Expertise Indices: Variants, Modifications, Advancements, and Computational Tools in R
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918466612625408 |
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| author | Nandy, Abhirup Das, Nilabhra R. |
| author_facet | Nandy, Abhirup Das, Nilabhra R. |
| contents | In the academic landscape, scientific research has been primarily conducted through research institutions, which requires a massive influx of funds from various sources. Presently, these funding bodies have been moving from trust-based funding to performance-based evaluation systems for granting funds to the research bodies. This has led to the rise in popularity of various indices or statistics that measure institutional research strength or expertise. Institutional research expertise usually focuses on publication volume and its impact measured using the widely used h- and g-indices. However, these indices fail to capture the thematic expertise of research for institutions. To address this gap, two new expertise indicators, namely the x-index, the x_d-index, and bias-adjusted variants, the field-normalised x_d-index, and the fractional x_d-index, were introduced recently. Additionally, we propose two new variants, the category-adjusted x-index and the inverse variance weighted x_d-index, which further account for resolvable bias, and a novel statistic, the x_o-index, which acts as a measure of the overall research expertise. While several packages that calculate the traditional h- and g-indices exist, these novel expertise indices are yet to be included in such existing packages. The 'xxdi' R package provides simple functions that implement these expertise indices and their variants, enabling their utilisation by the wider research community. A stable version of the package is available on CRAN (https://doi.org/10.32614/CRAN.package.xxdi) and an in-development version on GitHub (https://github.com/nilabhrardas/xxdi). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_22811 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Expertise Indices: Variants, Modifications, Advancements, and Computational Tools in R Nandy, Abhirup Das, Nilabhra R. Digital Libraries In the academic landscape, scientific research has been primarily conducted through research institutions, which requires a massive influx of funds from various sources. Presently, these funding bodies have been moving from trust-based funding to performance-based evaluation systems for granting funds to the research bodies. This has led to the rise in popularity of various indices or statistics that measure institutional research strength or expertise. Institutional research expertise usually focuses on publication volume and its impact measured using the widely used h- and g-indices. However, these indices fail to capture the thematic expertise of research for institutions. To address this gap, two new expertise indicators, namely the x-index, the x_d-index, and bias-adjusted variants, the field-normalised x_d-index, and the fractional x_d-index, were introduced recently. Additionally, we propose two new variants, the category-adjusted x-index and the inverse variance weighted x_d-index, which further account for resolvable bias, and a novel statistic, the x_o-index, which acts as a measure of the overall research expertise. While several packages that calculate the traditional h- and g-indices exist, these novel expertise indices are yet to be included in such existing packages. The 'xxdi' R package provides simple functions that implement these expertise indices and their variants, enabling their utilisation by the wider research community. A stable version of the package is available on CRAN (https://doi.org/10.32614/CRAN.package.xxdi) and an in-development version on GitHub (https://github.com/nilabhrardas/xxdi). |
| title | Expertise Indices: Variants, Modifications, Advancements, and Computational Tools in R |
| topic | Digital Libraries |
| url | https://arxiv.org/abs/2604.22811 |