Finding developers to assist with project issues is essential in Global Software Development (GSD) contexts, where various individuals with distinct characteristics are involved. Several recommending approaches lead to identifying the same group of individuals who end up work overloaded. Aiming to diversify the recommendation process, we introduced DRecSys, a diversity-based recommendation system. Our approach seeks to identify individuals with characteristics similar to those previously requested to collaborate. To that end, we proposed a hybrid process composed of supervised (classification) and unsupervised (clustering) techniques. We provided evidence that DRecSys is able to recommend suitable non-obvious developers to assist with project issues.

The Internet of Things (IoT) is a technological paradigm that aims to connect millions of networked devices to provide more complex functionality. However, the heterogeneity of application/device communication standards precludes support for interoperability, which impacts developer collaboration. There are many works that propose solutions to support syntactic and semantic interoperability in IoT context. This paper aims to propose a service capable of supporting pragmatic IoT interoperability with the goal of enriching developer collaboration. This being possible through the use of inferences and similarity calculations about information provided by the developers.

With increasingly complex activities, scientific workflows are becoming more data-intensive. In this context, may require a collaborative, distributed or high performance (HPC) environment such as grids or clouds for their execution. Considering its extensibility feature, resources pool and pay-to-use, cloud computing environments have been increasingly adopted. Scientists are formulating their scientific experiments in a collaborative way, provisioning resources (software, hardware) and managing large volumes of data, based on cloud infrastructures. In data-driven collaborative scientific experiments, aspects such interoperability, privacy and trust in shared provenance data should be considered to allow the reproducibility of the results. In this paper, we present the BlockFlow architecture, which aims to bring trust to scientists of a scientific ecosystem platform (E-SECO) in the execution of their collaborative scientific experiments on cloud platforms.

Digital transformation in e-health is a well-known challenge problem reported from several studies and from several dimensions. In addition, it has been verified a gap in the utilization of new technologies as differential tool in the war against the Covid-19 pandemic. In this paper, we present an ongoing research effort which is characterized for supporting a digital transformation gap found in a public primary healthcare system. Therefore, it can be seen as an interesting case study approach to tackle some challenges found in Covid-19. Utilizing smart bands by groups of different type of voluntaries, where vital signals were collected in a digital data fashion and then evaluated in public health unit. A recommendation system (RS) algorithm was also developed to understand userś behaviors, based upon their vital signals. In addition, we utilized a simulator software to highlight people movement and predictable scenarios of Covid-19 contamination. This last effort provides a visualization on how the proposal could also help in a real ordinary monitoring scenario. Initial results from this research work indicates a differentiated approach to tackle challenges in digital transformation in a public health scenario, especially in a pandemic. In addition, our experiments illustrate that the adoption of some computational technologies require mainly changes on the present behavior, from governments and people, to be successful approaches to individual protection inside public environments.

[Motivation] The science has evolved giving space to the open and data-intensive science. In this context, information sharing, and data integration are essential issues. [Problem] Data integration in the e-Science domain is a complex task, as researchers use different scientific software tools, and perform their experiments in different sites and contexts, thus using specific data models. Without supporting the execution of different geographically distributed tools, scientists may not collaborate with the experiment. [Objective] The goal of this article is to enhance collaboration between researchers throughout the process of scientific experimentation in a scientific software ecosystem platform. [Method] To achieve this goal, the article addresses an approach to support the integration of a scientific software ecosystem with external scientific platforms. [Results] An evaluation of the proposed solution showed that integration solutions implemented can enhance collaboration among researchers.

With advances in technologies, such as the Internet of Things (IoT), scientific research has become data-driven, based mainly on the intensive collection of raw data and analyses of heterogeneous data sets. Computational resources has allowed a somehow agile data production and analytics, which can be collaboratively assisted by teams of researchers. However, in order to achieve a full collaboration, with members altogether engaged, it is necessary to ensure a continuous data flow between the parts and to validate the compatibility of their data. To accomplish such aim, it is required to stress some aspects of the collaboration model, composed by processes of communication, cooperation, and coordination. This paper presents a collaborative research environment built in a scientific software ecosystem platform that implements an ontology-based data integration strategy to semantically provide comprehensive data models of disparate data sources, with the aim of enhancing the scientific collaboration.

This paper presents a solution based on analyses of complex networks in order to address context alignment and context loss between researchers working in projects and possibly in scientific experiments. Network edges with weights indicate the level of alignment between the researchers' contexts. The analyses showed that the context mismatch between members of a project might reveal the reasons why researchers quit their collaborative activities. It was also possible to identify the links that could support the decision of inserting a particular researcher into a project.

In the information age, people are getting more and more connected. In this context, collaborative activities are accomplished in different domains, and their relations allow researchers to model them as complex networks in order to obtain insights and competitive advantage. Software Ecosystems are emerging as a new way to contribute to developing and maintaining software through an active community of engaged users. Recognizing how users collaborate is a complex task that can help understand the network. This paper aims at analyzing a Software Ecosystem through its social layer. To this end, different complex networks were modeled using data extracted from the Eclipse ecosystem version control system. The networks represent interactions among users and projects. The network analyses conducted indicate the viability of using this approach to recognize important contributors in the ecosystem.

Summary Providing historical information to deal with knowledge loss about a scientific experiment has been the focus of some several researches. However, computational support for large‐scale scientific experiments is still incipient and is considered one of e‐science's greatest challenges. In this vein, providing provenance information to a scientist is part of these challenges. Provenance information helps to assure the reliability and reproducibility of experiments. Therefore, this work has as its main objective to present a new ontology—Open Provenance Model Ontology‐e (OPMO‐e)—which is part of an architecture, named SciProvMiner. Open Provenance Model Ontology‐e is a new ontology that encompasses both prospective and retrospective provenance. SciProvMiner captures the provenance and implements all inference and completeness rules defined by Open Provenance Model to provide provenance information beyond those already established. We hypothesize that the capture and management of provenance data will provide scientists with implicit strategical information about the experiment through OPMO‐e. Case studies were carried out to evaluate OPMO‐e use considering SciProvMiner. The obtained results revealed that the use of the proposed ontology and SciProvMiner can enhance scientist's knowledge about an experiment.

Social iterations in the scientific environment can be analyzed to enhance collaboration between researchers. Scientific social networks are complex networks that represent researchers' iterations through academic tasks. Analyzing the structure of those networks researchers can establish new relationships and to understand the potential of collaboration of their relationships. In this paper we use topological aspects from a Brazilian scientific social network to identify the key researchers to the collaboration flow. The relationships between researchers are used to provide insights about researchers' influence in the network.

Analyzing social iterations in a scientific environment will assist researchers in expanding their collaborative networks. Scientific social networks represent the researchers' social iterations in an academic environment. The analysis of these networks requires a detailed study of their structure and it is important the use of visual resources in order to a better understanding of how the social iterations occur. In this paper we will use centrality metrics and a clustering algorithm to analyze the structure of a Brazilian scientific social network. A scientific social network visualization tool will be used to allow a visual analysis of the collaboration between researchers from different educational institutions.

In recent years the scientific research has undergone substantial changes. In particular, there is a greater collaboration between research groups, which leads to an increase in the use of information processing techniques, and, therefore, the need to share results and observations among participants of a research. This work has as main goal to propose an architecture to support distributed processing of scientific experiments, as an implementation of so-called collaborative laboratories.

Link Prediction is a classic social networks analysis problem. Knowing in advance future actions in social network can help, for example, agents decision. Link Prediction techniques are based on metrics that have different approaches. In this paper, we model a multi-relational scientific social network to assess the impact of content extraction on topological metrics. Thus, a metric composed of topological and semantic approach is proposed to solve link prediction problem. The results were compared with those presented by Katz metric.

Companies have increasingly distributed their teams to increase productivity and quality, and to reduce their costs in software development processes. This scenario has brought new challenges to effective collaboration and to enhance communication across peers. Pragmatic interoperability has been considered as one of the key requirements to tackle these challenges. However, there is lack of research investigating how to support development of pragmatic interoperability across globally dispersed groups. This paper proposes a model to support pragmatic interoperability in software projects to foster collaboration in distributed environments. The proposed model was implemented and evaluated in a collaborative architecture which supports collaborative scientific experiments development.

In scientific scenario, there is an increasing collaboration among research groups, which leads to the growth in the use of information processing techniques, and the creation of new experiments as well as the sharing of their results. In this article, we present an architecture named e-ScienceNet, that creates an access point to distributed scientific applications, where scientists can work with heterogeneous data, form groups based on their interests, and create new experiments based on service compositions. Therefore, e-ScienceNet, a semantic peer-to-peer network system, aims to provide support to scientist in the e-Science context, enabling the use and sharing of scientific resources.

Middleware infrastructure has been widely adopted to support the development and execution of collaborative systems. With these systems many challenges arise, those related to interoperability gained remarkable importance. This work aims to increase the semantic interoperability support in an infrastructure named Web-based Groupware Service-Oriented Architecture (WGWSOA) that uses middleware services to support the development and execution of collaborative systems. Services were built on this infrastructure to extend the interoperability semantic support. Evidences pointing to the validity of the proposed solution, to support the development and execution of collaborative systems, were produced through an experimental study.