Some example apps developed by lab members.
LeslieMatrixShiny: stochastic population projections
This app projects a user-defined Leslie (age-classified) matrix to examine population changes through time. This fully customisable app includes a density-feedback function on survival relative to desired initial population size and carrying capacity, stochastic projections with user-defined variance for survival, a generationally scaled catastrophe function, a single ‘pulse’ disturbance function, and a ‘press’ disturbance function with a user-defined time application. This Github repository provides all the ‘under-the-bonnet’ code for the app. Read the related blog post.
Existing citation-based indices used to rank research performance do not permit a fair comparison of researchers among career stages or disciplines, nor do they treat women and men equally. We designed the ε-index, which is simple to calculate, is based on open-access data, corrects for disciplinary variation, can be adjusted for career breaks, and sets a sample-specific threshold above and below which a researcher is deemed to be performing above or below expectation. This R Shiny App estimates the ε-index and its variants using user-provided data files. This Github repository provides all the ‘under-the-bonnet’ code for the app. Read the related paper (pre-print) and/or blog post.
AltmetricShiny: Altmetric data-fetch & analysis
Ever wanted to collate the Altmetric data for your articles, but couldn’t be bothered to do it manually? We have made the process substantially easier by designing this R Shiny app. All you need to do is collate a list of ‘digital object identifiers’ (‘doi’) for the articles of interest, and the app does it all for you. The app also produces outputs that plot the distribution of not only the relevant Altmetric scores, but also the rank percentiles for each article relative to articles of the same age in the journal, and to all articles in the journal with Altmetric data. The app also gives you the option of fetching citation data from Crossref to examine the patterns between Altmetric and citation trends. This Github repository provides all the ‘under-the-bonnet’ R code for the app. Note that attempting to fetch Altmetric data for an incorrect doi or for a doi with no associated Altmetric entry will cause the algorithm to fail.
JournalRankShiny: a multi-index bootstrap function to rank a sample of peer-reviewed journals
This R Shiny app provides a κ-resampled composite journal rank incorporating six user-supplied citation indices. There are many methods available to assess the relative citation performance of peer-reviewed journals. Regardless of their individual faults and advantages, citation-based metrics are used by researchers to maximise the citation potential of their articles, and by employers to rank academic track records. The absolute value of any particular index is arguably meaningless unless compared to other journals, and different metrics result in divergent rankings. To provide a simple yet more objective way to rank journals within and among disciplines, this app provides a κ-resampled composite journal rank incorporating six user-supplied citation indices: Impact Factor (IF), Immediacy Index (IM), Google 5-year h-index (h5), CiteScore (CS), Source-Normalized Impact Per Paper (SNIP), and SCImago Journal Rank (SJR). The output gives an index of relative rank uncertainty for all sample journals provided by the user. This Github repository provides all the ‘under-the-bonnet’ code for the app. Read the related paper and/or blog post.