#  1\. Executive Summary

This deliverable contains the first version of the Data Management Plan (M6).
It describes the nature of the data that will be collected and used in the
Open Data Lab Platform. The open data has been divided into two overarching
data categories:

  1. Open Data 

     * **Category A** : _Open and ready to use_

  

     * **Category B** : _Open - but not yet in a ready to use format_

  

  

  2. Closed Data 

     * **Category C** : data that will not be used in this project 

  

  

  

It also describes the overall architecture of the OpenDataLab Platform that
will contain the data and touch base with the current development on how the
data will be stored, made available, shared, reused and used during and after
the project. It also outlines the ethics related to working with Open Data as
well as presenting an outlook for the further work with the deliverable.

#  2\. Introduction

The purpose of this deliverable is to ‘provide an analysis of the main
elements of the data management policy that will be used in the project with
regards to all the datasets that will be generated by the project’ as stated
in the document ‘Guidelines of data Management in Horizon 2020  1  . According
to the DoA (p. 37), the aims of this deliverable (in M6) are to ‘describe the
nature of the data that will be collected and integrated and the way it will
be stored, made available and used during and after the project.’ According to
the ‘Guidelines of data Management in Horizon 2020  2  ’ p.5, the Data
Management Plan should be written from a dataset-by-dataset point of view.
However since we do not yet have the specific datasets this initial draft of
the Data Management Plan will focus on describing the overarching data
categories that will be encountered during the hackathon cycles as well as
describing the overall architecture of the OpenDataLab Platform that will
contain the data. As each pilot comes closer to defining its specific
challenges during the pre-hack phase, we will gain a clearer picture on which
datasets to collect and upload to the OpenDataLab Platform. This will
contribute to shaping the next editions of this deliverable, which are
contractually bound to appear by Month 15 and Month 30.

##  2.1. Overview on structure

This initial draft of the Data Management Plan focuses on describing the
overarching data categories that will be encountered during the hackathon
cycles followed by how each category will be managed.

Firstly the two data categories are presented in section 3.0. The subsections
describe the origin of the data as well as the possible outcome of using the
particular category (concept, mobile application, use case, etc.). Then the
structure of the Open Data Lab Platform where the data will be managed is
described in section 4.1. The subsections elaborate on access, data standards,
data sharing, archiving and preservation, reuse of the data and ethics. Lastly
the outlook for the development is presented.

#  3\. Description of the Data Categories

A large quantity of data concerning e.g. urban environments, weather, census,
etc. is being produced every day. Such data can be found, for example as
published government data sets, in private companies who have recorded the
behaviour of their customers or by scientific research, or generated by users,
who often record their activities and in some cases share such data with
friends or social networks. The term ‘Open Data’ refers to those data sets
that are made publicly available. These data have a large potential to
generate new applications and to enhance several aspects of human life,
including transport, healthcare, climate and even human behaviour. The way to
manage these data as well as realising the real potential of open data is
still to be fully discovered.

The O4C project intends to contribute to create a demand for applications
based on open data and to include citizens as a driver for innovation in the
open data arena. However many citizens are not necessarily able to handle Open
Data, or even to imagine what to do with them. To help close this gap between
users and data, a clarification of the different types of data is needed. In
general ‘Open Data’ means that the data is publically available and can be
used, modified, and shared freely by anyone for any purpose  3  . With this in
mind the following categories have been created:

3) Open Data

  * Category A: _Open and ready to use_

  

  * Category B: _Open - but not yet in a ready to use format_

  

4) Closed Data

  * Category D: data that will not be used in this project 

  

The categories will be described in detail in the following sections. The
colour: green, yellow and red corresponds to the level of employment of the
data.

  * Green: the data is ready to use. 

  

  * Yellow: the data needs to be converted to a usable format or there are restrictions on the use 

  

  * Red: the data is not available in the O4C project 

  

_**3.1. Open Data - Category A** _

The general topic for this category is that the data can be classified as
open. It means that it can be freely used, re-used and redistributed by anyone
- subject only, at most, to the requirement to attribute and sharealike  4  .  
  
<table>  
<tr>  
<th>

**Category**

</th>  
<th>

**Public Data**

</th>  
<th>

**User Generated Data**

</th>  
<th>

**Datasets generated by Hackathon Participants**

</th> </tr>  
<tr>  
<td>

Category A:

Open and ready to use

</td>  
<td>

Publicly available datasets that are available in a structured format and
ready

to be used in the Platform

E.g. CSV - Comma Separated

Values

</td>  
<td>

Basically any content that users of a service have created that is accessible
through a graphical user interface

Examples:

\- GPS data

</td>  
<td>

Datasets that have been generated by the users for the O4C project

Merged datasets also belongs in this category

</td> </tr> </table>

**Table 1: Category A - Open and ready to use data**

##  3.1.1 Public data

The data is presented in a repository – an online platform – owned and
maintained by e.g. a company, an organization or by local, municipal or
national governments who have decided to make a selection of data available to
the public. Examples on this type of data may include data related to
demographics or urban planning, as well as to public transport or real-time
road usage. Data from this category can be located through repositories like
Open Data DK, Humanitarian Data Exchange, DR Archives, HIP.se – Health
Innovation Platform, etc. and is typically presented in a structured CSV
format. If the data has a restricted/limited use or isn’t free of charge it
belongs in Category B.

##  3.1.2 User Generated Data

When a user creates content that will be visible to the general public they
usually agree to this in the Terms of Use of the service. Examples for this
category are posts that are publically available via a graphical user
interface on e.g. Twitter. If the data has a restricted/limited use or isn’t
free of charge it belongs in Category B.

##  3.1.3 Data Generated by Hackathon Participants

This category refers to datasets that have been created during the hackathons.
The datasets can e.g. come from merging ‘Public’ or ‘User Generated Data’ into
new datasets. Since the data in this category is created from Open Data there
are no restrictions related to use, modification or sharing of the data.

##  3.1.4 Possible Outcomes and Available Datasets

It will be possible to manipulate Data from this category in the Open Data Lab
Platform. The outcome from this is mainly helping the hackathon participants
gain understanding of Open Data as well as aiding the development of new
services/improve existing ones during the hackathon cycle.

The data can be used as:

  * Components in mobile or web applications 

  

  * Concepts - i.e. mockups of mobile or web application 

  

  * Data examples for the participants to gain a greater understanding of Open Data 

  

  * Some data may not be off limits because of privacy reasons, as explained in category D, but due to the fact that the owners of the datasets have not yet made them available. Thus existing Open Data can be used as components in use-cases - proof of concept - that can clear the way for opening data that is not currently available as open data. 

  

##  3.2. Open Data - Category B

This category refers to data that needs to be converted or have certain
restrictions on its use. It means that the data is publically available but it
is not ready-to-use as the data from category A.  
  
<table>  
<tr>  
<th>

**Category**

</th>  
<th>

**Public Data**

</th>  
<th>

**User Generated Data**

</th>  
<th>

**Datasets generated by Hackathon Participants**

</th> </tr>  
<tr>  
<td>

Category B: Open - but not yet in an optimal format

</td>  
<td>

Publically available unstructured data like:

  * PDF 

  

  * Addresses 

  

  * Menus 

  

  * Library Catalogues Formats that need to be converted. 

  

As well as CSV files with restricted downloads (e.g. number of rows/file size)

</td>  
<td>

Data generated by everyday users of services and technology.

Examples:

\- Twitter

</td>  
<td>

Hackathon participants creating datasets as PDF or

XLS files

or merging of datasets with restricted use

</td> </tr> </table>

**Table 2: The three sub-categories of Category B**

###  3.2.1 Public Data

Public data refers to the often _unstructured_ data that is publically
available like online menus, libraries of information, catalogues, etc. Data
from this category is typically owned by a for-profit company or an
organization (for-profit or non-profit). This category also includes data
owned by public institutions, which have not published the data in a format
that makes them immediately useable (e.g. PDF)

The unstructured data requires more preparation than structured data, since it
will have to be converted into a useable format and perhaps be extracted from
the websites by using Dataproces’ software robots.

If the use is restricted it means that there can be a limited number of
downloads. The restriction can also refer to the ownership of the data, which
means that the data is free to use but is owed by someone.

###  3.2.2 User Generated Data

User generated data is data that is quite literally generated directly by
users of everyday services and technology. Examples from this category can be
GPS data, Geo-tagged Twitter data. Also for this category the unstructured
data requires more preparation than structured data, since it will have to be
converted into a useable format and perhaps be extracted from the websites by
using Dataproces’ software robots. Restricted use can also apply which means
that there can be a limited number of downloads or a payment is required to
use the data. The restriction can also refer to the ownership of the data -
that the data is free to use but is owed by someone.

###  3.2.3 Data Generated by Hackathon Participants

This category encompasses Hackathon participants creating datasets as PDF or
XLS files or merging of the Open Data that have restricted use.

###  3.2.4 Possible Outcomes and Available Datasets

It is more time consuming to get hold of the data from Category B than
Category A. It is possible to retrieve, collect or create datasets during the
pre-hack, hack or post-hack phase but as a general rule the possible final
outcome when using data from this category is _concepts_ . If the data is
converted it “moves” to category A. Data from this category can be used as
concepts - i.e. mock-ups of mobile or web applications.

##  3.3. Closed data - Category C

Data that is not open refers to personal, medical and other sensitive data
that is not open to the public and cannot be found by the general public
online. It also encompasses data that is only meant as internal information to
public administration. Data from this category will _not_ be available in the
O4C project. The same rule applies to data that are only available after
purchase, such as business data, etc.  
  
<table>  
<tr>  
<th>

**Category**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Category C: Closed data - data that will not be used in this project

</td>  
<td>

Any data that contain personal information e.g. medical data or data that is
only meant as internal information to public administration

</td> </tr> </table>

**Table 3: Category C**

#  4\. The OpenDataLab Platform

The OpenDataLab will be the place or playground where citizens can make their
ideas more concrete. The Platform will help support this vision by leaning
against the O4C approach: Explore, Learn/Apply, Consolidate and letting the
participants build on different levels with Open Data. Starting out with
building an understanding of Open Data, then moving on to building concepts
and finally building applications and possibly a viable business (figure 1).

**Figure 1: How the Open Data Lab Platform supports the O4C approach.**

The figure (1) shows an ideal process. In real life the path will be more
intricate and winding – however, one of the goals of the platform, as well as
the O4C project, is “straightening” out the process – helping citizens getting
data and getting there!

By helping citizens concretize their ideas, concepts and applications can be
developed and where the data is not yet open a well-founded use-case can be
created and used as an argument to ask the data owner to open the data. In
order to let the participants to build with Open Data both datasets and
selected tools will be available inside the Open Data Lab Platform.

The basic function of the Platform is to allow citizens to work with Open
Data. In order to work with Open Data you must have access to datasets and you
must have access to relevant tools.

Figure 2 shows a representation of the content of the Platform. It shows the
relationship between the local hackathon teams/the hackathon participants (the
box to the right – figure 2) and the Platform (the box to the right – figure
2).

  * Upload of datasets: The local hackathon teams will at all times be able to upload datasets to the platform while the hackathon participants - for now - will not have this opportunity. This division of roles is a measure to ensure relevance and quality of the uploaded datasets. It also helps ensure that there is sufficient storage space so that the platform will run smoothly. 

  

  * Access to the uploaded datasets: The Open Data Lab Platform contains five data storages - one storage for each location/country - and a selection of tools to work with Open Data. The participants at the hackathon can then access the platform, fetch datasets and use the tools to work with the Open Data. For now, each local hackathon team will only be able to access its own data storage. This is again to ensure quality, structure and stability. At a later time a feature might be added to the platform that allows mixing datasets from the different local hackathon teams. 

  

  * The platform will be linked to a developer toolbox from IBM, called Bluemix. Arrangements are being made at the time of the editing of this deliverable, to make sure that the toolbox could be linked to the platform, in order to give access to a large range of tools to analyse, search, visualise and working with data. 

  

**Figure 2 The OpenDataLab platform structure**

##  4.1. Access to the Data inside the Open Data Lab Platform

All hackathon participants – and anyone else who accesses the Open Data Lab
Website  _www.opendatalab.eu_ – can use the datasets and the tools inside.

##  4.2. Standards and metadata

Data standards for Category A (Table 1): CSV, XML, RDF, JSON, OIS, API
queries, etc.

Data standards for Category B (Table 2): formats that require transformation
to become machine readable such as PDF, JPG, TIFF, etc.

##  4.3. Data sharing

The datasets that are created during the hackathons will be shared through the
Open Data Lab website  _www.opendatalab.eu_ .  The essence of Open Data is
that it is open – which means that anyone can use it without asking permission
or informing the data owner about how or where it is being used and for what
purpose. The datasets that belong to Category A are therefore embraced by
unrestricted and unlimited use as well as unlimited sharing and manipulation.
The datasets from Category B might present some restrictions in terms of
usage. This has been elaborated upon in section 3.2.1.

**4.4. Archiving and preservation (including storage and backup):**

The plan is that all datasets that are uploaded to the Open Data Lab Platform
will be stored on a server at Dataproces who will ensure preservation and
backup throughout the project.

##  4.5. Tools

The tools inside the platform will allow the participant to perform different
actions, including (among others):

  * visualise data, 

  

  * build understanding through data examples and use cases 

  

  * construct concepts and mobile applications 

  

The specific data visualisations tools will be described in detail in the next
deliverable (M15). In order to build mobile applications the participants will
additionally have access to Bluemix. This toolbox includes a large number of
tools to handle, transform and analyse data, including tools to:

  * transform data in HTML, PDF and Word format into Json and other appropriate formats 

  

  * create on-demand relational databases 

  

  * create apps for Web or mobile 

  

  * perform geospatial analysis 

  

The work on the specific cases and the definition of detailed requirements
will give the project team the possibility to better define and select the
most useful tools.

##  4.6. Reuse of the data

The Open Data Lab Platform will form a virtual component of the five Open Data
Labs. The platform is also meant to be active after the O4C project has been
completed. This means that the Open Data that has been collected, generated
and uploaded to the Platform during the project lifetime will be accessible
both after each hackathon cycle and after the end of the funding period of the
O4C project. This will be elaborated in the upcoming version of the Data
Management Plan.

##  4.7. Ethics

When organising the events Open4Citizens collects information from public
repositories, which contain Open Data. Open Data consist of information
databases that are public domain, and therefore data that can be freely used
and redistributed by anyone. Open4Citizens is thus not subject to any
regulations regarding confidential or sensitive data storage, including
principles stipulated in The Data Protection Directive 95/46/ECP and the
General Data Protection Regulation (EU) 2016/679.

When providing Open Data for the events, Open4Citizens, its employees, and any
contributing partners of Open4Citizens or its employees, shall not be liable
for any harm arising from the use of the collected datasets shared through the
Open Data Lab Platform, including but not limited to, how participating
parties handle and develop the Open Data available on the Open Data Lab
Platform.

In regards to the Open data from various data sources that is made available
on the Open Data Lab Platform, Open4Citizens does not guarantee that this data
has been published with the prior, necessary and informed approval that it
requires. However the Open4Citizens team and the hackathons’ team will verify
the reliability of the source of the publication case by case.

#  5\. Conclusions and Outlook

As mentioned in the preface this first version of the Data Management Plan
presents the overarching categories and how data will enter into the Open Data
Lab Platform. The next iteration of the Data Management Plan (M15) will
provide a more detailed description of specific datasets while the final
version (M30) will present the complete and detailed description of datasets
and the final architecture of the platform. The next iteration will also
elaborate on the concrete tools that will be available in the Open Data Lab
Platform.