scholarly journals Implementing FAIR principles for dissemination of data from the French OZCAR Critical Observatory network: the Theia/OZCAR information system

2020 ◽  
Author(s):  
Isabelle Braud ◽  
Véronique Chaffard ◽  
Charly Coussot ◽  
Sylvie Galle ◽  
Rémi Cailletaud
Keyword(s):  
Information System ◽  
Data Management ◽  
Data Model ◽  
Distribution Systems ◽  
Critical Zone ◽  
Research Infrastructure ◽  
Common Data Model ◽  
In Situ Observation ◽  
Data Discovery ◽  
Fair Principles

<p>OZCAR-RI, the French Critical Zone Research Infrastructure gathers 20 observatories sampling various compartments of the Critical Zone, and having historically developed their own data management and distribution systems. However, these efforts have generally been conducted independently. This has led to a very heterogeneous situation, with different levels of development and maturity of the systems and a general lack of visibility of data from the entire OZCAR-RI community. To overcome this difficulty, a common Information System (Theia/OZCAR IS) was built to make these in situ observation FAIR (Findable, Accessible, Interoperable, Reusable). The IS will allow the data to be visible in the European eLTER-RI (European Long Term Ecosystem Research) Research Infrastructure to which OZCAR-RI contributes.</p><p>The IS architecture was designed after consultation of the users, data producers and IT teams involved in data management. A common data model including all the requested information and based on several metadata standards was defined to set up information fluxes between observatories IS and the Theia/OZCAR IS. Controlled vocabularies were defined to develop a data discovery web portal offering a faceted search with various criteria, including variables names and categories that were harmonized in a thesaurus published on the web. The communication will describe the IS architecture, the pivot data model and open source solutions used to implement the data portal that allows data discovery. The communication will also present future steps to implement data downloading and interoperability services that will allow a full implementation of these FAIR principles.</p>

scholarly journals Accessing OMOP Common Data Model Repositories with the i2b2 Webclient – Algorithm for Automatic Query Translation

2021 ◽  
Author(s):  
Raphael W. Majeed ◽  
Patrick Fischer ◽  
Andreas Günther
Keyword(s):  
User Interface ◽  
Data Integration ◽  
Data Model ◽  
Research Data ◽  
Feasibility Analysis ◽  
Common Data Model ◽  
Wide Spread ◽  
Query Translation ◽  
Fair Principles ◽  
Intuitive User Interface

In the era of translational research, data integration and clinical data warehouses are important enabling technologies for clinical researchers. The OMOP common data model is a wide-spread choice as a target for data integration in medical informatics. It’s portability of queries and analyses across different institutions and data are ideal also from the viewpoint of the FAIR principles. Yet, the OMOP CDM lacks a simple and intuitive user interface for untrained users to run simple queries for feasibility analysis. Aim of this study is to provide an algorithm to translate any given i2b2 query to an equivalent query which can then be run on the OMOP CDM database. The provided algorithm is able to convert queries created in the i2b2 webclient to SQL statements which can be executed on a standard OMOP CDM database programmatically.

scholarly journals Transfer of Clinical Drug Data to a Research Infrastructure on OMOP – A FAIR Concept

2021 ◽  
Author(s):  
Ines Reinecke ◽  
Michéle Zoch ◽  
Markus Wilhelm ◽  
Martin Sedlmayr ◽  
Franziska Bathelt
Keyword(s):  
Claim Data ◽  
Research Infrastructure ◽  
Common Data Model ◽  
Evidence Based ◽  
Medical Outcomes ◽  
Real World Data ◽  
Open Issue ◽  
Fair Principles ◽  
The Common ◽  
Clinical Drug

Generating evidence based on real-world data is gaining importance in research not least since the COVID-19 pandemic. The Common Data Model of Observational Medical Outcomes Partnership (OMOP) is a research infrastructure that implements FAIR principles. Although the transfer of German claim data to OMOP is already implemented, drug data is an open issue. This paper provides a concept to prepare electronic health record (EHR) drug data for the transfer to OMOP based on requirements analysis and descriptive statistics for profiling EHR data developed by an interdisciplinary team and also covers data quality issues. The concept not only ensures FAIR principles for research, but provides the foundation for German drug data to OMOP transfer.

scholarly journals Application for Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool based on the Common Data Model (Preprint)

2019 ◽  
Author(s):  
Jaehyeong Cho ◽  
Seng Chan You ◽  
Seongwon Lee ◽  
DongSu Park ◽  
Bumhee Park ◽  
...  
Keyword(s):  
United States ◽  
Health Care ◽  
Information System ◽  
South Korea ◽  
Health Outcomes ◽  
Open Source ◽  
Data Model ◽  
Endemic Area ◽  
The United States ◽  
Common Data Model

BACKGROUND Although spatial epidemiology is widely used to evaluate geographic variations and disparities in health outcomes, constructing geographic statistical models usually requires a labor-intensive process that limits its overall utility. OBJECTIVE This study aimed to develop open-source software for scalable spatial epidemiological analysis based on standardized geocode and a health care database and to demonstrate its applicability and methodological quality across countries. METHODS We developed Application for Epidemiological Geographic Information System (AEGIS) based on a standardized geocode and common data model (CDM) for health care data. AEGIS was implemented to access the geographic distribution in the incidences and health outcomes of non–communicable and communicable diseases in South Korea and the United States, specifically, the (1) geographical distribution of incident cancers, (2) spatial heterogeneity of 5-year mortality in Korean patients with cancer, and (3) identification of an endemic area of malaria in South Korea and the United States. The results from South Korea were compared with those of previous studies to assess the reliability of AEGIS. RESULTS AEGIS provides two widely used spatial analysis methods for health outcome assessment: disease mapping and detection of concentrated clusters of medical conditions or outcomes. It was possible to describe the spatial distribution, assess the spatial heterogeneity, and detect the focused area of a medical condition or outcome in various databases from different countries. The AEGIS-generated spatial distribution of incident cancer in Korea was consistent with those of previous reports. AEGIS was able to detect the known endemic area of malaria in South Korea. CONCLUSIONS As an open-source, cross-country, spatial analytics solution, AEGIS may globally expedite the assessment of differences in geographic health outcomes through the use of standardized geocode and health care databases.

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