scholarly journals Supporting FAIR data: categorization of research data as a tool in data management

2018 ◽  
Vol 37 (4) ◽  
Author(s):  
Jessica Parland-von Essen ◽  
Katja Fält ◽  
Zubair Maalick ◽  
Miika Alonen ◽  
Eduardo Gonzalez
Keyword(s):  
Data Management ◽  
Research Data ◽  
Data Types ◽  
Specific Research ◽  
Research Results ◽  
Data Services ◽  
Operational Data

The demand for implementation of the FAIR data principles is in many cases difficult for a researcher to adhere to in efficient ways due to lacking tools. We suggest categorizing data in a more extensive and systematic way with focus on the inherent properties of the data as means to enhancing research data services. After discussing different approaches to categorizing data, we propose a tripartite research data categorization based around the inherent aspect of stability. The three research data types are operational data, generic research data and research data publications. Generic research data is validated data and can be cumulative, i.e. data can be added without versioning, however if it is dynamic it should be versioned. Generic research data should be separated from immutable dataset publications that are published for reasons of reproducibility of specific research results.

scholarly journals FAIR Data Austria - Aligning the Implementation of FAIR Tools and Services

2021 ◽  
Vol 74 (2) ◽  
Author(s):  
Susanne Blumesberger ◽  
Nikos Gänsdorfer ◽  
Raman Ganguly ◽  
Eva Gergely ◽  
Alexander Gruber ◽  
...  
Keyword(s):  
Data Management ◽  
Support Services ◽  
Research Data ◽  
Research Results ◽  
Management Plans ◽  
Project Objectives ◽  
Training And Support

This article gives an overview of the FAIR Data Austria project objectives and current results. In collaboration with our project partners, we work on the development and establishment of tools for managing the lifecycle of research data, including machine-actionable Data Management Plans (maDMPs), repositories for long-term archiving of research results, RDM training and support services, models, and profiles for Data Stewards and FAIR Office Austria.

Research Data Management Services in Tertiary Institutions in Zimbabwe

2021 ◽  
pp. 419-437
Author(s):  
Josiline Phiri Chigwada
Keyword(s):  
Data Management ◽  
Research Data ◽  
Management Policy ◽  
Online Questionnaire ◽  
Tertiary Institutions ◽  
Data Services ◽  
Security Issues ◽  
Research Data Management ◽  
Survey Monkey ◽  
Management Services

The chapter seeks to analyze how librarians in Zimbabwe are responding to increasing librarian roles in the provision of research data services. The study sought to ascertain librarians' awareness and preparedness to offer research data management services at their institutions and determine support required by librarians to effectively deliver research data services. Participants were invited to respond to the survey, and survey monkey was used to administer the online questionnaire. The collected data was analyzed using content analysis, and it was thematically presented. Findings revealed that librarians in Zimbabwe are aware of their role in research data management, but the majority are not prepared to offer research data management services due to a lack of the required skills and resources. Challenges that were noted include lack of research data management policy at institutional levels and information technology issues such as obsolescence and security issues.

scholarly journals Research Data Services at ETH-Bibliothek

IFLA Journal ◽  
2016 ◽  
Vol 42 (4) ◽  
pp. 284-291 ◽  
Author(s):  
Ana Sesartic ◽  
Matthias Töwe
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Service Providers ◽  
Scientific Practice ◽  
Research Data ◽  
Data Services ◽  
Mutual Learning ◽  
Data Life Cycle ◽  
Long Term Preservation

The management of research data throughout its life-cycle is both a key prerequisite for effective data sharing and efficient long-term preservation of data. This article summarizes the data services and the overall approach to data management as currently practised at ETH-Bibliothek, the main library of ETH Zürich, the largest technical university in Switzerland. The services offered by service providers within ETH Zürich cover the entirety of the data life-cycle. The library provides support regarding conceptual questions, offers training and services concerning data publication and long-term preservation. As research data management continues to play a steadily more prominent part in both the requirements of researchers and funders as well as curricula and good scientific practice, ETH-Bibliothek is establishing close collaborations with researchers, in order to promote a mutual learning process and tackle new challenges.

scholarly journals Research data services in veterinary medicine libraries

2017 ◽  
Vol 104 (4) ◽  
Author(s):  
Erin E. Kerby, MSI
Keyword(s):  
Veterinary Medicine ◽  
Data Management ◽  
Online Survey ◽  
Research Data ◽  
Management Support ◽  
Job Responsibilities ◽  
Data Services ◽  
Medical Libraries ◽  
Definition Of ◽  
Interest In Research

Objective: The study investigated veterinary medicine librarians’ experience with and perceptions of research data services. Many academic libraries have begun to offer research data services in response to researchers’ increased need for data management support. To date, such services have typically been generic, rather than discipline-specific, to appeal to a wide variety of researchers.Methods: An online survey was deployed to identify trends regarding research data services in veterinary medicine libraries. Participants were identified from a list of contacts from the MLA Veterinary Medical Libraries Section.Results: Although many respondents indicated that they have a professional interest in research data services, the majority of veterinary medicine librarians only rarely or occasionally provide data management support as part of their regular job responsibilities. There was little consensus as to whether research data services should be core to a library’s mission despite their perceived importance to the advancement of veterinary research. Furthermore, most respondents stated that research data services are just as or somewhat less important than the other services that they provide and feel only slightly or somewhat prepared to offer such services.Conclusions: Lacking a standard definition of ‘‘research data’’ and a common understanding of precisely what research data services encompass, it is difficult for veterinary medicine librarians and libraries to define and understand their roles in research data services. Nonetheless, they appear to have an interest in learning more about and providing research data services.

scholarly journals Collaboration in RDM activities – Practices and development at Aalto University

2017 ◽  
Author(s):  
Maria Söderholm ◽  
Anne Sunikka
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Research Data ◽  
Service Planning ◽  
Open Access Publishing ◽  
Service Development ◽  
It Services ◽  
Data Services ◽  
Service Portfolio ◽  
Starting Point

Watch the VIDEO here. Presenter – Maria SöderholmResearch data management (RDM) is a complex and dynamic topic, and demands diverse expertise, skills and knowledge. The RDM expertise includes subtopics like collection/provision of data; storage and processing of data; long-term preservation requirements of the data; and funders’ demands and solutions to share, re-find and re-use research data. Usually the expertise related to these RDM subtopics is spread to several university units, both academic and administrative. Therefore, many tasks related to RDM, for example, day-to-day practices, the supply of services and the development work are best carried out in a network-based cooperation.In the presentation, we will introduce our RDM related partnership and networking of Aalto University. As a starting point, we introduce the internal service development working principles that our RDM network work is based on. However, the focus will be on describing our RDM working group and development activities.Aalto’s Research Data Management Programme forms the backbone for RDM work. It establishes seven separate action points for RDM activities: 1) open access  publishing; 2) implementation of data management planning tool; 3) metadata catalogue for open data; 4) data publishing; 5) comprehensive repository service for storage, back-up and collaboration; 6) combining openness and innovation; and 7) RDM awareness building activities. The actors in the RDM network consist of Research and Innovation Services (leading the group), IT Services, and Learning Centre (previously Library).In the presentation, we will introduce the core actors in more detail, paying attention to the complementarity of the roles; and the activities and the aims, which steer the work. We also discuss the meaning and consequences of the network-based cooperation for the working group.First, the RDM group acts as a joint platform for comprehensive RDM information. Important means of data gathering are surveys and informal discussion with researcher. In the future, formal group discussions on RDM needs are hosted. The previous discussions with researchers have underlined the importance of arranging services for both disciplinary and data specific needs in addition to common university level service needs.Second, the group is a messenger of the RDM topics. Our task is to rise internal RDM awareness and disseminate national and international information and development trends in the university.The third and the most challenging aim is the RDM service planning and delivery. Our task is to identify the existing in-house services, to map the suitable services provided by national and international agents as well as to recognize the needs for new services. Our service portfolio covers both consultation/informational services and technical, hands-on services. However, many of our research data services are still in the planning or piloting stage, thus cooperation with researchers is essential.In our experience, the network-based collaboration model that foster individuals’ interconnectedness is crucial for surviving with the built-in dynamism of RDM. This model provides a non-hierarchical and flexible environment for actions to meet the increasing expectations for research data services we face from the funders, governments, and researchers.

Variability in academic research data management practices

2015 ◽  
Vol 49 (4) ◽  
pp. 382-407 ◽  
Author(s):  
Amanda L. Whitmire ◽  
Michael Boock ◽  
Shan C. Sutton
Keyword(s):  
Data Management ◽  
Online Survey ◽  
Management Practices ◽  
Academic Research ◽  
Research Data ◽  
Data Types ◽  
Content Type ◽  
Progressive Development ◽  
Research Data Management ◽  
Basic Services

Purpose – The purpose of this paper is to demonstrate how knowledge of local research data management (RDM) practices critically informs the progressive development of research data services (RDS) after basic services have already been established. Design/methodology/approach – An online survey was distributed via e-mail to all university faculty in the fall of 2013, and was left open for just over one month. The authors sent two reminder e-mails before closing the survey. Survey data were downloaded from Qualtrics survey software and analyzed in R. Findings – In this paper, the authors reviewed a subset of survey findings that included data types, volume, and storage locations, RDM roles and responsibilities, and metadata practices. The authors found that Oregon State University (OSU) researchers are generating a wide variety of data types, and that practices vary between colleges. The authors discovered that faculty are not utilizing campus-wide storage infrastructure, and are maintaining their own storage servers in surprising numbers. Faculty-level research assistants perform the majority of data-related tasks at OSU, with the exception of data sharing, which is primarily handled by the professorial ranks. The authors found that many faculty on campus are creating metadata, but that there is a need to provide support in how to discover and create standardized metadata. Originality/value – This paper presents a novel example of how to efficiently move from establishing basic RDM services to providing more focussed services that meet specific local needs. It provides an approach for others to follow when tackling the difficult question of, “What next?” with regard to providing academic RDS.

Managing collaborative research data for integrated, interdisciplinary environmental research

2020 ◽  
Author(s):  
Michael Finkel ◽  
Albrecht Baur ◽  
Tobias K.D. Weber ◽  
Karsten Osenbrück ◽  
Hermann Rügner ◽  
...  
Keyword(s):  
Data Management ◽  
Collaborative Research ◽  
Large Scale ◽  
Full Range ◽  
Research Data ◽  
Environmental Research ◽  
Data Types ◽  
Research Data Management ◽  
Management Concepts

<p>The consistent management of research data is crucial for the success of long-term and large-scale collaborative research. Research data management is the basis for efficiency, continuity, and quality of the research, as well as for maximum impact and outreach, including the long-term publication of data and their accessibility. Both funding agencies and publishers increasingly require this long term and open access to research data. Joint environmental studies typically take place in a fragmented research landscape of diverse disciplines; researchers involved typically show a variety of attitudes towards and previous experiences with common data policies, and the extensive variety of data types in interdisciplinary research poses particular challenges for collaborative data management.We present organizational measures, data and metadata management concepts, and technical solutions to form a flexible research data management framework that allows for efficiently sharing the full range of data and metadata among all researchers of the project, and smooth publishing of selected data and data streams to publicly accessible sites. The concept is built upon data type-specific and hierarchical metadata using a common taxonomy agreed upon by all researchers of the project. The framework’s concept has been developed along the needs and demands of the scientists involved, and aims to minimize their effort in data management, which we illustrate from the researchers’ perspective describing their typical workflow from the generation and preparation of data and metadata to the long-term preservation of data including their metadata.</p>

scholarly journals A model for initiating research data management services at academic libraries

2019 ◽  
Vol 107 (3) ◽  
Author(s):  
Kevin B. Read ◽  
Jessica Koos ◽  
Rebekah S. Miller ◽  
Cathryn F. Miller ◽  
Gesina A. Phillips ◽  
...  
Keyword(s):  
Data Management ◽  
Research Data ◽  
Data Series ◽  
Pilot Program ◽  
Data Services ◽  
Research Data Management ◽  
Hands On ◽  
Medical Libraries ◽  
Training Resources ◽  
Educational Modules

Background: Librarians developed a pilot program to provide training, resources, strategies, and support for medical libraries seeking to establish research data management (RDM) services. Participants were required to complete eight educational modules to provide the necessary background in RDM. Each participating institution was then required to use two of the following three elements: (1) a template and strategies for data interviews, (2) a teaching tool kit to teach an introductory RDM class, or (3) strategies for hosting a data class series.Case Presentation: Six libraries participated in the pilot, with between two and eight librarians participating from each institution. Librarians from each institution completed the online training modules. Each institution conducted between six and fifteen data interviews, which helped build connections with researchers, and taught between one and five introductory RDM classes. All classes received very positive evaluations from attendees. Two libraries conducted a data series, with one bringing in instructors from outside the library.Conclusion: The pilot program proved successful in helping participating librarians learn about and engage with their research communities, jump-start their teaching of RDM, and develop institutional partnerships around RDM services. The practical, hands-on approach of this pilot proved to be successful in helping libraries with different environments establish RDM services. The success of this pilot provides a proven path forward for libraries that are developing data services at their own institutions.

Investigation and analysis of research data services in university libraries

2015 ◽  
Vol 33 (3) ◽  
pp. 417-449 ◽  
Author(s):  
Li Si ◽  
Wenming Xing ◽  
Xiaozhe Zhuang ◽  
Xiaoqin Hua ◽  
Limei Zhou
Keyword(s):  
Data Management ◽  
Academic Libraries ◽  
Management Training ◽  
Site Investigation ◽  
Research Data ◽  
Data Service ◽  
University Libraries ◽  
Content Type ◽  
Data Services ◽  
And Storage

Purpose – This paper aims to find the current situation of research data services by academic libraries and summarize some strategies for university libraries to reference. Recent years have seen an increasing number of university libraries extended their traditional roles and provided research data services. Design/methodology/approach – This paper selected 87 libraries of the top 100 universities listed in the World’s Best Universities released by the USA News in October 2012 as samples and conducted a Web site investigation to check if there were any research data services provided. In addition, it made an interview with the Wuhan University Library’s Research Data Service Workgroup to understand the procedure, difficulties and experiences of their research data service. Based on the survey and interview, it analyzed the current status and difficulties of research data services in university libraries and proposed some strategies for others to reference. Findings – Of the 87 university libraries investigated, 50 libraries have offered research data services. Most of the services can be divided into six aspects: research data introduction, data management guideline, data curation and storage service, data management training, data management reference and resource recommendation. Among these services, research data introduction is the most frequently provided (47.13 per cent), followed by data curation and storage services (43.68 per cent), data management guideline (42.53 per cent), data management reference (41.38 per cent), resource recommendation (41.38 per cent) and data management training (24.14 per cent). The difficulties met by research data service of Chinese academic libraries are also concluded. Originality/value – Through Web site investigation and interview with the Wuhan University Library’s Research Data Service, this paper presented an overall picture of research data services in university libraries and identified the difficulties and experiences of research data services of the Wuhan University Library. Based on some successful examples, it put forward some strategies for university libraries to reference. This study is very useful for academic libraries to promote their research data services.

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