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Scoulas, Jung Mi. "Learning from data reuse: successful and failed experiences in a large public research university library." IASSIST Quarterly 44, no. 1-2 (2020): 1–15. http://dx.doi.org/10.29173/iq966.
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This paper illustrates a large research university library experience in reusing the data for research collected both within and outside of the library to demonstrate data reuse practice. The purpose of the paper is to 1) demonstrate when and how data are reused in a large public research university library, 2) share tips on what to consider when reusing data, and 3) share challenges and lessons learned from data reuse experiences. This paper presents five proposed opportunities for data reuse conducted by three researchers at the institution’s library which resulted in three successful instances of data reuses and two failed data reuses. Learning from successful and failed experiences is critical to understand what works and what does not work in order to identify best practices for data reuse. This paper will be helpful for librarians who intend to reuse data for publication.
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Chauvette, Amelia, Kara Schick-Makaroff, and Anita E. Molzahn. "Open Data in Qualitative Research." International Journal of Qualitative Methods 18 (January 1, 2019): 160940691882386. http://dx.doi.org/10.1177/1609406918823863.
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There is a growing movement for research data to be accessed, used, and shared by multiple stakeholders for various purposes. The changing technological landscape makes it possible to digitally store data, creating opportunity to both share and reuse data anywhere in the world for later use. This movement is growing rapidly and becoming widely accepted as publicly funded agencies are mandating that researchers open their research data for sharing and reuse. While there are numerous advantages to use of open data, such as facilitating accountability and transparency, not all data are created equally. Accordingly, reusing data in qualitative research present some epistemological, methodological, legal, and ethical issues that must be addressed in the movement toward open data. We examine some of these challenges and make a case that some qualitative research data should not be reused in secondary analysis.
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Briney, Kristin, Heather Coates, and Abigail Goben. "Foundational Practices of Research Data Management." Research Ideas and Outcomes 6 (July 27, 2020): e56508. https://doi.org/10.3897/rio.6.e56508.
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The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity. Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-consuming. By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free up more time for research. This paper provides an introduction to best practices for managing all types of data.
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Boté, Juan-José, and Miquel Termens. "Reusing Data Technical and Ethical Challenges." DESIDOC Journal of Library & Information Technology 39, no. 06 (2019): 329–37. http://dx.doi.org/10.14429/djlit.39.06.14807.
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Research centres, universities and public organisations create datasets that can be reused in research. Reusing data makes it possible to reproduce studies, generate new research questions and new knowledge, but it also gives rise to technical and ethical challenges. Part of these issues are repositories interoperability to accomplish FAIR principles or issues related to data privacy or anonymity. At the same time, funding institutions require that data management plans be submitted for grants, and research tends to be increasingly interdisciplinary. Interdisciplinarity may entail barriers for researchers to reuse data, such as a lack of skills to manipulate data, given that each discipline generates different types of data in different technical formats, often non-standardized. Additionally, the use of standards to validate data reuse and better metadata to find appropriate datasets seem necessary. This paper offers a review of the literature that addresses data reuse in terms of technical, ethical-related issues.
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Li, Kai, Pao-Pei Huang, and Wei Jeng. "Are data papers cited as research data? Preliminary analysis on interdisciplinary data paper citations." Information Research an international electronic journal 30, iConf (2025): 1225–33. https://doi.org/10.47989/ir30iconf46918.
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Introduction. Research data sharing and reuse have become increasingly important in modern science, and data papers represent a new academic publication genre aimed at enhancing the visibility, sharing, and reuse of research data. However, whether citations to data papers reflect actual data reuse remains largely unexplored. This paper presents preliminary findings from a project designed to address this gap. Method. we conducted a content analysis to manually annotate 437 citation sentences from 309 research articles referencing 50 data papers published in Data in Brief, a chief academic journal that only publishes data papers. The data papers were sampled from five knowledge domains based on a paper-level classification system. Results. Our results show that most citations to all selected data papers (89%) are unrelated to the research data being described in the paper, instead focusing on the research findings or methodologies. This suggests that data papers are being cited similarly to traditional research articles, despite their unique purpose and content. Conclusion. These findings raise questions about the effectiveness of data papers as representations of research data within the scholarly communication system, as well as their utility in quantitative studies on data reuse.
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Semeler, Alexandre Ribas, Luana Farias Sales, Adilson Luiz Pinto, Roberta Pereira da Silva de Paula, Valquer Cleyton Paes Gandra, and Heloisa Costa. "Defining geosciences research data through metadata reuse:." Biblios Journal of Librarianship and Information Science, no. 87 (February 7, 2025): e009. https://doi.org/10.5195/biblios.2024.1233.
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Objective. Research data refers to factual records used as primary scientific research resources. Reusing research data metadata provides a new perspective, allowing the presentation of new tests, hypotheses, and new research developments. This study aims to identify the nature of the types of Geosciences research data based on the reuse of metadata from the PANGEA Data Publisher for Earth and Environmental Science available at (https://www.pangaea.de/). The research question to be analyzed is “Can the processes of analyzing and manipulating PANGEA research data metadata be used to define a concept of Geosciences research data?” To address this question, we considered data specification attributes used by data journals to describe the nature of research data: domain of specialization, accessibility, language, data type, acquisition, source location, specific subject area, and related publications. Method. The methodology in question involved collecting, analyzing, and visualizing PANGEA research data metadata. In total, (426,272) records were downloaded from the data repository and compared to the data specifications used by data journals to describe the nature of research data in data papers. The methodology required the application of techniques and technologies used for descriptive analysis, information retrieval, data manipulation, and visualization of Dublin Core metadata. These techniques were implemented using the Python programming language and other data manipulation software, including OpenRefine and VOSviewer. Results. The results of our analysis suggest a detailed examination of the metadata for (137,218) research data records from (6) six Geosciences collections. The number of records in the Geochemistry collection is (73,992), in the Atmospheric Sciences collection it is (32,314), in the Paleontology collection it is (25,903), in the Oceanography collection it is (22,287), in the Geophysics collection it is (4,175), and in the Hydrology collection, it is (834). PANGEA's (6) six research data metadata collections allow for the discussion of a concept of Geosciences research data as a type of data on studies related to the Earth, atmosphere, and oceans, across different geo-disciplines. The data come from a range of disciplines, including geochemistry, atmospheric science, paleontology, oceanography, geophysics, and hydrology, using technologies such as satellites, electronics microscopes, climate sensors, ships, computer modeling, and others. In addition, the data are augmented by other sources related to the study of the Earth and its processes. Conclusions. In conclusion, research data metadata are domain-specific objects that serve as valuable research resources, regardless of their usage timing, purpose, data characteristics, or user. Geosciences research data combine laboratory and fieldwork techniques, utilizing technologies like satellites and climate sensors to study Earth’s processes. PANGEA metadata defines Geosciences research data as including observations, experiments, and modeling. Geosciences research data support replication, reinterpretation, and new research across disciplines, showcasing various facets of data reuse in scientific research.
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Bishop, Libby, and Arja Kuula-Luumi. "Revisiting Qualitative Data Reuse." SAGE Open 7, no. 1 (2017): 215824401668513. http://dx.doi.org/10.1177/2158244016685136.
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Secondary analysis of qualitative data entails reusing data created from previous research projects for new purposes. Reuse provides an opportunity to study the raw materials of past research projects to gain methodological and substantive insights. In the past decade, use of the approach has grown rapidly in the United Kingdom to become sufficiently accepted that it must now be regarded as mainstream. Several factors explain this growth: the open data movement, research funders’ and publishers’ policies supporting data sharing, and researchers seeing benefits from sharing resources, including data. Another factor enabling qualitative data reuse has been improved services and infrastructure that facilitate access to thousands of data collections. The UK Data Service is an example of a well-established facility; more recent has been the proliferation of repositories being established within universities. This article will provide evidence of the growth of data reuse in the United Kingdom and in Finland by presenting both data and case studies of reuse that illustrate the breadth and diversity of this maturing research method. We use two distinct data sources that quantify the scale, types, and trends of reuse of qualitative data: (a) downloads of archived data collections held at data repositories and (b) publication citations. Although the focus of this article is on the United Kingdom, some discussion of the international environment is provided, together with data and examples of reuse at the Finnish Social Science Data Archive. The conclusion summarizes the major findings, including some conjectures regarding what makes qualitative data attractive for reuse and sharing.
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LaFlamme, Marcel, Marion Poetz, and Daniel Spichtinger. "Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science." PLOS ONE 17, no. 8 (2022): e0272153. http://dx.doi.org/10.1371/journal.pone.0272153.
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Considerable resources are being invested in strategies to facilitate the sharing of data across domains, with the aim of addressing inefficiencies and biases in scientific research and unlocking potential for science-based innovation. Still, we know too little about what determines whether scientific researchers actually make use of the unprecedented volume of data being shared. This study characterizes the factors influencing researcher data reuse in terms of their relationship to a specific research project, and introduces subjectification as the mechanism by which these influencing factors are activated. Based on our analysis of semi-structured interviews with a purposive sample of 24 data reusers and intermediaries, we find that while both project-independent and project-dependent factors may have a direct effect on a single instance of data reuse, they have an indirect effect on recurring data reuse as mediated by subjectification. We integrate our findings into a model of recurring data reuse behavior that presents subjectification as the mechanism by which influencing factors are activated in a propensity to engage in data reuse. Our findings hold scientific implications for the theorization of researcher data reuse, as well as practical implications around the role of settings for subjectification in bringing about and sustaining changes in researcher behavior.
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Joo, Soohyung, Sujin Kim, and Youngseek Kim. "An exploratory study of health scientists’ data reuse behaviors." Aslib Journal of Information Management 69, no. 4 (2017): 389–407. http://dx.doi.org/10.1108/ajim-12-2016-0201.
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Purpose The purpose of this paper is to examine how health scientists’ attitudinal, social, and resource factors affect their data reuse behaviors. Design/methodology/approach A survey method was utilized to investigate to what extent attitudinal, social, and resource factors influence health scientists’ data reuse behaviors. The health scientists’ data reuse research model was validated by using partial least squares (PLS) based structural equation modeling technique with a total of 161 health scientists in the USA. Findings The analysis results showed that health scientists’ data reuse intentions are driven by attitude toward data reuse, community norm of data reuse, disciplinary research climate, and organizational support factors. This research also found that both perceived usefulness of data reuse and perceived concern involved in data reuse have significant influences on health scientists’ attitude toward data reuse. Research limitations/implications This research evaluated its newly proposed research model based on the theory of planned behavior using a sample from the community of scientists’ scholar database. This research showed an overall picture of how attitudinal, social, and resource factors influence health scientists’ data reuse behaviors. This research is limited due to its sample size and low response rate, so this study is considered as an exploratory study rather than a confirmatory study. Practical implications This research suggested for health science research communities, academic institutions, and libraries that diverse strategies need to be utilized to promote health scientists’ data reuse behaviors. Originality/value This research is one of initial studies in scientific data reuse which provided a holistic map about health scientists’ data sharing behaviors. The findings of this study provide the groundwork for strategies to facilitate data reuse practice in health science areas.
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Joo, Yeon Kyoung, and Youngseek Kim. "Engineering researchers’ data reuse behaviours: a structural equation modelling approach." Electronic Library 35, no. 6 (2017): 1141–61. http://dx.doi.org/10.1108/el-08-2016-0163.
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Purpose The purpose of this research is to investigate the factors that influence engineering researchers’ data reuse behaviours. Design/methodology/approach The data reuse behaviour model of engineering researchers was investigated by using a survey method. A national survey was distributed to engineering researchers in the USA, and a total of 193 researchers responded. Findings The results showed that perceived usefulness, perceived concerns and norms of data reuse have significant relationships with attitudes toward data reuse. Also, attitudes toward data reuse and the availability of data repositories were found to have significant influences on engineering researchers’ intention to reuse data. Research limitations/implications This research used a combined theoretical framework by integrating the theory of planned behaviour (TPB) and the technology acceptance model (TAM). The combination of the TPB and the TAM effectively explained engineering researchers’ data reuse behaviours by addressing individual motivations, norms and resource factors. Practical implications This research has practical implications for promoting more reliable and beneficial data reuse in the engineering community, including encouraging positive motivations toward data reuse, building community norms of data reuse and setting up more data repositories. Originality value As prior research on data reuse mainly used interviews, this research used a quantitative approach based on a combined theoretical framework and included diverse research constructs which were not tested in the previous research models. As one of the initial studies investigating data reuse behaviours in the engineering community, the current research provided a better understanding of data reuse behaviours and suggested possible ways to facilitate engineering researchers’ data reuse behaviours.
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Faniel, Ixchel M., and Ann Zimmerman. "Beyond the Data Deluge: A Research Agenda for Large-Scale Data Sharing and Reuse." International Journal of Digital Curation 6, no. 1 (2011): 58–69. http://dx.doi.org/10.2218/ijdc.v6i1.172.
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There is almost universal agreement that scientific data should be shared for use beyond the purposes for which they were initially collected. Access to data enables system-level science, expands the instruments and products of research to new communities, and advances solutions to complex human problems. While demands for data are not new, the vision of open access to data is increasingly ambitious. The aim is to make data accessible and usable to anyone, anytime, anywhere, and for any purpose. Until recently, scholarly investigations related to data sharing and reuse were sparse. They have become more common as technology and instrumentation have advanced, policies that mandate sharing have been implemented, and research has become more interdisciplinary. Each of these factors has contributed to what is commonly referred to as the "data deluge". Most discussions about increases in the scale of sharing and reuse have focused on growing amounts of data. There are other issues related to open access to data that also concern scale which have not been as widely discussed: broader participation in data sharing and reuse, increases in the number and types of intermediaries, and more digital data products. The purpose of this paper is to develop a research agenda for scientific data sharing and reuse that considers these three areas.
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Dijkers, Marcel. "Reduce, reuse, recycle: good stewardship of research data." Spinal Cord 57, no. 3 (2019): 165–66. http://dx.doi.org/10.1038/s41393-019-0246-8.
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Curty, Renata Gonçalves, and Jian Qin. "Towards a model for research data reuse behavior." Proceedings of the American Society for Information Science and Technology 51, no. 1 (2014): 1–4. http://dx.doi.org/10.1002/meet.2014.14505101072.
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Yoon, Ayoung, and Youngseek Kim. "The role of data-reuse experience in biological scientists’ data sharing: an empirical analysis." Electronic Library 38, no. 1 (2020): 186–208. http://dx.doi.org/10.1108/el-06-2019-0146.
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Purpose The purpose of this paper is to investigate how scientists’ prior data-reuse experience affects their data-sharing intention by updating diverse attitudinal, control and normative beliefs about data sharing. Design/methodology/approach This paper used a survey method and the research model was evaluated by applying structural equation modelling to 476 survey responses from biological scientists in the USA. Findings The results show that prior data-reuse experience significantly increases the perceived community and career benefits and subjective norms of data sharing and significantly decreases the perceived risk and effort involved in data sharing. The perceived community benefits and subjective norms of data sharing positively influence scientists’ data-sharing intention, whereas the perceived risk and effort negatively influence scientists’ data-sharing intention. Research limitations/implications Based on the theory of planned behaviour, the research model was developed by connecting scientists’ prior data-reuse experience and data-sharing intention mediated through diverse attitudinal, control and normative perceptions of data sharing. Practical implications This research suggests that to facilitate scientists’ data-sharing behaviours, data reuse needs to be encouraged. Data sharing and reuse are interconnected, so scientists’ data sharing can be better promoted by providing them with data-reuse experience. Originality/value This is one of the initial studies examining the relationship between data-reuse experience and data-sharing behaviour, and it considered the following mediating factors: perceived community benefit, career benefit, career risk, effort and subjective norm of data sharing. This research provides an advanced investigation of data-sharing behaviour in the relationship with data-reuse experience and suggests significant implications for fostering data-sharing behaviour.
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Chao, Tiffany. "Mapping Methods Metadata for Research Data." International Journal of Digital Curation 10, no. 1 (2015): 82–94. http://dx.doi.org/10.2218/ijdc.v10i1.347.
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Understanding the methods and processes implemented by data producers to generate research data is essential for fostering data reuse. Yet, producing the metadata that describes these methods remains a time-intensive activity that data producers do not readily undertake. In particular, researchers in the long tail of science often lack the financial support or tools for metadata generation, thereby limiting future access and reuse of data produced. The present study investigates research journal publications as a potential source for identifying descriptive metadata about methods for research data. Initial results indicate that journal articles provide rich descriptive content that can be sufficiently mapped to existing metadata standards with methods-related elements, resulting in a mapping of the data production process for a study. This research has implications for enhancing the generation of robust metadata to support the curation of research data for new inquiry and innovation.
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Hasselbring, Wilhelm, Leslie Carr, Simon Hettrick, Heather Packer, and Thanassis Tiropanis. "From FAIR research data toward FAIR and open research software." it - Information Technology 62, no. 1 (2020): 39–47. http://dx.doi.org/10.1515/itit-2019-0040.
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AbstractThe Open Science agenda holds that science advances faster when we can build on existing results. Therefore, research data must be FAIR (Findable, Accessible, Interoperable, and Reusable) in order to advance the findability, reproducibility and reuse of research results. Besides the research data, all the processing steps on these data – as basis of scientific publications – have to be available, too.For good scientific practice, the resulting research software should be both open and adhere to the FAIR principles to allow full repeatability, reproducibility, and reuse. As compared to research data, research software should be both archived for reproducibility and actively maintained for reusability.The FAIR data principles do not require openness, but research software should be open source software. Established open source software licenses provide sufficient licensing options, such that it should be the rare exception to keep research software closed.We review and analyze the current state in this area in order to give recommendations for making research software FAIR and open.
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Kansa, Sarah Whitcher, and Eric C. Kansa. "Data Beyond the Archive in Digital Archaeology." Advances in Archaeological Practice 6, no. 2 (2018): 89–92. http://dx.doi.org/10.1017/aap.2018.7.
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ABSTRACTThis special section stems from discussions that took place in a forum at the Society for American Archaeology's annual conference in 2017. The forum, Beyond Data Management: A Conversation about “Digital Data Realities”, addressed challenges in fostering greater reuse of the digital archaeological data now curated in repositories. Forum discussants considered digital archaeology beyond the status quo of “data management” to better situate the sharing and reuse of data in archaeological practice. The five papers for this special section address key themes that emerged from these discussions, including: challenges in broadening data literacy by making instructional uses of data; strategies to make data more visible, better cited, and more integral to peer-review processes; and pathways to create higher-quality data better suited for reuse. These papers highlight how research data management needs to move beyond mere “check-box” compliance for granting requirements. The problems and proposed solutions articulated by these papers help communicate good practices that can jumpstart a virtuous cycle of better data creation leading to higher impact reuses of data.
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Shen, Yi. "Research Data Sharing and Reuse Practices of Academic Faculty Researchers: A Study of the Virginia Tech Data Landscape." International Journal of Digital Curation 10, no. 2 (2016): 157–75. http://dx.doi.org/10.2218/ijdc.v10i2.359.
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This paper presents the results of a research data assessment and landscape study in the institutional context of Virginia Tech to determine the data sharing and reuse practices of academic faculty researchers. Through mapping the level of user engagement in “openness of data,” “openness of methodologies and workflows,” and “reuse of existing data,” this study contributes to the current knowledge in data sharing and open access, and supports the strategic development of institutional data stewardship. Asking faculty researchers to self-reflect sharing and reuse from both data producers’ and data users’ perspectives, the study reveals a significant gap between the rather limited sharing activities and the highly perceived reuse or repurpose values regarding data, indicating that potential values of data for future research are lost right after the original work is done. The localized and sporadic data management and documentation practices of researchers also contribute to the obstacles they themselves often encounter when reusing existing data.
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Labastida, Ignasi, and Thomas Margoni. "Licensing FAIR Data for Reuse." Data Intelligence 2, no. 1-2 (2020): 199–207. http://dx.doi.org/10.1162/dint_a_00042.
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The last letter of the FAIR acronym stands for Reusability. Data and metadata should be made available with a clear and accessible usage license. But, what are the choices? How can researchers share data and allow reusability? Are all the licenses available for sharing content suitable for data? Data can be covered by different layers of copyright protection making the relationship between data and copyright particularly complex. Some research data can be considered as a work and therefore covered by full copyright while other data can be in the public domain due to their lack of originality. Moreover, a collection of data can be protected by special rights in Europe to acknowledge the investment in time and money in obtaining, presenting, arranging or verifying the data. The need of using a license when sharing data comes from the fact that, under current copyright laws, when rights exist, the absence of any legal notice must be understood as the default “all rights reserved” regime. Unless an exception applies, the authorisation of right holders is necessary for reuse. Right holders could use any text to state the reusability of data but it is advisable to use some of the existing licenses, and especially the ones that are suitable for data and databases. We hope that with this paper we can bring some clarity in relation to the rights involved when sharing research data.
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Oza, Vishal H., Jordan H. Whitlock, Elizabeth J. Wilk, et al. "Ten simple rules for using public biological data for your research." PLOS Computational Biology 19, no. 1 (2023): e1010749. http://dx.doi.org/10.1371/journal.pcbi.1010749.
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With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public biological data are: (1) use public data purposefully in your research; (2) evaluate data for your use case; (3) check data reuse requirements and embargoes; (4) be aware of ethics for data reuse; (5) plan for data storage and compute requirements; (6) know what you are downloading; (7) download programmatically and verify integrity; (8) properly cite data; (9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share; and (10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.
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Weber, Nicholas M. "The relevance of research data sharing and reuse studies." Bulletin of the American Society for Information Science and Technology 39, no. 6 (2013): 23–26. http://dx.doi.org/10.1002/bult.2013.1720390609.
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Jones, Kerina H., Elizabeth M. Ford, Nathan Lea, et al. "Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper." Journal of Medical Internet Research 22, no. 6 (2020): e16760. http://dx.doi.org/10.2196/16760.
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Background Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product. Objective This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit. Methods We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text. Results We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders. Conclusions By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.
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Kim, Jihyun, Kara Suzuka, and Elizabeth Yakel. "Reusing qualitative video data: matching reuse goals and criteria for selection." Aslib Journal of Information Management 72, no. 3 (2020): 395–419. http://dx.doi.org/10.1108/ajim-08-2019-0215.
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PurposeThis research investigated the reuse of Video Records of Practice (VRPs) – i.e. a type of qualitative data documenting teaching and learning in educational settings. It studied how reusers' purposes and experience-level with VRP reuse influence the importance of various VRP selection criteria and how these differ depending on whether the main goal for reuse was research or teaching. It also examined whether two different dimensions of qualitative research – reflexivity and context – were factors in VRP reuse.Design/methodology/approachThe study reports on surveys of reusers at four VRP repositories. Questions were based on the literature and interviews with VRP reusers. The response rate was 20.6% (180 of 872 distributed surveys). This paper focused on 126 respondents who affirmatively responded they reused VRPs from a repository.FindingsResearchers using VRPs were primarily interested in examining a broad range of processes in education and studying/improving ways to measure differences and growth in education. Reusers with teaching goals were commonly interested in VRPs to engage learners in showing examples/exemplars of – and reflecting on – teaching and learning. These differences between research and teaching led to varied expectations about VRPs, such as the amount of content needed and necessary contextual information to support reuse.Research limitations/implicationsWhile repositories focus on exposing content, understanding and communicating certain qualities of that content can help reusers identify VRPs and align goals with selection decisions.Originality/valueAlthough qualitative data are increasingly reused, research has rarely focused on identifying how qualitative data reusers employ selection criteria. This study focused on VRPs as one type of qualitative data and identified the attributes of VRPs that reusers perceived to be important during selection. These will help VRP repositories determine which metadata and documentation meet reusers' goals.
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Yoon, Ayoung, and Yoo Young Lee. "Factors of trust in data reuse." Online Information Review 43, no. 7 (2019): 1245–62. http://dx.doi.org/10.1108/oir-01-2019-0014.
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Purpose The purpose of this paper is to quantitatively examine factors of trust in data reuse from the reusers’ perspectives. Design/methodology/approach This study utilized a survey method to test the proposed hypotheses and to empirically evaluate the research model, which was developed to examine the relationship each factor of trust has with reusers’ actual trust during data reuse. Findings This study found that the data producer (H1) and data quality (H3) were significant, as predicted, while scholarly community (H3) and data intermediary (H4) were not significantly related to reusers’ trust in data. Research limitations/implications Further disciplinary specific examinations should be conducted to complement the study findings and fully generalize the study findings. Practical implications The study finding presents the need for engaging data producers in the process of data curation, preferably beginning in the early stages and encouraging them to work with curation professionals to ensure data management quality. The study finding also suggests the need for re-defining the boundaries of current curation work or collaborating with other professionals who can perform data quality assessment that is related to scientific and methodological rigor. Originality/value By analyzing theoretical concepts in empirical research and validating the factors of trust, this study fills this gap in the data reuse literature.
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Kansa, Sarah Whitcher. "Using Linked Open Data to Improve Data Reuse in Zooarchaeology." Ethnobiology Letters 6, no. 2 (2015): 224–31. http://dx.doi.org/10.14237/ebl.6.2.2015.467.
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The inability of journals and books to accommodate data and to make it reusable has led to the gradual loss of vast amounts of information. The practice of disseminating selected sub-sets of data (usually in summary tables) permits only very limited types of reuse, and thus hampers scholarship. In recent years, largely in response to increasing government and institutional requirements for full data access, the scholarly community is giving data more attention, and solutions for data management are emerging. However, seeing data management primarily as a matter of compliance means that the research community faces continued data loss, as many datasets enter repositories without adequate description to enable their reuse. Furthermore, because many archaeologists do not yet have experience in data reuse, they lack understanding of what “good” data management means in terms of their own research practices. This paper discusses Linked Open Data (LOD) as an approach to improving data description, intelligibility and discoverability to facilitate reuse. I present examples of how annotating zooarchaeology datasets with LOD can facilitate data integration without forcing standardization. I conclude by recognizing that data sharing is not without its challenges. However, the research community’s careful attention and recognition of datasets as valuable scholarly outputs will go a long way toward ensuring that the products of our work are more widely useful.
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Law, Margaret. "Reduce, Reuse, Recycle: Issues in the Secondary Use of Research Data." IASSIST Quarterly 29, no. 1 (2006): 5. http://dx.doi.org/10.29173/iq599.
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Fowler, Dan, Jo Barratt, and Paul Walsh. "Frictionless Data: Making Research Data Quality Visible." International Journal of Digital Curation 12, no. 2 (2018): 274–85. http://dx.doi.org/10.2218/ijdc.v12i2.577.
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There is significant friction in the acquisition, sharing, and reuse of research data. It is estimated that eighty percent of data analysis is invested in the cleaning and mapping of data (Dasu and Johnson,2003). This friction hampers researchers not well versed in data preparation techniques from reusing an ever-increasing amount of data available within research data repositories. Frictionless Data is an ongoing project at Open Knowledge International focused on removing this friction. We are doing this by developing a set of tools, specifications, and best practices for describing, publishing, and validating data. The heart of this project is the “Data Package”, a containerization format for data based on existing practices for publishing open source software. This paper will report on current progress toward that goal.
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Rueda, Laura, Martin Fenner, and Patricia Cruse. "DataCite: Lessons Learned on Persistent Identifiers for Research Data." International Journal of Digital Curation 11, no. 2 (2017): 39–47. http://dx.doi.org/10.2218/ijdc.v11i2.421.
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Data are the infrastructure of science and they serve as the groundwork for scientific pursuits. Data publication has emerged as a game-changing breakthrough in scholarly communication. Data form the outputs of research but also are a gateway to new hypotheses, enabling new scientific insights and driving innovation. And yet stakeholders across the scholarly ecosystem, including practitioners, institutions, and funders of scientific research are increasingly concerned about the lack of sharing and reuse of research data. Across disciplines and countries, researchers, funders, and publishers are pushing for a more effective research environment, minimizing the duplication of work and maximizing the interaction between researchers. Availability, discoverability, and reproducibility of research outputs are key factors to support data reuse and make possible this new environment of highly collaborative research. An interoperable e-infrastructure is imperative in order to develop new platforms and services for to data publication and reuse. DataCite has been working to establish and promote methods to locate, identify and share information about research data. Along with service development, DataCite supports and advocates for the standards behind persistent identifiers (in particular DOIs, Digital Object Identifiers) for data and other research outputs. Persistent identifiers allow different platforms to exchange information consistently and unambiguously and provide a reliable way to track citations and reuse. Because of this, data publication can become a reality from a technical standpoint, but the adoption of data publication and data citation as a practice by researchers is still in its early stages. Since 2009, DataCite has been developing a series of tools and services to foster the adoption of data publication and citation among the research community. Through the years, DataCite has worked in a close collaboration with interdisciplinary partners on these issues and we have gained insight into the development of data publication workflows. This paper describes the types of different actions and the lessons learned by DataCite.
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Faniel, Ixchel M., Rebecca D. Frank, and Elizabeth Yakel. "Context from the data reuser’s point of view." Journal of Documentation 75, no. 6 (2019): 1274–97. http://dx.doi.org/10.1108/jd-08-2018-0133.
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Purpose Taking the researchers’ perspective, the purpose of this paper is to examine the types of context information needed to preserve data’s meaning in ways that support data reuse. Design/methodology/approach This paper is based on a qualitative study of 105 researchers from three disciplinary communities: quantitative social science, archaeology and zoology. The study focused on researchers’ most recent data reuse experience, particularly what they needed when deciding whether to reuse data. Findings Findings show that researchers mentioned 12 types of context information across three broad categories: data production information (data collection, specimen and artifact, data producer, data analysis, missing data, and research objectives); repository information (provenance, reputation and history, curation and digitization); and data reuse information (prior reuse, advice on reuse and terms of use). Originality/value This paper extends digital curation conversations to include the preservation of context as well as content to facilitate data reuse. When compared to prior research, findings show that there is some generalizability with respect to the types of context needed across different disciplines and data sharing and reuse environments. It also introduces several new context types. Relying on the perspective of researchers offers a more nuanced view that shows the importance of the different context types for each discipline and the ways disciplinary members thought about them. Both data producers and curators can benefit from knowing what to capture and manage during data collection and deposit into a repository.
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Duan, Qingyu, Mengli Liang, and Xiaoguang Wang. "The Human‐Data Interaction Driven by Data Reuse." Proceedings of the Association for Information Science and Technology 61, no. 1 (2024): 905–7. http://dx.doi.org/10.1002/pra2.1135.
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ABSTRACTThe data‐intensive research paradigm is sweeping through the scientific community, and new interaction challenges for interacting with data have emerged. Data reuse emerges as a critical driver of human‐data interaction, positioning data repositories at the forefront as the optimal solution for facilitating this process. This study adopts a diary study methodology to analyze the data repository‐supported human‐data interaction behaviors driven. The findings reveal that human‐data interaction extends beyond mere engagement with data, encapsulating elements of human‐computer interaction, engagement with literature, interactions with agent systems, and interpersonal communication.
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Leslie, Heather. "openEHR Archetype Use and Reuse Within Multilingual Clinical Data Sets: Case Study." Journal of Medical Internet Research 22, no. 11 (2020): e23361. http://dx.doi.org/10.2196/23361.
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Background Despite electronic health records being in existence for over 50 years, our ability to exchange health data remains frustratingly limited. Commonly used clinical content standards, and the information models that underpin them, are primarily related to health data exchange, and so are usually document- or message-focused. In contrast, over the past 12 years, the Clinical Models program at openEHR International has gradually established a governed, coordinated, and coherent ecosystem of clinical information models, known as openEHR archetypes. Each archetype is designed as a maximal data set for a universal use-case, intended for reuse across various health data sets, known as openEHR templates. To date, only anecdotal evidence has been available to indicate if the hypothesis of archetype reuse across templates is feasible and scalable. As a response to the COVID-19 pandemic, between February and July 2020, 7 openEHR templates were independently created to represent COVID-19–related data sets for symptom screening, confirmed infection reporting, clinical decision support, and research. Each of the templates prioritized reuse of existing use-case agnostic archetypes found in openEHR International's online Clinical Knowledge Manager tool as much as possible. This study is the first opportunity to investigate archetype reuse within a range of diverse, multilingual openEHR templates. Objective This study aims to investigate the use and reuse of openEHR archetypes across the 7 openEHR templates as an initial investigation about the reuse of information models across data sets used for a variety of clinical purposes. Methods Analysis of both the number of occurrences of archetypes and patterns of occurrence within 7 discrete templates was carried out at the archetype or clinical concept level. Results Across all 7 templates collectively, 203 instances of 58 unique archetypes were used. The most frequently used archetype occurred 24 times across 4 of the 7 templates. Total data points per template ranged from 40 to 179. Archetype instances per template ranged from 10 to 62. Unique archetype occurrences ranged from 10 to 28. Existing archetype reuse of use-case agnostic archetypes ranged from 40% to 90%. Total reuse of use-case agnostic archetypes ranged from 40% to 100%. Conclusions Investigation of the amount of archetype reuse across the 7 openEHR templates in this initial study has demonstrated significant reuse of archetypes, even across unanticipated, novel modeling challenges and multilingual deployments. While the trigger for the development of each of these templates was the COVID-19 pandemic, the templates represented a variety of types of data sets: symptom screening, infection report, clinical decision support for diagnosis and treatment, and secondary use or research. The findings support the openEHR hypothesis that it is possible to create a shared, public library of standards-based, vendor-neutral clinical information models that can be reused across a diverse range of health data sets.
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Usée, Franziska, Christiane A. Melzig, and Dirk Ostwald. "Use it or lose it: Facilitating the use of interactive data apps in psychological research data sharing." Europe’s Journal of Psychology 20, no. 3 (2024): 202–19. http://dx.doi.org/10.5964/ejop.12811.
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The value of open research data (ORD), a key feature of open science, lies in their reuse. However, the mere online availability of ORD does not guarantee their reuse by other researchers. Specifically, previous meta-scientific research has indicated that the underutilization of ORD is related to barriers at the level of the ORD themselves, potential reusers of ORD, and the broader academic ecosystem. At the same time, sharing large datasets in an understandable and transparent format that motivates researchers to explore these datasets remains a fundamental challenge. With the present work, we propose interactive data apps (IDAs) as innovative ORD supplements that provide a means to lower barriers of ORD reuse. We demonstrate the use of two open-source Python libraries (Dash, Gradio) for IDA development using two psychological research use cases. The first use case pertains to an experimental quantitative dataset acquired in a clinical psychology setting. The second use case concerns the familiarization with data analysis workflows that are characteristic of natural language processing (NLP). For both use cases, we provide easy-to-adapt Python code that can form the basis for IDA development in similar scenarios.
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Wernick, Alina. "Defining Data Intermediaries." Technology and Regulation 2020 (July 16, 2020): 65–77. https://doi.org/10.71265/fk0zcq05.
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Data intermediaries may foster data reuse, thus facilitating efficiency and innovation. However, research on the subject suffers from terminological inconsistency and vagueness, making it difficult to convey to policymakers when data governance succeeds and when data sharing requires regulatory intervention. The paper describes what distinguishes data intermediaries from other data governance models. Building on research on intellectual property governance, we identify two distinct types of data intermediaries, data clearinghouses and data pools. We also discover several governance models that are specific to data and not present in the context of intellectual property. We conclude that the use of more refined terminology to describe data intermediaries will facilitate more accurate research and informed policy-making on data reuse.
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Di, Staso Davide, Ingrid Mulder, Marijn Janssen, and Fernando Kleiman. "Serious Games for Building Data Capacity." Interdisciplinary Description of Complex Systems 20, no. 2 (2022): 179–89. https://doi.org/10.7906/indecs.20.2.9.
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Abstract: Open data can support the creation of new services, facilitate research, and provide insights into everyday issues affecting citizens. Although public administrations are making efforts to create sustainable and inclusive open data systems, there is limited capacity to identify suitable datasets, clean, release, and reuse them. Serious games offer a possible solution for data capacity building and have already been used to train civil servants and citizens on the topic of open data. This research presents a review of serious games and discusses their potential for data capacity building. The games selected in the review are classified and described according to their different learning outcomes, formats, and type of media. Most serious games found in this review can be categorized as teaching games and are designed to raise data awareness, which is only a limited aspect of building data capacity. We found a lack of design games, research games, and policy games. Given their success for ideation in other fields, design games offer a particular opportunity to build data capacity by generating new ideas about how to reuse open datasets.
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Arora, Surbhi, and Rupak Chakarvarty. "Making research data discoverable: an outreach activity of each activity of Datacite." Making r Making research data disco ch data discoverable: an outr able: an outreach activity of each activity of Datacite 1, no. 1 (2021): 1–18. https://doi.org/10.5281/zenodo.4944852.
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The enormous growth in research data generated today has highlighted the value of data management (RDM) to make research FAIR (Findable, Accessible, Interconnected and Reusable). Appropriate data instructs researchers to use and reuse that data within appropriate citations and attribute it to the author. And Data citation refers to the process of presenting a reference to data in the same way as a bibliographic reference to printed resources is regularly provided by researchers. In this regard, the objective of this paper is to investigate the activities of the Datacite website in managing research data.
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Devarinti, Revanasiddappa, and Madiwalayya Shivakantayya Ganachari. "Assessment of Knowledge, Attitude, and Practices on Genetic Research Data Reuse for Future Research: Clinical Trial Investigators Perspectives." Asian Journal of Pharmaceutical Research and Health Care 16, no. 1 (2024): 58–66. http://dx.doi.org/10.4103/ajprhc.ajprhc_138_23.
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ABSTRACT Introduction: According to the principle of good clinical practice, it is imperative that the personnel involved in the pharmaceutical or genetic research possess an educational background and comprehensive training. The clinical trial investigator must ensure the safeguard the privacy and confidentiality and prevent inadvertent reuse of research results of study participants. Aim: The objective of the current research is to assess the knowledge, attitude, and practices of clinical trial investigators concerning the reuse of genetic research data. Settings and Design: As a cross-sectional study, there was only one-time collection of knowledge, attitude, and practice on genetics and its data for future research from clinical trial investigators from the Tertiary Care Hospital and Clinical Research Centers in Belagavi Karnataka, India. Subjects and Methods: A study was conducted using validated knowledge, attitude, and practices questionnaire on the clinical trial investigators. Statistical Analysis Used: Data were analyzed using the SPSS software version 21one-way analysis of variance. Results: The present study was comprised n = 50 clinical trial investigators calculated by using the general formula for the calculation of sample size with a confidence of interval limit, of whom 64% exhibited awareness of genetic data reuse in clinical trials. Seventy-four percent of clinical trial investigators expressed concerns about confidentiality issues related to the reuse of genomic data. Conclusions: The lack of synchronized international conference on harmonization of technical requirements for pharmaceuticals for human use (ICH) guidelines directives concerning genomic sampling and data management in health-care research presents a formidable obstacle for pharmaceutical sponsors in achieving consistent and standardized genomic research samples and its data execution in pharmaceutical research on a global scale.
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Giannopoulou, Alexandra. "Access and Reuse of Machine-Generated Data for Scientific Research." Erasmus Law Review 12, no. 2 (2019): 155–65. http://dx.doi.org/10.5553/elr.000136.
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Wagner, Michael, Christin Henzen, and Ralph Müller-Pfefferkorn. "A Research Data Infrastructure Component for the Automated Metadata and Data Quality Extraction to Foster the Provision of FAIR Data in Earth System Sciences." AGILE: GIScience Series 2 (June 4, 2021): 1–7. http://dx.doi.org/10.5194/agile-giss-2-41-2021.
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Abstract. Metadata management is core to support discovery and reuse of data products, and to allow for reproducibility of the research data in Earth System Sciences (ESS). Thus, ensuring acquisition and provision of meaningful and quality assured metadata should become an integral part of data-driven ESS projects.We propose an open-source tool for the automated metadata and data quality extraction to foster the provision of FAIR data (Findable, Accessible, Interoperable Reusable). By enabling researchers to automatically extract and reuse structured and standardized ESS-specific metadata, in particular quality information, in several components of a research data infrastructure, we support researchers along the research data life cycle.
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Kethers, Stefanie, Andrew Treloar, and Mingfang Wu. "Building Tools to Facilitate Data Reuse." International Journal of Digital Curation 11, no. 2 (2017): 1–12. http://dx.doi.org/10.2218/ijdc.v11i2.409.
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The Australian National Data Service (ANDS) has been funded by the Australian Government since 2009, with a goal to increase the value of data to researchers, research institutions and the nation. To achieve this goal, ANDS has funded more than 200 projects under seven programs. This paper provides an overview of one of these programs, the Applications Program, which focused on funding software infrastructure to enable data reuse to demonstrate the value of making data available to researchers. The paper also presents some representative projects, a summary of what the program has achieved, and lessons learned.
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Meystre, S. M., C. Lovis, T. Bürkle, G. Tognola, A. Budrionis, and C. U. Lehmann. "Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress." Yearbook of Medical Informatics 26, no. 01 (2017): 38–52. http://dx.doi.org/10.1055/s-0037-1606528.
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Summary Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.
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Meystre, S. M., C. Lovis, T. Bürkle, G. Tognola, A. Budrionis, and C. U. Lehmann. "Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress." Yearbook of Medical Informatics 26, no. 01 (2017): 38–52. http://dx.doi.org/10.15265/iy-2017-007.
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Summary Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.
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Matusiak, Krystyna K., Anna Harper, and Chelsea Heinbach. "Use and reuse of visual resources in student papers and presentations." Electronic Library 37, no. 3 (2019): 490–505. http://dx.doi.org/10.1108/el-09-2018-0185.
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Purpose The purpose of this study is to explore how undergraduate and graduate students use visual resources in their papers and presentations and what role images play in their academic work. It also focused on analyzing the types of image use/reuse in academic work. Design/methodology/approach This study was designed using an exploratory, qualitative approach. In all, 15 participants were recruited. Multiple sources of data were collected, including visual evidence, questionnaires and interviews. It adopted consensual qualitative research for data analysis. Findings This study finds a prevalent reuse of images in student presentations but limited use and reuse in papers. Images in presentations were primarily reused as objects for engaging and esthetic purposes. Reuse of images as a source of information was not common and in some cases problematic when students were missing context. The type of use/reuse of images in the papers was more varied with examples of creative use and transformative reuse. Practical implications This paper contributes to a better understanding of how students use and reuse images for academic papers and presentations. Results have important implications for teaching visual literacy and re-purposing images in higher education. Originality/value This paper analyses educational use/reuse of images along the data/object spectrum and distinguishes between different types of image use and reuse.
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Šimko, Tibor, Lukas Heinrich, Harri Hirvonsalo, Dinos Kousidis, and Diego Rodríguez. "REANA: A System for Reusable Research Data Analyses." EPJ Web of Conferences 214 (2019): 06034. http://dx.doi.org/10.1051/epjconf/201921406034.
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The revalidation, reinterpretation and reuse of research data analyses requires having access to the original computing environment, the experimental datasets, the analysis software, and the computational workflow steps which were used by researchers to produce the original scientific results in the first place. REANA (Reusable Analyses) is a nascent platform enabling researchers to structure their research data analyses in view of enabling future reuse. The analysis is described by means of a YAML file that captures sufficient information about the analysis assets, parameters and processes. The REANA platform consists of a set of micro-services allowing to launch and monitor container-based computational workflow jobs on the cloud. The REANA user interface and the command-line client enables researchers to easily rerun analysis workflows with new input parameters. The REANA platform aims at supporting several container technologies (Docker), workflow engines (CWL, Yadage), shared storage systems (Ceph, EOS) and compute cloud infrastructures (Ku-bernetes/OpenStack, HTCondor) used by the community. REANA was developed with the particle physics use case in mind and profits from synergies with general reusable research data analysis patterns in other scientific disciplines, such as bioinformatics and life sciences.
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Mannheimer, Sara. "Data Curation Strategies to Support Responsible Big Social Research and Big Social Data Reuse." International Journal of Digital Curation 17, no. 1 (2022): 8. http://dx.doi.org/10.2218/ijdc.v17i1.823.
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Big social research repurposes existing data from online sources such as social media, blogs, or online forums, with a goal of advancing knowledge of human behavior and social phenomena. Big social research also presents an array of challenges that can prevent data sharing and reuse.
 This brief report presents an overview of a larger study that aims to understand the data curation implications of big social research to support use and reuse of big social data. The study, which is based in the United States, identifies six key issues relating to big social research and big social data curation through a review of the literature. It then further investigates perceptions and practices relating to these six key issues through semi-structured interviews with big social researchers and data curators.
 This report concludes with implications for data curation practice: metadata and documentation, connecting with researchers throughout the research process, data repository services, and advocating for community standards. Supporting responsible practices for using big social data can help scale up social science research, thus enhancing our understanding of human behavior and social phenomena.
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Chataigner, Julie, and Céline Nowak. "French Information System on Water Withdrawals: Challenges of a Data Reuse Project." Biodiversity Information Science and Standards 2 (May 22, 2018): e25577. https://doi.org/10.3897/biss.2.25577.
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In France, a national information system on water withdrawals called Banque Nationale des Prélèvements en Eau (BNPE) has been set up to comply with the Water Framework Directive (WFD) and national Law on Water and Aquatic Environments. The aims are to centralize information on the volume of water withdrawals and to share it on the website www.bnpe.eaufrance.fr, where data can both be viewed and exported without restriction. BNPE shares data in a form that can be used for water management studies, scientific research, or to assess impacts on aquatic habitats. <b>THE BNPE PROJECT SCOPE</b> The BNPE is a part of the French Water Information System (SIE), set up to share public data on water and aquatic environments*1. The BNPE project is managed by the French Biodiversity Agency (AFB) and the Adour-Garonne Water Agency, and is supervised by the French Ministry in charge of Environment. Database and related tools were developed with the French Geological Survey (BRGM). To achieve its goals, the project mainly reuses information from Water Agencies, based on taxes collected using the 'taker-payer' principle: persons who take water from the natural environment have to pay. Data on water withdrawals disseminated by BNPE can now be reused by land managers, decision-makers and researchers due to the single access of these data for all of France (metropolitan and overseas). These data are: Detailed data of water withdrawn: volume of water withdrawn (m<sup>3</sup>), geographic coordinates of the water pump, water uses (e.g. energy, irrigation, drinking water supply, industries), type of water (groundwater, surface water: river, lake or estuary), Aggregated data: synthesis is available by year, geography, use or type of water. In 2018, BNPE shared data from 2008 to 2016. <b>CHALLENGES OF CENTRALIZATION AND REUSE OF DATA : FEEDBACK FROM THE PROJECT</b> The BNPE project faced the challenges of centralization and reuse of data at a national level by making the data available to everyone. The reuse of data derived from taxes due to environmental issues is not easy, even in an open data context. We identified two main issues: <b>The data standardization issue</b> The stakeholders of the project set up a dictionary to define *2 common repositories and a data exchange format. This work was done with the collaboration of the Sandre*3, the French National Service for Water Data and Common Repositories Management. However, the definition of the standard is too broad and producers encounter issues in standardizing their data. This project shows us the need to define a limited core of data concepts to share, which are very well defined and cannot be misinterpreted. BNPE also focuses on the importance of using concepts that already exist in the producer's information system. Centralization and enrichment of datasets are two additional steps that need to be differentiated for a project to succeed. <b>The challenge of reusing data</b> The project is confronting issues related to assembling a relevant dataset of water withdrawals. Data from taxes paid by water takers lack key environmental information that limits its use for environmental studies. For example, only 50% of water withdrawn is linked to a specific river, lake or groundwater source. Moreover, because current water use datasets are derived from taxes on withdrawals greater than 7000 m<sup>3</sup> per year, the data are missing for some withdrawals. AFB is studying additional data sources to complete the dataset (e.g., local authorities, crowdsourcing, spatial joining).
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Sobotkova, Adela. "Sociotechnical Obstacles to Archaeological Data Reuse." Advances in Archaeological Practice 6, no. 2 (2018): 117–24. http://dx.doi.org/10.1017/aap.2017.37.
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ABSTRACTThe ease of digital data capture and the proliferation of concepts such as the “data deluge” suggest that modern researchers are drowning in datasets. Yet citations of archaeological datasets are few and far between, pointing to low rates of data reuse. This article explores the difficulties that surround data reuse in large-scale regional research, including the cost and coordination necessary to extract useful data from digitized PDF reports. The amount of correction and enhancement matches the effort needed to undertake a small field survey project and can only be circumvented with a thoughtful application of computer-assisted text analysis. Missing data in excavation report PDFs are not only intractable but also insidious due to their concealed nature, leading to poor outcomes in terms of (re)use. Consequently, the degree of data reuse in archaeology has been overestimated.
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Leachman, Siobhan. "How A Citizen Scientist Can Reuse & Link Biodiversity Heritage Library Data." Biodiversity Information Science and Standards 2 (May 17, 2018): e25298. https://doi.org/10.3897/biss.2.25298.
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The Biodiversity Heritage Library (BHL) provides open access to over 54 million pages of biodiversity literature. Much of this literature is either in the public domain or is licensed for reuse under the Creative Commons framework. Anyone can therefore freely reuse much of the information and data provided by BHL. This presentation will outline how the work of a citizen scientist using BHL content might benefit research scientists. It will discuss how a citizen scientist can reuse and link BHL literature and data in Wikipedia and Wikidata. It will explain the research efficiencies that can be obtained through this reuse and linking, for example through the consolidation of database identifiers. The presentation will outline the subsequent reuse of the BHL data added to Wikipedia and Wikidata by the internet search engine Google. It will discuss an example of the linking of this information in the citizen science observation platform iNaturalist. The presentation will explain how BHL, as a result of its open reuse licensing of information and data, helps in the creation of more accurate citizen science generated biodiversity data and assists with the wider and more effective dissemination of biodiversity information.
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Singh, Dr Manish Kumar, and Dr Gireesh Kumar T. K. "Research Data Curation in Academic Institutions Challenges & Expectations." DESIDOC Journal of Library & Information Technology 43, no. 01 (2023): 39–44. http://dx.doi.org/10.14429/djlit.43.01.18624.
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In the activity of Research Data Curation, the unpublished datasets generated during research are curated for possible reuse in future research by any researcher. Use of curated data, in several cases, may become helpful in avoiding repetition of efforts involved in generation of datasets. A large number of academic institutions in India are actively involved in research in various knowledge areas. Apart from the doctoral and post-doctoral research in academic institutions, academicians are involved in research projects sponsored by public or private bodies; thereby generating sizeable primary and secondary unpublished datasets worthy of curation in a data repository for possible reuse in some other research. For several reasons, curation efforts for research datasets in academic institutions in the country are negligible when compared to such efforts in research institutions. The present work makes an attempt to identify the cause of negligible research data curation efforts in academic institutions of India by uncovering the associated challenges and discusses the expectations from a Research Data Repository of academic institutions.
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Bishop, Bradley, Jaxx Fox, Sidney Gavel, Emily Chapin, and Sarah Kansa. "Biocollections Managers: Perspectives and Processes for Curating Physical Collections and their Digital Objects." Biodiversity Information Science and Standards 8 (November 27, 2024): e142801. https://doi.org/10.3897/biss.8.142801.
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Natural history collections retain a plethora of samples and objects for research purposes across domains. The data derived from these physical collections informs scientific discovery, but often aggregating data within even a single domain relies on navigating institutional and discipline-specific catalogs and repositories. Differing curation practices, shifts in methods for measurement, and changing theoretical and funding priorities, make the United States biocollections infrastructure a patchwork quilt of objects and their associated metadata. While the efforts of many have greatly improved the system, it still needs additional investment in these invaluable collections. In today's context of possible data reuse in AI-ready test beds and public access, building a cyberinfrastructure atop this foundation should include revisiting the existing practices in preserving, conserving, and managing the physical collections and the subsequent research data curation processes of the digital objects (Bishop and Hank 2016). Scientific domains that rely on physical collections to create knowledge and work within today's machine-actionable context need more FAIR (Findable, Accessible, Interoperable, and Resuable)-aligned data that also accounts for ethical reuse (i.e., CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) Principles for Indigenous Data Governance (Carroll et al. 2020)), as well as funding agency expectations for data that are as open as possible.The purpose of the Institute of Museum and Library Services study is to understand the curation perceptions and behaviors of managers of physical collections across biocollections to inform cross-disciplinary research, data management services, and resources. Six focus groups were conducted with eighteen participants across six physical biocollections: entomology, two herbaria, herpetology, ichthyology, and palaeontology. Participants responded to open-ended questions about their collection overview, storage, data collection, metadata, organization, findability, and reuse. Results indicate that these natural history collection managers use global metadata and storage standards to increase discoverability but reuse of these physical collections and associated digital objects require more investment in personnel and cyberinfrastructure to enhance reusability of these invaluable natural history collections.
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He, Lin, and Vinita Nahar. "Reuse of scientific data in academic publications." Aslib Journal of Information Management 68, no. 4 (2016): 478–94. http://dx.doi.org/10.1108/ajim-01-2016-0008.
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Purpose – In recent years, a large number of data repositories have been built and used. However, the extent to which scientific data are re-used in academic publications is still unknown. The purpose of this paper is to explore the functions of re-used scientific data in scholarly publication in different fields. Design/methodology/approach – To address these questions, the authors identified 827 publications citing resources in the Dryad Digital Repository indexed by Scopus from 2010 to 2015. Findings – The results show that: the number of citations to scientific data increases sharply over the years, but mainly from data-intensive disciplines, such as agricultural, biology science, environment science and medicine; the majority of citations are from the originating articles; and researchers tend to reuse data produced by their own research groups. Research limitations/implications – Dryad data may be re-used without being formally cited. Originality/value – The conservatism in data sharing suggests that more should be done to encourage researchers to re-use other’s data.