EGI: anOpen e-Infrastructure Ecosystem for the Digital European Research Area

Bringing the digital European Research Area (ERA) online means modernising Europe’s research infrastructure by promoting open science through the availability, accessibility and reuse of scientific data and results, the use of web- based tools that facilitate scientific collaboration and ensuring public access to research. As the European Grid Infrastructure (EGI) is the largest European distributed computing infrastructure providing 24/7 access to large scale computing, storage and data resources through a federation of national resource providers, it allows scientists from all disciplines to make the most out of the latest computing technologies for the benefit of their research. This paper describes the methodology and approach for defining EGI’s role in bringing this digital ERA online. The work presented defines the roles and functions of EGI as an open ICT ecosystem, required service redesign, the added value of EGI for the European research communities and demonstrates the role that EGI plays in contributing to the Europe 2020 strategy for social-economic impact

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

History of the Avon Longitudinal Study of Parents and Children , c.1980-2000

Annotated and edited transcript of a Witness Seminar held on 24 May 2011. Introduction by Professor David Gordon, World Federation for Medical Education.Annotated and edited transcript of a Witness Seminar held on 24 May 2011. Introduction by Professor David Gordon, World Federation for Medical Education.Annotated and edited transcript of a Witness Seminar held on 24 May 2011. Introduction by Professor David Gordon, World Federation for Medical Education.Annotated and edited transcript of a Witness Seminar held on 24 May 2011. Introduction by Professor David Gordon, World Federation for Medical Education.The Avon Longitudinal Survey of Parents and Children (ALSPAC) arose from a proposal in the mid-1980s to design a cohort study in Europe which concentrated on the health of children. The UK-side of this was developed in the former county of Avon and the study has followed more than 14000 children, due between April 1991 and December 1992, from their mothers' pregnancies onwards. A vast amount of data has been collected on health, lifestyle, and environment as well as biological samples of urine, blood and DNA. This has been used to establish genetic and environmental determinants of development and health. With an introduction by Professor David Gordon, this volume addresses the origin and development of ALSPAC, the problems of funding such a major study, the variety of methodologies employed, and the ethical questions associated with the project. Contributors include several of the major scientists involved with ALSPAC including Professor Jean Golding, Scientific and Executive Director of the study until December 2005, as well as clinical scientists, epidemiologists, members of the ethics committee, field workers and study parents.https://qmro.qmul.ac.uk/xmlui/handle/123456789/272

Mining Knowledge in Astrophysical Massive Data Sets

Modern scientific data mainly consist of huge datasets gathered by a very large number of techniques and stored in very diversified and often incompatible data repositories. More in general, in the e-science environment, it is considered as a critical and urgent requirement to integrate services across distributed, heterogeneous, dynamic "virtual organizations" formed by different resources within a single enterprise. In the last decade, Astronomy has become an immensely data rich field due to the evolution of detectors (plates to digital to mosaics), telescopes and space instruments. The Virtual Observatory approach consists into the federation under common standards of all astronomical archives available worldwide, as well as data analysis, data mining and data exploration applications. The main drive behind such effort being that once the infrastructure will be completed, it will allow a new type of multi-wavelength, multi-epoch science which can only be barely imagined. Data Mining, or Knowledge Discovery in Databases, while being the main methodology to extract the scientific information contained in such MDS (Massive Data Sets), poses crucial problems since it has to orchestrate complex problems posed by transparent access to different computing environments, scalability of algorithms, reusability of resources, etc. In the present paper we summarize the present status of the MDS in the Virtual Observatory and what is currently done and planned to bring advanced Data Mining methodologies in the case of the DAME (DAta Mining & Exploration) project.Comment: Pages 845-849 1rs International Conference on Frontiers in Diagnostics Technologie

The coarse fishes of Britain

The need for research upon the coarse fishes of Britain, questions of the breeding and rearing of large numbers of young fish, more especially roach, was established back in 1937. The investigation which was begun in 1939 at Barrington in Cambridgeshire was therefore, with the full approval of the National Federation of Anglers, devoted to the general life histories of as many species as possible. Now, five years later, the results of the investigation are presented in the hope that they will provide a basis of knowledge upon which sound policies can be devised for the improvement of fish stocks and the increase of sport. In this report the scientific data, which will be published in full elsewhere, have been condensed as much as possible, but nothing of importance has been omitted and nothing has been concealed

Flux of nutrients from Russian rivers to the Arctic Ocean: Can we establish a baseline against which to judge future changes?

Climate models predict significant warming in the Arctic in the 21st century, which will impact the functioning of terrestrial and aquatic ecosystems as well as alter land‐ocean interactions in the Arctic. Because river discharge and nutrient flux integrate large‐scale processes, they should be sensitive indicators of change, but detection of future changes requires knowledge of current conditions. Our objective in this paper is to evaluate the current state of affairs with respect to estimating nutrient flux to the Arctic Ocean from Russian rivers. To this end we provide estimates of contemporary (1970s–1990s) nitrate, ammonium, and phosphate fluxes to the Arctic Ocean for 15 large Russian rivers. We rely primarily on the extensive data archives of the former Soviet Union and current Russian Federation and compare these values to other estimates and to model predictions. Large discrepancies exist among the various estimates. These uncertainties must be resolved so that the scientific community will have reliable data with which to calibrate Arctic biogeochemical models and so that we will have a baseline against which to judge future changes (either natural or anthropogenic) in the Arctic watershed

Developing an open data portal for the ESA climate change initiative

We introduce the rationale for, and architecture of, the European Space Agency Climate Change Initiative (CCI) Open Data Portal (http://cci.esa.int/data/). The Open Data Portal hosts a set of richly diverse datasets – 13 “Essential Climate Variables” – from the CCI programme in a consistent and harmonised form and to provides a single point of access for the (>100 TB) data for broad dissemination to an international user community. These data have been produced by a range of different institutions and vary across both scientific and spatio-temporal characteristics. This heterogeneity of the data together with the range of services to be supported presented significant technical challenges. An iterative development methodology was key to tackling these challenges: the system developed exploits a workflow which takes data that conforms to the CCI data specification, ingests it into a managed archive and uses both manual and automatically generated metadata to support data discovery, browse, and delivery services. It utilises both Earth System Grid Federation (ESGF) data nodes and the Open Geospatial Consortium Catalogue Service for the Web (OGC-CSW) interface, serving data into both the ESGF and the Global Earth Observation System of Systems (GEOSS). A key part of the system is a new vocabulary server, populated with CCI specific terms and relationships which integrates OGC-CSW and ESGF search services together, developed as part of a dialogue between domain scientists and linked data specialists. These services have enabled the development of a unified user interface for graphical search and visualisation – the CCI Open Data Portal Web Presence

A Study on Green Economy Indicators and Modeling: Russian Context

This article aims to assess and forecast the dynamics of a regional green economy. The research relevance is determined by the need to develop theoretical and methodological basis of the green economy for the transition period and to identify criteria basis for assessing the state and regional level of it. The authors applied the modern methods, which allowed to model criteria considering data uncertainty and both static and dynamic criteria. The research process involved the methods of scientific analysis, comparison and synthesis, the theory of fuzzy sets, and fuzzy modeling. The main principles and methodology of the criteria evaluation for a regional green economy are proposed. The principal methodological approach in this research combines the current state and dynamics of the green economy in evaluating and forecasting the conditions of data uncertainty. The research results form a theoretical, methodological, and practical basis for assessing the current state and level of a regional green economy development, determining the effectiveness of environmental and economic programs, optimizing financial management, conducting environmental monitoring, and developing state plans.The research was funded by the grant of the Ministry of Education and Science of the Russian Federation to Perm National Research Polytechnic University # 26.6884.2017/8.9 "Sustainable development of urban areas and the improvement of the human environment.