UNDERSTANDING THE RELATIONSHIP OF FEDERAL FINANCIAL SUPPORT TO THE BACCALAUREATE STEM DEGREE PRODUCTION IN U.S. COLLEGES AND UNIVERSITIES

The purpose of this study was to examine STEM baccalaureate degree production in relationship to the receipt of National Science Foundation (NSF) undergraduate education awards by the included postsecondary institutions. Data from NSF Award Abstracts and Integrated Postsecondary Education Data System (IPEDS) from 2003 to 2012 were utilized to determine how receipt of NSF funding affects STEM baccalaureate degree production (total number and proportion of all baccalaureate degrees) when holding relevant independent variables constant. In addition to total STEM degree production and production rate, STEM degree awards for women and underrepresented minority students (URMs) were also analyzed. Findings revealed that, in most models, NSF funding was not a significant factor in the production outcomes. However, public institutions with NSF awards for URMs did produce a greater average number of STEM baccalaureate degrees by URM students. In addition, private institutions with NSF awards for STEM education produced a greater average number of STEM baccalaureate degrees in total. This study’s findings suggest that the presence of NSF funding could have an impact on STEM degree production for some student populations, in some institutions, but may not for others. The varied outcomes may inform institutions and policy makers, when reflecting on the stated goal of the federal government, to increase STEM baccalaureate degree production and the impact of federal funding for such endeavors. Additional research focused on NSF funding amounts and explicit outcomes of funded projects may prove helpful to further develop policy implications and create more directive outcomes for STEM funding by NSF

Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists

Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks

Introduction : The Alma-Tademas' Studio-Houses and Beyond

Introduction to a series of articles (edited by Prettejohn and Trippi) on Artists' Studio-Houses, in issue 9 of British Art Studie

Factors affecting the identification of individual mountain bongo antelope

The recognition of individuals forms the basis of many endangered species monitoring protocols. This process typically relies on manual recognition techniques. This study aimed to calculate a measure of the error rates inherent within the manual technique and also sought to identify visual traits that aid identification, using the critically endangered mountain bongo, Tragelaphus eurycerus isaaci, as a model system. Identification accuracy was assessed with a matching task that required same/different decisions to side-by-side pairings of individual bongos. Error rates were lowest when only the flanks of bongos were shown, suggesting that the inclusion of other visual traits confounded accuracy. Accuracy was also higher for photographs of captive animals than camera-trap images, and in observers experienced in working with mountain bongos, than those unfamiliar with the sub-species. These results suggest that the removal of non-essential morphological traits from photographs of bongos, the use of high-quality images, and relevant expertise all help increase identification accuracy. Finally, given the rise in automated identification and the use of citizen science, something our results would suggest is applicable within the context of the mountain bongo, this study provides a framework for assessing their accuracy in individual as well as species identification

Population monitoring of a Critically Endangered antelope, the mountain bongo, using camera traps and a novel identification scheme

Conservation monitoring is paramount for the gathering of information on species and populations in need of conservation actions. However, monitoring of the mountain bongo Tragelaphus eurycerus isaaci, a Critically Endangered antelope endemic to Kenya, has hitherto been limited to surveillance (i.e. focused on persistence of the species in particular areas), thus limiting the information that could be retrieved on the status and trends of these populations. Using a newly developed identification system, we implemented robust design mark–recapture using existing camera-trap records of four wild subpopulations of the bongo. We provide data on sex and age structure and the first estimates of population size in the wild. Males and calves seem to be suffering higher mortality than females, and only two of the four monitored populations include adults of both sexes and calves. The combined size of these two breeding populations is estimated to be 29–50. Our findings confirm the critical situation of the mountain bongo in the wild and highlight the need for conservation actions to reinforce the wild populations for the long-term conservation of this antelope