The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform

Ruediger Mutz - One of the best experts on this subject based on the ideXlab platform.

  • growth rates of modern science a bibliometric analysis based on the number of publications and cited references
    Association for Information Science and Technology, 2015
    Co-Authors: Lutz Bornmann, Ruediger Mutz
    Abstract:

    Many studies (in information science) have looked at the growth of science. In this study, we reexamine the question of the growth of science. To do this we (a) use current data up to publication year 2012 and (b) analyze the data across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data were analyzed with an Advanced Statistical Technique—segmented regression analysis—which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (a) the number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (b) the number of cited references in the publications of the source items per cited reference year. We looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three essential growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars, and 8 to 9% to 2010.

  • growth rates of modern science a bibliometric analysis based on the number of publications and cited references
    arXiv: Digital Libraries, 2014
    Co-Authors: Lutz Bornmann, Ruediger Mutz
    Abstract:

    Many studies in information science have looked at the growth of science. In this study, we re-examine the question of the growth of science. To do this we (i) use current data up to publication year 2012 and (ii) analyse it across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data are analysed with an Advanced Statistical Technique - segmented regression analysis - which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (1) The number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (2) the number of cited references in the publications of the source items per cited reference year. We have looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars and 8 to 9% to 2012.

Lutz Bornmann - One of the best experts on this subject based on the ideXlab platform.

  • growth rates of modern science a bibliometric analysis based on the number of publications and cited references
    Association for Information Science and Technology, 2015
    Co-Authors: Lutz Bornmann, Ruediger Mutz
    Abstract:

    Many studies (in information science) have looked at the growth of science. In this study, we reexamine the question of the growth of science. To do this we (a) use current data up to publication year 2012 and (b) analyze the data across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data were analyzed with an Advanced Statistical Technique—segmented regression analysis—which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (a) the number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (b) the number of cited references in the publications of the source items per cited reference year. We looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three essential growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars, and 8 to 9% to 2010.

  • growth rates of modern science a bibliometric analysis based on the number of publications and cited references
    arXiv: Digital Libraries, 2014
    Co-Authors: Lutz Bornmann, Ruediger Mutz
    Abstract:

    Many studies in information science have looked at the growth of science. In this study, we re-examine the question of the growth of science. To do this we (i) use current data up to publication year 2012 and (ii) analyse it across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data are analysed with an Advanced Statistical Technique - segmented regression analysis - which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (1) The number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (2) the number of cited references in the publications of the source items per cited reference year. We have looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars and 8 to 9% to 2012.

Randall J Wisser - One of the best experts on this subject based on the ideXlab platform.

  • multivariate mixed linear model analysis of longitudinal data an information rich Statistical Technique for analyzing plant disease resistance
    Phytopathology, 2012
    Co-Authors: Yogasudha Veturi, Kristen L Kump, Ellie Walsh, Oliver Ott, Jesse Poland, Judith M Kolkman, Peter Balintkurti, James B Holland, Randall J Wisser
    Abstract:

    The mixed linear model (MLM) is an Advanced Statistical Technique applicable to many fields of science. The multivariate MLM can be used to model longitudinal data, such as repeated ratings of disease resistance taken across time. In this study, using an example data set from a multi-environment trial of northern leaf blight disease on 290 maize lines with diverse levels of resistance, multivariate MLM analysis was performed and its utility was examined. In the population and environments tested, genotypic effects were highly correlated across disease ratings and followed an autoregressive pattern of correlation decay. Because longitudinal data are often converted to the univariate measure of area under the disease progress curve (AUDPC), comparisons between univariate MLM analysis of AUDPC and multivariate MLM analysis of longitudinal data were made. Univariate analysis had the advantage of simplicity and reduced computational demand, whereas multivariate analysis enabled a comprehensive perspective on disease development, providing the opportunity for unique insights into disease resistance. To aid in the application of multivariate MLM analysis of longitudinal data on disease resistance, annotated program syntax for model fitting is provided for the software ASReml.

Yogasudha Veturi - One of the best experts on this subject based on the ideXlab platform.

  • multivariate mixed linear model analysis of longitudinal data an information rich Statistical Technique for analyzing plant disease resistance
    Phytopathology, 2012
    Co-Authors: Yogasudha Veturi, Kristen L Kump, Ellie Walsh, Oliver Ott, Jesse Poland, Judith M Kolkman, Peter Balintkurti, James B Holland, Randall J Wisser
    Abstract:

    The mixed linear model (MLM) is an Advanced Statistical Technique applicable to many fields of science. The multivariate MLM can be used to model longitudinal data, such as repeated ratings of disease resistance taken across time. In this study, using an example data set from a multi-environment trial of northern leaf blight disease on 290 maize lines with diverse levels of resistance, multivariate MLM analysis was performed and its utility was examined. In the population and environments tested, genotypic effects were highly correlated across disease ratings and followed an autoregressive pattern of correlation decay. Because longitudinal data are often converted to the univariate measure of area under the disease progress curve (AUDPC), comparisons between univariate MLM analysis of AUDPC and multivariate MLM analysis of longitudinal data were made. Univariate analysis had the advantage of simplicity and reduced computational demand, whereas multivariate analysis enabled a comprehensive perspective on disease development, providing the opportunity for unique insights into disease resistance. To aid in the application of multivariate MLM analysis of longitudinal data on disease resistance, annotated program syntax for model fitting is provided for the software ASReml.

James B Holland - One of the best experts on this subject based on the ideXlab platform.

  • multivariate mixed linear model analysis of longitudinal data an information rich Statistical Technique for analyzing plant disease resistance
    Phytopathology, 2012
    Co-Authors: Yogasudha Veturi, Kristen L Kump, Ellie Walsh, Oliver Ott, Jesse Poland, Judith M Kolkman, Peter Balintkurti, James B Holland, Randall J Wisser
    Abstract:

    The mixed linear model (MLM) is an Advanced Statistical Technique applicable to many fields of science. The multivariate MLM can be used to model longitudinal data, such as repeated ratings of disease resistance taken across time. In this study, using an example data set from a multi-environment trial of northern leaf blight disease on 290 maize lines with diverse levels of resistance, multivariate MLM analysis was performed and its utility was examined. In the population and environments tested, genotypic effects were highly correlated across disease ratings and followed an autoregressive pattern of correlation decay. Because longitudinal data are often converted to the univariate measure of area under the disease progress curve (AUDPC), comparisons between univariate MLM analysis of AUDPC and multivariate MLM analysis of longitudinal data were made. Univariate analysis had the advantage of simplicity and reduced computational demand, whereas multivariate analysis enabled a comprehensive perspective on disease development, providing the opportunity for unique insights into disease resistance. To aid in the application of multivariate MLM analysis of longitudinal data on disease resistance, annotated program syntax for model fitting is provided for the software ASReml.