The Oporto School of Law of the Catholic University of Portugal awards dozens of students each year with a Master's degree in Law, in seven areas of specialization, as well as a Master's degree in Law and Management. In order to give visibility to the result of the research, the Oporto School of Law School has taken the decision to publish the master's dissertations that have obtained the highest grades.Thus, from 2019 and henceforth, the best master's dissertations, whose exams took place by December 8 of the previous calendar year, will be published each year. This disclosure allows quality research to be shared with the rest of the scientific community. In addition, it is an incentive for the qualification of the research of Master's students in Law and in Law and Management.It is our conviction that this initiative contributes to a useful sharing of knowledge and allows for the opening of a dialogue with the legal community and civil society.This issue includes thirty-eight dissertations. They are presented in alphabetical order and cover areas as diverse as criminal law, international and European law, business law, labor law, tax law, and private law.

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost