Joining in the Enterprise of Response in the Wake of the NSF Data Management Planning Requirement

2011 ◽  
pp. 11-17 ◽  
Author(s):  
Patricia Hswe ◽  
Ann Holt
Keyword(s):  
Data Management ◽  
Management Planning

scholarly journals Data Governance Maturity Level at the National Archives of the Republic of Indonesia

2020 ◽  
Vol 10 (1) ◽  
pp. 27-40
Author(s):  
Sari Agustin Wulandari
Keyword(s):  
Data Management ◽  
Good Governance ◽  
Maturity Level ◽  
Data Governance ◽  
Management Planning ◽  
Governance Model ◽  
Master Data ◽  
National Archives ◽  
Grand Design ◽  
The Republic

The National Archives of the Republic of Indonesia (ANRI) as an institution given mandate to carry out state duty in the field of archives has vision as a pillar of good governance and nation’s collective memory. To implement it, the study of the grand design of the archival system arranged. That is very related to the data governance implementation. Therefore, ANRI needs to know the maturity level of the data governance function which had been held. The assessment was done by referring to the Stanford Data Governance Model. The result showed that data governance is still at an initial level. The foundational aspects are on an average of 1,2 which contains awareness, formalization, and metadata. While on project aspects are on average of 1,5 consisting of stewardship, data quality, and master data. In total, ANRI is at the level of 1,35. ANRI needs to make improvements for data management planning activities referring to Data Management Body of Knowledge (DMBOK) with a focus on people, policies, and capabilities dimensions in all aspects. This research is expected to be helpful for ANRI to make improvements corresponding to the recommendations thus ANRI could implement national data archival properly.

scholarly journals Data Management Planning: How Requirements and Solutions are Beginning to Converge

2020 ◽  
Vol 2 (1-2) ◽  
pp. 208-219 ◽  
Author(s):  
Sarah Jones ◽  
Robert Pergl ◽  
Rob Hooft ◽  
Tomasz Miksa ◽  
Robert Samors ◽  
...  
Keyword(s):  
Data Management ◽  
Driving Forces ◽  
Management Planning ◽  
Current State ◽  
Adoption And Implementation ◽  
Key Characteristics ◽  
Data Stewardship ◽  
Fair Principles ◽  
Research Workforce ◽  
Research Initiatives

Effective stewardship of data is a critical precursor to making data FAIR. The goal of this paper is to bring an overview of current state of the art of data management and data stewardship planning solutions (DMP). We begin by arguing why data management is an important vehicle supporting adoption and implementation of the FAIR principles, we describe the background, context and historical development, as well as major driving forces, being research initiatives and funders. Then we provide an overview of the current leading DMP tools in the form of a table presenting the key characteristics. Next, we elaborate on emerging common standards for DMPs, especially the topic of machine-actionable DMPs. As sound DMP is not only a precursor of FAIR data stewardship, but also an integral part of it, we discuss its positioning in the emerging FAIR tools ecosystem. Capacity building and training activities are an important ingredient in the whole effort. Although not being the primary goal of this paper, we touch also the topic of research workforce support, as tools can be just as much effective as their users are competent to use them properly. We conclude by discussing the relations of DMP to FAIR principles, as there are other important connections than just being a precursor.

Data management planning and repository demands for qualitative research

KWALON ◽  
2016 ◽  
Vol 21 (1) ◽  
Author(s):  
René van Horik
Keyword(s):  
Data Management ◽  
Research Data ◽  
Digital Data ◽  
Data Repository ◽  
Data Sets ◽  
Data Repositories ◽  
Management Planning ◽  
Research Activities ◽  
Trusted Data ◽  
New Research

Summary Nowadays, research without a role for digital data and data analysis tools is barely possible. As a result, we see an increasing interest in research data management, as this enables the replication of research outcomes and the reuse of research data for new research activities. Data management planning outlines how to handle data, both during research and after the research is completed. Trusted data repositories are places were research data are archived and made available for the long term. This article covers the state of the art concerning data management and data repository demands with a focus on qualitative data sets.

scholarly journals Virtual exchanges as complex research environments: facing the data management challenge. A case study of Teletandem Brasil

2020 ◽  
Vol 3 ◽  
pp. 13-38
Author(s):  
Solange Aranha ◽  
Ciara R. Wigham
Keyword(s):  
Data Collection ◽  
Data Management ◽  
Interaction Analysis ◽  
Open Data ◽  
Critical Discussion ◽  
Video Data ◽  
Learning Theories ◽  
Management Planning ◽  
Management Plans ◽  
Two Phases

Although there is a move toward open data, with research funding bodies more frequently requiring data management plans and dissemination strategies, the data management challenges inherently linked to virtual exchange research are understudied. Data collection is often reported upon in papers addressing interaction analysis or language development, but little attention has been paid to offering critical discussion of data collection and structuration methods or practical advice to encourage data/corpora dissemination. This paper reports on two phases of the Multimodal Teletandem Corpus project (Aranha & Lopes, 2019) that structured 581 hours of video data from Portuguese-English teletandem sessions, 351 chat logs, 956 written productions exchanged between the partners (original, revised, and corrected versions), 91 initial and 41 final questionnaires, and 666 learning diaries. We describe the data management problems faced that included the organization of data collected, ethical consent, management of a large quantity of data, inclusion of sociolinguistic information, expansion of learning theories, and the solutions found. We then outline data management planning steps that, consequently, are being introduced for future telecollaboration instantiations.

scholarly journals Automating Research Data Management Using Machine-Actionable Data Management Plans

2022 ◽  
Vol 13 (2) ◽  
pp. 1-22
Author(s):  
Tomasz Miksa ◽  
Simon Oblasser ◽  
Andreas Rauber
Keyword(s):  
Data Management ◽  
Research Data ◽  
Use Cases ◽  
Full Potential ◽  
Institutional Research ◽  
Reference Architecture ◽  
Management Planning ◽  
Research Data Management ◽  
Management Plans ◽  
New Generation

Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks. In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs.

scholarly journals Data Governance Maturity Level at the National Archives of the Republic of Indonesia

2020 ◽  
Vol 10 (1) ◽  
pp. 27
Author(s):  
Sari Agustin Wulandari
Keyword(s):  
Data Management ◽  
Good Governance ◽  
Maturity Level ◽  
Data Governance ◽  
Management Planning ◽  
Governance Model ◽  
Master Data ◽  
National Archives ◽  
Grand Design ◽  
The Republic

<em><span>The National Archives of the Republic of Indonesia (ANRI) as an institution given mandate to carry out state duty in the field of archives has vision as a pillar of good governance and nation’s collective memory. To implement it, the study of the grand design of the archival system arranged. That is very related to the data governance implementation. Therefore, ANRI needs to know the maturity level of the data governance function which had been held. The assessment was done by referring to the Stanford Data Governance Model. The result showed that data governance is still at an initial level. The foundational aspects are on an average of 1,2 which contains awareness, formalization, and metadata. While on project aspects are on average of 1,5 consisting of stewardship, data quality, and master data. In total, ANRI is at the level of 1,35. ANRI needs to make improvements for data management planning activities referring to Data Management Body of Knowledge (DMBOK) with a focus on people, policies, and capabilities dimensions in all aspects. This research is expected to be helpful for ANRI to make improvements corresponding to the recommendations thus ANRI could implement national data archival properly</span><span lang="EN-US">.</span></em>

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