Usage data on research outputs such as books and journals is well established in the scholarly community. Yet, as research impact is derived from a broader set of scholarly outputs, such as data, code, and multimedia, more holistic usage and impact metrics could inform national innovation and research policy. While usage data reporting standards, such as Project COUNTER, provide the basis for shared statistics reporting practice, mandated access to publicly funded research has increased the demand for impact metrics and analytics. In this context, stakeholders are exploring how to scaffold and strengthen shared infrastructure to better support the trusted, multistakeholder exchange of usage data across a variety of outputs. In April 2023, a workshop on Exploring National Infrastructure for Public Access and Impact Reporting supported by the United States (US) National Science Foundation (NSF) explored these issues. This paper contextualizes the resources shared and recommendations generated in the workshop.

Recently, there has been much interest in detecting and measuring patterns of change in disparity. Although most studies have used one or two measures of disparity to quantify and characterize the occupation of morphospace, multiple measures may be necessary to fully detect changes in patterns of morphospace occupation. Also, the ability to detect morphological trends and occupation patterns within morphospace depends on using the appropriate measure(s) of disparity. In this study, seven measures were used to determine and characterize sensitivity to sample size of the data, number of morphological characters, percentage of missing data, and changes in morphospace occupation pattern. These consist of five distance measures—sum of univariate variances, total range, mean distance, principal coordinate analysis volume, average pairwise dissimilarity—and two non-distance measures—participation ratio and number of unique pairwise character combinations. Evaluation of each measure with respect to sensitivity to sample size, number of morphological characters, and percentage of missing data was accomplished by using both simulated and Ordovician crinoid data. For simulated data, each measure of disparity was evaluated for its response to changes of morphospace occupation pattern, and with respect to simulated random and nonrandom extinction events. Changes in disparity were also measured within the Crinoidea across the Permian extinction event.

Although all measures vary in sensitivity with respect to species sample size, number of morphological characters, and percentage of missing data, the non-distance measures overall produce the lowest estimates of variance (in bootstrap analyses). The non-distance measures appear to be relatively insensitive to changes in morphospace occupation pattern. All measures, except average pairwise dissimilarity, detect changes in occupation pattern in simulated nonrandom extinction events, but all measures, except number of unique pairwise character combinations and principal coordinate analysis volume, are relatively insensitive to changes in pattern in simulated random extinction events. The distance measures report similar changes in disparity over the Permian extinction event, whereas the non-distance measures differ. This study suggests that each measure of disparity is designed for different purposes, and that by using a combination of techniques a clearer picture of disparity should emerge.

If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.

Rogers & McClelland (R&M) criticize models that rely on structured representations such as categories, taxonomic hierarchies, and schemata, but we suggest that structured models can account for many of the phenomena that they describe. Structured approaches and parallel distributed processing (PDP) approaches operate at different levels of analysis, and may ultimately be compatible, but structured models seem more likely to offer immediate insight into many of the issues that R&M discuss.

Many ingenious mechanical methods were used by hydrographic surveyors for measuring the depth of water in the early part of the 20th Century. These were superseded by echo-sounding and sonar techniques in the later decades. Relatively few of the mechanical methods were used by merchant ships but, in the years before radar and satellite navigation, they, and the increasingly popular echo sounder, had a useful place in the navigator's armoury of navigation aids. This paper is not intended to provide a detailed description of the development of sounding devices. Sections 1 to 5 simply outline the principles of the sounding devices with which the authors were familiar during their time at sea. In Sections 6 to 9 the authors recount their anecdotal experiences of the way in which they, personally, used depth measuring devices during the middle years of the 20th century.