scholarly journals Playing Well on the Data FAIRground: Initiatives and Infrastructure in Research Data Management

2019 ◽  
Vol 1 (4) ◽  
pp. 350-367 ◽  
Author(s):  
Danielle Descoteaux ◽  
Chiara Farinelli ◽  
Marina Soares e Silva ◽  
Anita de Waard
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Current Status ◽  
Data Repositories ◽  
Data Preservation ◽  
Management Platform ◽  
Research Data Management ◽  
Future Goals ◽  
External Data

Over the past five years, Elsevier has focused on implementing FAIR and best practices in data management, from data preservation through reuse. In this paper we describe a series of efforts undertaken in this time to support proper data management practices. In particular, we discuss our journal data policies and their implementation, the current status and future goals for the research data management platform Mendeley Data, and clear and persistent linkages to individual data sets stored on external data repositories from corresponding published papers through partnership with Scholix. Early analysis of our data policies implementation confirms significant disparities at the subject level regarding data sharing practices, with most uptake within disciplines of Physical Sciences. Future directions at Elsevier include implementing better discoverability of linked data within an article and incorporating research data usage metrics.

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.

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.

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.

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 VARIABILITY IN CLINICAL RESEARCH DATA MANAGEMENT PRACTICES: LESSONS FROM THE MALARIA COMMUNITY

2017 ◽  
Vol 2 (Suppl 2) ◽  
pp. A19.1-A19
Author(s):  
Amélie Julé ◽  
Hazel Ashurst ◽  
Laura Merson ◽  
Piero Olliaro ◽  
Vicki Marsh ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Research Data Management ◽  
Clinical Research Data

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.

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