Software development teams depend on the constant and varied use of technological tools that contribute to the fluidity of development activities. The communication channels provided by these tools contribute to the participatory culture of software development, where requirements include getting involved, learning and co-producing the code. However, the choice of channels to support development is still an open question in the scientific community. Through an investigation of closed remote software teams from a large public educational institution in Brazil during the COVID-19 Pandemic, we identified their preferred communication channels and analyzed them to learn how to improve collaboration. We also drew some comparison with social developers and took a look at the challenges that these channels impose and solutions adopted to mitigate these problems.

The 2018 Brazilian presidential elections were marked by a massive use of social media. Promising to improve user's quality of experience, social media recommend, filter and reorder the posts that will be shown to users. These social media filtering and personalization algorithms determine the flow of posts on the network and shape users' information diets. In this paper, we report measurements and findings from Facebook in the 2018 Brazilian presidential elections. To this aim, a reproducible methodology encompassing a method to identify publishers and candidates alignment and measurements to evaluate the posts selection carried out by Facebook filtering algorithms. The obtained results leverage the understanding of social media influence on the Brazilian elections. This knowledge can be useful in future elections around the world to ensure that the population exercises their right to choose their representatives in a democratic way.

In global software development distributed teams are often challenged by global distances, such as cultural diversity and linguistic barriers. This stimulates the search for groups of people that can work together and collaborate in a positive way, even though they are working in a distributed environment. In addition, the demand for high quality software motivates the search for experienced developers who are able to solve complex tasks or to help others with their own tasks. This work aims to detect experts and to identify collaborative groups with experienced members in some topics of software development. To achieve these goals the StackOverflow forum was used and modeled as a complex network. The presented method uses NetSCAN algorithm to detect overlapping communities, expert developers and their topics of expertise in social networks. Through a temporal analysis over the social network multidisciplinary developers were found as well as people who are changing their interests over time. In a first evaluation a statistic test showed that experts (indicated by the proposed method) have a higher performance than common users. A second evaluation showed that these experts are suitable for recommendation systems in question-answer forums.

Nowadays, a lot of universities and research institutes are concerned with measuring their scientists’ productivity and the public awareness of its scientific discoveries, that is, how citizens interpret the efficiency of scientists and their efforts to find solutions. This scenario demands mechanisms to identify the experts’ reputation in specific domains or topics of interest, such as the Zika epidemic. In this paper we describe an altmetrics-based framework which allows the identification of specialists and important research in specific research scenarios. Besides, we did an implementation of the framework and applied it in the Zika scenario where the most important names and disease-related studies were identified and their public awareness was analysed via altmetrics.

The Future-oriented Technology Analysis (FTA) literature recommends to use diversified FTA methods to improve the quality of the results. As the FTA methods are performed, the output of one method is used as an input of the next one. We propose that FTA methods rely on collaboration in order to produce the final result of the FTA. In this work, we analyze the collaboration applied to the FTA, from the collaboration among the researches when performing a method to the collaboration between the FTA methods. Finally, we also discuss the awareness of the collaborative work and how the FTA methods may support them.

People make decisions every day. If the wrong decisions are made in governments and companies, this may cause huge financial losses or other negative consequences. There are several methods to help decision-makers with these hard decisions. Delphi is one of those methodologies. It relies on the wisdom of crowds to extract the best solution from a multidisciplinary group of experts. Long-term decisions may take more than a decade to be proven right or wrong, and as the time passes, the reasons for the decision may be forgotten. There is a lack of methods which enable storage of the reasons for a decision made using the Delphi methodology. We propose an ontology which stores the whole process, thereby allowing the decision-maker to validate the reasons for the decision. If the experts' opinions are still valid, it is likely that the decision is still valid. We also prove that our ontology is minimal and sufficient to satisfy our objective.

Software development projects with geographically disperse teams, especially when use UML models for code generation, may gain performance by using tools with collaborative capabilities. This study reviews the distributed collaborative UML editors available in the literature. The UML Editors were compared using a Workstyle Model. Then, we discuss the fundamental challenges which these kind of UML Editors face to assist distributed developers and stakeholders across disperse locations.

Crowdsourcing tools have improved e-learning over the last few years. However, these tools vary in many aspects. This work discusses and analyzes the basic e-learning processes based on these tools, defining ways to classify them through these processes. Finally, we define conceptual models tailored to each type of process, using a standard notation for software development. These conceptual models may be used to build new e-learning crowdsourcing tools.

Delphi is a traditional method for supporting the decision-making process. It relies on a panel of experts who collaboratively reach a consensus about a given topic. This method may be empowered with new communication technologies which can gather a larger and more geographically distant expert group. In this work, we discuss in depth the gathering of a panel of experts for Delphi, which may be constrained by geographic location and competence levels, using methods to find experts in social networks. The Delphi process was also adapted to use social network analysis methods to find experts, thus allowing faster expert selection from small local groups to huge worldwide groups.

The increasing adoption of mobile devices can improve the collaborative participation, especially in data acquiring, of people in their space: where they live or where they work. Based in this kind of crowdsourcing, we create a proposal for The Federal University of Rio de Janeiro (UFRJ), specially its campus of “Ilha do Fundão” (Fundão Island), which has a fragmented culture since its origin. Nowadays, it is still possible to find not only a geographical distance, but also informational disaggregation and waste of resources and opportunities. In order to minimize these problems, as well motivating the collaborative work, we create an approach using mobile computing, called UFRJ Social. The main idea is that the crowd (students, professor, employers, visitors and anyone who stay in the campus) can provide information or opinions of any place (infrastructure, events, classes, bus stations and others). This application is context-sensitive, and users can receive recommendations by their geographical location and interests. This approach was evaluated in a study case and the results were positive. In this paper we also analyze how this approach can be extended in other scenarios.

Recent crowd sourcing tools are changing the Education. However, each tool has a specific approach, business model and e-Learning goals. Due the increasing contribution of crowd sourcing in the e-Learning process, it has become important to study it and map the challenges intrinsic to this activity. This work discusses and classifies several crowd sourcing e-Learning tools that can be found in the Internet. We defined and used eleven dimensions to classify the 22 tools found, ranging from online Universities to marketplaces for online courses. The work contributions are: provide the first map the crowd sourcing tools and a framework for classify the future tools.

Social networks are sets of links that organize people, groups, and institutions in an equal and democratic way, and around a common purpose [4]. During the formation of such networks, problems can arise, such as elements that concentrate many relationships, very isolated individuals or peripheral members of a network, people who are the only link between two distinct groups, agglomerations of people in isolated points. This can cause a series of communications problems, which ultimately will cause losses to the most important element that flows through social networks: knowledge. Thus it is necessary a series of analysis on social networks so we can monitor possible collaborations and assist in the formation of work teams, this can be done through the concept of balancing social networks developed by us [20], whose main focus is to maintain the social network as cohesive as possible in order to maximize the flow of knowledge. A practical application of these analysis occurred in the project BRINCA (Analysis and Balancing of Scientific Social Networks in Cancer Control). BRINCA's goals are to analyze how members of the Cancer INCT (National Institute of Science and Technology) collaborate and how the scientific knowledge flows amongst the different researchers and institutions. With this project we could perform contextualized analysis (i.e. by relationship type, subject, institution, geographic location, time) and use the metrics of social network analysis to understand and verify how collaboration occurs in a multi-disciplinary, multi-team project. Moreover, we used the criteria of social network analysis to perform the balancing. In addition, some of the analysis made on the scenery of cancer are shown.

Design is a knowledge intensive activity, which depends on information to be undertaken. Previous designs, recent innovations, new techniques and materials must be taken into account when working on a new project, whatever its nature. Therefore, retrieving appropriate information is of paramount importance to the design process. Information retrieval is an established field, which has seen great progress in recent years. However, as the amount of unstructured information available increases, it becomes harder to retrieve the most relevant information using standard techniques and, frequently, users end up with results that don't fulfill their information needs. In this paper, we present an approach to improve retrieval using query expansion through ontologies allied to faceted navigation. We constructed the Ontoogle tool, which uses an ontology describing a domain to expand the query and define result ranking and presentation using Faceted Navigation. This proposal was evaluated in a case study and the results indicate that this approach contributes to increased satisfaction and confidence of users during their search process.

Modern society offers us many ways to socially interact with friends, colleagues, co-workers, etc. In this scenario, our social networks are in constantly changing and interests, objectives, and other things involved in each social interaction can be affected or even remain out of reach. For each change, the social network has to be re-analyzed to show us if the interests and objectives can still be attained. Each analysis carries its own variables, relevant information, contexts and difficulties, and this new analysis can be expensive for a Human entity. We argue that autonomic computing could help with the analysis and in decision-making, transforming the network into an autonomic social network: intelligent networks capable of analyzing themselves and suggesting actions to individuals or computational systems towards attaining one or more set goals.

This article describes the microblogging as an ecosystem that allows sharing of ideas and connects people with similar interests. Analyzing microblogging dynamics we can detect users profile information, their main topics, interests increasing on them, and also interaction and networking. All these information can be used as a resource for team formation, informal learning and collaboration. We present a social matching model based on microblogging and social networks analysis, and also the prototype created to evaluate this proposal.

Knowledge has always been an advantage in any social activity that involves dispute. To possess knowledge on the domain of a negotiation can give one the edge to attain one's desired goals. In order to obtain such knowledge, an alternative can be the collaboration between allied negotiators where past experiences, acquired in negotiation, can be important and a powerful tool to future efforts. To facilitate this collaboration, this work presents an application of the 3C model for negotiation, the NK-Sys, a tool whose goal is to support the negotiator in decision-making; this tool takes Knowledge Management into account to allow cooperation between negotiators.

Man as a social being, as a being in social relations, is in constant motion. Such mobility affects the social networks that Man builds throughout one's existence. Networks that can consist of dozens, hundreds or even thousands of other people, with various degrees of mobility. During the formation of such networks, problems can arise, such as elements that concentrate many relationships, very isolated individuals or peripheral members of a network, people who are the only link between two distinct groups, agglomerations of people in isolated points. This can cause a series of losses to the most important element that flows through social networks: knowledge. In scientific social networks, this assertion takes even more importance, mainly on account of the mobility of researchers and the excess of knowledge circulating in it, making it more vulnerable to the movements of Man. It is with the intent of solving this problem that our work seeks to achieve success, as it analyzes the scientific social networks based on the GCC tool detects the problems related to them, and suggests recommendations of relationships to users considered harmful to the flow of knowledge in the network. We call this process of social network balancing. For the evaluation of this work we conducted a comparison with several similar proposals, and developed a working prototype, which in turn was used to make our case studies.

Sources of alternative, clean and smart energies are a demand on our modern world. And as a design project, collaboration and knowledge management are elements that improve the work on multidisciplinary teams. This work describes a collaborative project to construct a Brazilian solid oxide fuel cell, its problems and the solution created to enable a collaborative work on a national context.

A social network is a social structure consisting of individuals or organizations usually represented by nodes tied by one or more types of relationships. The resulting structures are complex and analyzing them enables us to detect several inter and intra connection problems. This work focuses on using data mining techniques to identify intra and inter organization groups of people with similar profiles that could have relationships among them. The clusters identified allow us to identify the collaboration in the Brazilian scientific scenario of Computing Science, assessing how researchers in the best universities and research centres collaborate and relate to each other. This kind of approach can help in the identification and improvement of collaboration on innovative and multidisciplinary teams, especially on manufacturing and design scenarios.

Ontology alignment has impact in how ontologies will be treated by actors in semantic web and stills an unsolved issue. To contribute in this issue, we present an algorithm developed in the GNoSIS system. This solution uses both syntactic and semantic techniques in a structural approach. The algorithm uses distinct resemblance functions and calculates the degree of similarity between the concepts in a recursive manner, calculating the resemblance function result between concepts based on the total similarity degree between concepts with a close kinship. A validation of this approach is presented in this article.

Scientific environments are known for being highly dynamic, subject to rapid evolution and demanding constant renewal and update. Additionally, science is a highly social arena. However, interpersonal collaboration and knowledge flow in scientific environments are usually more restricted. Collaboration is intense among small groups of people working on specific problems within a domain, but low between groups. As user profiling has been extensively used as a basis for recommendation, personalization and matchmaking systems, a better profile identification can improve interaction levels among researchers belonging to the same domain but working in different laboratories. Profiles may be constructed in two ways: either through explicit declaration by the user or through the observation of users’ actions. Many systems employ one approach to the exclusion of the other. We contend that a combined approach will yield better results, especially on scientific scenario, providing a mix of declared and inferred information. In this article, we present inference-based profiles in scientific environments (i-ProSE), an integrated system that dynamically creates and maintains scientific user profiles based both on declared information and on observed behaviour. The i-ProSE can be used to locate experts, deliver content, build communities, find collaborators for long-term projects or detect instantaneous opportunities for informal collaboration, which is presented with a short study case.

In scientific and engineering scenarios we can notice the predominance of explicit knowledge being manipulated and distributed, which makes recommender systems very useful in this environment. But along with a knowledge management approach this kind of system can support the organization in better identifying competences, help engage users in a continuous and dynamic knowledge exchange, and customize knowledge dissemination as much as possible. In this work we detail a collaborative recommender system which is used in a Knowledge Management Environment for Scientific and Engineering contexts; we show how this approach can be aimed at a KM process and how this approach can deal with other kinds of knowledge used in research centers and universities.

Customer relationship management -CRM -is a broad term that can cover every mode of customer interaction. This concept has been widely used in business language, enabling companies to gain more knowledge about their customers which, in its turn, can be used to improve marketing and sales targeting, improving their results. University and research institutions (URIs), as any other organizations, also need to cater to the needs of their clients, which may be: society in general, their students, researchers and collaborators; other institutions -of a research or commercial nature with which they interact (supplying data, results from research work, consultancy work, and the execution of partnerships) -or governments that make use of their teaching practices and results from their research work. So, this work is a CRM proposal for URIs, studying the differences between the academic/scientific and business world and proposing an environment to manage all kinds of relationships in the academic and scientific scenario.

Knowledge is something that we're always seeking, but it is unthinkable that a person, with one's common tasks of the day, could take the time to follow people around and ask them about a certain type of knowledge one is interested in. However, imagine if we could share it with many people, even those unknown? Imagine if we didn't have to go one by one, asking them about common interests? Our gain would be huge. Not only in terms of time, but also in terms of gathered information. It is to fill this gap that the Mobile Exchange of Knowledge (MEK) concept came to life, so like its implications, which involve, in a mobile way, the interchange of knowledge between people who possess the same interests. Some issues like ad-hoc networks, social networks, location prediction and distributed knowledge management are also related with the MEK concept. However, to verify how effective our idea is, we conducted an experiment in the geographical space of an university in Brazil, where we analyzed the movements of students and also their interest and willingness to share them.

In project teams, having an unique vocabulary and a common understanding about terms is essential to the success of the project. This issue especially disturbs a design project, which has a multidisciplinary team and must consist of people with specific and different knowledge, from diverse domains, to execute special activities. Negotiation arises from this context as a process for the construction of consensus. The goal of this work is to present a model of negotiation to obtain the consensus of meanings, based on models of business negotiation, and consequently, deal with conflicts and the multiplicity of understandings of a concept, making this negotiation a way for creating value for all agents involved.

The activities executed in the scientific environment are extremely dependent on knowledge from their performers. Thus, knowledge management initiatives are very important in this environment. One of the possibilities for improving the knowledge flow is to explore past experiences, resulting from the modeling and execution of previous scientific activities and handled by a workflow management system. This work presents Thoth, a system which aims at knowledge reuse in the scientific environment, during the planning, design and execution of scientific activities. This approach is integrated with a CBR approach, also described in this paper, with workflow systems and the use of ontologies.

Currently, one problem faced by the organizations is the inability of the institution to know what it actually knows, in other words, the competences that the organization masters. This problem especially disturbs team formation for a design project, which must be composed of people with specific knowledge to execute different activities. Simple classification or a keyword approach is not sufficient for competence identification. The expertise of an employee or researcher often differs in small but significant details, and an approach is required to allow the accurate description of people's competences and to implement a retrieval strategy that finds a person with a specific competence. We propose a technique to identify competences by mining employee documents, especially publications. We have chose a number of criteria, which is described in this paper, to find people with specific expertise and propose than to projects. Finally, a possible indication for a person's participation in a community is her/his published knowledge and expertise degree.

Researchers create and change information faster than in the past. Although a large part of this exchange is in document form, there is also much informal or tacit knowledge exchange in peoples' interactions. The Epistheme environment was designed for scientific knowledge management. This environment is an instance of the SpeCS collaborative framework and aims at knowledge creation, according to Nonaka and Takeuchi's (1995) theory. Researchers can be provided with all necessary knowledge to execute their tasks, to make decisions, collaborate with one another and disseminate an individual's knowledge to transform it into organizational knowledge. This article shows the Epistheme framework with the modules of identification, creation, validation, integration, acquisition and knowledge distribution.

This work presents the development of the AGROMET environment: a platform that supports scientific work in a cooperative way providing the reutilization of data, metadata, models, programs and workflow definitions. To reach this goal this environment is composed of five modules that are responsible for: i) data integration; ii) support to the scientific workflows; iii) support to the collaborative decision making; iv) scientific knowledge management, and v) document and content management. To understand the questions involved in the scientific work a study case is accomplished in the agro-meteorology area in the Brazilian Agricultural Research Enterprise (Embrapa Solos). With this collaborative platform the access, sharing and consequent innovation of the scientific knowledge will be better disseminated in the environment work.