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Background: In France, the Ministry of Health has implemented a comprehensive program for rare diseases (RD) that includes an epidemiological program as well as the establishment of expert centers for the clinical care of patients with RD. Since 2007, most of these centers have entered the data for patients with developmental disorders into the CEMARA population-based registry, a national online data repository for all rare diseases. Through the CEMARA web portal, descriptive demographic data, clinical data, and the chronology of medical follow-up can be obtained for each center. We address the interest and ongoing challenges of this national data collection system 10 years after its implementation.Methods: Since 2007, clinicians and researchers have reported the "minimum dataset (MDS)" for each patient presenting to their expert center. We retrospectively analyzed administrative data, demographic data, care organization and diagnoses.Results: Over 10 years, 228,243 RD patients (including healthy carriers and family members for whom experts denied any suspicion of RD) have visited an expert center. Among them, 167,361 were patients affected by a RD (median age 11 years, 54% children, 46% adults, with a balanced sex ratio), and 60,882 were unaffected relatives (median age 37 years). The majority of patients (87%) were seen no more than once a year, and 52% of visits were for a diagnostic procedure. Among the 2,869 recorded rare disorders, 1,907 (66.5%) were recorded in less than 10 patients, 802 (28%) in 10 to 100 patients, 149 (5.2%) in 100 to 1,000 patients, and 11 (0.4%) in > 1,000 patients. Overall, 45.6% of individuals had no diagnosis and 6.7% had an uncertain diagnosis. Children were mainly referred by their pediatrician (46%; n = 55,755 among the 121,136 total children referrals) and adults by a medical specialist (34%; n = 14,053 among the 41,564 total adult referrals). Given the geographical coverage of the centers, the median distance from the patient's home was 25.1 km (IQR = 6.3 km-64.2 km).Conclusions: CEMARA provides unprecedented support for epidemiological, clinical and therapeutic studies in the field of RD. Researchers can benefit from the national scope of CEMARA data, but also focus on specific diseases or patient subgroups. While this endeavor has been a major collective effort among French RD experts to gather large-scale data into a single database, it provides tremendous potential to improve patient care. [ABSTRACT FROM AUTHOR]

In France and Europe, farmland represents a large fraction of land cover. The study and assessment of biodiversity in farmland is therefore a major challenge. To monitor biodiversity across wide areas, citizen science programs have demonstrated their effectiveness and relevance. The involvement of citizens in data collection offers a great opportunity to deploy extensive networks for biodiversity monitoring. But citizen science programs come with two issues: large amounts of data to manage and large numbers of participants with heterogeneous skills, needs and expectations about these data. In this article, we offer a solution to these issues, concretized by an information system. The study is based on a real life citizen science program tailored for farmers. This information system provides data and tools at several levels of complexity, to fit the needs and the skills of several users, from citizens with basic IT knowledge to scientists with strong statistical background. The proposed system is designed as follows. First, a data warehouse stores the data collected by citizens. This data warehouse is modelled depending on future data analysis. Secondly, associated with the data warehouse, a standard OLAP tool enables citizens and scientists to explore data. To complete the OLAP tool, we implement and compare four feature selection methods, in order to rank explanatory factors according to their relevance. Finally, for users with extended statistical skills, we use Generalized Linear Mixed Models to explore the temporal dynamics of invertebrate diversity in farmland ecosystems. The proposed system, a combination of business intelligence tools, data mining methods and advanced statistics, offers an example of complete exploitation of data by several user profiles. The proposition is supported by a real life citizen science program, and can be used as a guideline to design information systems in the same field. • The paper presents a global integrated Business Intelligence system for the analysis of agro-biodiversity data • Agro-biodiversity data is collected by the FBO over all French and several years • Data Warehouse and OLAP systems are designed non-skilled OLAP decision-makers to analyze these data • Data science methods are then applied over some subset of warehoused data to define more complex analysis [ABSTRACT FROM AUTHOR]

Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. Data are thus converted in data cubes characterized by a multidimensional structure on which exploration is based. However, multiple sources often lead to several data cubes defined by heterogeneous dimensions. In particular, dimensions definition can change depending on analyzed scale, territory and time. In order to consider these three issues specific to geographic analysis, this research proposes an original data cube metamodel defined in unified modeling language (UML). Based on concepts like common dimension levels and metadimensions, the metamodel can instantiate constellations of heterogeneous data cubes allowing SOLAP to perform multiscale, multi-territory and time analysis. Afterwards, the metamodel is implemented in a relational data warehouse and validated by an operational tool designed for a social economy case study. This tool, called "Racines", gathers and compares multidimensional data about social economy business in Belgium and France through interactive cross-border maps, charts and reports. Thanks to the metamodel, users remain independent from IT specialists regarding data exploration and integration. [ABSTRACT FROM AUTHOR]