Delivering the Next-Generation Research Repository

2019 ◽  
pp. 144-154
Author(s):  
Adi Alter ◽  
Eddie Neuwirth ◽  
Dani Guzman
Keyword(s):  
Data Management ◽  
Research Output ◽  
Scale Up ◽  
Research Data ◽  
Next Generation ◽  
Data Repositories ◽  
Institutional Repositories ◽  
Research Data Management ◽  
Library Research ◽  
Multiple Data

Academic libraries are looking for ways to grow their involvement in and scale-up their support for research activities. The successful transition depends to a large extent on the library's ability to systematically manage data, break down information silos and unify workflows across the library, research office and researchers. Data repositories are at the heart of this challenge, yet often institutional repositories are not built to address the needs of modern research data management due to inability to store all research assets, lack of consistent data models, and insufficient workflows. This chapter will present a new approach to research data management that ensures visibility of research output and data, data coherency, and compliance with open access standards. The authors will discuss a ‘Next-Generation Research Repository' that spans multiple data management activities, including automated data capture, metadata enrichment, dissemination, compliance-related workflows, automated publication to scholarly profiles, as well as open integration with the research ecosystem.

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 NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

2020 ◽  
Vol 6 ◽  
Author(s):  
Christoph Steinbeck ◽  
Oliver Koepler ◽  
Felix Bach ◽  
Sonja Herres-Pawlis ◽  
Nicole Jung ◽  
...  
Keyword(s):  
Data Management ◽  
Data Science ◽  
Open Data ◽  
Research Data ◽  
Data Standards ◽  
Data Repositories ◽  
Data Infrastructure ◽  
Research Data Management ◽  
Wide Range ◽  
Chemistry Community

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

scholarly journals Challenges and Practices of Research Data Management in Selected Iraq Universities

2019 ◽  
Vol 39 (06) ◽  
pp. 308-314
Author(s):  
Mahdi Salah Mohammed ◽  
Rafea Ibrahim
Keyword(s):  
Data Management ◽  
Research Data ◽  
Faculty Members ◽  
Data Repositories ◽  
Postgraduate Students ◽  
Research Data Management ◽  
Research Findings ◽  
Management Policies ◽  
Research Organisations

Research emphasises the fundamental role of research data management (RDM) in enhancing academic and scientific research. This paper intended to examine RDM in Iraqi Universities, identify the current challenges of RDM and propose influential RDM practices. Data collection employed a self-administered questionnaires distributed to 155 postgraduate students and 20 faculty members from five universities in Iraq. Research findings revealed that there is a lack of proper RDM. Postgraduate students and researchers were managing their own research data. Main challenges of maintaining a good RDM involve lack of guidelines on effective RDM practices, insufficient of adequate human resources, technological obsolescence, insecure and inefficient infrastructure, lack of financial resources, absence of research data management policies and lack of support by institutional authorities and researchers negatively influenced on research data management. Postgraduate students and researchers recommend building research data repositories and collaboration with other universities and research organisations.

scholarly journals Publishing trends on research data management in Sub-Saharan Africa: A bibliometrics analysis

2021 ◽  
Vol 45 (3-4) ◽  
Author(s):  
Tom Kwanya
Keyword(s):  
Best Practices ◽  
Data Management ◽  
Research Output ◽  
Developed Countries ◽  
Research Data ◽  
Sub Saharan Africa ◽  
Google Scholar ◽  
Publish Or Perish ◽  
Research Data Management ◽  
Sub Saharan

Research data management is an umbrella term used to describe activities related to the creation, organisation, structuring, naming, backing up, storage, conservation, and sharing of research data as well as all actions that guarantee security of research data. As is often the case, researchers from Sub-Saharan Africa are lagging behind their counterparts in developed countries in embracing the best practices of research data management. One of the factors to which this slow pace of adoption of research data management could be attributed, is inadequate research on the subject. The purpose of this paper is to analyse the quantity, quality, visibility and authorship of publications on research data management in Sub-Saharan Africa. Bibliometrics approaches were used to analyse publications on research data management from, and on, Sub-Saharan Africa which are currently indexed in Google Scholar. The index was chosen because it is free and is reputed to have liberal selection criteria which do not favour, or discriminate, any discipline or geographic regions. Data was retrieved from Google Scholar using Harzing’s “Publish or Perish” software and analysed using VOSviewer. The findings of the study revealed that the quantity, quality, visibility and authorship collaboration of scholarly publications on research data management in Sub-Saharan Africa is low. The findings may be used by libraries and research institutions in Sub-Saharan Africa to develop and promote best practices in research data management as a means of enhancing their research output and impact.

scholarly journals Research Data Management Tools and Workflows: Experimental Work at the University of Porto

2018 ◽  
Vol 42 (2) ◽  
pp. 1-16
Author(s):  
Cristina Ribeiro ◽  
João Rocha da Silva ◽  
João Aguiar Castro ◽  
Ricardo Carvalho Amorim ◽  
João Correia Lopes ◽  
...  
Keyword(s):  
Data Management ◽  
Research Data ◽  
Sensor Data ◽  
Preliminary Evaluation ◽  
Laboratory Notebook ◽  
Data Repositories ◽  
Long Tail ◽  
Management Platform ◽  
Research Data Management ◽  
The University

Research datasets include all kinds of objects, from web pages to sensor data, and originate in every domain. Concerns with data generated in large projects and well-funded research areas are centered on their exploration and analysis. For data in the long tail, the main issues are still how to get data visible, satisfactorily described, preserved, and searchable. Our work aims to promote data publication in research institutions, considering that researchers are the core stakeholders and need straightforward workflows, and that multi-disciplinary tools can be designed and adapted to specific areas with a reasonable effort. For small groups with interesting datasets but not much time or funding for data curation, we have to focus on engaging researchers in the process of preparing data for publication, while providing them with measurable outputs. In larger groups, solutions have to be customized to satisfy the requirements of more specific research contexts. We describe our experience at the University of Porto in two lines of enquiry. For the work with long-tail groups we propose general-purpose tools for data description and the interface to multi-disciplinary data repositories. For areas with larger projects and more specific requirements, namely wind infrastructure, sensor data from concrete structures and marine data, we define specialized workflows. In both cases, we present a preliminary evaluation of results and an estimate of the kind of effort required to keep the proposed infrastructures running.  The tools available to researchers can be decisive for their commitment. We focus on data preparation, namely on dataset organization and metadata creation. For groups in the long tail, we propose Dendro, an open-source research data management platform, and explore automatic metadata creation with LabTablet, an electronic laboratory notebook. For groups demanding a domain-specific approach, our analysis has resulted in the development of models and applications to organize the data and support some of their use cases. Overall, we have adopted ontologies for metadata modeling, keeping in sight metadata dissemination as Linked Open Data.

scholarly journals On a Quest for Cultural Change - Surveying Research Data Management Practices at Delft University of Technology

2019 ◽  
Author(s):  
Heather Andrews ◽  
Marta Teperek ◽  
Jasper van Dijck ◽  
Kees den Heijer ◽  
Robbert Eggermont ◽  
...  
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Management Training ◽  
Research Data ◽  
Data Repositories ◽  
Automatic Data ◽  
Awareness Campaigns ◽  
Research Data Management ◽  
University Of Technology ◽  
Data Stewardship

The Data Stewardship project is a new initiative from the Delft University of Technology (TU Delft) in the Netherlands. Its aim is to create mature working practices and policies regarding research data management across all TU Delft faculties. The novelty of this project relies on having a dedicated person, the so-called ‘Data Steward’, embedded in each faculty to approach research data management from a more discipline-specific perspective. It is within this framework that a research data management survey was carried out at the faculties that had a Data Steward in place by July 2018. The goal was to get an overview of the general data management practices, and use its results as a benchmark for the project. The total response rate was 11 to 37% depending on the faculty. Overall, the results show similar trends in all faculties, and indicate lack of awareness regarding different data management topics such as automatic data backups, data ownership, relevance of data management plans, awareness of FAIR data principles and usage of research data repositories. The results also show great interest towards data management, as more than ~80% of the respondents in each faculty claimed to be interested in data management training and wished to see the summary of survey results. Thus, the survey helped identified the topics the Data Stewardship project is currently focusing on, by carrying out awareness campaigns and providing training at both university and faculty levels.

scholarly journals Research Data Management Policy and Practice in China

2020 ◽  
Vol 15 (1) ◽  
pp. 18
Author(s):  
Yingshen Huang ◽  
Andrew Cox ◽  
Laura Sbaffi
Keyword(s):  
Data Management ◽  
Research Data ◽  
Management Policy ◽  
Current Status ◽  
Service Development ◽  
University Libraries ◽  
Data Repositories ◽  
Chinese Universities ◽  
Research Data Management ◽  
Website Analysis

On April 2, 2018, the State Council of China formally released a national research data management (RDM) policy “Measures for Managing Scientific Data”. Literature review shows that university libraries have played an important role in supporting Research Data Management at an institutional level in countries in North America, Europe and Australasia. The aim of this paper is to capture the current status of RDM in Chinese universities, in particular how university libraries have involved in taking the agenda forward. This paper uses mixed methods: a website analysis of university policies and services; a questionnaire for university librarians; and semi-structured interviews. Findings from website analysis and questionnaires indicate that RDS at a local level in Chinese Universities are in their infancy. On the whole there is more evidence of activity in developing data repositories than support services. Despite the existence of a national policy there remain significant barriers to further service development, such as the lag in the creation of local policy, insufficient funding for technical infrastructure, shortages of staff skills in data curation, and language barriers to international data sharing and open science. RDS in Chinese university libraries are still lagging behind the English-speaking countries and Europe.

Involving Data Creators in an Ontology-Based Design Process for Metadata Models

2017 ◽  
pp. 181-214 ◽  
Author(s):  
João Aguiar Castro ◽  
Ricardo Carvalho Amorim ◽  
Rúbia Gattelli ◽  
Yulia Karimova ◽  
João Rocha da Silva ◽  
...  
Keyword(s):  
Data Management ◽  
Data Interpretation ◽  
Research Data ◽  
Data Repositories ◽  
Vehicle Simulation ◽  
Management Platform ◽  
Biological Oceanography ◽  
Research Data Management ◽  
External Data ◽  
Data Management Platform

Research data are the cornerstone of science and their current fast rate of production is disquieting researchers. Adequate research data management strongly depends on accurate metadata records that capture the production context of the datasets, thus enabling data interpretation and reuse. This chapter reports on the authors' experience in the development of the metadata models, formalized as ontologies, for several research domains, involving members from small research teams in the overall process. This process is instantiated with four case studies: vehicle simulation; hydrogen production; biological oceanography and social sciences. The authors also present a data description workflow that includes a research data management platform, named Dendro, where researchers can prepare their datasets for further deposit in external data repositories.

A Lightweight, Microservice-Based Research Data Management Architecture for Large Scale Environmental Datasets

2020 ◽  
Author(s):  
Alexander Götz ◽  
Johannes Munke ◽  
Mohamad Hayek ◽  
Hai Nguyen ◽  
Tobias Weber ◽  
...  
Keyword(s):  
Data Management ◽  
Large Scale ◽  
Virtual Water ◽  
Research Data ◽  
Environmental Data ◽  
Data Repositories ◽  
Research Data Management ◽  
Water Value ◽  
Simulation Based ◽  
Core Components

<p>LTDS ("Let the Data Sing") is a lightweight, microservice-based Research Data Management (RDM) architecture which augments previously isolated data stores ("data silos") with FAIR research data repositories. The core components of LTDS include a metadata store as well as dissemination services such as a landing page generator and an OAI-PMH server. As these core components were designed to be independent from one another, a central control system has been implemented, which handles data flows between components. LTDS is developed at LRZ (Leibniz Supercomputing Centre, Garching, Germany), with the aim of allowing researchers to make massive amounts of data (e.g. HPC simulation results) on different storage backends FAIR. Such data can often, owing to their size, not easily be transferred into conventional repositories. As a result, they remain "hidden", while only e.g. final results are published - a massive problem for reproducibility of simulation-based science. The LTDS architecture uses open-source and standardized components and follows best practices in FAIR data (and metadata) handling. We present our experience with our first three use cases: the Alpine Environmental Data Analysis Centre (AlpEnDAC) platform, the ClimEx dataset with 400TB of climate ensemble simulation data, and the Virtual Water Value (ViWA) hydrological model ensemble.</p>

scholarly journals Over-Professional Skills and Professional Knowledge of Library Specialist: Demands of the Time

2020 ◽  
Vol 68 (6) ◽  
pp. 647-658
Author(s):  
Natalia S. Redkina
Keyword(s):  
Data Management ◽  
Information Technologies ◽  
Professional Knowledge ◽  
Research Data ◽  
Information Resources ◽  
Mobile Technologies ◽  
Process Data ◽  
Data Repositories ◽  
Research Data Management ◽  
Metadata Standards

Library specialists having competencies in the field of modern information technologies and knowledge of information resources, capable to analyse and synthesize heterogeneous information, process data, solve non-standard tasks, are able to develop innovative trends, increase the importance and competitiveness of libraries in the information space. The purpose of this study is to determine the most important skills and knowledge of librarians for the development of new forms and trends in the activities of research libraries: assistant services to scientists, work with research data, creation of intellectual centres, centres of intellectual leisure, organization of communication platforms, etc. The author highlights the key knowledge necessary for librarian: knowledge of modern and advanced information technologies (social networks, cloud, mobile technologies, new generation analytics, etc.), knowledge of the world market of information resources, as well as technologies of collection and processing of information/data. The article presents competences of librarians in the research data management, who provide consulting and assistant services to scientists in the life cycle of research. It is determined that the research data management librarian should know the methods of data management plan preparation, management methods, categories, metadata standards and schemes, data classifications and identifiers, data citation requirements, copyright, data repositories, long-term data preservation technologies, etc. The author concludes that the possession of non-specialized over-professional (“soft”) skills (communication skills, emotional intelligence, thinking by “results” and “processes”, etc.) along with the complex of professional knowledge is the key to the improvement of efficiency and demand of libraries in the conditions of intensively developing environment.

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