Do researchers use open research data? Exploring the relationships between usage trends and metadata quality across scientific disciplines from the Figshare case

Quarati, Alfonso; Raffaghelli, Juliana Elisa

doi:10.1177/0165551520961048

Cited by 21 publications

(13 citation statements)

References 58 publications

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“…Many potential synthesis datasets are inaccessible or of limited value due to poor‐quality metadata (Quarati & Raffaghelli, 2020). Data may be difficult to find or access because they are published in a language that is not native to the investigator, not digitized, or housed in databases with barriers to access (Haddaway & Bayliss, 2015).…”

Section: Process and Practice Of Synthesis Sciencementioning

confidence: 99%

Priorities for synthesis research in ecology and environmental science

et al. 2023

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Synthesis research in ecology and environmental science improves understanding, advances theory, identifies research priorities, and supports management strategies by linking data, ideas, and tools. Accelerating environmental challenges increases the need to focus synthesis science on the most pressing questions. To leverage input from the broader research community, we convened a virtual workshop with participants from many countries and disciplines to examine how and where synthesis can address key questions and themes in ecology and environmental science in the coming decade. Seven priority research topics emerged: (1) diversity, equity, inclusion, and justice (DEIJ), (2) human and natural systems, (3) actionable and use-inspired science, (4) scale, (5) generality, (6) complexity and resilience, and (7) predictability. Additionally, two issues regarding the general practice of synthesis emerged: the need for increased participant diversity and inclusive research practices; and increased and improved data flow, access, and skill-building. These topics and practices provide a strategic vision for future synthesis in ecology and environmental science.

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Section: Process and Practice Of Synthesis Sciencementioning

confidence: 99%

Priorities for synthesis research in ecology and environmental science

et al. 2023

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“…It has been pointed out that a factor often called into question for scientific data low usage is DQ (Quarati and Raffaghelli, 2020). According to the 37 researchers’ experiences in dealing with scientific data mentioned in Section 3, the authors analyse and explore the critical DQ issues that have frequently emerged in each step of the general scientific data life cycle model.…”

Section: Root Causes Identification For Data Quality Issues By Inform...mentioning

confidence: 99%

Process-driven quality improvement for scientific data based on information product map

Zong

Song-tao

Gao

et al. 2022

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Purpose This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data. Design/methodology/approach First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers’ experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward. Findings The scientific data manufacturing process and data stakeholders’ responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data. Research limitations/implications As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders. Practical implications The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues. Originality/value This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.

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“…Academic journal publishers are implementing supplementary data file hosting on large repositories. For example, Mendeley Data hosts supplementary material for Elsevier Journals, while Figshare 15 – 17 hosts data from Earth Science publications in journals from Springer Nature, The Geological Society of London, The Geological Society of America, and Taylor & Francis. Such repositories are important data resources and allow researchers to use automated techniques to search and collect data such as web scraping.…”

Section: Background and Summarymentioning

confidence: 99%

A resource for automated search and collation of geochemical datasets from journal supplements

2022

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This article presents a resource for automated search, extraction and collation of geochemical and geochronological data from the Figshare repository using web scraping code. To answer fundamental questions about the Earth’s evolution, such as spatial and temporal evolution and interrelationships between the planet’s solid and surficial reservoirs, researchers must utilize global geochemical datasets. Due to the volume of data being published, these datasets become quickly outdated. We present a resource that allows researchers to rapidly curate and update their own databases from existing published data. We use open-source Python code to web scrape the Figshare repository for journal supplementary files using the application programming interface, allowing for the collection and download of hundreds of supplementary files and metadata in minutes. Use of this web scraping tool is demonstrated here by collation of a zircon geochronology and chemistry database of >150,000 analyses. The database is consistent in reproducing trends in other published zircon compilations. Providing a resource for automated collection of Figshare data files will encourage data sharing and reuse.

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Do researchers use open research data? Exploring the relationships between usage trends and metadata quality across scientific disciplines from the Figshare case

Cited by 21 publications

References 58 publications

Priorities for synthesis research in ecology and environmental science

Priorities for synthesis research in ecology and environmental science

Process-driven quality improvement for scientific data based on information product map

A resource for automated search and collation of geochemical datasets from journal supplements

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