scholarly journals Reference models for e-infrastructures and related research data management: A use case from Austrian Universities

2015 ◽  
Author(s):  
Raman Ganguly
Keyword(s):  
Data Management ◽  
Research Data ◽  
Use Case ◽  
It Services ◽  
Sensitive Data ◽  
Related Research ◽  
Reference Models ◽  
Data Preservation ◽  
Research Data Management ◽  
The World

One goal of e-Infrastructures Austria, a project involving Austrian universities and non-university research institutions, is to find strategies for managing research data. We established a network of experts from libraries and IT services to develop architecture for efficient e-infrastructures and related reproach data management.Based on our experience from years of running a repository project for research data, we have developed models for designing e-infrastructures for research data management. This model helps us to design technical and non-technical services for preserving data. In this poster, we will present ways of defining the world of data, workflows for data preservation and models for defining roles in the entire process.These models help to structure different aspects for data preservation based on each data type. It is necessary to have different solutions for the different kinds of needs that researchers may have, whereas there must be alternate solutions for preventing sensitive data from being misused when handling petabytes of data.

scholarly journals Service Integration to Enhance Research Data Management: RSpace Electronic Laboratory Notebook Case Study

2015 ◽  
Vol 10 (1) ◽  
pp. 163-172 ◽  
Author(s):  
Stuart Macdonald ◽  
Rory Macneil
Keyword(s):  
Data Management ◽  
Research Output ◽  
Service Integration ◽  
Research Data ◽  
Responsible Conduct Of Research ◽  
It Services ◽  
Laboratory Notebook ◽  
Research Data Management ◽  
Research Administrators ◽  
Working Together

Research Data Management (RDM) provides a framework that supports researchers and their data throughout the course of their research and is increasingly regarded as one of the essential areas of responsible conduct of research. New tools and infrastructures make possible the generation of large volumes of digital research data in a myriad of formats. This facilitates new ways to analyse, share and reuse these outputs, with libraries, IT services and other service units within academic institutions working together with the research community to develop RDM infrastructures to curate and preserve this type of research output and make them re-usable for future generations. Working on the principle that a rationalised and continuous flow of data between systems and across institutional boundaries is one of the core goals of information management, this paper will highlight service integration via Electronic Laboratory Notebooks (ELN), which streamline research data workflows, result in efficiency gains for researchers, research administrators and other stakeholders, and ultimately enhance the RDM process.

scholarly journals Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic

2021 ◽  
Author(s):  
Núria Queralt-Rosinach ◽  
Rajaram Kaliyaperumal ◽  
César H. Bernabé ◽  
Qinqin Long ◽  
Simone A. Joosten ◽  
...  
Keyword(s):  
Semantic Web ◽  
Data Management ◽  
Research Data ◽  
Patient Data ◽  
Research Data Management ◽  
Hospitalised Patients ◽  
The World ◽  
Data Points ◽  
Fair Principles ◽  
Digital Objects

AbstractBackgroundThe COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data ‘silos’ that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR.ResultsIn this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors’ research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital.ConclusionsOur work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR digital objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.

scholarly journals ARCHER – e-Research Tools for Research Data Management

2009 ◽  
Vol 4 (1) ◽  
pp. 22-33 ◽  
Author(s):  
Steve Androulakis ◽  
Ashley M. Buckle ◽  
Ian Atkinson ◽  
David Groenewegen ◽  
Nick Nicholas ◽  
...  
Keyword(s):  
Data Management ◽  
Research Data ◽  
Scientific Instruments ◽  
Data Share ◽  
Related Research ◽  
Research Data Management ◽  
Work Spaces ◽  
Desktop Application ◽  
New Infrastructure ◽  
Research Resources

With new scientific instruments growing exponentially in their capability to generate research data, new infrastructure needs to be developed and deployed to allow researchers to effectively and securely manage their research data from collection, publication, and eventual dissemination to research communities.  In particular, researchers need to be able to easily acquire data from instruments, store and manage potentially large quantities of data, easily process the data, share research resources and work spaces with colleagues both inside and outside of their institution, search and discover across their accessible collections, and easily publish datasets and related research artefacts.  The ARCHER Project has developed production-ready generic e-Research infrastructure including: a Research Repository; Scientific Dataset Managers (both a web and desktop application); Distributed Integrated Multi-Sensor and Instrument Middleware; and a Collaborative Workspace Environment.  Institutions can selectively deploy these components to greatly assist their researchers in managing their research data.

Global presence of open-source research data management platform for libraries: the Dataverse project

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vineet Jamwal ◽  
Simran Kaur
Keyword(s):  
Data Management ◽  
Open Source ◽  
Local Level ◽  
Research Data ◽  
Content Type ◽  
Management Platform ◽  
Research Data Management ◽  
The World ◽  
Global Presence ◽  
The Usa

Purpose This paper aims to provide statistical information on the worldwide spread of the open-source research data management application, the Dataverse Project, to librarians, data managers and information managers who are considering using the application at their own institution. Design/methodology/approach To produce a list of dataverse repositories, the official Dataverse website was evaluated, and JSON data were downloaded and parsed. Data standardisation was performed to assess the state of installations in various nations and continents across the world. Findings Globally, the Dataverse repositories have seen a rise in overall installations. The year 2020 alone saw a 23.21% rise. In a country-by-country comparison, the USA (13) has the most dataverse installations, while Europe (25) has the highest number of installations worldwide. Originality/value This research will be useful to librarians, data managers and information managers, among others, who want to learn more about Dataverse repositories throughout the world before deploying at their local level.

Research Data Management Practices in University libraries: A study

2017 ◽  
Vol 37 (6) ◽  
pp. 417 ◽  
Author(s):  
Manorama Tripathi ◽  
Archana Shukla ◽  
Sharad Kumar Sonkar
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Data Reuse ◽  
University Libraries ◽  
Research Data Management ◽  
The World ◽  
The University

<p>The paper has studied the research data management (RDM) services implemented by different university libraries for managing, organizing, curating and preserving research data generated at their universities’ departments and laboratories, for data reuse and sharing. It has surveyed the central university libraries and the best 20 university libraries of the world to highlight how RDM is extended to the researchers. Further, it has suggested a model for the university libraries in the country to follow for actually deploying RDM services. </p>

scholarly journals Education Needs in Research Data Management for Science-Based Disciplines

2019 ◽  
Author(s):  
Judith E Pasek ◽  
Jennifer Mayer
Keyword(s):  
Graduate Students ◽  
Data Management ◽  
Data Visualization ◽  
Management Training ◽  
Research Data ◽  
Academic Community ◽  
Data Conversion ◽  
Data Preservation ◽  
Research Data Management ◽  
Doctoral Universities

Research data management is a prominent and evolving consideration for the academic community, especially in scientific disciplines. This research study surveyed 131 graduate students and 79 faculty members in the sciences at two public doctoral universities to determine the importance, knowledge, and interest levels around research data management training and education. The authors adapted 12 competencies for measurement in the study. Graduate students and faculty ranked the following areas most important among the 12 competencies: ethics and attribution, data visualization, and quality assurance. Graduate students indicated they were least knowledgeable and skilled in data curation and re-use, metadata and data description, data conversion and interoperability, and data preservation. Their responses generally matched the perceptions of faculty. The study also examined how graduate students learn research data management, and how faculty perceive that their students learn research data management. Results showed that graduate students utilize self-learning most often and that faculty may be less influential in research data management education than they perceive. Responses for graduate students between the two institutions were not statistically different, except in the area of perceived deficiencies in data visualization competency.

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.

Research data management practices of chemistry researchers in federal universities of technology in Nigeria

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatimah Jibril Abduldayan ◽  
Fasola Petunola Abifarin ◽  
Georgina Uchey Oyedum ◽  
Jibril Attahiru Alhassan
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Management Practice ◽  
Research Process ◽  
Management Plan ◽  
Research Data ◽  
Content Type ◽  
Data Preservation ◽  
Research Data Management ◽  
Research Findings

Purpose The purpose of this study was to understand the research data management practices of chemistry researchers in the five specialized federal universities of technology in Nigeria. Appropriate research data management practice ensures that research data are available for reuse by secondary users, and research findings can be verified and replicated within the scientific community. A poor research data management practice can lead to irrecoverable data loss, unavailability of data to support research findings and lack of trust in the research process. Design/methodology/approach An exploratory research technique involving semi-structured, oral and face-to-face interview is used to gather data on research data management practices of chemistry researchers in Nigeria. Interview questions were divided into four major sections covering chemistry researchers’ understanding of research data, experience with data loss, data storage method and backup techniques, data protection, data preservation and availability of data management plan. Braun and Clarke thematic analysis approach was adapted, and the Provalis Qualitative Data Miner (version 5) software was used for generating themes and subthemes from the coding framework and for presenting the findings. Findings Findings revealed that chemistry researchers in Nigeria have a good understanding of the concept of research data and its importance to research findings. Chemistry researchers have had several experiences of irrecoverable loss of data because of poor choice of storage devices, back-up methods and weak data protection systems. Even though the library was agreed as the most preferred place for long-term data preservation, there is the issue of trust and fear of loss of ownership of data to unauthorized persons or party. No formal data management plan is used while conducting their scientific research. Research limitations/implications The research focused on research data management practices of chemistry researchers in the five specialized federal universities of technology in Nigeria. Although the findings of the study are similar to perceptions and practices of researchers around the world, it cannot be used as a basis for generalization across other scientific disciplines. Practical implications This study concluded that chemistry researchers need further orientation and continuous education on the importance and benefits of appropriate research data management practice. The library should also roll out research data management programs to guide researchers and improve their confidence throughout the research process. Social implications Appropriate research data management practice not only ensures that the underlying research data are true and available for reuse and re-validation, but it also encourages data sharing among researchers. Data sharing will help to ensure better collaboration among researchers and increased visibility of the datasets and data owners through the use of standard data citations and acknowledgements. Originality/value This is a qualitative and in-depth study of research data management practices and perceptions among researchers in a particular scientific field of study.

Research Data Management Practices in University libraries: A study

2017 ◽  
Vol 37 (6) ◽  
pp. 417 ◽  
Author(s):  
Manorama Tripathi ◽  
Archana Shukla ◽  
Sharad Kumar Sonkar
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Data Reuse ◽  
University Libraries ◽  
Research Data Management ◽  
The World ◽  
The University

<p>The paper has studied the research data management (RDM) services implemented by different university libraries for managing, organizing, curating and preserving research data generated at their universities’ departments and laboratories, for data reuse and sharing. It has surveyed the central university libraries and the best 20 university libraries of the world to highlight how RDM is extended to the researchers. Further, it has suggested a model for the university libraries in the country to follow for actually deploying RDM services. </p>

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