#  Executive summary

The purpose of this document is to describe the data management life cycle for
all data sets that will be collected, processed or generated by the FIRES
project. This document provides a general overview of the nature of the
research data that will be collected and generated within the project and
outlines how these data will be handled during the project and after its
completion. This first version of the DMP serves as starting point and
guidelines for the researchers in FIRES project. The more elaborated versions
will be uploaded in later stages of the project, whenever it is relevant.

#  Prepare

##  Data Collection

Databases generated from the project will be submitted to the EC as part of
the deliverables planned in the Project:

D3.2 Pan European Database on Related Variety at NUTS-2 level

D4.2 Pan European Database Time Series GEDI at National Level

D4.4 Pan European Database REDI at Regional Level

D5.1 Database on Start up Processes

Data necessary for these deliverables will be collected mainly from public
data sources, proprietary and public sources and through surveys.

In particular, data that will be collected/generated in the FIRES project:  
  
<table>  
<tr>  
<th>

**Dataset name**

</th>  
<th>

**Data type**

</th>  
<th>

**Description of data**

</th>  
<th>

**Origin/collection source**

</th>  
<th>

**File**

**Format**

</th>  
<th>

**Scale**

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

D3.2 Pan

European

Database on

Related

Variety at

NUTS-2 level

</td>  
<td>

Numerical data at national and regional NUTS-2.

</td>  
<td>

Consists of a number European regions and countries of a certain number of
years.

</td>  
<td>

The data will be collected from different sources of which the GEM and the
Skill-relatedness data of Neffke & Henning (2013) are two.

</td>  
<td>

STATA

(.dta)

</td>  
<td>

Not known yet.

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

D4.2 Pan

European

Database

Time Series

GEDI at

National

Level

</td>  
<td>

Numerical data at national level from

2002 to 2014

</td>  
<td>

The database includes institutional and individual indicators that
characterize the national system of

entrepreneurship and refer on the performance of entrepreneurships in the
involved countries.

</td>  
<td>

Individual data: GEM; institutional data: various sources

(World Economic

Forum, UN, UNESCO,

Transparency

International,

Heritage

Foundation/World

Bank, OECD, KOF,

EMLYON Business

</td>  
<td>

Excel

(xlsx)

</td>  
<td>

7,5 MB

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

School, IESE Business School). As compared to previous GEDI data collection
the Coface risk measurement has been replaced by OECD indicator.

Owner: GEDI

</td>  
<td>

</td>  
<td>

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

D4.4 Pan

European

Database

REDI at

Regional

Level

</td>  
<td>

Numerical data in

NUTS-1 and/or

NUTS-2 (if feasible

– requires sufficient sample size)

</td>  
<td>

This only refers to the entrepreneurship indicators that feed into REDI.
Approximately 125 region cells for two time periods: 2007-2011 and 2012-2014.
In case this is not feasible: 125 regions for one time period: 2010-

2014

</td>  
<td>

Researchers are members with GEM and have access to the data

</td>  
<td>

.xlsx (Excel) and

.dta

(Stata)

</td>  
<td>

Limite d size

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

D5.1

Database on

Start up

Processes

</td>  
<td>

Mostly quantitative (numerical) data and some

qualitative

(interview quotes) data at corporate level that can, inter alia, be sorted by
country and industry (via NACE, NAICS, US

SIC codes)

</td>  
<td>

Venture creation processes of 800 start-up companies in the US, UK, Germany
and Italy.

Dataset is restricted to alternative energy and ICT companies. The sample is
based on external database Orbis.

</td>  
<td>

via CATIs with support of external call center. UU will be the owner

</td>  
<td>

.xlsx /

.sav

</td>  
<td>

60 MB

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

**Requirements for access to existing datasets (previously collected data):**  
  
<table>  
<tr>  
<th>

**Dataset name**

</th>  
<th>

**Description/summary**

</th>  
<th>

**Data owner/source**

</th>  
<th>

**Access issues (requirements to access existing data)**

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

Global

Entrepreneurship

Monitor (GEM)

</td>  
<td>

Data based on adult population surveys to adult population in

European countries

</td>  
<td>

GEM

</td>  
<td>

GEM members (including some FIRES members) have access to the micro data,
regional indicators can be compiled and published on mutual consent of the GEM
National Teams concerned

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

Perfect Timing (PT)

Database

</td>  
<td>

Venture creation processes of 420 start-up companies in the US, Germany and
the Netherlands.

</td>  
<td>

Utrecht University:

Andrea

</td>  
<td>

PI (Andrea Herrmann) is the owner of the data

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

</td>  
<td>

Dataset is restricted to alternative energy and ICT companies. The sample is
based on external database Orbis.

</td>  
<td>

Herrmann

</td>  
<td>

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

##  Data Documentation

The aim of the FIRES project is to document data in a way that will enable
future users to easily understand and reuse it.

All Datasets are Deliverable as a data file and will be labelled with a
persistent identifier received upon depositing the dataset. To all datasets,
there will be a separate report provided, describing in detail the collection
and presenting the descriptive statistics and data manipulations of each data
series in the dataset; and will be stored alongside the data.

Common _**metadata** _ that apply to all studies in your FIRES project on
study level will include. i.e. name, description, authors, date, subproject,
persistent identifier, accompanying publications, etc. For such generic
metadata the Dublin core or DDI metadata standard will be used. For D3.2 a new
metadata template must be developed; D4.2 and D4.4 can follow practice
developed in the GEDI- and REDI-indicators; whereas D5.1 can rely on earlier
work by Dr. Andrea Herrmann in her earlier Marie-Curie project, where she
collected exactly the same type of data in Germany and the US.

_**File naming and folder structure:** _

In order to better organize the data and save time the file naming convention
will be used to enable titling of folders, documents and records in a
consistent and logical way. The data will be available under filename composed
of the project Acronym and the Deliverable number, for example:
FIRESProjectD32.dta, FIRESProjectD42.dta, FIRESProjectD44.dta and
FIRESProjectD51.dta. reports will be stored under corresponding names.
Furthermore, specific project/data identifiers will be assigned. All variables
are given logical three letter codes and a complete codebook is provided, with
definitions and descriptive statistics.

#  Handling research data

##  Data Storage and Back-up

_**Raw data** _ will be stored on secure university fileservers and back up
versions will be saved on external portable storage devices (CD) and on
personal computers of responsible researchers.

For the duration of the project the research data _**master files** _ will be
stored on the university fileserver with the partner institution of the
responsible PI in order to ensure long term a and secure storage. From the
master file location, _**backups** _ will be made and stored on local drives –
on personal laptops with responsible researchers. _**Working copies** _ will
be accessible on cloud storage (Dropbox) that enables researchers to access
the data and allows editing environment. The updated working copies will be
synchronized regularly (after every edit) with the _master copy_ location. The
person responsible for the synchronization will be the responsible researcher
(the researcher who is responsible for generating the data, i.e. Deliverable
coordinator).

_**Version control** : _ Both master copy and back up versions will be using
the same identifier for newer versions to ensure the authenticity of the data
and to avoid work with outdated versions of files. For different versions
codes will be used: V1.00, V1.01; V2.01 etc. with ordinal numbers indicating
major and decimals minor changes. The original and definitive copy will be
retained. During the research also the intermediate major versions will be
retained to make it possible to go back in versions if needed.

STATA also allows for do-files that code all manipulations in the data. All
data sets generated in STATA will be thus presented as a collection of _raw
source files_ (with reference) and a series of _.dofiles_ that allow for exact
replication of aggregation, manipulation and analysis of the data. These
.dofiles are published with the raw and final cleaned data files.

##  Data Access and Security

Within the duration of the project only the directly responsible researchers
have _access_ to the data files. They are thereby also responsible for the
integrity of the datasets and required to carefully document collection and
any manipulations made to the data. Data will be made public only after
publication of the reports and deliverables. For privacy reasons, raw
microdata in D5.1 will remain restricted access after the project, as do the
proprietary parts of the data used in D4.2 and D4.4. We will publish data
required for the reproduction of analyses. Principal investigators will
control the data up to the delivery of the deliverables.

Ownership of the data generated in the project lies with the beneficiary (or
beneficiaries) that generates them, as stated in the FIRES Consortium
Agreement. In case of joint owners of the data, these shall agree on all
protection measures of the data.

The data collected through survey in D5.1 will be anonymized. No privacy
/sensitive data are involved in the project.

#  Preserve and Share

##  Data Preservation and Archiving

All data generated by the project should be preserved permanently. They will
be preserved in Stata .dta and .do as well as a simpler database formats.
Together with the data also reports in .pdf and STATA .do-files will be stored
as supportive documentation. For the purposes of long term sustainable
archiving of the data suitable archiving system will be chosen in the course
of the project.

##  Data Sharing and Reuse

Possible audiences identified for reuse of the data are mainly students and
scholars. In order to ensure that the data and its metadata can be easily
found, reused and cited and can also be retrieved even if at some point its
location changes, all data generated from the project will be deposited in a
public research data repository. Suitable repository that allows the
assignment of a persistent identifier as well as for long term storage and
open access, will be chosen through  _re3data.org_ \- registry of discipline-
specific repositories. In order to create clarity for potential users towards
the use of the data, suitable licenses will be assigned to the data, using
creative commons licenses (mostly CC-BY).

Once delivered to the European Commission and approved, the data files will
also be made public on the website of the project. The data for deliverables
D4.2 and D4.4 are proprietary, but aggregated data can be made public. Micro-
data for D5.1 will not be made public until all reports foreseen in the
project have been published.