A Platform to Generate FAIR Data for COVID-19 Clinical Research

in Brazil

Vânia Borges

, Natalia Queiroz de Oliveira

, Henrique F. Rodrigues

Maria Luiza Machado Campos

and Giseli Rabello Lopes

Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

Keywords: VODAN Brazil, FAIRification, Platform, ETL4FAIR, COVID-19 Clinical Research.

Abstract: The COVID-19 pandemic and the global actions to address it have highlighted the importance of clinical care

data for more detailed studies of the virus and its effects. Extracting and processing such data, in terms of

confidentiality issues, is a challenge. In addition, the mechanisms necessary for their publication are aimed at

reuse in research to better understand the effects of this pandemic or other viral outbreaks. This paper

describes a modular, scalable, distributed, and flexible platform, based on a generic architecture, to promote

the publication of FAIR clinical research data. This platform collects heterogeneous data from Electronic

Health Records, transforms these data into interconnected and interoperable (meta)data that are processable

by software agents, and publishes them through technological solutions such as repositories and FAIR Data

Point.

1 INTRODUCTION

Due to the COVID-19 pandemic, global actions have

been conceived to produce and deploy information to

support decision making on combating the virus and

its effects. Although the amount of information

available on the Web concerning COVID-19 has

grown far beyond expectation, most of them are

aggregated data, i.e., totals of people infected,

hospitalized, recovered, and deaths. Data regarding

clinical conditions, patients treatment, and their

outcome are, though, complementary to aggregate

data and essential for more detailed studies in clinical

research. However, access to these data outside the

Hospital Unit (HU) is often limited (Hallock et al.,

2021).

In order to accelerate and promote cooperation

among different initiatives referring to COVID-19

research results, the Virus Outbreak Data Network

(VODAN) (GO FAIR, 2020a) was created. VODAN

is an Implementation Network (IN) carried out by

CODATA (Committee on Data International Science

https://orcid.org/0000-0002-6717-1168

https://orcid.org/0000-0001-8371-142X

https://orcid.org/0000-0002-6777-3935

https://orcid.org/0000-0002-7930-612X

https://orcid.org/0000-0003-2482-1826

Council), RDA (Research Data Alliance), WDS

(World Data System), and the GO FAIR Initiative.

This IN was created under the goal to establish a

federated data infrastructure to support the capture

and use of data, following the FAIR data principles,

not only during this pandemic but also on future

disease outbreaks (Mons, 2020). This federated data

infrastructure is targeted for both human and machine

exploration, fostering reuse and reproducibility of

scientific resources (GO FAIR, 2020b).

The VODAN Brazil (VODAN BR) project is

responsible for the implementation of a pilot of this

federated data infrastructure in Brazil. It is

coordinated by the Oswaldo Cruz Foundation

(FIOCRUZ) in partnership with the Federal

University of Rio de Janeiro (UFRJ), the Federal

University of the State of Rio de Janeiro (UNIRIO),

and the University of Twente (FIOCRUZ, 2021). The

first phase of the implementation plan aims at

COVID-19 clinical cases, with data being collected

through a set of Brazilian hospitals, such as the Albert

Einstein hospital, the Gaffrée Guinle University

218

Borges, V., Queiroz de Oliveira, N., Rodrigues, H., Campos, M. and Lopes, G.

A Platform to Generate FAIR Data for COVID-19 Clinical Research in Brazil.

DOI: 10.5220/0011066800003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 218-225

ISBN: 978-989-758-569-2; ISSN: 2184-4992

Hospital, and the São José State Hospital of Duque de

Caxias. These data include clinical features about

anonymized patients related to COVID-19 cases,

following the World Health Organization (WHO)

Rapid Core Case Report Form (CRF).

VODAN BR is establishing an IT platform to

manage these data, addressing practical challenges

from the hospital partners, such as collecting data

from different Electronic Health Records (EHR) and

integrating these data with semantic-oriented CRF,

which is based on the WHO semantic model

developed by the VODAN IN. This platform

addresses requirements for an analytical environment

that is able to cope with distinct data sources formats

and semantics, e.g., CSV format and triplestore

Application Programming Interface (API). It also

includes a defined licensing authorization schema and

metadata management supported by FAIR Data

Points (FAIR DPs).

This position paper aims to present an overview

of a scalable, distributed, flexible and modular

platform, based on a generic architecture, with its

processes and computational assets developed to

support the VODAN BR project. This generic

architecture will attend an intensive data collection

process with high heterogeneity, turning the FAIR

data available on repositories. In these repositories,

interoperable data and metadata could be processed

by software agents, supporting the discovery of other

resources that can be linked with them. As a result,

the VODAN BR platform promotes greater agility in

discovery and knowledge generation through the

efficient reuse of research results.

This paper is organized as follows: Section 2

presents some background; Section 3 describes the

proposed platform for the VODAN BR; Section 4

presents a discussion about the challenges identified

in this work; and Section 5 concludes with final

comments and future works.

2 BACKGROUND

2.1 Semantic Web and FAIR Principles

The Semantic Web proposes that data on the Web can

be defined and connected in a way that allows

interpretation by both humans and machines,

stimulating sharing and reuse by applications,

companies, and communities. To achieve this goal, a

set of standards and best practices for publishing and

linking data on the Web has been defined (W3C,

2017). These best practices are based on annotating

data with controlled vocabularies and ontologies,

facilitating the identification of new connections

among items from different data sources, thus

forming a global data space, the so-called Web of

Data (Heath and Bizer, 2011).

The FAIR principles aim to make data (and, more

recently, digital objects in general) Findable,

Accessible, Interoperable, and Reusable (Wilkinson

et al., 2016). In essence, the principles emphasize the

importance of using metadata to facilitate data

discovery and understanding, especially by machines

(software agents), to the standards established by the

World Wide Web Consortium (W3C). It should be

noted that the FAIR principles neither establish

standards nor supporting technologies, but, rather,

guide the creation of FAIR data and metadata. Recent

initiatives consider it vital to ensure that data and

other associated resources are FAIR, in the original

sense of the acronym, and also in the sense of

“Federated and Artificial Intelligence (AI)-Ready

data”, therefore readable and actionable by machines

(GO FAIR, 2020b).

Metadata standards and content annotations are

established to promote common understanding about

the meaning of the data, ensuring correct

interpretation and proper usage. Metadata need to be

findable and structured to be interpreted by machines,

i.e., machine-actionable. These machine-actionable

metadata, essential to the FAIR principles, have

fostered discussion of Metadata for Machine (M4M),

stimulating the creation and reuse of metadata

components and metadata templates for machine

processing. In the VODAN implementation, M4M

has been active in standardizing metadata regarding

catalogs and datasets that will be made available via

FAIR DPs, as well as the services associated with

them (GO FAIR, 2021).

The formalism and flexibility required for

creating and making data and metadata available are

provided by the Resource Description Framework

(RDF) model, including in this context RDF Schema

(RDFS). The Web Ontology Language (OWL),

developed to create robust ontologies, also meets

these criteria. The set of statements represented by

RDF triples constitutes an RDF Knowledge Graph.

2.2 FAIRification Process

The process of making data FAIR is called

FAIRification. It is in fact a complex process,

requiring several areas of expertise and data

stewardship knowledge. In order to facilitate this

process, Jacobsen et al. (2020) proposed a generic

workflow comprising three defined phases: pre-

FAIRification, FAIRification, and post-

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219

FAIRification. The phases are further divided into

seven steps: 1) identify the FAIRification objective;

2) analyze data; 3) analyze metadata; 4) define

semantic model for data (4a) and metadata (4b); 5)

make data (5a) and metadata (5b) linkable; 6) host

FAIR data; and 7) assess FAIR data. Each step

describes how data and metadata can be processed,

which knowledge is required, and which procedures

and tools can be used to obtain FAIR (meta)data. This

FAIRification workflow is applicable to any kind of

data and metadata.

2.3 FAIR Data Point

One of the main components of a FAIR Ecosystem is

the FAIR DP. FAIR DP is a software that works as an

infrastructure for (meta)data storage and accessibility

with the goals of: (i) allowing data owners to expose

datasets in a FAIR manner; (ii) facilitating the

information discovery about FAIR DP by data users,

in a network of FAIR DPs; (iii) establishing

mechanisms that handle consumer access, according

to the licenses and restrictions imposed on the data by

their managers; (iv) providing access indicators on

the (meta)data made available for data owners; and

(v) providing data access for humans, through a

Graphical User Interface (GUI), and for software

agents, using API (Santos et al., 2016).

3 VODAN BRAZIL PLATFORM

In VODAN BR, the starting point is the COVID-19

patients’ clinical data, which are processed and

transformed into linked data according to the Rapid

Core Case Report Form - named in this paper by

“WHO-CRF” (WHO, 2021). This CRF was

developed by the WHO to standardize data collection

of clinical features of COVID-19 among hospitalized

patients. The generic FAIRification workflow was

extended to guide the processes on this platform,

ensuring FAIR data and metadata (Oliveira et al.,

2021). In addition, these processes obey the Semantic

Web standards and comply with established licensing

and anonymization criteria.

3.1 Desiderata and Requirements

Desiderata were established to guide the development

of the platform, with emphasis on data management

and FAIR (meta)data. They are intended to provide a

readily adjustable structure, i.e., one that significantly

reduces the impact of changes to each evolution and

versioning of CRFs or the semantic artifacts

(vocabularies, thesaurus, ontologies etc). Some of

these desiderata are highlighted below:

 creating an infrastructure capable of

implementing and making available a digital

CRF (application) for health care professionals,

meeting the epidemic episodes of COVID-19

pandemic or other viral outbreaks;

 storing the information established in the

CRFs, in an anonymized way, considering that

their versioning can include, alter or exclude

elements;

 allowing the creation of national CRFs or the

inclusion of specific additional questions. This

requirement emerged evaluating the different

CRF types used in Brazil, which, besides the

elements established by the WHO-CRF,

consider additional information for research,

such as participation in vaccination campaigns

and date of the last dose;

 developing a conceptual model that aligns CRF

elements with ontologies (semantic models),

contributing to the data FAIRification process;

 providing a flexible, modular, scalable, and

agile infrastructure to support software and

database adaptations;

 transforming the collected data, i.e., "non-

FAIR data" into linked data by mapping them

to machine-readable formats using RDF,

making them available in datasets, and

publishing the associated metadata also in

RDF, in a FAIR DP;

 providing a public FAIR DP configured to meet

the privacy requirements agreed by

participants, allowing access to data through

controlled queries rather than traditional

downloads

The main requirements of the platform

infrastructure are to be modular, scalable,

distributed, and flexible:

 Modular because the planned activities are

organized in the form of modules that interact

in a sequential manner, with the result of a

module being the input for the subsequent

module;

 Scalable and distributed because the idea is that

a staging database will be made available in

each HU, as well as repositories and/or

triplestores. These components will host the

structured data according to the WHO-CRF in

their different distributions or formats. This

means that as more hospitals join the project,

more IT infrastructure will be integrated,

leading to a natural horizontal scaling up;

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Figure 1: Schematic view of the VODAN BR architecture.

 Flexible platform because the heterogeneous

data produced by the HUs EHR are treated and

transformed into an RDF graph representation,

which is one of the formats that facilitate data

linkage (interconnection).

3.2 Generic Architecture and the

VODAN BR Platform

Figure 1 depicts the architecture designed for the

VODAN BR Project, considering the requirements

and the desiderata. It covers the process from

collection of clinical data provided by HUs to

metadata publishing in the FAIR DP.

The computational assets considered for this

architecture are: a staging database (2) to store data

processed and transformed using a CRF format;

automated solutions to handle ETL processes (1) and

(4); a triplestore (5) for publishing and querying RDF

triples; a repository (6) for datasets and their

associated publications; and a FAIR DP (7), as a

public metadata repository, for visibility and access

for the queries. To serve hospitals without access to

their EHR data, it makes available an eCRF

application (3). The(meta)data flow is from left to

right as described in the Figure 1.

Initially, the architecture captures data that can be

in several formats, such as text, CSV, or in the format

used in each HU. An Extract-Transform-Load (ETL)

(1) process is employed to clean and transform the

data, storing them in a staging database (2). The data

in the staging database are then transformed into

linked data (4) and annotated in vocabularies and

ontologies to satisfy the interoperability principle.

They are loaded into a triplestore (5) and/or made

available for download in a repository, as an RDF

dataset (6). The associated metadata is also treated (4)

and then loaded and published in a FAIR DP (7).

The VODAN BR platform is based on the

proposed architecture with data and services

treatment guided by an adapted FAIRification

workflow (Oliveira et al., 2021). The actions outlined

in this workflow allowed the analysis of solutions to

automate process steps. The actual version of this

platform adopts these components: (i) a MySQL

relational database as staging database; (ii) Pentaho

Data Integration (PDI) solution for HUs data ETL

process; (iii) the ETL4FAIR approach for generating

and publishing FAIR data into triplestores and

repositories; (iv) GraphDB as triplestore; (v)

Dataverse as data repository; and (vi) FAIR DP as

metadata repository.

Some mechanisms must be developed for the

platform to play its role. Among them, we shall

highlight, due to their relevance in the project and the

challenges in (meta)data management: (i)

mechanisms to capture and process data, that

contemplate different requirements and EHR of the

HUs; (ii) the approach for processing, transformation,

and annotation of linked (meta)data, i.e.,

FAIRification of data; and (iii) the alternatives for

publishing the (meta)data. The main tasks associated

with these mechanisms are described in the following

subsections.

3.2.1 Extracting and Processing Data

As presented before, the project foresees different

forms of collecting clinical research data on COVID-

19: an application (eCRF) developed to capture

information according to the WHO-CRF; an ETL

solution to load and process anonymized data from

files in text or CSV formats made available by the

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UHs; and an ETL solution that connects both

databases, staging and from HUs, collecting and

treating data from the EHR in the format established

by the WHO-CRF.

Extracting data from an existing EHR poses an

additional challenge, despite the use of the staging

database as a transition database to the WHO-CRF

format. Existing EHRs frequently allow some aspects

of treatment to be recorded in textual fields. Due to

the lack of standardization in these entries and the

significant amount of unstructured data, the collection

and transformation processes become difficult and

complex. In these cases, interpretation and coding

support by healthcare professionals is essential. This

problem is not new and has been a constant in health

care data interoperability studies (Santos, 2020).

3.2.2 FAIRification

Based on the recommendations of the original

FAIRification workflow, VODAN BR has been using

an adapted and extended version proposed by

(Oliveira et al., 2021). The adaptation follows the

phases and steps of the generic FAIRification

workflow. Although steps 6 and 7 have been renamed

to 6) host FAIR data and metadata and 7) assess FAIR

data and metadata, emphasizing the importance of

storing, publishing, and evaluating both FAIR data

and metadata. The adaptation followed an approach

of associated actions for the FAIRification process in

a delimited and specific way, justifying

implementation choices to support the transformation

and publishing of FAIR (meta)data.

The FAIRification phase occurs after processing

the raw data, generating the FAIR (meta)data

associated with the semantic models in RDF. The

semantic data model COVIDCRFRAPID (BioPortal,

2020) was adopted for the data representation

according to the WHO-CRF. This model associates

the questionnaire questions with a set of entities from

the health domain which refers to other existing and

well-documented ontologies, providing quality and

additional information for data reuse.

The reference metadata follow the specifications

established for the FAIR DP metadata schemas.

These schemas define a set of standardized metadata

that describe information such as licenses, access

conditions, context, and provenance (da Silva Santos,

2020).

The ETL4LOD+ tool (GRUPO-GRECO, 2019),

adopted in this phase, consists of a set of plugins

developed in JAVA that extend the functionalities of

PDI, which is a widely used ETL solution. In this

architecture, ETL4LOD+ provides the transformation

of data from different sources and formats into linked

(meta)data and their publication in Semantic Web

technologies such as triplestores and FAIR DP.

Potential solutions have been tested in the

VODAN BR project to support the FAIRification

phase (Oliveira et al., 2021). They contribute to

automating some of the established actions integrated

with ETL4LOD+. Some of them are presented below.

The ETL4LinkedProv approach was tested to

collect provenance metadata associated with an ETL

workflow. The approach uses ETL workflows and

employs the Provenance Collector Agent (PCA)

component to capture prospective and retrospective

provenance metadata at different granularity levels.

The approach also supports the assessment of the

quality and reliability of FAIR provenance metadata

(Mendonça et al., 2016). It is currently being

reengineered to be aligned to the FAIRification

process.

The CEDAR Workbench was analyzed with

respect to metadata schemas established for the FAIR

DP. Through CEDAR, it is possible to create

metadata schemas as templates (Gonçalves et al.,

2017). These templates must be instantiated with the

metadata for the dataset and distribution to be

generated.

3.2.3 Publishing FAIR Data

According to the established desiderata, research

(meta)data should be made available in linked data

format, using the RDF standard. Following trends in

research data management, these data can be

published in institutional or thematic repositories,

which become responsible for storing the RDF

dataset and its metadata.

In addition to RDF datasets, triplestores and FAIR

DP are used to improve data reuse. Both provide

graph stores for querying the data. The triplestores

can be accessed from the FAIR DP or the repository,

via API, by algorithms that collect the data of interest.

The FAIR DP facilitates transparent and

controlled access to metadata. This access is made

through four different hierarchical layers: beginning

with the metadata of the FAIR DP itself, followed by

the metadata of the catalog, datasets, and

distributions. Publishing metadata about COVID-19

data in the VODAN BR FAIR DP allows access to

these data by software agents and humans (Santos et

al., 2016) and their integration into the VODAN

federation.

It is important to note that, after the FAIRification

process, a set of data and metadata is obtained,

compliant with the FAIR principles. These well-

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Figure 2: Overview of the scalable and distributed platform for VODAN BR.

structured (meta)data can be exploited through

machine learning techniques, and other artificial

intelligence (AI) approaches. These techniques can

contribute to the discovery of significant patterns in

epidemic outbreaks, supporting decisions and actions

to address them.

As stated by the VODAN network, the datasets

must be "visited" by algorithms, respecting the access

established by the HUs. Therefore, the associated

metadata, such as information about the origin of the

existing data, distribution types, and access policy,

will be available and accessible in the FAIR DP.

4 DISCUSSION AND

CHALLENGES

The development of a platform to disseminate viral

outbreaks research data during a pandemic is a

challenge per se. The VODAN BR team is working

on implementing such a platform to ensure

appropriate FAIR data management. This

management includes data from the moment of their

capture to their publication in the FAIR technological

supporting solutions (triplestores, repositories, and

FAIR DP).

The proposed platform aims at a federated

infrastructure. Therefore, it respects the

independence of HUs in managing their data and IT

resources. However, it demands HUs compliance

with rules for the metadata publication/dissemination

established by FIOCRUZ. Figure 2 shows an example

of the implementation of this platform contemplating

three HU. In the figure, hospitals (a) and (c) process

clinical data from the EHR database and publish the

results in triplestore and data repository. The former

uses Blazegraph and DSpace, and the latter GraphDB

and Dataverse. Hospital (b) uses the eCRF

application to register the survey and publishes it in

the Virtuoso triplestore. The metadata are published

in the VODAN BR FAIR DP hosted by FIOCRUZ.

Data access is performed through this FAIR DP,

respecting the authorization rules established by each

hospital. In the depicted example, hospital (c)

requires authorization for access.

The main challenges encountered during the

development and deployment stages of this flexible

platform are related to (Campos et al., 2021): (i) an

extraction and collection strategy for the

heterogeneous EHR available at the HU; (ii) the

creation and maintenance of the staging relational

database adherent to the WHO-CRF, enriched with a

reference ontology and other associated standard

vocabularies; (iii) the development of an ETL4FAIR

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223

approach, supporting a process-oriented

FAIRification, presenting automated steps in order to

reduce human intervention and mitigating possible

errors caused by it; (iv) the technical

qualification/competence and the computing

infrastructure required to support local triplestores at

each participating HU; and (v) expertise to install,

configure and employ a FAIR DP.

During the implementation of the architecture, it

was observed the lack of solutions to support the

entire FAIRification process. FAIRification steps can

be automated, improving the process and making the

resulting platform more stable to meet new

challenges. The collection of provenance metadata

according to the granularity established by the

process manager is an example.

Other initiatives to improve FAIR data and

metadata management are being developed, usually

for specific domains, such as the Collaborative Open

Omics (COPO) platform (Shaw et al. 2020). It was

developed for researchers to publish their assets,

providing metadata annotation and mediation for data

submission to appropriate repositories. VODAN

AFRICA was the first VODAN IN. It is funded by the

Philips Foundation and aims to promote distributed

access to CRF data from African countries, serving

African universities, hospitals, and research

institutions. VODAN AFRICA proposes an

integrated architecture with clinical and research data

(Van Reisen et al., 2021). In this architecture, data are

made available in closed dashboards. Two levels of

dashboards are available: the first with data from each

clinic and the second with aggregated data from the

VODAN community. In contrast to the initiatives

presented, the VODAN BR project proposes a

generic architecture, which allows the establishment

of scalable, distributed, and flexible domain-oriented

platforms for the generation and publication of FAIR

(meta)data processable by software agents.

5 CONCLUSIONS AND FUTURE

WORKS

Data for more detailed clinical research studies are

highly valuable to the scientific community, but are

not always available (Hallock et al., 2021). Among

the main problems in extracting and collecting this

type of data are: (i) privacy protection issues

concerning personal data in the EHR, aligned to the

Brazilian Protection Law for Personal Data; (ii)

complexity in processing free text fields from EHRs,

hampering data extraction; (iii) the challenges of

publishing FAIR health data, with respect to

developing and deploying a federated infrastructure

to support this process; and (iv) the difficulty in

providing linked (meta)data with different semantic

artifacts to facilitate reuse by researchers.

The experience in the project reinforces the

importance of the FAIR DP as an essential

component for federated access points, as well as for

research and (re)use mechanisms for FAIR

(meta)data. In addition, it also supports sensitive

research data that require some level of privacy, such

as patient data. According to its specification, the

FAIR DP provides an appropriate authentication and

authorization infrastructure in distributed scenarios.

Hence, sensitive data are only accessible when

authorized, allowing for "data to be as open as

possible and as closed as necessary" (Mons, 2020).

This project is carried out with the support of

undergraduate, master, and doctoral students from the

universities involved. Currently, there are ongoing

studies to provide: a high availability FAIR DP; the

development of new clinical trials using eCRF

application; and automatic capture of provenance

metadata throughout the FAIRification process, with

the granularity established by the data stewards. Also,

the ETL4FAIR approach itself has been tested on the

VODAN BR project with an emphasis on the

FAIRification phase. It contributes to FAIRification

steps automatization, collaborating on the integration

of different solutions, and improving the federated

FAIR data ecosystem.

Once the platform defined by the project is

completely implemented, it will enable the country

participation in the federated data network of

epidemiological FAIR DPs. Once accessible to

researchers, it will contribute to future research

related to the COVID-19 pandemic or other potential

future outbreaks.

ACKNOWLEDGEMENTS

This work has been partially supported with students

grants from CAPES (Process numbers

223038.014313/2020-19, and 88887.613048/2021-

00), CNPq (Process number 158474/2020-1) and

UNIRIO university funding.

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