M. Haas

820 total citations
30 papers, 513 citations indexed

About

M. Haas is a scholar working on Astronomy and Astrophysics, Instrumentation and Artificial Intelligence. According to data from OpenAlex, M. Haas has authored 30 papers receiving a total of 513 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Astronomy and Astrophysics, 7 papers in Instrumentation and 5 papers in Artificial Intelligence. Recurrent topics in M. Haas's work include Galaxies: Formation, Evolution, Phenomena (15 papers), Astrophysics and Star Formation Studies (13 papers) and Stellar, planetary, and galactic studies (11 papers). M. Haas is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (15 papers), Astrophysics and Star Formation Studies (13 papers) and Stellar, planetary, and galactic studies (11 papers). M. Haas collaborates with scholars based in Netherlands, Germany and United Kingdom. M. Haas's co-authors include R. A. Scheepmaker, S. S. Larsen, Mark Gieles, H. J. G. L. M. Lamers, N. Bastian, Joop Schaye, Claudio Dalla Vecchia, S. Wolf, Volker Springel and C. M. Booth and has published in prestigious journals such as The Astrophysical Journal, Scientific Reports and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

M. Haas

28 papers receiving 505 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
M. Haas Netherlands	14	491	173	36	23	15	30	513
Lichen Liang United States	12	432 0.9×	189 1.1×	51 1.4×	10 0.4×	10 0.7×	14	454
Juan Rafael Martínez-Galarza United States	12	323 0.7×	91 0.5×	74 2.1×	17 0.7×	7 0.5×	24	353
A. Dariush United Kingdom	11	315 0.6×	181 1.0×	16 0.4×	11 0.5×	10 0.7×	18	327
Roan Haggar United Kingdom	10	279 0.6×	177 1.0×	31 0.9×	8 0.3×	12 0.8×	15	293
Mark Neeser Germany	8	336 0.7×	145 0.8×	45 1.3×	8 0.3×	9 0.6×	15	358
R. S. Collins United Kingdom	5	472 1.0×	198 1.1×	39 1.1×	10 0.4×	10 0.7×	11	488
T. F. Laganá Brazil	14	469 1.0×	236 1.4×	65 1.8×	5 0.2×	13 0.9×	31	477
Matteo Messa Sweden	13	466 0.9×	189 1.1×	29 0.8×	11 0.5×	7 0.5×	40	497
Z. L. Wen China	14	409 0.8×	241 1.4×	96 2.7×	11 0.5×	12 0.8×	31	424
Pamela M. Marcum United States	12	377 0.8×	171 1.0×	36 1.0×	10 0.4×	9 0.6×	30	396

Countries citing papers authored by M. Haas

Since Specialization

Citations

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

Fields of papers citing papers by M. Haas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Haas

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Haas, M., et al.. (2025). Fidelity-agnostic synthetic data generation improves utility while retaining privacy. Patterns. 6(10). 101287–101287. 1 indexed citations

Heuvel, Marion I. van den, M. Haas, Caspar J. Van Lissa, et al.. (2025). Identifying prenatal risk factors of postpartum depression with machine learning. Scientific Reports. 15(1). 34610–34610.

Haas, M., et al.. (2024). Understanding validity criteria in technology-enhanced learning: A systematic literature review. Computers & Education. 220. 105128–105128. 3 indexed citations

Islam, Saif ul, Hafiz Muhammad Waseem, Parisis Gallos, et al.. (2024). A Novel Taxonomy for Navigating and Classifying Synthetic Data in Healthcare Applications. Studies in health technology and informatics. 321. 259–263. 2 indexed citations

Haas, M., et al.. (2024). Extracting patient lifestyle characteristics from Dutch clinical text with BERT models. BMC Medical Informatics and Decision Making. 24(1). 151–151. 4 indexed citations

Vos, Rimke C., Albert T.A. Mairuhu, Jeroen N. Struijs, et al.. (2024). Data Resource Profile: Extramural Leiden University Medical Center Academic Network (ELAN). International Journal of Epidemiology. 53(4). 4 indexed citations

Haas, M., et al.. (2024). On the evaluation of synthetic longitudinal electronic health records. BMC Medical Research Methodology. 24(1). 181–181. 2 indexed citations

Haas, M., et al.. (2024). Measuring data drift with the unstable population indicator. Research portal (Tilburg University). 7(1). 1–12. 1 indexed citations

Haas, M., Joop Schaye, C. M. Booth, et al.. (2013). Physical properties of simulated galaxy populations at z = 2 – I. Effect of metal-line cooling and feedback from star formation and AGN. Monthly Notices of the Royal Astronomical Society. 435(4). 2931–2954. 61 indexed citations

10.

Silk, Joseph, et al.. (2012). Jet interactions with a giant molecular cloud in the Galactic centre and ejection of hypervelocity stars. Springer Link (Chiba Institute of Technology). 12 indexed citations

11.

Haas, M. & Peter Anders. (2010). Variations in integrated galactic initial mass functions due to sampling method and cluster mass function. Springer Link (Chiba Institute of Technology). 22 indexed citations

12.

Sales, Laura V., Julio F. Navarro, Joop Schaye, et al.. (2009). The origin of extended disc galaxies at z = 2. Monthly Notices of the Royal Astronomical Society Letters. 399(1). L64–L68. 13 indexed citations

13.

Haas, M., Mark Gieles, R. A. Scheepmaker, S. S. Larsen, & H. J. G. L. M. Lamers. (2008). ACS imaging of star clusters in M 51. Astronomy and Astrophysics. 487(3). 937–949. 15 indexed citations

14.

Haas, M., Mark Gieles, R. A. Scheepmaker, S. S. Larsen, & H. J. G. L. M. Lamers. (2008). ACS imaging of star clusters in M51 II. The luminosity function and mass function across the disk. arXiv (Cornell University). 14 indexed citations

15.

Scheepmaker, R. A., M. Haas, Mark Gieles, et al.. (2007). ACSimaging of star clusters in M 51. Astronomy and Astrophysics. 469(3). 925–940. 58 indexed citations

16.

Gieles, Mark, S. S. Larsen, R. A. Scheepmaker, et al.. (2006). Observational evidence for a truncation of the star cluster initial mass function at the high mass end. Springer Link (Chiba Institute of Technology). 44 indexed citations

17.

Hippelein, H., M. Haas, R. J. Tuffs, et al.. (2003). The spiral galaxy M33 mapped in the FIR by ISOPHOT: A spatially resolved study of the warm and cold dust. ArXiv.org. 324(3). 165–165. 46 indexed citations

18.

Haas, M., S. Müller, R. Chini, et al.. (2000). Dust in PG quasars as seen by ISO. A&A. 354. 453–466. 1 indexed citations

19.

Haas, M., D. Lemke, M. Stickel, et al.. (1998). Cold dust in the Andromeda Galaxy mapped by ISO. 338(1). 885. 2 indexed citations

20.

Haas, M. & D. K. Ross. (1975). Measurement of the angular momentum of Jupiter and the Sun by use of the Lense-Thirring effect. Astrophysics and Space Science. 32(1). 3–11. 5 indexed citations

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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