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Trevor Fenner - One of the best experts on this subject based on the ideXlab platform.
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A two-dimensional bibliometric index reflecting both quality and quantity
Scientometrics, 2020Co-Authors: Mark Levene, Martyn Harris, Trevor FennerAbstract:We propose a two-dimensional bibliometric index that strikes a balance between quantity (as measured by the number of publications of a researcher) and quality (as measured by the number of citations to those publications). While the square of h -index is determined by the maximum Area square that fits under the citation curve of an author when plotting the number of citations in decreasing order, the rec -index is determined by the maximum Area Rectangle that fits under the curve. In this context we may distinguish between authors with a few very highly-cited publications, who may have carried out some influential research, and prolific authors, who may have many publications but fewer citations per publication. The influence of a researcher may be measured via a restricted version of the rec -index, the $${rec}_{I}$$ rec I -index, which is the maximum Area vertical Rectangle that fits under the citation curve. Similarly, the prolificity of a researcher may be measured via the $${rec}_{P}$$ rec P -index, which is the maximum Area horizontal Rectangle that fits under the citation curve. This leads to the proposal of the two-dimensional bibliometric index $$({rec}_{I}, {rec}_{P})$$ ( rec I , rec P ) , which captures both aspects of a researcher’s output. We present a comprehensive empirical analysis of this two-dimensional index on two datasets: a large set of Google Scholar profiles (representing “typical” researchers) and a small set of Nobel prize winners. Our results demonstrate the potential of this two-dimensional index, since for both data sets there is a statistically significant number of researchers for whom $${rec}_{I}$$ rec I is greater than $${rec}_{P}$$ rec P . In particular, for nearly 25% of the Google Scholar researchers and for nearly 60% of the Nobel prize winners, $${rec}_{I}$$ rec I is greater than $${rec}_{P}$$ rec P .
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A two-dimensional bibliometric index reflecting both quality and quantity
Scientometrics, 2020Co-Authors: Mark Levene, Martyn Harris, Trevor FennerAbstract:We propose a two-dimensional bibliometric index that strikes a balance between quantity (as measured by the number of publications of a researcher) and quality (as measured by the number of citations to those publications). While the square of h-index is determined by the maximum Area square that fits under the citation curve of an author when plotting the number of citations in decreasing order, the rec-index is determined by the maximum Area Rectangle that fits under the curve. In this context we may distinguish between authors with a few very highly-cited publications, who may have carried out some influential research, and prolific authors, who may have many publications but fewer citations per publication. The influence of a researcher may be measured via a restricted version of the rec-index, the $${rec}_{I}$$-index, which is the maximum Area vertical Rectangle that fits under the citation curve. Similarly, the prolificity of a researcher may be measured via the $${rec}_{P}$$-index, which is the maximum Area horizontal Rectangle that fits under the citation curve. This leads to the proposal of the two-dimensional bibliometric index $$({rec}_{I}, {rec}_{P})$$, which captures both aspects of a researcher’s output. We present a comprehensive empirical analysis of this two-dimensional index on two datasets: a large set of Google Scholar profiles (representing “typical” researchers) and a small set of Nobel prize winners. Our results demonstrate the potential of this two-dimensional index, since for both data sets there is a statistically significant number of researchers for whom $${rec}_{I}$$ is greater than $${rec}_{P}$$. In particular, for nearly 25% of the Google Scholar researchers and for nearly 60% of the Nobel prize winners, $${rec}_{I}$$ is greater than $${rec}_{P}$$.
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A novel bibliometric index with a simple geometric interpretation.
PloS one, 2018Co-Authors: Trevor Fenner, Martyn Harris, Mark Levene, Judit Bar-ilanAbstract:We propose the χ-index as a bibliometric indicator that generalises the h-index. While the h-index is determined by the maximum square that fits under the citation curve of an author when plotting the number of citations in decreasing order, the χ-index is determined by the maximum Area Rectangle that fits under the curve. The height of the maximum Rectangle is the number of citations ck to the kth most-cited publication, where k is the width of the Rectangle. The χ-index is then defined as , for convenience of comparison with the h-index and other similar indices. We present a comprehensive empirical comparison between the χ-index and other bibliometric indices, focusing on a comparison with the h-index, by analysing two datasets—a large set of Google Scholar profiles and a small set of Nobel prize winners. Our results show that, although the χ and h indices are strongly correlated, they do exhibit significant differences. In particular, we show that, for these data sets, there are a substantial number of profiles for which χ is significantly larger than h. Furthermore, restricting these profiles to the cases when ck > k or ck < k corresponds to, respectively, classifying researchers as either tending to influential, i.e. having many more than h citations, or tending to prolific, i.e. having many more than h publications.
Mark Levene - One of the best experts on this subject based on the ideXlab platform.
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A two-dimensional bibliometric index reflecting both quality and quantity
Scientometrics, 2020Co-Authors: Mark Levene, Martyn Harris, Trevor FennerAbstract:We propose a two-dimensional bibliometric index that strikes a balance between quantity (as measured by the number of publications of a researcher) and quality (as measured by the number of citations to those publications). While the square of h -index is determined by the maximum Area square that fits under the citation curve of an author when plotting the number of citations in decreasing order, the rec -index is determined by the maximum Area Rectangle that fits under the curve. In this context we may distinguish between authors with a few very highly-cited publications, who may have carried out some influential research, and prolific authors, who may have many publications but fewer citations per publication. The influence of a researcher may be measured via a restricted version of the rec -index, the $${rec}_{I}$$ rec I -index, which is the maximum Area vertical Rectangle that fits under the citation curve. Similarly, the prolificity of a researcher may be measured via the $${rec}_{P}$$ rec P -index, which is the maximum Area horizontal Rectangle that fits under the citation curve. This leads to the proposal of the two-dimensional bibliometric index $$({rec}_{I}, {rec}_{P})$$ ( rec I , rec P ) , which captures both aspects of a researcher’s output. We present a comprehensive empirical analysis of this two-dimensional index on two datasets: a large set of Google Scholar profiles (representing “typical” researchers) and a small set of Nobel prize winners. Our results demonstrate the potential of this two-dimensional index, since for both data sets there is a statistically significant number of researchers for whom $${rec}_{I}$$ rec I is greater than $${rec}_{P}$$ rec P . In particular, for nearly 25% of the Google Scholar researchers and for nearly 60% of the Nobel prize winners, $${rec}_{I}$$ rec I is greater than $${rec}_{P}$$ rec P .
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A two-dimensional bibliometric index reflecting both quality and quantity
Scientometrics, 2020Co-Authors: Mark Levene, Martyn Harris, Trevor FennerAbstract:We propose a two-dimensional bibliometric index that strikes a balance between quantity (as measured by the number of publications of a researcher) and quality (as measured by the number of citations to those publications). While the square of h-index is determined by the maximum Area square that fits under the citation curve of an author when plotting the number of citations in decreasing order, the rec-index is determined by the maximum Area Rectangle that fits under the curve. In this context we may distinguish between authors with a few very highly-cited publications, who may have carried out some influential research, and prolific authors, who may have many publications but fewer citations per publication. The influence of a researcher may be measured via a restricted version of the rec-index, the $${rec}_{I}$$-index, which is the maximum Area vertical Rectangle that fits under the citation curve. Similarly, the prolificity of a researcher may be measured via the $${rec}_{P}$$-index, which is the maximum Area horizontal Rectangle that fits under the citation curve. This leads to the proposal of the two-dimensional bibliometric index $$({rec}_{I}, {rec}_{P})$$, which captures both aspects of a researcher’s output. We present a comprehensive empirical analysis of this two-dimensional index on two datasets: a large set of Google Scholar profiles (representing “typical” researchers) and a small set of Nobel prize winners. Our results demonstrate the potential of this two-dimensional index, since for both data sets there is a statistically significant number of researchers for whom $${rec}_{I}$$ is greater than $${rec}_{P}$$. In particular, for nearly 25% of the Google Scholar researchers and for nearly 60% of the Nobel prize winners, $${rec}_{I}$$ is greater than $${rec}_{P}$$.
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A novel bibliometric index with a simple geometric interpretation.
PloS one, 2018Co-Authors: Trevor Fenner, Martyn Harris, Mark Levene, Judit Bar-ilanAbstract:We propose the χ-index as a bibliometric indicator that generalises the h-index. While the h-index is determined by the maximum square that fits under the citation curve of an author when plotting the number of citations in decreasing order, the χ-index is determined by the maximum Area Rectangle that fits under the curve. The height of the maximum Rectangle is the number of citations ck to the kth most-cited publication, where k is the width of the Rectangle. The χ-index is then defined as , for convenience of comparison with the h-index and other similar indices. We present a comprehensive empirical comparison between the χ-index and other bibliometric indices, focusing on a comparison with the h-index, by analysing two datasets—a large set of Google Scholar profiles and a small set of Nobel prize winners. Our results show that, although the χ and h indices are strongly correlated, they do exhibit significant differences. In particular, we show that, for these data sets, there are a substantial number of profiles for which χ is significantly larger than h. Furthermore, restricting these profiles to the cases when ck > k or ck < k corresponds to, respectively, classifying researchers as either tending to influential, i.e. having many more than h citations, or tending to prolific, i.e. having many more than h publications.
Martyn Harris - One of the best experts on this subject based on the ideXlab platform.
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A two-dimensional bibliometric index reflecting both quality and quantity
Scientometrics, 2020Co-Authors: Mark Levene, Martyn Harris, Trevor FennerAbstract:We propose a two-dimensional bibliometric index that strikes a balance between quantity (as measured by the number of publications of a researcher) and quality (as measured by the number of citations to those publications). While the square of h -index is determined by the maximum Area square that fits under the citation curve of an author when plotting the number of citations in decreasing order, the rec -index is determined by the maximum Area Rectangle that fits under the curve. In this context we may distinguish between authors with a few very highly-cited publications, who may have carried out some influential research, and prolific authors, who may have many publications but fewer citations per publication. The influence of a researcher may be measured via a restricted version of the rec -index, the $${rec}_{I}$$ rec I -index, which is the maximum Area vertical Rectangle that fits under the citation curve. Similarly, the prolificity of a researcher may be measured via the $${rec}_{P}$$ rec P -index, which is the maximum Area horizontal Rectangle that fits under the citation curve. This leads to the proposal of the two-dimensional bibliometric index $$({rec}_{I}, {rec}_{P})$$ ( rec I , rec P ) , which captures both aspects of a researcher’s output. We present a comprehensive empirical analysis of this two-dimensional index on two datasets: a large set of Google Scholar profiles (representing “typical” researchers) and a small set of Nobel prize winners. Our results demonstrate the potential of this two-dimensional index, since for both data sets there is a statistically significant number of researchers for whom $${rec}_{I}$$ rec I is greater than $${rec}_{P}$$ rec P . In particular, for nearly 25% of the Google Scholar researchers and for nearly 60% of the Nobel prize winners, $${rec}_{I}$$ rec I is greater than $${rec}_{P}$$ rec P .
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A two-dimensional bibliometric index reflecting both quality and quantity
Scientometrics, 2020Co-Authors: Mark Levene, Martyn Harris, Trevor FennerAbstract:We propose a two-dimensional bibliometric index that strikes a balance between quantity (as measured by the number of publications of a researcher) and quality (as measured by the number of citations to those publications). While the square of h-index is determined by the maximum Area square that fits under the citation curve of an author when plotting the number of citations in decreasing order, the rec-index is determined by the maximum Area Rectangle that fits under the curve. In this context we may distinguish between authors with a few very highly-cited publications, who may have carried out some influential research, and prolific authors, who may have many publications but fewer citations per publication. The influence of a researcher may be measured via a restricted version of the rec-index, the $${rec}_{I}$$-index, which is the maximum Area vertical Rectangle that fits under the citation curve. Similarly, the prolificity of a researcher may be measured via the $${rec}_{P}$$-index, which is the maximum Area horizontal Rectangle that fits under the citation curve. This leads to the proposal of the two-dimensional bibliometric index $$({rec}_{I}, {rec}_{P})$$, which captures both aspects of a researcher’s output. We present a comprehensive empirical analysis of this two-dimensional index on two datasets: a large set of Google Scholar profiles (representing “typical” researchers) and a small set of Nobel prize winners. Our results demonstrate the potential of this two-dimensional index, since for both data sets there is a statistically significant number of researchers for whom $${rec}_{I}$$ is greater than $${rec}_{P}$$. In particular, for nearly 25% of the Google Scholar researchers and for nearly 60% of the Nobel prize winners, $${rec}_{I}$$ is greater than $${rec}_{P}$$.
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A novel bibliometric index with a simple geometric interpretation.
PloS one, 2018Co-Authors: Trevor Fenner, Martyn Harris, Mark Levene, Judit Bar-ilanAbstract:We propose the χ-index as a bibliometric indicator that generalises the h-index. While the h-index is determined by the maximum square that fits under the citation curve of an author when plotting the number of citations in decreasing order, the χ-index is determined by the maximum Area Rectangle that fits under the curve. The height of the maximum Rectangle is the number of citations ck to the kth most-cited publication, where k is the width of the Rectangle. The χ-index is then defined as , for convenience of comparison with the h-index and other similar indices. We present a comprehensive empirical comparison between the χ-index and other bibliometric indices, focusing on a comparison with the h-index, by analysing two datasets—a large set of Google Scholar profiles and a small set of Nobel prize winners. Our results show that, although the χ and h indices are strongly correlated, they do exhibit significant differences. In particular, we show that, for these data sets, there are a substantial number of profiles for which χ is significantly larger than h. Furthermore, restricting these profiles to the cases when ck > k or ck < k corresponds to, respectively, classifying researchers as either tending to influential, i.e. having many more than h citations, or tending to prolific, i.e. having many more than h publications.
Lip Chien Woon - One of the best experts on this subject based on the ideXlab platform.
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RAM - A divide and conquer algorithm for rectilinear region coverage
IEEE Conference on Robotics Automation and Mechatronics 2004., 1Co-Authors: Amit Agarwal, Meng-hiot Lim, Lip Chien WoonAbstract:We give an algorithm to generate a coverage motion plan for a single unmanned reconnaissance aerial vehicle (URAV) over a holed rectilinear polygonal region. The URAV is equipped with a stabilized downward-looking sensor which has a square footprint of a fixed Area. The algorithm is based on the principle of divide-and-conquer. In the first step, we generate the lexicographically maximum Area Rectangle partition of the polygon. We outline a sweep line based algorithm to compute this partition. Next, we rind a coverage motion plan for movement of the sensor over each Rectangle in the partition. Plans for adjacent Rectangles are finally merged to generate a complete plan for the entire region. Our algorithm yields paths with minimal length and minimal turns.
Amit Agarwal - One of the best experts on this subject based on the ideXlab platform.
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A Divide and Conquer Algor ithm for Rectilinear Region Coverage
2004Co-Authors: Amit Agarwal, Meng-hiot Lim, Chien Woon, Techno PlazaAbstract:Thus, an alternativ e equipped with a stabilized downward-looking sensor which has a solu tion strategy is needed. In this work, we describe a dividesquare footprint of a fixed Area. The algorithm is based on the and-conquer algorithm for covering a rectilinear polygo n. principle of divide-and-conqu er. In the tirst step, we generate the More specifically, our algorithm is invariant to the presence of lexicographically maximum Area Rectangle partition of the hole(s) in the polygon. The underlying polygon need not be polygon. We outline a sweep line based algorithm to compute this horizontally (or, vertically) convex. Henceforth, except in the partition. Next, we find a coverage motion plan for movement of Section II, unless stated otherwise, the terms 'holed rectilinear the sensor over each Rectangle in the partition. Plans for adjac ent polygon' and 'polygon' are used interchangeably and both refer Rectangles are finally merged to generate a complete plan for the to a simple holed rectilinear polygon. entire region. Our algorithm yields paths with minimal length and minimal turns.
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RAM - A divide and conquer algorithm for rectilinear region coverage
IEEE Conference on Robotics Automation and Mechatronics 2004., 1Co-Authors: Amit Agarwal, Meng-hiot Lim, Lip Chien WoonAbstract:We give an algorithm to generate a coverage motion plan for a single unmanned reconnaissance aerial vehicle (URAV) over a holed rectilinear polygonal region. The URAV is equipped with a stabilized downward-looking sensor which has a square footprint of a fixed Area. The algorithm is based on the principle of divide-and-conquer. In the first step, we generate the lexicographically maximum Area Rectangle partition of the polygon. We outline a sweep line based algorithm to compute this partition. Next, we rind a coverage motion plan for movement of the sensor over each Rectangle in the partition. Plans for adjacent Rectangles are finally merged to generate a complete plan for the entire region. Our algorithm yields paths with minimal length and minimal turns.