=Paper=
{{Paper
|id=Vol-3036/paper06
|storemode=property
|title=A FAIR Problem-Solving Lifecycle Architecture
|pdfUrl=https://ceur-ws.org/Vol-3036/paper06.pdf
|volume=Vol-3036
|authors=Nikolay A. Skvortsov
|dblpUrl=https://dblp.org/rec/conf/rcdl/Skvortsov21
}}
==A FAIR Problem-Solving Lifecycle Architecture==
https://ceur-ws.org/Vol-3036/paper06.pdf
A FAIR Problem-Solving Lifecycle Architecture
Nikolay A. Skvortsov[0000-0003-3207-4955]
Federal Research Center “Computer Science and Control”
of the Russian Academy of Sciences, Moscow, Russia
nskv@mail.ru
Abstract. Research infrastructures are intended to provide access to scientific
data and resources needed for problem-solving. Approaches to support interop-
erability and reuse of heterogeneous resources in such infrastructures need inves-
tigation. Researchers tend to integrate and reuse existing resources but do not
spend much effort to publish the resources created during solving scientific prob-
lems to make them reusable in communities. As the result, further users of these
results have to spend their time and effort on integrating resources to reuse them.
We propose an architecture of research infrastructures that are initially based on
the lifecycle of problem-solving in research communities providing interopera-
bility and reuse of the sources and previous research results. In this architecture,
most of the data maintenance tasks are moved from the data integration stage to
the data publishing stage, and data are manipulated following the domain speci-
fications accepted by communities. This makes providing the interoperability and
reuse of resources in the infrastructure more competent and easy and avoids re-
peated integration.
Keywords: research infrastructures, data reuse, problem-solving lifecycle, soft-
ware architecture.
1 Introduction
Contemporary research is mainly based on data about the research object, often col-
lected from different sources, initially focused on various aspects and purposes of ob-
ject describing and obtained by various methods and instruments. At the same time,
there are continuously growing needs for covering a wide variety and large volumes of
data and in possible directions of their analysis. That applies to almost any scientific,
commercial, or state field of research. There is an urgent need to create research infra-
structures that provide both the data themselves and tools for comprehensive support
of research on them.
The creation of data infrastructures was due to big volumes of research data needed
to be stored, replicated in data centers in different regions, and shared to different re-
search groups for immediate and probably long-term processing for obtaining new
knowledge from them. Thus, data infrastructures were aimed at data collecting, long-
term preserving, suitable organization of data for processing and analysis, and sharing
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
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between research groups with different interests. For instance, the CERN data pro-
cessing problems were similar. However, later, with the growth of data centers preserv-
ing and providing data of different nature and in different research domains. So the
problems of data preserving had shifted to the problems of interoperability and reuse of
heterogeneous research data for problem-solving.
Such infrastructures, in addition to long-term data preservation, need search and ac-
cess tools, description of data semantics, tools for data integration, automation of re-
search on data. Еhe ultimate goal of these tools is to support unimpeded, useful, and
productive reuse of data and related resources for solving research tasks. The guiding
principles for providing reusable data, known by the abbreviation FAIR (findable, ac-
cessible, interoperable, reusable data) [1] have become one of the main directions for
the actively discussed foundations for creating global interdisciplinary research infra-
structures.
Despite the universal recognition of the FAIR data guiding principles as a measure
of the quality of technologies to select, positions on organizing, automating, and sim-
plifying the process of solving problems in research infrastructures have not yet been
established and are actively being discussed. Moreover, there are obvious problems, the
overcoming of which is seen not so much in technical solutions, but in changing the
established research paradigm. Even if research infrastructures provide a wide range of
services, there are not enough incentives for researchers to use them in a way to meet
the FAIR data principles, to make the research process easier, and the results more
accessible and convenient for reuse by other members of the research community.
So there is a need for infrastructures with an architecture itself that would direct
researchers to a new paradigm and culture of research, in which providing the ability
to reuse any results of data management and data analysis and the continuity of research
in domain communities are put at the forefront.
In [2], a lifecycle for problem-solving in research infrastructures was proposed,
which was the result of thinking on the guiding principles of FAIR data. It uses semantic
approaches, domain specifications developed by research communities, and timely
publishing data at all stages of solving research problems, from the selection and inte-
gration of source data to long-term preservation of research results. Each stage of such
a life cycle is designed in such a way as to preserve the results of passing it and provide
their reuse in subsequent studies.
The purpose of this work is to propose an architecture of research infrastructures that
would provide the implementation of the problem-solving lifecycle mentioned above,
and therefore, fulfill the principles of FAIR data in them. The next two sections dis-
cusses the accustomed approaches used in research infrastructures. Section 4 briefly
introduces the theses of the proposed problem-solving lifecycle to overcome these de-
ficiencies. Section 5 describes the architecture of research infrastructures that imple-
ment the discussed approach. Then, in section 6, it is explained how the proposed ar-
chitecture provides the principles for managing the problem-solving declared as part of
FAIR.
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2 Issues of the Research Process
Data infrastructures were originally designed for the consolidation of research data,
access to them, and the ability to share them. In some specialized infrastructures, meth-
ods and workflows were provided to be used together with data. Later research infra-
structures appeared that include wider functions and provide tools for processing and
analyzing data. In other words, these were environments for supporting research on
data.
Among the basic set of services in existing research infrastructures are registration,
long-term preservation, and search for datasets, and support of data analysis.
The architecture of the EUDAT CDI [3] research infrastructure is initially designed
to support the collaboration of large communities as well as individual users in work
with data. EUDAT CDI can be considered as a research infrastructure with a classic set
of services. Long-term data preservation with support of permanent identifiers and
provenance is provided by B2SAFE and B2HANDLE services. Digital objects [4] are
supported that contain several datasets under one identifier, and all of them have their
identifiers. The B2SHARE and B2ACCESS provide data storage and access services.
The B2STAGE service provides replication of datasets for problem-solving in interme-
diate storage. A data type registry (DTR) is created to define descriptive metadata. The
B2NOTE service provides semantic annotation of data, and the B2FIND metadata cat-
alog allows searching for data by annotations.
The architecture of the open science cloud EOSC [5, 6] is designed to combine a set
of research infrastructures. It is defined as a system of systems based on registering
services by providers. EOSC functionalities are provided as services on nodes distrib-
uted across some organizations and regions. It is declared that EOSC services should
promote and support the principles of FAIR.
The architecture defines several dozen classes of services that are considered as a
minimum viable product. Such services include those specifically designed to serve
researchers, research administrators, third-party service providers, as well as EOSC
managers. Like in EUDAT, the metadata structure is an enhanced set of fields, which
were started in Dublin Core. Interestingly, the user interface and the search engine ser-
vices are related to EUDAT services.
GO FAIR [7] is an initiative aimed at the interaction of experts and the wide com-
munity to implement the FAIR data principles. Discussions, events, and initiatives in it
are held for popularization, making incentives, education, and direction for implement-
ing the principles. Technologies and components of architectures are proposed, inves-
tigation of certain aspects of the principles are initiated, the best practices are chosen,
and the response of the community is evaluated. One of the priorities of GO FAIR is
the coordination of different studies necessary to create the EOSC infrastructure.
An important initiative for the development of the Internet of FAIR Data and Ser-
vices (IFDS) [8] is also being discussed in the GO FAIR community in connection with
the EOSC infrastructure. It is aimed at the support of type-driven automatic data pro-
cessing. It links data with tools and calculations semantically relevant to them. Thus,
data processing research infrastructures can be performed automatically based on the
81
data semantics using relevant tools. Such studies comply with the guiding principles of
FAIR data as a kind of machine-actionable approach.
The FAIRsFAIR [9] project is aimed at promoting selected practices and support
representative projects such as data centers or repositories that support the FAIR data
principles and use certain sets of technologies for it. For this purpose, criteria for eval-
uating projects for FAIRness have been developed. Explanations of the meaning of
various aspects of the FAIR data principles and possible ways to follow these concepts
have been developed. Specifying ontologies, data schemas, interfaces, and protocols,
and mapping them to one another are recommended [10]. This simplifies the under-
standing of the principles themselves and in the research community, and technologies
implementing them can be selected from the project case studies. These projects show
the advantages of following the FAIR data principles for solving problems of interop-
erability and reuse of data in research.
The FREYA Project [11] was devoted to the study of global persistent identifiers.
As a result of its implementation, tools have been created to support identifiers of var-
ious entities types (such as research publications, data, programs, people, and organi-
zations), related standards, and their integration. In the extensible registry of the
Knowledge Hub, there are services to support existing standards of global persistent
identifiers, domain-specific identifier types, linking data with their metadata and prov-
enance information based on the graph of identifiers of different types [12], cross-res-
olution of identifiers in different standards, entity annotation, and others. This work
was carried out in connection with the needs of the EOSC research infrastructure and
the results are proposed as a part of the EOSC-hub. The PID Graph is a conceptual part
of EOSC. Another result of the project is a community that continues to promote the
results of the project [13].
All the described projects and initiatives indicate that research communities are
aware of the need to conceptualize their domains for greater interoperability and data
processing and research automation. However, in those projects, it is recognized that
the mentality of researchers is changing slowly, and willing and organizational efforts
are needed to use semantic descriptions of domains as community requirements, imple-
ment FAIR principles and change the situation with machine-actionable data pro-
cessing.
When solving problems in research infrastructures, users usually have to work with
heterogeneous data. In general, the mapping of data models and the integration of het-
erogeneous structures are considered as a non-trivial task, so solutions for it are
avoided. To overcome them, it is proposed to use widely used universal formats and
trivial data representation. However, this does not keep users from the semantic heter-
ogeneity of the data. Data and other resources are available in their original formats,
and the use of them for problem-solving, in any case, requires understanding and rec-
onciliation. To reconcile heterogeneous data, research infrastructures can provide ser-
vices that help to integrate resources. These tools solve the problems of data interoper-
ability but do not yet ensure the effectiveness of applying them.
Anyway, an accustomed approach to starting new research is to search for data rel-
evant to the problem being solved, find out their structure, semantics, and interfaces,
and reconcile them for the possibility to use them in a problem-solving application.
82
This usually takes a lot of time, effort, and manual work, moreover, it is done multiple
times for the same data in each independent research. The results of this big work often
remain hardly applicable for reuse.
The lack of support for specialized methods related to data in the domain community
becomes a problem due to the need to develop the same methods many times in each
research. Creating libraries of methods is not enough, since extending them with new
research results is difficult, and they remain unprepared for search and poorly inte-
grated.
The publishing results of problem-solving in research infrastructures is usually sup-
ported or even required to make the results available to reuse or reproduce in further
research. However, simple procedures of data and service publishing in research infra-
structures allow providers of newly obtained data and developed services to publish
them as-is with simple descriptions for potential users. This immediately becomes a
problem for their reuse, since users have to do a lot of work to reconcile them before
using them.
Developers of research infrastructures, including pilot projects of new infrastruc-
tures, discuss the weak development of researchers' skills in interaction with infrastruc-
tures. This probably happens because publishing is postponed until the end of the pro-
ject and so it is not done properly. Thus, the research paradigm of users is changing
slowly, the problem-solving in them remains involving a lot of manual work. This, in
turn, is a weak incentive for the use of infrastructures by research communities.
So leaving the final research results unintegrated, the inevitability of repeated man-
ual work with small variations for data integration and method implementation, and the
inability to reuse the results obtained at these intermediate stages of problem-solving
mean contradicts the principles of FAIR data, since data are not reusable in domain
communities.
3 Formal Domain Specification in Different Disciplines
The use of ontologies in some research domains is very natural and convenient. Ontol-
ogies are especially useful for classifying a large number of different types of entities
defined by their certain properties and the values of attributes inherent to these entities.
For example, the domains where research communities have developed and widely
used ontologies for a long time are bioinformatics and biomedicine. The gene structures
or proteins define physiological characteristics, organ functions, pathologies, and other
aspects of the living organism description. It allows to classify the properties and iden-
tify the types of structures that affect the characteristics of the organism. There are a lot
of subdomains in biomedicine. Each of them can be used by specific communities, de-
scribed by specific concepts, and participate in the classification of entities based on
correlation with other subdomain concepts. These subdomains are supplemented with
domains for the description of the observation tools and methods. Those tools and meth-
ods define sets of the observed and evaluated characteristics of domain entities. BioPor-
tal [15] collects ontologies describing different aspects of the biomedicine domain. The
need to harmonize these ontologies was realized and some of them were emerged and
83
reconciled with each other. The conceptual framework of the research process contains
concepts for laboratory research on physical entities, creating information entities, and
then analyzing information objects to generate secondary data and new knowledge. Se-
mantic annotation of data and resources in terms of these ontologies is widely used in
biomedicine and it is a good basis for achieving data interoperability in research domain
communities and infrastructures. Many of the rest ontologies in BioPortal remain over-
lapping, contradicting, and unformal.
Similarly, in materials science, the description of the microstructure of materials de-
fines their chemical and physical properties. Thus ontologies allow to define and clas-
sify all the variety of such characteristics. Domains with parameters dependent on the
microstructure may include chemical, magnetic, optical characteristics, crystal struc-
tures, and others. The OntoCommons [14] project aims to standardize a set of ontolo-
gies and data representations in materials science and to offer practical tools for it as
well. A system of ontologies related to each other is being developed and form the
hierarchy. At the most abstract level, there are widely used higher-level ontologies, then
middle-level ontologies related to the domain, in particular, those included in EMMO
[16] and their extension, and finally, ontologies of various subdomains based on them.
A set of use cases demonstrate the effectiveness of the approach.
In astronomical research, the correlation of the observed parameters of astronomical
objects with their astrophysical parameters is important. Experiments [17, 18] with for-
mal semantic specifications of various domains in this discipline show that during solv-
ing representative domain research problems the vocabularies of domain concepts have
been quickly saturated and become mostly sufficient for solving other problems in the
same domains. On the other hand, Observation Core Data Model (ObsCore) [19] can
be considered as one of the best standards in astronomy for common domain specifica-
tions and FAIR data management. It includes features of general domains of astronom-
ical observation such as descriptions of the spatial axis and time, observational proper-
ties and spectral characteristics of astronomical objects, annotations of data with se-
mantic definitions (descriptors in the UCD standard are unfortunately are sufficiently
ambiguous and not formally defined), metadata, and provenance model and allows que-
rying it all simultaneously.
The ontologies, data schemas, interfaces, and protocols in specific research domains
confirm that research communities move towards a formal conceptual definition of
their research domains. Some of the most common domains are ready for formal se-
mantic approaches since domain specifications are enhanced and used intensively used
in them.
4 The Research Problem-Solving Lifecycle
In [2], a problem-solving lifecycle was proposed, which was the result of reasoning
about the guiding principles of FAIR data. The core of such a lifecycle is the support
of formal domain specifications by communities since data semantics plays an im-
portant role in the integration of data and the capabilities of machine-controlled data
management processes. Based on the management of domain descriptions, the search
84
for relevant data, methods, and other resources related to data for their reuse is provided.
The problem-solving and the presentation of the research results are presented in the
domain terms. Thus, the research results remain available for reuse in subsequent re-
search within the community.
Communities of researchers in such infrastructures become interested in describing
the domains of their interests and in semantic integration of data related to their domains
for repeated reuse without additional integration.
The lifecycle of research problem-solving includes a framework of basic activities
(Fig. 1, highlighted in blue):
description of the domain, formulation, and analysis of the problem in it;
selection of data resources relevant for problem-solving;
selection of methods for solving the problem or implementing them;
getting the results of problem-solving and publishing them.
Knowlegde
Publishing
Problem
Specification
Result Data and Resource Search for Data
Problem Solving Resources
Method Publishing Selection
Method
Selection
Search for
Relevant
Methods
Fig. 1. The framework of the lifecycle of problem-solving on data
However, other important intermediate stages of problem-solving are put on this
basic framework. An infrastructure should provide the possibility of a formal search for
semantically relevant resources based on domain ontologies (Fig. 1, highlighted in
green):
search for domain concepts, related requirement models of previously solved (sub-)
problems, and publishing the knowledge obtained during the problem analysis;
search for relevant data resources that were previously registered in the community;
search for suitable methods and workflows for solving the problem, previously im-
plemented for use in the community (the principles of the formal ontological de-
scription of methods are presented in [20]);
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publishing the results of problem-solving and their metadata in terms of domain on-
tologies to provide the search for them during solving subsequent research problems
in the community.
If relevant resources have been integrated with the domain specifications, and the
results of integration have been published, they can be reused with the domain specifi-
cations without additional integration.
If some resources have not been previously used by the community at all or found
resources have been registered but not integrated with the domain descriptions, the
lifecycle offers the tools for integrating them with the domain specifications:
finding correspondences between the elements of the integrated data model and the
elements of the canonical model or already known extensions of the canonical data,
making data model mapped to the canonical one and work with a unified model [21]
to solve problems in it;
finding correspondences of the elements of the integrated schema with the concep-
tual schemas and formats used in the domain of the community, and mapping them
into each other, as well as integrating data at the object level between the reconciled
schemas;
search for matches of elements of the method and workflow specifications for inte-
gration and implementation.
Search for
Data Model Model Mapping Data Model
Data Model
Unification Verification Publishing
Elements
Search for
Schema Schema Mapping
Relevant Schema
Mapping Verification
Elements
Integrated
Entity Resolution Data Integration
Resource
and Fusion Verification
Publishing
Method
Implementation
Search for Method Refinement Integrated
Relevant Method
Methods Verification
Publishing
Method
Reuse
Fig. 2. Integration of heterogeneous resources for problem-solving
Each of these stages starts with a formal search for relevant resource elements from
the ontological point of view and finishes on publishing the integrated resources (data
models, data resources, methods, and workflows). At the same time, the integration
86
results themselves are published too. The established correspondences between the in-
tegrated resources and the specifications of the domains to which they are mapped, so
that the integration process is not repeated in the future, but reused (see Fig. 2, high-
lighted in green). Activities for formal verification (highlighted in purple) include ser-
vices for verifying the results of integration stages or reuse based on proof of the re-
finement relationship between specifications [22]. The integration support processes
themselves are highlighted in light blue in Fig. 2.
Semantic approaches to managing heterogeneous data and formally proving the cor-
rectness of their integration or reuse are combined with tools for quality publishing of
research and data management results at different stages.
To implement the principles of FAIR data, the principles for creating research infra-
structures based on the presented problem-solving lifecycle on heterogeneous data
could be the following.
The infrastructure is aimed at supporting communities working in certain research
domains. The basis of the infrastructure is the research problem-solving lifecycle
providing data and resource reuse in communities.
Domain knowledge is the basis for uniting research communities. Formal specifica-
tions of ontologies with the reasoning feature are used for the description of domains
and related resources.
Metadata in terms of domain specifications annotate and describe available re-
sources. They are used for the semantic search for resources in communities. Differ-
ent kinds of resources can be semantically annotated including specifications of da-
tasets, conceptual schemas, methods signatures, data model elements, and others.
Any manipulations with resource metadata are performed over metadata registries.
Providers should publish resources to make resources reusable in communities. The
publishing of problem-solving results includes describing them in terms of domain
ontologies and storing the metadata in registries to make available the search for
them in communities. Described data should be preserved and accessible by identi-
fiers. All useful results should be published as soon as they appear following the
problem-solving lifecycle or a special data management plan. The infrastructure en-
sures the reuse of different kinds of problem-solving results, including data, meth-
ods, integration results, so that any repeated work in communities is minimized.
Automated reasoning on semantic specifications and metadata is used as much as
possible at all stages of problem-solving (search for relevant specifications, resource
integration and reuse) so that machine-controlled data management and problem-
solving at a semantically significant level are possible.
5 The Proposed Architecture of Research Infrastructures
Following the described lifecycle of research problem-solving and the principles of
creating research infrastructures based on such a lifecycle, the following software ar-
chitecture is proposed for their implementation (Fig. 3).
87
Lifecycle Problem Workflow Data Model
Management Specification Execution Integration
Metadata Resource Resource Schema
Search Management Integration Integration
Method/
Metadata Method Entity
Workflow
Publishing Management Integration
Integration
Ontology Metadata Refinement
Reasoning Registries Verification
Fig. 3. The architecture supporting the problem-solving lifecycle
A person or a machine (possibly controlled by an expert) as an agent interacts with
the problem-solving lifecycle management workflow to use the components of the ar-
chitecture as its activities.
Metadata registries support long-term preservation and access to domain specifica-
tions, metadata of the integrated data resources, available implementations of methods
applicable to domain objects, descriptions of data models mapped to the canonical
model. In addition to metadata registries, repositories for replicated data and digital
objects are used.
Metadata search services are based on tools that support logical reasoning in terms
of formal ontologies (highlighted in green) over metadata in registries. They support
the search for data resources, methods, workflows relevant to the problem, and similar
specification elements during the process of heterogeneous resource integration.
Metadata publishing services relate resource identifiers with metadata and preserve
them in the registries. They support the publishing of data models, schemas, data re-
sources, methods, and workflows at different stages of the problem-solving lifecycle.
Resource integration services (highlighted in light blue) can provide support for the
integration of data resources at all levels, including the integration of their data models,
schema mapping, instance integration. Integration of methods and workflows follow
the resource integration.
Formal semantics refinement verification services (highlighted in purple) can pro-
vide formal proof of the correctness of mapping data models, schemas, objects, meth-
ods, or workflows to one another.
The issues of authorization, authentication, licensing, and access restriction are tied
to domains, communities and participating in them. Domains are organized as partially
88
ordered set where subdomains use knowledge of some more general domains. Commu-
nities of domains have access to resources of domains and more common domains.
Researches, research groups, projects, and machine agents must join the community to
get access to its resources. Access to community implies commitment to its domain
specifications and standards. Joining communities is regulated by license politics and
may be open or restricted. Digital objects are considered as resource access units.
The prototype of the research infrastructure architecture is being implemented using
Hadoop technologies. To materialize data from integrated sources in the form of files,
the distributed storage is applied. The Spark framework was chosen as a distributed
computing platform for data analysis. However, for structured data, it is possible to use
distributed databases and frameworks that work in Hadoop too, for example, HBase
and Hive. The implementation is also considered to be moved to a cloud or other dis-
tributed technologies. The variety of data presented here can be very large: copies of
the source data resources published intermediate data (collected, selected, transformed,
processed, generated data), data on resource integration, data obtained as a result of
solving problems (new data resources, models, programs, libraries, and others).
To implement metadata registries based on formal ontologies, an RDF repository or
a framework for RDF can be chosen. It must support the OWL language, processing in
RAM, and store or interact with a distributed DBMS for storing domain specifications
and metadata. The framework needs to integrate a tool for reasoning in description
logics for resource classification (for example, Pellet), and the SPARQL endpoint for
metadata queries.
A workflow execution framework is used to activate the problem-solving lifecycle
as well as problem-specific workflows.
Programming languages that support Spark, for example, Java, Python, Scala can be
used for the implementation of some tools in this architecture or problem-specific meth-
ods. Data can be stored in a distributed HDFS file system with the ability to extract
them by global identifiers. But it can be accessed without materialization in the research
infrastructure repositories by the same identifiers from the original long-term preserva-
tion locations.
6 Following the Principles of FAIR Data by the Research
Infrastructure Architecture
The presented architecture of research infrastructures for problem-solving on data aims
to cover the guiding principles of FAIR data as much as possible since any resources
related to FAIR data should be FAIR as well. The arguments given below allow evalu-
ating the decisions from the view of every FAIR data principle.
6.1 Findable Data
According to the FAIR data principles, finding datasets and services requires human-
and machine-readable metadata, global data identifiers that explicitly link metadata to
data, and tools of indexing or registering data based on such metadata.
89
Data and related resources are provided in the proposed architecture with metadata
representing their semantic annotations. They widely, comprehensively, and formally
describe the meaning and properties of data from different perspectives defined by a
set of domain ontologies. Any concepts existing in the domain should be expressible in
the annotations. Semantic annotations refer to the data using permanent unique global
identifiers.
The publishing data consists of defining metadata and registering them in registries.
This makes it possible to search for data and resources by specifying the necessary
concepts as queries as well.
6.2 Accessible Data
To make data accessible, compatibility at the level of access protocols, the ability to
extract data by global identifiers are important. Access restriction rules are defined.
And the access to metadata is maintained even if the data itself is no longer available.
In the proposed architecture, in this regard, data can be extracted from the original
places of their long-term preservation using standard Internet protocols and global iden-
tifiers. Or copies of them are available in distributed repositories using the same iden-
tifiers. Digital objects also provide identification and access through other integrated
protocols. Problems of identifying data fragments and reconciliation of identifiers of
partially mismatched data entities can be solved by using the identifier graph [12], alt-
hough the primary idea of creating it had other goals.
After publishing, the metadata are permanently preserved in the registries. If changes
are necessary, the metadata is supplemented with the expiration as provenance infor-
mation, and new versions of the metadata are created.
Any types of resources, such as ontologies, data resources, services, workflows, the
results of resource integration are also published, can be found and extracted by identi-
fiers. Thus, they meet the principles of FAIR data in their turn.
6.3 Interoperable Data
The principles of achieving data interoperability include the use of a formal, accessible,
and widely used knowledge representation language at the core of data management.
Various types of resources, in particular, dictionaries for describing related to data and
necessary for their description, should themselves comply with the principles of FAIR
data. It is also important to provide a qualitative description of linking the data related
to each other or used together. These principles are designed to solve the issues of com-
patibility of applications and workflows with data during their processing, analysis, and
preservation. It is noteworthy that the main principle of data interoperability is the use
of a formal language of knowledge representation. This gives hope for machine-con-
trolled data management but would not recognize machine learning methods perhaps
because of their probabilistic character and poorly interpretability.
In the proposed architecture, formal models, in particular, description logics, are
used for describing the domain knowledge and semantic annotation of data and re-
sources. During publishing in the registries, automatic formal logical reasoning is used
90
for the classification of data and resources from the point of view of ontologies. Auto-
matic reasoning provides the ability to interpret the meaning of data and resources when
solving problems by both a human and a machine.
Domain ontologies as dictionaries are themselves formal and available for reuse af-
ter their publishing in registries, thus they comply with the principles of FAIR data.
The proposed method of semantic annotation allows not only to use the concepts of
ontologies directly to define the data semantics but to describe data with expressions
that define subconcepts in terms of existing ontologies using the entire expressive
power of the knowledge representation language. Thus, it becomes possible to express
necessary constraints and relationships of concepts within one ontology, or between
several ontologies, and then use logical reasoning to classify data by annotations.
Data, method implementations, the results of resource integration, workflows,
metadata, and other resources refer to each other within digital objects. They are de-
scribed in the registries, and they are accessible using an ontology-based search and by
storing identifiers of each other.
6.4 Reusable Data
The reusability of data in solving various problems as the ultimate aim of applying the
principles of FAIR data is provided as follows. Datasets should be widely described by
a set of accurate and relevant attributes to assess their applicability in the problems
being solved. They should be supplied with a license for their use, accompanied by
detailed provenance information, and should comply with the community domain
standards. Thus, it is possible to deal with data copies and combinations for various
purposes.
Following these principles, in addition to the research domain specifications,
knowledge specifications include special ontologies for describing non-functional
properties (attributes) of data and evaluating their applicability. These are such domains
as data quality, measurement quality, and data provenance [23]. Non-functional data
requirements can be expressed in terms of such special ontologies, and become part of
queries when searching for relevant data for problem-solving. Metadata in terms of
these ontologies describe attributes of data.
Special standards supported by research communities are registered like data models
or conceptual schemas used in their domains. Within the framework of digital objects,
they are supplied with information about their integration. The data that meet these
standards refer to those data models and schemas. Licenses related to domain commu-
nities allow organizing access to digital objects and ensure commitment to formal spec-
ifications used for access, publishing and reuse of their resources.
7 Conclusion
An analysis of the principles for building research infrastructures that provide manage-
ment of the problem-solving lifecycle ensuring the reuse of various types of resources
is presented in the paper. An architecture designed to implement such a lifecycle has
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been developed. It is shown how the presented architecture of research infrastructures
complies with the principles of FAIR data.
The investigations related to the FAIR data principles in big research infrastructures
are wide. The real focus of them remains to provide services the research community
tends to use. And the mentality is being changed slowly. Therefore, formal semantic
technologies and commitment to specified domain knowledge are not in priority. The
proposed research infrastructure architecture is based on prospective semantics-based
solutions for changing the paradigm of the lifecycle of research over data. It does not
avoid them in favor of the short-term broad needs of communities in data infrastruc-
tures. It gives the possibility to reuse heterogeneous source data, results of their inte-
gration, methods developed for problem-solving, and research results without multiple
integrations inside the research community.
Acknowledgments. The work was carried out using the infrastructure of shared re-
search facilities CKP “Informatics” of FRC CSC RAS [24], supported by the Russian
Foundation for Basic Research, grants 19-07-01198, 18-29-22096.
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