Hyoungjoo Park

An examination of research data sharing and re-use: implications for data citation practice

This study examines characteristics of data sharing and data re-use in Genetics and Heredity, where data citation is most common. This study applies an exploratory method because data citation is a relatively new area. The Data Citation Index (DCI) on the Web of Science was selected because DCI provides a single access point to over 500 data repositories worldwide and to over two million data studies and datasets across multiple disciplines and monitors quality research data through a peer review process. We explore data citations for Genetics and Heredity, as a case study by examining formal citations recorded in the DCI and informally by sampling a selection of papers for implicit data citations within publications. Citer-based analysis is conducted in order to remedy self-citation in the data citation phenomena. We explore 148 sampled citing articles in order to identify factors that influence data sharing and data re-use, including references, main text, supplementary data/information, acknowledgments, funding information, author information, and web/author resources. This study is unique in that it relies on a citer-based analysis approach and by analyzing peer-reviewed and published data, data repositories, and citing articles of highly productive authors where data sharing is most prevalent. This research is intended to provide a methodological and practical contribution to the study of data citation.

Is informal data citation for data sharing and re-use more common than formal data citation?: Is Informal Data Citation for Data Sharing and Re-Use More Common than Formal Data Citation?

Data citation to reflect instances of data sharing and re‐use is becoming more common, although it is not yet widely adopted. We investigate how common formal and informal data citation are in bioscience/biomedical research. We found that informal data citation (i.e., acknowledgments embedded in publications outside of formal references) is far more common than formal citations. Informal citations go unrecorded, and therefore authors of datasets largely do not receive credit for having informed or influenced the research of others.

Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields

Data citation, where products of research such as data sets, software, and tissue cultures are shared and acknowledged, is becoming more common in the era of Open Science. Currently, the practice of formal data citation—where data references are included alongside bibliographic references in the reference section of a publication—is uncommon. We examine the prevalence of data citation, documenting data sharing and reuse, in a sample of full text articles from the biological/biomedical sciences, the fields with the most public data sets available documented by the Data Citation Index (DCI). We develop a method that combines automated text extraction with human assessment for revealing candidate occurrences of data sharing and reuse by using terms that are most likely to indicate their occurrence. The analysis reveals that informal data citation in the main text of articles is far more common than formal data citations in the references of articles. As a result, data sharers do not receive documented credit for their data contributions in a similar way as authors do for their research articles because informal data citations are not recorded in sources such as the DCI. Ongoing challenges for the study of data citation are also outlined.

Enhancing Data Curation of Cultural Heritage for Information Sharing: A Case Study Using Open Government Data

The purpose of this paper is to enhance cultural heritage data curation. A core research question of this study is how to share cultural heritage data by using ontologies. A case study was conducted using open government data mapped with the CIDOC-CRM (Conceptual Reference Model). Twelve library-related files in unstructured data format were collected from an open government website, Seoul Metropolitan Government of Korea (http://data.seoul.go.kr). By using the ontologies of the CIDOC CRM 5.1.2, we conducted a mapping process as a way of enhancing cultural heritage information to share information as a data component. We graphed each file then mapped each file in tables. Implications of this study are both the enhanced discoverability of unstructured data and the reusability of mapped information. Issues emerging from this study involve verification of detail for complete compatibility without further input from domain experts.