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Objetivo: Este estudo tem como objetivo contextualizar a Ciência Aberta, propondo uma versão brasileira da taxonomia desenvolvida originalmente pelo grupo Facilitate Open Science Training for European Research (Foster), presente no projeto Open Science do Programa Comunitário de Investigação e Inovação, intitulado Horizon 2020. Método: Adotou-se a pesquisa do tipo bibliográfica, documental, descritiva de abordagem dedutiva, com procedimentos do método Delphi. Utilizou-se a pesquisa bibliográfica com o intuito de recuperar documentos que pudessem dar aporte para conceituação do termo Ciência Aberta, em uma base de documentos adicionados no gerenciador de referências Mendeley. Foram recuperados 158 registros e optou-se por apresentar o conceito mencionado em 13 deles, considerando-se os melhores ranqueados e a quantidade de usuários que os adicionaram em suas bibliotecas Mendeley ou observado o contexto da publicação e deste estudo. A segunda etapa da pesquisa envolveu a tradução e ampliação da taxonomia utilizando o método Delphi, reunindo 13 pesquisadores especialistas na temática sobre Ciência Aberta em geral ou em algumas de suas facetas, os quais cooperaram na proposta de inclusão de novos termos da taxonomia, bem como a validação e sugestões de novos recursos e conceitos na tradução livre realizada inicialmente. Resultado: Para a primeira etapa, o resultado das definições apresenta aspectos do ecossistema da Ciência Aberta envolvendo particularidades filosóficas, científicas, sociais, tecnológicas, políticas e econômicas. O segundo resultado desta pesquisa apresenta a proposta de incorporação de novos termos, compondo uma taxonomia com 11 facetas e 82 rótulos na taxonomia. Conclusões: Vivencia-se um momento transitório de transformação na comunicação científica, envolvendo o surgimento de novos movimentos relacionados à ciência e ao fortalecimento de uma infraestrutura que transpassa a tecnologia utilizada, o que, certamente, pode ser notada na proposta de taxonomia construída em colaboração de pesquisadores e especialista que atuam com a Ciência Aberta ou alguma de suas facetas. Objective: This study aims to contextualize Open Science by proposing a Brazilian version of the taxonomy originally developed by the Facilitate Open Science Training for European Research (Foster) group, present in the Open Science project of the Community Research and Innovation Program, entitled Horizon 2020. Method: Bibliographic, documental, descriptive, and deductive approach research was adopted, with procedures of the Delphi method. Bibliographic research was used to retrieve documents that could contribute to the concept of the term Open Science, in a base of documents added in the Mendeley reference manager. We recovered 158 records and chose to present the concept mentioned in 13 of them, considering the best ranks and the number of users who added them in their Mendeley libraries or observed the context of the publication and this study. The second stage of the research involved the translation and expansion of taxonomy using the Delphi method, bringing together 13 researchers specializing in the subject of Open Science in general or in some of its facets, who cooperated in the proposal to include new terms of taxonomy, as well as the validation and suggestions of new resources and concepts in the free translation initially performed. Result: For the first stage, the result of the definitions presents aspects of the Open Science ecosystem involving philosophical, scientific, social, technological, political, and economic particularities. The second result of this research presents the proposal to incorporate new terms, composing a taxonomy with 11 facets and 82 labels in taxonomy. Conclusions: There is a transitory moment of transformation in scientific communication, involving the emergence of new movements related to science and the strengthening of an infrastructure that goes beyond the technology used, which certainly can be noted in the proposal of taxonomy built in collaboration with researchers and experts who work with Open Science or some of its facets.

‘e-Science’ represents the global collaborations of people and shared resources to solve new and challenging problems in science and engineering (Hey & Trefethen, 2003) on the basis of the IT infrastructure, typically referred to as the Grid (Foster & Kesselman, 1999). As we can easily infer, e-Science initially meant a virtual environment where a new and challengeable research can be accomplished using latest infrastructures. That virtual environment usually has a form of a web portal page or an independent application with a bunch of computer scientific components inside: high-end application researches include large-scale computations for complex multi-physical mechanisms, coupled works of computation and experiments for design-to-development processes, and/or data-intensive researches. The infrastructure consists of computational and experimental facilities, valuable datasets, knowledge, and so on. Researchers can be referred to as a core component of e-Science environment as their discussions and collaborations are promoted by, managed by and integrated to the environment. Meanwhile, the meaning of ‘e-Science’ is becoming broader nowadays. Though e-Science first intended to enrich high-end research activities, it is soon proven to be also effective on academic activities as a cyber education system. (On the other hand, use on inter-disciplinary collaborative researches is not much vitalized as expected, because of diverse preference on internal workflow, I/O and interface among research domains.) Thus, the term ‘e-Science’ is rather used to represent ‘all scientific activities on high performance computing and experimental facilities with the aid of user-friendly interface and system middleware’ these days. As a virtual academic system for aerospace engineering, ‘e-AIRS’ (e-Science Aerospace Integrated Research System) has been designed and developed since 2005. After three years’ development, e-AIRS educational system is finally open, where non-experts can intuitively conduct the full process of computational and experimental fluid dynamic study. Also, the

In Operational Research conventional simulation practices typically focus on the conceptualization, development and use of a single model simulated on a single computer by a single analyst. Since the late 1970s the field of Distributed Simulation has led research into how to speed up simulation and how to compose large-scale simulations consisting of many reusable models running using distributed computers. There have been significant advances in the theories and technologies underpinning Distributed Simulation and there have been major successes in defence, computer systems design and smart urban environments. However, from an Operational Research perspective, Distributed Simulation has had little impact on mainstream research and practice. To argue the potential benefits of Distributed Simulation for Operational Research, this article gives an overview of Distributed Simulation approaches and technologies as well as discussing the state-of-the-art of Distributed Simulation applications. It will investigate the potential advantages of Distributed Simulation for Operational Research and present a possible sustainable future, based on experiences from e-Science, that will help Operational Research meet future challenges such as those emerging from Big Data Analytics, Cyber-physical systems, Industry 4.0, Digital Twins and Smart environments.

Seeking information is of the great key for both researchers and academic li-brarians and for that matter getting data plays a key role. The act of sharing, preserving, collecting, disseminating, and managing of data with respect to re-search data is of the essence in our current system of retrieving information. This has been made possible by the collaboration with information communi-cation and technology for both researchers and academic librarians. Scholars are now producing, disseminating and storing digital data in much larger vol-ume than the text. Management of research data is a major challenge for many universities in Ghana. In case, having access to such data is really difficult because some data are not stored in libraries. The study reveals that some academic libraries are beginning to provide frameworks for these services with some degree of success as policies are being formulated. Also, infrastructures are being set up, and library staff trained, and awareness and advocacy campaigns held with academic staff and researchers. The study seeks to explore academic librarians’ perceptions of their roles and involvement in RDM support services. In view of that, the researchers used the qualitative method and gathered data using semi-structured interviews. Thematic analysis method was used to analyze the data collected. A sample of 116 librarians was targeted for the survey in the data collection process. Out of one hundred and sixteen (116), only eighty-one (81) agreed to participate. The findings reveal that academic libraries in Ghana need to improve their technical skills and competency sets through training and other capacity building program. Based on the findings recommendations were also provided which can help major Ghanaian academic libraries to improve.

In contrast with practice in recent times past, computational and data intensive processes are increasingly driving collaborative research in science and technology. Large amounts of data are being generated in experiments or simulations and these require real-time, or near real-time, analysis and visualisation. The results of these evaluations need to be validated and then published quickly and openly in order to facilitate the overall progress of research on a national and international basis. Research is increasingly undertaken in large teams and is also increasingly interdisciplinary as many of the major research challenges lie at the boundaries between existing disciplines. The move to open access for peer reviewed publications is rapidly becoming a required option in the sector. At the same time, communication and dissemination procedures are also utilising non-traditional forms facilitated by burgeoning developments in social networking. It is proposed that these elements, when combined, constitute a paradigm shift in the model of research and the dissemination of research results.

A review and critical analysis of publications devoted to the competence issues of digital librarians of scientific libraries has been carried out. The range of functional requirements and librarian knowledge related to information technology is generalized. The basic tendencies of involvement of librarians in digital scientific communication are found out. The scientific and educational measures necessary for the successful participation of library specialists in digital projects of the scientific library are determined.

Effective planning and optimized execution of the e-Science workflows in distributed systems, such as the Grid, need predictions of execution times of the workflows. However, predicting the execution times of e-Science workflows in heterogeneous distributed systems is a challenging job due to the complex structure of workflows, variations due to input problem-sizes, and heterogeneous and dynamic nature of the shared resources. To this end, we propose two novel workflow execution time-prediction methods based on the machine learning ensemble models. In this paper, we showcase our approach for two different real Grid environments. Our approach can effectively predict the execution time of the scientific workflow applications in the Grid for various problem sizes, Grid sites, and runtime environments. We characterized the workflow performance in the Grid using the attributes that define structure of workflow as well as the execution environment. Contrary to common ensembles, our ensemble systems employed three strong learners, which balance the weaknesses of each other by their strengths to model the workflow execution times. The proposed methods have been thoroughly evaluated for three real-world e-science workflow applications. The experimental results demonstrated that our proposed multi-model ensemble models can significantly decrease the prediction error (by 50%, on average) as compared with methods based on the radial basis function neural network, local learning, and performance templates. The proposed methods can also be applied with similar effectiveness and without any major modification for other heterogeneous distributed environments, such as the Cloud.

During “the 13th Five-Year Plan” period, the Chinese experts and scholars make full use of advanced information technology to carry out scientific research work, and have achieved a series of scientific and technological achievements. It reflects the level of e-Science application in China. This article systematically summarized the e-Science research from three aspects, the application in the frontier research of science and technology, the progress of e-Science in major projects and the achievements of informatization in interdisciplinary. During the past two years, so as to provide a reference for the scholars in this field for the further e-Science research.

Our daily life has experienced significant changes in the Internet age. The emergence of e-science is regarded as a dramatic one for science. Wikis, blogs, virtual social networks, grid computing and open access are just a brief selection of related new technologies. In order to understand the changes, it is necessary to define these aspects of e-science precisely. Right now, no generally used term or common definition of e-science exists, which limits the understanding of the true potential of the concept. Based on a well-known approach to science in terms of three dimensions—human, task and technology—the author provides a framework for understanding the concept which enables a distinctive view of its development. The concept of e-science emerged in coherence with the technological development of web 2.0 and infrastructure and has reached maturity. This is impacting on the task and human dimensions as in this context, the letter “e” means more than just electronic.

High-throughput gene expression studies yielded a great number of large datasets, and these are freely available in biological databases. Re-analyzing these studies individually or in clusters can produce new results relevant to the scientific community. The purpose of this work is to develop a WEB system based on the e-Science paradigm. The system should read massive amounts of data from the Gene Expression Omnibus (GEO) database, pre-process, mine, and display it in a user-friendly interface. Thus, it is intended to mitigate the difficulty in interpreting data from transcriptomic studies made using the DNA microarray technique. Also presented will be the preliminary results obtained from the initial stages of development, as well as the proposed architecture for the system.

The questions of whether and how doctoral students are motivated for enhanced research collaboration deserve thorough consideration. Even though collaboration in general and its mediated forms, such as computer-supported cooperative work and collaborative learning (CSCW and CSCL), are prominent research topics, only a little is known about the methods necessary to design various activities to support research collaboration. With the upcoming generation of tools such as Mendeley, Conference Chair, ResearchGate, or Communote, scholars suspect that web 2.0 services play a decisive role in enabling and enhancing research collaboration. However, there is almost no data available on the extent to which researchers adopt these technologies, and how they do so. Therefore, the authors first present an overview of the current usage of web 2.0 among doctoral researchers in their daily academic routines, based on a survey (n = 140) conducted in the German Federal State of Saxony. It confirms a wide and often specified usage of web 2.0 services for research collaboration. For theoretical analysis, the authors propose a conceptual framework that reflects the requirements of scientific participation and scholarly collaboration within an average international doctoral programme adopting current digital technologies. The aim of this framework is to understand, support, and enhance research collaboration among doctoral researchers. Our fish model highlights the mutual relationship between the following dichotomous factors: (a) tasks/time factors; (b) beliefs/activities; (c) support/context; and (d) incentives/ethical issues. Our results indicate a significant relationship in terms of research collaboration. This relationship has particularly been identified between two dichotomous factors: beliefs/activities and incentives/ethics.

The results of recent foresight projects reveal the impact of future ICT tools on the practice of scientific research. This paper presents several aspects of the process of building scenarios and trends of selected advanced ICT technologies. We point out the implications of emerging global expert systems (GESs) and AI-based learning platforms (AILPs). GESs will be capable of using and processing global knowledge from all available sources, such as databases, repositories, video streams, interactions with other researchers and knowledge processing units. In many scientific disciplines, the high volume, density and increasing level of interconnection of data have already exhausted the capacities of any individual researcher. Three trends may dominate the development of scientific methodology. Collective research is one possible coping strategy: Group intellectual capacity makes it possible to tackle complex problems. Recent data flow forecasts indicate that even in the few areas, which still resist ICT domination, research based on data gathered in non-ICT supported collections will soon reach its performance limits due to the ever-growing amount of knowledge to be acquired, verified, exchanged and communicated between researchers. Growing automation of research is the second option: Automated expert systems will be capable of selecting and processing knowledge to the level of a professionally edited scientific paper, with only minor human involvement. The third trend is intensive development and deployment of brain–computer interfaces (BCIs) to quickly access and process data. Specifically, GESs and AILPs can be used together with BCIs. The above approaches may eventually merge, forming a few AI-related technological scenarios, as discussed to conclude the paper.

The project of “E-Science Application for Knowledge Discovery in Stem Cells” is an important part of the 13th Five-Year Plan e-Science Programme of Chinese Academy of Sciences. Focusing on the critical needs of science and technology (S&T) innovation and subject knowledge discovery in stem cell research for e-Science, the project makes comprehensive use of big data and new generation of artificial intelligence technologies to develop a platform of “Stem Cell Subject Knowledge Discovery (SCKD)”, which can provide professional, efficient and accurate subject knowledge discovery services for the research and S&T management activities in stem cell. This paper introduces the project’s objectives, tasks, key technologies, achievements and service effect, and discusses the future work of SCKD.

This open access book shows the breadth and various facets of e-Science, while also illustrating their shared core. Changes in scientific work are driven by the shift to grid-based worlds, the use of information and communication systems, and the existential infrastructure, which includes global collaboration. In this context, the book addresses emerging issues such as open access, collaboration and virtual communities and highlights the diverse range of developments associated with e-Science. As such, it will be of interest to researchers and scholars in the fields of information technology and knowledge management.

As an important tool of scientific research activities, instruments and equipment play an important role in scientific creation. Open sharing of large-scale instruments and equipment is an important part of the current sharing economic model. The sharing and use of instruments and equipment is an important manifestation of eScience. Taking the practice of open sharing of instruments and equipment of the Chinese Academy of Sciences as an example, this paper analyzes the design and practice of IoT and Internet in the intensive sharing management of large-scale instruments and equipment, the application effect achieved, and puts forward the prospect for the future development of the sharing platform of instruments and equipment

Science ecosystem has been evolved during modern human life. Teaching, learning, education, and doing research connecting elements of this ecosystem include students, professors, administrators and researchers from academia and industry. Different learning systems have been developed in different countries results in various science ecosystems that interact with each other. While the main goals are the same and there are many collaborations between them, but their reactions are different during the world paradigm shifts such as ICTs, IoTs and the Internet revolution and recently emerged Covid-19 Pandemic. In this paper we are going to study and share some experiences of different education systems. This may help developed and developing courtiers to tune current or determine new strategies, especially when the world faces with the crisis of Covid-19 Pandemic. We will discuss about the digital transformation in academic society and innovative ecosystems in the world beyond Covid19-Pandemic by using 7PS model and the 5th wave theory.

Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains, like geoscience, climate science, and health science. In these domains, users need to perform comprehensive data analyses combining scientific data and ML models to provide for critical requirements, such as reproducibility, model explainability, and experiment data understanding. However, scientific ML is multidisciplinary, heterogeneous, and affected by the physical constraints of the domain, making such analyses even more challenging. In this work, we leverage workflow provenance techniques to build a holistic view to support the lifecycle of scientific ML. We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design principles to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946 GPUs. The experiments show that the principles enable queries that integrate domain semantics with ML models while keeping low overhead (, 21 pages, 10 figures, text overlap with arXiv:1910.04223, a workshop paper being extended in this journal paper

A Venn diagram is used in mathematics to graphically symbolise properties, axioms, and problems concerning sets and their theories. Thus, this study applied a Venn diagram to describe a theoretical background for data librarianship as a field relating to information science, e-science, and data science. Data librarianship is a new area of study that is located within the thematic core of the triad. The first set on the proposed Venn diagram is information science. Information technology concepts are fundamental to the comprehension of data librarianship in the context of information science. The second set is e-science, an innovative field that incorporates software and hardware that have been built by technology into science. The third set is data science, a way of representing data-driven research in the most diverse knowledge fields; it is a set of the skills, methods, techniques, and technologies of statistics and computer science used to extract knowledge and to create new products and services from data. To ensure greater comprehension of data librarianship, a relatively new field, we suggest some reading materials. The formal discipline of data librarianship is yet to be established in many countries across the globe. Thus, there is the lack of adequate information and certification on data librarianship.

The fact that satellite planning in Strategic Pilot Projects in space science had opened a new vigorous era in China’s space science was firstly depicted, and then the demands, chances, and challenges of space science e-Science that meet the condition of this new era were analyzed. The “Domain Cloud of Space Science and Technology” (DCSST) project supported by “e-Science projects” in the “12th Five-Year Plan” was taken as a typical example to introduce the space science e-Science practice and progress. Its framework, construction process, and system achievements—the STAR-Network was subsequently introduced, and STAR-Network functions and abilities were described in detail. Furthermore, its application results and serving effects in satellite mission collaborative demonstration, operation, and scientific research assistance were also elaborated. Finally, the future goals were determined, focusing on improving the e-Science core element levels and abilities, and devoting to the construction of a deeply fused and extensive cloud e-Science platform.

Over the past two years, the domestic experts and scholars had completed a lot of research on the development of e-Science in China, and received a series of outstanding achievements. This chapter mainly summarized the fruitful achievements which supported by e-Science and information technology, so as to provide a reference for the researchers in this field for the further e-Science research.

The development of major scientific instruments has indispensable impacts on the modern science and technology, however, it is a complex and difficult mission. Comparing to common scientific projects, the development of major scientific instruments not only requires progresses in relevant theories and technologies, but also requires efficient projects management. In this chapter, quoting the Special Fund for Research on National Major Research Instruments as an example, we will discuss the applications of the information for the program of developing major scientific instruments, and comment on their importance and necessity. Our discussion and comments are made on the basis of following five parts, including sources of project ideas, the process of developing instruments, the integration of instrument systems, the implementation of the projects management, and sharing information after the completion of instruments.

The information tidal wave is changing social life and human scientific research activities and scientific research management paradigm. E-Science has become a vital approach of scientific research and management. As an important part of national science research funding system, after many years of informatization construction, National Natural Science Foundation of China (NSFC) has built a whole process management system for research funds and an information opening and sharing environment, which effectively support various NSFC transactions, meanwhile provide advanced information techniques for NSFC to play a guiding and coordinating role in science research.

The analysis of time series in the era of Big Data has become a major challenge for computational framework research. Furthemore, in the areas of space science which deals with a large variety of data, the practical consistence between workload, workflow and cloud computing is crucial. Here, such consistence is provided by an innovative e-Science framework project named Sentinel which is based on a NoSQL data base (MongoDB) and a containerization platform (Docker). This web framework supports researchers for time series analysis in a cloud environment where they can easily access, parameterize, initialize and monitor their applications. As a case study in the Brazilian Space Weather Program, we consider the intensive analysis of time series from a complex information system for solar activity monitoring and forecasting. As a prototype for implementing the framework, the DFA (detrended fluctuation analysis) technique was used as a nonlinear spectrum analyzer applied to the solar irradiance measurements from 1978 to 2012. Moreover, new applications can be added and managed by researchers on the portal easily to complement their data analysis purposes.

National research and education networks (NRENs) play a critical role in the development of communication network infrastructure and networked services for researchers and educators. They help close ‘digital divides’ between and within countries and are an essential factor for national and international development. In collaboration with the West and Central African Research and Education Network (WACREN), the TANDEM project has developed a roadmap for the development of NRENs in the region. This was based on the results of a survey that was conducted to investigate user requirements of networked services. The analysis of the 561 responses to a three-part questionnaire divided into 11 education, 22 research and 2 technical management questions identified key educational and research service needs. This article reports on the results of the survey with respect to research services. Highlights include respondents wanting regular access to online conference and academic articles (89%), a range of research services including online library resources, video conferencing, collaboration tools, online data access and storage, online library resources and inter-university login (>87%), access to remote computing facilities (80%) and high performance computing facilities (77%). A desire to share data with others online (74%) was also identified. Respondents also indicated that they would like to access research services through a range of device types–Laptops (96%), Fixed PC (86%) and Mobile Devices (81%). Poor network connectivity was consistently identified as being a major barrier to research in the region. H2020 TransAfrican Network Development (TANDEM) and the H2020 Energising Scientific Endeavour

Accurate and comprehensive storage of provenance information is a basic requirement for modern scientific computing. A significant effort in recent years has developed robust theories and standards for the representation of these traces across a variety of execution platforms. Whilst these are necessary to enable repeatability they do not exploit the captured information to its full potential. This data is increasingly being captured from applications hosted on Cloud Computing platforms, which offer large scale computing resources without significant up front costs. Medical applications, which generate large datasets are also suited to cloud computing as the practicalities of storing and processing such data locally are becoming increasingly challenging. This paper shows how provenance can be captured from medical applications, stored using a graph database and then used to answer audit questions and enable repeatability. This static provenance will then be combined with performance data to predict future workloads, inform decision makers and reduce latency. Finally, cost models which are based on real world cloud computing costs will be used to determine optimum strategies for data retention over potentially extended periods of time.

As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.

The purpose of this study is to examine how institutional pressures, individual motivations, and resources all affect scientists diverse data sharing behaviors, including (a) making data accessible through data repositories, (b) submitting data as journal supplements, and (c) providing data via personal communication methods upon request. A combined theoretical framework integrating institutional theory and theory of planned behavior was used to create a research model which presents how scientists make the decision to share data in diverse ways, and how the data sharing factors differ across diverse data sharing behaviors. A survey method was employed to evaluate the research model by using multivariate regression analysis technique with a total of 2172 survey responses in the U.S. The results of this research show the dynamic relationships between diverse data sharing factors and different forms of data sharing behaviors. For data sharing via data repository, journal pressure, perceived effort, and availability of data repositories are significant factors; for data sharing through journal supplement, journal pressure, perceived career benefit, perceived effort, and availability of data repository are significant factors; for personal data sharing, funding agency pressure, normative pressure, perceived career risk, perceived effort, and availability of data repositories are significant factors. This research suggests that funding agencies, journal publishers, and scientific communities that different strategies need to be employed to promote different forms of data sharing behaviors.

LIBER's 45th Annual Conference 2016, Helsinki, FIN, -; In the context of E-science and open access, visibility and impact of scientific results and data have become important aspects for spreading information to users and to the society in general. The objective of this general trend of the economy is to feed the innovation process and create economic value. In our institute, the French National Research Institute of Science and Technology for Environment and Agriculture, Irstea, the department in charge of scientific and technical information, with the help of other professionals (Scientists, IT professionals, ethics advisors...), has recently developed suitable services for the researchers and for their needs concerning the data management in order to answer European recommendations for open data. This situation has demanded to review the different workflows between databases, to question the organizational aspects between skills, occupations, and departments in the institute. In fact, the data management involves all professionals and researchers to assess their ways of working together.

The application of digital cameras to monitor the environment is becoming global and changing the way of phenological data collection. The technique of repeated digital photographs to monitor plant phenology (phenocams) has increased due to its low-cost investment, reduced size, easy set up installation, and the possibility of handling high-resolution near-remote data. Considering the widespread use of phenocams worldwide, our main goals here are: (i) to provide a step-by-step guide for phenocam set up in the tropics, reinforce its appliance as an efficient tool for monitoring tropical phenology and foster networking, (ii) to discuss phenocam applications for biological conservation, management, and ecological restoration. We provide the concepts and properties for image analysis which allow representing the phenological status of the vegetation. The association of a long-term imagery data with local sensors (e.g., meteorological stations and surface-atmosphere flux towers) allows a wide range of studies, especially linking phenological patterns to climatic drivers; and the impact of climate changes on plant responses. We show phenocams applications for conservation as to document disturbances and changes on vegetation structure, such as deforestation, fire events, and flooding and the vegetation recovery. Networks of phenocams are growing globally and represent an important tool for conservation and restoration, as it provides hourly to daily information of monitored systems spread over several sites, ecosystems, and climatic zones. Moreover, websites enriched by vegetation dynamic imagery data can promote science knowledge by engaging citizen science participation.

This article again asks, What is nursing science? Who knows? Who cares? The author describes the threat to the survival of nursing science grounded in nursing frameworks and theories. This threat is magnified by the proposal of the Council for the Advancement of Nursing Science (CANS) to change the curricula of PhD education. The aim of CANS is to prepare nurse scientists for lifelong competitive careers in interdisciplinary research, often focused on funding priorities of the National Institute of Nursing Research (NINR). Curricula would include preparation for conducting research in topics such as omics, e-science, translation science, biobehavioral science, symptom science, and team science. How can this be nursing science? It is argued that this focus might obliterate nursing’s discipline-specific phenomenon of concern, the human-universe-health process.

Data provenance is a fundamental concept in scientific experimentation. However, for their proper understanding and use, efficient and user-friendly mechanisms are needed. Research in software visualization, ontologies and complex networks can help in this process. This paper presents a framework to assist in the understanding and use of data provenance using visualization techniques, ontologies and complex networks. The framework capture the provenance data and generates new information using ontologies and provenance graph analysis. The graph is analyzed through complex networks techniques and provide some metrics to help in each node analyzes. The visualization presents and highlights the inferences and results. The framework was used in the E-SECO scientific ecosystem to support the scientific experimentation.

There is a growing need for cyberinfrastructure to support science-based decision making in management of natural resources. In particular, our motivation was to aid the development of cyberinfrastructure for Integrated Ecosystem Assessments (IEAs) for marine ecosystems. The IEA process involves analysis of natural and socio-economic information based on diverse and disparate sources of data, requiring collaboration among scientists of many disciplines and communication with other stakeholders. Here we describe our bottom-up approach to developing cyberinfrastructure through a collaborative process engaging a small group of domain and computer scientists and software engineers. We report on a use case evaluated for an Ecosystem Status Report, a multi-disciplinary report inclusive of Earth, life, and social sciences, for the Northeast U.S. Continental Shelf Large Marine Ecosystem. Ultimately, we focused on sharing workflows as a component of the cyberinfrastructure to facilitate collaboration and reproducibility. We developed and deployed a software environment to generate a portion of the Report, retaining traceability of derived datasets including indicators of climate forcing, physical pressures, and ecosystem states. Our solution for sharing workflows and delivering reproducible documents includes IPython (now Jupyter) Notebooks. We describe technical and social challenges that we encountered in the use case and the importance of training to aid the adoption of best practices and new technologies by domain scientists. We consider the larger challenges for developing end-to-end cyberinfrastructure that engages other participants and stakeholders in the IEA process.

ENVRI-FAIR is a recently launched project of the European Union’s Horizon 2020 program (EU H2020), connecting the cluster of European Environmental Research Infrastructures (ENVRI) to the European Open Science Cloud (EOSC). The overarching goal of ENVRI-FAIR is that all participating research infrastructures (RIs) will provide a set of interoperable FAIR data services that enhance the efficiency and productivity of researchers, support innovation, enable data- and knowledge-based decisions and connect the ENVRI cluster to the EOSC. This goal will be reached by: (1) defining community policies and standards across all stages of the data life cycle, aligned with the wider European policies and with international developments; (2) creating for all participating RIs sustainable, transparent and auditable data services for each stage of the data life cycle, following the FAIR principles; (3) implementing prototypes for testing pre-production services at each RI, leading to a catalogue of prepared services; (4) exposing the complete set of thematic data services and tools of the ENVRI cluster to the EOSC catalogue of services.

SAGE (the Scalable Adaptive Graphics Environment) and its successor SAGE2 (the Scalable Amplified Group Environment) are operating systems for managing content across wideband display environments. This paper documents the prevalent usage patterns of SAGE-enabled display walls in support of the e-Science enterprise, based on nearly 15 years of observations of the SAGE community. These patterns will help guide e-Science users and cyberinfrastructure developers on how best to leverage large tiled display walls, and the types of software services that could be provided in the future.

The European Network for Earth System Modelling (ENES) Climate Analytics Service (ECAS) is a new service from the EOSC-hub project. It offers a Virtual Research Environment (VRE) to scientific users, combining a Python (Jupyter) work environment with support services for data access, computing and data sharing. ECAS is motivated by providing users with remote access to extensive computing and storage resources beyond what they may have access to locally, reducing the need to conduct costly data transfer, and helping to realize the vision of FAIR data management. ECAS aims at providing a paradigm shift for the ENES community and beyond with a strong focus on data intensive analysis, provenance management, and server-side approaches as opposed to the current ones mostly client-based, sequential and with limited or missing end-to-end analytics workflow and provenance capabilities. Furthermore, the integrated data analytics service enables basic data provenance tracking by establishing a graph of persistent identifiers (PIDs) through the whole chain, and thereby improving reusability, traceability, and reproducibility. ECAS targets multiple user groups, including researchers in lack of local computing and storage resources, researchers with interest in the high-volume climate data pools, and use within education and training scenarios.

The main aim of the development of a Spanish Network of Excellence on Biodiversity is to strengthen the Spanish Scientific Network Community participating in LifeWatch, the E-Science European Infrastructure for Biodiversity and Ecosystem Research ERIC (https://www.lifewatch.eu). Through this e-infrastructure several resources and services are provided to the scientific community working on this field (catalogues of virtual labs (VREs), data and services) (https://www.lifewatch.eu/web/guest/catalogue-of-virtual-labs). The LifeWatch ERIC Statutes foresee the creation of National Nodes, which in Spain has been translated into the creation of the Joint Research Unit LifeWatch Spain (JRU - LW.ES) in 2014. This brings together several organizations and institutions carrying out research, development and innovation (RDI) activities in this field. Among others, to date the JRU - LW.ES counts on the participation of Public Research Organizations (Center for Energy, Environmental and Technological Research (CIEMAT), The Spanish National Research Council (CSIC), Spanish Institute of Oceanography (IEO), National Institute of Aerospace Technology (INTA)); International Excellence Campus & Universities (ANDALUCIA TECH Universities of Seville and Málaga, Universities of Cantabria, Navarra, Extremadura, Granada, Huelva, Alcalá de Henares (Madrid), Pablo de Olavide); Ministry of Agriculture, Fishing, Food and Environment (Spanish Inventory for Natural and Biodiversity Heritage, Fundación Biodiversidad, Directorate-General for Water-River Basin Authorities, Green Infrastructure); Technology and Innovation Parks-Institutions (Andalusica Technological Park (PTA), Cartuja93, Technological Institute Galicia (ITG)); Regional and Local Authorities (Junta Andalucía), and Port Authorities (Cartagena, Huelva, Seville). These organizations have expressed their interest in integrating into a network of excellence to promote biodiversity research, addressing major environmental challenges, and supporting knowledge-based strategic solutions for environmental preservation. This scientific network, associated with the e-infrastructure LifeWatch ERIC, aims to bring together the critical mass of resources and experience in the field of biodiversity present within Spain, taking advantage of the information and communication technology (ICT) tools and facilities that offer the necessary infrastructure, operating in synergy with national research plans. In essence, the creation of this network aims to consolidate a focus of excellence in Spain in the field of biodiversity, promoting the implementation of joint European projects and contributing to the objectives set in the LifeWatch ERIC.

This work reviews and compares the literature about ITSM practices in libraries, public administration, commercial sector and e-Science infrastructures. Six aspects taken out were properties or structure of digital components, source of finances to support the daily running of business in these sectors, forms of organization, frameworks developed in the form of guidelines to structure the sectors, the factors that influence implementation of ITSM guidelines. It was found out that though there are similarities between all of these sectors, yet the case of e-infrastructures is special and practical implementation of ITSM practices is needed.

In this paper, we present a bibliographic review that contains the most important aspects of the digital transformation in scientific collaboration. We reviewed 162 scientific papers where authors have identified and analysed the digital changes in scientific research and the impact they have generated in the scientific community.

The e-Science program was initiated in the United Kingdom in the early 2000s with the aim of bringing together researchers in large scale, collaborative projects involving software and computation to solve grand challenges. A legacy of this program has been an understanding of the importance of the people behind the software, the researchers and research software engineers, as well as the challenges of developing and maintaining code that is reusable given the problems of software decay. The Software Sustainability Institute was established in the UK to provide support and direction for the research software community through consultancy, training, engagement, and policy campaigns. Through this it has worked with an international community of collaborators, in the UK, in Europe, and across the world to support reusability, research integrity, and transparency, recognizing that to achieve the goals of e-Science, we must change research culture globally.

This paper investigates the role of feedback in big data projects for cyberinfrastructure (CI) diffusion in e-science. For many of these projects, large-scale and heterogeneous datasets, multidisciplinary and dispersed experts, and advanced technologies are brought together to harness analytic insights. However, without effective CI and computational tools, the accuracy and meaningfulness of analytics results are compromised. In fact, without CI tools, raw data remain raw with hidden insights, as data analytics cannot be executed at all. In order to improve such tools for meaningful results, we argue to conceptualize the communication mechanism of ‘feedback’ in agile software development, with the goal of producing CI tools that are responsive to users. Based on a grounded analysis of interview data, we concluded that feedback helps developers in big data projects understand users’ needs, makes tools user-friendly, prevents emergencies, and is better for developers than no feedback. Furthermore, good feedback is often structured, specific, actionable, timely, generalizable, and delivered in a tactful way. Despite the limitation of the findings being exploratory and yet to be evaluated experimentally, we argued that they still can motivate developers to be proactive seekers of feedback for their tools, productively guide developers’ communication with users, and ultimately promote further adoption and diffusion of CI tools in e-science.

The development of modern information technology generates the need in scientific research on an innovation platform called the e-science. Implementation of projects aimed at informational and technological support for research activities requires effective communication among members of virtual teams and information workers. The authors have developed a project aimed at creating a complex system of informational and technological support for scientific research conducted by virtual research teams on the platform of electronic science using cloud computing technologies. The information system provides the establishment of effective scientific communication when conducting research on the platform of electronic science. This approach increases the effectiveness of communication processes of participants in the virtual scientific team for interdisciplinary research; contributes to solving the informational aspects of scientific communication and the exchange of scientific data, information and knowledge for the convenient interaction of researchers, it also facilitates the effective search, consolidation, preservation and dissemination of research results among the scientific community.

This article commences the special section on scientific workflows. A brief history is given of workflow approaches in general. More detail is given of scientific workflows, including sample applications. Challenges and research issues are then identified. Finally, an overview is given of the articles appearing in this special section.

This paper explores the potential for improving the performance of e-Science applications on commercial clouds through the detailed examination, and characterization, of the underlying cloud network using network tomography. Commercial cloud providers are increasingly offering high performance and GPU-enabled resources that are ideal for many e-Science applications. However, the opacity of the cloud's internal network, while a necessity for elasticity, limits the options for e-Science programmers to build efficient and high performance codes. We introduce health indicators, markers, metrics, and score as part of a network health system that provides a model for describing the overall network health of an e-Science application. We then explore the suitability of a range of tomographic techniques to act as health indicators using two testbeds-the second of which spanned one hundred AWS instances. Finally, we evaluate our work using a real-world medical image reconstruction application. We identify and characterize network performance in commercial clouds.An overall health system is constructed using tomographic probes to establish and compare an instance's network performance.We deploy the health system over a testbed of 100 AWS instances and explore its ability to scale.We apply the health system to a medical imaging e-Science application and demonstrate performance benefits.

Cyberinfrastructure is a research infrastructure that provides an environment in which research communities can get access to distributed resources and collaborate at unprecedented levels of computation, storage, and network capacity. The Worldwide LHC Computing Grid (WLCG) is a global collaborative project of computing or data centers that enables access to scientific data generated by the Large Hadron Collider (LHC) experiments at CERN. This case study analyzes the WLCG as a model of cyberinfrastructure in research collaboration. WLCG provides a useful case of how cyberinfrastructure can work in providing an infrastructure for collaborative researches under data-intensive paradigm. Cyberinfrastructure plays the critical role of facilitating collaboration of diverse and widely separated communities of researchers. Data-intensive science requires new strategies for research support and significant development of cyberinfrastructure. The sustainability of WLCG depends on the resources of partner organizations and virtual organizations at international levels, essential for research collaboration.

Nowadays, the adoption of Cloud Computing platforms and Service Computing technologies are almost natural for the different e-Science communities. Cost benefits for data-intensive applications, ease of access, rich and varied offers for services are examples of positive returns by users. However, beyond this favorable welcome for the technology, some research problems remain and are still challenging. In this paper, we focus on the problems of automatically deploying IaaS for computing and for data management, using the SlapOS Cloud. The core of the system is a distributed protocol for orchestrating data and compute nodes. Using this interaction scheme, users are able to deploy, without any system administrator intervention, a PaaS inside the IaaS basically a Desktop Grid middleware. The aim of this paper is to demonstrate that the Desktop Grid and Cloud paradigms may merge and may be widely used by non-experts in the different areas of e-Science. We propose a fully self-organized volunteer Cloud for researchers where they can carry out e-Science experiments and process large amounts of data in a coherent way.

Conducting scientific research on the platform of electronic science requires the establishment of effective communication between members of virtual teams. It requires the implementation of creative projects aimed at informational and technological support of research work. The authors developed a project aimed at creating a complex system of informational and technological support for scientific research on the platform of electronic science using cloud computing technologies. The information system solves a number of key issues for the establishment of effective scientific communication in conducting research on the e-science platform, in particular, it improves the efficiency of the communication processes of participants in the virtual creative team for conducting multidisciplinary research; contributes to solving the financial aspects of scientific communication and the exchange of scientific data, information and knowledge for the convenient interaction of researchers, and also contributes to the efficient search, consolidation, storage and dissemination of research results among the scientific community.