More scientific papers use ESB data

We're pleased to report to our recorders that two more scientific papers have been published that have referenced data collated by the Earthworm Society of Britain and published through the Global Biodiversity Information Facility. Details of these two new articles are provided below, and clicking on the headings will take you to the full article (these articles are about subject matters outside of our expertise so please direct any questions to the authors!).

You can find more information about the data that we collate earthworm data, how we make this open to all and a full list of journal articles that have referenced our data on the Earthworm Data page.

Landscape Analysis for the Specimen Data Refinery

Walton, S. et al. (2020) ‘Landscape Analysis for the Specimen Data Refinery’, Research Ideas and Outcomes, 6. doi: 10.3897/rio.6.e57602.

This report reviews the current state-of-the-art applied approaches on automated tools, services and workflows for extracting information from images of natural history specimens and their labels. We consider the potential for repurposing existing tools, including workflow management systems; and areas where more development is required. This paper was written as part of the SYNTHESYS+ project for software development teams and informatics teams working on new software-based approaches to improve mass digitisation of natural history specimens.

Deeply digging the interaction effect in multiple linear regressions using a fractional-power interaction term

Li, X. et al. (2020) ‘Deeply digging the interaction effect in multiple linear regressions using a fractional-power interaction term’, MethodsX, 7, p. 101067. doi: 10.1016/j.mex.2020.101067.

In multiple regression Y ~ β0 + β1X1 + β2X2 + β3X1 X2 + ɛ., the interaction term is quantified as the product of X1 and X2. We developed fractional-power interaction regression (FPIR), using βX1M X2N as the interaction term. The rationale of FPIR is that the slopes of Y-X1 regression along the X2 gradient are modeled using the nonlinear function (Slope = β1 + β3MX1M-1 X2N), instead of the linear function (Slope = β1 + β3X2) that regular regressions normally implement. The ranges of M and N are from -56 to 56 with 550 candidate values, respectively. We applied FPIR using a well-studied dataset, nest sites of the crested ibis (Nipponia nippon).We further tested FPIR by other 4692 regression models. FPIRs have lower AIC values (-302 ± 5003.5) than regular regressions (-168.4 ± 4561.6), and the effect size of AIC values between FPIR and regular regression is 0.07 (95% CI: 0.04–0.10). We also compared FPIR with complex models such as polynomial regression, generalized additive model, and random forest. FPIR is flexible and interpretable, using a minimum number of degrees of freedom to maximize variance explained. We have provided a new R package, interactionFPIR, to estimate the values of M and N, and suggest using FPIR whenever the interaction term is likely to be significant.