USING AND REUSING DATA IN DATA CURATION




The process of obtaining, evaluating, distributing, and applying pre-existing datasets for present or future uses is referred to as "using and reusing data" in data curation. In order for researchers, institutions, and organizations to efficiently reuse data, data curation makes sure that the data is credible, comprehensible, and accessible (Abbott, 2008). One of the main objectives of data curation is to maximize the long-term value of information resources by enabling the reuse of data.

Accessing carefully selected datasets for purposes like research, decision-making, policy creation, instruction, or innovation is known as data use. To help consumers comprehend its structure, meaning, and context, curated data is frequently arranged and recorded (Johnston, 2017). Properly curated data saves time and resources because users do not need to collect the same information repeatedly.

When pre-existing datasets are used for reasons other than those for which they were intended, this is known as data reuse. Researchers may integrate datasets for more comprehensive studies, undertake new analysis, perform comparison studies, or reuse data to validate earlier findings (Borgman, 2015). Reusing data encourages transparency in research procedures and fosters scientific cooperation. Additionally, it lowers the expense and work involved in gathering fresh data.

In order to facilitate the usage and reuse of data, metadata is crucial. According to the National Information Standards Organization [NISO], 2004, metadata offers descriptive information about datasets, such as the inventor, collection techniques, variables, dates, and usage conditions. Users may find it difficult to accurately comprehend or apply data in the absence of appropriate metadata. Effective reuse and discoverability are enhanced by well-documented datasets.


                                      

Another important component of data reuse is accessibility. Repositories, databases, or cloud platforms that make it simple for authorized individuals to access datasets should be used to keep curated data (Yakel, 2007). Digital storage and sharing platforms that provide data access across various locations and users are offered by companies like Google, Microsoft, and Amazon.

When utilizing and reusing data, ethical and legal considerations are also crucial. Certain datasets contain sensitive or private information that needs to be accessed under strict guidelines and with consent from participants or data owners (Sayogo & Pardo, 2013). To guarantee responsible information reuse, data users must abide by copyright laws, license agreements, and privacy restrictions.

In conclusion, by facilitating ongoing access, analysis, cooperation, and innovation, the use and reuse of data in data curation increases the value of data. The generation of metadata, safe storage, and accessibility management are examples of proper curation techniques that guarantee datasets stay dependable and significant for usage in the future. Reusing data effectively reduces costs, enhances the caliber of research, and encourages interdisciplinary information exchange.

References

Abbott, D. (2008). What is digital curation? Digital Curation Centre.

Borgman, C. L. (2015). Big data, little data, no data: Scholarship in the networked world. MIT Press.

Johnston, L. R. (2017). Curating research data: Practical strategies for your digital repository. Association of College and Research Libraries.

National Information Standards Organization. (2004). Understanding metadata. NISO Press.

Sayogo, D. S., & Pardo, T. A. (2013). Exploring the determinants of scientific data sharing. Government Information Quarterly, 30(S1), S19–S31.

Yakel, E. (2007). Digital curation. OCLC Systems & Services, 23(4), 335–340.


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