Data management and sharing: Practices and perceptions of psychology researchers

Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.

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Data Management and Sharing: Practices and Perceptions of Psychology Researchers

10.31234/osf.io/7g3ae ◽

2020 ◽

Author(s):

John Borghi ◽

Ana Van Gulick

Keyword(s):

Data Management ◽

Management Practices ◽

New Technologies ◽

Research Process ◽

Data Organization ◽

Scholarly Output ◽

Data Practices ◽

Data Collection And Analysis ◽

Human Participants

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The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

GigaScience ◽

10.1093/gigascience/giaa107 ◽

2020 ◽

Vol 9 (10) ◽

Cited By ~ 1

Author(s):

Daniel Arend ◽

Patrick König ◽

Astrid Junker ◽

Uwe Scholz ◽

Matthias Lange

Keyword(s):

Data Management ◽

Data Storage ◽

Best Practice ◽

Research Process ◽

Research Data ◽

Primary Data ◽

Data Repository ◽

Plant Genomics ◽

Quality Of Data

Abstract Background The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. Results To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a “bring the infrastructure to the data” approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. Conclusion The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.

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Research data management in Turkey: perceptions and practices

Library Hi Tech ◽

10.1108/lht-11-2016-0134 ◽

2017 ◽

Vol 35 (2) ◽

pp. 271-289 ◽

Cited By ~ 7

Author(s):

Arsev Umur Aydinoglu ◽

Guleda Dogan ◽

Zehra Taskin

Keyword(s):

Data Management ◽

Online Survey ◽

Management Practices ◽

Cost Effective ◽

Science Research ◽

Research Data ◽

Content Type ◽

Research Data Management ◽

Data Practices ◽

The Subject

Purpose The massive increase in research data being produced nowadays has highlighted the importance of research data management (RDM) to science. Research data not only have to be cost effective but also reliable, discoverable, accessible, and reusable. In this regard, the purpose of this paper is to investigate the perceptions and practices of Turkish researchers on the subject of RDM. Design/methodology/approach An online survey was distributed to the academicians in 25 universities in Turkey, and 532 responses were gathered. Findings Results indicate that although Turkish researchers are aware of the benefits of data management, are willing to share their research data with certain groups, and have decent preservation habits, they express that they lack the technical skills and knowledge needed for RDM. In addition, no institutionalized support (staff, training, software, and hardware) is provided to researchers. Research limitations/implications A well-structured data strategy or policy that includes resource allocation (awareness, training, software/hardware) and is supported by Turkish research agencies is required for better data management practices among researchers in Turkey. Originality/value This is the first study that investigates the data practices of Turkish academics who produce around 30,000 scientific articles annually that are indexed by Web of Science. It contributes to the growing literature on RDM.

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A survey of research quality in core facilities

eLife ◽

10.7554/elife.62212 ◽

2020 ◽

Vol 9 ◽

Author(s):

Isabelle C Kos-Braun ◽

Björn Gerlach ◽

Claudia Pitzer

Keyword(s):

Data Management ◽

Management Practices ◽

Research Process ◽

Research Quality ◽

Research Practice ◽

Experimental Equipment ◽

Research Practices ◽

Core Facilities ◽

First Contact ◽

Good Research

Core facilities are an effective way of making expensive experimental equipment available to a large number of researchers, and are thus well placed to contribute to efforts to promote good research practices. Here we report the results of a survey that asked core facilities in Europe about their approaches to the promotion of good research practices, and about their interactions with users from the first contact to the publication of the results. Based on 253 responses we identified four ways that good research practices could be encouraged: (i) motivating users to follow the advice and procedures for best research practice; (ii) providing clear guidance on data-management practices; (iii) improving communication along the whole research process; and (iv) clearly defining the responsibilities of each party.

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Electronic Data Collection Methods

Handbook of Research on Electronic Surveys and Measurements ◽

10.4018/978-1-59140-792-8.ch004 ◽

2007 ◽

pp. 28-43 ◽

Cited By ~ 1

Author(s):

M. Singh ◽

S. Burgess

Keyword(s):

Data Collection ◽

Online Survey ◽

New Technologies ◽

Virtual Community ◽

Research Process ◽

Survey Method ◽

Online Data ◽

Electronic Data Collection ◽

Focus Group Research ◽

Data Collection And Analysis

This chapter discusses the application of new technologies to scholarly research. It highlights the process, benefits and challenges of online data collection and analysis with three case studies, the online survey method, online focus groups and email interviews. The online survey method is described as it was undertaken to collect and collate data for the evaluation of e-business in Australia. The online focus group research is described as it was applied to complete research on e-commerce with small business. The email interviews applied to collect information from a virtual community of global respondents to assess the impact of interaction between members on B2C e-commerce. The research process, its advantages and disadvantages are elaborated for all three e-research methods.

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Coordination of long-term data management in the Gulf of Mexico: Lessons learned and recommendations from two years of cross-agency collaboration

Shore & Beach ◽

10.34237/1008812 ◽

2020 ◽

pp. 17-22

Author(s):

Kathryn Keating ◽

Melissa Gloekler ◽

Nancy Kinner ◽

Sharon Mesick ◽

Michael Peccini ◽

...

Keyword(s):

Gulf Of Mexico ◽

Data Management ◽

Management Practices ◽

Collaborative Work ◽

Lessons Learned ◽

The Many ◽

Diverse Data ◽

The University ◽

Term Data

This paper presents a summary of collaborative work, lessons learned, and suggestions for next steps in coordinating long-term data management in the Gulf of Mexico in the years following the Deepwater Horizon oil spill (DWH). A decade of increased research and monitoring following the DWH has yielded a vast amount of diverse data collected from response and assessment efforts as well as ongoing restoration efforts. To maximize the benefits of this data through proper management and coordination, a cross-agency and organization Long-Term Data Management (LTDM) working group was established in 2017 with sponsorship from NOAA’s Office of Response and Restoration (OR&R) and NOAA’s National Marine Fisheries Service Restoration Center (NMFS RC) and facilitated by the University of New Hampshire’s Coastal Response Research Center. This paper will describe the LTDM working group’s efforts to foster collaboration, data sharing, and best data management practices among the many state, federal, academic and non-governmental entities working to restore and improve the coastal environment in the Gulf following the DWH. Through collaborative workshops and working groups, participants have helped to characterize region-specific challenges, identify areas for growth, leverage existing connections, and develop recommended actions for stakeholders at all organizational levels who share an interest in data coordination and management activities.

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Modifying researchers’ data management practices

IFLA Journal ◽

10.1177/0340035216673856 ◽

2016 ◽

Vol 42 (4) ◽

pp. 253-265 ◽

Cited By ~ 7

Author(s):

Susan Hickson ◽

Kylie Ann Poulton ◽

Maria Connor ◽

Joanna Richardson ◽

Malcolm Wolski

Keyword(s):

Data Management ◽

Best Practice ◽

Management Practices ◽

Social Science Research ◽

Science Research ◽

Current Data ◽

Research Centre ◽

Data Collection And Analysis ◽

Management Plans

Data is the new buzzword in academic libraries, as policy increasingly mandates that data must be open and accessible, funders require formal data management plans, and institutions are implementing guidelines around best practice. Given concerns about the current data management practices of researchers, this paper reports on the initial findings from a project being undertaken at Griffith University to apply a conceptual (A-COM-B) framework to understanding researchers’ behaviour. The objective of the project is to encourage the use of institutionally endorsed solutions for research data management. Based on interviews conducted by a team of librarians in a small, social science research centre, preliminary results indicate that attitude is the key element which will need to be addressed in designing intervention strategies to modify behaviour. The paper concludes with a discussion of the next stages in the project, which involve further data collection and analysis, the implementation of targeted strategies, and a follow-up activity to assess the extent of modifications to current undesirable practices.

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

Digital Library Perspectives ◽

10.1108/dlp-06-2020-0051 ◽

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.

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Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development

Remote Sensing ◽

10.3390/rs11151797 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1797 ◽

Cited By ~ 4

Author(s):

Jane Wyngaard ◽

Lindsay Barbieri ◽

Andrea Thomer ◽

Josip Adams ◽

Don Sullivan ◽

...

Keyword(s):

Data Management ◽

Remote Sensing Data ◽

Unmanned Aircraft ◽

Data Capture ◽

Use Cases ◽

Complex Data ◽

Data Practices ◽

Wide Range ◽

Near Future

The use of small Unmanned Aircraft Systems (sUAS) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on a four-year, community-based investigation into the tools, data practices, and challenges that currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data—as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases—is characterized as both sharing many traits with traditional remote sensing data and also as exhibiting—as common across the spectrum of disciplines and use cases—novel characteristics that require novel data support infrastructure; and (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost.

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Between administration and research - Understanding data management practices in a mid-sized technical university

10.31235/osf.io/75ac6 ◽

2020 ◽

Author(s):

Stefan Reichmann ◽

Thomas Klebel ◽

Ilire Hasani-Mavriqi ◽

Tony Ross-Hellauer

Keyword(s):

Data Management ◽

Management Practices ◽

The State ◽

Data Sets ◽

Data Archiving ◽

Research Institution ◽

University Of Technology ◽

Data Collection And Analysis ◽

Administrative Task ◽

Good Research

Research Data Management (RDM) promises to make research outputs more transparent, findable, and reproducible. Strategies to streamline data management across disciplines are of key importance. This paper presents the results of an institutional survey (N=258) at Graz University of Technology, supplemented with interview data (N=18), to give an overview of the state-of-play of RDM practices across faculties and disciplinary contexts. RDM services are on the rise but remain somewhat behind leading countries like the Netherlands and UK, showing only the beginnings of a culture attuned to RDM. There is considerable variation between faculties and institutes with respect to data amounts, complexity of data sets, data collection and analysis, and data archiving. Data sharing practices within fields tend to be inconsistent. RDM is predominantly regarded as an administrative task, to the detriment of considerations of good research practice. Problems with RDM fall in two categories: Generic problems transcend specific research interests, infrastructures, and departments while discipline-specific problems need a more targeted approach. The paper extends the state-of-the-art on RDM practices by combining in-depth qualitative material with quantified, detailed data about RDM practices and needs. The findings should be of interest to any comparable research institution with a similar agenda.

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