Treffer: Standardization of Journal Title Information from Interlibrary Loan Data: A Customized Python Code Approach

Interlibrary loan (ILL) data plays a crucial role in making informed journal subscription decisions. However, inconsistent or incomplete data associated with journal titles and International Standard Serial Numbers (ISSNs) as data points often entered inaccurately by requestors, presents challenges when attempting to make use of the ILL data. This article introduces a solution utilizing customized Python code to standardize journal titles obtained from user-entered data. The solution incorporates a preprocessing workflow that filters out irrelevant information and employs Application Programming Interfaces (APIs) to replace inaccurate titles with precise ones based on retrieved ISSNs, ensuring data accuracy. The solution then presents the processed data in a dashboard format, highlighting the most requested journals and enabling librarians to interactively explore the data. By adopting this approach, librarians can make well-informed decisions and conduct thorough analysis, resulting in more efficient and effective management of library resources.