scholarly journals Spatio-Temporal Research Data Infrastructure in the Context of Autonomous Driving

2020 ◽  
Vol 9 (11) ◽  
pp. 626
Author(s):  
Colin Fischer ◽  
Monika Sester ◽  
Steffen Schön
Keyword(s):  
Data Storage ◽  
Autonomous Driving ◽  
Research Data ◽  
Structured Data ◽  
Data Management System ◽  
Metadata Management ◽  
Data Preparation ◽  
Data Infrastructure ◽  
Spatio Temporal ◽  
And Storage

In this paper, we present an implementation of a research data management system that features structured data storage for spatio-temporal experimental data (environmental perception and navigation in the framework of autonomous driving), including metadata management and interfaces for visualization and parallel processing. The demands of the research environment, the design of the system, the organization of the data storage, and computational hardware as well as structures and processes related to data collection, preparation, annotation, and storage are described in detail. We provide examples for the handling of datasets, explaining the required data preparation steps for data storage as well as benefits when using the data in the context of scientific tasks.

scholarly journals IOER Monitor: A Spatio-Temporal Research Data Infrastructure on Settlement and Open Space Development in Germany

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Gotthard Meinel ◽  
Sujit Kumar Sikder ◽  
Tobias Krueger
Keyword(s):  
Land Use ◽  
Open Space ◽  
Spatial Development ◽  
Research Data ◽  
Data Infrastructure ◽  
Domain Specific ◽  
Multi Temporal ◽  
Spatial Domains ◽  
Spatio Temporal ◽  
Development Assessment

Abstract This paper gives a comprehensive introduction to the IOER Monitor – an open research data infrastructure (RDI) in Germany providing domain-specific multi-temporal geospatial datasets, services and visualizations for land use and land cover (LULC)-related development of settlements and open space and closely related topics. Its easy-to-use information system provides multi-scale data offers to form a discussion platform that supports spatial development assessment and evidence-based decision making. It contributes to public land-use change discourses by enhancing information offers that can be adopted by other multi-disciplinary data users - even from non-spatial domains. All data and services are freely available. IOER Monitor is committed to offering continuous services implementing FAIR principles (findable, accessible, interoperable and re-usable) and policy-relevant inputs for transformative spatial development.

scholarly journals RESEARCH DATA MANAGEMENT SYSTEM DESIGN (CASE ICT RESEARCH AND HUMAN RESOURCES DEVELOPMENT AGENCY MCIT)

2017 ◽  
Vol 7 (2) ◽  
pp. 143
Author(s):  
Onny Rafizan
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Research Data ◽  
Data Management System ◽  
Primary Data ◽  
Management Information ◽  
User Requirement ◽  
Research Activities ◽  
Data Credibility ◽  
Research Institute

<p class="JGI-AbstractIsi"><span lang="EN">Management of research data is important for every research institute. The absence of activity for centralized data management and data storage within the research institute has the potential to make the research data disappear or not reusable. Data management information system is needed by research institute in order to manage data centrally by paying attention to the business process of research and requirement to the process of storage and sharing of research data. Primary data collection by means of interviews to the technical sources with expertise and direct experience are related to the management of research data, as well as validation of data credibility are tiered to higher research positions (madya and main) and to structural officials who facilitate research activities to obtain business modeling and user requirement. The result of this study is the design of data management information systems in accordance with the conditions of the R &amp; D organizations to facilitate the activities of storage and sharing access the research data.</span></p>

scholarly journals A Research Data Infrastructure Component for the Automated Metadata and Data Quality Extraction to Foster the Provision of FAIR Data in Earth System Sciences

2021 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Michael Wagner ◽  
Christin Henzen ◽  
Ralph Müller-Pfefferkorn
Keyword(s):  
Life Cycle ◽  
Data Quality ◽  
Open Source ◽  
Research Data ◽  
Data Driven ◽  
Quality Information ◽  
Metadata Management ◽  
Earth System ◽  
Data Infrastructure ◽  
Data Life Cycle

Abstract. Metadata management is core to support discovery and reuse of data products, and to allow for reproducibility of the research data in Earth System Sciences (ESS). Thus, ensuring acquisition and provision of meaningful and quality assured metadata should become an integral part of data-driven ESS projects.We propose an open-source tool for the automated metadata and data quality extraction to foster the provision of FAIR data (Findable, Accessible, Interoperable Reusable). By enabling researchers to automatically extract and reuse structured and standardized ESS-specific metadata, in particular quality information, in several components of a research data infrastructure, we support researchers along the research data life cycle.

scholarly journals COEMS — open traces from the industry

10.29007/gp13 ◽  
2018 ◽  
Author(s):  
Svetlana Jakšić ◽  
Martin Leucker ◽  
Dan Li ◽  
Volker Stolz
Keyword(s):  
Data Management ◽  
Scientific Data ◽  
Research Data ◽  
Runtime Verification ◽  
Metadata Management ◽  
Continuous Observation ◽  
Open Research ◽  
Technical Issues ◽  
And Storage ◽  
The Eu

The runtime verification community is still fragmented with non-interoperable specifications and tools. Within the EU H2020 project COEMS “Continuous Observation of Embedded Multicore Systems”, we are contributing to the European Open Research Data Pilot that makes scientific data available to other researchers. We describe our first contributions and experience with the required data management and discuss technical issues such as metadata management, format and storage on practical examples. Based on our experience, we make suggestions on tools and formats for future RV Competitions and for desired artefacts from the EU COST Action IC1402 "ARVI -- Runtime Verification Beyond Monitoring".

scholarly journals A FRAMEWORK FOR SPATIO-TEMPORAL DATA WAREHOUSE

2013 ◽  
Vol 4 (1) ◽  
pp. 146-150
Author(s):  
Lax Maiah ◽  
DR.A.GOVARDHAN DR.A.GOVARDHAN ◽  
DR. C.SUNIL KUMAR
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Data Warehouse ◽  
Data Storage ◽  
Multidimensional Data ◽  
Metadata Management ◽  
Temporal Data ◽  
Application Layer ◽  
Data Process ◽  
Spatio Temporal

Data Warehouse (DW) is topic-oriented, integrated, static datasets which are used to support decision-making. Driven by the constraint of mass spatio-temporal data management and application, Spatio-Temporal Data Warehouse (STDW) was put forward, and many researchers scattered all over the world focused their energy on it.Although the research on STDW is going in depth , there are still many key difficulties to be solved, such as the design principle, system framework, spatio-temporal data model (STDM), spatio-temporal data process (STDP), spatial data mining (SDM) and etc. In this paper, the concept of STDW is discussed, and analyzes the organization model of spatio-temporal data. Based on the above, a framework of STDW is composed of data layer, management layer and application layer. The functions of STDW should include data analysis besides data process and data storage. When users apply certain kind of data services, STDW identifies the right data by metadata management system, then start data processing tool to form a data product which serves the data mining and OLAP. All varieties of distributed databases (DDBs) make up data sources of STDW, including Digital Elevation Model (DEM), Diagnosis-Related Group (DRG), Data Locator Group (DLG), Data Objects Management (DOM), Place Name and other databases in existence. The management layer implements heterogeneous data processing, metadata management and spatio-temporal data storage. The application layer provides data products service, multidimensional data cube, data mining tools and on-line analytical process.

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

A System Architecture for Data Storage in the Cloud

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Maryam Hammami ◽  
Hatem Bellaaj
Keyword(s):  
Data Storage ◽  
System Architecture ◽  
Cloud Storage ◽  
The Other ◽  
Important Data ◽  
Work In Progress ◽  
Flexible System ◽  
Other Hand ◽  
Data Manager ◽  
And Storage

The Cloud storage is the most important issue today. This is due to a rapidly changing needs and a huge mass of varied and important data to back up. In this paper, we describe a work in progress and propose a flexible system architecture for data storage in the Cloud. This system is centered on the Data Manager module. This module provides various functions such as the dispersion of data in fragments, encryption and storage of fragments... etc. This architecture proves to be very relevant. It ensures consistency between different components. On the other hand, it ensures the security and availability of data.

scholarly journals The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Daniel Arend ◽  
Patrick König ◽  
Astrid Junker ◽  
Uwe Scholz ◽  
Matthias Lange
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Best Practice ◽  
Research Process ◽  
Research Data ◽  
Primary Data ◽  
Data Repository ◽  
Plant Genomics ◽  
Quality Of Data

Abstract Background The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. Results To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a “bring the infrastructure to the data” approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. Conclusion The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.

Sign in / Sign up

Export Citation Format

Share Document