scholarly journals Personal Data Management: An Abstract Personal Data Lifecycle Model

2018 ◽  
pp. 685-697 ◽  
Author(s):  
Majed Alshammari ◽  
Andrew Simpson
Keyword(s):  
Data Management ◽  
Personal Data ◽  
Lifecycle Model ◽  
Data Lifecycle

scholarly journals Research Data Management Practices: Synergies and Discords between Researchers and Institutions

2018 ◽  
Vol 13 (1) ◽  
pp. 73-90
Author(s):  
Sally Vanden-Hehir ◽  
Helena Cousijn ◽  
Hesham Attalla
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Qualitative Interviews ◽  
Research Data ◽  
Research Data Management ◽  
Early Stages ◽  
Lifecycle Model ◽  
Data Lifecycle

The aim of this study was to explore the synergies and discords in attitudes towards research data management (RDM) drivers and barriers for both researchers and institutions. Previous work has studied RDM from a single perspective, but not compared researchers’ and institutions’ perspectives. We carried out qualitative interviews with researchers as well as institutional representatives to identify drivers and barriers, and to explore synergies and discords of both towards RDM. We mapped these to a data lifecycle model and found that the contradictions occur at early stages in the lifecycle of data and the synergies occur at the later stages. This means that for future successful RDM, the points of discord at the start of the data lifecycle must be overcome. Finally, we conclude by proposing key recommendations that could help institutions when addressing both researcher and institutional RDM needs.

scholarly journals DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

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.

scholarly journals Public perceptions of good data management: Findings from a UK-based survey

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172093561
Author(s):  
Todd Hartman ◽  
Helen Kennedy ◽  
Robin Steedman ◽  
Rhianne Jones
Keyword(s):  
Data Management ◽  
Personal Data ◽  
Current Approach ◽  
Data Driven ◽  
Policy Action ◽  
Driven Systems ◽  
General Data Protection Regulation ◽  
Good Data ◽  
Opt Out ◽  
The Eu

Low levels of public trust in data practices have led to growing calls for changes to data-driven systems, and in the EU, the General Data Protection Regulation provides a legal motivation for such changes. Data management is a vital component of data-driven systems, but what constitutes ‘good’ data management is not straightforward. Academic attention is turning to the question of what ‘good data’ might look like more generally, but public views are absent from these debates. This paper addresses this gap, reporting on a survey of the public on their views of data management approaches, undertaken by the authors and administered in the UK, where departure from the EU makes future data legislation uncertain. The survey found that respondents dislike the current approach in which commercial organizations control their personal data and prefer approaches that give them control over their data, that include oversight from regulatory bodies or that enable them to opt out of data gathering. Variations of data trusts – that is, structures that provide independent stewardship of data – were also preferable to the current approach, but not as widely preferred as control, oversight and opt out options. These features therefore constitute ‘good data management’ for survey respondents. These findings align only in part with principles of good data identified by policy experts and researchers. Our findings nuance understandings of good data as a concept and of good data management as a practice and point to where further research and policy action are needed.

scholarly journals Towards privacy in personal data management

2009 ◽  
Vol 17 (4) ◽  
pp. 311-329 ◽  
Author(s):  
Pavlos S. Efraimidis ◽  
Georgios Drosatos ◽  
Fotis Nalbadis ◽  
Aimilia Tasidou
Keyword(s):  
Data Management ◽  
Personal Data

scholarly journals Modelling the Research Data Lifecycle

2018 ◽  
Vol 12 (2) ◽  
pp. 331-361 ◽  
Author(s):  
Stacy T Kowalczyk
Keyword(s):  
Data Management ◽  
Analytical Techniques ◽  
Research Process ◽  
Initial Study ◽  
Research Data ◽  
Management Support ◽  
File Formats ◽  
Metadata File ◽  
Data Lifecycle ◽  
Digital Research

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.

scholarly journals PlutoF: Biodiversity data management platform for the complete data lifecycle

2019 ◽  
Vol 3 ◽  
Author(s):  
Urmas Kõljalg ◽  
Kessy Abarenkov ◽  
Allan Zirk ◽  
Veljo Runnel ◽  
Timo Piirmann ◽  
...  
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Data Types ◽  
Ecological Data ◽  
Biodiversity Data ◽  
Management Platform ◽  
Project Description ◽  
Data Lifecycle

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).

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