Reflexões sobre a competência em informação nos arquivos, o combate à desinformação e a relação com a Agenda 2030

A reflection on archives, information literacy and disinformation, pertinent to sustainable development, was addressed in terms of the right to access information, providing the guarantee of this fundamental right provided for in the Federal Constitution. The objective of this study is to present which CoInfo actions the archives can develop to combat disinformation, considering the 2030 Agenda, in its goal 16.10, to ensure public access to information and protect fundamental freedoms, in accordance with national legislation and international agreements. The methodology used is descriptive, exploratory, qualitative, bibliographical and documental. The strategies available for this study are part of the collection of data in the Reference Database of Journal Articles in Information Science and the analysis of the Political Manifesto on Information Literacy 2022 Librarian: Professional Luz. The results of this research indicate that of the seventeen actions suggested for libraries, nine can be adapted for archives, aiming to combat disinformation. In this way, it is expected to provoke a constructive and promising reflection.Aborda-se uma reflexão sobre os arquivos, a competência em informação e a desinformação, pertinentes para um desenvolvimento sustentável, na questão do direito ao acesso à informação, proporcionando a garantia desse direito fundamental previsto na Constituição Federal. O objetivo deste estudo é apresentar quais as ações da CoInfo os arquivos podem desenvolver para combater a desinformação, considerando a Agenda 2030, em sua meta 16.10, para assegurar o acesso público à informação e proteger as liberdades fundamentais, em conformidade com a legislação nacional e os acordos internacionais. A Metodologia utilizada é a descritiva, exploratória, qualitativa, bibliográfica e documental. As estratégias dispostas para este estudo enquadram-se na coleta de dados na Base de Dados Referenciais de Artigos de Periódicos em Ciência da Informação e da análise do Manifesto Político sobre Competência em Informação 2022 Bibliotecário: Profissional Luz. Os resultados desta pesquisa indicam que das dezessete ações sugeridas para bibliotecas, nove podem ser adaptadas para os arquivos, visando o combate à desinformação. Desta forma, espera-se provocar uma reflexão construtiva e promissora

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

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq