International Geosphere and Biosphere Program (IGBP) metadata information system for globally distributed data sets

2002 ◽  
Author(s):  
S.I. Rasool ◽  
L. Andres
Keyword(s):  
Information System ◽  
Data Sets ◽  
Distributed Data ◽  
Globally Distributed

scholarly journals Building a successful international research community through data sharing: The case of the Wheat Information System (WheatIS)

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 536
Author(s):  
Taner Z. Sen ◽  
Mario Caccamo ◽  
David Edwards ◽  
Hadi Quesneville
Keyword(s):  
Information System ◽  
International Research ◽  
Research Community ◽  
Future Research ◽  
Data Sets ◽  
Standard Data ◽  
Data Formats ◽  
Expert Working Group ◽  
Geographically Distributed ◽  
Globally Distributed

The International Wheat Information System (WheatIS) Expert Working Group (EWG) was initiated in 2012 under the Wheat Initiative with a broad range of contributing organizations. The mission of the WheatIS EWG was to create an informational infrastructure, establish data standards, and build a single portal that allows search, retrieval, and display of globally distributed wheat data sets that are indexed in standard data formats at servers around the world. The web portal at WheatIS.org was released publicly in 2015, and by 2020, it expanded to 8 geographically-distributed nodes and around 20 organizations under its umbrella.   In this paper, we present our experience, the challenges we faced, and the answer we brought for establishing an international research community to build an informational infrastructure. Our hope is that our experience with building wheatis.org will guide current and future research communities to facilitate institutional and international challenges to create global tools and resources to help their respective scientific communities.

Synoptic analysis of globally-distributed data sets of cosmogenic-nuclide exposure ages

2021 ◽  
Author(s):  
Greg Balco
Keyword(s):  
Global Analysis ◽  
Glacier Change ◽  
Data Sets ◽  
Distributed Data ◽  
Data Set ◽  
Cosmogenic Nuclide ◽  
Synoptic Analysis ◽  
Exposure Age ◽  
Exposure Ages ◽  
Globally Distributed

<p>This abstract describes a project to make large data sets of cosmogenic-nuclide measurements useable for synoptic global analysis of paleoclimate, glacier change, and landscape change. It is based on the 'ICE-D' (Informal Cosmogenic-nuclide Exposure-age Database), a transparent-middle-layer infrastructure for compiling and storing cosmogenic-nuclide measurements and generating internally consistent exposure-age data. The prototype implementation of this project focuses on a global data set of exposure ages from glacial deposits that are, potentially, useful for synoptic analysis of glacier change and paleoclimate. The aim is to address a number of messy data-management and analysis problems associated with cosmogenic-nuclide data, thus making it possible to apply unbiased, automated quantitative analysis to the entire globally-distributed data set. The presentation will highlight (i) examples of error-tolerant hypothesis testing using this approach; (ii) means of quantifying the importance of the details of cosmogenic-nuclide production-rate calculations to global paleoclimate inferences, and (iii) likewise, approaches to understanding the importance of geomorphic processes and landform evolution to global paleoclimate inferences drawn from exposure-dated landforms.</p>

Cell volumes of marine phytoplankton from globally distributed coastal data sets

2015 ◽  
Vol 162 ◽  
pp. 130-142 ◽  
Author(s):  
Paul J. Harrison ◽  
Adriana Zingone ◽  
Michael J. Mickelson ◽  
Sirpa Lehtinen ◽  
Nagappa Ramaiah ◽  
...  
Keyword(s):  
Marine Phytoplankton ◽  
Data Sets ◽  
Globally Distributed ◽  
Cell Volumes

scholarly journals Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2737
Author(s):  
Leandro Ordonez-Ante ◽  
Gregory Van Seghbroeck ◽  
Tim Wauters ◽  
Bruno Volckaert ◽  
Filip De Turck
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Temporal Trends ◽  
Multidimensional Data ◽  
Data Sets ◽  
Distributed Data ◽  
Key Factors ◽  
Distributed Data Processing ◽  
Spatial And Temporal Trends ◽  
Query Latency

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving—on ingestion time—synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach.

IMPACT ANALYSIS AND PREDICTION OF THE TOTAL NUMBER OF BACTERIA IN DIFFERENT RESERVOIRS USING SELAR INFORMATION SYSTEM

2021 ◽  
Author(s):  
Ivana Radojević ◽  
◽  
Aleksandar Ostojić ◽  
Nenad Stefanović
Keyword(s):  
Information System ◽  
Impact Analysis ◽  
Trophic Status ◽  
Data Sets ◽  
Relative Impact ◽  
Classification Analysis ◽  
The Relationship ◽  
Influential Parameters ◽  
Number Of Bacteria

This study was performed using the SeLaR information system (IS). SeLaR IS combines relevant data on reservoirs in Serbia and enables advanced methods of analysis, such as statistical analysis and data mining. For the data analysis, three accumulations with different morphometric properties, trophic status, and dominant community of microorganisms were selected: Gruža, Grošnica, and Bovan. The material in this research is data sets that include standard routine and broader scientific hydrobiological tests of freshwater from certain periods. The data include physicochemical, biochemical, microbiological, and other biological parameters. The analysis aimed to determine the relationship between the entities, to discover unknown relations, the regularity in the dynamics of the specific characteristics, and for predictions. Classification, analysis of influential parameters, and scenario analysis were used for this analysis. The results indicate a clear classification of the values of the total number of bacteria. The obtained models have a small number of influential parameters (one to four) with a large relative impact for each class separately. Influence parameters are different for distinct accumulations. For prediction of the total number of bacteria selected tools did not provide satisfactory results for all three reservoirs.

An Ontology-based approach to enable data-driven research in the field of NDT in Civil Engineering

2021 ◽  
Author(s):  
Benjamin Moreno-Torres ◽  
Christoph Völker ◽  
Sabine Kruschwitz
Keyword(s):  
Knowledge Representation ◽  
Civil Engineering ◽  
Common Knowledge ◽  
Measurement Data ◽  
Data Access ◽  
Semantic Knowledge ◽  
Data Sources ◽  
Data Sets ◽  
Extensive Literature ◽  
Distributed Data

<div> <p>Non-destructive testing (NDT) data in civil engineering is regularly used for scientific analysis. However, there is no uniform representation of the data yet. An analysis of distributed data sets across different test objects is therefore too difficult in most cases.</p> <p>To overcome this, we present an approach for an integrated data management of distributed data sets based on Semantic Web technologies. The cornerstone of this approach is an ontology, a semantic knowledge representation of our domain. This NDT-CE ontology is later populated with the data sources. Using the properties and the relationships between concepts that the ontology contains, we make these data sets meaningful also for machines. Furthermore, the ontology can be used as a central interface for database access. Non-domain data sources can be integrated by linking them with the NDT ontology, making them directly available for generic use in terms of digitization. Based on an extensive literature research, we outline the possibilities that result for NDT in civil engineering, such as computer-aided sorting and analysis of measurement data, and the recognition and explanation of correlations.</p> <p>A common knowledge representation and data access allows the scientific exploitation of existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis or material characterization) and simplifies bidirectional knowledge and data transfer between engineers and NDT specialists.</p> </div>

Mining Environmental Data in the ADMIRE Project Using New Advanced Methods and Tools

2012 ◽  
pp. 296-308
Author(s):  
Ondrej Habala ◽  
Martin Šeleng ◽  
Viet Tran ◽  
Branislav Šimo ◽  
Ladislav Hluchý
Keyword(s):  
Data Mining ◽  
Environmental Data ◽  
Environmental Applications ◽  
Data Sets ◽  
Distributed Data ◽  
New Methods ◽  
Prospective Application ◽  
Using Data ◽  
Computer Power

The project Advanced Data Mining and Integration Research for Europe (ADMIRE) is designing new methods and tools for comfortable mining and integration of large, distributed data sets. One of the prospective application domains for such methods and tools is the environmental applications domain, which often uses various data sets from different vendors where data mining is becoming increasingly popular and more computer power becomes available. The authors present a set of experimental environmental scenarios, and the application of ADMIRE technology in these scenarios. The scenarios try to predict meteorological and hydrological phenomena which currently cannot or are not predicted by using data mining of distributed data sets from several providers in Slovakia. The scenarios have been designed by environmental experts and apart from being used as the testing grounds for the ADMIRE technology; results are of particular interest to experts who have designed them.

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