Towards conceptual representation and invocation of scientific computations

H. Rijgersberg, J.L. Top, B. Wielinga

    Research output: Contribution to journalArticleAcademicpeer-review

    Abstract

    Computers are central in processing scientific data. This data is typically expressed as numbers and strings. Appropriate annotation of bare data is required to allow people or machines to interpret it and to relate the data to real-world phenomena. In scientific practice however, annotations are often incomplete and ambiguous let alone machine interpretable. This holds for reports and papers, but also for spreadsheets and databases. Moreover, in practice its often unclear how the data has been created. This hampers interpretation, reproduction and reuse of results and thus leads to suboptimal science. In this paper we focus on annotation of scientific computations. For this purpose we propose the ontology OQR (the Ontology of Quantitative Research). It includes a way to represent generic scientific methods and their implementation in software packages, invocation of these methods and handling of tabular datasets. This ontology promotes annotation by humans, but also allows automatic, semantic processing of numerical data. It allows scientists to understand the selected settings of computational methods and to automatically reproduce data generated by others. A prototype application demonstrates this can be done, illustrated by a case in food research. We evaluate this case with a number of researchers in the considered domain
    Original languageEnglish
    Article number447
    JournalInternational Journal of Semantic Computing
    Volume6
    Issue number4
    DOIs
    Publication statusPublished - 2012

    Fingerprint

    spreadsheet
    software
    food
    method
    science

    Cite this

    @article{fde2aa45077d44b882f41aa8ca141ea0,
    title = "Towards conceptual representation and invocation of scientific computations",
    abstract = "Computers are central in processing scientific data. This data is typically expressed as numbers and strings. Appropriate annotation of bare data is required to allow people or machines to interpret it and to relate the data to real-world phenomena. In scientific practice however, annotations are often incomplete and ambiguous let alone machine interpretable. This holds for reports and papers, but also for spreadsheets and databases. Moreover, in practice its often unclear how the data has been created. This hampers interpretation, reproduction and reuse of results and thus leads to suboptimal science. In this paper we focus on annotation of scientific computations. For this purpose we propose the ontology OQR (the Ontology of Quantitative Research). It includes a way to represent generic scientific methods and their implementation in software packages, invocation of these methods and handling of tabular datasets. This ontology promotes annotation by humans, but also allows automatic, semantic processing of numerical data. It allows scientists to understand the selected settings of computational methods and to automatically reproduce data generated by others. A prototype application demonstrates this can be done, illustrated by a case in food research. We evaluate this case with a number of researchers in the considered domain",
    author = "H. Rijgersberg and J.L. Top and B. Wielinga",
    year = "2012",
    doi = "10.1142/S1793351X12500079",
    language = "English",
    volume = "6",
    journal = "International Journal of Semantic Computing",
    issn = "1793-351X",
    publisher = "World Scientific Publishing",
    number = "4",

    }

    Towards conceptual representation and invocation of scientific computations. / Rijgersberg, H.; Top, J.L.; Wielinga, B.

    In: International Journal of Semantic Computing, Vol. 6, No. 4, 447, 2012.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Towards conceptual representation and invocation of scientific computations

    AU - Rijgersberg, H.

    AU - Top, J.L.

    AU - Wielinga, B.

    PY - 2012

    Y1 - 2012

    N2 - Computers are central in processing scientific data. This data is typically expressed as numbers and strings. Appropriate annotation of bare data is required to allow people or machines to interpret it and to relate the data to real-world phenomena. In scientific practice however, annotations are often incomplete and ambiguous let alone machine interpretable. This holds for reports and papers, but also for spreadsheets and databases. Moreover, in practice its often unclear how the data has been created. This hampers interpretation, reproduction and reuse of results and thus leads to suboptimal science. In this paper we focus on annotation of scientific computations. For this purpose we propose the ontology OQR (the Ontology of Quantitative Research). It includes a way to represent generic scientific methods and their implementation in software packages, invocation of these methods and handling of tabular datasets. This ontology promotes annotation by humans, but also allows automatic, semantic processing of numerical data. It allows scientists to understand the selected settings of computational methods and to automatically reproduce data generated by others. A prototype application demonstrates this can be done, illustrated by a case in food research. We evaluate this case with a number of researchers in the considered domain

    AB - Computers are central in processing scientific data. This data is typically expressed as numbers and strings. Appropriate annotation of bare data is required to allow people or machines to interpret it and to relate the data to real-world phenomena. In scientific practice however, annotations are often incomplete and ambiguous let alone machine interpretable. This holds for reports and papers, but also for spreadsheets and databases. Moreover, in practice its often unclear how the data has been created. This hampers interpretation, reproduction and reuse of results and thus leads to suboptimal science. In this paper we focus on annotation of scientific computations. For this purpose we propose the ontology OQR (the Ontology of Quantitative Research). It includes a way to represent generic scientific methods and their implementation in software packages, invocation of these methods and handling of tabular datasets. This ontology promotes annotation by humans, but also allows automatic, semantic processing of numerical data. It allows scientists to understand the selected settings of computational methods and to automatically reproduce data generated by others. A prototype application demonstrates this can be done, illustrated by a case in food research. We evaluate this case with a number of researchers in the considered domain

    U2 - 10.1142/S1793351X12500079

    DO - 10.1142/S1793351X12500079

    M3 - Article

    VL - 6

    JO - International Journal of Semantic Computing

    JF - International Journal of Semantic Computing

    SN - 1793-351X

    IS - 4

    M1 - 447

    ER -