Adrian Calma
Impact in
- Computer Science Applications top 10%
- Mobile Crowdsensing and Crowdsourcing
Papers in
-
- Mobile Crowdsensing and Crowdsourcing 5
- Software 2
- Co-authors
- Bernhard SickPhilipp EbelDominik DellermannNikolaus LipuschSarah Oeste-ReißJan Marco LeimeisterSven TomfordeGerd Stumme
- Journals
- Information Sciences (2 papers)IEEE Access (1 paper)Proceedings of the ... Annual Hawaii International Conference on System Sciences (2 papers)Alexandria (UniSG) (University of St.Gallen) (1 paper)Data Archiving and Networked Services (DANS) (1 paper)
- Partner nations
- GermanySwitzerlandNetherlands
In The Last Decade
Adrian Calma
17 papers receiving 243 citations
Peers
Comparison fields: 5 of 69
- Health Informatics 9
- Computer Science Applications 35
- Artificial Intelligence 150
- Safety Research 31
- Management Information Systems 25
Countries citing papers authored by Adrian Calma
This map shows the geographic impact of Adrian Calma'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 Adrian Calma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adrian Calma more than expected).
Fields of papers citing papers by Adrian Calma
This network shows the impact of papers produced by Adrian Calma. 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 Adrian Calma. The network helps show where Adrian Calma may publish in the future.
Co-authorship network
The 15 scholars most cited alongside Adrian Calma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 9 | |
| 2 | 2019 | 106 | |
| 3 | 2019 | 3 | |
| 4 | 2018 | 3 | |
| 5 | 2018 | 2 | |
| 6 | 2018 | 28 | |
| 7 | 2018 | 3 | |
| 8 | 2018 | 2 | |
| 9 | 2018 | 1 | |
| 10 | 2018 | 6 | |
| 11 | Simulation of Annotators for Active Learning: Uncertain Oracles. | 2017 | 4 |
| 12 | Challenges of Reliable, Realistic and Comparable Active Learning Evaluation | 2017 | 11 |
| 13 | 2017 | 15 | |
| 14 | From Active Learning to Dedicated Collaborative Interactive Learning | 2016 | 17 |
| 15 | 2016 | 2 | |
| 16 | 2016 | 11 | |
| 17 | 2014 | 33 |
About Adrian Calma
Adrian Calma is a scholar working on Computer Science Applications, Software, Artificial Intelligence, Safety Research and Management Information Systems, having authored 17 papers that have together received 256 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (12 papers), Machine Learning and Data Classification (8 papers), Data Stream Mining Techniques (5 papers), Mobile Crowdsensing and Crowdsourcing (5 papers), Algorithms and Data Compression (2 papers), AI-based Problem Solving and Planning (1 paper), Modular Robots and Swarm Intelligence (1 paper) and Big Data and Business Intelligence (1 paper). The work is most often cited by research in Health Informatics (9 citations), Computer Science Applications (35 citations), Artificial Intelligence (150 citations), Safety Research (31 citations) and Management Information Systems (25 citations). Adrian Calma has collaborated with scholars based in Germany, Switzerland and Netherlands. Frequent co-authors include Bernhard Sick, Philipp Ebel, Dominik Dellermann, Nikolaus Lipusch, Sarah Oeste-Reiß, Jan Marco Leimeister, Sven Tomforde, Gerd Stumme, Paul Lukowicz and Katharina A. Zweig. Their work appears in journals such as Information Sciences, IEEE Access, Proceedings of the ... Annual Hawaii International Conference on System Sciences, Alexandria (UniSG) (University of St.Gallen) and Data Archiving and Networked Services (DANS).
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.