Larissa Chazette

454 citations
14 papers · 212 indexed · h-index 8
Topics
Software Engineering Research (6 papers)Ethics and Social Impacts of AI (6 papers)Explainable Artificial Intelligence (XAI) (4 papers)
Partner nations
GermanySweden

In The Last Decade

Larissa Chazette

14 papers receiving 208 citations

Peers

Larissa Chazette
Comparison fields: 5 of 57
  • Artificial Intelligence 113
  • Information Systems 56
  • Safety Research 46
  • Information Systems and Management 28
  • Management Information Systems 26
Replace David Gray Widder with:
David Gray Widder United States
Ángel Alexander Cabrera United States
Claudia Müller-Birn Germany
Cristina Sarasua Switzerland
Bifei Mao China
Katherine Lee United States
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Megha Srivastava United States
Agathe Balayn Netherlands
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Citations per field
00.5×3.8×
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Citations per year

Countries citing papers authored by Larissa Chazette

Since Specialization
Citations

This map shows the geographic impact of Larissa Chazette'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 Larissa Chazette with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Larissa Chazette more than expected).

Fields of papers citing papers by Larissa Chazette

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Larissa Chazette. 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 Larissa Chazette. The network helps show where Larissa Chazette may publish in the future.

Co-authorship network of co-authors of Larissa Chazette

This figure shows the co-authorship network connecting the top 25 collaborators of Larissa Chazette. A scholar is included among the top collaborators of Larissa Chazette 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 Larissa Chazette. Larissa Chazette is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
#WorkIndexed citations
1 8
2 7
3 13
4 10
5 6
6 8
7 17
8 6
9 2
10 99
11 10
12 4
13
A Scenario-Based MDE Process for Dynamic Topology Collaborative Reactive Systems - Early Virtual Prototyping of Car-to-X System Specifications.
1
14 21

About Larissa Chazette

Larissa Chazette is a scholar working on Safety Research, Software and Management Information Systems, having authored 14 papers that have together received 212 indexed citations. Recurring topics across this work include Software Engineering Research (6 papers), Ethics and Social Impacts of AI (6 papers) and Explainable Artificial Intelligence (XAI) (4 papers). The work is most often cited by research in Health Informatics (25 citations), Safety Research (46 citations) and Information Systems and Management (28 citations). Larissa Chazette has collaborated with scholars based in Germany and Sweden. Frequent co-authors include Kurt Schneider, Matthias Becker, Helena Szczerbicka, Timo Speith, Jil Klünder, Walid Maalej, Oliver Karras, Florian Herzog, Joel Greenyer and Eric Knauss. Their work appears in journals such as Computer, Journal of Systems and Software and Requirements Engineering.

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.

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