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Research on fine-grained linked data creation for papers stored in digital library
Jing Huang1, Zhongyi Wang2
1Wuhan Polytechnic, Wuhan, Hubei ,China, People's Republic of; 2Central China Normal University, Wuhan, Hubei, China, People's Republic of
The best practices for publishing linked data have been adopted by an increasing number of libraries, leading to the creation of a global data space-the web of digital library data. However, in library linked data publishing, most of the existing researches mainly focus on structured and semi-structured digital library resources (for example catalogue data). Researches on publishing unstructured digital library resources (for example: contents of papers) are seldom. In order to overcome this problem, this paper proposes a fine-grained linked data creation method to publish the papers stored in digital libraries into linked data. At last, in order to evaluate this method, this paper conducted an experiment on the papers on “text segmentation”. From the experiment results we find that our fine-grained linked data creation method is feasible and will promote the opening access to digital libraries resources.
Earth Science Data Management: Mapping Actual Tasks to Conceptual Actions in the Curation Lifecycle Model
Bradley Wade Bishop, Carolyn Hank
University of Tennessee, United States of America
Earth science, like other data intensive sciences, requires data that are discoverable and usable by a variety of designated communities for a multitude of purposes in our transforming digital world. Data must be collected, documented, organized, managed, and curated with data sharing in mind. Actual, rather than supposed, practices of data managers provide insight into how earth science data are preserved and made available, and the requisite skills required to do so. This study’s purpose is to explore the job practices of earth science data managers as they relate to the data lifecycle. Twelve earth science data managers were interviewed using a job analyses approach focused on job tasks and their frequencies. Data managers identified tasks related to preservation and curation in the data lifecycle, though the most mentioned tasks do not relate directly to sequential actions in the data lifecycle, but rather are more oriented toward full-life cycle actions. These are communication and project management activities. Data managers require domain knowledge of science and management skills beyond the data lifecycle to do their jobs. Several tasks did relate to the data lifecycle, such as data discovery, and require an understanding of the data, technology, and information infrastructures to support data use, re-use and preservation. Most respondents lacked formal education, acquiring necessary skills through informal, self-directed study or professional training, indicating opportunity for integrating information science and data management curriculum in disciplinary academic programs.
Limits to the Pursuit of Reproducibility: Emergent Data-Scarce Domains of Science
University of Illinois at Urbana-Champaign, United States of America
Recommendations and interventions to promote reproducibility in science have so far largely been formulated in the context of well-established do-mains characterized by data- and computationally-intensive methods. How-ever, much promising research occurs in little data domains that are emergent and experience data scarcity. This paper presents a longitudinal study of such a domain, deep subseafloor biosphere research. Two important challenges this domain faces in establishing itself are increasing production and circulation of data, and strengthening relationships between domain re-searchers. Some potential interventions to promote reproducibility may al-so help the domain to establish itself. However, other potential interventions could profoundly damage the domain’s long-term prospects of maturation by impeding production of new data and undermining critical relationships between researchers. This paper challenges the dominant framing of the pursuit of reproducible science as identifying, and overcoming, barriers to reproducibility. Instead, those interested in pursuing reproducibility in a domain should take into account multiple aspects of that domain’s epistemic culture to avoid negative unintended consequences. Further, pursuing reproducibility is premature for emergent, data-scarce domains: scarce resources should instead be invested to help these domains to mature, for instance by addressing data scarcity.