Monica Bobra

2.8k total citations · 2 hit papers
34 papers, 1.5k citations indexed

About

Monica Bobra is a scholar working on Astronomy and Astrophysics, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Monica Bobra has authored 34 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Astronomy and Astrophysics, 12 papers in Artificial Intelligence and 7 papers in Molecular Biology. Recurrent topics in Monica Bobra's work include Solar and Space Plasma Dynamics (27 papers), Stellar, planetary, and galactic studies (12 papers) and Solar Radiation and Photovoltaics (11 papers). Monica Bobra is often cited by papers focused on Solar and Space Plasma Dynamics (27 papers), Stellar, planetary, and galactic studies (12 papers) and Solar Radiation and Photovoltaics (11 papers). Monica Bobra collaborates with scholars based in United States, United Kingdom and Japan. Monica Bobra's co-authors include J. T. Hoeksema, Xudong Sun, Yang Liu, Keiji Hayashi, M. Turmon, G. Barnes, K. D. Leka, A. A. Norton, J. Schou and Sébastien Couvidat and has published in prestigious journals such as Science, The Astrophysical Journal and Geophysical Research Letters.

In The Last Decade

Monica Bobra

33 papers receiving 1.3k citations

Hit Papers

The Helioseismic and Magnetic Imager (HMI) Vector Magneti... 2014 2026 2018 2022 2014 2014 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs – Space-Weather HMI Active Region Patches
    2014 453 citations Monica Bobra, Xudong Sun et al. Solar Physics profile →
  2. The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance
    2014 368 citations J. T. Hoeksema, Yang Liu et al. Solar Physics profile →

Peers

Monica Bobra
Ju Jing United States
D. A. Falconer United States
V. I. Abramenko United States
D. Berghmans Belgium
J. Ireland United States
Huaning Wang China
R. T. J. McAteer United States
G. Stenborg United States
T. D. Tarbell United States
Ju Jing United States
Monica Bobra
Citations per year, relative to Monica Bobra Monica Bobra (= 1×) peers Ju Jing

Countries citing papers authored by Monica Bobra

Since Specialization
Citations

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

Fields of papers citing papers by Monica Bobra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Monica Bobra

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

All Works

20 of 20 papers shown
1.
Zhao, Lulu, et al.. (2022). Interpretable Machine Learning to Forecast SEP Events for Solar Cycle 23. Space Weather. 20(2). 18 indexed citations
2.
Bobra, Monica, Xiantong Wang, Yu Wang, et al.. (2022). Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data. The Astrophysical Journal. 931(2). 163–163. 40 indexed citations
3.
Bobra, Monica, et al.. (2022). Incorporating polar field data for improved solar flare prediction. Frontiers in Astronomy and Space Sciences. 9. 3 indexed citations
4.
Bobra, Monica, Paul Wright, Xudong Sun, & M. Turmon. (2021). SMARPs and SHARPs: Two Solar Cycles of Active Region Data. The Astrophysical Journal Supplement Series. 256(2). 26–26. 25 indexed citations
5.
Bobra, Monica, Stuart Mumford, Russell J. Hewett, et al.. (2020). A Survey of Computational Tools in Solar Physics. UCL Discovery (University College London). 4 indexed citations
6.
Barnes, Will, Mark C. M. Cheung, Monica Bobra, et al.. (2020). aiapy: A Python Package for Analyzing Solar EUV Image Data from AIA. The Journal of Open Source Software. 5(55). 2801–2801. 51 indexed citations
7.
Morris, Brett M., Monica Bobra, Eric Agol, Y.-J. Lee, & Suzanne L. Hawley. (2020). The stellar variability noise floor for transiting exoplanet photometry with PLATO. Monthly Notices of the Royal Astronomical Society. 493(4). 5489–5498. 13 indexed citations
8.
Bobra, Monica, et al.. (2019). Machine Learning, Statistics, and Data Mining for Heliophysics. 3 indexed citations
9.
Fouhey, David F., Meng Jin, Andrés Muñoz‐Jaramillo, et al.. (2019). A Machine-learning Data Set Prepared from the NASA Solar Dynamics Observatory Mission. The Astrophysical Journal Supplement Series. 242(1). 7–7. 50 indexed citations
10.
Glogowski, Kolja, et al.. (2019). drms: A Python package for accessing HMI and AIA data. The Journal of Open Source Software. 4(40). 1614–1614. 18 indexed citations
11.
Bobra, Monica, et al.. (2019). lucaspauker/hmi-time-series-analysis: Time Series Analysis with HMI SHARP data. Figshare. 1 indexed citations
12.
Liu, Yang, et al.. (2018). Using Sun's Far-Side Images Inferred by the Time-Distance Helioseismic Imaging to Improve Synoptic Maps of Magnetic Field: Importance and Methodology. 147. 1 indexed citations
13.
Bobra, Monica. (2017). Predicting Solar Activity Using Machine-Learning Methods. AGU Fall Meeting Abstracts. 2017. 2 indexed citations
14.
Bobra, Monica, et al.. (2015). CME Prediction Using SDO, SoHO, and STEREO data with a Machine Learning Algorithm. AGU Fall Meeting Abstracts. 2015. 1 indexed citations
15.
Bobra, Monica, et al.. (2015). Solar Flare Forecasting Using Time Series of SDO/HMI Vector Magnetic Field Data and Machine Learning Methods. 1 indexed citations
16.
Bobra, Monica, et al.. (2015). Solar Flare Predictions Using Time Series of SDO/HMI Observations and Machine Learning Methods. 29. 2258117. 1 indexed citations
17.
Hayashi, Keiji, J. T. Hoeksema, Yang Liu, et al.. (2015). The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Magnetohydrodynamics Simulation Module for the Global Solar Corona. Solar Physics. 290(5). 1507–1529. 35 indexed citations
18.
Turmon, M., J. T. Hoeksema, & Monica Bobra. (2014). Tracked Active Region Patches for MDI and HMI. AAS. 224. 1 indexed citations
19.
Bobra, Monica, et al.. (2011). SHARPs - A New Space Weather Data Product from SDO/HMI. AGUFM. 2011.
20.
Reale, F., S. Parenti, Katharine K. Reeves, et al.. (2007). Magnetic activity and the solar corona: first results from the Hinode satellite .. Nova Science Publishers (Nova Science Publishers, Inc.). 78. 591. 1 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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