Read Only: "Data Paper as a Reward? Motivation, Consideration, and Perspective behind Data Paper Submission"


Data papers, as one of the channels to encourage researchers to open up research data under the open science movement, are expected to provide strong incentives through formal citations. . . . This study examines researchers’ motivations, and considerations for data paper submission, as well as their perspectives on this scholarly publication. . . . Although the academic community widely recognizes the benefits of publishing data papers, some still cast a doubtful eye on its academic value and impact.

https://doi.org/10.1002/pra2.648

| Research Data Publication and Citation Bibliography | Research Data Sharing and Reuse Bibliography | Research Data Curation and Management Bibliography | Digital Scholarship |

"The Interdependence of Data Producers and Data Users: How Researchers’ Behaviors Can Support or Hinder Each Other"


Sharing and reusing data is widely viewed as advancing knowledge, but researchers often view it as a burdensome and time-consuming process. We sought to identify specific research practices that have the potential to decrease burden and increase benefits for researchers from any discipline while retaining the broad scholarly benefits, complementing investigations that have identified approaches and standards within specific fields. We conducted a literature search and engaged in qualitative interviews with 20 academic researchers who had diverse disciplinary backgrounds and experience sharing and/or reusing publicly accessible data. The connection points between data producers and data users throughout the data sharing and reuse cycle indicate that sharing and reusing data is an interdependent process, meaning producers and users depend on each other to achieve their respective goals successfully and efficiently. For example, data producers can simplify and ease the user’s work of finding data by posting on a visible repository or directly linking to their data in publications. Relatedly, data users who perceive the linked nature of reuse can simplify the producer’s ability to track impact of the data and facilitate the reward and credit the producer receives by citing the data products in publications. We highlight areas of interdependencies throughout the research process and provide recommendations for data producers and users to make their sharing and reuse practices, respectively, more efficient. We also recommend practices to reduce burden for producers, who bear the initial effort in preparing data properly for reuse. Because many of our participants did not consider the downstream success and impact of their data and the researchers who produce and use data, we call for increased awareness of the interconnections between producers and users as an important step to reduce burden and increase the effectiveness of data sharing and reuse.

https://doi.org/10.31222/osf.io/yp3ct

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Paywall: "A Perspective on Computational Research Support Programs in the Library: More than 20 Years of Data from Stanford University Libraries"


Presentation of data is a major component to academic research. However, programming languages, computational tools, and methods for exploring and analyzing data can be time consuming and frustrating to learn and finding help with these stages of the broader research process can be daunting. In this work, we highlight the impacts that computational research support programs housed in library contexts can have for fulfilling gaps in student, staff, and faculty research needs. The archival history of one such organization, Software and Services for Data Science (SSDS) in the Stanford University Cecil H. Green Library, is used to outline challenges faced by social sciences and humanities researchers from the 1980s to the present day.

https://doi.org/10.1177/09610006221124619

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"Guest Post – The Door to Data Sharing is Slowly Creaking Open "


Looking to the future, it is interesting to dive deeper into researchers’ perceived incentives for sharing data. Overall, just 19% of respondents believed that researchers get sufficient credit for sharing data, while fully three-quarters indicated they receive too little credit. Those who report more ingrained behaviors to sharing their research data openly were more likely to agree that researchers get sufficient credit for sharing data – for example 40% of those who share their data immediately on collection believe that researchers get sufficient credit – however they are still in the minority.

https://cutt.ly/8BKwneK

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"Introducing the FAIR Principles for Research Software"


The FAIR for Research Software (FAIR4RS) Working Group has adapted the FAIR Guiding Principles to create the FAIR Principles for Research Software (FAIR4RS Principles). The contents and context of the FAIR4RS Principles are summarised here to provide the basis for discussion of their adoption. Examples of implementation by organisations are provided to share information on how to maximise the value of research outputs, and to encourage others to amplify the importance and impact of this work.

https://doi.org/10.1038/s41597-022-01710-x

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"Nine Best Practices for Research Software Registries and Repositories"


Scientific software registries and repositories improve software findability and research transparency, provide information for software citations, and foster preservation of computational methods in a wide range of disciplines. Registries and repositories play a critical role by supporting research reproducibility and replicability, but developing them takes effort and few guidelines are available to help prospective creators of these resources. To address this need, the FORCE11 Software Citation Implementation Working Group convened a Task Force to distill the experiences of the managers of existing resources in setting expectations for all stakeholders. In this article, we describe the resultant best practices which include defining the scope, policies, and rules that govern individual registries and repositories, along with the background, examples, and collaborative work that went into their development.

https://doi.org/10.7717/peerj-cs.1023

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The State of Open Data Report 2022


Based on a global survey, the report is now in its seventh year and provides insights into researchers’ attitudes towards and experiences of open data. With more than 5,400 respondents, the 2022 survey is the largest since the COVID-19 pandemic began.

This year’s report also includes guest articles from open data experts at the National Institutes of Health (NIH), the White House Office of Science and Technology Policy (OSTP), the Chinese Academy of Sciences (CAS), publishers and universities.

https://cutt.ly/iBTuXpe

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"Synchronic Curation for Assessing Reuse and Integration Fitness of Multiple Data Collections"


SC is a framework that can be implemented to curate data collections to solve multiple research use cases in different scientific fields. SC fills an urgent need in data driven research that requires usage of large and diverse data collections. To reuse data, the first step is to assess its quality and its fitness to address the research use case at hand. SC proposes modelling data collections to research questions to enable targeted analyses and comparisons that can help users identify which collections are more reliable and adequate to solve them. Importantly, SC enables curators and researchers to assess multiple datasets at the same time.

https://doi.org/10.2218/ijdc.v17i1.847

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"Boundaries, Extensions, and Challenges of Visualization for Humanities Data: Reflections on Three Cases"


This paper discusses problems of visualizing humanities data of various forms, such as video data, archival data, and numeric-oriented social science data, with three distinct case studies. By describing the visualization practices and the issues that emerged from the process, this paper uses the three cases to each identify a pertinent question for reflection. More specifically, I reflect on the difficulty, thoughts, and considerations of choosing the most effective and sufficient forms of visualization to enhance the expression of specific cultural and humanities data in the projects. Discussions in this paper concern some questions, such as, how do the multi-modality of humanities and cultural data challenge the understanding, roles, and functions of visualizations, and more broadly, visual representations in humanities research? What do we lose of the original data by visualizing them in those projects? How to balance the benefits and disadvantages of visual technologies to display complex, unique, and often culturally saturated humanities datasets.

https://arxiv.org/abs/2210.03630

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"OpenStack Swift: An Ideal Bit-Level Object Storage System for Digital Preservation "


A bit-level object storage system is a foundational building block of long-term digital preservation (LTDP). To achieve the purposes of LTDP, the system must be able to: preserve the authenticity and integrity of the original digital objects; scale up with dramatically increasing demands for preservation storage; mitigate the impact of hardware obsolescence and software ephemerality; replicate digital objects among distributed data centers at different geographical locations; and to constantly audit and automatically recover from compromised states. . . . In this paper, we present OpenStack Swift, an open-source, mature and widely accepted cloud platform, as a practical and proven solution with a case study at the University of Alberta Library. We emphasize the implementation, application, cost analysis and maintenance of the system, with the purpose of contributing to the community with an exceedingly robust, highly scalable, self-healing and comparatively cost-effective bit-level object storage system for long-term digital preservation.

https://doi.org/10.2218/ijdc.v17i1.782

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"Increasing the Reuse of Data through FAIR-enabling the Certification of Trustworthy Digital Repositories"


To address this gap the FAIRsFAIR project developed a number of tools and resources that facilitate the assessment of FAIR-enabling practices at the repository level as well as the FAIRness of datasets within them. These include the CoreTrustSeal+FAIRenabling Capability Maturity model (CTS+FAIR CapMat), a FAIR-Enabling Trustworthy Digital Repositories-Capability Maturity Self-Assessment template, and F-UJI, a web-based tool designed to assess the FAIRness of research data objects.

https://doi.org/10.2218/ijdc.v17i1.852

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"Uncommon Commons? Creative Commons Licencing in Horizon 2020 Data Management Plans"


I find that 36% of DMPs mention creative commons and among those a number of different approaches towards licencing exist (overall policy per project, licencing decisions per dataset, licencing decisions per partner, licensing decision per data format, licensing decision per perceived stakeholder interest), often clad in rather vague language with CC licences being “recommended” or “suggested”.

https://doi.org/10.2218/ijdc.v17i1.840

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Paywall: "A Comparative Study of Social Media Data Archiving Software"

"This study compares the features of three SMDAS: ArchiveSocial, Pagefreezer, and Smarsh. First, by surveying the developers’ websites and catalogs, the features of all three software products are identified and classified into four areas. After using statistical methods and the Chi-square test, significant differences among features of the software in each domain are investigated."

https://doi.org/10.1515/pdtc-2022-0013

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"FAIREST: A Framework for Assessing Research Repositories "

"In this article, we introduce the FAIREST principles, a framework inspired by the well-known FAIR principles, but designed to provide a set of metrics for assessing and selecting solutions for creating digital repositories for research artefacts. The goal is to support decision makers in choosing such a solution when planning for a repository, especially at an institutional level.. . . We further describe an assessment of 11 widespread solutions, with the goal to provide an overview of the current landscape of research data repository solutions, identifying gaps and research challenges to be addressed."

https://doi.org/10.1162/dint_a_00159

Paywall: "Where Are They Now? The 2020 Status of Early (1996–2003) Online Digital Humanities Projects and an Analysis of Institutional Factors Correlated to Their Survival"

"A study of fifty-nine websites created with funds provided by the National Endowment for the Humanities Education Development and Demonstration program 1996–2003 reveals a different situation. The data show that 68% of these websites remained online for free use in September, 2020, suggesting an online lifespan of approximately eleven to sixteen years."

https://doi.org/10.1515/pdtc-2022-0011

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"Many Researchers Were Not Compliant with Their Published Data Sharing Statement: A Mixed-Methods Study – Journal of Clinical Epidemiology"

https://doi.org/10.1016/j.jclinepi.2022.05.019

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"Surveying Research Data-Sharing Practices in Us Social Sciences: A Knowledge Infrastructure-Inspired Conceptual Framework"

https://doi.org/10.1108/OIR-03-2020-0079

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"Are We Building the Data Discovery Infrastructure Researchers Want? Comparing Perspectives of Support Specialists and Researchers"

"This is a meta-synthesis of work the authors have conducted over the last six years investigating the data discovery practices of researchers and support specialists, like data librarians. We bring together data collected from in-depth interview studies with 6 support specialists in the field of social science in Germany, with 21 social scientists in Singapore, an interview with 10 researchers and 3 support specialists from multiple disciplines, a global survey with 1630 researchers and 47 support specialists from multiple disciplines, an observational study with 12 researchers from the field of social science and a use case analysis of 25 support specialists from multiple disciplines."

https://arxiv.org/abs/2209.14655

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"Going Qual In: Towards Methodologically Inclusive Data Work in Academic Libraries"

https://cutt.ly/EV9wSla

"In this paper, we report on the results of interviews with academic librarians about their understanding of data literacy, qualitative research, and academic library infrastructure around qualitative research. From the interviews, we propose a model of data literacy that incorporates both interpretive and instrumental elements. We conclude with suggestions for incorporating qualitative data and analysis methods into academic library programming and services around data literacy and research data."

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"Developing Data Literacy: How Data Services and Data Fellowships Are Creating Data Skilled Social Researchers"

https://cutt.ly/VV9wBEF

"This paper describes two successful approaches to quantitative data literacy training within the UK and the synergies and collaborations between these two programmes. The first is a data literacy training programme, being delivered by the UK Data Service, which focuses on training in basic data literacy skills. The second is a Data Fellows programme that has been developed to help undergraduate social science students gain real-world experience by applying their classroom skills in the workplace."

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"Factors Contributing to Repository Success in Recruiting Data Deposits"

https://doi.org/10.29173/iq1037

"While quite a few studies outline researchers’ data management needs and how repositories can meet those needs, few have assessed the success of various approaches. This study examines infrastructure for accepting data into repositories and identifies factors influential in recruiting data deposits."

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Fostering Data Literacy: Teaching with Quantitative Data in the Social Sciences

https://doi.org/10.18665/sr.317506

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"NIH Launches Bridge2AI Program to Expand the Use of Artificial Intelligence in Biomedical and Behavioral Research"

https://cutt.ly/gVPCJVw

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