Synopsis: Ict: Data:

data on social enterprises in Hungary, Romania, Spain, Sweden, and the UK22 tell us that 75%of these

and reflections and this data is analysed at a local and regional level by project staff to identify any patterns

41 Recent data tobe found on CIRIEC 2012 42 http://www. euclidnetwork. eu/news-and-events/sector-news/517-research-a-new-approach-to-welfare-generating-experiences-study

Member States on Innovation and Mainstreaming. http://ec. europa. eu/employment social/equal/data/document/200606-reflection

3. 3. 1. 2. Database of labels and certifications of social enterprises...110 3. 3. 1. 3. Social innovation prizes...

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 and

-tity, 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, distribut -ed search and distributed social networking.

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. Citizens should be aware that technical solutions do not work by

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

insufficiently precise and systematic use of measurement and data There are efforts underway to address these barriers, both in the European union (e g

mobility, big data, cloud computing packaged in new digital government offerings y Adopting an attitude of experimentation and entrepreneurship (government itself

through primary and secondary research) and analyse data on the scale, characteristics and patterns of development of social enterprise in each country studied

Europe 2020 priorities in areas like innovation, the digital economy, employment, youth industrial policy, poverty, and resource efficiency.

manages to give the digital economy the necessary political attention. It gave rise to the cooperation and commitment of various Commission services around a common

of social enterprises, database of labels support for local and national authorities to build integrated strategies for social

data necessary to measure the efficacy of the programme 3. 1. 4. New practices for making policy

growth and jobs in the digital economy through an online platform. Citizens will identify barriers to growth, job creation and investment,

More information and data collection concerning the use and volume of dormant ac -counts in each Member State is necessary

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

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 edi -torial. Moreover, the website features case studies of the most successful social innova

-cial media, distributed knowledge creation and data from real environments (†Internet of Thingsâ€) in order to create new forms of social innovation.

-tional Oceanic and Atmospheric Association and the World bank will provide a rich stream of input data and amplify

data is collected and produced y Increasing awareness of the potential of the network effect (CAP2020:

with statistics to life so that we can enjoy easier access to data and improve policy †beyond GDP€ in all kinds of

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

*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

The CAPS participants share data and collaborate to reach collective sustainability goals on open source platforms (open

data on European higher education learning mobility and employment in cooperation with Eurostat Furthermore, a European Alliance for Apprenticeships has been established to drive

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

Early School Leaving (ESL) aimed at in depth analyses of existing data and the collec -tion 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

-duction and data sharing, the internet of things, and social or user-generated content Results in progress are being presented at conferences aimed at involving entrepreneurs

-estimation because of lack of empirical data 133p 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

3. 3. 1. 2. Database of labels and certifications of social enterprises 3. 3. 1. 3. Social innovation prizes

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

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

much more interested 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.

of the research process †from design, recruitment, ethics and data collection to data analysis, writing up, and dissemination.

toward prescriptions emerging out of the data which can be employed for the improvement of future action

and analyse large quantities of data has been the basis for remarkable changes †for example: in flexible manufacturing, and

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

311 complaint system, embedded with GPS data pinpointing the exact location of the problem. These complaints will then get forwarded to the

18) Integrated user-centred data such as Electronic Patient Records in the UK, which, when linked through grid and cloud computing models

19) Citizen-controlled data, such as the health records operated by Group Health in Seattle, and the ideas being developed by Mydex that adapt

service which provide a database that can be analysed for patterns of recurring problems and requests

The gathering and presentation of data requires a process of interpretation. This should ideally include those

In analysing an issue or a set of data, it is useful to have the

online repository of ideas and experiences †that has a database of 4, 000 ideas online, receives a quarter of a million visitors a year, and, of those

and research data to demonstrate effectiveness and value for money (see list of metrics below) as well as adapting models to reduce costs or improve

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

provide funders or investors with data on impact; and to provide a tool for organisations to manage their own choices internally;

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

to gather chronic disease data in Sheffield and metrics geared to self -monitoring such as those used by Active Mobs in Kent

coaches, increasingly backed up by shared data services and networks Service design in the 1980s and 1990s often focused on disaggregating

Solutions & How-Tos Get the Data Economic Empowerment -A year-round conversation Forums, media spokespeople

•Girls Database/Scorecards •Girls Count Task force Reports •Partners & research initiatives measure girls more broadly

databases). ) With the increasing mixing of voluntary and professional roles (for example around care for the elderly,

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,

†for self-care for chronic disease, that combines rich data feedback with support structures which help patients understand

So while familiar data on income, employment, diseases or educational achievement continues to be gathered, there is growing interest in other types

public agencies to publish data on their balance sheets, or to show disaggregated spending patterns, or flows of costs, can then contribute

spending data for particular areas or groups of people. Too often, public accounting has been structured around the issues of targets, control

Wikiprogress, bringing together data and analysis on progress. The same year President Sarkozy commissioned Joseph Stiglitz to chair an inquiry

This includes file sharing services such as Napster, and open-source software such as the Linux operating system, the Mozilla Firefox browser,

transparent access to public financial and other data 342) Audit and inspection regimes which overtly assess and support

Guidestar†s services and databases in many countries worldwide, and New Philanthropy Capital in the UK

provides a useful platform for aggregating ultra local data Prosumption There has been marked a development of users becoming more engaged in

Data 17-18; 21-2; 101-105; 112; 114; 116 119-120; 204 De Bono, Edward 32

6. Analysing network data: Exploring DSI Network effect (WP2) 58 6. 1 Network analysis Methods 58

labs, including Fab labs, Living Labs, Hackerspaces and Makerspaces (4) the open data and open knowledge

The project†s most substantial challenge is to develop a crowdmapping facility based on open and linked data

Tanks to the open data mapping facility, in combination with our hybrid iterative strategy of case study interviews, workshops,

analyse the relationship data from the mapping, we are adopting social network analysis to detect patterns

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

Data is aslo categorised by tï¿""ï¿UZQPMPHZÏ¿PGÏ¿PSHBOJTBUJPOT (e g. Government and public sector organisations, businesses, academia and

USFOET the organisations and their activities fit under (open data, open networks open knowledge, open hardware

of strong and weak DSI network in Europe, based on the open data set on organisations captured on www

for open data distributed repositories, distributed cloud, distributed search, and distributed social networking The Future of privacy, data protection, trust & ethics, emphasising the need for privacy-aware technologies

(PPE, 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,

the open data and open knowledge community,(5) smart citizens, and (6) the open democracy community

data mapping website •&ohbhjohï¿UIFÏ¿%%4*ï¿DPNNVOJUZÏ¿ï¿an overview of the engagement strategies to involve the DSI community

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

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, case studies and potential funding opportunities

visualisations showing all the relevant dimensions in the data, such as EU countries with most DSI projects

the survey data Figure 1. A view of the European section of the map. At this scale organisations are clustered to show how many exist in

meet, such as the Open Data community at the Open Knowledge Conference and the Maker community at the

to open data and also in  encouraging more women to participate in learning to code through open

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

engaged extensively with other related research projects to both engage their networks and access the data

working on Open Data, Open Networks, Open Hardware and Open Knowledge need to overcome to scale their work

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. Since then, Mrs Neelie

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

open data incubator within Horizon 2020 aimsâ to help SMES set up supply chains, and to get access to

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

Behavioural, design-led and data-driven Network structure Centralized and hierarchical Decentralized and digitally connected

innovation space (e g. open data, open knowledge, open hardware, open networks), and identifies the key

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, 3) citizen advocates who want services and information to make their lives better, 4) open

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

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;

accepted premise underlying these objectives is that the publishing of government data in a reusable format

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

are successfully developing open data portals. In the United states, the cities of Chicago, San francisco Philadelphia, and New york are only a few examples worth mentioning.

and Metropolitan Rennes in France have also set up open data websites at the regional level that can be considered good practices

Open/big data (Local) govern -ments Competition organ -izers Networks of develop -ers Open data evange

-lists Top-down (govern -ments decide what when and how to open Lack of standardization Lack of reuse

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.

Their goal is usually twofold: on one hand, they aim to be more transparent; on the other, they pursue an increase

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

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

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 promoted an initiative 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

open data among the members of the network. It also backs up open data individual requests to governments

Usually, networks of developers are virtual. In this respect, social media networks play a significant role. They

data portals. Data. gov. uk77 the open data portal of the United kingdom, has an â€oeinteract†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 PWFSOBODF 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 already

referred 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

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

generally used 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

Open/big data Organization of competi -tions Support for networking Knowledge sharing and dissemination New services

large data sets and offers, for a fee, training, consulting, and technical support services. Though the services

for citizen-led environmental data collection supported by a small data platform for analysis and advocacy.

tool enables civic-minded groups to empirically verify government data and inaugurating a new generation of

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, in this section we will refer to the organization of

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.

Several hackathons have been organized since them across the country. In Queensland (Australia), between February and March 2014, the Science for Solutions open data competition took place

in order to encourage data visualisations application development or other unique treatments of the science datasets provided by the Department of

what is said to be one of the biggest competitions of open data in the region:

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

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

opening data will result in transparency and the idea that transparency automatically leads to more trust in the

Research has shown that the assumption that open data automatically results in transparency is too simple.

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

public data, creating services and applications from those free data. This means a new market niche, based

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

Incentives for the open/big data community should take into account the instruments†flaws and the needs of the community in terms of motivations.

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.

Technical support in order to address the make the most of opened data is another incentive. There are some

Open Data Support, for example, is a 36-month project of the DG CONNECT of the European commission toâ improve the visibility

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 servicesâ that will enable them to share the metadata

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 servicesâ in the areas of linked open data technologies, data and metadata licensing,

and business aspects and externalities of (linked) open data. Â 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.

In section 5. 4, we have referred already to online platforms for both crowdsourcing and crowdfunding initiatives

open data, previously analysed, governments use transparency portals as well, which give information about different topics,

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

digital social innovation is enabled often by open data, free software, and open hardware platforms. In many

might be used such as digital human rights and data as knowledge commons 49 1 2 3 4 5

making available open data, ubiquitous broadband Enabling some of the radical, disruptive innovations emerging from digital SI †new approaches to money

Open Standards for social, 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 re -quire the European Public sector and EC funded

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 innova -tors who often rely too much on Facebook, Twitter

ect. for data. It will create more space for innova -tors to build easier and better tools

data available online which threatens individual privacy and freedom. By having set guidelines and rules on this data and helping individuals

maintain control over their own data will prevent infringements on privacy Citizens engagement and feedback

Democratic and distributed social network Social network based on open source code to promote the most interesting news decided by the people,

including open data distributed repositories, distr buted cloud, distributed search, decentralised social networking, public identity management

concentration of power in the hands of a few data aggregators (e g. over the top players), none of which is

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 â€oeoil of the Internet economyâ€.

and weakens data protection but also commodifies knowledge, identity, and personal data. 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

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.

awareness, and ensure that businesses receive guidance on data anonymisation and pseudonymisation 3. 0qfoï¿ï¿

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

â€oeinterest graphs†(what people like and do) are harnessed and sold to advertisers to extract and †mineâ€

an ocean of commercially valuable Big data. Technical Solutions do not work by themselves, therefore legal and commercial solutions have to be based in technology and integrated with the appropriate

Defining sensible governance modalities for big data will requires a large collaboration between public and private actors

important issue in the digital economy, since social interaction and relations are mediated increasingly by the network and their instruments.

The aggregated data extracted from the analysis of our identities what companies define as â€oesocial graphsâ€)

mechanisms are crucial for the understanding of future bottom-up digital economies. The agency that public or private providers have today on identity is mainly at device level.

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, the Directive on the reuse

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

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 and argue that the causal success of DSI located in the social structure.

open-ended field such as DSI is how to direct the multiple diverse streams of data from interviews to social

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 and

This means we can use this ever-expanding visualization and network database as a tool for

One of the tasks of this second interim report is to both determine how the current data can help to answer a

with such a framework can data and hypotheses be interpreted in a sensible manner without projecting pre

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 TJHOJMDBODFÏ¿UFTUJOH, as network-based data often assumes a non-Gaussian distribution such as a power

-law 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

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

broad-stroked analysis of the data. From this analysis will come a number of hypotheses that we will more

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.

so that the data-set will be a more representative sample of digital social innovation in Europe. We earlier estimated that we need approximately

Currently we still have only half the data we need for a full analysis. However, we can â€oeeyeball†the results of the data-set

and determine general trends, as well as commence with a basic quantitative analysis 6. 2 What is the distribution of social innovation across Europe

the data is disconnected mostly. There are only 136 organizations with connections to other organisations (23%.%It appears that the vast majority of DSI

Indeed, if we graph the data-set of only connected organisations, we can see a clear â€oepower-law†style

organisations (89%of entire data has three or less links, including zero links). In the final version of the

report, we will do significance testing on this hypothesis with a larger data-set. The distribution of links is

In detail, there is a clustering coefficient of. 887, signalling a fairly high density of interconnections in existing communities (Latapy, 2008.

to interpret a clustering coefficient is that it is the measurement of how likely it is that the organisations

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, it is likely that there are other networks that have not been captured yet in this

we muct (1) still collect more data and to take into account the fact that (2 our hypotheses,

we have doubled approximately the data we gathered in the first phase, we will need to almost double that

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;

standardisation of delivery and you will need data on costs of production and acceptable price point for your customers

78. http://data. gov. uk /79. https://data. cityofchicago. org /80. http://www. socrata. com

/81. https://okfn. org 82. http://www. mycityway. com /83. http://en. seeclickfix. com /84. http://earthquake. usgs. gov/earthquakes/dyfi

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