Performance benchmarks for scholarly metrics associated with fisheries and wildlife faculty

Research productivity and impact are often considered in professional evaluations of academics, and performance metrics based on publications and citations increasingly are used in such evaluations. To promote evidence-based and informed use of these metrics, we collected publication and citation data for 437 tenure-track faculty members at 33 research-extensive universities in the United States belonging to the National Association of University Fisheries and Wildlife Programs. For each faculty member, we computed 8 commonly used performance metrics based on numbers of publications and citations, and recorded covariates including academic age (time since Ph.D.), sex, percentage of appointment devoted to research, and the sub-disciplinary research focus. Standardized deviance residuals from regression models were used to compare faculty after accounting for variation in performance due to these covariates. We also aggregated residuals to enable comparison across universities. Finally, we tested for temporal trends in citation practices to assess whether the law of constant ratios , used to enable comparison of performance metrics between disciplines that differ in citation and publication practices, applied to fisheries and wildlife sub-disciplines when mapped to Web of Science Journal Citation Report categories. Our regression models reduced deviance by 1/4 to 1/2. Standardized residuals for each faculty member, when combined across metrics as a simple average or weighted via factor analysis, produced similar results in terms of performance based on percentile rankings. Significant variation was observed in scholarly performance across universities, after accounting for the influence of covariates. In contrast to findings for other disciplines, normalized citation ratios for fisheries and wildlife sub-disciplines increased across years. Increases were comparable for all sub-disciplines except ecology. We discuss the advantages and limitations of our methods, illustrate their use when applied to new data, and suggest future improvements. Our benchmarking approach may provide a useful tool to augment detailed, qualitative assessment of performance. © 2016 Swihart et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

A novel bibliometric index with a simple geometric interpretation

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

Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform

The potential of close-range hyperspectral imaging (HSI) as a tool for detecting early drought stress responses in plants grown in a high-throughput plant phenotyping platform (HTPPP) was explored. Reflectance spectra from leaves in close-range imaging are highly influenced by plant geometry and its specific alignment towards the imaging system. This induces high uninformative variability in the recorded signals, whereas the spectral signature informing on plant biological traits remains undisclosed. A linear reflectance model that describes the effect of the distance and orientation of each pixel of a plant with respect to the imaging system was applied. By solving this model for the linear coefficients, the spectra were corrected for the uninformative illumination effects. This approach, however, was constrained by the requirement of a reference spectrum, which was difficult to obtain. As an alternative, the standard normal variate (SNV) normalisation method was applied to reduce this uninformative variability. Once the envisioned illumination effects were eliminated, the remaining differences in plant spectra were assumed to be related to changes in plant traits. To distinguish the stress-related phenomena from regular growth dynamics, a spectral analysis procedure was developed based on clustering, a supervised band selection, and a direct calculation of a spectral similarity measure against a reference. To test the significance of the discrimination between healthy and stressed plants, a statistical test was conducted using a one-way analysis of variance (ANOVA) technique. The proposed analysis techniques was validated with HSI data of maize plants (Zea mays L.) acquired in a HTPPP for early detection of drought stress in maize plant. Results showed that the pre-processing of reflectance spectra with the SNV effectively reduces the variability due to the expected illumination effects. The proposed spectral analysis method on the normalized spectra successfully detected drought stress from the third day of drought induction, confirming the potential of HSI for drought stress detection studies and further supporting its adoption in HTPPP