scholarly journals Data Management

Author(s):  
Carolin Helbig ◽  
Uwe-Jens Görke ◽  
Mathias Nest ◽  
Daniel Pötschke ◽  
Amir Shoarian Sattari ◽  
...  

AbstractData management includes the development and use of architectures, guidelines, practices and procedures for accurate managing of data during the entire data lifecycle of an institutional unit or a research project. Data are defined as different information units such as numbers, alphabetic characters, and symbols that are particularly formatted and can be processed by computer. The data in the project is provided by various actors which can be GeomInt partners, their legal representatives, employees, and external partners.

2015 ◽  
Vol 49 (4) ◽  
pp. 494-512 ◽  
Author(s):  
Constanze Curdt ◽  
Dirk Hoffmeister

Purpose – Research data management (RDM) comprises all processes, which ensure that research data are well-organized, documented, stored, backed up, accessible, and reusable. RDM systems form the technical framework. The purpose of this paper is to present the design and implementation of a RDM system for an interdisciplinary, collaborative, long-term research project with focus on Soil-Vegetation-Atmosphere data. Design/methodology/approach – The presented RDM system is based on a three-tier (client-server) architecture. This includes a file-based data storage, a database-based metadata storage, and a self-designed user-friendly web-interface. The system is designed in cooperation with the local computing centre, where it is also hosted. A self-designed interoperable, project-specific metadata schema ensures the accurate documentation of all data. Findings – A RDM system has to be designed and implemented according to requirements of the project participants. General challenges and problems of RDM should be considered. Thus, a close cooperation with the scientists obtains the acceptance and usage of the system. Originality/value – This paper provides evidence that the implementation of a RDM system in the provided and maintained infrastructure of a computing centre offers many advantages. Consequently, the designed system is independent of the project funding. In addition, access and re-use of all involved project data is ensured. A transferability of the presented approach to another interdisciplinary research project was already successful. Furthermore, the designed metadata schema can be expanded according to changing project requirements.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.


Author(s):  
Hugo Guerrero

Today, much of the focus on integrating Geospatial technology and data has been on the operations side of the business. Not much attention has been paid to the workflow within the project environment even though most of the data that is used to populate enterprise datasets is created or prepared as a requirement of a project; that said; it is early on at the project level when geospatial integration needs to be implemented and incorporated into the project workflow. On the other hand, project teams have historically focused on strictly satisfying the needs of the project. This is typically limited to the minimum work required to design, permit & build a given work scope. This approach has left many companies with the task of paying high costs for the project data to be translated, captured or in some cases recreated after the fact. Too many times, Gas Company X hires multiple consultants with different disciplines responsible for different project scope items (i.e. Environmental, Right-of-way, Engineering, etc...). Each company has established methods for preparing and organizing their respective data without ever thinking how Gas Company X intends on using the data for other enterprise needs during the project and after the project has been completed. This presentation outlines methods by which companies can require that their project consultants produce project data with geospatial integration in mind. This includes identification of required resources & workflows to specify and manage the data that is prepared and/or collected in a structured environment that is geospatially & data aware.


2018 ◽  
Vol 12 (2) ◽  
pp. 331-361 ◽  
Author(s):  
Stacy T Kowalczyk

This paper develops and tests a lifecycle model for the preservation of research data by investigating the research practices of scientists.  This research is based on a mixed-method approach.  An initial study was conducted using case study analytical techniques; insights from these case studies were combined with grounded theory in order to develop a novel model of the Digital Research Data Lifecycle.  A broad-based quantitative survey was then constructed to test and extend the components of the model.  The major contribution of these research initiatives are the creation of the Digital Research Data Lifecycle, a data lifecycle that provides a generalized model of the research process to better describe and explain both the antecedents and barriers to preservation.  The antecedents and barriers to preservation are data management, contextual metadata, file formats, and preservation technologies.  The availability of data management support and preservation technologies, the ability to create and manage contextual metadata, and the choices of file formats all significantly effect the preservability of research data.


Author(s):  
Urmas Kõljalg ◽  
Kessy Abarenkov ◽  
Allan Zirk ◽  
Veljo Runnel ◽  
Timo Piirmann ◽  
...  

PlutoF online platform (https://plutof.ut.ee) is built for the management of biodiversity data. The concept is to provide a common workbench where the full data lifecycle can be managed and support seamless data sharing between single users, workgroups and institutions. Today, large and sophisticated biodiversity datasets are increasingly developed and managed by international workgroups. PlutoF's ambition is to serve such collaborative projects as well as to provide data management services to single users, museum or private collections and research institutions. Data management in PlutoF follows a logical order of the data lifecycle Fig. 1. At first, project metadata is uploaded including the project description, data management plan, participants, sampling areas, etc. Data upload and management activities then follow which is often linked to the internal data sharing. Some data analyses can be performed directly in the workbench or data can be exported in standard formats. PlutoF includes also data publishing module. Users can publish their data, generating a citable DOI without datasets leaving PlutoF workbench. PlutoF is part of the DataCite collaboration (https://datacite.org) and so far released more than 600 000 DOIs. Another option is to publish observation or collection datasets via the GBIF (Global Biodiversity Information Facility) portal. A. new feature implemented in 2019 allows users to publish High Throughput Sequencing data as taxon occurrences in GBIF. There is an additional option to send specific datasets directly to the Pensoft online journals. Ultimately, PlutoF works as a data archive which completes the data life cycle. In PlutoF users can manage different data types. Most common types include specimen and living specimen data, nucleotide sequences, human observations, material samples, taxonomic backbones and ecological data. Another important feature is that these data types can be managed as a single datasets or projects. PlutoF follows several biodiversity standards. Examples include Darwin Core, GGBN (Global Genome Biodiversity Network), EML (Ecological Metadata Language), MCL (Microbiological Common Language), and MIxS (Minimum Information about any (x) Sequence).


1991 ◽  
pp. 97-112 ◽  
Author(s):  
Stephen B. Richards ◽  
John D. Morris ◽  
Les Sternberg

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