-In this paper the FAIR principles are summarized, with a focus on (meta)data management infrastructure as the conduit for developing these principles in practice. The paper serves as an overview of the European Open Science Cloud in supporting the FAIR data approach in general, and as an overview of the Lithuanian practice in supporting the sharing, use and sustainability of language resources specifically: the CLARIN infrastructure as a networked federation of repositories and service providers is presented, Lithuanian experience in developing national language resources infrastructure is detailed, and the practical benefits that Lithuania gained with joining the European infrastructure are reviewed.
Keywords–FAIR; data management; infrastructures; European Open Science Cloud; CLARIN; language resources
It is reasonably stated that humans and machines often
face distinct barriers when attempting to find and process
data on the web. This is because humans have an intuitive
sense of “semantics” (the meaning or intent of a digital
object): they are capable of identifying and interpreting a
wide variety of contextual cues, whether those take the form
of structural/visual/iconic cues in the layout of a web page,
or the content of narrative notes. The primary limitation of
humans, however, is that they are unable to operate at the
scope, scale, and speed necessitated by the scale of
contemporary scientific data and complexity of e-science [
Machines are necessitated to be capable of autonomously
and appropriately acting when faced with the wide range of
types, formats, and access-mechanisms/protocols. It also
necessitates that the machines keep an exquisite record of
provenance such that the data they are collecting can be
accurately and adequately cited [
The focus on assisting machines in their discovery and exploration of data through application of more generalized interoperability technologies and standards at the data/repository level, becomes a first-priority for good data management. There are some guidelines, summarized as acronym FAIR that put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. They are discussed in the paper below.
The paper is organized as follows. The FAIR principles are theoretically presented in section II, practical implementation of developing these principles is specified in section III, the Lithuanian practical results in supporting the FAIR approach are detailed in section IV.
The goal of this paper is to present infrastructures as one of the means to overcome data discovery and reuse obstacles in order to be more FAIR, with a focus on Lithuanian experience in developing language resources infrastructure.
Data-intensive sciences meet the challenges to facilitate knowledge discovery. Computational analysis to discover meaningful patterns in massive, interlinked datasets is rapidly becoming a routine research activity. However datasets should be prepared for such analysis, so this kind of activity, in turn, urges an ever closer cooperation of humans and machines in the access to, integration and analysis of, scientific data.
Providing machine-readable data as the main substrate for
knowledge discovery and for the e-scientific processes to run
smoothly and sustainably is considered to be one of the
biggest challenges of e-science. As it is observed, the reason
that we often need several weeks (or months) of specialist
technical effort to gather the data necessary to answer
research questions is not the lack of appropriate technology;
the reason is, that we do not pay our valuable digital objects
the careful attention when we create and preserve them [
A key enabler to achieve international-grade data
management is for research data and information to be
published in a “FAIR” manner. “FAIR” summarizes several
aspects, or in other words, a set of guiding principles,
regarding data, both for machines and for people, and these
principles assist the interaction between those who want to
use data and those who provide them. In the FAIR data
approach, data should be [
F1. (Meta)data are assigned a globally unique and eternally persistent identifier.
F2. Data are described with rich metadata (defined by R1 below).
F3. (Meta)data are registered or indexed in a searchable resource.
F4. Metadata specify the data identifier. Accessible (A)
A1. (Meta)data are retrievable by their identifier using a standardized communications protocol.
A1.1. The protocol is open, free, and universally implementable.
A1.2. The protocol allows for an authentication and authorization procedure, where necessary.
A2. Metadata are accessible, even when the data are no longer available.
Interoperable (I)
I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2. (Meta)data use vocabularies that follow FAIR principles.
I3. (Meta)data include qualified references to other (meta)data.
Reusable (R)
R1. Meta(data) have a plurality of accurate and relevant attributes.
R1.1. (Meta)data are released with a clear and accessible data usage license.
R1.2. (Meta)data are associated with their provenance.
R1.3. (Meta)data meet domain-relevant community standards.
So putting data on the web is not enough. The FAIR
principles speak about a “knowledge representation” language
for data representation. To be actually interoperable and
reusable, data should not only be properly licensed, but the
methods to access and/or download them should also be well
described and preferably fully automated and using well
established protocols [
The idea of being machine-actionable applies in two
contexts: first, when referring to the contextual metadata
surrounding a digital object (“what is it?”), and second, when
referring to the content of the digital object itself (“how do I
process it/integrate it?”). Here also to be drawn a distinction
between machine-actionable data as a result of specific
investment in software supporting that data-type, and data
that are machine-actionable exclusively through the
utilization of general-purpose, open technologies [
FAIRness can be achieved with a wide range of technologies and implementations. One of the solutions to supporting FAIRness is data infrastructures. The FAIR principles guide the development of infrastructure and tooling to make data optimally reusable for machines and people alike, which is considered to be a crucial step forward. III. INFRASTRUCTURES FOR SUPPORTING THE “FAIR”
DATA APPROACH
Infrastructure in general is defined as (usually large-scale)
basic physical and organizational resources, structures and
services needed for the operation of a society or enterprise [
The European Commission suggested “European Cloud
initiative” [
Fig. 1. European Open Science Cloud [
The EOSC is a data infrastructure to support Open
Research Data and Open Science in Europe: it is intended as
an open and trusted environment where research data can be
safely stored and made openly available, and is dedicated to
enable trusted access to services, systems and the re-use of
shared scientific data across disciplinary, social and
geographical borders [
The European Open Science Cloud will start by federating
existing scientific data infrastructures, today scattered across
disciplines and geographically. This step will make access to
scientific data easier, cheaper and more efficient. It will enable
the creation of new market opportunities and new solutions in
key areas such as health, environment, or transport [
The European Open Science Cloud will be also open for
education and training purposes in higher education and, over
time, to government and business users as the technologies
developed will be promoted for wider application [
Fig. 3. EOSC benefits [
Practically, the European Open Science Cloud will offer
1.7 million European researchers and 70 million professionals
in science and technology a virtual environment with free at
the point of use, open and seamless services for storage,
management, analysis and re-use of research data, across
borders and scientific disciplines [
While the European Open Science Cloud is still on the way, the other - European infrastructure dedicated to researches that deal with language resources - already works and is one of the good practices in creating infrastructures. IV. “FAIR” IN LITHUANIAN PRACTICE. LANGUAGE
RESOURCES INFRASTRUCTURE
In January of 2015 Lithuania became a full member of
CLARIN ERIC (Common Language Resources and
Technology Infrastructure) (see [
The CLARIN infrastructure at the European scale is
intended for the humanities and the social sciences, and these
two domains, in turn, include a wide range of disciplines. It is
distributed, i.e., implemented in a network of CLARIN
Centres, and virtual, i.e., it provides services via the Internet
[
It also includes software to browse and search in digital
language data (e.g. software to search in a linguistically
annotated text corpus), as well as software to analyze, enrich,
process, and visualize digital language data (e.g., a parser,
which enriches each sentence in a text corpus with a syntactic
structure) [
The case of CLARIN ERIC points to the means for
overcoming data discovery and reuse obstacles in language
technologies. The CLARIN infrastructure supports the
sharing, use and sustainability of language data and tools
through a networked federation of centres: language data
repositories, service centres and knowledge centres, with
single sign-on access for all members of the academic
community in all participating countries. Tools and data from
different centres are interoperable, so that data collections can
be combined and tools from different sources can be chained
to perform complex operations to support researchers in their
work. Interoperability is ensured through the standards
adopted in the CLARIN framework. A large number of
participating centres are offering access services to data, tools
and expertise [
The access provided to data and content is in principle
sustainable and in accordance with the principles of open
science, and thus free for scholars. The same criteria for data
and services interoperability, access conditions, quality of
data and services are adhered to by all members, however
countries have a large degree of freedom to decide what they
contribute to the CLARIN infrastructure [
CLARIN Repositories
As shown in the figure above, CLARIN offers a number of
basic services to researchers, in particular:
Services to find digital data and get access to digital
data.
Services to find software for processing digital data.
Services to apply the software to the digital data in a
user friendly manner.
Services to create and describe new data and software.
Services to store new data and software in CLARIN
for long term preservation and for making them
accessible to other researchers [
Before Lithuania became a member of the CLARIN infrastructure, its language-based resources and advanced tools were accessible via special websites. With joining the infrastructure, Lithuanian resources and services are available to the various research communities at large, and Lithuania as a member of the infrastructure gained practical benefits: Access to services provided by the infrastructure: existing tools and datasets, e.g. online access to national corpora, lexica, audio and video recordings, annotations, grammars, etc. Long-term archiving: a storage guarantee can be given for a long period (up to 50 years in some cases). Resources can be archived and made available to the community in a reliable manner. Persistent identifiers for data. Resources can be cited easily with a persistent identifier. The resources and their metadata are integrated into the infrastructure, making it possible to search them efficiently. Password-protected resources can be made available via an institutional login. Once resources are integrated in the CLARIN infrastructure, they can be analyzed and enriched more easily with various linguistic tools (e.g. automated part-of-speech tagging, phonetic alignment or audio/video analysis).
Practical workshops at which a diverse group of people, including those with research questions and software developers, get together to work on producing some real solutions over a short but intense period. The outputs of those workshops - tools, datasets, linked data, etc. that are to be maintained. Such activity stimulates the adoption or better use of digital methods for the community of the infrastructure.
Repository. Lithuanian language data and services are
stored in CLARIN-LT Repository [
annotated corpus MATAS; 5. Wordlist of the
Data in CLARIN-LT Repository (Fig. 5.) are made
available under licences attached to resources. In case there is
no licence, data is made freely available for access, printing
and download for the purposes of non-commercial research or
private study [
So Lithuanian language resources and tools can now be reached not only via special websites of those resources and tools, but also via a specific platform provided by the CLARIN infrastructure (Accessible).
An important step in the FAIR data approach is to publish existing and new datasets in a semantically interoperable format that can be understood by computer systems. By semantically annotating data items and metadata, we can use computer systems to (semi)automatically combine different data sources, resulting in richer knowledge discovery activities. Rich metadata facilitate such discovery, including clear rules regarding the process for accessing the data.
Metadata for language resources and tools exists in a
multitude of formats. Often these descriptions contain
specialized information for a specific research community
(e.g. TEI headers for text, IMDI for multimedia collections
[
For metadata purposes, CLARIN ERIC provides a special
metadata-based portal for language resources - Virtual
Language Observatory [
There are numerous and diverse stakeholders who benefit from being a part of the infrastructure: researchers share and reuse each other’s data; professional data publishers offer their services; software and tool-builders provide data analysis and process services;
funding agencies (private and public) increasingly concerned with long-term data stewardship;
a data science communities mine, integrate and analyse new and existing data.
Lithuania as a member of the CLARIN infrastructure makes its resources (Fig. 6.) available and accessible in the infrastructure for other researchers. Other members, in turn, provide access to their tools and resources incorporated in the infrastructure.
Fig. 6. Lithuanian resources in CLARIN-LT Repository
It should be stated that CLARIN has a lot to offer: it has adapted existing data and software, and it has created new easy and user-friendly software for searching, analysing and visualising data. However, there is still work to do: some improvements and extensions are needed, especially in terms of (meta)data functionality (the same applies also to CLARINLT).
In the paper the FAIR principles were summarized, with a focus on infrastructure initiatives as the conduit for developing these principles in practice: the European Open Science Cloud for supporting the FAIR data approach was presented, and Lithuanian experience in developing language resources infrastructure was overviewed.
Good data management is not a goal in itself, but rather is the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse by the community after the data publication process.
Infrastructures are one of the means to overcome data discovery and reuse obstacles for those wishing to become more FAIR. Lithuanian case of membership in the infrastructure shows that to be a part of infrastructures means to reach more and wider.