Are academic studies reliable in Brazil? Financial variables in an inflationary environment

After the stabilization of inflation rates in Brazil, Monetary Correction for financial data was extinguished. Since then, the distinct reflections of prices variations on monetary and non-monetary variables presented on balance sheets are no longer considered. From 2004 to 2013, however, the cumulative inflation reaches 226%. In this situation, questions about the reliability of the greatest tools we have in the finance research world - financial data of companies - emerges. This work compared financial indicators and empirical models built with data adjusted for inflation and original data issued by firms. A database of 143 Brazilian companies traded on Bovespa were adjusted for inflation, from 2004 to 2013, on the precepts of the extinct Monetary Correction. We obtained two different samples: the first containing financial data adjusted for inflation and the second corporate data originally released. Statistical tests showed that financial indicators such as ROI, Asset Turnover, Debt and Market-to-book are significantly higher when we do not consider the effects of inflation. In addition, the panel regression models, when adjusted, had higher predictable power (greater R²) and representative changes of significance on the variables and coefficients. The results indicated that inflation is essential in the analysis of financial data and must be considered in the preparation of reliable databases, although assumed as stable in recent years.

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

Pervasive gaps in Amazonian ecological research

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 lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Are academic studies reliable in Brazil? Financial variables in an inflationary environment

After the stabilization of inflation rates in Brazil, Monetary Correction for financial data was extinguished. Since then, the distinct reflections of prices variations on monetary and non-monetary variables presented on balance sheets are no longer considered. From 2004 to 2013, however, the cumulative inflation reaches 226%. In this situation, questions about the reliability of the greatest tools we have in the finance research world - financial data of companies - emerges. This work compared financial indicators and empirical models built with data adjusted for inflation and original data issued by firms. A database of 143 Brazilian companies traded on Bovespa were adjusted for inflation, from 2004 to 2013, on the precepts of the extinct Monetary Correction. We obtained two different samples: the first containing financial data adjusted for inflation and the second corporate data originally released. Statistical tests showed that financial indicators such as ROI, Asset Turnover, Debt and Market-to-book are significantly higher when we do not consider the effects of inflation. In addition, the panel regression models, when adjusted, had higher predictable power (greater R²) and representative changes of significance on the variables and coefficients. The results indicated that inflation is essential in the analysis of financial data and must be considered in the preparation of reliable databases, although assumed as stable in recent years.

Self-assessment Accuracy, Overconfidence and Student Performance

We have analyzed results of the Brazilian National Student Performance exam, applied by the Ministry of Education, to verify overconfidence in students. Looking at 549,487 student-level observations, we estimated an overconfidence score, comparing the perceived performance with the actual grade in two different parts of the exam. Ordered logit models suggest that overconfidence in Brazilian students is positively correlated with income and that overconfident students took less time to complete the exam. Contrary to previous studies conducted in other countries, male students presented lower overconfidence in our sample. Moreover, performance was inversely related to the overconfidence score, indicating that students with better performance “know more about what they do not know”, as already widely discussed in the education literature

Brazilian Journal of Veterinary Pathology

The purpose of this paper is to establish criteria that could guide the diagnosis, prognosis and treatment of canine mammary neoplasias. It was elaborated during the Mammary Pathology Meeting: Diagnosis, Prognosis and Treatment of the Canine Mammary Neoplasm, held on November 6th and 7th, 2010 in Belo Horizonte – MG, Brazil, sponsored by the Laboratory of Comparative Pathology – UFMG, with the support of the Brazilian Association of Veterinary Pathology (ABPV) and Brazilian Association of Veterinary Oncology (ABROVET). Academics from several regions of Brazil were present and contributed to this work