Higher Education Teaching and Learning Support Areas Differences in England and Brazil: A Case Study / Áreas de Apoio ao Ensino e Aprendizagem no Ensino Superior Diferenças na Inglaterra e no Brasil: um estudo de caso

Teaching and Learning processes have been relevant in higher education institutions. There have been a wide range of skills provided by the universities teaching and learning support areas. This paper shows a comparative analysis of these areas from University of Bath and the State University of Campinas-Unicamp. To show the strengths and weaknesses of each university, this paper shows their ranking data used within The Times Higher Education World University Rankings. Data gathered from both universities websites show their support areas with specific skills. While the University of Bath has high scores in international outlook and citations, Unicamp has its strength in teaching, research, and industry income. Unicamp has more than the double of students. Similar teaching and learning support areas in both universities are shown. Most skills listed for the University of Bath are present in the State university of Campinas-Unicamp. Three support areas under the School of Civil Engineering from Unicamp are shown. Data from other league tables were not considered. These support areas exist to help academic and professional staff when teaching, and planning to teach, and students when learning to learn. It might be relevant to run some scales, questionnaires, surveys among the undergraduate and graduate students, concerning motivation, procrastination, learning strategies, learning styles, and feedback from them about their needs and solutions they believe could help. These might lead to reinforcement or upgrade of these teaching and learning support areas bringing a more appropriate range of skills needed by the students and staff

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data