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1. See, eg., Open Data Handbook: ‘What is Open Data?’ (Open Knowledge). Available at
opendatahandbook.org/guide/en/what-is-open-data.
2. See, eg., What are Open Educational Resources (OERs)? (UNESCO). Available at
www.unesco.org/new/en/communication-and-information/access-to-knowledge/open-educational-resources/what-are-
open-educational-resources-oers.
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3. See, eg., Making the most of data: Data skills training in English universities (Universities UK, 2015). Available at
http://www.universitiesuk.ac.uk/highereducation/Pages/MakingthemostofdataDataskillstraininginEnglishuniversities.aspx
#.Vi-pf9_tlBd.
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Chiara Ciociola & Luigi Reggi
A Scuola di OpenCoesione, Italy
Keywords: 78777
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1. ASOC was launched thanks to a partnership between the Department for Economic Development and Cohesion (now
Agency for Territorial Cohesion and Department for Cohesion Policies at the Italian Presidency of the Council of
Ministers) and the Ministry of Education. The project also involves the European Commission’s network of information
centres, “Europe Direct”. Additional information on the team and the institutions involved can be found on the ASOC
website: http://www.ascuoladiOpenCoesione.it/team/.
2. Cohesion policies aim at improving economic well-being in regional and local contexts, by transforming regional
disparities into opportunities for growth and development. European Cohesion Policy is financed by EU Structural and
Investment Funds in order to promote economic and social development in the regions lagging behind in terms of
growth, most of which are located in Southern and Eastern Europe. In Italy, national policies for cohesion provide
additional financial resources, concentrated in the Italian mezzogiorno (Southern Italy). In Italy, the overall 2007-13
cohesion budget, made up of national and European funds, amounted to approximately 100 billion Euros over a 7-year
period, which on a yearly basis accounted for as much as 1% of GDP. Italy is a net contributor to the EU budget and the
third biggest beneficiary country of EU structural funds (after Poland and Spain). However, Italy is also among the
countries with the lowest absorption rates of the funds, and issues about misuse of the funding have been raised. This
explains why cohesion policy is a particularly hot topic in Italy.
27
Figure 1: Participant schools
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3. The project uses the Monithon initiative’s tools and methodologies to take action and report malpractice, but also to
play a role in making all these projects work, accelerating their completion and understanding whether they actually
respond to local needs (Buttiglione, P. L.; Reggi, L. 2015). Monithon (literally "monitoring marathon”) and promotes the
citizen monitoring of cohesion policy through the active involvement of communities and a shared methodology. The
Monithon idea, initially conceived by the OpenCoesione team, was presented to the “civic hacking” community and soon
transformed into an independent civil society initiative run by an enthusiastic group of developers, activists and
journalists, based on a web monitoring platform operational since 2013. Within the project, Monithon’s expertise lies in
monitoring not only of public funds in general, but of cohesion funds specifically. Through “monitoring marathons”, groups
of citizens – sometimes under the guidance of local CSOs – embark on investigations within an area in order to gather
information on specific projects of interest. By doing so, participants pool together useful material with a view to
evaluating the effectiveness of public spending, and apply bottom-up methods of exercising control over public policies.
The OpenCoesione-Monithon partnership reached 4th place and received the silver award at the 2014 Open
Government Awards for citizen engagement.
29
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4. During the last edition 86 schools have participated in the course. 26% of the teams were supported by both an EDIC
center and an association; 33% were supported only by a EDIC; 15% by only one association.
5. Two surveys are conducted among the teachers. The first survey is to assess the characteristics of the selected
schools (e.g. the teachers’ digital skills, the number of team members and their different characteristics, etc.) at the
beginning of the school year. The second survey, conducted upon conclusion of the ASOC project, assesses the results.
30
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6. The students participate in a “Data Expedition”, which is adapted from the original model developed by Open
Knowledge (see http://schoolofdata.org/data-expeditions/). During this exercise, the students divide into groups with
specific roles: project manager; coder; analyst; social media manager; storyteller; blogger; head of research (see
http://www.slideshare.net/ascuoladioc/i-ruoli-di-a-scuola-di-OpenCoesione).
7. 82% of the teacher has expressed favourable judgement about the efficiency of the working groups. 74% has
positively evaluated the attribution of roles and the level of inclusion of the students in the activities. Also, 76% of the
teachers has expressed a very positive opinion about the interaction among the students.
31
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Figure 3: Activity board
8. See the following examples of data analysis that the students have conducted:
•http://www.ascuoladiOpenCoesione.it/i-dati-ancorati-negli-abissi-del-liri/
•http://www.ascuoladiOpenCoesione.it/il-dato-e-tratto/
•http://www.ascuoladiOpenCoesione.it/iii-lezione-analizzare-iiss-giulio-cesare/
•http://www.ascuoladiOpenCoesione.it/tell-me-more-about-open-data/
•http://www.ascuoladiOpenCoesione.it/terza-lezione-analizzare-liceo-scientifico-la-cava/
32
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9. Content Analysis: Acropolis E-Team Alatri (see, http://prezi.com/ya4ynbelgpmo/?
utm_campaign=share&utm_medium=copy&rc=ex0share).
10. Only 24% of the top 100 Italian municipalities publish local data as open data (Between, 2014).
11. Map of the Castles, https://a.tiles.mapbox.com/v4/soniamurruni.lm6504jg/page.html?
access_token=pk.eyJ1Ijoic29uaWFtdXJydW5pIiwiYSI6ImJvRlF0ZGMifQ.wokEt0Yv5nEvQ3YusUrWKw#10/38.2603/15.9
233
12. In 2014, the Stanislao Cannizzaro high school in Palermo requested the publication of local open data on local
transportation. The local government responded and published the dataset on its website (see
http://www.comune.palermo.it/opendata_dld.php?id=318#.VFyhLvSG-hM). In 2015, the Veronica Gambara high school
in Brescia added new items to a public dataset on local bike lanes. The Lombardy Region then published this improved
version of the dataset on its open data portal (see https://www.dati.lombardia.it/Turismo/Ciclabili-Progetto-Garda-
Bresciano/e6qe-v3v4).
33
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13. Some examples:
•Liceo Scientifico Peano Pellico, Cuneo: http://liceocuneo.it/oc1e
•Liceo Bertarelli Ferraris, Milano: https://youtu.be/9AR6YmiTTLo
•Liceo V. Gambara, Brescia: http://www.powtoon.com/embed/dPzsSkI3TuJ/
•Istituto E. Medi, Galatone: https://youtu.be/lC29Z66flPg
•Liceo G. Giordani, Monte Sant'Angelo : https://youtu.be/wdk7Ul8372M
•Liceo G. Asproni, Nuoro: https://youtu.be/8Z6SOV1q_BQ
34
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14. Art. 115 of the EU Regulation no. 1303/2013 forces the all European Managing Authorities of EU Structural and
Investment Funds to publish highly detailed data on the beneficiaries and the projects as open data.
36
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Institute of Educational Technology,
The Open University, UK
Keywords:79*%(&77(&779
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1. https://www.gutenberg.org/.
2. http://www.opendatanottingham.org.uk/.
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Figure 6: Open Data Nottingham: Car park occupancy data. Available from:
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Figure 7: An example solution produced for the Car Parks Monitor coursework.
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Figure 8: Excerpt of Open Nottingham Car Park Occupancy Data in CSV format. This shows only the
relevant columns for the coursework. Each row represents the current status of an individual car park.
Nottingham City Council, 2015. Licensed under the Open Government Licence.
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Bordini
Publishing Program, Simon Fraser University,
Canada
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Alan Dix(1) & Geo3rey Ellis(2)
(1) Talis and University of Birmingham, UK
(2) University of Konstanz, Germany
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1. Summary of available data:
•Daily blogs: approximately 150,000 words. The blogs are in the process of being semantically tagged, but this
is incomplete, so only the raw text was available to the students.
•Photos: approximately 19,000 photos, on average more than one every 100 metres, all time stamped.
•Audio blogs: short audio notes, approximately 10 per day, again time-stamped.
•GPS: Collected by two different devices (a Garmin dedicated GPS device, and a tracking app on a mobile
phone).
•ECG: Full ECG data (heart trace) for 60 days of the walk (the largest ECG trace in the public domain).Typically
including one overnight for every two days to yield baseline long-term data as well as more dynamic daily data.
•EDA (Electro-Dermal Activity —previously known as GSR, galvanic skin response) as used in lie detectors to
measure emotional reaction.
•Accelerometers: On both wrist and chest.
58
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Figure 11: GPS data cleaning (Kolb, 2015): a) raw data from Garmin, b) cleaned merged data.
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2. Here are two examples the authors’ have encountered in dealing with open government data. While the kinds of
'rawness' seen in the AWW data set, are less evident in most government open data, which is already sanitized, this
does not necessarily mean they are easier to use.
In 2010 one of the major issues in the UK General Election was the national deficit, and in particular the performance of
the last Labour government. The first author tried to find historic data giving actual figures —it was surprisingly hard. He
first looked to the National Statistics Office, the holder of official data for the UK. Undoubtedly the numbers were there,
but spread over so much detailed data with terms that only an expert economist would understand. Happily this data is
now available at the Guardian Data blog, which gives a simple graph and spreadsheet of deficit and national debt by
year (Guardian, 2010).
In another example, the OnSupply project gathered data related to renewable energy usage and related demographics
on Tiree, one of the Scottish islands (Catalyst, 2015; Simm, et al., 2015). Some data was available from government
open data, but listed using region codes. While these would clearly be well understood by those dealing with the data
day to day, it took the team (hardly digital novices) a considerable time to find the tables to decode these and find the
right code corresponding to the area including Tiree.
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Virginia Power
University of the West of England (UWE), Bristol,
UK
Keywords:77N(7676776'7
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1. http://courses.uwe.ac.uk/P11012/2015#coursecontent.
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2. http://en.unesco.org/gem-report/sites/gem-report/files/2015_report_dataviz/index.html.
3. http://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx.
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4. http://www1.uwe.ac.uk/aboutus/visionandmission/sustainability/education/ourapproach.aspx.
5. http://www.hefce.ac.uk/pubs/year/2014/201430/.
6. http://www.qaa.ac.uk/en/Publications/Documents/Education-sustainable-development-Guidance-June-14.pdf.
7. http://www.oecd.org/sti/sci-tech/1913021.pdf.
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8. https://en.wikipedia.org/wiki/Knowledge_Economic_Index#The_4_pillars_of_the_Knowledge_Economy_framework.
9. http://www.gesci.org/.
10. https://www.youtube.com/watch?v=oBC7bA-PeSI.
11. https://www.youtube.com/watch?v=4mWYA3Z-YJ0.
12. http://www.cfses.com/documents/knowledgeeconprimer.pdf.
13. http://www.bbc.co.uk/news/business-12686570.
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14. https://openknowledge.worldbank.org/.
15. https://www.jisc.ac.uk/news/data-literacy-and-skills-development-vital-to-uk-economic-health-13-jul-2015.
16. http://issuu.com/universitiesuk/docs/makingthemostofdata.
17. http://www.nesta.org.uk/publications/skills-datavores-talent-and-data-revolution.
18. http://www.britac.ac.uk/policy/count_us_in_report.cfm.
71
Figure 13: Literacy rainbow. CC BY-SA 2.0 (Justin Grimes).
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20. http://en.unesco.org/gem-report/sites/gem-report/files/2015_report_dataviz/index.html.
21. http://www.education-inequalities.org/.
22. http://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx.
23. http://www.ordnancesurvey.co.uk/education-research/index.html.
24. http://schoolofdata.org/data-expeditions/.
25. http://ukdataservice.ac.uk/media/398744/learningteaching.pdf.
26. https://defradigital.blog.gov.uk/category/open-data/.
73
Figure 14: Open Government Data. CC BY-SA 2.0 (Justin Grimes).
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