FORSCHUNGSBERICHT
Terminology for a FAIR Framework for the Virus
Outbreak Data Network-Africa
Ruduan Plug1†, Yan Liang1, Aliya Aktau1, Mariam Basajja1, Francisca Oladipo2, Mirjam van Reisen1,3
1Leiden University, 2331 GL Leiden, die Niederlande
2Kampala International University, 260101 Kampala, Uganda
3Leiden University Medical Centre (LUMC), Leiden University, 1310 Leiden, die Niederlande
Schlüsselwörter: Data management; Distributed data; Federated data; Data governance; FAIR Guidelines; FAIR Data
and Services; FAIR Data Point; FAIR framework
Zitat: Plug, R., Liang, Y., Aktau, A., Basajja, M., Oladipo, F., Van Reisen, M.: Terminology for a FAIR framework for the Virus
Outbreak Data Network-Africa. Datenintelligenz 4(4), 698–723 (2022). doi: 10.1162/dint_a_00167
Submitted: Marsch 10, 2021; Überarbeitet: Juni 10, 2022; Akzeptiert: Juli 15, 2022
ABSTRAKT
The field of health data management poses unique challenges in relation to data ownership, the privacy
of data subjects, and the reusability of data. The FAIR Guidelines have been developed to address these
Herausforderungen. The Virus Outbreak Data Network (VODAN) architecture builds on these principles, using the
European Union’s General Data Protection Regulation (GDPR) framework to ensure compliance with local
data regulations, while using information knowledge management concepts to further improve data
provenance and interoperability. In this article we provide an overview of the terminology used in the field
of FAIR data management, with a specific focus on FAIR compliant health information management, als
implemented in the VODAN architecture.
ACRONYMS
CEDAR
DMP
ETL
EU
GERECHT
FDP
Center for Expanded Data Annotation and Retrieval
data management plan
extract, verwandeln, and load
European Union
Findable, Accessible, Interoperable, Reusable
FAIR Data Point
†
Korrespondierender Autor: Ruduan Plug, Leiden University (Email: r.b.f.plug@umail.leidenuniv.nl; ORCID: 0000-0001-5146-6116).
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© 2022 Chinesische Akademie der Wissenschaft. Veröffentlicht unter einer Creative Commons Namensnennung 4.0 International (CC BY 4.0) Lizenz.
Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa
GDPR
HMIS
IN
KPI
OWL
RDF
URI
VODAN
General Data Protection Regulation
health management information system
Implementation Network
key performance indicator
Web Ontology Language
Resource Description Framework
universal resource identifier
Virus Outbreak Data Network
1. EINFÜHRUNG
Data management has become one of the prime factors of concern in all fields of contemporary research.
The volume and velocity of data is rapidly increasing, causing serious bottlenecks in data processing,
storage and reusability. To tackle this issue, a multimodal process that advances the human-data relationship
may offer a viable approach [1]. This is achieved by developing theoretical frameworks for automated data
management and technological architectures that distribute data, as well as by expanding human expertise.
Jedoch, these developments towards automated data processing pose numerous challenges, von dem
perspective of society [2] and technology [3]. These challenges are magnified in the field of health, Wo
privacy, security and the ownership of patient data are critical concerns. Coincidentally, these data typically
contain vital, yet untapped, information for the advancement of scientific research. Health data is by
definition personal data, which may contain sensitive and personal information. The Universal Declaration
of Human Rights (1948) Staaten, in Article 12, that “No one shall be subjected to arbitrary interference with
his privacy, family, home or correspondence” [4]; daher, personal data protection is enshrined in the
foundations of international law.
The Virus Outbreak Data Network (VODAN) initiative, guided by the FAIR Guidelines, provides a
framework that addresses these concerns through a multimodal approach to data management and data
stewardship [5]. By developing an architecture in which data is Findable, Accessible (under well-defined
Bedingungen), Interoperable and Reusable (GERECHT), we may address technical concerns about the use of modern
metadata processing techniques, while data stewardship empowers scientific communities with expertise
to interact with these data across their field in a meaningful way.
The that way we deal with medical data within VODAN is inherently distributed, in order to ensure data
sovereignty. Jedoch, there are concerns over the convergence between localised instances. To reconcile
such localised instances with a common vocabulary, in this article we have developed a set of shared
terminologies that allow for the unambiguous exchange of controlled vocabularies and development of
consistent data stewardship expertise.
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Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa
This article investigates and reviews the basic concepts and terminology in the context of VODAN, Und
specifically VODAN-Africa, which was established as an Implementation Network (IN) under the GO FAIR
initiative, jointly with FAIR IN-Africa [6]. The VODAN-Africa initiative has been established as a pilot
deployment to produce clinical patient data, which is by nature sensitive data (Article 1 of this Special
Issue) [7]. Important is the full retention of data ownership in residence, through data-visiting, and recognising
the fragmented nature of the regulatory frameworks applicable in each locale [6].
T his article sets out to review how data terminology can be defined in the context of health data
management, for the investigation of VODAN-Africa. Zusätzlich, we seek to facilitate the further investigation
of FAIR-based clinical patient data generation, processing and analytics within distributed and federated
healthcare data applications.
2 . DATA CONCEPTS
To develop our terminology framework, we built upon the core terminologies used in the process of
data management. The first concepts we developed for our framework were ‘data’, ‘information’ and
‘knowledge’ [8], as they are procured within a clinical setting. In diesem Rahmen, we start with unprocessed
Daten, which are the first elements we encounter in the operational sphere in the data stack.
Metaphorically, data can be seen as the technological equivalent to the stimuli humans receive through
their senses. These stimuli are raw bits of information and, before they are processed in the brain, are not
attached to any meaning. Ähnlich, data entered into a computer, either through automated recording or
human data entry, does not have any meaning until it is compartmentalised and processed. From the clinical
Perspektive, meaning is central to the subsequent application of data, which is defined through biosemantics.
Data
Data is a set of numeric values, characters and/or symbols.
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This definition of data is very broad and includes both ordered and unordered data. In der Praxis, the vast
majority of data originates from observation, such as observational patient data, and is initially unstructured.
To provide data with meaning, we need to process the data in accordance with standardised methods of
formalisation. The three most common forms of data processing are: (ich) select or sample the data relevant
to the purpose by filtering, (ii) compartmentalise data into separate attributes, Und (iii) provide an index to
the data (d.h., a time-stamp, identifier, numeric ordering) [9].
All the techniques that structure and give meaning to data are considered data processing techniques.
The simplest example of this employed at VODAN is ad-hoc data processing, with composite forms based
on controlled vocabularies, in which the structure of the form indicates the assignment of entered data to
specific attributes under specified conditions.
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Information
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Information is data that has been structured and processed in such a way
that meaning has been assigned to it, which can be interpreted and from
which analyses can be drawn.
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Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa
The process of transforming data into information involves giving structure to the data, which is primarily
aimed at making the data suitable for human interpretability and machine interoperability. These processes
can be either performed manually, d.h., by assigning certain data to a type or attribute field, or by automated
methods based on ontology specifications.
An example of this can be found in the transcription of written medical documents. A digital image of
a medical form consists of nothing but raw pixel values that can be rendered on a screen. In diesem Kontext,
the machine is not inherently able to determine whether or not a certain group of pixels has a specific
Bedeutung. We can, daher, state that the semantics of such an image cannot be directly derived by a machine
from the raw data.
Jedoch, these data can be transcribed by human annotators, provided they possess such domain
Wissen. In the medical field this is traditionally performed by clinicians, but many such tasks can be
performed data clerks and data stewards (after training), who are extensively involved in VODAN. Von
gathering the data from the form, the data can be entered into appropriate attribute fields in a digital format.
In this way, the human annotator assigns meaning to the visual data, based on their existing knowledge,
and transforms these data into a structured format, which is information that can be used by both humans
and machines without requiring additional context. These processes can also be automated; Zum Beispiel,
optical character recognition (OCR) may be used to extract the characters, numbers and letters from the
form—but these technologies typically fail to compartmentalise data further, are prone to error, requiring
manual review and possess no accountability, unlike data stewards. While both methods produce
Information, the information is unequal in terms of specificity and granularity [10].
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Figur 1. (A) Flowchart indicating the generalised process to transform data into information [11]. (B) Example of
Daten (top) and possible resulting information (bottom) [11].
Another factor we have to consider when processing data is that relationships may exist between data
or derived information. There are many types of relationships that can exist between data and the type of
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relationship can depend on the type of data. Zum Beispiel, two numerical attributes may be correlated or
one attribute may be associated with, or causal of, another attribute.
This is important in the context of the sensitive data processed in VODAN, as the context and meaning
of these data are crucial to localised data methods. Analysing data in isolation may remove context and,
daher, Bedeutung. Appropriate metadata and semantics, in the form of provenance, may be key to preserve
these relationships when deidentification is applied to sensitive data.
By mapping the relationships between the information we have extracted from the data, wir sind
transforming information into knowledge [9, 10], which is one of the primary methods used in VODAN.
Knowledge typically takes the form of a graph representation, in which nodes identify instances that have
attributes and the edges indicate relationships between such instances. This type of graph structure can be
visualised for human interpretation, as well as traversed by computational algorithms for a process we
consider knowledge discovery [12].
Knowledge
Knowledge is a tectonic description of information and the interconnected
relationships between elements of information.
A widely used methodology to represent knowledge is the Resource Description Framework (RDF) [13].
This is a data structure framework that implements a machine interoperable language to represent semantic
graphs. In this context, each node is a universal resource identifier (URI) specifying a resource with
associated attributes, and each edge is a directional relationship between two resources. The combination
of the URI and the locale can be employed to produce a globally unique identifier when accessing and
querying metadata across different services, which is important to enable unambiguous data access within
VODAN.
As relational descriptions in RDF are primarily used for machine interoperability, and through linkages
compatible with JSON data produced by non-relational health databases, they have no spatial structure.
The visualisation of these graphs in complex relational schemas is non-trivial [14], but an RDF-based
knowledge representation provides a very powerful machine interpretable data structure that can be readily
used for relational knowledge discovery [15], which is one of the core aims of the knowledge base developed
within VODAN.
Knowledge discovery
Knowledge discovery is the derivation of new relational properties in a
knowledge graph, based on the properties of the graph structure.
Thus far, we have described the framework that incorporates data to produce information and knowledge
graphs. The motivation behind this process is twofold: both to incorporate the domain-specific meaning
of the data and to provide machine interoperability. The most important properties of these three core
terminologies that will be used to develop the FAIR health data management framework are listed in
Tisch 1.
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Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa
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Figur 2. An example of an RDF graph for drug-gene interaction using knowledge discovery; equivalent
interactions R1 and R2 have been associated with rd:equivalent [11].
Tisch 1. Properties of data, information and knowledge [11].
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As we have discussed in the previous section, the core principle underlying the transformation of data
into information and knowledge is the attribution of meaning to the data. As meaning is fundamentally a
philosophical concept, we need a formalised methodology to ascribe meaning to data.
Th ese formalisations are shaped by metadata, which in epistemology designates the self-referential
denomination of data with respect to data [16]. The conceptual foundation of this formalisation is that
meaning can be structured as data; Zum Beispiel, in the form of a description or a caption. These data can
be used in reference to other data to attach meaning; in the above example a caption could be attached
to an image to provide meaning to the image.
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As a consequence, we can derive that metadata are the building blocks that allow us to transform data
into information and knowledge [17]. For us to transform data into information, we have to specify metadata
that conveys context over the particular data. Likewise, transforming information into knowledge requires
the production of metadata that specifies the relationship between elements of information. Mit anderen Worten,
metadata form the mechanism that provides a link to the insights with respect to the semantics of the data,
primarily in facilitating information seeking, retrieval, understanding and use [16].
Metadaten
Metadata is data that describes other data in order to convey information
that guides understanding, specifi city, retrieval and interoperability.
Herein also lies the fundamental problem: with self-reference, there is always the risk of unresolved or
inconsistent references. This is problematic in some complex data sets, in which the metadata itself may
require references to the data to convey its meaning. Another issue is that without some form of domain
standardisation across an implementation network like VODAN, the meaning of the metadata may be
ambiguous or unspecified [18].
To standardise metadata, we define different types of metadata based on the objective that is associated
with the denotation [17]. To illustrate the paradigm, some metadata may be produced to aid human
Verständnis, while other metadata describe properties for machine interoperability. We define three main
archetypes of metadata, which form the building blocks of our data management framework in VODAN.
The first type of metadata we consider is metadata that is centred around human understanding, providing
descriptions of, or annotations about, Daten. This type of contextual metadata provides the link between
machine interoperable data and human interpretability.
Contextual metadata
Contextual metadata is metadata that provides descriptions about data to
aid human understanding.
The next type of metadata we discuss is focused on the machine interpretability of the data—or what
we consider the syntactic metadata, which provides information about the format of the data, the way the
data should be operated on, and the way the data is structured. Being able to specify the syntactic format
of data is essential in cross-machine interoperability.
Syntactic metadata
Syntactic metadata is metadata that provides structural specifi cations about
data to aid machine interoperability.
Endlich, we consider semantic metadata, which specifies the meaning of data and is the broadest concept
for which metadata can be produced [19]. These metadata define the broad context, and may be used to
specify unique identifiers and link different concepts or data together. These metadata are central to the
structure of interlinked data and form the building blocks of the concept of the Semantic Web, as proposed
by Berners-Lee et al. [20]. Semantic metadata is central to frameworks such as RDF to represent knowledge
graphs [13] and the Web Ontology Language (OWL) [21], which is used to formalise knowledge
Darstellungen [21] that are used by clinicians and implemented by data stewards in VODAN.
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Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa
Semantic metadata
Semantic metadata is metadata that associates objective meaning with the
data in relation to other data.
An operational example of how these three types of metadata work in conjunction with one another in
medical data records is provided in Table 2. The metadata in this table supports the entered data, such that
the individual data points can be isolated using the semantic metadata, the data is interoperable due to the
syntactic metadata providing instructions for machine interpretation, and the contextual metadata provides
annotations on the relationship of the data to domain-specific knowledge.
Tisch 2. (A) Example of data produced for the given metadata using a controlled vocabulary [11]. (B) Metadata as
Daten, describing the properties of the various metadata [11].
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As shown in Table 2, what constitutes metadata cannot always be inferred simply by considering the
attribute values. The table to the left (A) shows the classical example, where the metadata is structured as
semantic metadata. These metadata provide a structural specification about the meaning of each different
attribute in the data, in which each row is a uniquely indexed record in the table, which forms an essential
part of the VODAN URI.
Andererseits, we can also construe metadata as the records themselves, as shown in the table to
the right (B). We consider this synergy of ‘metadata as data’ [22], in which for each semantic identifier we
also have the syntactic and contextual metadata associated with that semantic concept. The composite of
these three elements forms the complete metadata specification of a particular concept in the information
or knowledge specification of our domain, which formalises the data generation and traversal throughout
VODAN.
Metadata specification Metadata specifi cation is the complete specifi cation of all metadata
associated with a concept within a domain.
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Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa
As there are potentially uncountable different methods by which metadata can be specified for linked
concepts, a standardisation process is typical used within domain-specific knowledge bases [16, 18]. Der
baseline of VODAN community standardisation is expressed through the use of agreed-upon vocabularies,
defined as controlled vocabularies, which limits the potential set of concepts to a finite and enumerable set.
Vocabulary
Vocabulary is a fi nite set of terms and symbols derived from expressions
within a domain.
As vocabularies may continuously change and evolve as new concepts are generated by domain experts
within VODAN, there is the inherent prospect that the vocabulary itself may become ambiguous. Für
Beispiel, in the case of synonyms, where two terms are linked to the same concept, or in the case of
homonyms, where a single term may be linked to multiple concepts in a controlled vocabulary [23]. To
maintain the specificity and integrity of the knowledge base, it is important that such ambiguities are
avoided by using lemmatised concepts across VODAN in order to achieve convergence within the knowledge
Rahmen. For instance, if two research facilities use a different terminology for the same concept, it is
important that these terminologies are grouped together as a single lemma, instead of being treated as
separate entities for the purposes of convergence within health communities.
In order to achieve this within VODAN, a centralised, controlled vocabulary can be used. Diese
vocabularies are organised in such a way as to optimise the knowledge base, minimise ambiguities and
streamline data retrieval in relational entity-based knowledge bases [24]. The controlled vocabulary consists
of a curated list of terms used to transform information into knowledge, by associating these terms as
metadata to convey the specification, links and descriptors of unique conceptual entities.
Controlled vocabulary
Controlled vocabulary is a curated set of terms and symbols from which
concepts and relationships between concepts can be expressed.
We can further specify this by formalising the method we use to structure a controlled vocabulary by
the means of specified grammars to form an ontology [25]. These grammars define the way that terms within
the controlled vocabulary can be used together. Zum Beispiel, in a medical ontology we may choose that
a phenotype expression can only be linked to an instance of a gene, but not to an instance of a
pharmacological compound. By formally defining these constraints, we can ensure, by using an ontology,
that only semantically valid and uniquely identified knowledge is created as a product of input data.
Ontology
Ontology is a domain-specifi c language from which knowledge can be
represented as the product of a controlled vocabulary and semantic rules
governed by formal grammar.
A concept that arises from the use of ontologies is that of templating metadata, which is an essential
element of VODAN-wide data formalisation. As ontologies control for both the vocabulary and grammar
of the knowledge base, any data entered within the knowledge base should belong to an entity within that
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knowledge base [25]. This limits the metadata that may be associated with data, and can be expressed by
constraining the metadata to a template format that controls for terms and semantic properties.
Metadata template
A metadata template is a set of semantically valid, domain-specifi c
metadata specifi cations derived from constraints specifi ed by an ontology.
By using metadata templates in VODAN, which are produced from the domain ontology, we can
standardise the way that products of data, information and knowledge are represented within an information
System, in this instance a health information system. The standardisation of terms and semantics defined
by metadata is a core element in producing data that is interoperable and reusable, and is key in the process
of knowledge discovery.
3. FAIR HEALTH DATA MANAGEMENT
As health facilities have started collecting more data about physiology, pharmacology and treatment
efficacy, there has been an increasing need for the digitisation of health data to keep these increases in
data volume manageable and usable. This is especially relevant to digitalisation in VODAN, across which
a multitude of health facilities have thus far operated using manual data entry or handwritten patient
records. Eysenbach describes these digitisation efforts as eHealth, representing the relationship between
medicine and computers and how this combination can benefit the healthcare and pharmacological
industries [26].
Jedoch, because of the rapid development of data collection and healthcare information technologies,
the academic definition of eHealth extends to include the enhancement of health services and information
supported by the onset of relevant technologies. This can be represented as the development and application
of digital technologies in the field of medicine [27] in an effort to improve interoperability. Examples of
health information in eHeath are patients’ electronic health records (EHRs), genomic data, digital prescription,
and even extending to remote diagnostics, each of which are data encompassed in VODAN.
Care facilities frequently use health key performance indicators (KPIs), based on which VODAN defines
the key analytical factors unique to each locale. These are employed to compare their performance to that
of other care facilities, which makes it particular relevant in cross-facility analytics and knowledge exchange.
KPIs can be specially used to identify areas for improvement. Zusätzlich, KPIs can be correlated with
measures directly related to treatment efficacy within the local context. Zum Beispiel, average hospital stay
and outpatient rate are some of the commonly used healthcare KPIs within VODAN, measured for various
treatment types [28].
Healthcare key
Leistung
indicators (KPIs)
Healthcare key performance indicators are a well-defi ned performance
metric that is used to track, analyse, improve, and transform all essential
healthcare operations in order to enhance patient satisfaction.
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Different KPIs may be recognised at different levels of healthcare in VODAN, which addresses health at
both the clinical as well as the population level. From the perspective of a nation we are most interested
in metrics such as life expectancy, while at the clinic level treatment outcomes and patient turnaround are
critical. One of the primary issues that VODAN-Africa addresses is the need for both in residence and
aggregate analytics, using a specifically designed data management framework [29, 30].
Data in residence
Data in residence is data produced and stored at a research institute or at
the point-of-care, and is used to enable and enhance healthcare and
scientifi c research, as well as to perform analytics.
The data that is present in residence within VODAN is stored in local database architectures, welche sind
defined as data repositories, driven by local ownership [31]. The repository is the technical implementation
of the system that collects, aggregates, manages and stores data in residence. What differentiates the
repository from a standardised database is that the repository also maintains services for generating and
maintaining domain specific ontologies, pooled from a central controlled vocabulary, and knowledge bases
to support data management and access.
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A data repository is the point of storage and management of all data,
information and knowledge relating to the primary purpose of a facility.
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These operations, and the underlying operations performing these transactions, are part of a larger
architecture, which we consider a health management information system (HMIS). Most of the current
HMISs in Africa are proprietary [31], which is a large drawback that VODAN seeks to address. Typical, Die
HMIS forms the layer between the end-user (z.B., researchers and health professionals) and the data
Repository [32]. This allows for the management of access levels and for interfacing directly with other
applications that are used within departments of a healthcare facility.
Health management
information system
(HMIS)
A health management information system is a system for entering, storing,
maintaining, retrieving, and processing health data stored in repositories. Es
provides functionality to aid in the planning, management, and decision-
making processes of healthcare institutions.
Two processes that are primarily monitored by a HMIS are data integrity and data quality, which are
critical to the operation of a health facility. Within VODAN-Africa, data quality is maintained through
provenance, rich metadata and domain specific accuracy measures, while data integrity is maintained by
means of data redundancy and strictly regulated access and control patterns [6].
Data integration can be considered one of the main data management processes in operating an
HMIS, and represents the process of combining data from various data sources into a single, unified and
cohesive dataset with the purpose of supporting users with the consistent data access and delivery [33].
When consolidating healthcare data into a HMIS, there are some challenges involved in the processing
pipeline, which impose constraints on accessing data, the retention of data quality, and validation of data
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consistency [34]. The FAIR framework provides a workable solution to these issues through the accessibility
and interoperability specifications, which in case of VODAN are transparent and locale-dependent [6].
Not all healthcare (meta)data are case-specific; there are some common data elements through VODAN,
such as patient age, Geschlecht, and marital status, that are common in a lot of clinical datasets from the
different healthcare systems. Common domain specific data elements also exist in health metadata and are
defined in biomedical ontologies, specified by the VODAN community. These describe commonly used
clinical data and can be used in directly transforming data to a common VODAN format, as well as for
secondary data analysis.
Common data elements
(CDEs)
Common data elements are standardised terms or concepts that can be
used or shared with other healthcare and research institutions as controlled
vocabularies or ontologies for clinical research.
When doing clinical research, the data management plan (DMP) plays an important role. After the
proposal stage and before the funding stage, the DMP helps researchers to organise the use of data and
includes data management and data analysis during and after the research. Zusätzlich, it is a critical
component in validating whether or not the data management process is compliant with local data
Vorschriften [35].
Data management plan
(DMP)
A data management plan is a formal written document that outlines the
process for accessing or producing data; the standards for managing,
describing, and storing data; and the system for handling and protecting
data during and after research.
The process specification involved in a DMP helps researchers to manage the research data specification
and requirements, which in total specifies the data lifecycle [36]. Data lifecycle phases typically include
data collection, data storage, data usage, data archiving and, finally, data destruction. For a viable DMP
the entire process must be well-defined.
Data lifecycle
The data lifecycle is an overview of all the stages of data existence from its
production, storage, use, and reuse to destruction.
The process of data generation involves measuring or acquiring data according to a pre-specified
collection protocol. While this process can differ across locales in VODAN, the steps afterwards are
standardised [6]. After the data creation stage, the data must be stored and protected with different security
levels within the organisation, based on the specifications and regulations. In the data usage phase, Daten
can be read, analysed, manipulated, edited, and saved. Data archiving stores data as a backup without
additional maintenance. Endlich, data destruction removes the data from the repository, ensuring, from a
security and privacy perspective, that the data can no longer be restored or subsequently used.
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Contemporary data, information and knowledge management in healthcare and research faces emerging
and ever-increasing difficulties in dealing with the challenges posed by big data [37]. Simple increases
in computational performance, storage capacity and algorithm efficiency alone are not enough to handle
the magnitude of data that is being generated [2]. Aus diesem Grund, the FAIR Guidelines were conceptualised
by Wilkinson et al. [5], consisting of four foundational principles, nämlich: Findability, Barrierefreiheit,
Interoperabilität, and Reusability.
These principles were developed in order to improve data management and stewardship and ensure
Transparenz, reproducibility, and reusability for digital assets that contain not only data, but also related
Algorithmen, tools and workflows [5]. These are the key principles that are used throughout the VODAN-
Africa implementation of the VODAN health data management architecture.
The primary requirement of FAIR compliance with respect to data management, is the baseline specification
for data to be discoverable through the concept of findability. For data to be findable, there must be a well-
documented path to index, organise and query data through the use of unambiguously readable metadata
and traversable knowledge graphs, defined by a standards-driven ontology specification.
Findable health data
Health data is fi ndable when it is discoverable by humans and machines
through the use of metadata and data linkages defi ned by biomedical
ontologies.
Once data has been properly indexed and integrated into a health information system for findability,
there must be a well-specified method to perform a repository query. At the point of data access, typically
implemented by an application programming interface (API), data queries are handled under well-defined
Bedingungen, such as methods of authorisation and credential verification audited by data stewards either in
residence or at the relevant ministry of health.
Accessible health data Health is accessible when health data, information or knowledge in residence
is accessible, possibly in an anonymised format, under well-defi ned and
transparent authorisation conditions.
A critical component that revolves around the findability and accessibility of health data is the machine
interoperability of the data throughout VODAN. For this, a baseline requirement is that the ontology,
produced from the central controlled vocabulary, must be resolvable by all locales and the unique identifiers
associated with the metadata must be unique.
The representation of knowledge, and the entity-attributed metadata through templating, must be
interpretable by automated evaluation to make the underlying data machine-actionable. From the perspective
of formal graph representation, this means that the knowledge graph that is implemented must be well
in Verbindung gebracht. Semantic metadata that is not referenced or indexed by the health system is not operable, als
the data pertaining to these metadata are not findable through automated methods in the repository.
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Interoperable health
Daten
Health knowledge bases are interoperable when they are interlinked and
operable for secure, automated data processing, storage and analysis
across health facilities.
Through interoperability, by making the health data architecture well-specified, resolvable and machine-
actionable, the conditions under which data become reusable are expressed in a formal framework.
Interoperability throughout VODAN allows for techniques such as automated knowledge discovery [38] Zu
maximise the information and knowledge that can be extracted from existing data, or combinations of old
and new data.
For the reuse of data to comply with data protection regulations, it is essential that the reposited data
within VODAN remains in good provenance, which is done by maintaining all associated metadata specified
in the DMP. Zusätzlich, the laws of each VODAN locale under which accessibility is regulated must be
well-documented, and both data and metadata have to be provided with a specification describing the
conditions under which access may be provided.
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Reusable health data
Health data is reusable when it is in good provenance, with documented
metadata to allow for the replication or reuse of data across health facilities
and locales.
The architecture of VODAN has been designed as a FAIR ecosystem, in which every aspect has been
specified, with the FAIR Guidelines as key design elements. This is aimed at achieving the primary objective,
which is to support the transnational reusability of medical (Forschung) data and the exchange of knowledge,
while maintaining data sovereignty [39].
Data sovereignty
Data sovereignty is maintained when data is reposited at the place of
production, full data ownership is retained and data is subject to local laws
and regulations.
By keeping data in residence in VODAN, and maintaining the rights of the data owner, data controllers
and processors work under the local laws and regulations in the jurisdiction. This ensures that the rights of
the data subject are always maintained in accordance with the government processes, which are influenced
by local constituents. A key problem that hampers data reusability and the exchange of knowledge is the
lack of a framework in which data can be exchanged or used under controlled conditions outside the
jurisdiction. This requires the architecture of VODAN-Africa to be inherently distributed. From the perspective
of data localisation, each of the data repositories within the network form individual FAIR Data Points (FDPs)
[29] that are compliant with the General Data Protection Regulation (GDPR) [30] and further regulated
under the data protection laws of the locale. Within the network, FDPs represent the individual repositories
where data is both controlled and processed using FAIR compliant health management processes.
FAIR Data Point (FDP)
A FAIR Data Point is a local data repository (with accompanying services)
that is compliant with the FAIR Guidelines.
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The design of this network is specified in the design of a FAIR digital health infrastructure by van Reisen
et al. [6], in which communication between FDPs is integrated in the Internet of FAIR Data and Services
(IFDS) through the concept of data visiting. Conceptually, data visiting involves the provision of aggregate
and inferential data, produced from the original data in residence at each of the FDPs, without exposing
the actual data records. This allows for a robust, distributed community analytics framework, in which
meta-analyses can be performed on VODAN aggregate data, while retaining full data sovereignty, und ist,
daher, also compliant with regulatory frameworks in regard to privacy and data protection.
Data visiting
Data visiting refers to the retrieval of aggregate analyses or statistics from
a FAIR Data Point, where analysis processing is fully performed at the
repository and no underlying data is exposed.
This ecosystem is defined as the Internet of FAIR Data and Services, where FAIR data is produced and
interacted with through FAIR services, which interface through FDPs. To establish the process of data visiting
within this ecosystem, unambiguous resource identification is required. These resources are conceptualised
in a digital object model, in which each resource has a unique identifier that is persistent as well as
resolvable [40].
Unique, persistent and
resolvable identifier
(UPRI)
This refers to a unique, persistent and resolvable identifi er for digital objects.
A FAIR compliant system to support the data processing and management of VODAN-Africa FDPs is
implemented at the Center for Expanded Data Annotation and Retrieval (CEDAR) [41], which is responsible
for the management of the ontologies, knowledge bases and all activities related to FAIR-based data
Verarbeitung. This provides individual facilities in VODAN-Africa with tools to perform both data controlling
and data processing, without requiring external parties, based on controlled vocabularies that are agreed
upon through community and stakeholder driven decision making. The comprehensive implementation
defined as the FDP, implemented as a repository managed by CEDAR with services that provide a data
visiting interface, forms the central unit within the VODAN architecture.
4. JURISDICTION AND DATA GOVERNANCE
The question of data ownership is both a legal and philosophical challenge and plays a central role in
VODAN. As data is non-tangible, from a legal standpoint data may be interpreted as intellectual property.
Jedoch, some data are ‘matter of fact’, to which no rights can be attributed [42]. This is further complicated
by the question of who the true legal owner of data is, and whether or not it is even possible to identify
the legal owner of data, in which provenance plays a key role. Each of these matters may depend on the
jurisdiction in which the data is produced and the geospatial location where the data is physically stored.
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Data owner
The data owner is the individual or party who has full control and legal
rights over specifi ed data, and who can, daher, defi ne the terms
pertaining to access to and control of the data.
A baseline principle that must always be upheld for data governance in cross-national instances is data
provenance [43]. Data is said to be in good provenance when meta-causality is upheld: d.h., the origin and
the processes that generated the data are known and well-documented through a clear data-lineage. Aus
the perspective of VODAN, provenance is a critical element for the data to have meaning in the place where
it was produced, which increases its relevance, but also serves as a way to measure the data’s veracity.
For scientific purposes, the quality of data provenance is critical to an investigation of the environmental
interactions of data in the context in which it was generated, not only in terms of the locale, but also the
data subject cluster. From the quality of data provenance, the question of data ownership can be addressed
by means of identifying who the subject of the data is, if applicable, and the party that initially collected
or sampled the data.
Data provenance
Data is in good provenance when the origin of the data and the processes
that generated the data are known, well-documented and kept current.
Apart from concerns about data ownership in VODAN, there are also legal and ethical concerns
surrounding both collecting and storing data. Most of these legal concerns are focused on the privacy of
Fächer [44], which is further driven by the rapidly increasing scope and variety of the medical data that
is being collected on individuals since the SARS-CoV-2 pandemic [45]. Data are by definition heterogeneous,
as such different types of data may warrant different levels of legal protection. Medical data typically
warrants the highest level of legal protection, due to the sensitive nature of such information [46, 47], welche
is one of the main concerns of VODAN stakeholders [6].
The legal concerns surrounding the handling and storing of data are placed within the perspective of the
jurisdiction in which the data resides. The legal policies and standards that are in place within a jurisdiction
fall under the data governance and regulatory framework, which aim to standardise the way data is handled
according to the applicable laws and regulations [48].
Data governance
Data governance is the enactment of regulations and policies surrounding
the collection, handling and storage of data, as well as the authorisation
and management of cross-border data fl ows.
When designing an information management system that can be localised, it is essential that it is
compatible with the different modes of data governance—as in the applicable laws and regulations
surrounding data in the place where it is produced. One approach that may be taken is an open source
Ansatz, in which localisation is performed by manually customising every aspect of the implementation
to comply with regulations. An information management system across different geographies requires that
it be flexible to handle regulatory fragmentation across locales, as each implementation may use radically
different methodologies to comply with the terms of the jurisdiction it operates under.
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Data localisation
Data localisation is the practice of repositing data at the location where the
data was produced.
An implementation of this is to use ethnographic design principles across VODAN. Within the community,
which seeks the convergence of information systems, all stakeholders representing each different locale are
actively participating in the design and development process. This approach promotes transparency and
allows for agreed-upon solutions to issues when differences in laws and regulations are identified. Through
a participatory and collaborative ethnographic process, an implementation is created that provides an
optimised baseline for all stakeholders and streamlined, well-documented options for divergence from the
baseline when needed for any practical or regulatory reason.
Ethnographic design
An ethnographic design is a participatory collaborative design that aims to
satisfy the requirements of cross-national stakeholders.
At the centre of a participatory and collaborative ethnographic design is transparency about the process
and implementation. As both data collection and data analysis are becoming increasingly complex and
‘black-box’, there is an increased need for transparency when it comes to the intermediate processes by
which data are stored and archived [49].
A step further is the concept of a completely transparent information system, in which non-sensitive data
is anonymised and published in an interoperable and reusable manner. Such a concept is implemented in
the European Open Science Cloud (EOSC) [50], while upholding the same principles with regards to
ethnographic design and full-scale interoperability [51].
In relation to legal concepts regarding data, information and knowledge management, VODAN uses the
GDPR as the foundational legislative frame of reference [52, 53]. The GDPR revolves around transnational
legislation for increasing operational transparency, promoting integrity, necessitating confidentiality and
specifying the constraints of data processing. This applies to personal data, which is data that pertains to a
natural person and over which the natural person should have control.
Personal data
Personal data is any data, information or directly resulting knowledge that
relates to, and legally belongs to, the data subject (Article 4(1), GDPR).
At the centre of the GDPR framework is the legal arbitration between the data owner, data controller
and data processor. While data ownership, as we have previously defined, pertains to the party that has
control over and legal obligations in relation to a specified set of data, under the GDPR we fully recognise
the rights of the individual from whom data has been collected. As VODAN provides full data provenance,
this becomes feasible to implement over the entire implementation network. As a consequence, we assume
that the individual from whom data has been drawn retains full ownership over their data, while another
party may process or control data under strict guidelines. These guidelines are only exempt under documented
derogations that are jurisdiction-specific, and typically cover matters of security, defence, public security
and the judicial process (Article 23(1), GDPR), which overrule, by local means, the conditions defined by
VODAN’s stakeholders.
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Zum Beispiel, medical data that has been collected to perform toxicological tests are sensitive in nature.
While these data are stored and operated by the medical facility, from a data protection regulation framework
perspective the data subject still has full legal rights over the data and the facility must have legal permission
to use and store these data, unless a legal exemption clause was signed. Exemptions in relation to data
ownership, such as data used for scientific research, are subject to strict regulations and typically require
a DMP that involves a process of pseudonymisation or anonymization of the data to protect the data subject.
The aggregation process, such as that used in VODAN, depersonalises data and, as such, they no longer
pertain to a specific data subject and are, daher, not considered personal data.
Data subject
A data subject is a natural person about whom data has been collected and
who can be identifi ed, directly or indirectly, by reference to that data
(Article 4(1), GDPR).
We consider here the difference between ‘data objects’, which we consider any non-human entity from
which data can be sampled, as compared to ‘data subjects’, a term that exclusively covers data relating to
a natural person. From the perspective of the data collector and regulator within VODAN, we can relate
this to data from which we can, directly or indirectly, identify any natural person. In this instance, the data
collector does not have full legal rights over the data, rather the rights remain with the data subject who
needs to give exclusive and sole permission for their data to be stored and used, which requires findability
as a baseline property.
The conditional requirements under which a data subject may be able to provide permission to store
and use their personal data fall under the GDPR, which stipulates that the data subject can only provide
consent if given full information about the processing and use of their personal data. These conditions are
typically given by the domain experts, who drive the semantic and purpose of data within VODAN.
This underlines the importance of data provenance in the implementation of an information system that
holds data about data subjects. It is of critical importance to maintain well-documented contextual metadata
that specifies the ownership of the data, the conditions under which the data may be used or processed,
and the extent of the consent that has been provided by the data subject. It should also be noted that under
the GDPR, consent can be withdrawn at any time and the data subject has the right to request a record of
the personal data, as defined under right of access, as well as to have personal data erased.
Informed consent
Informed consent is consent that is voluntary, specifi c and unambiguously
given by a data subject who is informed of all available data processing
Aktivitäten (Article 4(11), GDPR).
From the perspective of medical data processing, such as that performed in residence or in medical
Repositories, we are dealing with special categories of personal data. If a non-privileged party wishes to
process these data in VODAN, they must receive explicit consent for every single purpose that the data will
be used for and local regulations can impose limitations on the permissions that a data subject may give
to other parties over special categories of personal data. As VODAN-Africa covers a wide variety of legislative
frameworks, these limitations may vary, but should not be more permissive than the implementation.
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There are exemptions for certified public services that require more permissive data processing capabilities
to function, such as ministries of health associated with VODAN. These categories allow for secure processing
and storage under professional secrecy, by certified individuals, under strict conditions stipulated by the
national regulating body, without receiving explicit consent (Article 9(3), GDPR). Examples in VODAN are
if the processing and controlling of data is necessary for medical diagnosis, occupational medicine,
provisional healthcare or the management of healthcare systems by individuals under non-disclosure.
Special categories of
personal data
Special categories of personal data refers to sensitive personal data that are
subject to strict regulations, which may only be processed and used by
legally certifi ed parties (Article 9(1-3), GDPR).
In addition to the data subject, we identify two entities in VODAN that may handle personal data: Die
clinician as data controller and the data steward as data processor. The data controller is the contingent
that is given the right to control personal data belonging to a data subject, which is typically provided
through informed consent. The controller determines the conditions, purpose and means by which personal
data is stored and used by the data processor. Under these conditions, from the perspective of medical data
management, the data controller is typically the residence at which the data was produced.
Data controller
The data controller is the entity that specifi es the purpose for, and the
means by, which personal data belonging to a data subject is processed
(Article 24(1-3), GDPR).
The controller of the data is legally responsible for acquiring consent or legal permission and providing
a statement of purpose and DMP. The controller does not need to be a singular entity. Multiple organisations,
such as VODAN-Africa, may form a group that jointly determines and states the purpose and conditions
under which data may be stored and processed, while complying with the GDPR guidelines.
While clinicians as controllers specify the purpose and means by which data is handled, the data steward
as data processor is the party responsible for processing and storing the data on behalf of the data controller.
It is the responsibility of the data processor to implement a data repositing process with sufficient security
measures and the ability to certify the integrity and security of personal data that is stored at the locale.
Potential security risks and measures taken to minimise these risks have to be documented in a data
protection impact assessment (DPIA) Bericht (Article 35(1), GDPR).
Data protection impact
assessment (DPIA)
Potential security risks and measures taken to minimise these risks have to
be documented in a data protection impact assessment report (Article
35(7), GDPR).
The data controller and the data processor may, in some cases, be the same entity, for instance, in a
small clinic where medical professionals process data. Jedoch, data processing is typically covered by a
specialised party, Zum Beispiel, a cloud service provider, that is contracted by the data controller. Alle
responsibilities, legal obligations and non-disclosure stipulations must be documented in a contract between
data controller and data processor.
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Data processor
The data processor is the entity that is responsible for processing the
complete lifecycle of the personal data belonging to a data subject on
behalf of the data controller (Article 28(3), GDPR).
Figur 3. Diagram showing each of the steps between the data subject and legal use of personal data [11].
The GDPR applies to any identified or identifiable natural person. In order to process the information
for research purposes in VODAN, a common technique that the data processor, in agreement with the data
controller, may employ to provide privacy protection over accessed data is anonymization. This involves
replacing all directly and indirectly identifiable information in a data set with a unique identifier that does
not disclose the identity of the data subject when records are retrieved, and thus cannot be linked to a data
subject by combining separately stored data-sets. At the point of full anonymization, such as aggregation
used by VODAN, the GDPR no longer applies to the data, meaning that the data subject cannot be
identified in any way and, daher, the data is not considered personal data.
Anonymization
Anonymization is the process of ensuring that personal data cannot be
attributed to a data subject in any way, directly or indirectly, including by
combining separately stored data sets (Preamble 26, GDPR).
Pseudonymisation is the process in which directly identifiable personal information is removed, but by
means of processing the different data available, the data can still lead to the data subject. Infolge, Die
natural person is indirectly identifiable. In VODAN, these data are not allowed to leave the localised
Beispiel.
Pseudonymisation
Pseudonymisation is the process of ensuring that personal data can only
be attributed to a data subject indirectly, by utilising separately stored
Information, to which access is strictly regulated (Article 4(5), GDPR).
The process of pseudonymisation is an important protection mechanism for sensitive data, such as
medical data, used with research exemption clauses, as the identity of the data subject is usually only of
concern in extenuating circumstances or for verification of the integrity of the data.
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As the GDPR does not apply to completely anonymous data, a method that has been conceptualised in
VODAN to improve the ease of data exchange for big data applications is to synthesise data based on the
statistical properties of the original data belonging to the data subjects [54]. This process of data synthesis,
im Wesentlichen, extracts knowledge from the data through computational or mathematical processing, and then
uses the knowledge to create new data that has not originated from a data subject.
Repositing synthetic data with proper provenance has certain benefits for VODAN, especially in relation
to security and privacy, and increases the ease of data exchange. Jedoch, specific care has to be taken
to ensure that combinations of the underlying distributions of these synthetic data does not contain the
granularity that would allow indirect or approximate identification of the individuals from which these data
were synthesised. This phenomenon is described as ‘k-anonymity’ [55]. Another point of concern is the
quality of the data, as synthetic data is the result of sampling from a modelled distribution, rather than from
a population that can be verified. In VODAN, transparency and provenance are important tools for upholding
data quality when synthetic data is employed to model population health.
Synthetic data
Synthetic data is data that has been generated from a measured distribution
or computational process, and has not been obtained from direct
measurement or observation.
Robust mechanisms for verification that may determine that synthetic data do indeed match the
characteristics of the original data subjects through federated data could ultimately result in synthetic data
being verifiable through pseudonymous data, as their generative process could be linked to a population
of data subjects. While these methods are developed in VODAN, the GDPR has not yet elaborated on novel
federated data concepts.
While the data steward, as data processor within VODAN, bears responsibility for the technical security
aspects of a data repository, the clinicians, as data controllers, have to perform due diligence through a
privacy impact assessment (PIA) documenting all identifiable information that will be obtained, the risks
beteiligt, and the conditions under which the data will be obtained. This documents the risk evaluation
and impact assessment with respect to the risks to the rights of data subjects, which can be evaluated under
the GDPR throughout VODAN.
Endlich, it is the responsibility of the data controller to notify the supervisory authority about data breaches,
such as unauthorised access or access control failures. When managing health data, this would require
immediate reporting to the regulatory health authority, such as the ministry of health of the relevant country
under Article 33 of the GDPR, in accordance with Article 55 of the GDPR. While outside the European
Union (EU) this is not a legal requirement, VODAN supports transparency and liability for the security of
personal data as an important safeguard. This underlines the well-documented and specified access and
control patterns, as well as record keeping of access, within VODAN, which are crucial when handling
protected categories of personal data and form an essential basis for community trust in health data
management.
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5. DISCUSSION AND CONCLUSION
The purpose of this article was to develop a set of shared terminologies that allow for the unambiguous
exchange of controlled vocabularies and the development of consistent data stewardship expertise
throughout VODAN. At the core of the implementation of VODAN-Africa lies the concept of knowledge
management, which uses ontologies to manage data using graph representations that aid in findability and
knowledge discovery in data when causality is highly relevant such as the health domain. The core elements
of the architecture are transferable to other research areas and may be considered by other domains to
establish data stewardship expertise and FAIR data networks. The core concepts, defined in this article, Sind
each crucial to the deployment of a FAIR implementation network.
This article considers the elements involved in traditional health data management, identifies the
challenges involved and discusses how these challenges are addressed in the FAIR architecture. Some of
these challenges are technical in nature, while others deal with societal challenges. such as compliance
with regulations and the rights of individuals. These may vary in different locales and the FAIR Guidelines
help to bridge potentially fragmented realities concerning data management with different customs or rights
awarded to protecting individuals and society.
Utilising both the GDPR, as well as the FAIR Guidelines, and respecting the principle of personal privacy
protection enshrined in the Universal Declaration of Human Rights, VODAN-IN shapes the way forward
for sovereignty over health data, in the place where the data is produced and mindful of societal differences
in relation to the management of the data. Through the definitions we have developed, we specify a
framework of terms that build upon the VODAN architecture. This architecture is highly distributed and
interoperabel, ultimately managed and controlled in residence by data stewards that rely on unambiguous
specifications.
This article was conceptualised as a review of how data terminology can be defined in the context of
health data management, with a focus on aspects of FAIR and regulatory compliance. To this extent we
have developed a comprehensive framework that will support the further development and deployment of
FAIR data architectures in the domain of health, such as VODAN-Africa, and the modernisation knowledge
on health data management to educate a new generation of data stewards.
DANKSAGUNGEN
I would like to acknowledge the help of Prof. Dr Mark Musen and John Graybeal of the Stanford
Biomedical Informatics Research institute for research support; Misha Stocker for management and
coordination of the Special Issue (Volumen 4); and Susan Sellars for copyediting and proofreading. We would
also like to acknowledge VODAN-Africa, the Philips Foundation, the Dutch Development Bank FMO,
CORDAID, and the GO FAIR Foundation for supporting this research.
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AUTHORS’ CONTRIBUTIONS
Ruduan Plug (ruudplug@gmail.com, 0000-0001-5146-6116): Writing—original draft preparation, investigation,
conceptualization. Yan Liang (liangyan12138@gmail.com, 0000-0002-3860-725X): Writing—original draft
preparation, investigation. Aliya Aktau (aleka.aktau@gmail.com, 0000-0003-4942-2725): Writing—review
and editing, investigation. Mariam Basajja (mariam.basajja@gmail.com, 0000-0001-7710-8843): Schreiben-
review and editing, investigation. Francisca Oladipo (francisca.oladipo@kiu.ac.ug, 0000-0003-0584-9145):
Writing—review and editing, validation, project administration. Mirjam van Reisen (mirjamvanreisen@
gmail.com, 0000-0003-0627-8014): Aufsicht, validation, conceptualization, project administration.
CONFLICT OF INTEREST
All of the authors declare that they have no competing interests.
ETHICS STATEMENT
Tilburg University, Research Ethics and Data Management Committee of Tilburg School of Humanities
and Digital Sciences REDC#2020/013, Juni 1, 2020-Mai 31, 2024 on Social Dynamics of Digital Innovation
in remote non-western communities and the Uganda National Council for Science and Technology,
Reference IS18ES, Juli 23, 2019-Juli 23, 2023.
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