Browsing by Author "Salsabil, Lamia"

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    MetaEnhance: Metadata Quality Improvement for Electronic Theses and Dissertations of University Libraries
    Choudhury, Muntabir Hasan; Salsabil, Lamia; Jayanetti, Himarsha R.; Wu, Jian; Ingram, William A.; Fox, Edward A. (ACM, 2023)
    Metadata quality is crucial for discovering digital objects through digital library (DL) interfaces. However, due to various reasons, the metadata of digital objects often exhibits incomplete, inconsistent, and incorrect values. We investigate methods to automatically detect, correct, and canonicalize scholarly metadata, using seven key fields of electronic theses and dissertations (ETDs) as a case study. We propose MetaEnhance, a framework that utilizes state-of-the-art artificial intelligence (AI) methods to improve the quality of these fields. To evaluate MetaEnhance, we compiled a metadata quality evaluation benchmark containing 500 ETDs, by combining subsets sampled using multiple criteria. We evaluated MetaEnhance against this benchmark and found that the proposed methods achieved nearly perfect F1-scores in detecting errors and F1-scores ranging from 0.85 to 1.00 for correcting five of seven key metadata fields. The codes and data are publicly available on GitHub11https://github.com/lamps-lab/ETDMiner/tree/master/metadata-correction.
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    A Study of Computational Reproducibility using URLs Linking to Open Access Datasets and Software
    Salsabil, Lamia; Wu, Jian; Choudhury, Muntabir; Ingram, William A.; Fox, Edward A.; Rajtmajer, Sarah; Giles, C. Lee (ACM, 2022-04-25)
    Datasets and software packages are considered important resources that can be used for replicating computational experiments. With the advocacy of Open Science and the growing interest of investigating reproducibility of scientific claims, including URLs linking to publicly available datasets and software packages has become an institutionalized part of research publications. In this preliminary study, we investigated the disciplinary dependency and chronological trends of including open access datasets and software (OADS) in electronic theses and dissertations (ETDs), based on a hybrid classifier called OADSClassifier, consisting of a heuristic and a supervised learning model. The classifier achieves the best F1 of 0.92.We found that the inclusion of OADS-URLs exhibited a strong disciplinary dependence and the fraction of ETDs containing OADS-URLs has been gradually increasing over the past 20 years.We developed and share a ground truth corpus consisting of 500 manually labeled sentences containing URLs from scientific papers. The dataset and source code are available at https://github.com/lamps-lab/oadsclassifier.