EXPERIMENTAL RESEARCH DATA QUALITY IN MATERIALS SCIENCE

International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 6, December 2014
DOI : 10.5121/ijait.2014.4601 1
EXPERIMENTAL RESEARCH DATA QUALITY IN
MATERIALS SCIENCE
Thorsten Wuest1
, Rainer Tinscher2
, Robert Porzel3
and Klaus-Dieter Thoben1,4
1
Department ICT applications for production, BIBA - Bremer Institut für Produktion und
Logistik GmbH, Hochschulring 20, 28359 Bremen, Germany
2
Stiftung Institut für Werkstofftechnik, Division Materials Science, Badgasteiner Straße
3, 28359 Bremen, Germany
3
University of Bremen, Research Group Digital Media, Bibliothekstraße 1, 28359
Bremen, Germany
2
University of Bremen, Institute for Integrated Product Development, Bibliothekstraße 1,
28359 Bremen, Germany
ABSTRACT
In materials sciences, a large amount of research data is generated through a broad spectrum of different
experiments. As of today, experimental research data including meta-data in materials science is often
stored decentralized by the researcher(s) conducting the experiments without generally accepted standards
on what and how to store data. The conducted research and experiments often involve a considerable
investment from public funding agencies that desire the results to be made available in order to increase
their impact. In order to achieve the goal of citable and (openly) accessible materials science experimental
research data in the future, not only an adequate infrastructure needs to be established but the question of
how to measure the quality of the experimental research data also to be addressed. In this publication, the
authors identify requirements and challenges towards a systematic methodology to measure experimental
research data quality prior to publication and derive different approaches on that basis. These methods are
critically discussed and assessed by their contribution and limitations towards the set goals. Concluding, a
combination of selected methods is presented as a systematic, functional and practical quality measurement
and assurance approach for experimental research data in materials science with the goal of supporting
the accessibility and dissemination of existing data sets.
KEYWORDS
data quality; experimental research data; materials science; meta-data; quality measurement; continuous
exploitation
1. INTRODUCTION
The topic of data quality gains importance due to various factors such the rapid development of
the so-called “big data” movement (Lohr, 2012; Aggrawal, 2013; Küll, 2013; Lycett, 2013;
Kwon, Lee, & Shin, 2014), increasing automation of manufacturing systems, and developments
in Information and Communication Technology (ICT). With this development, practitioners and
academics in various domains (Ozmen-Ertekin & Ozbay, 2012) have to face the challenge of
ensuring the quality of generated or captured data. In academia, where researchers base their
conclusions on research data, the quality of the data has a tremendous impact on the results.
Especially when sharing information, the importance of inherited data quality can not be rated

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high enough (Hartono, Li, Na & Simpson, 2010). However, Büttner et al., state in their handbook
on research data management, that the absence of a chapter “research data quality” is highlighting
the need to begin exploring this issue (Büttner, Hobohm & Müller, 2011).
There are various publications and practical handbooks about data quality issues (e.g., Pierce,
2004; Evans, 2006; Naumann, 2007; Büttner, Hobohm & Müller, 2011). However, most of them
are very specific for a certain type of data sets or domain. However, research data has certain
requirements and differentiates itself from other data sets. As scientific data are in many cases
very heterogeneous and can even be based on unique experimental designs, it is quite difficult to
assess and measure data quality. This is where this publication seeks to make a contribution by
first presenting the specifications of experimental research data per se and then focus on the
domain of materials science, followed by a section elaborating on data quality and the special
requirements of experimental research data with respect to data quality. Based on the findings, the
fourth section will present approaches that support the quality assurance of experimental research
data and map them in a matrix to the requirements of the research domain of materials science. In
the fifth section, a methodology to assure experimental research data quality in materials science
is presented and critically discussed. The final section provides a short conclusion and an outlook
on this important topic.
2. STATE OF THE ART IN RESEARCH DATA IN MATERIALS SCIENCE
In this section, a brief recapitulation of the definition and meaning of research data itself and its
significance within the materials science domain is presented. Furthermore, characteristics of
experimental research data in materials science are derived as a foundation for the presented
research in the following sections.
2.1. Experimental research data
Most research domains share similar problems when looking at the research data they produce. In
most cases there are no standards in form of format style or documentation. Therefore, the
research data is generated or saved in various formats, which are most of the time not consistent
semantically and meta-data is provided incompletely (Neuroth et al. 2009). This holds true for
most empirical research fields. The research domain of materials science can be located in the
group of research domains, which conduct empirical experiments in order to obtain new findings.
The absence of standards in format and incomplete provision of meta-data hinders the reuse and
thus the continuous exploitation of experimental research data to a large extent. Be it reuse by the
same institute/researchers or another, each one requires a certain quality of the data and its
documentation, thus data management in order to be able to work with the research data. In order
to ensure the sustainability of the data and the maximum impact of mostly expensive experiments
conducted to generate it, the data quality of the research data has to be secured.
Experimental research data is often ‘generated’ by informal processes, which are adapted based
on the research question (Neuroth et al., 2009). Major funding organizations put an increased
focus on research data management over the last years. In 2009 the German Research Foundation
(DFG) published a number of recommendations concerning the handling of research data. Within
the first recommendation the term research data is broadly defined as all data, which is generated
during study of sources, experiments, measurements, surveys and polls. An important aspect is
that research data is seen as the foundation of scientific publications (DFG, 2009). Within this
definition, every institution has the freedom to choose to what extent and what state of
aggregation the research data is to be saved. However, the second recommendation puts forward
the need for domain specific organizational standards for the handling of research data. It is

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argued, that the diverse nature of the different research domains is reflected in the data and thus
the individual requirements towards a sufficient data management (DFG, 2009).
Monteagudo (1995) described the increasing challenges for the management of experimental
research data put forward by e.g. the developments in instrumentation. The amount of data
increases due to automatic measurements by sensors and/or computers (Scott, et al., 2013). The
contextual information is often not considered anymore (Monteagudo, 1995) and it is almost
impossible to access this contextual information at a later stage of the experiment. This is a key
factor of success for any platform handling research data and any future infrastructure has to be
measured by its contribution to this. The next sub-section further analyses the research data in
materials science.
2.2. Materials science research data
Data management and corresponding infrastructure is to a large extent domain specific (Büttner,
Hobohm & Müller, 2011). In materials science, as mentioned before, research data is the results
of a wide variation of different experiments and observations. Occurrences in and on materials,
e.g. happening during an experiment, represent the time dimension of an observation and describe
internal and external changes of the material sample (Ilschner & Singer, 2010). Experiments and
series of experiments test the properties of materials under a variation of circumstances. In order
to perform the experiments, major investments are often necessary. Depending on the experiment,
the material and the other factors, e.g., test-equipment and pre-treatment of the material, the
generation of a research data set in the materials sciences can be very costly and time consuming.
One lever to realize monetary and time savings within material testing is seen in the increasing
amount of simulation activities in the field of materials science during the last 20 years. On the
other hand, solid simulation models need to be feed with reliable experimental research data
function probably and provide dependable results.
Most of the experimental research data is based on measurements and observations and provide
generally a good foundation for processing in ICT systems/infrastructures. In many cases the data
is based on standardized tests and experiments. To give an impression on the diversity and
heterogeneity of the research data, it is important to understand the wide range of different
experiments, which are conducted within the materials sciences. This can be for example tensile
tests, fatigue tests, bend or hardness tests all described by standards like DIN, EN, ASTM, ISO
etc. specified for different materials, e.g. metals, polymers, carbon fiber reinforced plastics, etc.
These standards however to not specify how and to what extent the resulting experimental
research data shall be saved or archived in an information infrastructure. Nor do they specify how
to measure the quality of the resulting experimental research data. Hence, there is a lack of
guidance concerning a standardized handling of experimental research data in the materials
sciences.
However, it has to be noted, that each of the experiments can be altered to some extent, changing
the results significantly or just slightly. This is an important part of the ongoing research activity
in the materials science domain and therefore has to be considered when talking about the
handling of research data. The adaption of existing or definition of new experiments plus the
large amount of different materials increases the complexity continuously.
Nevertheless, it is important to create an environment where the generated research data is as well
prepared in terms of data quality as possible for future continuous exploitation and to ensure a
quick search and find for older data. Not only because the generation involved large investments
both in time and money, but also because it allows for a later verification of the conducted
research results as well as the employment of data mining and knowledge discovery approaches.

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3. QUALITY OF EXPERIMENTAL RESEARCH DATA IN MATERIALS SCIENCE
Data quality is a one of the major factors when it comes to potential reuse of existing data not
only in materials science but also in most research domains. Before looking into methods and
tools designed for measuring and thus ensuring data quality, a common understanding of quality,
data quality and, more specifically, data quality of experimental research data in materials science
will be established in this section.
3.1. Quality
Quality has been a focus area of various engineering domains like manufacturing for several
decades and the market success of companies successful in utilizing their understanding of quality
and customer requirements highlight the importance of quality. De Weck, Ross & Rhodes (2012)
found in their recent study on system lifecycle properties (‘Ilities’) that quality is and was the
most dominant ‘ility’ of engineering systems for over a century, rated higher than e.g., reliability
and safety (De Weck, Ross & Rhodes, 2012).
In this paper the term quality is understood as “the degree to which a set of inherent
characteristics fulfills requirements” (DIN EN ISO 9001:2008). Requirement within this context
is defined as the “need or expectation that is stated, generally implied or obligatory” (DIN EN
ISO 9001:2008). According to this definition, quality depends on the fulfillment of requirements.
The fulfillment of these requirements depends on the planning of processes (commands) and the
execution of processes (executions) (see Figure 1) (Jacob & Petrick, 2007). Quality of the final
product is regarded as achieved to a higher degree when more of the original customer
requirements match with the achieved final product characteristics (Sitek, 2012).
Figure 1. Elements of quality (adapted from Jacob & Petrick, 2007; Sitek, 2012)
It has to be considered that a product can inherit different qualities, the sum of these qualities like
e.g., security, workmanship, durability or stable value represent the final product quality
(Kamiske & Brauer, 2008). Several of these qualities can be represented by material properties. In
the next section, the meaning of quality of data, so-called data quality, is explained.
3.2. Data quality
Data quality is a multi-dimensional concept (Pipino, Lee & Wang, 2002). Data and information
quality is usually defined in terms of contribution to the objectives of the end-user (Helfert,
2002). It can be additionally described as the adequacy for the relevant data processing
application (Naumann, 2007). Poor data quality can be a major cause for damages and losses on
organizational processes (Storey et al., 2012). To avoid the damages and losses data quality
problems and solutions should be considered as early as possible, best at the design stage of the
information sys-tem (Storey et al., 2012).

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Pipino, Lee & Wang, 2002 lists under the data quality dimensions the following attributes:
accessibility, appropriate amount of data, believability, completeness, concise representation,
consistent representation, ease of manipulation, free-of-error, interpretability, objectivity,
relevancy, reputation, security, timeliness, understandability, value-added (Pipino, Lee & Wang,
2002). Data quality can thus be described as a set of quality characteristics (Naumann, 2007).
Many of the listed attributes contribute to the overall data quality, as tested by a third-peer.
The starting point for consideration of data quality is the user-oriented quality concept. Helfert
divides data quality in design and execution quality. The fulfillment of end-user requirements and
specifications can be met through a choice of properties in the data design. Design quality refers
as such to the collection of specific quality requirements from the user's perspective. Execution
quality includes compliance with the specifications (Helfert, 2002). Helfert’s basic data quality
criteria are: correctness, completeness, consistency and timeliness (Helfert, 2002). Data quality
criteria developed by English are: data standards, data definitions and information architecture
(English, 1999). These criteria can be understood as the access capability, timeliness and
interpretability of the data and the data system. Data quality as understood by Wang and Strong
can be divided into internal data quality, contextual data quality, presentation, and access quality
(Wang & Strong, 1996). Wang and Strong focus on user-related data quality - interpretability,
usefulness, credibility, time reference, and availability have been rated as important criteria
(Wang & Strong, 1996; Helfert, 2002). Jarke, Jeusfeld, Quix & Vassilidis’s data quality criteria
are: completeness, credibility, accuracy, consistency, and interpretability (Jarke et al., 1999). A
poll conducted by Helfert delivers additional quality criteria important to organizations: clearly
defined data descriptions, formal data syntax, delivery times (for data), and specific information
about selected properties of the data, e.g., number of errors (Helfert, 2002).
Rohweder et al. describe data quality as the degree the characteristics suffice the requirements on
the data product. The requirements for the data are determined through particular decisions and
goals set on data quality. Rohweder et al. define data quality with the help of 15 IQ (Information
Quality) dimensions (Rohweder, Kasten, Malzahn, Piro & Schmid, 2011). These can be applied
to e.g., master data, to assess if the data is useful or not acceptable. The IQ dimensions have been
divided into four categories and form a regulatory concept for data quality (Rohweder et al.,
2011). The 15 IQ dimensions are as follows:
System support (e.g., user interface)
• Accessibility: accessible & easy to access.
• Ease of manipulation: easy to use & to change.
Inherent (content examination)
• Reputation: data source & processing highly trustworthy.
• Free of error: error free and consistent with reality.
• Objectivity: strictly objective and value-free.
• Believability: reinforced with quality standards, etc.
Representation (overall presentation, e.g., the form of statistics)
• Understandability: ability of users to directly understand & use information.
• Concise representation: clear, saved in appropriate & understandable format.
• Consistent representation: uniform & held in consecutive & equal manner.
• Interpretability: understandable in same, technically correct manner.

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Purpose-dependant (data use in the processes)
• Timeliness: actual properties of data (described) accurately & up-to-date.
• Value-added: usage leads to quantifiable increase in monetary cost function.
• Completeness: no missing information contained.
• Appropriate amount of data: amount meets requirements set on data.
• Relevancy: provides all necessary information for user.
Overall, it is accepted, that all IQ dimensions should exhibit a high or at least sufficient quality
for an information system to be functional (Rohweder et al., 2011). The following elaboration of
data quality of experimental research data in materials science and the development of suitable
measures is partly based upon the IQ dimensions presented in this section. Furthermore, the
chosen approach is evaluated taking the requirements derived from the IQ dimensions into
account.
3.3. Quality of experimental research data in materials science
The previous section presented a domain-independent elaboration of data and information quality.
In the following section, the applicability of the described dimensions and specific characteristics
of the materials science domain, which need to be considered, are described.
The understanding of experimental research data quality in materials science complies with the
before established understanding of information and data quality and its 15 dimensions. There are
however certain aspects specific to the domain of materials science which have to be highlighted.
For once, data quality of published data sets is very important. This is of course also true for other
domains, but as in materials science basic parameters for later design and construction of critical
product/parts may be set, there is an increased emphasis on this fact. Inaccurate data cannot only
lead to wrong research results but endanger human life through e.g., failure of critical parts of a
plane due to wrong dimensioning based on the inaccurate data available for a certain material.
This can lead directly to the thread of large liability claims. Also in the materials science domain,
research is often industry driven or at least involves industry, which is not only relevant by
differing quality standards and sharing cultures but also concerning possible confidentiality
agreements. This often limits the freedom of handling and processing the data and thus may limit
the potential leverage of quality assurance approaches.
A more technical challenge of materials science research data towards quality is the existence of
various data format (e.g., photos, graphs, plots, data all analog and/or digital) the data can be
utilizing. Often combinations of the above-mentioned formats are represented in the resulting data
set of an experiment. Other factors like e.g., the large number of tests, process parameters, quality
of test equipment, available standards (e.g., DIN, ISO, EN, ASTM), etc. add to the inherit
complexity and the diverse nature and high-dimensionality of the resulting data and meta-data.
Finally the scientists often set own quality levels in accordance to the specific targets of the
individual project. E.g. tensile tests performed in order to be used as a basis for numerical
simulations require a higher data- and test-quality compared to tensile tests performed within a
daily pre-delivery check of incoming basic materials. Nevertheless, both data-types need to fulfill
specific quality requirements but there is no standardized scale to set them into a relation. In a
database, both data sets might have a “green traffic light” for data quality but on different quality
levels which cannot be compared. Information about the target quality within the meta data of a
data set is therefore needed for making it usable for external third party users.

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In the following section, different approaches to measure experimental research data quality are
developed on the basis of a requirements analysis of the previously presented state of the art and
an analysis of generally accepted and often-occurring data errors and challenges for data quality.
4. APPROACHES TO MEASURE DATA QUALITY IN MATERIALS SCIENCE
In this section, current requirements towards a systematic measurement of experimental research
data quality are presented. This is fundamental for the subsequent development of practical
approaches to measure research data quality in materials science. After the approaches are
theoretically developed, they are compared in an illustrative matrix by their impact on certain key
aspects of future application within the materials science domain.
4.1. Requirements of tools to assure data quality
Data errors can be avoided most effectively and most sustainably at the moment of their
emergence, e.g. throughout the manual data entry or automatic data collection (Naumann, 2007).
A direct capture of the data from the source to an electronic device without human interference is
the best way to minimize data input errors. The same stands true for media discontinuity which
may lead to loss or alteration of data. When human interface or media discontinuity is
unavoidable, input errors may occur and consequently de-grade data quality (Verma, 2012). To
prevent quality degradation, a quality check can be performed in the moment of data delivery.
The data can be further checked by end-users through the use of complaints-forms or other rating-
systems, e.g. statistical methods (Helfert, 2002). In case of data sets from external sources, it is
essential for the researcher/information-manager to deal consciously with the data and the data
quality; it is crucial to mark the problematic data to be able to deal consciously with it (Naumann,
2007). The external party and the person responsible for integrating the data into the target system
should be clear about the purpose of why the data are being collected (e.g., goal of the
experiment), and it should be clearly stated (Verma, 2012).
The most common data quality issues are incorrect or missing values, duplicates, and errors in the
recording process (Helfert, 2002; Winkler, 2003; Naumann, 2007; Verma, 2012). Errors in data
cause errors in reports generated from the data, thus reinforcing the “garbage-in-garbage-out-
effect” (Naumann, 2007).
Errors can be found within the schema and/or the data level of the data. The schema level
describes the errors in the structural, semantic and schematic heterogeneity of the data
characteristics. The data level includes value, unit, accuracy, and duplicates errors (Naumann,
2007). Duplicates, one of the most costly data errors (Naumann, 2007) can arise, e.g. due to
typographical errors in the unique identifiers (e.g. the name of the researcher) (Winkler, 2003).
Missing identifiers and contradictions in data indicate low quality (Winkler, 2003). These issues
can be prevented with data quality ensuring practices, e.g. marking of problematic data, auto
correction of format errors, manual correction of the data values, troubleshooting and
coordination with the data suppliers, and organizational rules (Helfert, 2002). Furthermore file-
linkage can be used to create “more complete” data (Winkler, 2003).
The most important and critical characteristics of high-quality data are consistency, completeness
and correctness, timeliness, referential integrity and syntactical correctness. The traceability of
data origin and documentation of discrepancies is also relevant (Helfert, 2002). Semantics and
identifiability, as well as the precision of value ranges, granularity of data models, and technical
aspects of data are less critical for the overall data quality (Helfert, 2002).

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Data quality can be assessed by a third-peer. The assessment can either be task-independent,
where no contextual knowledge is required, or task-dependent, with specific application context
(Pipino, Lee & Wang, 2002). The data quality methodologies can be classified according to
various criteria (Batini & Scannapieco, 2006):
• Data-driven vs. process-driven
• Measurement vs. improvement (assessment or improvement of the data quality)
• General-purpose vs. specific-purpose
• Intra-organizational vs. inter-organizational
Assessing data quality is an on-going process (Pipino, Lee & Wang, 2002). The most common
phases of the process for assessing and improving the data quality are finding the cause of error,
designing improvement solutions on data, establishing process control, managing improvement
solutions, and the regular measurement of the improvements (Batini & Scannapieco, 2006). Thus,
e.g. after the data quality has been assessed, the results of the assessment have to be compared,
the discrepancies identified, and the root causes of discrepancies determined. Last but not least
the necessary actions for improvement have to be determined and taken into action (Pipino, Lee
& Wang, 2002).
Helfert specifies the following checks as important when testing: value ranges, data types,
duplicates, referential integrity, comparison to reference data, plausibility, review by end users or
customers (Helfert, 2002).
Pipino, Lee & Wang, 2002 suggests the use of data quality metrics, e.g. a questionnaire to
measure data quality dimensions (Pipino, Lee & Wang, 2002). Auditors can also use the method
of the control matrix to evaluate information integrity, and thus the reliability of the information
product, the data quality (Pierce, 2004). Pierce suggests the use of the following data quality
dimensions in the control matrix: accuracy, accessibility, consistency and timeliness (Pierce,
2004). The dimensions chosen by Pierce are similar to the aforementioned criteria. Further
methods for quality evaluation are questionnaires, data set sampling (small control sets) and the
use of data profiling tools (Naumann, 2007). If the data is not reliable it will lose its importance
and cease to be trustworthy. The data should always be up-to-date and each data item clearly
defined (Verma, 2012).
Data quality as such is an organizational issue. Each step in the organizational process, from data
capture to processing, has an impact on the final quality of the data. The costs of data
management have to be evaluated and the process responsibilities (data management
responsibilities) assigned within the organization. Because the value and costs of data quality are
not identical for the data users, Storey suggests decentralized information systems, so the
departments that value data quality most are given the responsibility over it (Storey, 2012).
Identifying key data quality characteristics, setting clear data quality goals and building incentive
systems to reward individuals who add value to the data quality are important parts in an
organizational architecture set to maintain high data quality (Storey, 2012).
Different requirements on the data lead to different solutions on maintaining the data quality
(Rohweder et al., 2011). Information managers have to choose the appropriate methods to
maintain data quality and integrate these practices into the organizational processes to achieve
long-term benefits of data quality.

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Based on the presented challenges and requirements and additional, underlying requirements
towards a successful measurement of research data quality, the following aspects have to be
considered before deciding on how data quality can be measured and data errors avoided:
• Access to and knowledge of data and meta-data
• Ability to prevent fraud
• Ability to assure the same or comparable (quality) standards
• Cost of quality assurance approach (e.g., cost experimental data set)
• Amount of time and effort needed (by personnel involved)
• Starting point of quality assurance / Influence during process of data generation
• Acceptance of quality assurance approach in the respective research community
These points have to be targeted by potential approaches in order to have a chance for successful
implementation in a research data infrastructure. All are important, whereas some are essential,
e.g., the acceptance in the research community is essential as without this trust, the available
research data will not be able to utilize its full potential for high impact reuse.
4.2. Analysis of approaches to measure experimental research data quality
Based on the previously described common data quality challenges and requirements, in this
section, six different approaches on how research data quality within an information infrastructure
in materials science can be secured are discussed.
4.2.1. Author responsible
The author (owner, in most cases the researcher) of a research data set is the first person with
significant influence on the data quality, being responsible for the accuracy and completeness.
The author (researcher) in this case is the person responsible for an experiment, not necessary the
person conducting the actual physical work (technician). The author therefore has the best insight
on the data available and can set the lever for what meta-data is to be included as well as
additional information which might be of relevance for future reuse. On the other hand, the author
might be biased as the experiment is most likely conducted having a certain objective. The author
might be tempted to just include the data relevant for reaching this objective, whereas neglecting
other information which might be beneficial for other utilization. Also the sole responsibility of
the author might not be beneficial for the comparability of different data sets due to varying
standards concerning data quality in different areas and/ or institutions, etc.
It can be assumed that the author has a high interest in the quality of the data as he is the first user
in most cases. However, research from various domains indicates that some researchers might
have second thoughts of sharing their data with others (Olsen & Downey, 2012). The reasons may
be manifold, reaching for the fear of competition to the insecurity of their own results. This
indicates the need for an additional/complementary approach at the very least supplementing the
author’s sole responsibility of data quality assurance. The additional time and effort and not to
forget cost of ensuring data quality by the author is, even considering variations, depend on the
already existing archiving mechanisms and the type of experiment/machine/ material used,
relatively low when the integration in the data generation process is smooth.
4.2.2. Internal review (e.g., advisor/supervisor)
Internal review, in this case by a direct superior (advisor/supervisor/project manager) not by a
peer (this case is included in the following sub-section) is another possible approach to ensure
research data quality. In this case, a party not directly involved in the data generation but

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connected to the same department/institution is checking the data for consistency, completeness
and the other dimensions described beforehand. The principle is based on a four-eye governing
principle common in many disciplines. The advantage is that the internal reviewer has access not
only to the original data but also to the process owner and/or resources used to generate the data.
Therefore eventual questions concerning the data quality can be answered with relative little
effort. The additional cost and time required depends strongly on the organizational structure and
the number of assigned tasks per reviewer but can be considered higher than a quality assurance
solely depending on the author. The possible bias, a main concern with the author based quality
assurance, might be reduced when applying this approach as the supervisor/advisor may have a
certain distance and intrinsic interest in an as broad as possible applicability of the generated data.
However, this largely depends on the implementation and the review processes.
Concerning the comparability of used standards, this might lead to a separation and clustering of
the existing research data in ‘trustworthy’ and ‘not trustworthy’ institutions, similar to what is
happening to some extent in the field of journal publication with the dawn of many ‘for-profit’
online journals with questionable references. It would be beneficial for future users to avoid this
and integrate some commonly accepted, non-biased ‘proof’ or ‘indicator’ for research data
quality.
4.2.3. External review (peers)
Peer review is commonly accepted by scholars as of assuring the accuracy of results in scientific
publications. However, even though the blind/non-blind peer review process is accepted as the
best-known way to ensure the quality of new publications in various research fields, in the area of
data publications the applicability and benefit has to be critically questioned. Whereas a
publication describes the methodology, data and results in a comprehensive way, a data set on the
other hand and especially surrounding meta-data and/or information about the experimental
environment (e.g., type of machine, day/night, temperature, etc.) represent information which is
hard to assess without being involved in the process itself.
The time and effort external reviewers would have to invest in order to achieve the in-depth
understanding of the data needed to achieve acceptable results in judging the research data quality
realistically exceeds the requirements the formerly presented two approaches with direct access to
the data source might experience. It is even questionable if e.g., a blind peer review is possible in
the case of data publications, as the chance for the reviewer needing additional information from
the author may be considerably higher. The time-to-publication will most likely increase in case
of external peer review activities, which has two important effects: first, the research data is not
directly available to the interested public and second, in case of questions from the reviewer to the
author, the chance that this information can be provided decreases with the time passed since the
data is generated.
However, as peer reviews are commonly accepted, it can be assumed, that the community will
accept a similar approach for data publications as well. And, similar to the existing practice for
regular publications, the direct cost are very limited, as the reviewers mostly contribute free of
charge. The indirect cost however may be significant, given the time spend on the reviews by
researchers. Regarding the comparability and standardization, the peer review approach faces
similar challenges as others, depending on individual input.
4.2.4. External review (independent 3rd party, e.g., classification society)
The fourth approach presented in this paper describes an independent third party being
responsible for the (external) review of research data sets. This is known from standardized

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certifications like e.g., ISO9001 Quality Management audits. Specialized and trusted agencies are
responsible for a neutral and professional review. The major advantage of such an approach is the
(assumed) lack of bias and the comparability/standardization of results. The acceptance in the
community can be assumed as being high if the independence and professional behavior of the
reviewing party can be assured. Concerning the access to knowledge and meta-data, the access
can be considered less problematic than for a (blind) peer review but higher than any internal
review approach.
However, there is a rather large disadvantage as of today. Not only does no such certification
and/or agency exist for the specific problem of research data quality in materials science but also
the cost must be considered as significantly higher than the other presented approaches given the
evidence of ISO9001 certification suggests. As long as these factors are not changed, a successful
utilization of such an approach is highly unlikely.
4.2.5. External review (social web, e.g., “likes” - facebook; “comments/stars” - amazon)
Another possible approach based on external, independent review is to utilize the increasing
power of the possibilities the social web offers. Inspired by established systems like the online
review system (stars) by amazon (www.amazon.com) for products, ratings (stars) for seller/buyer
on ebay (www.ebay.com), the recommendation system on LinkedIn (www.linkedin.com) or the
‘like’ button on facebook (www.facebook.com). The system utilizes the power of many and
allows even a dedicated rating of certain aspects of a data set comparable to the seller rating on
ebay/amazon regarding quality of the product, timeliness of delivery and communication. This
could be translated as independent aspects concerning the quality of research data sets e.g., based
on the IQ dimensions presented above. Researchgate (www.researchgate.com) already offers the
possibility to publish data sets (all disciplines and formats) and allows the community to discuss
and up-/down-vote it. There are many different options of structuring such an approach. The
reviews can be in the form of a written comment, where either everybody can be allowed to
review the data or just reviewers who used the data in an accepted, reviewed publication (proof
by citation) may be allowed to review. This would have the advantage that the reviewer can be
assumed to have gained the needed knowledge of the data set during the preparation of the
publication to review the data set. However, this might introduce another bias as the reviewer
then might lean towards having a positive attitude towards the data quality from the beginning.
Overall, such an approach offers both advantages and disadvantages. Among the advantages are
the low cost and minimal time and effort required from the data generating party. Disadvantages
are the problem of preventing fraud, common standards/comparability and the acceptance within
the community. As it is a very special domain, the design of the social review system can help to
minimize certain disadvantages. For example, an obligatory registration with an institutional
email address may help to legitimize the reviews authenticity. Furthermore, a mandatory and
openly accessible personal profile including links of the reviewer / reviewers institution to the
author / authors institution as well as a review history may help to uncover potential bias.
Such an approach, depending on the organization (see above), in general has a problem with its
legitimacy and the number of potential reviewers willing to invest time. The amazon approach of
using the power of the many in order to ensure the legitimacy of the merged reviews depends on a
large number of reviewers. An approach leaning more to the written comment can be seen as a
voluntary open peer review with all the advantages and disadvantages such an approach offers.

12
4.2.6. Automated review (e.g., software semantics / statistical methods)
The last approach is based on an automated review based on e.g., software semantics or statistical
methods. Such an approach offers the advantage of high comparability and standardization as
well as being comparable low cost (Blumberg, Chaty & Kleinhof, 2011). Furthermore, the time
and effort invested in the data generating party can be considered low. If implemented right, such
an approach can even support the data generating process by alerting the parties involved when
certain data pieces are missing or indicate incoherency. This is an important factor none of the
other approaches offer (except maybe the first, having the author responsible) as they launch after
the data set is already generated. Furthermore, an automated system is less likely to being
exposed to fraud, even though it might trigger a certain preparation of the data once the audience
is aware of the focus area of the algorithm. Overall, it can be considered very invulnerable of
fraud and manipulation.
An automated system depends strongly on the programming. This determines if there is a bias
involved and in the long run, the acceptance within the community. The possibility to include
artificial intelligence (AI) and machine learning (ML) techniques would allow the system to learn
actively from past reviews and increase the review quality over time. However, this may involve
significant resources to develop and evaluate the to-be developed AI and/or ML systems
depending on the requirements. On the other hand, even a relatively simple automated approach
requiring little development resources, might improve the data set review and thus the data
quality in combination with other approaches by automatically checking the data set for
inconsistencies, missing values and statistical errors and providing a report to the (human)
reviewers for a more detailed review.
The six approaches described in this section are evaluated in the following section against the
previously presented aspects of what has to be considered when research data quality needs to be
assured.
4.3. Assessment of suitable research data quality insurance approaches
In this section, the previously presented approaches are mapped to the identified requirements in
form of a table (see Table 1). The assessment of suitability is conducted qualitatively based on the
discussion in the previous section. It is conducted in form of an attribution of one (*) to five
(*****) stars, one star meaning the approach does not address the requirement whereas five stars
indicate high compliance with the requirement. For approach five, there are two ratings, one
solely evaluating the impact on the described requirement when generally everybody is allowed
to write a review, the other, including the additional stars in brackets, describes the evaluation
result when the reviewers have to proof their suitability through usage of the data set in a
publication. In the last column, the possible combinations with other approaches, which are
assumed to have a positive effect on the impact, are indicated by the number of the other
approaches.

13
Table 1. Mapping of individual approach to identified requirements of methodology
Requirements
Approach
Access
to
and
knowledge
of
data
&
meta-data
Preventing
fraud
Same
standards
/
comparable
Cost
Needed
time
/
effort
Starting
point
/
Influence
during
process
Overall
acceptance
by
research
community
Positive
effects
of
combination
with
following
other
approaches
1. Author
responsible
***** * ** ***** **** ***** ** 2. / 3. / 4. / 5. / 6.
2. Internal review
(e.g., superior)
**** ** ** **** *** *** *** 1. / 3. / 4. / 5. / 6.
3. External review
(peers)
** *** *** *** * * **** 1. / 2. / 6.
4. External review
(3rd
party)
*** ***** ***** * ** ** **** 1. / 2. / 6.
5. External review
(social web)
* (**) **** *** *** *** * * (**) 1. / 2. / 6.
6. Automated
review
*** ***** ***** ***** ***** ***** *** 1. / 2. / 3. / 4. / 5. / 6.
5. DEVELOPMENT OF SYSTEMATIC METHODOLOGY
In this section, the previous (mostly theoretical) discussion is included in a practical
methodology, which is implemented in a developed materials science research data infrastructure.
Therefore, the chosen approaches and their implementation are described including our
fundamental motivation.
5.1. Systematic methodology and selected approaches
As all of the previously presented approaches have their specific strength and weaknesses
concerning the overall goal of ensuring data quality, thus support the reuse of experimental
research data in materials science in a different way. Therefore, in order to create a
comprehensive and sustainable methodology, a combination of different approaches is chosen for
the systematic approach presented in the following.
Table 1 indicates that a combination of different approaches is possible and might have a positive
impact on the overall performance of the methodology. Whereas the approaches dealing with the
different forms of external review (3. / 4. / 5.) are not positively correlated to each other, each
individual one can be combined with a set of the other three approaches (1. / 2. / 6.). Overall, the

14
basic principle of the methodology is that the author is responsible for the experimental design,
generation of the research data set and its quality in the first place. This is necessary as in
materials science, the experimental layout has a great influence on the data and the knowledge
(access to knowledge) is necessary to set up the data set according to standards. This is a basic
requirement for future continuous exploitation of the research data. All other approaches are
additionally installed to support this basic responsibility of the author.
The project manager or the advisor/supervisor of the author, depending on the organizational
structure, is additionally responsible for a first quality check, especially for the compliance with
established quality management processes within the organization. This is an important step
towards a standardized and therefore comparable creation of experimental research data in
materials science.
From the three variations of external review, the social web approach (5.) is chosen within this
methodology. The reasons are that in the moment, no established external third party exists which
could be utilized to act as a reviewer according to approach No. 4. and the additional cost of such
an approach would hinder the acceptance within the field even further. The third approach
however, external peer review, is partly combined with the chosen variation of the fifth approach.
At the beginning, the review of the data set is limited to researchers who have worked with the
data set, which has to be proven through citations and their unique profile, indication of their
position, relation to the author and review history. Thus, the reviewers can be assumed to have in-
depth knowledge of the data set and, through the transparency, the acceptance of the review
within the community can be assured.
The approach dealing with automated quality assurance is a very important one, especially
considering its future development possibilities. In a first step, the developed methodology
incorporates the following automated quality assurance features supporting the author in creating
a high quality research data set:
• Automatic check calculation of Key Performance Indicators (KPIs) (e.g., coefficient of
elasticity) based on entered (manually and/or automatically) values
• Automated guidance of author to enter the required values for standardized experiments
so human error is minimized concerning missing values
• Automated check for unusual values based on existing data sets and standardized
calibration values allowing the author to double check the experimental parameters
directly
• Automatically connecting the data set to environmental parameters (e.g., temperature,
machine number, etc.) to create important meta-data
The methodology, incorporating different approaches to assure research data quality is
implemented in an information infrastructure for materials science research data and the
supporting organizational processes are designed as well in order to support the authors in the
creation of their data sets. In the following section, the methodology’s application within the
system is briefly described before the methodology is evaluated according to the IQ dimensions
in the section thereafter.
5.2. Discussion of results
In this section, the presented methodology and its incorporating approaches to assure research
data quality for potential research in the materials science domain is evaluated. In the following
table (Table 2), the 15 Information Quality (IQ) dimensions (Rohweder et al., 2011) presented

15
before are utilized to structure the evaluation. The (expected) impact of the methodology is
mapped to each individual dimension and judged by its ability to support the effort.
Table 1. Evaluation of developed research data quality assurance methodology based on the 15 IQ
dimensions (Rohweder et al., 2011)
IQ dimensions
(Rohweder et al., 2011)
Addressed by developed research data quality assurance
methodology
System
support
Accessibility Quality of data set is focused on and therefore resistance to make it
publically available may be minimized
Data sets will be uniquely identified through DOI number
Ease of manipulation Methodology supports the usage of data for different purposes by putting
a focus of the continuous exploitation ability of the data set from the
beginning
Inherent
Reputation By combining the above mentioned approaches, the acceptance within
the community is assumed to be high
Free of error Through the combination of the quality assurance approaches, the
chances of potential errors within a data set are be minimized
Objectivity By combining automated approaches and external reviewers (low bias)
with internal quality control, the objectivity of the results may increase
Believability Implementing not only quality assurance approaches but also
organizational procedures addressing and supporting generation of data
in experiments may increase credibility
Representation
Understandability A main goal of the information infrastructure developed is the semantic
annotation of research data which increases the understandability of the
presented data especially for external users
Concise representation The data is saved following standardized rules in an open format
Consistent representation Through semantic annotation and standardized processes the
consistency of representation can be assured
Interpretability Semantic annotation assures interpretability of the data and meta-data
Purpose-dependent
Timeliness Different starting points of the incorporated approaches assure the
timeliness of quality assurance when it is needed
Value-added Every reuse and the continuous exploitation increases the value add of
the data sets as it involves a fraction of the cost needed to generate it
again by experiments
Completeness Both human and automated approaches focus on the assurance of
completeness of the data set
Appropriate amount of data Processes and automated support approaches are based on existing
standards and experience. Following credo ‘as much as necessary, as
little as possible’
Relevancy Both automated and human approaches focus on creating data sets
including all necessary data for continuous exploitation of the data set in
different applications
As can be seen in the previous table, all IQ dimensions are addressed by the developed
methodology. Furthermore, the performance in each IQ dimension is expected to improve after
the methodology is utilized and implemented in the system. However, there are certain limitations
and challenges, which may hinder the successful implementation of the methodology. First, the
existing variety of experiments and new experimental designs are challenging when it comes to
the development of standardized processes. This directly influences the possible future

16
development of an automated quality assurance approach. Within the presented methodology, the
semantic annotation allows a continuous extension of the system. Thus, additional experimental
designs can be incorporated in the existing system. Second, the involvement of external reviewers
through the social web features may have to reach a critical mass and this will take a certain
amount of time. This is especially true due to the (at the beginning) agreed selection of reviewers
by usage of the data set provable by citations. Until a publication using a data set is cited, at least
six months can be calculated. A third major point is the question, how to motivate researchers to
participate in sharing their data especially when a culture of sharing is not yet established in the
community. This cannot be solved solely by a technical solution, but the usability and added
values such a system presents, might help in the process. Whereas a system with immanent
problems and an unclear structure will most likely hinder the development towards a sharing
culture. Other limitations and challenges are more of a strategic relevance. Among those is the
important question of how data and experiments of industrial projects with a signed
confidentiality agreement may be incorporated in such systems.
6. CONCLUSION AND OUTLOOK
In this paper, first the specific nature of experimental research data in the domain of materials
science was established. Following, the current state of the art concerning information and data
quality was presented, including the information quality (IQ) dimensions (Rohweder et al., 2011).
After looking into the specific meaning of data quality for materials science experimental
research data, requirements for research data quality assurance approaches for information
infrastructures were derived. Based on the findings, six data quality assurance approaches were
developed and subsequently evaluated regarding their compliance with the previously developed
requirements. Afterwards, a systematic methodology incorporating a combination of suitable
approaches based on the previous discussion was presented including case based practical
experience. Finally, the methodology and its indicated results were evaluated in a structured way
using the implication on the information quality dimensions.
The findings indicate that experimental research data in the materials science domain represents
specific challenges concerning the diverse nature and high-dimensionality of the resulting data
and meta-data. This is caused by a variety of factors like e.g., the large number of tests, process
parameters, machines, etc. adding to the inherit complexity are the many different data formats
(data format (e.g., photos, graphs, plots, data all analog and/or digital) and available standards
(e.g., DIN, ISO, EN, ASTM).
Overall, no individual approach on its won was found to ensure the experimental research data
quality according to the derived requirements. However, by combining suitable approaches within
the methodology, the requirements may be fulfilled to a satisfying degree. Subsequently, the
acceptance within the community and, in the long run, the availability of high quality materials
science research data sets for continuous exploitation may increase. Hence, the return on
investment for funding agencies might increase accordingly when the generated data can be
exploited for various research objectives in different institutions.
However, there are several areas where further research is necessary. First of all, the practical
incorporation of the methodology and the continuous development including feedback from users
especially of the automated approach need to be investigated further. Secondly, the social web
features and its acceptance by the community, as well as measures to actively promote the usage
present further research opportunities. Lastly, the impact on the data quality has to be critically
evaluated after the system is fully functional and running based on a comparison of existing
materials science experimental data sets before and after implementation of the methodology.

17
ACKNOWLEDGEMENT
This work was partly funded by the “Deutsche Forschungsgemeinschaft” via the project
“Informationssystem für werkstoffwissenschaftliche Forschungsdaten” and the German Academic
Exchange Service (DAAD), Germany, through a Ph.D. fellowship (DAAD Jahresstipendium für
Doktoranden). The authors would like to extend their gratitude to the funding parties for their
generous support.
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AUTHORS
Thorsten Wuest earned a Master's from University of Bremen and a Master's from
Auckland University of Technology. He has worked at BIBA, Bremen since 2009, while
concurrently pursuing his dissertation project. In 2013/2014 he was furthering his
research for ten months at the University of Southern California (USC). He is currently
working among other things in the areas of knowledge, information and data
management as well as closed-loop, item-level PLM with a focus on manufacturing.
Rainer Tinscher, born in 1969, studied from 1990 to 1995 production engineering at the
University of Bremen and earned his PhD in 2003 in the field of materials science. Since
2000 he has been working in the Stiftung Institut für Werkstofftechnik Bremen (IWT
Bremen), where he works today as chief engineer.
Robert Porzel earned degrees in general linguistics, computational linguistics and
computer science. At the University of Bremen, Robert Porzel started to work on
contextual computing for autonomous collaborative systems in the DFG-funded special
research program "Autonmous Logistic Processes" (SFB 637). He initiated and chaired the
biannual ScaNaLU workshop series on scalable natural language understanding systems
(2002, 2004 and 2006) and is member of the steering board of the Interdisciplinary
College (IK) - an international spring school on cognitive science, neurosciences and AI.
Klaus-Dieter Thoben studied mechanical engineering with a focus on product
development at the TU Braunschweig, Germany. He received Ph.D. in the field of
CAD/PDM applications. In 1989 he joined BIBA as Head of the Department of
Computer Aided Design, Planning and Manufacturing. In 2001 he received a state
doctorate (Habilitation) including a venia legendi for the domain production systems /
production systematic. He was appointed a full professor at the University of Bremen,
Germany in 2002 and currently is head of the "Institute of integrated Product
Development" at the production engineering faculty. Additionally, he was named director at BIBA in the
same year.

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