scholarly journals DMP Online and DMPTool: Different Strategies Towards a Shared Goal

2012 ◽  
Vol 7 (2) ◽  
pp. 123-129 ◽  
Author(s):  
Andrew Sallans ◽  
Martin Donnelly
Keyword(s):  
Research Data ◽  
Joint Paper ◽  
Management Planning ◽  
Online Tool ◽  
Digital Curation ◽  
Future Developments ◽  
Research Data Management ◽  
Shared Goal ◽  
Compare And Contrast ◽  
Sixth International

This paper provides a comparative discussion of the strategies employed in the UK’s DMP Online tool and the US’s DMPTool, both designed to provide a structured environment for research data management planning (DMP) with explicit links to funder requirements. Following the Sixth International Digital Curation Conference, held in Chicago in December 2010, a number of US institutions partnered with the Digital Curation Centre’s DMP Online team to learn from their experiences while developing a US counterpart. DMPTool arrived in beta in August 2011 and released a production version in November 2011. This joint paper will compare and contrast use cases, organizational and national/cultural characteristics that have influenced the development decisions, outcomes achieved so far, and planned future developments.

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 PROVIDING RESEARCH DATA MANAGEMENT SERVICES AND PRACTICES AT PDDI-LIPI: PREPAREDNESS, ROLES, CHALLENGES, AND TRAINING

2020 ◽  
Vol 41 (2) ◽  
pp. 169
Author(s):  
Hermin Triasih ◽  
Rahmi Rahmi ◽  
Katrin Setio Devi
Keyword(s):  
Human Resources ◽  
Data Management ◽  
Online Survey ◽  
Training Needs ◽  
Research Data ◽  
Management Planning ◽  
Data Archiving ◽  
Research Data Management ◽  
And Training ◽  
Management Services

This study aims to analyse the implementation of RDM at PDDI-LIPI and to assess its staff’s understanding about RDM services. This article also discusses the challenges and obstacles PDDI faces in providing RDM services. The data was collected via an online survey from 28 July to 7 August 2020. The survey consisted of 35 questions and was shared with 36 respondents via social media. The results identified categories such as research data management services, data management planning services, data archiving services, funding, and staff competency and training needs. In addition, this article also discusses the approach and assessment of RDM services, challenges in providing RDM services, and plans for further developing RDM services at PDDI-LIPI. The results showed that the PDDI staff's understanding of RDM services is adequate. As a new service, the implementation of RDM at PDDI-LIPI continues to develop toward optimisation. RIN is a platform used by PDDI to support this goal. The three biggest obstacles faced by PDDI-LIPI in developing RDM services are limited human resources, competence and budget.  Various trainings related to RDM, both sending staff off campus and inviting trainers to campus, were carried out by PDDI to overcome these obstacles. It is recommended to conduct further research on the mapping and upskilling of staff in charge of RDM services.

scholarly journals Incorporating Software Curation into Research Data Management Services: Lessons Learned

2018 ◽  
Vol 13 (1) ◽  
pp. 235-247
Author(s):  
Fernando Rios
Keyword(s):  
Data Management ◽  
Research University ◽  
Service Level ◽  
Research Data ◽  
Lessons Learned ◽  
Related Services ◽  
Management Planning ◽  
Research Data Management ◽  
Management Context ◽  
Large Research

Many large research universities provide research data management (RDM) support services for researchers. These may include support for data management planning, best practices (e.g., organization, support, and storage), archiving, sharing, and publication. However, these data-focused services may under-emphasize the importance of the software that is created to analyse said data. This is problematic for several reasons. First, because software is an integral part of research across all disciplines, it undermines the ability of said research to be understood, verified, and reused by others (and perhaps even the researcher themselves). Second, it may result in less visibility and credit for those involved in creating the software. A third reason is related to stewardship: if there is no clear process for how, when, and where the software associated with research can be accessed and who will be responsible for maintaining such access, important details of the research may be lost over time. This article presents the process by which the RDM services unit of a large research university addressed the lack of emphasis on software and source code in their existing service offerings. The greatest challenges were related to the need to incorporate software into existing data-oriented service workflows while minimizing additional resources required, and the nascent state of software curation and archiving in a data management context. The problem was addressed from four directions: building an understanding of software curation and preservation from various viewpoints (e.g., video games, software engineering), building a conceptual model of software preservation to guide service decisions, implementing software-related services, and documenting and evaluating the work to build expertise and establish a standard service level.

scholarly journals Reshaping the DCC Institutional Engagement Programme

2014 ◽  
Vol 9 (2) ◽  
pp. 47-64
Author(s):  
Sarah Jones ◽  
Jonathan Rans ◽  
Diana Sisu ◽  
Angus Whyte
Keyword(s):  
Higher Education ◽  
Research Data ◽  
Digital Curation ◽  
Research Data Management ◽  
Policy And Strategy ◽  
Future Strategy ◽  
The Uk ◽  
Uk Higher Education ◽  
The Impact ◽  
Institutional Engagement

This paper shares results from the Digital Curation Centre’s programme of Institutional Engagements (IEs), and describes how we continue to provide tailored support on Research Data Management (RDM) to the UK higher education sector.Between Spring 2011 and Spring 2013, the DCC ran a series of 21 Institutional Engagements. The engagement programme involved helping institutions to assess their needs, develop policy and strategy, and begin to implement a range of RDM services.We have conducted a synthesis and evaluation of the programme, analysing the types of assistance requested and the impact of our support. The findings and lessons to emerge from these exercises have informed our future strategy and helped reshape the programme.

scholarly journals Making Sense: Talking Data Management with Researchers

2011 ◽  
Vol 6 (2) ◽  
pp. 265-273 ◽  
Author(s):  
Catharine Ward ◽  
Lesley Freiman ◽  
Sarah Jones ◽  
Laura Molloy ◽  
Kellie Snow
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Ad Hoc ◽  
Research Data ◽  
Creative Commons ◽  
Digital Curation ◽  
Research Data Management ◽  
Making Sense ◽  
Raising Awareness ◽  
Technical Solutions

Incremental is one of eight projects in the JISC Managing Research Data programme funded to identify institutional requirements for digital research data management and pilot relevant infrastructure. Our findings concur with those of other Managing Research Data projects, as well as with several previous studies. We found that many researchers: (i) organise their data in an ad hoc fashion, posing difficulties with retrieval and re-use; (ii) store their data on all kinds of media without always considering security and back-up; (iii) are positive about data sharing in principle though reluctant in practice; (iv) believe back-up is equivalent to preservation. The key difference between our approach and that of other Managing Research Data projects is the type of infrastructure we are piloting. While the majority of these projects focus on developing technical solutions, we are focusing on the need for ‘soft’ infrastructure, such as one-to-one tailored support, training, and easy-to-find, concise guidance that breaks down some of the barriers information professionals have unintentionally built with their use of specialist terminology.We are employing a bottom-up approach as we feel that to support the step-by-step development of sound research data management practices, you must first understand researchers’ needs and perspectives. Over the life of the project, Incremental staff will act as mediators, assisting researchers and local support staff to understand the data management requirements within which they are expect to work, and will determine how these can be addressed within research workflows and the existing technical infrastructure.Our primary goal is to build data management capacity within the Universities of Cambridge and Glasgow by raising awareness of basic principles so everyone can manage their data to a certain extent. We will ensure our lessons can be picked up and used by other institutions. Our affiliation with the Digital Curation Centre and Digital Preservation Coalition will assist in this and all outputs will be released under a Creative Commons licence.

scholarly journals Doctoral Students' Educational Needs in Research Data Management: Perceived Importance and Current Competencies

2021 ◽  
Vol 16 (1) ◽  
pp. 36
Author(s):  
Jukka Rantasaari
Keyword(s):  
Data Management ◽  
Doctoral Students ◽  
Educational Needs ◽  
Research Data ◽  
Structured Interviews ◽  
High Quality Research ◽  
Management Planning ◽  
Data Preservation ◽  
Research Data Management ◽  
Data Lifecycle

Sound research data management (RDM) competencies are elementary tools used by researchers to ensure integrated, reliable, and re-usable data, and to produce high quality research results. In this study, 35 doctoral students and faculty members were asked to self-rate or rate doctoral students’ current RDM competencies and rate the importance of these competencies. Structured interviews were conducted, using close-ended and open-ended questions, covering research data lifecycle phases such as collection, storing, organization, documentation, processing, analysis, preservation, and data sharing. The quantitative analysis of the respondents’ answers indicated a wide gap between doctoral students’ rated/self-rated current competencies and the rated importance of these competencies. In conclusion, two major educational needs were identified in the qualitative analysis of the interviews: to improve and standardize data management planning, including awareness of the intellectual property and agreements issues affecting data processing and sharing; and to improve and standardize data documenting and describing, not only for the researcher themself but especially for data preservation, sharing, and re-using. Hence the study informs the development of RDM education for doctoral students.

scholarly journals Exploring research data management planning challenges in practice

2020 ◽  
Vol 62 (1) ◽  
pp. 29-37
Author(s):  
Armel Lefebvre ◽  
Baharak Bakhtiari ◽  
Marco Spruit
Keyword(s):  
Data Management ◽  
Scientific Information ◽  
Public Funding ◽  
Research Data ◽  
High Quality ◽  
Management Planning ◽  
Research Data Management ◽  
Barriers To Access ◽  
Funding Agencies ◽  
Management Plans

AbstractResearch data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP.

scholarly journals Research Data Management and Digital Curation as a Library Activity

2020 ◽  
Vol 61 ◽  
pp. 85
Author(s):  
Svitlana Chukanova
Keyword(s):  
Data Management ◽  
Research Data ◽  
Digital Curation ◽  
Research Data Management

<p>W epoce technologii informacyjnych dane badawcze nabrały większego znaczenia niż zwykłe publikacje naukowe bez zbiorów danych. Zbiory danych traktowane są jako teksty, mogą posiadać DOI i mogą być cytowane zgodnie ze standardem opisu bibliograficznego. Zarządzanie danymi badawczymi stanowi wieloetapowy i zawiły proces, który może być realizowany we współpracy z biblioteką lub jednostkami zajmującymi się zachowaniem zasobów cyfrowych w laboratoriach instytucji badawczych. Dla zaawansowanych analityków ważną sprawą jest praca nad tworzeniem sieci umożliwiającej szeroką eksplorację rezultatów badań i włączenie w ten proces także bibliotek i ekspertów danych.</p>

scholarly journals The Third DCC-RIN Research Data Management Forum

2009 ◽  
Vol 4 (1) ◽  
pp. 152-158
Author(s):  
Martin Donnelly
Keyword(s):  
Data Management ◽  
Research Data ◽  
Information Network ◽  
Research Information ◽  
Digital Curation ◽  
The Third ◽  
Research Data Management ◽  
Management Forum

A report on the third meeting of the Research Data Management Forum which was held in Manchester, UK on April 30 and May 1, 2009, with an overarching  theme entitled "Value and Benefits". The event was co-sponsored by the Digital Curation Centre (DCC) and the Research Information Network (RIN).

scholarly journals Tuuli project: accelerating data management planning in Finnish research organisations

2018 ◽  
Vol 12 (2) ◽  
pp. 107-115 ◽  
Author(s):  
Minna Ahokas ◽  
Mari Elisa Kuusniemi ◽  
Jari Friman
Keyword(s):  
Best Practices ◽  
Data Management ◽  
Data Sharing ◽  
Research Data ◽  
Planning Tool ◽  
Management Planning ◽  
Research Data Management ◽  
Management Plans ◽  
Research Organisations ◽  
And Training

Many research funders have requirements for data sharing and data management plans (DMP). DMP tools are services built to help researchers to create data management plans fitting their needs and based on funder and/or organisation guidelines. Project Tuuli (2015–2017) has provided DMPTuuli, a data management planning tool for Finnish researchers and research organisations offering DMP templates and guidance. In this paper we describe how project has helped both Finnish researchers and research organisations adopt research data management best practices. As a result of the project we have also created a national Tuuli network. With growing competence and collaboration of the network, the project has reached most of its goals. The project has also actively promoted DMP support and training in Finnish research organisations.

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