RESUMO
Infectious disease (ID) cohorts are key to advancing public health surveillance, public policies, and pandemic responses. Unfortunately, ID cohorts often lack funding to store and share clinical-epidemiological (CE) data and high-dimensional laboratory (HDL) data long term, which is evident when the link between these data elements is not kept up to date. This becomes particularly apparent when smaller cohorts fail to successfully address the initial scientific objectives due to limited case numbers, which also limits the potential to pool these studies to monitor long-term cross-disease interactions within and across populations. CE data from 9 arbovirus (arthropod-borne viruses) cohorts in Latin America were retrospectively harmonized using the Maelstrom Research methodology and standardized to Clinical Data Interchange Standards Consortium (CDISC). We created a harmonized and standardized meta-cohort that contains CE and HDL data from 9 arbovirus studies from Latin America. To facilitate advancements in cross-population inference and reuse of cohort data, the Reconciliation of Cohort Data for Infectious Diseases (ReCoDID) Consortium harmonized and standardized CE and HDL from 9 arbovirus cohorts into 1 meta-cohort. Interested parties will be able to access data dictionaries that include information on variables across the data sets via Bio Studies. After consultation with each cohort, linked harmonized and curated human cohort data (CE and HDL) will be made accessible through the European Genome-phenome Archive platform to data users after their requests are evaluated by the ReCoDID Data Access Committee. This meta-cohort can facilitate various joint research projects (eg, on immunological interactions between sequential flavivirus infections and for the evaluation of potential biomarkers for severe arboviral disease).
Assuntos
Infecções por Arbovirus , Humanos , Infecções por Arbovirus/epidemiologia , Estudos de Coortes , América Latina/epidemiologia , Masculino , Feminino , Criança , Arbovírus , Estudos Retrospectivos , Adolescente , Pré-Escolar , AdultoRESUMO
Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data. This makes it difficult, or even impossible, to access and integrate the data, which can result in the loss of significant amounts of information and significantly affect the understanding of the overall data. To address this problem, we propose a data portal architecture and open data deployment that enable Big Data processing, human interaction, and download-oriented approaches with tools that help users catalog, publish and visualize atmospheric data. Thus, we describe the architecture developed, based on the experience of the Atmospheric Radiation Measurement Data Center, which incorporates the principles of FAIR, the infrastructure and content management system for managing scientific data. The portal partial results were tested with environmental data from contaminated areas at the University of São Paulo. Overall, this data portal creates more shared knowledge about atmospheric processes by providing users with access to open environmental data.
Assuntos
Publicações , Editoração , Humanos , Brasil , AerossóisRESUMO
The FAIR principles have become a data management instrument for the academic and scientific community, since they provide a set of guiding principles to bring findability, accessibility, interoperability and reusability to data and metadata stewardship. Since their official publication in 2016 by Scientific Data Nature, these principles have received worldwide recognition and have been quickly endorsed and adopted as a cornerstone of data stewardship and research policy. However, when put into practice, they occasionally result in organisational, legal and technological challenges that can lead to doubts and uncertainty as to whether the effort of implementing them is worthwhile. Soon after their publication, the European Commission and other funding agencies started to require that project proposals include a Data Management Plan (DMP) based on the FAIR principles. This paper reports on the adherence of DMPs to the FAIR principles, critically evaluating ten European DMP templates. We observed that the current FAIRness of most of these DMPs is only partly satisfactory, in that they address data best practices, findability, accessibility and sometimes preservation, but pay much less attention to metadata and interoperability.
Os princípios FAIR tornaram-se um instrumento de gestão de dados para a comunidade acadêmica e científica, uma vez que fornecem um conjunto de princípios orientadores que facilitam a localização, acessibilidade, interoperabilidade e reutilização de dados e metadados. Desde sua publicação oficial em 2016 pela Scientific Data - Nature, esses princípios receberam reconhecimento mundial e foram rapidamente endossados e adotados como pilares da gestão de dados e das políticas de pesquisa. No entanto, quando postos em prática, apresentam ocasionalmente desafios organizacionais, jurídicos e tecnológicos que podem levar a dúvidas e incertezas quanto ao esforço em implementá-los. Logo após sua publicação, a Comissão Europeia e outras agências de financiamento começaram a exigir nas suas propostas de projetos um Plano de Gestão de Dados (PGD) com base nos princípios da FAIR. Este artigo relata a aderência dos PGDs aos princípios FAIR, avaliando criticamente dez modelos europeus de PGD. Observamos que o nível de FAIRness da maioria dos PGDs analisados ainda é parcialmente satisfatório, uma vez que abordam as melhores práticas de dados, localização, acessibilidade e, às vezes, preservação, mas dão pouca atenção aos metadados e a interoperabilidade.
Los principios FAIR se han convertido en una herramienta de gestión de datos para la comunidad académica y científica, ya que proporcionan un conjunto de principios rectores que facilitan la localización, accesibilidad, interoperabilidad y reutilización de la gestión de datos y metadatos. Desde su publicación oficial en 2016 por Scientific Data - Nature, estos principios han recibido reconocimiento mundial y fueron rápidamente respaldados y adoptados como pilares de la política de investigación y gestión de datos. Sin embargo, cuando se ponen en práctica, ocasionalmente presentan desafíos organizativos, legales y tecnológicos que pueden generar dudas e incertidumbres sobre el esfuerzo para implementarlos. Poco después de su publicación, la Comisión Europea y otras agencias de financiación comenzaron a exigir en sus propuestas de proyectos un Plan de Gestión de Datos (PGD) basado en los principios de FAIR. Este artículo informa sobre la adherencia de los PGD a los principios FAIR, evaluando críticamente diez modelos europeos de PGD. Observamos que el nivel de FAIRness de la mayoría de los PGD analizados sigue siendo parcialmente insatisfactorio, ya que abordan las mejores prácticas de datos, ubicación, accesibilidad y, a veces, preservación, pero prestan poca atención a los metadatos y la interoperabilidad.