**Funder:**

European Commission (Horizon 2020).

**1 DATA SUMMARY**

At the core of the REFLEX project is the development of the comprehensive
“Energy Models System” (EMS), coupling different models and tools from the
REFLEX partners. A good understanding of data types and data flows is
therefore indispensable. In what follows, we discuss the purpose of data
collection and generation, the types and format of data, data utility for
third parties as well as the REFLEX data protection and exploitation strategy.

##  1.1 PURPOSE OF DATA COLLECTION AND GENERATION

As briefly introduced above, the main objective of the REFLEX project is to
analyse and evaluate the development towards a low-carbon energy system in the
EU up to the year 2050. The focus is laying on the evaluation of several
flexibility options to support the system integration of increasing generation
from intermittent RES (see Deliverable 4.1: Overview of techno-economic
characteristics of different options for system flexibility provision; and
Deliverable 4.3: Report on cost optimal energy technology portfolios for
system flexibility in the sectors heat, electricity and mobility). Thereby,
the analysis and assessment are based on a modelling environment that
considers the full extent to which current and future energy technologies and
policies interfere and how they affect the environment and society while
considering technological learning of low-carbon and flexible technologies.

For analysing and answering the given research questions (see Administrative
details), the different models and approaches are coupled to the EMS (see
Deliverable 2.3: Report on modelling coupling framework). The _purpose for
data collection_ and their preparation within REFLEX is to provide the needed
input data for the applied mathematical energy system models. The _purpose of
data generation_ is to provide the quantitative background which will help to
understand and investigate the complex links, interactions and
interdependencies between the different actors and technologies within the
energy system as well as their impact on society and environment.

The following sections give an overview on data flows in the REFLEX project
and introduce the project’s data structure and different data types.

###  1.1.1 DATA FLOWS IN THE REFLEX PROJECT

The **model pool** of the REFLEX partners contains bottom-up simulation tools
and fundamental system optimisation models on national and European level as
well as approaches for Life Cycle Assessment (LCA). Typically, one model
cannot cover all aspects of an energy system or the implications of specific
policies. Each of these different models focuses on a specific sector or
aspect (e.g. heat, electricity, mobility, environmental / social impacts etc.)
of the energy system. The models applied in REFLEX can be grouped into four
fields:

  * Energy supply (ELTRAMOD, TIMES-Heat), 

  

  * Energy demand/usage (TE3, ASTRA, FORECAST, eLOAD), 

  

  * Energy market design (PowerACE), and 

  

  * Impacts on environment, society and economy (eLCA, sLCA,  ߨ  ESA). 

  

For analysing and answering the given research questions, the different models
and approaches are **coupled to the integrated Energy Models System (EMS)** .
Applying the EMS allows to perform an in-depth and at the same time holistic
assessment of the system transformation and shall contribute to the scientific
underpinning of the EU’s SET-Plan. Final modelling results shall help to
understand and investigate the complex links, interactions and
interdependencies between the different actors and technologies within the
energy system as well as their impact on society and environment.

All models used within the project have already been used as stand-alone
applications. Thus, each model has its own database with already existing
data. In the course of the project, **common input data** as well as **model-
specific input data** have been defined. Moreover, through model coupling,
essential exogenous parameters of the models become endogenous variables of
the EMS, i.e. relevant output data of one model serve as input data of another
model. It is necessary that collective input variables are harmonized.
Therefore, a **common REFLEX database** with a scenario storyline has been
developed. This allows for alternative assumptions regarding e.g. the
development of macroeconomic parameters, or the impact of fuel prices. Figure
1 illustrates REFLEX data flows.

**Figure 1: REFLEX data flows**

###  1.1.2 DATA STRUCTURE IN THE REFLEX PROJECT

The results of a model-based analysis depend not only on the chosen
methodology, but also on the quality of the data used. For a consistent
analysis within the EMS in REFLEX, a common database with harmonised datasets
was implemented in a Data Warehouse (DWH).  1

The database of the REFLEX project contains **four groups of data** , i.e. (1)
existing model input data, or so-called background data, (2) collected and
generated new model input data, or so-called foreground data, (3) generated
intermediate model output data for exchange between the models during the
iteration process, and (4) generated final result data of the EMS. Table 1
gives a brief overview on these four groups, which will be described in more
detail in the following sections.

**Table 1: Groups of data within the database of the REFLEX project**  
  
<table>  
<tr>  
<th>

Data group

</th>  
<th>

Description / content

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

**Existing model input data**

</td>  
<td>

  * Data collected, generated or purchased from commercial providers by a project partner before the start of the REFLEX project 

  

  * Input data collected, generated or purchased from commercial providers by a project partner in the context of projects on behalf of other clients run in parallel to the REFLEX project 

  

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

**Collected and generated new model input data**

</td>  
<td>

  * Data collected from publicly available sources or purchased from commercial providers by a project partner in the context of REFLEX 

  

  * Data collected through surveys conducted by a project partner in the context of REFLEX 

  

  * Data generated based on existing, new collected or new purchased data by a project partner in the context of REFLEX 

  

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

**Generated intermediate model output data**

</td>  
<td>

\- Intermediate results of the model applications for data exchange between
the models or for further assessments within the project

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

**Generated final**

**result data of the**

**EMS**

</td>  
<td>

\- Final results of REFLEX generated by model applications, e.g. CO  2
-emissions, energy demand, technology impact evaluation, etc.

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

Regarding model input data we thus can differentiate among existing and new
data, as well as among input data collected from publicly available sources,
input data generated by project partners in the context of REFLEX, purchased
data coming from commercial providers, and confidential data that have been
provided to REFLEX project partners after the signature of non-disclosure
agreements (see Figure 2). These different types of data sources will also
have an impact on our ability to (re-)publish respective datasets in an openly
accessible research data repository (see below).

**Figure 2: REFLEX data structure**

##  1.2 TYPES AND FORMATS OF DATA COLLECTED AND GENERATED

The following sections describe the four groups of REFLEX data regarding the
specific types and formats of datasets collected and generated.

###  1.2.1 EXISTING MODEL INPUT DATA

Each of models applied in REFLEX has already been used as a stand-alone
application, and thus each model has its own database with already existing
data input. These datasets originate from own previous work and own
assumptions of the project partners as well as from literature and have been
developed over many years. Most of these input data are rather model-specific
and an unconditional application over several models is limited. Some of the
existing data, however, will be reused in the EMS, given that they are up to
date, or that no better data are available.

The **REFLEX data warehouse** thereby is the central element of common data
storage, use and exchange. It includes

  * all data needed for more than one model within the EMS and which therefore have to be harmonized; and/or 

  

  * all data needed to validate the results presented in project reports and publications (so called "underlying data"). 

  

The harmonization of input data is necessary to ensure a consistent analysis
within the EMS. For the same information (e.g. power plants’ start-up costs,
or efficiency factors) the same dataset (values) have to be used in all
models. The consortium has decided which of the existing datasets are applied.
These are included in the project’s database and provided to all models before
initializing the EMS runs. Table 2 gives an overview on the essential existing
model input datasets reused in REFLEX.

#  Table 2: Essential existing model input datasets reused in REFLEX  
  
<table>  
<tr>  
<th>

Dataset Time period covered Spatial scope Sources

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

Power plants’ availability

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

DIW 2013

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

Power plants’ efficiency

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

DIW 2013

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

Power plants’ emission factor

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

UBA 2014

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

Power plants’ interest rate

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

IEA 2010

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

Power plants’ lifetime of investment

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

IEA 2010

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

Power plants’ load change costs (depreciation)

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

DIW 2013, Traber & Kemfert 2011, own assumptions

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

Power plants’ load change costs (fuel factor)

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

DIW 2013, Traber & Kemfert 2011

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

Power plants’ operation management costs (fixed)

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

DIW 2013, VGB 2011a

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

Power plants’ operation management costs (variable)

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

DIW 2013, Traber & Kemfert 2011

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

Power plants’ specific investment

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

DIW 2013

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

Power plants’ start-up costs (depreciation)

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

Traber & Kemfert 2011

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

Power plants’ start-up costs (fuel factor)

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

Traber & Kemfert 2011

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

Vehicles CO  2  standard

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

EU regulation, own assumptions

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

Vehicles fuel consumption factors

</td>  
<td>

2010-2050

</td>  
<td>

EU28

</td>  
<td>

GHG-TransPoRD project, ASSIST project

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

###  1.2.2 COLLECTED AND GENERATED NEW MODEL INPUT DATA

Some of the needed input data for the EMS have been updated or newly defined
according to the research questions and the focus of analysis within the
REFLEX project. Furthermore, publicly and commercially available data are
used. Additionally, required input data not available in the literature, in
existing data repositories or on the market, have been generated by the
project partners via empirical surveys and/or appropriate assumptions. These
data are included in the project’s database, too, and in that way provided as
harmonized datasets for all models within the EMS.

The group of collected and generated new model input data includes:

  * data for the REFLEX scenario framework; 

  

  * data for demand side management as one source of flexibility; and 

  

  * data for experience curves to allow for an endogenous modelling of technological learning. 

  

##  _DATA FOR THE REFLEX SCENARIO FRAMEWORK_

Data for the REFLEX scenario storylines describe the overall framework for the
model-based analysis and include the main macro-economic and societal drivers
as well as technoeconomic parameters and regulatory conditions of the
political environment.

Two main scenarios are distinguished (for a detailed description see
Deliverable 1.1: Scenario Description). First, a reference scenario (“Mod-
RES”) based on observed trends and most recent projections is in line with the
PRIMES 2016 Reference Case (Capros et al., 2016). Second, for a more ambitious
policy scenario (“High-RES”), framework conditions are similar to those of
Mod-RES in terms of population and economic growth, while both fuel- and CO  2
prices are assumed to be higher. More ambitious climate policies are
considered. In order to capture the different possible stances on a future
energy system, a centralized and a decentralized version are distinguished.

The defined scenario storylines are translated into quantitative model input
parameters until the year 2050, which is the defined horizon for the analysis.
The data may be aggregated for whole Europe, or disaggregated on a national,
sectoral or technological level. The macroeconomic trends and the societal
drivers are based upon official projections provided by the European
Commission. All political assumptions have been elaborated considering current
and past policy implementations and are discussed with the European Commission
and various stakeholders. Table 3 gives an overview on the data for the REFLEX
scenario framework.

#  Table 3: Data for the REFLEX scenario framework  
  
<table>  
<tr>  
<th>

Dataset

</th>  
<th>

Time period covered

</th>  
<th>

Spatial scope

</th>  
<th>

Sources

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

**Gross domestic product**

</td>  
<td>

2000-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

</td>  
<td>

Capros et al. 2016 (assumptions for NO+CH based on other Horizon 2020
projects)

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

**Population**

</td>  
<td>

2000-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

</td>  
<td>

Capros et al. 2016 (assumptions for NO+CH based on other Horizon 2020
projects)

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

**Price electricity** (initial average costs of gross electricity generation)

</td>  
<td>

2000-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

</td>  
<td>

Capros et al. 2016

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

**Price fossil energy carriers**

</td>  
<td>

2015-2050

</td>  
<td>

EU28

</td>  
<td>

Capros et al. 2016

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

**CO 2  -price ETS **

</td>  
<td>

2015-2050

</td>  
<td>

EU28

</td>  
<td>

Capros et al. 2016, own assumptions

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

##  _DATA FOR DEMAND SIDE MANAGEMENT_

Relevant data for investigating system flexibility in the form of demand side
management (DSM) are rarely available from public or commercial sources. In
particular, the available database for the tertiary sector with regard to DSM
is highly incomplete. Therefore, an empirical survey on DSM in the tertiary
sector has been conducted with the aim to improve the model input data and to
fill data gaps (see Deliverable 2.2 Report on Survey Findings). Based on the
analysis of the collected specific empirical data, existing datasets have been
extended as well as new datasets generated.

The design of the survey has been established by the REFLEX partners. The
survey itself has been conducted for 4 European countries by an international
market research institute. Empirical data for one further country (Germany)
was provided in-kind by REFLEX partner Fraunhofer ISI. DSM data for further
countries are deduced from the survey results. Relevant model input parameters
for modelling DSM options which should be deduced from the empirically
ascertained data are given in Table 4\.

#  Table 4: Data for modelling DSM options  
  
<table>  
<tr>  
<th>

**Dataset**

</th>  
<th>

**Time period covered**

</th>  
<th>

**Spatial** **scope**

</th>  
<th>

**Sources**

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

**DSM potential** (share of flexible load per energy usage process)

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

</td>  
<td>

empirical survey, public and commercial sources

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

**DSM costs** (activation costs per energy usage process)

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

</td>  
<td>

empirical survey, public and commercial sources

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

**DSM time of interfere** (maximum load reduction time)

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

</td>  
<td>

empirical survey, public and commercial sources

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

**DSM number of interventions** (frequency of DSM measures)

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

</td>  
<td>

empirical survey, public and commercial sources

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

**DSM shifting time** (allowed points of time or time periods/frames for DSM)

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

</td>  
<td>

empirical survey, public and commercial sources

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

##  _DATA FOR EXPERIENCE CURVES_

In order to enable an endogenous modelling of technological developments and
resulting production cost reductions, experience curves for the most relevant
technologies for each sector (electricity including storage and power-to-X,
industry, mobility, heating/cooling, energy end-use) have been developed in a
first step (see Deliverable D3.2 Comprehensive Report on Experience Curves).
In a second step, they have been implemented in the sectoral models of the
EMS.

Special attention is given to the determination of uncertainty ranges of
progress ratios (i.e. the slopes of the experience curves), as these can have
a major impact on modelling results, especially for long-term modelling up to
the year 2050. Especially for technologies that depend strongly on either the
available geographical potential (e.g. wind onshore, offshore) or on raw
material prices, a decomposition of the experience curve using a multi-level
experience curve is performed. This allows the determination of the most
important factors behind cost development, such as variations in steel or oil
prices, as well as scale effects.

The needed empirical data for defining experience curves has been collected by
means of interviewing industry experts, conducting specific survey methods and
analysing detailed statistics (e. g. construction, production and consumer
price indices as well as installed capacities and cost developments in the
electricity, heat and mobility sectors). Table 5 gives an overview on the
technologies for which experience curves have been developed.

#  Table 5: Technologies by sector for which experience curves are developed  
  
<table>  
<tr>  
<th>

Category

</th>  
<th>

Technology

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

**Electricity generation**

</td>  
<td>

CCS (membrane, oxyfuel, pre-combustion, post-combustion)

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

Photovoltaics: modules (mono/poly, CdTe), BOS, systems

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

Wind onshore

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

Wind offshore

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

Fuel cell micro-CHP

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

**Electricity storage**

</td>  
<td>

Battery: Lithium-ion (utility, residential)

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

Battery: Redox-flow

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

**Heating/cooling**

</td>  
<td>

Heat pump (air/water)

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

**Industry**

</td>  
<td>

Industrial heat pumps (large scale)

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

Industrial heat/steam (Industrial CCS)

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

**Mobility**

</td>  
<td>

Battery electric vehicles (EV Lithium-ion battery)

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

Fuel cell vehicles (EV fuel cell stack)

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

Hybrid electric car (HEV NiMH battery)

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

**Power-to-X**

</td>  
<td>

Power-to-hydrogen (alkaline electrolysis)

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

To estimate the potential of alternative fuel technologies in Europe, the
_global_ automotive market (especially including North America and Asia) is
considered. The analysis is focused on major driving patterns. The reason for
analysing global passenger car markets is to identify the global market
penetration of electric vehicles, which in turn will influence the demand for
Li-ion batteries substantially and thus will have a crucial impact on
technological learning. This information helps to assess the future prices of
batteries and fuel cells based on the learning curve theory.

###  1.2.3 GENERATED INTERMEDIATE MODEL OUTPUT DATA

By coupling the different approaches of the REFLEX partners, the system
boundaries of each stand-alone model are partly disbanded and most **exogenous
parameters of each model become endogenous variables of the EMS** . This is
done by using the relevant output data of one model as input data of another
model.

The REFLEX database, which was developed in the course of the project,
facilitates the data exchange between the models and stores relevant input and
output datasets. Technical routines are thereby able to overcome several
compatibility issues, i.e. models may use different levels of detail or
aggregation, time intervals of the same variables may differ, and finally,
different models use different identifiers for the same datasets. Therefore,
when importing the intermediate results into the database, a mapping and any
necessary value aggregations or data splits take place at the same time. For
this purpose, each data table contains the identifier structure used for both,
the model from which these intermediate results originate and for other models
using these intermediate data as own input data. To achieve a stable final
state of the EMS within each REFLEX scenario storyline, several iterations
with all models are performed.

Table 6 gives an overview on main input and output variables of the different
models coupled to the EMS. It shows which input is exogenous if the respective
model is used as a stand-alone application but becomes an endogenous input
dataset in the EMS.

#  Table 6: Main inputs and outputs of the different models  
  
<table>  
<tr>  
<th>

Model

</th>  
<th>

Needed input i. a.

Endogenous in EMS?

(exogenous if stand-alone Provided output i. a.

(If yes, provided by) application)

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

</td>  
<td>

Electricity demand

</td>  
<td>

Yes (eLOAD)

</td>  
<td>

Electricity prices

Capacity and operation of power plants RES curtailment

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

Techno-economic data for power plants

</td>  
<td>

No

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

H2-demand for mobility

</td>  
<td>

Yes (ASTRA)

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

Fuel prices

</td>  
<td>

No

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

Heat demand

</td>  
<td>

Yes (FORECAST)

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

</td>  
<td>

Heat demand

</td>  
<td>

Yes (FORECAST)

</td>  
<td>

Capacity and operation of heat plants

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

Capacity and operation of power plants

</td>  
<td>

Yes (ELTRAMOD)

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

Electricity wholesale prices

</td>  
<td>

Yes (ELTRAMOD)

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

Techno-economic data for power plants

</td>  
<td>

No

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

</td>  
<td>

Macro-economic framework data

</td>  
<td>

No

</td>  
<td>

Used transport technologies in global key markets

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

Techno-economic data for vehicles

</td>  
<td>

No

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

Fuel prices

</td>  
<td>

No

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

</td>  
<td>

Electricity prices

</td>  
<td>

Yes (ELTRAMOD)

</td>  
<td>

Used transport technologies and energy demand for

mobility in EU

H2-demand for Mobility

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

Macro-economic framework data

</td>  
<td>

No

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

Techno-economic data for vehicles

</td>  
<td>

No

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

Fuel prices

</td>  
<td>

No

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

</td>  
<td>

Electricity wholesale prices

</td>  
<td>

Yes (ELTRAMOD)

</td>  
<td>

Yearly energy demand by sector

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

Electricity demand mobility

</td>  
<td>

Yes (ASTRA)

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

Macro-economic framework data

</td>  
<td>

No

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

Techno-economic data for demand side technologies

</td>  
<td>

No

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

Fuel prices

</td>  
<td>

No

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

</td>  
<td>

Electricity wholesale prices

</td>  
<td>

Yes (ELTRAMOD)

</td>  
<td>

Electricity demand structure (load profiles)

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

Electricity demand (yearly)

</td>  
<td>

Yes (FORECAST)

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

</td>  
<td>

Electricity demand

</td>  
<td>

Yes (eLOAD)

</td>  
<td>

Capacity and operation of power plants under different market designs

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

Framework data for energy markets

</td>  
<td>

No

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

Techno-economic data for power plants

</td>  
<td>

No

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

Fuel prices

</td>  
<td>

No

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

</td>  
<td>

Capacity, operation and emissions of power plants

</td>  
<td>

Yes (ELTRAMOD)

</td>  
<td>

Emissions

Impacts on humans and environment

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

Capacity and operation of heat plants

</td>  
<td>

Yes (TIMES-Heat)

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

Energy / electricity

demand

</td>  
<td>

Yes (FORECAST, eLOAD, ASTRA, TE3)

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

Table 7 summarizes datasets of generated intermediate model output data for
the data exchange within the EMS.

#  Table 7: Generated intermediate model output data for data exchange within
the EMS  
  
<table>  
<tr>  
<th>

Dataset

</th>  
<th>

Time period covered (10-year steps)

</th>  
<th>

Spatial scope

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

**Price electricity (hourly)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

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

**Demand electricity (hourly)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

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

**Demand electricity for mobility (yearly)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

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

**Demand district heating (yearly)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

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

**Power plants installed capacity and operating (yearly and hourly)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

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

**Power plants emissions (yearly)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

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

**Power plants demand energy (yearly)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

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

**Mobility demand energy (yearly)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

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

**Mobility emissions (yearly)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH

(NUTS 0 level)

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

###  1.2.4 GENERATED FINAL RESULT DATA OF THE EMS

After achieving a stable state based on several iterations with all models
within the EMS for each REFLEX scenario storyline, the result data of the
different models are collected and combined within the project’s database to
the final result data of the EMS. These data are analysed to derive the key
findings on the impacts of technological development and innovation on the
energy system and on the environment, society and economy and are the basis
for answering the research questions of the REFLEX project (see Administrative
details). Table 8 gives an overview of the major generated final result data
of the EMS.

#  Table 8: Major generated final result data of the EMS  
  
<table>  
<tr>  
<th>

Dataset

</th>  
<th>

Time period covered (10-year steps)

</th>  
<th>

Spatial scope

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

**Price electricity average yearly**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Demand electricity**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Demand district heating**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Power plants installed capacity**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Power plants operation**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Power plants emissions**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Mobility demand energy**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Mobility emissions**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Life cycle environmental and resource impacts**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Life cycle human health (damage / toxicity)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Life cycle societal impacts (risk level)**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

**Costs external**

</td>  
<td>

2015-2050

</td>  
<td>

EU28+NO+CH (NUTS 0 level)

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

###  1.3 DATA UTILITY

As elaborated above, REFLEX collects and generates a certain amount of
research data. On the one hand, these data are necessary to meet the
objectives of the project and to answer the research questions. On the other
hand, most of the collected and generated data will be useful for further
research by the project partners themselves or by third parties, as well as
for stakeholders in the energy industry and for policy makers.

####  1.3.1 EXISTING MODEL INPUT DATA

During the last years, comprehensive databases have been created by the
project partners in order to answer a wide range of research questions based
on their individual models in stand-alone applications. These data have been
collected from a variety of sources, including institutional reports (e.g.
from the EC or IEA), scientific publications (e.g. published by Elsevier) and
data documentations from third modelling works (e.g. from the DIW Berlin).
Moreover, a part of the data has been generated based on previous own
modelling exercises as part of previous research projects and own assumptions.

As these existing model input data are essential underlying data for the
REFLEX model runs, their publication ensures the transparency of the generated
results and allows third parties to compare project outcomes to other studies
with a similar scope. Moreover, the collection of comprehensive datasets then
re-published in one single place and in one single re-usable format, e.g.
related to the variety of power plant parameters, facilitates future research
as searching the various data coming from various sources and having been
published in various formats (pdf, data-files, etc.) will not be necessary
again.

####  1.3.2 COLLECTED AND GENERATED NEW MODEL INPUT DATA

In what follows, we distinguish among the three categories for collected and
generated model input data introduced above, i.e. data for the REFLEX scenario
framework, data for DSM and data for experience curves.

##  _DATA FOR THE REFLEX SCENARIO FRAMEWORK_

The collected and prepared data for the scenario framework regarding macro-
economic and societal drivers, techno-economic parameters and regulatory
conditions are tailored to the aim and scope as well as the specific research
questions of the REFLEX project and the applied models to answering them. They
are thus primarily useful as underlying data to ensure the transparency of the
generated results and the comparability of the project outcomes to other
(existing and future) studies with similar analysing scope.

Their publication as open research data will also allow a critical discussion
of the impact of scenario framework assumptions on model outcomes. Whereas the
forecasts of the development of GDP and population have been relatively stable
over time, this has not been the case for fuel prices, and even less for the
level of costs related to CO  2  emissions. The data will be used for updating
the existing databases of the different models and for further model-based
research of the project partners outside of REFLEX.

##  _DATA FOR DEMAND SIDE MANAGEMENT_

Parameters related to different DSM measures have been generated in the course
of the REFLEX project based on elaborate empirical surveys. With the collected
specific empirical data, the database for investigating system flexibility by
DSM has been improved substantially because relevant data – especially for the
tertiary sector – have been rarely available from public and commercial
sources. Thus, existing, highly restricted datasets have been extended and at
the same time new datasets have been created.

These data have a high potential for re-use after the end of the REFLEX
project. The survey data will not only be a valuable resource for future
research activities in general, but it will also allow the identification of
promising energy applications and DSM potentials in the selected sector in
different European countries. To date, this is a very topical issue as
European electricity systems are evolving towards a generation mix that is
more decentralized and less predictable with additional flexibility being
expected to be provided by the demand side. This implies that – also small-
scale – consumers must be shifted from the current ‘passive’ role to providing
‘active’ demand response, and thus, new business models valorizing flexibility
provided by the tertiary sector and adaptations in market design and
regulation are required. REFLEX research data on DSM are therefore useful not
only for the research community, but also for stakeholders from industry and
policy making.

##  _DATA FOR EXPERIENCE CURVES_

Endogenizing technological learning through experience curves allows for an
enhanced assessment of the evaluation of impacts from policy measures or
alternative incentive schemes on realizable future cost reduction. In
addition, in view of current rapid and necessary changes in energy systems
(driven partially by policies and partially by markets) and the ensuing need
for flexibility, the endogenous modelling of cost development of existing and
new innovative energy-related technologies in bottom-up models will become
even more important.

However, the data and experience curves required to do so have not been
available when the REFLEX project started. A review of certain energy supply
and demand technologies has been published by Junginger et al. (2010), but
these required an updating. More recent studies have been published for some
individual technologies (e.g. Bolinger and Wiser 2012; Candelise et al. 2013
or Chen et al. 2012). However, a comprehensive and up-to-date overview was
missing. Especially with regard to technologies needed for increasing the
flexibility in energy systems (such as storage technologies or DSM-devices)
little or no experience curves had been published. Thus, to advance the energy
models included in REFLEX beyond the state-of-the-art, data collection was
required to devise or update experience curves for existing technologies and
to estimate experience curves for new ones.

Furthermore, it will require smart and innovative incorporation and
interlinkage of these experience curves in various sectoral energy models to
comprehensively assess the effects of technological learning and the demand
for increased flexibility in energy systems.

The collected data have a high potential for re-use after the end of the
REFLEX project. The outcome – a comprehensive state-of-the-art and up-to-date
overview of experience curves and underlying databases – will be of high value
for other energy models outside the project (developed in the EU as well as
worldwide) in order to meet the challenges of modelling our changing energy
systems with increasing penetrations of innovative (improved and new)
technological solutions for the coming decades.

###  1.3.3 GENERATED INTERMEDIATE MODEL OUTPUT DATA

These data are only intermediate results of the EMS and are transferred
between the models during the iteration processes. A relevant benefit of these
data for further applications outside the framework of REFLEX is not expected.

###  1.3.4 GENERATED FINAL RESULT DATA OF THE EMS

The overall objective of REFLEX is to support the EU’s SET-Plan by
strengthening the knowledge base for transition paths towards a low-carbon
energy system based on a crosssectoral analysis for the entire energy system
of the EU. Due to the complexity of this system, it is obvious that the
implementation of the SET-Plan requires in-depth knowledge on the
interrelationship between the different sectors (electricity, heat and
mobility), energy technologies but also on the interdependencies between
energy and non-energy industries, environment (beyond greenhouse gas
emissions) and society.

The result data of the EMS within REFLEX help to understand and investigate
the complex links, interactions and interdependencies between the different
actors and technologies within the energy system as well as their impact on
society and environment. Based on the EMS result data, recommendations for
effective strategies for a transition of the European energy system to a low-
carbon system can be derived. Policy makers at EU level as well as at regional
level can use these findings when developing policy measures. Furthermore, the
generated final result data of the REFLEX project can be used as a reference
or starting point for further research work by the project partners or third
parties on the future design of the energy system of the European Union.

###  1.4 DATA PROTECTION AND EXPLOITATION STRATEGY

In order to ensure efficient dissemination and exploitation activities, free
of any legal conflicts, the REFLEX project partners signed a Consortium
Agreement (CA).  2  The CA is, among other things, dealing with the details on
the partners’ background data and on the rights to, the protection and the
exploitation of pre-existing datasets and results generated solely and/or
jointly during the lifetime of the project. Moreover, the CA sets up specific
rules on how to deal with dissemination activities (see also Deliverables D7.3
and D7.4 Dissemination and Communication Plan) and to ensure open access to
peer-reviewed scientific publications.

The following basic CA rules regarding data protection and exploitation apply:

  * All partners define their individual existing background data required for their successful participation in the project. Background data are own and/or commercial model input data generated/purchased before the start of the project. The rights to these data remain with the respective partner, but royalty-free access to the others is granted if not restricted by third parties, and if it is required to enable the research activities in the context of the project. 

  

  * Data that are acquired by individual partners during the project without using REFLEX funds, e.g. in the context of projects on behalf of other clients run in parallel, will be also treated as pre-existing background data. 

  

  * The property rights to data collected and datasets/results generated during the project by using REFLEX funds (foreground data) belong to those partners involved in the collection and generation processes. When more than one consortium member is involved, the dataset will be jointly owned by the respective consortium members. 

  

Dissemination and exploitation of data and results is executed in accordance
with EU laws and with respect to specific laws in the participating countries.
Before any dissemination activity takes place, respective legal aspects are
examined and clarified. This is particularly the case for data from the DSM-
survey and for experience curve data as well as data purchased from commercial
providers. The possibility for protection of generated results within the
project (consortium) is also examined before publication. All participants
have departments specifically devoted to managing intellectual property. These
departments manage the relevant protection processes.

Within REFLEX, the dissemination and exploitation of results not only in terms
of knowledge and insights, but also in terms of data, is coordinated by the
Exploitation and Innovation Manager (EIM) regarding knowledge management and
innovation activities. Thus, and as specified in the Grant Agreement, the EIM
is responsible for:

  * maintaining a registry of relevant background data, 

  

  * maintaining a registry of foreground data gathered and generated in the work packages during the project, 

  

  * assessing the opportunities for exploitation, for example by following political events in the energy sector or searches of other scientific databases for similar developments, and 

  

  * proposing specific exploitation measures, e.g. policy briefs and events. 

  

During the project, periodic analyses of transfer opportunities to adjust the
exploitation strategies take place. Thereby, the EIM can identify synergies to
ensure the best and suitable use and exploitation of results. All consortium
partners contribute to the exploitation plan of the project throughout its
life span. The EIM is in close contact and regularly informed about the
exploitation plans of the partners and regularly advises the consortium and
individual partners about possible strategies.

The exploitation strategy related to REFLEX research data is outlined below:

  * First, it is decided whether to (re-)publish a dataset in the REFLEX data repository or not, and if yes in what way (data format, metadata provided, open or restricted access). 

  

  * Participants inform the EIM and other consortium members if they wish to publish or disseminate any datasets, whether in a direct way or indirectly. 

  

  * Before any dissemination activity takes place, the participants examined the possibility of protecting generated results, for instance with regard to potential reuse for commercial purposes. 

  

  * In case of collected input data: It is examined whether the rights of third parties are affected and, if necessary, their consent to the re-publication of these data is obtained. 

  

  * Upon (affirmative) dissemination decision, the dataset is made available (regarding different dissemination types see Sections 2.2 and 2.4). 

  

**2 FAIR DATA**

Collected and generated data relevant for EMS model runs are implemented in
the developed REFLEX database in a standardized way. The database is
implemented in postgreeSQL. For managing the database, the management-tool
“pgADMIN” and a proprietary developed interface tool is used, which provides
several data-preparing and identifier-mapping functions.

A selection of existing data as well as data collected and generated during
the project will be made available to interested research groups and
stakeholders from policy and industry. The following sections outline how the
data are exploited and made accessible for verification and re-use and how
data will be curated and preserved upon closure of the project.

##  2.1 MAKING DATA FINDABLE – REFLEX DATA REPOSITORY

For making data findable, a data catalogue – the so-called **REFLEX data
repository** – has been prepared. The consortium will continue to provide the
data via the REFLEX project website for a limited period of time after the end
of the REFLEX project (<18 months). The project website will be maintained
during this period (only ensuring online accessibility).

**For long-term data provision,** the following approaches (or a combination
of them) are currently evaluated:

  1. The data remain in the _DWH of the project partner ESA²_ . The data provision is transferred to the website of the ESA² Company ( _http://www.esa2.eu_ ). There a reference to the project will be created and access to the data will be enabled. 

  

  2. The data are published in the _ZENODO repository_ ( _https://zenodo.org/_ ). This is an online, free of charge storage created through the European Commission’s OpenAIREplus project and is hosted at CERN, Switzerland. It encourages open access deposition of any data format, but also allows deposits of content under restricted or embargoed access. 

  

  3. The data are published in the inter-disciplinary _OpARA repository_ ( _https://tudresden.de/zih/forschung/projekte/opara_ ). This is an online, long-term repository for research data, hosted by Technische Universität Dresden. 

  

  4. The data are published in the _“OpenEnergy Platform”_ ( _https://wiki.openmodinitiative.org/wiki/Proposal_for_the_OpenEnergy_Platform_ ). This platform is still under development and aims to expand the existing “OpenMod” online presence by, amongst others, offering a place to store and exchange data (raw data and processed data), which are needed for modelling works. 

  

  

Approach i) is preferred. For the case that a permanent provision via the
website of ESA² should no longer be possible at any time, option iv) is
preferred. The structure of the REFLEX data has already been designed for this
option. Additionally, this platform offers the possibility to implement
information about the REFLEX project. In any case the data will be available
for third parties after the end of the project with free access to metadata
and the open access data contents. The length of time for which the data will
remain re-usable is not restricted.

The **REFLEX data catalogue** is currently implemented on the REFLEX project
website ( _http://reflex-project.eu/public/data-publication/_ ) with a
simplified preliminary frontend version which has a format similar to Table 9
below. The catalogue thus gives a comprehensive overview on all datasets
available in the data repository and at the same time allows users to access
metadata sheets with detailed information on the content and scope of a
specific dataset directly. Moreover, a short-link for downloading the
individual dataset in a userfriendly format is provided.

#  Table 9: Data catalogue in the REFLEX data repository  
  
<table>  
<tr>  
<th>

Dataset

</th>  
<th>

ID

</th>  
<th>

Version

</th>  
<th>

Description

</th>  
<th>

Download

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

[dataset name and _link_ _to metadata sheet_ ]

</td>  
<td>

[dataset identifier]

</td>  
<td>

[version number, i.e. year_month_day]

</td>  
<td>

[brief description]

</td>  
<td>

[direct _link for_ _download_ ]

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

Dataset 1

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Dataset 2

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Dataset 3

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Dataset 4

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

…

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

The frontend-concept of the final version is shown in Figure 3. This is
currently under construction and will give a user-friendly overview with a
content related hierarchic (tree) structure about the available data. By
clicking on a data set, the associated metadata and a data preview are
displayed and the data set can be downloaded.

The scope and design of the metadata has been oriented on the metadata
structure of the “Open Power System Data” platform ( _http://www.data.open-
power-system-data.org/_ ). Table 10 shows the **REFLEX metadata sheet** . It
includes not only general descriptive issues (e.g. name, description, version
number, etc.), but also detailed information on its scope (time, sectoral,
spatial) as well as administrative matters (e.g. recommended text for
attribution or the licensing of the dataset). Each dataset can be
unambiguously identified via the combination of dataset name and the version
label. Both will be included in the unique dataset ID.

**Figure 3: Concept of the frontend for the REFLEX data repository**

#  Table 10: Scope and contents of the metadata for a provided dataset  
  
<table>  
<tr>  
<th>

Category

</th>  
<th>

Content

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

Name

</td>  
<td>

Name of the dataset (a concise one, short but informative)

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

ID

</td>  
<td>

Dataset identifier (unique dataset ID including the dataset name and version
number)

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

**Version**

</td>  
<td>

Year_month_day (e.g. 2015_04_21)

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

**Keywords**

</td>  
<td>

List of keywords

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

**Description**

</td>  
<td>

Short description of the dataset

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

**Remarks**

</td>  
<td>

Specific remarks (e.g. restrictions, data gaps)

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

**Timescale**

</td>  
<td>

Time period covered (e.g. 2005 to 2030) and timesteps (e.g. hourly, yearly,
5-year steps)

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

**Spatial scope**

</td>  
<td>

Countries/regions covered with scope and level of differentiation or
aggregation (e.g. EU-28, national data, list of countries)

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

**Sectoral scope**

</td>  
<td>

Sectors covered (e.g. households, industry, traffic) as well as sub-categories
if available (e.g. road traffic, rail traffic, aviation)

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

**Sources and input data**

</td>  
<td>

Used sources to prepare/provide the dataset, if possible with links to the
primary data

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

**Attribution**

</td>  
<td>

Recommended text for attribution

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

**Contact**

</td>  
<td>

Contact information for questions/remarks

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

**Access**

</td>  
<td>

Terms of data access/usage, ideally a standard license

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

**Download**

</td>  
<td>

Here link to the dataset for download, e.g. _CAP_installed.xls_

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

**Field documentation**

</td>  
<td>

_Link_ to extra file/page [see Table 11]

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

The field documentation as last item of the metadata sheet and being fully
displayed on a separate page/window includes the complete list of variables
within the dataset with the subcategories field name, -type, -unit, and
-description (see Table 11).

#  Table 11: Field documentation  
  
<table>  
<tr>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

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

Field name

</td>  
<td>

e.g. CAP_inst

</td>  
<td>

…

</td>  
<td>

…

</td>  
<td>

…

</td>  
<td>

…

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

Field type

</td>  
<td>

e.g. Number

</td>  
<td>

…

</td>  
<td>

…

</td>  
<td>

…

</td>  
<td>

…

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

Field unit

</td>  
<td>

e.g. MW

</td>  
<td>

…

</td>  
<td>

…

</td>  
<td>

…

</td>  
<td>

…

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

Field description

</td>  
<td>

e.g. Installed capacity end of the year

</td>  
<td>

…

</td>  
<td>

…

</td>  
<td>

…

</td>  
<td>

…

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

With regard to the publication and transparency of results of the project work
(e.g. in scientific journals, book chapters, conference proceedings or policy
briefs), the preparation of tailored dataset packages for the different
publications is planned. Such a package contains, together with a short
content description, a compilation of (1) the metadata of the published
results, and (2) the metadata of the relevant datasets required to verify the
results, as long as these can be made available.

##  2.2 MAKING DATA OPENLY ACCESSIBLE – REFLEX OPEN DATA

At the beginning of the REFLEX project, three different possibilities for data
dissemination have been considered by the project partners, i.e.

  1. _Open access data publication_ : In this case, data owners grant a royalty-free access of a meaningful selection of generated results to other participants and to the public, possibly restricted by appropriate embargo periods and/or respecting restrictions from editors of scientific journals and organizers of conferences. 

  

  2. _Commercial data exploitation_ : In this case, data suitable for commercial exploitation (e.g. for a commercial re-use by consulting companies) will be managed by the project partner ESA  2  , with the explicit purpose of exploiting research results (including research data) related to (coupled) energy systems modelling. 

  

  3. _Indirect data publication_ : In this case, parts of the generated data are disseminated only indirectly as part of intermediate or final results of models and/or as qualitative outcome based on post-analysis of results. 

  

  

The following discussions concentrate on direct data publication. Both, input
data collected from publicly available sources (existing and new model input
data) as well as data generated via the project partners’ application of the
different simulation models are made available as open data as far as possible
according to the guidelines of the EU. Thereby, any legal conflicts have to be
avoided (see Section 2.5 for details). The same holds for generated final
results of the EMS (see Table 12).

The project partners are in discussion to make the results of the DSM-survey
and the generated different DSM-datasets openly available for research
purposes with added value for society. However, the commercial use by third
parties shall be subject to explicit permission and an adequate royalty
payment.

A similar discussion is taking place for data on experience curves. The
required data for technological learning curves have been collected by means
of interviewing industry experts, conducting specific survey methods and
analysing detailed statistics (e. g. construction, production and consumer
price indices as well as installed capacities and cost developments in the
electricity, heat and mobility sectors).

Model input data purchased by the project partners, in contrast, cannot be re-
published as open data due to the data usage conditions of the commercial
providers. Finally, the same holds for confidential data on fuel price
developments provided by the European Commission (Capros et al. 2016), for
which non-disclosure agreements have been signed.

#  Table 12: REFLEX data published in the data repository (the right to make
changes is reserve)  
  
<table>  
<tr>  
<th>

Data group Data source

</th>  
<th>

(Re-)publication in the REFLEX data repository?

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

**Existing model input data**

</td>  
<td>

Data collected from publicly available sources

</td>  
<td>

**YES** – if no legal conflicts

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

Data generated by project partners

</td>  
<td>

Model outcomes: **YES**

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

Purchased data

</td>  
<td>

**NO**

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

**Collected and generated new model input data**

</td>  
<td>

Data collected from publicly available sources

</td>  
<td>

**YES** – if no legal conflicts

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

Data generated by project partners

</td>  
<td>

Model outcomes: **YES**

Data for DSM: **YES, but** restricted access  1

Data for experience curves: **YES, but** restricted access  1

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

Purchased data

</td>  
<td>

**NO**

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

Confidential data

</td>  
<td>

**NO** – NDAs have been signed

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

**Generated intermediate model output data**

</td>  
<td>

EMS models

</td>  
<td>

**NO** – transfer between models during iteration process, no relevant benefit
for further applications

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

**Generated final result data of the EMS**

</td>  
<td>

EMS models

</td>  
<td>

**YES**

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

1

Open and royalty-free access restricted to non-commercial use

</td>  
<td>

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

In the case of an _Open Access Data Publication_ , the dataset can be easily
downloaded in a standard format. The planned standard format for data
(re-)publication is “.csv”. The download links for different formats are given
within the metadata sheet in the category “Download”.

In case of _Commercial Exploitation_ of a dataset, a registration procedure
for all those interested in such datasets is an option. This includes the
opportunity to differ the conditions for access depending on the type of the
inquirer or planned re-usage (e.g. dataset being free of charge for public
scientific institutions, but subject to a charge in case of commercial reuse
by a company). After registration of the request of a dataset, a time-limited
download link is provided via e-mail to the registered contact together with
the terms of usage and, as the case may be, with the invoice. The requisition
could be implemented in the respective metadata sheets, which are available
free of charge in any case, in the category “Access”.

The datasets are made available to third parties as soon as they are
generated, prepared and reviewed for publication/commercial exploitation and
when the conditions of dissemination are decided and possible protections of
datasets are clarified within the consortium. However, additional restrictions
by setting appropriate embargo periods and/or respecting restrictions from
editors of scientific journals and organizers of conferences are also
possible. A generally valid statement regarding the embargo periods is not
possible at the moment. It can differ from case to case.

##  2.3 MAKING DATA INTEROPERABLE

To increase the interoperability of provided data, commonly used vocabularies
for the metadata contents as well as for the identifiers and the contents of
the identifiers within the datasets will be applied. These include
standardized name conventions and codes used in official statistics (e.g. for
countries, regions etc.). Furthermore, specific energy system topics related
name conventions will be orientated on the “Open Power System Data” platform (
_http://www.data.open-power-system-data.org/_ ). An additional mapping
procedure or the provision of mapping tools for data users is not envisaged.

##  2.4 INCREASE DATA REUSE – LICENSING OF REFLEX DATA

Intellectual property rights on databases are subject to different pieces of
legislation. First, copyright applies to a wide range of creative,
intellectual and artistic works – including data. Second, in 1996, the EU
adopted the Database Directive (96/9/EG), which had to be implemented into
national law by the Member States. Moreover, rights and duties resulting from
data privacy law or existing licenses on datasets have to be respected.

In order to increase data re-use for REFLEX research data and avoid any legal
ambiguities for data users, the (re-)publication of REFLEX data goes hand in
hand with a clear licensing procedure. In what follows, we discuss different
types of licenses available (i.e. standard vs. custom; open vs. restricted),
give a more detailed overview on standardized licenses, describe how the data
are licensed in order to permit the widest reuse possible, and elaborate on
data quality assurance.

###  2.4.1 TYPES OF LICENSES

A license is a contract by which the licensor (in our case the data owner)
allows the licensee (the data user) to use otherwise protected material. Thus,
it clarifies the conditions under which data can be reused. In contrast, in
the absence of a license, the data owner still retains proprietary copyright
(see also Section 2.5 for further elaborations thereon).

A first distinction can be made among customized and standardized licenses.
Drafting a new **customized license** , allows tailoring it to the individual
needs and desires of the partners. However, it might leave issues open, and
thus might in the end leave data users with default copyright (i.e. no open
data). Moreover, it might be unclear or ambiguous on certain issues. An
established, **standardized license** , in contrast, has the advantages of
content and scope being well understood, and the legal text being rigorously
written and suitable for different legal systems. The REFLEX project partners,
therefore, agreed on the use of standardized licenses for datasets published
in the REFLEX data repository.

A second distinction can be made among open data and non-open data licenses.
Thereby, “ **open data** […] can be freely used, modified, and shared by
anyone for any purpose” (see also _https://opendefinition.org/_ ).  3  More
specifically, data to be classified as open data must be “available under an
open license; available as a whole and at no more than a reasonable one-time
reproduction cost, preferably downloadable via the internet; and it must be
provided in a convenient and modifiable form, machine-readable, available in
bulk and provided in an open format” (see also _https://open-power-system-
data.org/legal_ ).  4

Three main types of **open data licenses** – with increasing obligations for
users – exist. These are _public domain licenses_ under which users are
completely free to use, modify and share the data; _attribution licenses_
which demand users to appropriately credit the dataset creator; and finally
_share-alike licenses_ which in addition to the ones above also condition that
any derivative work is made available under the same license.

**Licenses for non-open data** – with increasing restrictions for users –
include _no-derivatives_ _licenses_ where it is only allowed to copy,
distribute and display original copies of the work and any modification is
subject to explicit permission; as well as _non-commercial use only_
_licenses_ and different combinations thereof. Figure 4 illustrates the
different types of licenses distinguishing among open and non-open data.

**Figure 4: Different types of licenses – open vs. non-open data**

Several initiatives developed sets of standardized licenses for specific works
such as artwork in general (e.g. _Creative Commons_ ), software (e.g. _MIT_ ),
or even explicitly for data publication ( _Open Data Commons_ ). In what
follows, we give an overview on the different standardized licenses available
including examples.

  * **Public domain licenses** – ZERO licenses (i.e. no copyright): Users are completely free to use, modify and share the data, even for commercial purposes and all without asking permission. 

  

Examples: Creative Commons Zero ( _CC0 1.0_ ); Open Data Commons Public Domain
Dedication and Licence ( _PDDL 1.0_ ); Data licence Germany - Zero 2.0 ( _dl-
de-2-0_ ).

  * **Attribution licenses** – BY licenses (i.e. name the author): Users are completely free to use, modify and share the data, even for commercial purposes and all without asking permission. The only requirement is the adequate attribution, i.e. name of the data provider and references to the license and dataset. 

  

Examples: Creative Commons Attribution ( _CC-BY 4.0_ ); Open Data Commons
Attribution ( _ODC-BY 1.0_ ); Data licence Germany – attribution – 2.0 ( _dl-
de/by-2-0_ ).

  * **Share-alike licenses** – SA licenses: Users are completely free to use, modify and share the data, even for commercial purposes and all without asking permission. The only requirements are an adequate attribution (see above) plus that in case a database is amended or modified, it shall be published under the same license as the original one. 

  

Examples: Creative Commons Attribution-ShareAlike 4.0 ( _CC BY-SA 4.0_ ); Open
Data Commons Open Database License ( _ODbL 1.0_ ).

  * **No derivatives license** – ND licenses: It is allowed to copy, distribute and display only original copies of the work. Any modification is subject to explicit permission. 

  

Example: Creative Commons Attribution-NoDerivs ( _CC BY-ND 4.0_ ).

  * **Non-commercial use only license** – NC licenses: It is allowed to use, copy, distribute, modify and display the work for non-commercial purpose only. Any commercial use is subject to explicit permission. 

  

Example: Creative Commons Attribution-NonCommercial ( _CC BY-NC 4.0_ ).

  * **Different combinations** : Creative Commons Attribution-NonCommercial-NoDerivs ( _CC BY-NC-ND 4.0_ ); Creative Commons Attribution-NonCommercial-ShareAlike ( _CC_ _BY-NC-SA 4.0_ ). 

  

###  2.4.2 LICENSING REFLEX DATA

The REFLEX project partners decided to apply the widely used **Creative
Commons licenses** for the (re-)publication of REFLEX datasets (see Table 13).
For data collected from publicly available sources (existing and new) as well
as for input data generated by the project partners’ modelling works, the open
CC-BY license has been chosen, providing open access to any user for any use
on the single condition of an adequate attribution to the original dataset.
The same holds for generated final project result data of the EMS.

For generated data for demand side management as well as experience curves the
open CC-BY-NC license has been chosen. It restricts open access to non-
commercial use only. Any commercial use will be subject to explicit permission
and the payment of royalty fees to be specified in the respective agreement
between the REFLEX representative and the commercial party.

The REFLEX project partners refrained from using share-alike licenses as their
application to a certain extent restricts future users of a dataset in the re-
publication of processed data. Especially if he/she might rely on several
share-alike licenses, these might be incompatible and leave the user with a
potential conflict.

#  Table 13: Draft of REFLEX data licensing (the right to make changes is
reserved)  
  
<table>  
<tr>  
<th>

Data group Data source

</th>  
<th>

General license type

</th>  
<th>

Standard license chosen

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

**Existing model input data**

</td>  
<td>

Data collected from publicly available sources

</td>  
<td>

**Open data** with attribution

</td>  
<td>

Creative Commons

Attribution **(CC-BY 4.0)**

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

Data generated by project partners

</td>  
<td>

Model outcomes: **Open data** with attribution

</td>  
<td>

Creative Commons

Attribution **(CC-BY 4.0)**

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

Purchased data

</td>  
<td>

**-**

</td>  
<td>

**-**

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

**Collected and generated new model input data**

</td>  
<td>

Data collected from publicly available sources

</td>  
<td>

**Open data** with attribution

</td>  
<td>

Creative Commons

Attribution **(CC-BY 4.0)**

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

Data generated by project partners

</td>  
<td>

_Model outcomes:_ **Open data** with attribution

_Data for DSM:_

**Restricted access** for non-commercial use only  1  and with attribution
_Data for experience curves:_ **Restricted access** for noncommercial use only
1  and with attribution

</td>  
<td>

_Model outcomes:_

Creative Commons Attribution **(CC-BY 4.0)** _Data for DSM:_

Creative Commons

Attribution-NonCommercial ( **CC BY-NC 4.0)**

_Data for experience curves:_ Creative Commons

Attribution-NonCommercial

**(CC BY-NC 4.0)**

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

Purchased data

</td>  
<td>

**-**

</td>  
<td>

**-**

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

Confidential data

</td>  
<td>

**-**

</td>  
<td>

**-**

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

**Generated intermediate model output data**

</td>  
<td>

EMS models

</td>  
<td>

**-**

</td>  
<td>

**-**

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

**Generated final result data of the EMS**

</td>  
<td>

EMS models

</td>  
<td>

**Open data** with attribution

</td>  
<td>

Creative Commons

Attribution **(CC-BY 4.0)**

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

1

Any commercial use subject to explicit permission.

</td>  
<td>

</td>  
<td>

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

In theory, the **compatibility of different licenses** could be an issue (see
e.g. _www.github.com_ ). When processing data and publishing results, the
license(s) of input data need(s) to be respected. Typical questions that might
arise include: Can data published under license A be merged with other data
published under license B? What license could be applied to such a derived or
aggregated dataset? Are there any provisions associated with the license of an
input dataset that constrain the creation and publication of derivations?
Precisely, a data user might face the conflict of two input datasets being
licensed under two different, incompatible share-alike licenses. For the
REFLEX project no issues related to incompatibility of licenses occurred.

The REFLEX project partner **ESA 2  is responsible for data management and
(re-) publication. ** For datasets collected from project-external sources, an
agreement on the use and republication of the respective data will be signed
between the data owner and ESA  2  (see Section 2.5 and Figure 6 below for a
detailed discussion of the proceeding). For data generated by REFLEX project
partners themselves – whether outside (background data) or inside the project
(foreground data) – the CA says that “results shall be vested in the party
that has generated them” and that “where results are generated from work
carried out jointly by two or more parties […], they shall have joint
ownership.” To allow ESA  2  to take care of data publication and licensing,
and to also manage commercial exploitation where envisaged, a contract between
the concerned partner(s) and ESA  2  will be signed (see Figure 5). Thereby,
data ownership remains with the party who has collected/generated the dataset.

**Figure 5: Procedure for giving ESA 2  the right to republish data and manage
licensing **

###  2.4.3 DATA QUALITY ASSURANCE

Open data obviously are only of use for future research or stakeholders from
industry and policy, if they are up-to-date, as complete as possible and free
of any human-made mistakes in data collection, storage and reporting.
Moreover, coupled models – as is the case for the EMS – should use harmonized
input data and be based on common scenario storylines. Within the REFLEX
project, quality assurance processes as described in Table 14 therefore have
been implemented for the different data groups.

#  Table 14: Processes of data quality assurance  
  
<table>  
<tr>  
<th>

**Data group**

</th>  
<th>

**Processes**

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

**Existing model input data**

</td>  
<td>

  * Harmonization of model input data to ensure a consistent analysis within the EMS and regarding the defined REFLEX scenario storylines. For the same information, the same dataset (values) have been used in all models (consortium decision). 

  

  * Harmonized data being provided to all models before initializing the EMS runs. 

  

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

**Collected and generated new model input data**

</td>  
<td>

  * A minimum of two internal reviews of the generated new model input data took place. 

  

  * Additionally, external peer-reviews in case of publication of selected modelling results and/or quantitative research in scientific journals. 

  

  * Harmonization of model input data to ensure a consistent analysis within the EMS and regarding the defined REFLEX scenario storylines. For the same information have to be used the same dataset (values) in all models (consortium decision). 

  

  * Harmonized data being provided to all models before initializing the EMS runs. 

  

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

**Generated intermediate model output data**

</td>  
<td>

\- Plausibility check for intermediate output data of a model during EMS runs
by the responsible modeller before implementing the data in the DHW for data
transfer to another model.

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

**Generated final**

**result data of the**

**EMS**

</td>  
<td>

  * A minimum of two internal reviews of the generated final result data took place. 

  

  * Additionally, external peer-reviews in case of publication of selected modelling results and/or quantitative research in scientific journals. 

  

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

##  2.5 REFLEX ANSWERS TO POTENTIAL LEGAL CONFLICTS

Data management in the REFLEX project on the one hand refers to the management
of data flows of the different models forming the EMS. Issues related to data
harmonization, data storage, data exchange, and data up-/download have been
dealt with by implementing the REFLEX data warehouse (see also Deliverable
2.3: Report on modelling coupling framework). Legal aspects associated to the
ownership of input and result data, the rights to use them and possible
confidentiality restrictions are defined in the Consortium Agreement. It
provides details on the partners’ background data and on the rights to, the
protection and the exploitation of datasets/results generated solely and/or
jointly during the lifetime of the project. Thus, no potential legal conflicts
did arise here.

On the other hand, data management in the REFLEX project refers to the
**(re-)publication** of input data in order to support a transparent research
environment, and to the publication of final project modelling results in a
format that makes it possible for third parties to access, mine and exploit
the data. In general, potential legal conflicts might relate to the following
questions:

  * How to avoid any copyright infringements when using input data from project-external sources for modelling works and re-publishing those in the REFLEX data repository? 

  

  * How to avoid any legal conflicts when using these data for modelling works and publishing EMS modelling results in an open data format? 

  

  * How to avoid any legal ambiguities for third parties who wish to use REFLEX final result data? 

  

Copyright may apply to a wide range of creative, intellectual or artistic
works, including data. One thereby has to be clear in the use of terminology.
Whereas a **single date is not protected** under copyright, structured or
organized data which have been collocated to a database via a substantial
investment (e.g. time, manpower) might be. In this context, the EC’s Database
Directive (96/9/EG, Art. 1.2) defines a **database** as “a collection of
independent works, data or other materials arranged in a systematic or
methodical way and individually accessible by electronic or other means.”

Besides copyright law and the EU Database Directive, different other legal
concepts may govern data ownership and rights for data use and publication.
These include amongst others data privacy laws, national legislation,
licenses, or terms-of-use clauses.

When searching for power system data, one will find that many data collections
are available online, free of any charge. However, this does not imply that
one is allowed to use such data freely. In the absence of any license
agreement, the default is that the copyright holder reserves, or holds for
his/her own use, all the rights provided by copyright and related law. This
includes that already the use of such data – without an explicit consent of
the owner – can be a copyright infringement. Discussions with operators of
energy data platforms, who are dealing with these issues since several years,
confirmed the concerns of the REFLEX partners. It has come to light that long
and comprehensive debates are ongoing regarding the question if datasets can
be (re-)published and what implications such a publication has on the rules
for access, usage etc. This seems still a grey zone in which many researchers
and platform operators work.

Having these issues in mind, potential legal conflicts that did arise in the
course of the REFLEX project were related to

_(i) the use of datasets – i.e._

  * Are we allowed to use and process the dataset for our modelling work? If “reuse is authorized provided the source is acknowledged” what is meant by “reuse”? 

  

  * Are we allowed to copy, present, share, print, or transmit the dataset? 

  

  * Are we allowed to modify or merge with own data and with the data of others to form new datasets? 

  

_(ii) the (re-)publication of datasets – i.e._

\- Are we allowed to re-publish the dataset in public and non-public
electronic networks, and in our project data repository?

and finally

_(iii) implications of non-open-access input data on output data publication –
i.e._

  * Are there any legal conflicts when using data with unclear licensing (or purchased/ confidential data) as input and then publishing our modelling results as open data? 

  

Point (iii) could be neglected, as EMS modelling results are completely new
created datasets, not mirroring input data. No conclusion to the values of
input datasets can be drawn. Points (i) and (ii), in contrast, are more
complex and will be discussed in-depth below. Table 15 summarizes potential
legal conflicts for REFLEX input data, which are provided under a number of
different legal regimes, i.e.:

  * © Public institutions & “All rights reserved.”; 

  

  * © European Union & “Reuse is authorised provided the source is acknowledged.”; 

  

  * © Publisher of academic journal; 

  

  * data published in online reports without any specification on licensing/reuse; 

  

  * data purchased; 

  

  * confidential data coming from EC; or 

  

  * data generated by project partners (model outcomes, data from surveys, etc.) 

  

with respect to the use of a dataset in the project context and its
(re-)publication in the REFLEX data repository.

#  Table 15: Potential legal conflicts for REFLEX data related to use and
(re-)publication  
  
<table>  
<tr>  
<th>

Legal rules on REFLEX input data

</th>  
<th>

Potential legal conflicts regarding the use of the datasets?

</th>  
<th>

Potential legal conflicts regarding the (re-) publication of the dataset?

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

**© Public institutions & “All rights reserved.” **

</td>  
<td>

**YES** – Full copyright with the author

</td>  
<td>

**YES** – Full copyright with the author

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

**© European Union & “Reuse is authorised provided the source is
acknowledged.” **

</td>  
<td>

**Maybe YES** – What is meant by “reuse”? Only processing? Or also
modification, combination with another database? Etc.

</td>  
<td>

**Maybe YES** – No specification regarding re-publication

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

**© Publisher of academic journal**

</td>  
<td>

**Maybe YES** – Depends on

specific rules

</td>  
<td>

**Maybe YES** – Depends on specific rules; possibly re-publication not allowed
for certain time period

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

**Data published in online reports without any**

**specification on licensing/reuse**

</td>  
<td>

**YES** – Default is full copyright with the author

</td>  
<td>

**YES** – Default is full copyright with the author

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

**Data purchased**

</td>  
<td>

**NO** – explicitly purchased for use in the modelling works

</td>  
<td>

Not relevant here, re-publication forbidden

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

**Confidential data coming from EC**

</td>  
<td>

**NO** – explicitly provided for use in the modelling works

</td>  
<td>

Not relevant here, re-publication forbidden

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

**Data generated by project partners (model outcomes, data from surveys,
etc.)**

</td>  
<td>

**NO**

</td>  
<td>

**NO**

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

Copyright law only becomes relevant in case of the use of “substantial parts”
of a database. As a rule of thumb, one can say that the use of less than 5%
will not cause any legal conflicts; for the use of more than 15%, however,
full copyright law applies. As REFLEX partners use parts of the respective
databases to an extent above this last threshold, copyright law cannot solve
the conflicts identified in Table 15. Thus, well-defined contracts are needed
for the different datasets at hand in order to avoid any copyright
infringements with the use and (re-) publication of REFLEX data.

Within REFLEX, the project partner ESA  2  , being responsible for data
management and the research data repository, therefore prepared an
**“Agreement on the use and republication of research data in the REFLEX
project data repository”** , including an annex with the respective metadata.

One thereby had to consider that the **author is not always the copyright
holder** , but only the copyright holder her-/himself has the right to assign
a license. Thereby, copyright ownership is determined by a number of
(national) laws depending on the type of employment (see RUI (2018) for
further details). For instance, under private employment which is the case for
a researcher at a research institute, in Germany all works created during
working hours belong to the employer according to §43 UrhG and §69b UrhG.
This, for instance, is the case for all REFLEX input datasets coming from DIW
(2013).

In the first step, consequently, the copyright holder has been identified.
Second, a contact person being endowed with the right to sign such an
agreement letter had to be found. Third, the agreement letter, including the
annex and an e-mail explaining the context has been sent out, asking the
respective contact person for completion and signature. Signed agreement
letters finally have been archived by ESA².

**Figure 6: REFLEX process for gathering agreements on use and re-publication
of data**

Besides copyright law, severe legal conflicts can originate from **data
privacy law** , which becomes relevant if data refer to particular
individuals. The publication of anonymous data, in contrast, is unproblematic
from the data privacy law perspective. In the case of REFLEX research data, no
related issue could be identified:

  * _DSM parameters_ (flexible load, shifting time, etc.) – absence of any reference to particular households or individuals in the respective datasets summarizing the DSM parameters. Representative and average values resulting from the empirical survey conducted for ten EU Member States have been used. DSM data for further countries have been deduced from the survey results. 

  

  * _Power plant parameters_ (availability, efficiency, cost factors, etc.) – absence of any reference to particular installations. Partly average values for the different generation technologies have been used. 

  

  * _Electricity demand_ – absence of any reference to particular consumers. Data aggregated over regions and/or sectors has been used. 

  

  * _Heating demand_ – absence of any reference to particular consumers. Data aggregated over regions and time has been used. 

  

  3. **ALLOCATION OF RESOURCES**

  

  

The EIM is responsible for data management within the REFLEX project (see
Section 1.4). Estimated costs for making REFLEX data FAIR are 150.000 Euro
(over all partners).

These costs include:

  * the clarification of data protection issues and licences available, 

  

  * the final preparation of data by each project partner for publishing (without effort/costs for data collection/purchasing/generation etc.), 

  

  * the processes for assurance of data quality, 

  

  * the development and implementation of the data catalogue in the project website, 

  

  * the implementation of the registration procedure for access to commercially exploited datasets, 

  

  * the data hosting and backup for security, 

  

  * the data updating and maintenance of the data and of the data provision 

  

during the project lifespan. These costs are covered by the project funds,
mainly by the budgeted personnel costs.

The costs for long-term preservation after the end of the project are
difficult to estimate. They depend mainly on the chosen platform, the
effort/cost for maintaining but also on the scope and size of the collected
and generated datasets. The permanent costs of preserving datasets on the ESA²
DWH provided via ESA² Website are estimated with 800 EUR per year (under
current conditions for the v-server and without personnel costs for
maintenance). The OpenEnergy Platform and the ZENODO repository would be free
of charge as long as the single dataset storage is no greater than the maximum
2GB of data. The permanent costs of preserving datasets on the OpARA
repository are planned to be free of charge for TUD members, but the final
decision on costs has not been taken yet. The costs for long term preservation
shall be covered by the collected charges from the commercial exploitation of
datasets during the project lifespan and after.

  4. **DATA SECURITY**

  

  

Most of the data handled in the REFLEX project are not sensitive regarding the
laws governing data protection and data security. An exception represents the
data from the DSM survey. A provision/publication of these data is only
possible in an anonymous form.

The DWH as well as the data provision via websites will be implemented on
servers with regular backup and data recovery procedures.

  5. **ETHICAL ASPECTS**

  

  

Data collection, data storage, data usage, data generation and data
dissemination in this project do not raise ethical issues.

  6. **OTHER**

  

  

No other national-, funder-, sectorial-, or departmental procedures for data
management will be used.