Chiara Masci
- Education top 10%
- Computer Science Applications top 5%
- Radiology, Nuclear Medicine and Imaging
- Artificial Intelligence
- Surgery
- Co-authors
- Tommaso AgasistiFrancesca IevaAnna Maria PaganoniGeraint JohnesKristof De WitteLuca ViganòFrancesco FizGuido Torzilli
- Topics
- School Choice and Performance (9 papers)Online Learning and Analytics (4 papers)Radiomics and Machine Learning in Medical Imaging (2 papers)
- Journals
- Scientific ReportsEuropean Journal of Operational ResearchEuropean Journal of Nuclear Medicine and Molecular Imaging
- Partner nations
- ItalyBelgiumNetherlands
In The Last Decade
Chiara Masci
18 papers receiving 300 citations
Peers
Comparison fields: 5 of 87
- Education 104
- Computer Science Applications 78
- Radiology, Nuclear Medicine and Imaging 52
- Artificial Intelligence 50
- Surgery 27
Countries citing papers authored by Chiara Masci
This map shows the geographic impact of Chiara Masci's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chiara Masci with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chiara Masci more than expected).
Fields of papers citing papers by Chiara Masci
This network shows the impact of papers produced by Chiara Masci. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chiara Masci. The network helps show where Chiara Masci may publish in the future.
Co-authorship network of co-authors of Chiara Masci
This figure shows the co-authorship network connecting the top 25 collaborators of Chiara Masci. A scholar is included among the top collaborators of Chiara Masci based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chiara Masci. Chiara Masci is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | 47 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 31 | |
| 9 | 16 | |
| 10 | 37 | |
| 11 | 4 | |
| 12 | 12 | |
| 13 | 3 | |
| 14 | 22 | |
| 15 | 67 | |
| 16 | 32 | |
| 17 | Analysis of pupils’ INVALSI achievements by means of bivariate multilevel models. | 1 |
| 18 | 14 | |
| 19 | 16 |
About Chiara Masci
Chiara Masci is a scholar working on Computer Science Applications, Statistics and Probability and Education, having authored 19 papers that have together received 317 indexed citations. Recurring topics across this work include School Choice and Performance (9 papers), Online Learning and Analytics (4 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). The work is most often cited by research in Computer Science Applications (78 citations), Health Information Management (21 citations) and Health Informatics (6 citations). Chiara Masci has collaborated with scholars based in Italy, Belgium and Netherlands. Frequent co-authors include Tommaso Agasisti, Francesca Ieva, Anna Maria Paganoni, Geraint Johnes, Kristof De Witte, Luca Viganò, Francesco Fiz, Guido Torzilli, Guido Costa and Martina Sollini. Their work appears in journals such as Scientific Reports, European Journal of Operational Research and European Journal of Nuclear Medicine and Molecular Imaging.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.