Archives des mots-clés : research data

COVID‐19 and the generation of novel scientific knowledge: Evidence‐based decisions and data sharing

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Authors : Lucie Perillat, Brian S. Baigrie

Rationale, aims and objectives

The COVID‐19 pandemic has impacted every facet of society, including medical research. This paper is the second part of a series of articles that explore the intricate relationship between the different challenges that have hindered biomedical research and the generation of novel scientific knowledge during the COVID‐19 pandemic.

In the first part of this series, we demonstrated that, in the context of COVID‐19, the scientific community has been faced with numerous challenges with respect to (1) finding and prioritizing relevant research questions and (2) choosing study designs that are appropriate for a time of emergency.

Methods

During the early stages of the pandemic, research conducted on hydroxychloroquine (HCQ) sparked several heated debates with respect to the scientific methods used and the quality of knowledge generated.

Research on HCQ is used as a case study in both papers. The authors explored biomedical databases, peer‐reviewed journals, pre‐print servers and media articles to identify relevant literature on HCQ and COVID‐19, and examined philosophical perspectives on medical research in the context of this pandemic and previous global health challenges.

Results

This second paper demonstrates that a lack of research prioritization and methodological rigour resulted in the generation of fleeting and inconsistent evidence that complicated the development of public health guidelines.

The reporting of scientific findings to the scientific community and general public highlighted the difficulty of finding a balance between accuracy and speed.

Conclusions

The COVID‐19 pandemic presented challenges in terms of (3) evaluating evidence for the purpose of making evidence‐based decisions and (4) sharing scientific findings with the rest of the scientific community.

This second paper demonstrates that the four challenges outlined in the first and second papers have often compounded each other and have contributed to slowing down the creation of novel scientific knowledge during the COVID‐19 pandemic.

DOI : https://doi.org/10.1111/jep.13548

Research data management and data sharing behaviour of university researchers

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Authors : Yurdagül Ünal, Gobinda Chowdhury, Serap Kurbanoğlu, Joumana Boustany, Geoff Walton

Introduction

The aim of this study is to understand how university researchers behave in the context of using and sharing research data in OA mode.

Method

An online questionnaire survey was conducted amongst academics and researchers in three countries – UK, France and Turkey. There were 26 questions to collect data on: researcher information, e.g. discipline, gender and experience; data sharing practices, concerns; familiarity with data management practices; and policies/challenges including knowledge of metadata and training.

Analysis

SPSS was used to analyse the dataset, and Chi-Square tests, at 0.05 significance level, were conducted to find out association between researchers’ behaviour in data sharing and different areas of research data management (RDM).

Findings

Findings show that OA is still not common amongst researchers. Data ethics and legal issues appear to be the most significant concerns for researchers. Most researchers have not received any training in RDM such as data management planning metadata, or file naming. However, most researchers would welcome formal training in different aspects of RDM.

Conclusion

This study indicates directions for further research to understand the disciplinary differences in researchers’ data access and management behaviour so that appropriate training and advocacy programmes can be developed to promote OA to research data.

URL : http://www.informationr.net/ir/24-1/isic2018/isic1818.html

A Review of Open Research Data Policies and Practices in China

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Authors: Lili Zhang, Robert R. Downs, Jianhui Li, Liangming Wen, Chengzan Li

This paper initially conducts a literature review and content analysis of the open research data policies in China. Next, a series of exemplars describe data practices to promote and enable the use of open research data, including open data practices in research programs, data repositories, data journals, and citizen science.

Moreover, the top four driving forces are identified and analyzed along with their responsible guiding work. In addition, the “landscape of open research data ecology in China” is derived from the literature review and from observations of actual cases, where the interaction and mutual development of data policies, data programs, and data practices are recognized.

Finally, future trends of research data practices within China and internationally are discussed. We hope the analysis provides perspective on current open data practices in China along with insight into the need for additional research on scientific data sharing and management.

URL : A Review of Open Research Data Policies and Practices in China

DOI : http://doi.org/10.5334/dsj-2021-003

Deep Learning in Mining Biological Data

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Authors : Mufti Mahmud, M. Shamim Kaiser, T. Martin McGinnity, Amir Hussain

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature.

Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems.

To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data.

This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided.

Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

URL : Deep Learning in Mining Biological Data

DOI : https://doi.org/10.1007/s12559-020-09773-x

Biomedical Data Sharing Among Researchers: A Study from Jordan

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Authors : Lina Al-Ebbini, Omar F Khabour, Karem H Alzoubi, Almuthanna K Alkaraki

Background

Data sharing is an encouraged practice to support research in all fields. For that purpose, it is important to examine perceptions and concerns of researchers about biomedical data sharing, which was investigated in the current study.

Methods

This is a cross-sectional survey study that was distributed among biomedical researchers in Jordan, as an example of developing countries. The study survey consisted of questions about demographics and about respondent’s attitudes toward sharing of biomedical data.

Results

Among study participants, 46.9% (n=82) were positive regarding making their research data available to the public, whereas 53.1% refused the idea. The reasons for refusing to publicly share their data included “lack of regulations” (33.5%), “access to research data should be limited to the research team” (29.5%), “no place to deposit the data” (6.5%), and “lack of funding for data deposition” (6.0%).

Agreement with the idea of making data available was associated with academic rank (P=0.003). Moreover, gender (P-value=0.043) and number of publications (P-value=0.005) were associated with a time frame for data sharing (ie, agreeing to share data before vs after publication).

Conclusion

About half of the respondents reported a positive attitude toward biomedical data sharing. Proper regulations and facilitation data deposition can enhance data sharing in Jordan.

URL : Biomedical Data Sharing Among Researchers: A Study from Jordan

DOI : https://doi.org/10.2147/JMDH.S284294

Implementing the RDA Research Data Policy Framework in Slovenian Scientific Journals

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Authors: Janez Štebe, Maja Dolinar, Sonja Bezjak, Ana Inkret

The paper aims to present the implementation of the RDA research data policy framework in Slovenian scientific journals within the project RDA Node Slovenia. The activity aimed to implement the practice of data sharing and data citation in Slovenian scientific journals and was based on internationally renowned practices and policies, particularly the Research Data Policy Framework of the RDA Data Policy Standardization and Implementation Interest Group.

Following this, the RDA Node Slovenia coordination prepared a guidance document that allowed the four pilot participating journals (from fields of archaeology, history, linguistics and social sciences) to adjust their journal policies regarding data sharing, data citation, adapted the definitions of research data and suggested appropriate data repositories that suit their disciplinary specifics.

The comparison of results underlines how discipline-specific the aspects of data-sharing are. The pilot proved that a grass-root approach in advancing open science can be successful and well-received in the research community, however, it also pointed out several issues in scientific publishing that would benefit from a planned action on a national level.

The context of an underdeveloped data sharing culture, slow implementation of open data strategy by the national research funder and sparse national data service infrastructure creates a unique environment for this study, the result of which can be used in similar contexts worldwide.

URL : Implementing the RDA Research Data Policy Framework in Slovenian Scientific Journals

DOI : http://doi.org/10.5334/dsj-2020-049

Investigation and Development of the Workflow to Clarify Conditions of Use for Research Data Publishing in Japan

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Authors : Yasuyuki Minamiyama, Ui Ikeuchi, Kunihiko Ueshima, Nobuya Okayama, Hideaki Takeda

With the recent Open Science movement and the rise of data-intensive science, many efforts are in progress to publish research data on the web. To reuse published research data in different fields, they must be made more generalized, interoperable, and machine-readable.

Among the various issues related to data publishing, the conditions of use are directly related to their reuse potential. We show herein the types of external constraints and conditions of use in research data publishing in a Japanese context through the analysis of the interview and questionnaire for practitioners.

Although the conditions of research data use have been discussed only in terms of their legal constraints, we organize the inclusion of the non-legal constraints and data holders’ actual requirements.

Furthermore, we develop practical guideline for examining effective data publishing flow with licensing scenarios. This effort can be positioned to develop an infrastructure for data-intensive science, which will contribute to the realization of Open Science.

URL : Investigation and Development of the Workflow to Clarify Conditions of Use for Research Data Publishing in Japan

DOI : http://doi.org/10.5334/dsj-2020-053