People analytics - flight risk

Trabalho de projeto de mestrado em Matemática Aplicada à Economia e Gestão, Universidade de Lisboa, Faculdade de Ciências, 2020Nos últimos anos, tem-se vindo a registar um aumento da rotatividade laboral. Efectuar uma avaliação adequada aos riscos associados à rotatividade dos colaboradores, pode contribuir para a diminuição dos custos associados às novas contratações e evitar a perda de produtividade da organização. Nesse sentido, com este trabalho pretende-se, como objectivo global, criar um modelo capaz de prever antecipadamente as saídas voluntárias dos colaboradores, com o recurso a metodologia de Human Resources Analytics (HRA), que corresponde ao conjunto de competências, tecnologias e práticas que permite aos Recursos Humanos (RH), com base na exploração de dados, fornecer insights de suporte à tomada de decisão, na gestão e resolução de desafios de negócio. Como tal, pretende-se criar dois modelos distintos de previsão de saída dos colaboradores, através do uso de modelos de regressão logística e de árvores de decisão. Estas metodologias permitem, através de um conjunto de variáveis independentes, prever a rescisão voluntária do contrato de trabalho. Para além disso, é possível averiguar qual é a variável que tem o maior ganho de informação no modelo. Para a construção dos modelos, a variável resposta é definida como a vinculação, ou não, de um colaborador à empresa, consoante as variáveis que melhor caracterizam o seu perfil. Por neste trabalho se ter assumido o propósito de identificar e quantificar os colaboradores que pretendem rescindir o contrato de trabalho, está-se perante a um modelo de classificação. Desta forma, são estabelecidas duas amostras distintas, de treino e de teste. Assim, o conjunto de observações da amostra de teste nunca irá influenciar a construção do modelo e, por sua vez, é possível testar e avaliar a capacidade discriminatória do mesmo. O melhor modelo obtido foi através do uso da regressão logística, que permitiu predizer corretamente todas as observações em 74,71%, sendo a variável idade aquela que tem maior importância no modelo.In recent years, there has been an increase in labor turnover. Carrying out an adequate assessment of the risks associated with employee turnover may contribute to the reduction of costs associated with new hires and avoid the loss of productivity of the organization. In this sense, the overall objective of this work is to create a model capable of predicting in advance the voluntary departures of employees, using the Human Resources Analytics methodology, which corresponds to the set of skills, technologies and practices that allow Human Resources (HR), based on data exploration, to provide insights to support decision making, management and resolution of business challenges. As such, it aims to create two distinct models of employee exit forecasting, through the use of logistic regression models and decision trees. These methodologies allow, through a set of independent variables, the voluntary termination of the employment contract. In addition, it is possible to find out which variable has the greatest information gain in the model. For the construction of the models, the response variable is defined as the link, or not, of an employee to the company, depending on the variables that best characterize his/her profile. Since the purpose of this work is to identify and quantify the employees who intend to terminate the employment contract, this is a classification model. In this way, two distinct training and test samples are established. Thus, the set of observations in the test sample will never influence the construction of the model and, in turn, it is possible to test and evaluate its discriminatory capacity. The best model obtained was through the use of logistic regression, which allowed the correct prediction of all observations at 74.71%, the age variable being the one that has the greatest importance in the model

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

A importância do povoamento da produção acadêmica de TCCs/TCRs no Arca – Repositório Institucional da Fiocruz

O trabalho tem como objetivo apresentar o novo fluxo de depósito das coleções Trabalhos de Conclusão de Curso e de Residências (TCCs/TCRs) dos programas de pós-graduação da Fiocruz no Arca – Repositório Institucional, que será realizado através de autoarquivamento feito pelos próprios alunos, conferido pela secretaria acadêmica e depois aprovado pelo bibliotecário. Foram elaborados dois tutoriais para ajudar no procedimento do novo fluxo, visando facilitar o trabalho de gestão dos gestores Bibliotecários das Unidades da Instituição e permitir uma melhor recuperação destas coleções. Para a realização deste trabalho foram realizados diversos treinamentos e criados dois tutoriais para facilitar a inserção destas coleções no Repositório. Como resultado, espera-se contribuir para disponibilizar em acesso aberto, como também permitir uma visualização e quantificação destas coleções de forma ágil e eficiente.Ibero-American Science and Technology Education Consortiu

Ciência, Crise e Mudança. 3.º Encontro Nacional de História das Ciências e da Tecnologia. ENHCT2012

III Encontro Nacional de História das Ciências e da Tecnologia. O Centro de Estudos de História e Filosofia da Ciência, organiza o 3.º Encontro Nacional de História da Ciência e da Técnica, sob o tema «Ciência, Crise e Mudança» que tem lugar na Universidade de Évora, nos dias 26, 27 e 28 de Setembro de 2012. O Primeiro Encontro Nacional de História da Ciência teve lugar em 21 e 22 Julho de 2009, no seguimento do programa de estímulo ao de¬senvolvimento da História da Ciência em Portugal e de valorização do património cultural e científico do País, lançado pelo Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) em 31 de Janeiro desse ano. A sua organização coube a investigadores do Instituto de História Contemporânea (IHC), da FCSH da UNL, e do Centro Científico e Cultural de Macau (CCCM), em cujas instalações se realizou. De en¬tre as conclusões do Encontro, destacou-se a de realizar periodicamen¬te novos Encontros Nacionais, a serem organizados de forma rotativa por diferentes centros e núcleos de investigadores. Na sequência deste Primeiro Encontro, o Centro Interuniversitário de História das Ciências e da Tecnologia (CIUHCT) organizou, entre 26 e 28 de Julho de 2010, o II Encontro, dedicado ao tema “Comunicação das Ciências e da Tecnologia em Portugal: Agentes, Meios e Audiências”. Cabe agora ao CEHFCi cumprir o que foi decidido no final deste Encontro. Na situação económica e política que hoje vivemos torna-se particularmente urgente aprofundar o estudo e o debate sobre a interação entre a Sociedade, a Ciência e a sua História. Coordenação Científica e Executiva do encontro estiveram a cargo de dois investigadores CEHFCi: Maria de Fátima Nunes, José Pedro Sousa Dia

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora