Farshidi, S., Liao, X., Li, N., Goldfarb, D., Magagna, B., Stocker, M., Jeffery, K., Thijsse, P., Pichot, C., Petzold, A., & Zhao, Z. (2023). Knowledge sharing and discovery across heterogeneous research infrastructures. Open Research Europe, 1, Article 68. https://doi.org/10.12688/openreseurope.13677.3[details]
Li, N., Zhang, Y., & Zhao, Z. (2023). A Dense Retrieval System and Evaluation Dataset for Scientific Computational Notebooks. In 2023 IEEE 19th International Conference on e-Science: (e-Science) : October 9-14, 2023, Limassol, Cyprus : proceedings (pp. 179-188). Article 19 IEEE. https://doi.org/10.1109/e-Science58273.2023.10254859[details]
Li, N., Zhang, Y., & Zhao, Z. (2023). CNSVRE: A Query Reformulated Search System with Explainable Summarization for Virtual Research Environment. In The ACM Web Conference 2023: Companion of the World Wide Web Conference WWW 2023 : April 30-May 4, 2023, Austin, Texas, USA (pp. 254-257). Association for Computing Machinery. https://doi.org/10.1145/3543873.3587360[details]
2022
Li, N., Farshidi, S., Bianchi, R., Koulouzis, S., & Zhao, Z. (2022). Context-Aware Notebook Search in a Jupyter-Based Virtual Research Environment. In eScience '22 : Democratizing science : 2022 IEEE 18th International Conference on e-Science: proceedings : eScience 2022 : Salt Lake City, Utah, USA, 10-14 October 202 (pp. 393-394). Conference Publishing Services, IEEE Computer Society. https://doi.org/10.1109/eScience55777.2022.00054[details]
Wang, Y., Koulouzis, S., Bianchi, R., Li, N., Shi, Y., Timmermans, J., Kissling, W. D., & Zhao, Z. (2022). Scaling Notebooks as Re-configurable Cloud Workflows. Data Intelligence, 4(2), 409-425. https://doi.org/10.1162/dint_a_00140[details]
Zhao, Z., Koulouzis, S., Bianchi, R., Farshidi, S., Shi, Z., Xin, R., Wang, Y., Li, N., Shi, Y., Timmermans, J., & Kissling, W. D. (2022). Notebook-as-a-VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment. Software - Practice and Experience, 52(9), 1947-1966. https://doi.org/10.1002/spe.3098[details]
Poon, L., Farshidi, S., Li, N., & Zhao, Z. (2021). Unsupervised Anomaly Detection in Data Quality Control. In Y. Chen, H. Ludwig, Y. Tu, U. Fayyad, X. Zhu, X. Hu, S. Byna, X. Liu, J. Zhang, S. Pan, V. Papalexakis, J. Wang, A. Cuzzocrea, & C. Ordonez (Eds.), 2021 IEEE International Conference on Big Data: proceedings : Dec 15-Dec 18, 2021 : virtual event (pp. 2327-2336). IEEE. https://doi.org/10.1109/BigData52589.2021.9671672[details]
Li, N., Qi, Y., Xin, R., & Zhao, Z. (2023). Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, JC.-W. Lin, & R. Agrawal (Eds.), 2023 IEEE International Conference on Big Data (BigData) (pp. 102-107). IEEE. https://doi.org/10.1109/BigData59044.2023.10386969[details]
De UvA gebruikt cookies voor het meten, optimaliseren en goed laten functioneren van de website. Ook worden er cookies geplaatst om inhoud van derden te kunnen tonen en voor marketingdoeleinden. Klik op ‘Accepteren’ om akkoord te gaan met het plaatsen van alle cookies. Of kies voor ‘Weigeren’ om alleen functionele en analytische cookies te accepteren. Je kunt je voorkeur op ieder moment wijzigen door op de link ‘Cookie instellingen’ te klikken die je onderaan iedere pagina vindt. Lees ook het UvA Privacy statement.