A large-scale dataset of solar event reports from automated feature recognition modules | Journal of Space Weather and Space Climate

Statistical Challenges in Solar Information Processing

Open Access

Issue		J. Space Weather Space Clim. Volume 6, 2016 Statistical Challenges in Solar Information Processing


Article Number		A22
Number of page(s)		18
DOI		https://doi.org/10.1051/swsc/2016015
Published online		04 May 2016

Aydin, B., D. Kempton, V. Akkineni, R. Angryk, and K. Pillai. Mining spatiotemporal co-occurrence patterns in solar datasets. Astron. Comput., 13, 136–144, 2015, DOI: 10.1016/j.ascom.2015.10.003. [CrossRef] [Google Scholar]
Banda, J.M., R. Angryk. Selection of image parameters as the first step towards creating a CBIR system for the solar dynamics observatory. In Digital Image Computing: Techniques and Applications (DICTA) 2010 International Conference on, IEEE, 528–534, 2010, DOI: 10.1109/DICTA.2010.94. [Google Scholar]
Banda, J.M., M.A. Schuh, R.A. Angryk, K.G. Pillai, and P. McInerney. Big data new frontiers: mining, search and management of massive repositories of solar image data and solar events. In New Trends in Databases and Information Systems, Springer International Publishing, Cham, 151–158, 2014, DOI: 10.1007/978-3-319-01863-8_17. [CrossRef] [Google Scholar]
Bernasconi, P.N., D.M. Rust, and D. Hakim. Advanced automated solar filament detection and characterization code: description, performance, and results. Sol. Phys., 228 (1–2), 97–117, 2005, DOI: 10.1007/s11207-005-2766-y. [Google Scholar]
Council. N. R. Severe Space Weather Events – understanding societal and economic impacts: a workshop report. The National Academies Press, 2008, DOI: 10.17226/12507. [Google Scholar]
Hurlburt, N., M. Cheung, C. Schrijver, L. Chang, S. Freeland, et al. Heliophysics event knowledgebase for the Solar Dynamics Observatory (SDO) and beyond. In The Solar Dynamics Observatory, Springer, 67–78, 2012, DOI: 10.1007/978-1-4614-3673-7_5. [Google Scholar]
Kempton, D., and R. Angryk. Tracking solar events through iterative refinement. Astron. Comput., 13, 124–135, 2015, DOI: 10.1016/j.ascom.2015.10.005.3.1. [CrossRef] [Google Scholar]
Kempton, D., K. Pillai, and R. Angryk. Iterative refinement of multiple targets tracking of solar events. In 2014 IEEE International Conference on Big Data (Big Data), 36–44, 2014, DOI: 10.1109/BigData.2014.7004402. [Google Scholar]
Lemen, J., A. Title, D. Akin, P. Boerner, C. Chou, et al. The Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). Sol. Phys., 275, 17–40, 2012, DOI: 10.1007/s11207-011-9776-8. [NASA ADS] [CrossRef] [Google Scholar]
Martens, P., G. Attrill, A. Davey, A. Engell, S. Farid, et al. Computer Vision for the Solar Dynamics Observatory (SDO). In: P. Chamberlin, W.D. Pesnell, and B. Thompson, Editors. The Solar Dynamics Observatory, Springer, 79–113, ISBN: 978-1-4614-3672-0, 2012, DOI: 10.1007/978-1-4614-3673-7_6. [Google Scholar]
Pesnell, W., B. Thompson, and P. Chamberlin. The Solar Dynamics Observatory (SDO). Sol. Phys., 275, 3–15, 2012, DOI: 10.1007/s11207-011-9841-3. [NASA ADS] [CrossRef] [Google Scholar]
Pevtsov, A.A., K. Balasubramaniam, and J.W. Rogers. Chirality of chromospheric filaments. Astrophys. J., 595 (1), 500, 2003, DOI: 10.1086/377339. [Google Scholar]
Pillai, K.G., R.A. Angryk, J.M. Banda, T. Wylie, and M.A. Schuh. Spatiotemporal co-occurrence rules. In New Trends in Databases and Information Systems, Springer International Publishing, Cham, 27–35, 2014, DOI: 10.1007/978-3-319-01863-8_3. [CrossRef] [Google Scholar]
Scherrer, P., J. Schou, R. Bush, A. Kosovichev, R. Bogart, et al. The Helioseismic and Magnetic Imager (HMI) Investigation for the Solar Dynamics Observatory (SDO). Sol. Phys., 275, 207–227, 2012, DOI: 10.1007/s11207-011-9834-2. [Google Scholar]
Schuh, M., R. Angryk, K.G. Pillai, J. Banda, and P. Martens. A large scale solar image dataset with labeled event regions. In Proc. International Conference on Image Processing (ICIP), 4349–4353, 2013, DOI: 10.1109/ICIP.2013.6738896. [Google Scholar]
Schuh, M., J. Banda, P. Bernasconi, R. Angryk, and P. Martens. A comparative evaluation of automated solar filament detection. Sol. Phys., 289 (7), 2503–2524, 2014, DOI: 10.1007/s11207-014-0495-9. [CrossRef] [Google Scholar]
Schuh, M., J. Banda, T. Wylie, P. McInerney, K.G. Pillai, and R. Angryk. On visualization techniques for solar data mining. Astron. Comput., 10, 32–42, 2015, DOI: 10.1016/j.ascom.2014.12.003. [CrossRef] [Google Scholar]
Schuh, M.A., and R.A. Angryk. Massive labeled solar image data benchmarks for automated feature recognition. In 2014 IEEE International Conference on Big Data (Big Data), IEEE, 53–60, 2014, DOI: 10.1109/BigData.2014.7004404. [Google Scholar]
Schuh, M.A., R.A. Angryk, and P.C. Martens. Supporting data: a large-scale dataset of solar event reports from automated feature recognition modules, 2016, DOI: 10.5281/zenodo.48187. [Google Scholar]
Thompson, W. Coordinate systems for solar image data. A&A, 449 (2), 791–803, 2006, DOI: 10.1051/0004-6361:20054262.3. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Verbeeck, C., V. Delouille, B. Mampaey, and R. De Visscher. The SPoCA-suite Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images. A&A, 561, A29, 2014, DOI: 10.1051/0004-6361/201321243. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Withbroe, G.L. Living With a Star. In AAS/Solar Physics Division Meeting #31, vol. 32 of Bulletin of the American Astronomical Society, 839, 2000, DOI: 10.1029/GM125p0045. [Google Scholar]
Zharkov, S., V.V. Zharkova, and S.S. Ipson. Statistical properties of sunspots in 1996–2004: I. Detection, North-South asymmetry and area distribution. Sol. Phys., 228 (1), 377–397, 2005, DOI: 10.1007/s11207-005-5005-7. [NASA ADS] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.