and entity called Helsinki Region Infoshare37 Helsinki and three of its neighbouring cities publish all of their data in formats that make it easy for software developers
researchers, journalists and others to analyse, combine or turn into web-based or mobile applications that citizens may find useful.
There are other local governments around the world that are successfully developing open data portals. In the United states, the cities of Chicago, San francisco, Philadelphia,
and Metropolitan Rennes in France have also set up open data websites at the regional level that can be considered good practices.
labs themselves Networks Networked Formal enabling/servicing structures Lack of interconnection between different types of labs Cost of being a network member Difficulty to involve the community Open/big data (Local governments Competition organizers
Networks of developers Open data evangelists Top-down (governments decide what, when and how to open) Lack of standardization Lack of reuse Little sharing of
The open/big data community It has already been stated that the open/big data community includes a set of governments, usually at the local level,
that decide to open their data. Governments are, therefore, the focal actors of this community.
businesses and individual developers to use their data, engaging with the local community is key.
Innovation is the result of using the data governments open and offer for free. The open/big data community's enablers connect (local governments with those who are potential users
and who will boost innovation. One example is that of competitions. Particularly competitions'organisers make sure developments
and innovation takes place by means of using government open data. This is the case of the Open Data Challenge74, one of Europe's biggest open data competitions.
It was organized by the Open Knowledge Foundation, the Openforum Academy and Share-PSI. eu. It offered 20,000 Euros in prizes
Prize Idea, Prize App, Price Visualization, Better Data Award, Open Data Award, and Talis Award for Linked data.
In total, 13 awards were given. There are many other competitions, some of them organized by governments themselves.
Apps4finland75, for example, is an open data contest that has been running since 2009. It encourages the public sector
and other actors to make their data accessible to citizens and 33 developers. The competition has welcomed new data sources, applications, visualisations and ideas as entries.
Apps4ottawa76 is another open data contest organised by the City of Ottawa in Canada. Apps for Amsterdam has also been analysed widely.
It was an initiative promoted by the City of Amsterdam, the Waag Society, and the Amsterdam Economic Board, to make accessible as much data of the City of Amsterdam as possible.
Developers were invited to send in their applications that used at least one available source of information from the (local government.
Interaction between developers promotes the use of open data among the members of the network.
It also backs up open data individual requests to governments. Usually, networks of developers are virtual.
Of particular interest are also those sites devoted to developers'interaction that are embedded in open data portals.
Data. gov. uk77 the open data portal of the United kingdom, has an Interact section, with blogs and forums.
At the local level, the open data portal of Chicago is worth mentioning; it has aimed a section at developers78.
Open data evangelists are also enablers within the open/big data community. There are organisations that encourage the use of open data.
In the private world, Socrata79 is one interesting example. Building on the experience of open data portals developed throughout the United states,
it offers an open data field guide that is particularly aimed at government and elected officials. The Open Knowledge Foundation80 is another example, from the nonprofit field, that advocates and campaigns for the open release of key information.
It has published an open data handbook that anyone can use but that is especially designed for those who are seeking to open up data.
It has developed also an open data index which assess the state of open government data around the world.
Individuals can also be considered open data evangelists: Andrea Di Maio (VP Distinguished Analyst at Gartner), David Eaves (open data innovator and thought leader), Tariq Khokhar (open data evangelist at the World bank),
or Jay Nath (San francisco's Mayor Chief Innovation Officer) are only a few examples. of the open/big data community is top down, that is,
governments decide what, when and how to open. Some Governments do not interact with other stakeholders
and there are many differences between them, both in terms of speed and pace and commitment. As a result, the success of open data portals regarding innovation is very diverse.
This does not mean the open/big data community does not have references. There are outstanding good practices
such as the case of Helsinki, to which we have referred already in section 3, other local governments turn to
and followbut there is not a formal network of local governments, connected to each other on a regular basis around open data issues.
In terms of governance, therefore, we can only refer to the governance of relationships with stakeholders (users, first data providers, the information environment),
such as Helbig et al (2012) do, but still in this case, it is each government which decides what governance structure it wants
and how it manages stakeholders and relationships between them. Lastly a lot has been written on open/big data failures.
Huijboom & Van den Broek (2012) identified several barriers for open/big data initiatives to progress. After reviewing open data strategies in several European countries,
they describe a closed government culture, privacy legislation, limited quality of data, lack of standardisation (due to individual decisions), security threats,
existing charging models (some government charge for the data), and uncertain economic impact (it is still not clear
what the use/reuse of open data gives rise to). Other authors have referred also to some of these pitfalls,
such as data quality and lack of reuse, two topics that are related very. According to the United kingdom Public Accounts Committee (2012), businesses and developers are being hindered in making open data products
and services due to the poor quality of information being opened up. In this respect, the release of incomplete datasets such as patchy price and performance information for adult social care, plus factors such as inconsistent reporting across local authorities, mean that the data quality does not help developers.
Dawes (2012) adds that data quality is used generally to mean accuracy but that research studies identify multiple aspects of information quality that go well beyond simple accuracy of the data:
intrinsic quality (it includes accuracy and objectivity, but also involves believability and the reputation of the data source),
contextual quality (it refers to the context of the task for which the data will be used
and includes considerations of timeliness, relevancy, completeness, sufficiency, and value-added to the user), representational quality (it relates to meaning and format),
and accessibility (it comprises ease and means of access as well as access security). Actually, according to Kitchin (2013), it is not clear that open data is leading to innovative products that create new markets.
This may well be the case with high value datasets such as mapping and transport data,
but much less likely with most other datasets. He mentions De vries et al (2011), who reported that the average 34 apps developer made only 3, 000 USD per year from apps sales,
with 80%of paid Android apps being downloaded fewer than 100 times. In addition, they noted that even successful apps, such as Mycityway81
which had been downloaded 40 million times, were not yet generating profits. Competitions and hackatons have aimed at making datasets visible as well as at promoting apps development
but these created solutions often remain at version 1. 0, with little after event follow-up, maintenance or development.
and reputation Open/big data Organization of competitions Support for networking Knowledge sharing and dissemination New services Generation of economic value Transparency Political incentives (reputation) Technical support Monetary incentives
Revolution R Enterprise92, a proprietary spin-off, markets a faster version of R. The company can process very large data sets and offers, for a fee, training, consulting,
open hardware and software packages for citizen-led environmental data collection supported by a small data platform for analysis and advocacy.
This tool enables civic-minded groups to empirically verify government data and inaugurating a new generation of civic information tools to hold government accountable.
In the past, Gaana. com, Cleartrip, Vserv, Reverie Language Technology, App Virality, PCLOUDY and Betaglide have launched also their API and support developers in the app development cycle.
The open data and open knowledge community As was the case with the community of developers,
the open/big data community's instruments are very similar to the so-called enablers in section X. In particular,
Competitions aim to bring together the data sets made available by (local governments, with the app developers or the community of open data users.
Competitions are aimed at developers, researchers, journalists and anyone who has a keen interest in the reuse of open data,
as their main goal is to promote the use/reuse of data sets. 41 Many open data competitions have been organised throughout the years by (local governments themselves or by other organisations.
In November 2013, for example, the Energy department of the United states launched a competition to encourage the creation of innovative energy apps built with open data109.
between February and March 2014, the Science for Solutions open data competition took place in order to encourage data visualisations,
what is said to be one of the biggest competitions of open data in the region:
In this respect, many open data portals include a section for developers. These same sites can also be an interesting tool
in order to share examples of using/reusing open data. Some of them list the apps that have been developed by companies
or the public administration itself by means of suing the open data sets. It is the case of Open Data Euskadi in Spain111
Open Data Vienna112, or Open Data Toronto113. Regarding motivations, there is a need to differentiate between (local governments'motivations and open data users'motivations.
We have approached already the latter when analysing the community of developers. Thus, we will now focus on the former.
Local governments have three important motivations when launching open data portals. First of all, most of them aim at being more transparent.
For them, open data enhances transparency because it shows what the government is doing. Increased transparency also relates to other benefits that open data could contribute to, namely increased participation in political life, stronger democracy or e-governance.
Much literature and many policy reports are actually based on the assumption that open data is a tool to enhance transparency.
In addition, it is argued often that transparency could lead to better accountability of the government. However
several researchers have challenged also the idea that opening data will result in transparency and the idea that transparency automatically leads to more trust in the government.
Research has shown that the assumption that open data automatically results in transparency is too simple.
There are at least four factors which we believe influence open data transparency: 1) the type of data opened, 2) what one can do opened with the data
and how they are displayed, 3) the undesired effects of opened data and 4) the costs of open data transparency apart from the systems, resources,
capabilities and other means to make sense out of data. Offering better and new services is another motivation to engage in open data initiatives.
According to Berners-Lee (2012), opening up data is fundamentally about more efficient use of resources and improving service delivery for citizens.
More and more, citizens expect city services to be available online. Reusing public sector data can lead to the development of improved
more efficient online public services. Also, merging data and information digitally leads to improved collaboration between city departments and more efficient internal information sharing.
This can also lead to improved e-government services being developed by public administrations. What's more, local authorities are actively pursuing open data strategies to collaborate with citizens and the private sector in developing services from this data.
Co-created or co-produced public services better meet the citizens'demands. Also, local governments can use their data to provide (real time) information to address issues from traffic congestion to peak load electricity management.
Other services such as reporting tools can allow citizens to report local problems to the council just by locating them on maps.
Finally local governments are driven also by the possibility that companies produce economic value from their public data,
creating services and applications from those free data. This means a new market niche, based on digital contain,
what helps to create richness and the possibility to offer added value services. Additionally, it promotes the competitiveness among companies,
affording the possibility of tendering this public and free information and obtaining a benefit. Indeed, according to the Eurocities Statement on Open Data, opening
and reusing public sector information can potentially create economic gains of up to 40 Billion euros annually in the European union.
Incentives for the open/big data community should take into account the instruments'flaws and the needs of the community in terms of motivations In this respect,
political incentives aimed at increasing the government's reputation are key. Thus, if it is true that opening data does not necessarily lead to more 42 transparency,
efforts are needed to enhance the links between opening data, increasing transparency and increasing trust and legitimacy.
Because reputation from a marketing/image point of view also matters, political incentives in terms of communication, diffusion and knowledge sharing are important as well.
Technical support in order to address the make the most of opened data is another incentive. There are some programmes that offer this type of support.
Open Data Support for example, is a 36-month project of the DG CONNECT of the European commission to improve the visibility
and facilitate the access to datasets published on local and national open data portals in order to increase their reuse within and across borders.
1) data and metadata preparation, transformation and publication services that will enable them to share the metadata of their datasets on the pan-European linked metadata infrastructure delivered by the project,
2) training services in the area of (linked) open data, aiming to build both theoretical and technical capacity to European union public administrations,
in particular to favour the uptake of linked open data technologies, and 3) information technology advisory and consultancy services in the areas of linked open data technologies, data and metadata licensing,
and business aspects and externalities of (linked) open data. Certainly, monetary incentives also matter. Funding open data projects may encourage the release of public data.
The Cabinet Office and the Department of Business Innovation and Skills, in the United kingdom, are, for example,
supporting organisations who want to improve their data publication. In this respect, they are helping to unlock data from public bodies by awarding 1. 5 Million pounds to projects as part of the Release of Data Fund and the Breakthrough Fund.
Smart citizens Two are the instruments mainly used by those citizens who want to take part in crowdsourcing initiatives:
projects and platforms. Both of them are related, assome crowdsourcing platforms revolve around specific projects and others (mainly crowdfunding platforms) display a list of projects that need citizens'input.
The authors suggest that financial incentives may be used to control trade-off between accuracy speed and total effort.
Leaving aside portals that display public open data, previously analysed, governments use transparency portals as well,
The indicators aim at evaluating the data and the information public organisations publish on their transparency portals.
using the internet to gather instantaneous real world data from which knowledge is extracted and used to dynamically (re) shape policy actions.
The speakers, all leading DSI practitioners highlighted how digital social innovation is enabled often by open data, free software,
and data as knowledge commons. 49 1 2 3 4 5 6 Opportunities 7 and chalenges Generating Ideas Developing
common standards) Increasing the potential value of digital SI (eg making available open data, ubiquitous broadband) Enabling some of the radical, disruptive innovations emerging from digital SI new approaches to money, consumption, education,
identity and payment data Many US companies have patents on identity, social and payment data.
There is a need to require the European Public sector and EC funded projects to not fall into this trap
and provide open data sets on social, identity and payment. Many US companies have patents on identity, social and payment data.
There is a need to require the European Public sector and EC funded projects to not fall into this trap
and provide open data sets on social, identity and payment. Public data sets available to encourage innovation By ensuring there are open data sets available from the European public sector
and EC funded projects will remove barriers from social innovators who often rely too much on Facebook,
Twitter ect. for data. It will create more space for innovators to build easier and better tools.
rights and fundamental freedoms There is increasingly more personal and social data available online which threatens individual privacy and freedom.
and rules on this data and helping individuals maintain control over their own data will prevent infringements on privacy.
create an open decentralised digital ecosystem including open data distributed repositories, distr buted cloud, distributed search, decentralised social networking, public identity management,
The internet ecosystem today is highly centralised The current Internet is dominated by a handful of mainly US companies that control all the layers of the tecosystem (app store, cloud, machine learning, devices),
an increasing concentration of power in the hands of a few data aggregators (e g. over the top players), none of which is located in Europe (Google controlling nearly 82%of the global search market and 98%of the mobile search market,
Furthermore, the Digital economy is now mainly based on business models that aggregate, analyse and sell personal data, turning personal data in what has been defined as the oil of the Internet economy.
European SMES, developers and social innovators are innovating with cheap open hardware, open source software, open knowledge, open data and analytics faster,
and are producing valuable data about people, the environment, biometric and sensor data (as shown in the DSI map129
but these data are used not yet to enhance the public good at a systemic level. What needs to happen is to channel more resources
and coordinated policy actions to support grassroots and social innovation. There is a common sentiment that a strong public intervention at EU level is needed to properly support these areas of developments which,
This includes the need for open data distributed repositories, distributed cloud, distributed search, and distributed social networking.
including data portability. In the Iot there will thus be a social contract between people and objects with ethical implications.
and ensure that businesses receive guidance on data anonymisation and pseudonymisation. 3. The main questions in a data-driven society emerge around new governance modalities for Big data, collective ownership of data, data portability,
and how to valorize data as knowledge commons). Citizens should trust the institutions that control
and negotiate their data and take decision on their behalf. Users'social graphs (personal attributes, friends and relationships) and interest graphs
(what people like and do) are harnessed and sold to advertisers to extract and‘mine'targeted market information.
The question is how to assure user control over personal information in an ocean of commercially valuable Big data.
Defining sensible governance modalities for big data will requires a large collaboration between public and private actors. 56 4. Identity Management is becoming a very important issue in the digital economy
The aggregated data extracted from the analysis of our identities (what companies define as social graphs)
A broader investigation and the understanding of the implication of such mechanisms are crucial for the understanding of future bottom-up digital economies.
Regulation matters, particularly regarding certain issues as open access, open data, open standards, and public sector information reuse,
topics already tackled by the European commission (see, for example, the Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020 or the PSI Directive,
that digital social innovation is a lot about open knowledge and open data policies. Therefore, regulating open data standardization across Europe or setting up a European open data agency would be interesting ideas.
Funding is critical as well. The analyses of communities have shown that the lack of money hinders innovation within the communities.
But action is needed at all levels. 58 6. Analysing network data: Exploring DSI Network effect (WP2)
In order to analyse the relationship data from the mapping, we are adopting social network analysis to detect patterns of relations
One of the primary problems facing the mapping of an open-ended field such as DSI is how to direct the multiple diverse streams of data from interviews to social media into a central repository capable of giving a big picture of European
The data collected at http://data. digitalsocial. eu network represents DSI organisations and their social relationships mapped in the form of graph that is a collection of nodes
This means we can use this ever-expanding visualization and network database as a tool for crowdsourcing even more information about DSI in Europe,
therby achieving the critical mass necessary to large-scale European social problems. 6. 1 Network analysis Methods One of the tasks of this second interim report is to both determine how the current data can help to answer a set
Only with such a framework can data and hypotheses be interpreted in a sensible manner without projecting preconceived,
and often wrong, assumptiosn onto the data-Set in particular in the longer term, this requires both an unbalanced sample, in
which we assume the data adequately reflects the empirical phenomena at hand, and, as network-based data often assumes a non-Gaussian distribution such as a powerlaw.
Phrasing both the null hypothesis and alternative hypotheses in terms of network theory must be done with care. There must then be enough data to adequately test the hypotheses
using mathematical techniques that can statistically quantify the level of confidence in the proof of the data for any given hypothesis. For non-Gaussian distributions such as power-laws,
traditional t-tests against Gaussian distributions and even traditional statistics around averages and means are scientifically invalid134.
due to the small and mostly disconnected data-set we currently have gathered, where it seems there is a large bias towards the United kingdom
therefore ourselves to a more broad-stroked analysis of the data. From this analysis will come a number of hypotheses that we will more rigorously quantify
We still have concerns that the data-set is biased heavily towards English speakers due the lack of translation of the website into languages outside English We still believe that many more actors in countries such as Italy, France,
so that the data-set will be a more representative sample of digital social innovation in Europe. We earlier estimated that we need approximately 1
Currently we still have only half the data we need for a full analysis. However,
we can eyeball the results of the data-set and determine general trends, as well as commence with a basic quantitative analysis. 6. 2
the data is disconnected mostly. There are only 136 organizations with connections to other organisations (23%.%It appears that the vast majority of DSI organisations in Europe are disconnected from each other.
Indeed, if we graph the data-set of only connected organisations, we can see a clear power-law style distribution arise,
and a vast long tail of not very well connected organisations (89%of entire data has three
In the final version of the report, we will do significance testing on this hypothesis with a larger data-set.
In detail, there is a clustering coefficient of. 887, signalling a fairly high density of interconnections in existing communities (Latapy, 2008).
The way to interpret a clustering coefficient is that it is the measurement of how likely it is that the organisations linked to each other are linked also.
If we take our data at face value for the most part that does not seem to be happening organically.
when data has been added, given that otherwise the experiments will be very hypothetical and possibly erroneous for example,
we muct (1) still collect more data and to take into account the fact that (2) our hypotheses,
While we have doubled approximately the data we gathered in the first phase, we will need to almost double that amount again to get the kinds of robust results we want, namely to around 1000 organisations.
and draw upon existing data and research from other sources. Level 2 You are gathering data that shows some change amongst those using your product/service At this stage,
data can begin to show effect but it will not evidence direct causality. You could consider such methods as:
pre and post survey evaluation; cohort/panel study, regular interval surveying Level 3 You can demonstrate that your product/service is causing the impact,
and you will need data on costs of production and acceptable price point for your customers.
://www. apps4ottawa. ca/en 78. http://data. gov. uk/79. https://data. cityofchicago. org/80. http://www. socrata. com/81
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