Improving data identification and tagging for more effective decision making in agriculture

International audienc

Profile Diversity for Phenotyping Data Search and Recommendation

Session: Applications innovantesNational audienceDans ce travail, nous étudions la diversité de profils. Il s'agit d'une approche nouvelle dans la recherche de documents scientifiques. De nombreux travaux ont combinés la pertinence des mots clés avec la popularité des documents au sein d'une fonction de score " sociale ". Diversifier le contenu des documents retournés a également été traité de mani'ere approfondie et la recherche, la publicité, les requêtes en base de données et la recommandation. Nous pensons que notre travail est le premier à traiter de la diversité de profils afin de traiter le problème des listes de résultats hautement populaires mais trop ciblées. Nous montrerons comment nous adaptons l'algorithme de Fagin sur les algorithmes à seuil pour retourner les documents les plus pertinents, les plus populaires mais aussi les plus divers que ce soit en terme de contenus ou de profils. Nous avons également un ensemble de simulations sur deux benchmarks afin de valider notre fonction de score

Profile Diversity for Phenotyping Data Search and Recommendation

Session: Applications innovantesSession: Applications innovantesNational audienceDans ce travail, nous étudions la diversité de profils. Il s'agit d'une approche nouvelle dans la recherche de documents scientifiques. De nombreux travaux ont combinés la pertinence des mots clés avec la popularité des documents au sein d'une fonction de score " sociale ". Diversifier le contenu des documents retournés a également été traité de mani'ere approfondie et la recherche, la publicité, les requêtes en base de données et la recommandation. Nous pensons que notre travail est le premier à traiter de la diversité de profils afin de traiter le problème des listes de résultats hautement populaires mais trop ciblées. Nous montrerons comment nous adaptons l'algorithme de Fagin sur les algorithmes à seuil pour retourner les documents les plus pertinents, les plus populaires mais aussi les plus divers que ce soit en terme de contenus ou de profils. Nous avons également un ensemble de simulations sur deux benchmarks afin de valider notre fonction de score

Vitioeno, un centre de ressources numériques pour la production de données scientifiques FAIR

Résumé INRAE et l’IFV ont mis en place un centre de ressources numériques ouvertes et librement accessibles, avec l’objectif d’aider et de faciliter la structuration et la qualification des données selon les principes FAIR. Ce centre de ressource pour l’aide à la gestion des données « Vitioeno » est disponible en ligne, en français et en anglais, à l’adresse https://vitioeno.mistea.inrae.fr/resource/app/. Il recense et donne un accès ouvert à différents types de ressources numériques existantes, en particulier des référentiels (matériel végétal et variables expérimentales), des documents téléchargeables de type protocoles, et des applications ou liens utiles

Position measurement and the nonlinear regime of cavity quantum optomechanics

Position measurement is central to cavity quantum optomechanics and underpins a wide array of sensing technologies and tests of fundamental physics. Excitingly, several optomechanics experiments are now entering the highly sought nonlinear regime where optomechanical interactions are large even for low light levels. Within this regime, new quantum phenomena and improved performance may be achieved, however, an approach for mechanical position measurement and a corresponding nonlinear theoretical toolbox are needed to unlock these capabilities. Here, we develop a framework of cavity quantum optomechanics that captures the nonlinearities of both the radiation-pressure interaction and the cavity response and propose how position measurement can be performed in this regime. Our proposal utilizes optical general-dyne detection to obtain mechanical position information imprinted onto both the optical amplitude and phase quadratures and enables both pulsed and continuous modes of operation. Moreover, our proposal and theoretical framework are readily applicable to current and near-future experiments and will allow a range of advances to be made in e.g. quantum metrology, explorations of the standard quantum limit, and quantum measurement and control.Comment: Main and supplemental material in single file. 20 pages, 7 figure

Final Report of the ModSysC2020 Working Group - Data, Models and Theories for Complex Systems: new challenges and opportunities

Final Report of the ModSysC2020 Working Group at University Montpellier 2At University Montpellier 2, the modeling and simulation of complex systems has been identified as a major scientific challenge and one of the priority axes in interdisciplinary research, with major potential impact on training, economy and society. Many research groups and laboratories in Montpellier are already working in that direction, but typically in isolation within their own scientific discipline. Several local actions have been initiated in order to structure the scientific community with interdisciplinary projects, but with little coordination among the actions. The goal of the ModSysC2020 (modeling and simulation of complex systems in 2020) working group was to analyze the local situation (forces and weaknesses, current projects), identify the critical research directions and propose concrete actions in terms of research projects, equipment facilities, human resources and training to be encouraged. To guide this perspective, we decomposed the scientific challenge into four main themes, for which there is strong background in Montpellier: (1) modeling and simulation of complex systems; (2) algorithms and computing; (3) scientific data management; (4) production, storage and archiving of data from the observation of the natural and biological media. In this report, for each theme, we introduce the context and motivations, analyze the situation in Montpellier, identify research directions and propose specific actions in terms of interdisciplinary research projects and training. We also provide an analysis of the socio-economical aspects of modeling and simulation through use cases in various domains such as life science and healthcare, environmental science and energy. Finally, we discuss the importance of revisiting students training in fundamental domains such as modeling, computer programming and database which are typically taught too late, in specialized masters

Identifying, naming and interoperating data in a Phenotyping platform network : the good, the bad and the ugly

The EPPN2020 is a research project funded by Horizon 2020 Programme of the EU that will provide European public and private scientific sectors with access to a wide range of state-of-the-art plant phenotyping installations, techniques and methods. Specifically, EPPN2020 includes access to 31 plant phenotyping installations, and joint research activities to develop: novel technologies and methods for environmental and plant measurements.Here we present the results of the discussions of the 2019 annual project meeting to adopt community-approved architectural choices. It focuses on persistent identification of data and real objects, the naming of variables and the priorities for increasing interoperability among phenotyping installations. We describe the main elements to prioritize (the good) in order to enhance Findable, Accessible, Interoperable and Reusable (FAIR) quality for each data management system with a pragmatic concern for all partners. The plant phenotyping community gathers different actors with various means and practices. Among all the recommendations (including the bad: avoiding bad practices), the community requests identification methods (including the use of ontologies) compatible with the ‘local’ pre-existing ones. The identification scheme being adopted is based on Uniform Resource Identifiers (URIs) with independant left and right parts for each identifier. It focuses on the associated objects and variables common to all EPPN2020 members, namely the experimental units (which can be a plant in a pot or a plot), sensors and variables. A common architecture for identifiers and variable names is presented in order to enable a first level of interoperation between information systems.In conclusion, we present some of the next challenges (the ugly) that need to be addressed by the EPPN2020 community related with i) the partial reuse of pre-existing ontologies, ii) the persistence of long-term access to data iii) interoperation between all potential users of the phenotyping data

InfraPhenoGrid: A scientific workflow infrastructure for Plant Phenomics on the Grid

International audiencePlant phenotyping consists in the observation of physical and biochemical traits of plant genotypes in response to environmental conditions. Challenges , in particular in context of climate change and food security, are numerous. High-throughput platforms have been introduced to observe the dynamic growth of a large number of plants in different environmental conditions. Instead of considering a few genotypes at a time (as it is the case when phenomic traits are measured manually), such platforms make it possible to use completely new kinds of approaches. However, the data sets produced by such widely instrumented platforms are huge, constantly augmenting and produced by increasingly complex experiments, reaching a point where distributed computation is mandatory to extract knowledge from data. In this paper, we introduce InfraPhenoGrid, the infrastructure we designed and deploy to efficiently manage data sets produced by the PhenoArch plant phenomics platform in the context of the French Phenome Project. Our solution consists in deploying scientific workflows on a Grid using a middle-ware to pilot workflow executions. Our approach is user-friendly in the sense that despite the intrinsic complexity of the infrastructure, running scientific workflows and understanding results obtained (using provenance information) is kept as simple as possible for end-users

PHENOPSIS DB: an Information System for Arabidopsis thaliana phenotypic data in an environmental context

<p>Abstract</p> <p>Background</p> <p>Renewed interest in plant × environment interactions has risen in the post-genomic era. In this context, high-throughput phenotyping platforms have been developed to create reproducible environmental scenarios in which the phenotypic responses of multiple genotypes can be analysed in a reproducible way. These platforms benefit hugely from the development of suitable databases for storage, sharing and analysis of the large amount of data collected. In the model plant <it>Arabidopsis thaliana</it>, most databases available to the scientific community contain data related to genetic and molecular biology and are characterised by an inadequacy in the description of plant developmental stages and experimental metadata such as environmental conditions. Our goal was to develop a comprehensive information system for sharing of the data collected in PHENOPSIS, an automated platform for <it>Arabidopsis thaliana </it>phenotyping, with the scientific community.</p> <p>Description</p> <p>PHENOPSIS DB is a publicly available (URL: <url>http://bioweb.supagro.inra.fr/phenopsis/</url>) information system developed for storage, browsing and sharing of online data generated by the PHENOPSIS platform and offline data collected by experimenters and experimental metadata. It provides modules coupled to a Web interface for (i) the visualisation of environmental data of an experiment, (ii) the visualisation and statistical analysis of phenotypic data, and (iii) the analysis of <it>Arabidopsis thaliana </it>plant images.</p> <p>Conclusions</p> <p>Firstly, data stored in the PHENOPSIS DB are of interest to the <it>Arabidopsis thaliana </it>community, particularly in allowing phenotypic meta-analyses directly linked to environmental conditions on which publications are still scarce. Secondly, data or image analysis modules can be downloaded from the Web interface for direct usage or as the basis for modifications according to new requirements. Finally, the structure of PHENOPSIS DB provides a useful template for the development of other similar databases related to genotype × environment interactions.</p