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New scientific knowledge is needed more urgently than ever, to address global challenges such as climate change, sustainability, health, and societal well-being. Could artificial intelligence (AI) accelerate science to meet these global challenges in time? AI is already revolutionizing individual scientific disciplines, but we argue here that it could be more holistic and encompassing. We introduce the concept of virtual laboratories as a new perspective on scientific knowledge generation and a means to incentivize new AI research and development. Despite the often perceived domain-specific research practices and inherent tacit knowledge, we argue that many elements of the research process recur across scientific domains and that even common software platforms for serving different domains may be possible. We outline how virtual laboratories will make it easier for AI researchers to contribute to a broad range of scientific domains, and highlight the mutual benefits virtual laboratories offer to both AI and domain scientists.

Am Bundesinstitut für Risikobewertung (BfR) ist das Forschungsdatenmanagement (FDM) als Bestandteil des wissenschaftlichen Forschungszyklus definiert. Der Fokus liegt darauf, dass FDM-Konzepte, -Methoden und -Dienstleistungen auf allen Ebenen einer öffentlichen Einrichtung berücksichtigt werden müssen: auf politischer, strategischer und operativer Ebene für verschiedene Interessengruppen in der Forschung, IT und auf Leitungsebene. Der Aufbau des FDM am BfR steht darüber hinaus vor der Herausforderung, die gesetzlichen Regelungen des E-Government-Gesetzes sowie die Koordination mit den weiteren Ressortforschungseinrichtungen unter der Leitung des Bundesministeriums für Ernährung und Landwirtschaft in seinem FDM-Framework einzubinden. Das in diesem Beitrag vorgestellte FDM-Framework des BfR basiert auf einem FDM-Konzept, das auf drei Säulen aufgebaut ist: Strategie, Kommunikation und Pilotierung. In einer ersten Pilotphase startet das BfR 2022 die Etablierung eines institutionellen FDM. Dieser Beitrag stellt das FDM-Framework am BfR vor und beschreibt erste Beispiele für die Umsetzung.

Introduction: In the eScience environment, digital research objects are characterized by having a complex and long-life cycle, which depends on different disciplinary contexts and perspectives of (re)use. This lifecycle starts before the start of research and extends beyond the end of the project, along this journey, various types of metadata are added to objects, assigned by different actors, including those generated automatically by scientific instruments and workflow tools, in a continuous process of adding value to datasets and other research objects. In this context, digital research objects are accompanied by a wide range of metadata - with many functions and properties - that often surpass the data themselves in volume and even in importance, configuring a "scientific big metadata" that is difficult to organize and manage. Objective: Systematically present the functions of new metadata to support metadata management and the construction of disciplinary schemes. Methodology: Underlying the construction of the proposal, four axes provide methodological support to the study: historical, pragmatic, standardization, and epistemological. Results: As a result, a model is proposed for schematizing the various elements of metadata based on their functionalities, based on the assumption of the connection between eScience and Information Science established by big metadata. Conclusion: It is concluded that big metadata creates a connection between eScience and CI, and that in addition to the need to curate research objects, specific FAIR management of metadata is also necessary. [ABSTRACT FROM AUTHOR]

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 decisions 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 decisions enable queries that integrate domain semantics with ML models while keeping low overhead (<1%), high scalability, and an order of magnitude of query acceleration under certain workloads against without our representation. [ABSTRACT FROM AUTHOR]

Distributed e-Infrastructure is a key component of modern BIG Science. Service discovery in e-Science environments, such as Worldwide LHC Computing Grid (WLCG), is a crucial functionality that relies on service registry. In this paper we re-formulate the requirements for the service endpoint registry based on our more than 10 years experience with many systems designed or used within the WLCG e-Infrastructure. To satisfy those requirements the paper proposes a novel idea to use the existing well-established Domain Name System (DNS) infrastructure together with a suitable data model as a service endpoint registry. The presented ARC Hierarchical Endpoints Registry (ARCHERY) system consists of a minimalistic data model representing services and their endpoints within e-Infrastructures, a rendering of the data model embedded into DNS-records, a lightweight software layer for DNS-record management and client-side data discovery. Our approach for the ARCHERY registry required minimal software development and inherits all the benefits of one of the most reliable distributed information discovery source of the internet, the DNS infrastructure. In particular, deployment, management and operation of ARCHERY is fully relying on DNS. Results of ARCHERY deployment use-cases are provided together with performance analysis. [ABSTRACT FROM AUTHOR]

The Carbon Capture Simulation Initiative (CCSI) project has developed and deployed scientific infrastructure called the CCSI Toolset. The CCSI Toolset provides state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. Carbon capture technologies have the potential to dramatically reduce the carbon emissions from power plants. The CCSI Toolset provides end users in industry with a comprehensive, integrated suite of leading-edge, scientifically validated models with simulation, uncertainty quantification, optimization, risk analysis and decision making support. The CCSI Toolset has at its core an integrated framework that enables execution of simulations and workflows including optimization and uncertainty parameter sweeps using a wide variety of computing platforms including desktops, clusters, Clouds, and HPC systems. The integration framework enables the running of a variety of commercial process simulation packages as well as custom simulators. Moreover, the framework enables scientists to run and manage thousands of concurrent simulations to perform optimizations and uncertainty quantification. Components of the CCSI Toolset are connected through the use of a data management system that stores data to a repository and enables the tracking of provenance for each simulation as well as its associated components. The data management system tracks all the configurations, models, simulations, and results created during the design of a carbon capture system and supports the design life-cycle as well as decision making. The primary contribution of this paper is thus the design and implementation of the integration framework within the CCSI Toolset, which provides both data management and simulation support for CCSI. This integration framework has been deployed and is in use by several groups of researchers and commercial entities.

Scientific experiments involve complex interactions between geographically distributed researchers, who act as units and require a substantial volume of data and services. This scenario categorizes a Scientific Software Ecosystem, which involves researchers and scientists working together, using scientific software and related services through scientific workflows. However, to develop scientific workflows, users need suitable platforms to comply with experiment requirements. This paper describes two services designed on top of an open-source E-science Software Ecosystem (E-SECO) platform to support researchers’ activities during the scientific workflow life cycle. Such services focus on data integration and provenance data to support experiment reuse. We conducted two different case studies in a Brazilian Agricultural Research Corporation. Our results show that the proposed platform facilitates the reuse of scientific experiments through data integration in an agriculture context. [ABSTRACT FROM AUTHOR]

The availability and use of geographic information technologies and data for describing the patterns and processes operating on or near the Earth's surface have grown substantially during the past fifty years. The number of geographic information systems software packages and algorithms has also grown quickly during this period, fueled by rapid advances in computing and the explosive growth in the availability of digital data describing specific phenomena. Geographic information scientists therefore increasingly find themselves choosing between multiple software suites and algorithms to execute specific analysis, modeling, and visualization tasks in environmental applications today. This is a major challenge because it is often difficult to assess the efficacy of the candidate software platforms and algorithms when used in specific applications and study areas, which often generate different results. The subtleties and issues that characterize the field of geomorphometry are used here to document the need for (1) theoretically based software and algorithms; (2) new methods for the collection of provenance information about the data and code along with application context knowledge; and (3) new protocols for distributing this information and knowledge along with the data and code. This article discusses the progress and enduring challenges connected with these outcomes. [ABSTRACT FROM AUTHOR]

Allergic rhinitis has become a global health problem in recent decades because airborne pollen is a primary trigger of this respiratory disorder. Moreover, pollinosis can exacerbate the symptoms of asthma and favour respiratory infections. Seasonal pollen trends and climatic circumstances (such as temperature, precipitation, relative humidity, wind speed and direction, and other variables) can impact daily airborne pollen concentrations, influencing local pollen emission and dispersion. Because of that, pollen monitoring and prediction are becoming more relevant to the urban population and scientific interest is put into them. Due to such tasks' high volume of data, scientists are starting to use computational tools like workflows to automate and speed up the process. Furthermore, using the expert scientific domain is critical for improving the analysis, allowing, among others, a better workflow configuration and data provenance. As semantic web technologies have been revealed as an essential means for knowledge representation, we implemented this workflow information as an ontology using formats like RDF(S) and OWL. Consequently, this paper provides a semantic-enhanced e-Science workflow based on the TITAN framework for pollen forecasting analysis using meteorological data. Furthermore, a catalogue of components is developed on the TITAN framework, which allows the creation of different workflow versions. Two case studies of pollen prediction were developed to test the implementation of the aforementioned methodologies. Both were elaborated with airborne pollen data obtained in the city of Málaga (Spain). Still, one was elaborated for Platanus pollen type (narrow annual main pollination period), while the other was done for Amaranthaceae pollen type (extensive annual main pollination period). The predictions have been conducted using machine and deep learning algorithms like SARIMA or CNN-LSTM that intend to optimise the pollen prediction procedure depending on its stational and seasonal profile. [ABSTRACT FROM AUTHOR]

Copyright of Critical Hermeneutics: Biannual Journal of Philosophy is the property of Universita degli Studi di Cagliari and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dados, informação e conhecimento são ativos corporativos valiosos e, por isso, devem ser bem governados e geridos de modo a maximizar o seu valor. Um dos mecanismos de governança é a implementação de políticas, que são instrumentos formais onde estão definidos os princípios a serem adotados, assim como as diretrizes, responsabilidades e como a estrutura organizacional irá conduzir e monitorar a governança. A Empresa Brasileira de Pesquisa Agropecuária (Embrapa), assim como outras instituições de P&D que integram a Administração Pública Federal, tem refletido e mobilizado esforços para atender ao arcabouço legal vigente, acompanhar e beneficiar-se das oportunidades trazidas pela nova economia digital e, ainda, fortalecer-se no novo paradigma científico global. Como parte desse esforço, a Embrapa publicou, em 5 de abril de 2019, a sua ‘Política de Governança de Dados, Informação e Conhecimento’. A elaboração dessa política foi um processo complexo e contou com a participação de diversas instâncias organizacionais, inclusive das 42 Unidades de Pesquisa distribuídas pelo País. Esse trabalho relata o processo de construção da Política de Governança de Dados, Informação e Conhecimento da Embrapa e os resultados de análise qualitativa das contribuições recebidas dos diversos atores institucionais na fase de elaboração do documento, destacando as principais dúvidas, preocupações e questionamentos que emergiram. Como principal resultado, constatou-se uma grande assimetria no entendimento dos principais conceitos abarcados na Política e resistência a alguns dos princípios e diretrizes.

This paper addresses the collaborative development of information infrastructure for supporting data-rich scientific collaboration. Studying infrastructure development empirically not only in terms of spatial issues but also, and equally importantly, temporal ones, we illustrate how the long-term matters. Our case is about the collaborative development of a metadata standard for an ecological research domain. It is a complex example where standards are recognized as one element of infrastructure and standard-making efforts include integration of semantic work and software tools development. With a focus on the temporal scales of short-term and long-term, we analyze the practices and views of the main parties involved in the development of the standard. Our contributions are three-fold: 1) extension of the notion of infrastructure to more explicitly include the temporal dimension; 2) identification of two distinct temporal orientations in information infrastructure development work, namely ‘project time’ and ‘infrastructure time’, and 3) association of related development orientations, particularly ‘continuing design’ as a development orientation that recognizes ‘infrastructure time’. We conclude by highlighting the need to enrich understandings of temporality in CSCW, particularly towards longer time scales and more diversified temporal hybrids in collaborative infrastructure development. This work draws attention to the manifold ramifications that ‘infrastructure time’, as an example of more extended temporal scales, suggests for CSCW and e-Research infrastructures.

E-Science is a tool that helps scientists to store, interpret, analyze and make a network of their data, and it can play a critical role in different aspects of the scientific goals and research. This commentary, under the topic of Cross Border E-Science and Research Partnership: Bridging the Gap between Science and Media,[1] attempts to shed light on E-Science with emphasis on three important points illustrated by the UNESCO and World Summit on the Information Society (WSIS). Hence, science and e-science in the WSIS documents are examined, and the UNESCO Action Line C9 on the media is reviewed. In addition, the Harold Lasswell’s model of communication is studied and applied to the case of Iran to show Iran’s stance in this regard. [1]. For further information refer to E-Science and Research in the Information Society: Key Factors for Sustainable Development (2015, May). Paper presented at WSIS Forum, Geneva, Switzerland.

This article describes the development and implementation of e-science and research support services in the Health Sciences Libraries (HSL) at the University of Minnesota (UMN). A review of the broader e-science initiatives within the UMN demonstrates the needs and opportunities that the University Libraries face while building knowledge, skills, and capacity to support e-research. These experiences are being used by the University Libraries administration and HSL to apply support for the growing needs of researchers in the health sciences. Several research areas that would benefit from enhanced e-science support are described. Plans to address the growing e-research needs of health sciences researchers are also discussed. [ABSTRACT FROM AUTHOR]

With the increasing demand for dynamic and customised resource provisioning for computational experiments in e-Science, solutions are required to mediate different participants' varied demands for such resource provision. This paper presents a novel negotiation protocol based on a new collaboration model. The protocol allows e-Scientists, the manager of an e-Scientist's collaboration, and resource providers to reach resource provisioning agreements. By considering the manager of an e-Scientist collaboration for negotiation decisions, the protocol enables fine-grained accountable resource provision on a per job basis for e-Scientist collaborations, without binding the e-Scientist collaboration to resource providers. A testbed built with the protocol is also presented, making use of a production e-Science gateway, use cases, and infrastructures. The testbed is experimentally evaluated, via designed scenarios and comparison with existing production tools. It demonstrates that the proposed negotiation protocol can facilitate accountable resource provision per job, based on resource sharing rules defined and managed by e-Scientist collaborations. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Qualitative Forschung (ZQF) is the property of Verlag Barbara Budrich GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Big data, the massive amount of structured and unstructured data, too large or too complex to be analyzed by conventional database software, signals for new methods to record, organize, analyze and provide access to information. To help understand how information science (IS) has incorporated big data as a topic of study, this paper researched the scientific output of the area indexed in main referential databases. The paper reviews the emergence of large volumes of data, the definition, dimensions and components of big data, and the related concepts of e-science and data science. In this bibliometric study, data collection was done through searches in the main referential IS databases to identify the scientific literature on big data. Authors and their programs/ departments, institutions and countries were identified. Then, the works were categorized through content analysis of their titles, keywords and abstracts. The results present a map of how IS scientific output has approached big data, showing topics of research and a ranking of universities/organizations, graduate programs/ departments, authors and countries that produced the articles on big data indexed in the LISA and LISTA databases. [ABSTRACT FROM AUTHOR]

Summary: Advances in computer networking, storage technologies, and high‐performance computing are helping global communities of researchers to address increasingly ambitious problems in Science collaboratively. E‐Science is the "science of this age"; it is realized through collaborative scientific enquiry which requires the utilization of non‐trivial amounts of computing resources and massive data sets. Core to this is the integrated set of technologies collectively known as e‐Infrastructures. In this paper, we explore the e‐Science and the e‐Infrastructure knowledge base through co‐citation analysis of existing literature. The dataset for this analysis is downloaded from the ISI Web of Science and includes over 12,000 articles. We identify prominent articles, authors, and articles with citation bursts. The detection of research clusters and the underlying seminal papers provide further insights. Our analysis is an important source of reference for academics, researchers, and students starting research in this field. [ABSTRACT FROM AUTHOR]

Computing-intensive experiments in modern sciences have become increasingly data-driven illustrating perfectly the Big-Data era. These experiments are usually specified and enacted in the form of workflows that would need to manage (i.e., read, write, store, and retrieve) highly-sensitive data like persons' medical records. We assume for this work that the operations that constitute a workflow are 1-to-1 operations, in the sense that for each input data record they produce a single data record. While there is an active research body on how to protect sensitive data by, for instance, anonymizing datasets, there is a limited number of approaches that would assist scientists with identifying the datasets, generated by the workflows, that need to be anonymized along with setting the anonymization degree that must be met. We present in this paper a solution privacy requirements of datasets used and generated by a workflow execution. We also present a technique for anonymizing workflow data given an anonymity degree. [ABSTRACT FROM AUTHOR]

Open science represents a paradigm shift in the way science is conducted that involves performing all stages of scientific research (design, data collection, review, publication, etc.) with an "open" vision. Our aim is to analyze the evolution of the denomination and the concept of open science from its beginnings (e-science, interconnected science, science 2.0, open science). We also want to determine which are the fundamental elements that make up open science. This is a theoretical research based on the selection and analysis of the most important conceptual documents about open science, which have been found in the Scopus and GoogleScholar databases. As a result, it is shown that the denominations have undergone more variations than the concept itself. Regarding the components, open science started with four basic elements (open access, open data, open software, reproducibility) that have been extended to the eight that are considered in the model proposed by OSPP recommendations. [ABSTRACT FROM AUTHOR]

Copyright of Perspectivas em Ciência da Informaçao is the property of Nova Economia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)