The Experts below are selected from a list of 4107 Experts worldwide ranked by ideXlab platform

Mark G Weiner - One of the best experts on this subject based on the ideXlab platform.

  • Metadata Tables to enable dynamic data modeling and web interface design the seer example
    International Journal of Medical Informatics, 2002
    Co-Authors: Mark G Weiner, Micah Sherr, Abigail Cohen
    Abstract:

    A wealth of information addressing health status, outcomes and resource utilization is compiled and made available by various government agencies. While exploration of the data is possible using existing tools, in general, would-be users of the resources must acquire CD-ROMs or download data from the web, and upload the data into their own database. Where web interfaces exist, they are highly structured, limiting the kinds of queries that can be executed. This work develops a web-based database interface engine whose content and structure is generated through interaction with a Metadata Table. The result is a dynamically generated web interface that can easily accommodate changes in the underlying data model by altering the Metadata Table, rather than requiring changes to the interface code. This paper discusses the background and implementation of the Metadata Table and web-based front end and provides examples of its use with the NCI's Surveillance, Epidemiology and End-Results (SEER) database.

Abigail Cohen - One of the best experts on this subject based on the ideXlab platform.

  • Metadata Tables to enable dynamic data modeling and web interface design the seer example
    International Journal of Medical Informatics, 2002
    Co-Authors: Mark G Weiner, Micah Sherr, Abigail Cohen
    Abstract:

    A wealth of information addressing health status, outcomes and resource utilization is compiled and made available by various government agencies. While exploration of the data is possible using existing tools, in general, would-be users of the resources must acquire CD-ROMs or download data from the web, and upload the data into their own database. Where web interfaces exist, they are highly structured, limiting the kinds of queries that can be executed. This work develops a web-based database interface engine whose content and structure is generated through interaction with a Metadata Table. The result is a dynamically generated web interface that can easily accommodate changes in the underlying data model by altering the Metadata Table, rather than requiring changes to the interface code. This paper discusses the background and implementation of the Metadata Table and web-based front end and provides examples of its use with the NCI's Surveillance, Epidemiology and End-Results (SEER) database.

Burford M - One of the best experts on this subject based on the ideXlab platform.

  • Corrigendum: A database of marine phytoplankton abundance, biomass and species composition in Australian waters (Scientific Data (2016) 3 (160043) DOI: 10.1038/sdata201643))
    'Springer Science and Business Media LLC', 2016
    Co-Authors: Ch Davies, Coughlan A, Hallegraeff G, Ajani P, Armbrecht L, Atkins N, Bonham P, Brett S, Brinkman R, Burford M
    Abstract:

    © The Author(s) 2016. A series of errors in our database were brought to our attention by readers, and have been corrected in an updated version of this database, which is accessible via the AODN at the following link: https://portal.aodn.org.au/search?uuid =75f4f1fc-bee3-4498-ab71-aa1ab29ab2c0 The custodian details of several datasets were incorrect. These fields in the Metadata Table have been updated to correctly assign P744, P746, P748, and P778 to the Australian Antarctic Division, and P752 to the Royal Belgian Institute of Natural Sciences. Species names and functional group assignments have been changed for a small number of records to fix identified errors. Tripos brevis and Tripos arietinus were spelt incorrectly, and have been duly corrected. Pedinellaceae was wrongly assigned to dinoflagellate as a functional group, and has now been re-assigned to flagellate. The 'Naked flagellate' group has been renamed 'Flagellate' as there is some inconsistency in the use of the term 'Naked flagellate' and what precisely would be included. The functional group 'Other', has also been excluded as this contained data that was not necessarily phytoplankton but had been found in phytoplankton counts. The macroalgae Murrayella australica, Cladophora spp., Chlorohormidium sp., Eudorina spp., Tribonema spp., Chlorohormidium spp. were also removed. In addition to these corrections, three datasets have been extended to include more recently acquired data: P 597 IMOS Australian Continuous Plankton Recorder survey (ongoing dataset, 59089 new records as of 2016-08-31); P599 IMOS National Reference Stations (ongoing dataset, 14669 new records as of 2016-08-31); and P1068 Great Barrier Reef Expedition 1928-29 (new dataset, 1340 new records). Table 1 provides a summary of the overall change in database contents. (Table Presented). This dataset will continue to grow and will be regularly updated with new data and any further corrections to the data. Users can email imos-planktonatcsiro.au with any comments, which will be reviewed and included in future updates if applicable. The AODN portal will always direct the user to the most recent version, the original version will remain available at http://dx.doi.org/10.4225/69/ 56454b2ba2f79, and interim versions will be available on request

Burford Michele - One of the best experts on this subject based on the ideXlab platform.

  • Corrigendum: A database of marine phytoplankton abundance, biomass and species composition in Australian waters (Scientific Data (2016) 3 (160043) DOI: 10.1038/sdata201643))
    'Springer Science and Business Media LLC', 2016
    Co-Authors: Davies, Claire H., Coughlan Alex, Hallegraeff Gustaaf, Ajani Penelope, Armbrecht Linda, Atkins Natalia, Bonham Prudence, Brett Steve, Brinkman Richard, Burford Michele
    Abstract:

    A series of errors in our database were brought to our attention by readers, and have been corrected in an updated version of this database, which is accessible via the AODN at the following link: https://portal.aodn.org.au/search?uuid =75f4f1fc-bee3-4498-ab71-aa1ab29ab2c0 The custodian details of several datasets were incorrect. These fields in the Metadata Table have been updated to correctly assign P744, P746, P748, and P778 to the Australian Antarctic Division, and P752 to the Royal Belgian Institute of Natural Sciences. Species names and functional group assignments have been changed for a small number of records to fix identified errors. Tripos brevis and Tripos arietinus were spelt incorrectly, and have been duly corrected. Pedinellaceae was wrongly assigned to dinoflagellate as a functional group, and has now been re-assigned to flagellate. The 'Naked flagellate' group has been renamed 'Flagellate' as there is some inconsistency in the use of the term 'Naked flagellate' and what precisely would be included. The functional group 'Other', has also been excluded as this contained data that was not necessarily phytoplankton but had been found in phytoplankton counts. The macroalgae Murrayella australica, Cladophora spp., Chlorohormidium sp., Eudorina spp., Tribonema spp., Chlorohormidium spp. were also removed. In addition to these corrections, three datasets have been extended to include more recently acquired data: P 597 IMOS Australian Continuous Plankton Recorder survey (ongoing dataset, 59089 new records as of 2016-08-31); P599 IMOS National Reference Stations (ongoing dataset, 14669 new records as of 2016-08-31); and P1068 Great Barrier Reef Expedition 1928-29 (new dataset, 1340 new records). Table 1 provides a summary of the overall change in database contents. (Table Presented). This dataset will continue to grow and will be regularly updated with new data and any further corrections to the data. Users can email imos-planktonatcsiro.au with any comments, which will be reviewed and included in future updates if applicable. The AODN portal will always direct the user to the most recent version, the original version will remain available at http://dx.doi.org/10.4225/69/ 56454b2ba2f79, and interim versions will be available on request

Goebel François-régis - One of the best experts on this subject based on the ideXlab platform.

  • ECOFI: A new generic database to analyse complex agroecological experimentation
    2017
    Co-Authors: Auzoux Sandrine, Martiné Jean-françois, Poser Christophe, Marnotte Pascal, Loison Romain, Goebel François-régis
    Abstract:

    Agroecological studies on sugarcane dealing with genotype by environment by management interactions commonly generate complex datasets. Data are collected separately and can be multi-scale (from phytomer to field), multi-species (sugarcane and companion plants), and multi-disciplinary (agronomy, entomology, ecophysiology, weed science, etc), making their use complex in system data analysis. This paper presents a solution to manage these heterogeneous data using database and Metadata technology. A relational database, named ECOFI, was designed from the analysis of the content and the structure datasets of multi-disciplinary experiments with sugarcane. The results of this analysis showed that most datasets shared data corresponding to common environmental by management factors and the same measurements, such as yield, weed occurrence and insect incidence. However, each of them had its own structure. To analyse biology by environment by management interactions efficiently, the structure of ECOFI was built into three parts: the environmental conditions, the agricultural practices, and the impacts observed on biotic and abiotic variables using agronomic measurements. In standard databases, each additional observed variable generally forces an update to the existing database model. The model of the ECOFI database does not require such modification. Taking into account a new variable is very easy, consisting solely of the addition of a new record in a Table, after having stored the new variable label and its definition (unit, type, scale) in a generic Metadata Table. This technology minimizes the number of Tables, columns and empty cells and improves database query performance. ECOFI is already used to manage, explore and analyse the data from agroecological experiments, ecophysiological observations and sugarcane modelling in Reunion Island and other sugar-producing countries. ECOFI is a generic database that improves analysis and facilitates access to multi-disciplinary and multi-scale data. Its data model can be applied to any type of project. (Résumé d'auteur

  • ECOFI: a new generic database to analyse complex agroecological experimentation
    ISSCT, 2016
    Co-Authors: Auzoux Sandrine, Martiné Jean-françois, Poser Christophe, Marnotte Pascal, Goebel François-régis
    Abstract:

    Agroecological studies on sugarcane dealing with genotype by environment by management interactions commonly generate complex datasets. Data are collected separately and can be multi-scale (from phytomer to field), multi-species (sugarcane and companion plants), and multi-disciplinary (agronomy, entomology, ecophysiology, weed science, etc), making their use complex in system data analysis. This paper presents a solution to manage these heterogeneous data using database and Metadata technology. A relational database, named ECOFI, was designed from the analysis of the content and the structure datasets of multi-disciplinary experiments with sugarcane. The results of this analysis showed that most datasets shared data corresponding to common environmental by management factors and the same measurements, such as yield, weed occurrence and insect incidence. However, each of them had its own structure. To analyse biology by environment by management interactions efficiently, the structure of ECOFI was built into three parts: the environmental conditions, the agricultural practices, and the impacts observed on biotic and abiotic variables using agronomic measurements. In standard databases, each additional observed variable generally forces an update to the existing database model. The model of the ECOFI database does not require such modification. Taking into account a new variable is very easy, consisting solely of the addition of a new record in a Table, after having stored the new variable label and its definition (unit, type, scale) in a generic Metadata Table. This technology minimizes the number of Tables, columns and empty cells and improves database query performance. ECOFI is already used to manage, explore and analyse the data from agroecological experiments, ecophysiological observations and sugarcane modelling in Réunion Island and other sugar-producing countries. ECOFI is a generic database that improves analysis and facilitates access to multi-disciplinary and multi-scale data. Its data model can be applied to any type of project. (Résumé d'auteur