#  EXECUTIVE SUMMARY

This Deliverable describes the technical and organisational measures to be
implemented within the XILforEV project for the data management of the project
results and assets. These results and assets are subject of both internal
access by the consortium participants and open access in selected cases. The
document introduces general information about relevant data, standards, and
quality assurance methods. Detailed data specifications are formulated for
four use cases, which are being investigated in the XILforEV project.
Particular attention is given to procedures for data archiving and
preservation as well as data repository.

_Attainment of the objectives and explanation of deviations:_ The project
works related to this Deliverable are being carried out in full compliance
with the XILforEV objectives. There are no deviations from the actions set in
the Grant Agreement.

**KEYWORDS:** Data management, Open Access, Research data.

#  GENERAL INFORMATION

##  Background

The Data Management Plan, established in the XILforEV project, is based on
internal practices of the consortium organisations and also uses outcomes of
relevant measures, which are efficiently realised by the consortium
coordinator (TUIL) in previous Horizon 2020 projects EVE  1  and ITEAM  2  .

The objectives of the data management are:

  * Realise a proper access to project results for both consortium participants and target audiences outside of the consortium; 

  

  * Ensure easy public search and access to publications, which are directly arising from the research funded by the EU; 

  

  * Allow reuse of research results, produced in the project, to enhance value of the project outcomes to all potential stakeholders; • Avoid unnecessary duplication of research activities; 

  

  * Guarantee transparency of research process. 

  

##  Data Categories

The research and development works of the project will produce three general
categories of analytical and experimental data:

  * Publishable raw data; 

  

  * Publishable analysed data; 

  

  * Data not selected for publication. 

  

###  Publishable Raw Data

The digitised data of parameters, which are recorded during the experiments,
are allocated to the category of publishable raw data. The recorded parameters
can include:

  * Signals of sensors installed on test rigs, driving simulators and vehicle systems used in experiments; 

  

  * Data describing the testing environments, i.e. ambient temperature and moisture; 

  

  * State signals from communication and measuring devices used on test equipment. 

  

The publishable raw data are stored in a digital format, which is determined
by corresponding data processing and acquisition systems. For instance, most
common data formats in this case are MATLAB data files and Microsoft Excel
spreadsheet files. The stored data will be supplemented with “readme” file
describing the raw content and the procedures of collecting raw data.

###  Publishable Analysed Data

The publishable analysed data may include figures, tables, charts, video
recordings and other relevant visual objects, which are created during the
processing and analysis of raw data. These data can be used for the project
reporting as well as for publications, presentations and other related
dissemination and communication activities.

###  Data not Selected for Publication

Some raw and analysed data can be tagged by the consortium as unpublished.
First of all, such a tagging can be applied in the cases predefined by IP/IPR
management procedures. Another tagging case is when the data have intermediate
character and used for preliminary works. Nevertheless, the data from this
category will be screened for quality and available upon request for potential
external users.

##  Relevant Regulatory Documents

The consortium uses instructions and recommendations from corresponding
regulatory documents to fulfil data management cycle during the project life.
It concerns detailing the character of data generated in the project and
linked metadata, exploitation, sharing, curation and preservation of these
data. All these actions are being performed in a strong compliance with the
following documents:

  * ISO/IEC JTC 1/SC 32 - Data management and interchange; 

  

  * ISO 9001:2008 - Quality management systems; 

  

  * ISO 27001:2013 - Information Security Management Systems; 

  

  * Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. 

  

##  Quality Assurance and Control

The quality assurance and control measures, related to the data management,
are under responsibility of all project beneficiaries and includes supervision
of the project coordinator. The relevant measures cover three groups
identified next.

_**Measures for quality assurance before data collection:** _

  * Definition of the standard(s) for measurements and recording prior to the data collection; 

  

  * Definition of the digital format for the data to be collected; 

  

  * Specification of units of measurement; 

  

  * Definition of required metadata; 

  

  * Assignment of responsibility to a person over quality assurance for each test series; 

  

  * Design of Experiments (DoE) for each test series; 

  

  * Design of a data storage system with sufficient performance; 

  

  * Design of a purpose-built database structure for data organization. 

  

_**Measures for quality assurance and control during data collection and
entry:** _

  * Calibration of sensors, measuring devices and other relevant instruments to check the precision, bias and scale of measurements; 

  

  * Taking multiple measurements and observations in accordance with the established DoE; 

  

  * Setting up validation rules and input masks in data entry software; 

  

  * Unambiguous labelling of variable and record names; 

  

  * Implementation of double entry rule – ensuring that two persons, performing the tests, can independently enter the data; 

  

  * Use of reference mechanisms (a relational database) to minimize the number of times the data need to be entered. 

  

_**Measures for quality control during data checking:** _

  * Documentation of any modifications to the dataset to avoid duplicate error checking; 

  

  * Checking the dataset for missing or irregular data entries to ensure the data completeness; 

  

  * Performing statistical summaries with checking for outliers by using graphical methods as probability and regression plots, scatterplots et al.; 

  

  * Verifying random samples of the digital data against the original data; 

  

  * Ensuring the data peer review both by scientific and technical criteria. 

  

#  CONTENT OF DATA SETS

Next sections specifies the content of expected data sets for each use case
defined in the XILforEV project description.

##  Use Case 1 – Brake Blending

Data sets:

  * Documentation to testing environment, incl. specification of software, hardware and communication components; 

  

  * Documentation to the hardware-in-the-loop brake test rig, the powertrain test rig, the brake dynamometer, and the brake robot; 

  

  * Vehicle dynamics model incl. a full vehicle, multi-body dynamics model of the target vehicle and real-time versions of the models for the use in dSPACE environment; 

  

  * Programme code of brake blending controller and related control functions; 

  

  * Results of validation and testing of models and brake blending controller in XILforEV testing environment, incl. recorded signals of sensors and measurement techniques on test rigs / setups. 

  

Features of data sets:

  * Online storage on the project SharePoint server; 

  

  * Data collected by the operators of test rigs / setups; 

  

  * No personal data and no ethical issues are identified; 

  

  * Use in documentation, reports and publications; 

  

  * Selected parts of data sets can be shared with external partners or provided for open access; 

  

  * Preservation in the archive. 

  

##  Use Case 2 – Ride Blending

Data sets:

  * Documentation to testing environment, incl. specification of software, hardware and communication components; 

  

  * Documentation to the driving simulator and suspension test rig; 

  

  * Vehicle dynamics model incl. a full vehicle, multi-body dynamics model of the target vehicle, tyre model, and real-time versions of the models for the use in dSPACE environment; 

  

  * Programme code of ride blending controller and related control functions; 

  

  * Results of validation and testing of models and ride blending controller in XILforEV testing environment, incl. recorded signals of sensors and measurement techniques on test rigs / setups. 

  

Features of data sets:

  * Online storage on the project SharePoint server; 

  

  * Data collected by the operators of test rigs / setups; 

  

  * Ethical issues can be related to the test persons operating the driving simulator that will be handled in accordance with the procedures stated in the project Deliverables 8.1-8.3; 

  

  * Use in documentation, reports and publications; 

  

  * Selected parts of data sets can be shared with external partners or provided for open access; 

  

  * Preservation in the archive. 

  

##  Use Case 3 – Integrated Chassis Control

Data sets:

  * Documentation to testing environment, incl. specification of software, hardware and communication components; 

  

  * Documentation to the driving simulator, hardware-in-the-loop brake test rig, the powertrain test rig, and suspension test rig; 

  

  * Vehicle dynamics model incl. a full vehicle, multi-body dynamics model of the target vehicle, tyre model, and real-time versions of the models for the use in dSPACE environment; 

  

  * Programme code of integrated chassis controller and related control functions; 

  

  * Results of validation and testing of models and integrated chassis controller in XILforEV testing environment, incl. recorded signals of sensors and measurement techniques on test rigs / setups. 

  

Features of data sets:

  * Online storage on the project SharePoint server; 

  

  * Data collected by the operators of test rigs / setups; 

  

  * Ethical issues can be related to the test persons operating the driving simulator that will be handled in accordance with the procedures stated in the project Deliverables 8.1-8.3; 

  

  * Use in documentation, reports and publications; 

  

  * Selected parts of data sets can be shared with external partners or provided for open access; 

  

  * Preservation in the archive. 

  

##  Use Case 4 – Fail-safe and Robustness Study

Data sets:

  * Documentation to testing environment, incl. specification of software, hardware and communication components; 

  

  * Documentation to the powertrain and chassis component test rigs; 

  

  * Models of vehicle subsystems and operational environments, and real-time versions of the models for the use in dSPACE environment; 

  

  * Programme code of fail-safe controllers of powertrain and chassis subsystems as well as related control functions; 

  

  * Results of validation and testing of models and fail-safe controllers in XILforEV testing environment, incl. recorded signals of sensors and measurement techniques on test rigs / setups. 

  

Features of data sets:

  * Online storage on the project SharePoint server; 

  

  * Data collected by the operators of test rigs / setups; 

  

  * No personal data and no ethical issues are identified; 

  

  * Use in documentation, reports and publications; 

  

  * Selected parts of data sets can be shared with external partners or provided for open access; 

  

  * Preservation in the archive. 

  

#  HANDLING OF DATA SETS

##  Archiving and Preservation

All data sets will be centrally stored on the secured server established by
the project coordinator TUIL. This server is linked to the project webpage
under the address

_HTTPS://SHAREPOINT.TU-ILMENAU.DE/WEBSITES/KFT/PROJEKTE/XILFOREV/_ , Figure 1.
It can be seen on Figure 1 the corresponding folders “Use Case #”, where the
corresponding data sets are being stored.

**Figure 1 – Screenshot of the secured consortium area of the XILforEV
project.**

The access to this secure consortium area is organized for the persons
involved in the project through individual login names and passwords. The
login names and passwords are issued by the secure server administrator from
TUIL.

The data sets will be stored on this server at least for five years following
the project end date. The access for this time frame will be ensured for all
registered users as well.

The consortium has no restrictions for participating beneficiaries to keep
archives of data sets on their institutional servers under conditions that all
required data management actions will be properly handled.

##  Data Sharing

The knowledge sharing outside of the consortium will be realized through two
main instruments:

  * The consortium will define a set of documents and reports with the analysis of the project results and assets that will be available for open access on the project website. Most of the project presentations delivered on professional events will be also published on the website for free download. 

  

  * The consortium will aim at granting free access for all the scientific publication that will be prepared during the project activities. The planned publications will be subjected to the “green” open access model. In addition to this, presentation of program activities and relative results will be published on the consortium website. 

  

The publishable and analysed raw data can be reused upon request in exchange
for authorship and/or establishment of a formal collaboration.

**Figure 2 – Screenshot of the XILforEV webpage with the publishable project
results.**

The data with open access will be available through two channels: the project
webpage and the project area on the ResearchGate portal. The data on the
project webpage are available through the link < _HTTPS://XIL.CLOUD/RESULTS/_
> , Figure 2. The project area on the ResearchGate portal is created and
accessible through the link

< _HTTPS://WWW.RESEARCHGATE.NET/PROJECT/XILFOREV_ > , Figure 3.

**Figure 3 – Screenshot of the XILforEV project area on the ResearchGate
portal.**

Individual consortium participants can share any type of data linked to the
project, such as articles, conference papers, presentations, posters et al.
Specifically for the XILforEV project area on the ResearchGate portal, there
is the possibility to preserve the data by means of the DOI codification. In
the “Digital Object Identifier” field it is either possible to assign a new
DOI, automatically generated by the ResearchGate service, or to use the
original code in order to allow the other users to easily and unambiguously
cite the uploaded file.

#  CLOSING REMARKS

The presented document regarding the data management plan in the XILforEV
project has a living character and will be regularly updated to address
eventual emergence of new data sets, changes in regulatory guidelines and
other relevant issues. In the case of changes, the content of the data
management plan will be adapted, and the applied actions will be reported in
project dissemination reports (Deliverables 6.3 and 6.4).