scholarly journals A Vision for Global Research Data Infrastructures

2013 ◽  
Vol 12 (0) ◽  
pp. 71-90 ◽  
Author(s):  
Costantino Thanos
Keyword(s):  
Research Data ◽  
Data Infrastructures ◽  
Global Research

scholarly journals Technological and Organisational Aspects of Global Research Data Infrastructures Towards Year 2020

2013 ◽  
Vol 12 (0) ◽  
pp. GRDI1-GRDI5
Author(s):  
Fotis Karagiannis ◽  
Dimitra Keramida ◽  
Yannis Ioannidis ◽  
Erwin Laure ◽  
Dejan Vitlacil ◽  
...  
Keyword(s):  
Research Data ◽  
Data Infrastructures ◽  
Global Research

Cloud-based Research Data Infrastructures Integrating In-Situ and Remote Sensing Data

2021 ◽  
Author(s):  
Simon Jirka ◽  
Benedikt Gräler ◽  
Matthes Rieke ◽  
Christian Autermann
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Research Data ◽  
Monitoring Networks ◽  
Sensing Data ◽  
Data Infrastructures ◽  
Practical Experiences ◽  
The One ◽  
Cloud Infrastructures

<p>For many scientific domains such as hydrology, ocean sciences, geophysics and social sciences, geospatial observations are an important source of information. Scientists conduct extensive measurement campaigns or operate comprehensive monitoring networks to collect data that helps to understand and to model current and past states of complex environment. The variety of data underpinning research stretches from in-situ observations to remote sensing data (e.g., from the European Copernicus programme) and contributes to rapidly increasing large volumes of geospatial data.</p><p>However, with the growing amount of available data, new challenges arise. Within our contribution, we will focus on two specific aspects: On the one hand, we will discuss the specific challenges which result from the large volumes of remote sensing data that have become available for answering scientific questions. For this purpose, we will share practical experiences with the use of cloud infrastructures such as the German platform CODE-DE and will discuss concepts that enable data processing close to the data stores. On the other hand, we will look into the question of interoperability in order to facilitate the integration and collaborative use of data from different sources. For this aspect, we will give special consideration to the currently emerging new generation of standards of the Open Geospatial Consortium (OGC) and will discuss how specifications such as the OGC API for Processes can help to provide flexible processing capabilities directly within Cloud-based research data infrastructures.</p>

scholarly journals Research Data Infrastructures and Engineering Metadata

2021 ◽  
pp. 13-30
Author(s):  
Martin Thomas Horsch ◽  
Silvia Chiacchiera ◽  
Welchy Leite Cavalcanti ◽  
Björn Schembera
Keyword(s):  
Knowledge Representation ◽  
Research Data ◽  
Semantic Technology ◽  
Metadata Model ◽  
Materials Modelling ◽  
Data Infrastructures ◽  
General Architecture

AbstractThis chapter introduces metadata models as a semantic technology for knowledge representation to describe selected aspects of a research asset. The process of building a hierarchical metadata model is reenacted in this chapter and highlighted by the example of EngMeta. Moreover, an overview on data infrastructures is given, the general architecture and functions are disscussed, and multiple examples of data infrastructures in materials modelling are given.

scholarly journals The Topologies of Data Practices: A Methodological Introduction

2021 ◽  
Vol 9 (2) ◽  
pp. 67 ◽  
Author(s):  
Mathias Decuypere
Keyword(s):  
Research Data ◽  
Methodological Framework ◽  
Digital Education ◽  
Actual Usage ◽  
Worked Example ◽  
Data Practices ◽  
Data Points ◽  
Data Infrastructures ◽  
Generic Sense

This paper offers a methodological framework to research data practices in education critically. Data practices are understood in the generic sense of the word here, i.e., as the actions, performances, and the resulting consequences, of introducing data-producing technologies in everyday educational situations. The paper first distinguishes between data infrastructures, datafication and data points as three distinct, yet interrelated, phenomena. In order to investigate their concrete doings and specificities, the paper proposes a topological methodology that allows disentangling the relational nature and interwovenness of data practices. Based on this methodology, the paper proceeds with outlining a methodical toolbox that can be employed in studying data practices. Starting from nascent work on digital education platforms as a worked example, the toolbox allows researchers to investigate data practices as consisting of four unique topological dimensions: the Interface of a data practice, its actual Usage, its concrete Design, and its Ecological embeddedness - IUDE.

scholarly journals Bridging the Data Talent Gap: Positioning the iSchool as an Agent for Change

2015 ◽  
Vol 10 (1) ◽  
pp. 111-122 ◽  
Author(s):  
Liz Lyon ◽  
Aaron Brenner
Keyword(s):  
Data Science ◽  
Core Curriculum ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
University Of Pittsburgh ◽  
Data Infrastructures ◽  
New Research ◽  
Agent Of Change ◽  
The University

This paper examines the role, functions and value of the “iSchool” as an agent of change in the data informatics and data curation arena. A brief background to the iSchool movement is given followed by a brief review of the data decade, which highlights key data trends from the iSchool perspective: open data and open science, big data and disciplinary data diversity. The growing emphasis on the shortage of data talent is noted and a family of data science roles identified. The paper moves on to describe three primary functions of iSchools: education, research intelligence and professional practice, which form the foundations of a new Capability Ramp Model. The model is illustrated by mini-case studies from the School of Information Sciences, University of Pittsburgh: the immersive (laboratory-based) component of two new Research Data Management and Research Data Infrastructures graduate courses, a new practice partnership with the University Library System centred on RDM, and the mapping of disciplinary data practice using the Community Capability Model Profile Tool. The paper closes with a look to the future and, based on the assertion that data is mission-critical for iSchools, some steps are proposed for the next data decade: moving data education programs into the mainstream core curriculum, adopting a translational data science perspective and strengthening engagement with the Research Data Alliance.

scholarly journals On the Complexities of Federating Research Data Infrastructures

2021 ◽  
Vol 3 (1) ◽  
pp. 79-87
Author(s):  
Atif Latif ◽  
Fidan Limani ◽  
Klaus Tochtermann
Keyword(s):  
Open Access ◽  
Data Management ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
Open Educational Resources ◽  
Digital Resources ◽  
Different Types ◽  
Data Infrastructures ◽  
Metadata Mapping

Federated Research Data Infrastructures aim to provide seamless access to research data along with services to facilitate the researchers in performing their data management tasks. During our research on Open Science (OS), we have built cross-disciplinary federated infrastructures for different types of (open) digital resources: Open Data (OD), Open Educational Resources (OER), and open access documents. In each case, our approach targeted only the resource “metadata”. Based on this experience, we identified some challenges that we had to overcome again and again: lack of (i) harvesters, (ii) common metadata models and (iii) metadata mapping tools. In this paper, we report on the challenges we faced in the federated infrastructure projects we were involved with. We structure the report based on the three challenges listed above.

scholarly journals Recommendations for Future Data Management Plans in Earth System Sciences

2021 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Christin Henzen ◽  
Stefano Della Chiesa ◽  
Lars Bernard
Keyword(s):  
Data Management ◽  
Interdisciplinary Research ◽  
Research Data ◽  
Data Reuse ◽  
Specific Information ◽  
Earth System ◽  
Domain Specific ◽  
Research Activities ◽  
Management Plans ◽  
Data Infrastructures

Abstract. Most research activities in Earth System Sciences (ESS) are data-driven. There is a growing need to establish innovative, cross-cutting data management and data analysis methods in ESS to support the collaboration of interdisciplinary research building on heterogeneous sources. Data management plans (DMPs) are structured documents that outline data handling and include for instance agreements on roles, specifications of data products, and definition of workflows. However, the structure of existing DMP templates is mostly designed for funder’s requirements and consequently address only the broad and interdisciplinary research community. Thus, these templates do lack (1) guidance on how to structure domain-specific information in a DMP – by providing domain-specific profiles, e.g. to harmonize the structure and improve the comprehensibility of DMP instances and (2) (linking into) tools enabling efficient management and reuse of information / sections of DMP instances. Therefore, we provide a concept of future DMP templates and address geo-domain-specific requirements, and the integration of DMPs into research data infrastructures. We recommend integrating structured provenance and quality information, using established concepts, and define a pathway to link tools into research data infrastructures, such that they foster automation of data management workflows and data reuse.

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