In recent years, concern about the misuse of natural resources has been increasing. It is essential to know in detail the biodiversity of an ecosystem to understand and analyze the impact of human activities on nature, as well as to promote the economic growth of a country. To achieve these goals, public and private institutions are aggregating and sharing biological data around the world by means of biodiversity data portals. The main purpose of those portals is to provide a set of tools that help users and institutions catalog, analyze, and publish raw data about different species in a manner that is open and freely available to any interested party. Normally the process of choosing the best software solution is not straightforward. This paper proposes a methodology to evaluate a collection of data portals to establish a clear and consistent selection process that analyzes a collection of requirements and research purposes. The proposed approach is based on three strategies: the use of software engineering techniques to identify the desired group of features to be available in the data portal; the application of the Kano Satisfaction Model to score each requirement according to a preset weight of importance; and the use of tree-maps to visualize the requirements based on their implementation priority, to establish a portal deployment road-map. The proposed methodology is broadly applicable to portal analyses for many communities of practice.

In the last decade, Science has been faced with an enormous amount of data from a wide range of sources, such as the Internet, social media, IoT devices, and so on. Increasingly it is essential to develop methods and tools that enable the most varied forms of understanding of the data and transformation processes involved in an experiment. The veracity dimension plays an essential role in meeting these needs, and it must be incorporated into a data management process. This paper presents a proposal of an architecture model for a Data Portal that provides users the ability of assessment of the legitimacy of datasets. Legitimacy consists of the communication of decisions in the construction of computational models and the provenance of the data. We present the proposed architecture and how it can be embedded in scientific data portals.

Long-term research and environmental monitoring are essential for the improved management of ecosystems and natural resources. However, to reuse this data for new experiments, decision-making processes, and integrate these data with other long-term initiatives, scientists need more information related to data creation and its evolution, intellectual property rights, and technical information in order to evaluate the use of this data. Provenance metadata emerges as a way to evaluate the quality and reliability of data, audit processes and the data versioning, while enabling the data reuse and the reproducibility of experiments and analysis. However, most solutions for the capture and management of provenance metadata are based on specific tools, restricted scopes, and they are difficult to apply in distributed and heterogeneous environments. In this paper, we present an approach for capturing, managing, and publishing the provenance metadata generated in the environmental monitoring processes. Our computational architecture comprises three main components: (1) a data model based in PROV-DM and Dublin Core, (2) a repository of RDF Graphs, and (3) a Web API that provides services for collecting, storing, and querying provenance metadata. We demonstrate the application of our approach and show its practical usefulness by evaluating this architecture to manage provenance metadata generated during an environmental monitoring simulation. The results show that our approach is effective in collecting and storing provenance metadata and allows the query of an entire provenance of datasets and data products, thus enabling reuse, discovery, and visualization of raw data, processes, and scientists involved in its generation and evolution.

Long-term research and environmental monitoring are essential for the improved management of ecosystems and natural resources. However, to reuse this data for new experiments, decision-making processes, and integrate these data with other long-term initiatives, scientists need more information related to data creation and its evolution, intellectual property rights, and technical information in order to evaluate the use of this data. Provenance metadata emerges as a way to evaluate the quality and reliability of data, audit processes and the data versioning, while enabling the data reuse and the reproducibility of experiments and analysis. However, most solutions for the capture and management of provenance metadata are based on specific tools, restricted scopes, and they are difficult to apply in distributed and heterogeneous environments. In this paper, we present an approach for capturing, managing, and publishing the provenance metadata generated in the environmental monitoring processes. Our computational architecture comprises three main components: (1) a data model based in PROV-DM and Dublin Core; (2) a repository of RDF Graphs; and (3) a Web API that provides services for collecting, storing, and querying provenance metadata. We demonstrate the application of our approach and show its practical usefulness by evaluating this architecture to manage provenance metadata generated during an environmental monitoring simulation. The results show that our approach is effective in collecting and storing provenance metadata and allows the query of an entire provenance of datasets and data products, thus enabling reuse, discovery, and visualization of raw data, processes, and scientists involved in its generation and evolution.

With the increasing production of information from e-government initiatives, there is also the need to transform a large volume of unstructured data into useful information for society. All this information should be easily accessible and made available in a meaningful and effective way in order to achieve semantic interoperability in electronic government services, which is a challenge to be pursued by governments round the world. Our aim is to discuss the context of e-Government Big Data and to present a framework to promote semantic interoperability through automatic generation of ontologies from unstructured information found in the Internet. We propose the use of fuzzy mechanisms to deal with natural language terms and present some related works found in this area. The results achieved in this study are based on the architectural definition and major components and requirements in order to compose the proposed framework. With this, it is possible to take advantage of the large volume of information generated from e-Government initiatives and use it to benefit society

Usability evaluation is a group of systematic activities that should be performed by software developers in order to identify usability problems. A widely used strategy to evaluate usability is known as User Observation and aims to analyze whether the interaction capability provided by the software is appropriated or not. Techniques, such as, Verbalization is widely used to support the evaluations based on observations due to the reason that it provides data about the quality of user interaction. In order to automate the usability evaluation test and to minimize the evaluation time, we developed a tool named ErgoSV, which is based on the verbalization technique and is capable of automatically recognize words defined in a keyword list. ErgoSV also collects, analyzes and provides relevant information about the usability test, such as: a list of pronounced words, pronounced words counter, interfaces (set of screens / video) spoken at the moment of the pronunciation and the exact time moment of pronunciation. Some usability evaluations were conducted with applications from diverse domains and showed the effectiveness of utilizing keywords to perform usability evaluation based on verbalization technique.

The digitization and integration of biodiversity data are essential for supporting environmental conservation and sustainable use of natural resources. Nowadays an increasing amount of data are made available by regional, national and global initiatives, but the efficient use of data still a challenge. New techniques are needed to enable efficient manage and the use of these various types of biotic and abiotic data to generate useful knowledge for decision-making processes. We present a work in progress research that proposes a computational framework to manage biodiversity data and to enable an efficient information retrieval process.

The Forest production is an activity with fundamental importance for the Brazilian economy.Studies show that the illegality in timber production is around 80% of total productive.This illegal wood becomes legalized in your supply chain due the failures in control and monitoring systems.This paper analyzes a computational problems existing in managing and monitoring productive process in the Amazon Forest and presents the modeling of a computational system, based on a serviceoriented architecture, which seeks through the involved systems integration the recording of information about the various productive stages.For this was created an information model that uses the eFIDS metadata standard, designed to handle electronic transactions in the forestry industry, and a centralized database, where the process information are recorded and linked, enabling the maintenance of product and raw material traceability throughout its lifecycle, ensuring identification of the forest products origin for businesses and consumers.

The wood production is an activity of fundamental importance for Brazilian economy. Studies show that the illegality in wood production is around 80% of the total production. This illegal wood becomes legalized in its supply chain due to the failures in controlling and monitoring systems. This paper analyzes some computational problems existing in managing and monitoring the production process in Amazon Forest and identifies new requirements to create a traceability system more efficient and appropriate to the region's characteristics.