Journals
- Ramalli, E. and Pernici, B., 2023. Challenges of a Data Ecosystem for scientific data. Data & Knowledge Engineering. Link
- Ramalli, E. and Pernici, B., 2023. Knowledge graph embedding for experimental uncertainty estimation. Information Discovery and Delivery. Link
- Ramalli, E., Dinelli, T., Nobili, A., Stagni, A., Pernici, B. and Faravelli, T., 2022. Automatic validation and analysis of predictive models by means of big data and data science. Chemical Engineering Journal. Link
- Ramalli, E., Scalia, G., Pernici, B., Stagni, A., Cuoci, A. and Faravelli, T., 2021. Data ecosystems for scientific experiments: managing combustion experiments and simulation analyses in chemical engineering. Frontiers in big Data, p.67. Link
- Scalia, G., Pelucchi, M., Stagni, A., Cuoci, A., Faravelli, T. and Pernici, B., 2019. Towards a scientific data framework to support scientific model development. Data Science, 2 (1-2), (pp.245-273). Link
Conferences
- Ramalli, E. and Pernici, B., 2023, July. Sustainability and Governance of Data Ecosystems. In 2023 IEEE International Conference on Web Services (ICWS) (pp. 740-745). IEEE. Link
- Ramalli, E. and Pernici, B., 2022. From a prototype to a data ecosystem for experimental data and predictive models. In DEco at VLDB 2022.
- Ramalli, E. and Pernici, B., 2021. Know your experiments: interpreting categories of experimental data and their coverage. In SeaData at VLDB 2021 (pp. 27-33). Link
- Stagni, A., Pelucchi, M., Scalia, G., Cuoci, A., Pernici, A., Faravelli, T., 2019. Numerical Combustion Conference, Aachen, abstract.
- Scalia, G., Pelucchi, M., Stagni, A., Faravelli, T. and Pernici, B., 2017. Storing combustion data experiments: New requirements emerging from a first prototype. In Semantics, Analytics, Visualization (pp. 138-149). Springer, Cham. Link
Invited Talk
- Stagni, A, 2023. Big data and data science for the intelligent development of kinetic mechanisms – AI for Energy, KAUST, Saudi Arabia.
- Pernici, B., 2019. DataBench: indicators and metrics to assess benchmarks to evaluate Big Data technologies – A focus on scientific domains”. Numerical Combustion, Aachen.
Book Chapter
- Scalia, G., 2022. Machine Learning for Scientific Data Analysis. In Special Topics in Information Technology (pp. 115-126). Springer, Cham. Link
- Pernici, B., Ratti, F. and Scalia, G., 2021. About the Quality of Data and Services in Natural Sciences. Next-Gen Digital Services 2021 (pp. 236-248). Link
- Pelucchi, M., Stagni, A. and Faravelli, T., 2019. Addressing the complexity of combustion kinetics: Data management and automatic model validation. In Computer Aided Chemical Engineering (Vol. 45, pp. 763-798). Elsevier. Link