Astroinformatics is an interdisciplinary field of study involving the combination of astronomy , data science , informatics , and information / communications technologies. [1] [2]


Astroinformatics is primarily focused on developing tools, methods, and applications of computational science , data science , and statistics for research and education in data-oriented astronomy. [1] Early efforts included in this management data discovery , metadata standards development, data modeling , astronomical data dictionary development, data access , information retrieval , [3] data integration , and data mining [4] in the astronomical Virtual Observatory initiatives.[5] [6] [7] Further development of the field, along with astronomy community endorsement, was presented to the National Research Council (United States) in 2009 in the Astroinformatics “State of the Profession” Position Paper for the 2010 Astronomy and Astrophysics Decadal Survey . [8] This position paper provides the basis for a more detailed exposition of the field in the Informatics Journal. Astroinformatics: Data-Oriented Astronomy Research and Education . [1]

Astroinformatics as a separate field of research Was inspired by work in the fields of Bioinformatics and Geoinformatics , and through the eScience work [9] of Jim Gray (computer scientist) at Microsoft Research , Whose legacy Was Remembered and continued through the Jim Gray eScience Award . [10]

The primary focus of Astroinformatics is on the world wide distributed collection of digital astronomical databases, image archives, and research tools, the field recognizes the importance of legacy data sets using modern technologies to preserve and analyzing historical astronomical observations. Some astroinformatics practitioners help to digitize historical and recent astronomical observations and images in a large database for efficient retrieval through web- based interfaces. [2] [11] Another aim is to help develop new methods and software for astronomers, as well as to help the process and analysis of the rapid growth of data in the field of astronomy. [12]

Astroinformatics is described as the Fourth Paradigm of astronomical research. [13] There are many research areas involved with astroinformatics, such as data mining, machine learning, statistics, visualization, scientific data management, and semantic science. [6] Data mining and machine learning plays a significant role in astroinformatics as a scientific research discipline due to their focus on “knowledge discovery from data” ( KDD ) and “learning from data”. [14] [15]

The amount of data collected from astronomical sky surveys has grown from large to large in the past decade, and is predicted to grow in the next decade in large scale telescopes and microstructures with the Square Kilometer Array. [16]This plethora of new data both enables and challenges effective astronomical research. Therefore, new approaches are required. In part due to this, data-driven science is becoming a recognized academic discipline. Consequently, astronomy (and other scientific disciplines) are developing information-intensive and data-intensive sub-disciplines to an extent that these sub-disciplines are now becoming (or have already become) standalone research disciplines and full-fledged academic programs. While many institutes of education do not boast an astroinformatics program, such programs are likely to be developed in the near future.

Informatics has been recently defined as “the use of digital data, information, and related services for research and knowledge generation”. The usual, or commonly used definition is “the discipline of organizing, accessing, integrating, and mining data from multiple sources for discovery and decision support.” Therefore, the discipline of astroinformatics includes many naturally-related specialties including data modeling, data organization, etc. It can also include data retrieval and technicalization, data retrieval, data retrieval and data mining methods. Classification schemes (eg, taxonomies , ontologies , folksonomies, and / or collaborative tagging [17] ) more Astrostatistics will also be heavily involved. Citizen science projects (such as Galaxy Zoo ) also contribute to a highly valued novelty discovery, meta-tagging feature, and object characterization within large astronomy data sets. All of these specialties enable scientific discovery across massive data collections, collaborative research, and data re-use, in both research and learning environments.

In 2012, two position papers [18] [19] were presented to the Council of the American Astronomical Society in Astroinformatics and Astrostatistics for the Profession of Astronomy in the USA and elsewhere. [20]

Astroinformatics provides a natural context for the integration of education and research. [21] The experience of research in the classroom and the development of data literacy . [22] It also has many other uses, such as repurposing archival data for new projects, literature-data links, intelligent retrieval of information, and many others. [23]


year Square Link
2017 Cape Town , South Africa [1]
2016 Sorrento , Italy [2]
2015 Dubrovnik , Dalmatia [3]
2014 University of Chile [4]
2013 Australia Telescope National Facility , CSIRO [5]
2012 Microsoft Research [6]
2011 Sorrento , Italy [7]
2010 Caltech [8]

See also

  • Astronomy and Computing
  • Astrophysics Data System
  • Astrophysics Source Code Library
  • Astrostatistics
  • Galaxy Zoo
  • International Astrostatistics Association
  • International Virtual Observatory Alliance (IVOA)
  • MilkyWay @ home
  • Virtual Observatory
  • WorldWide Telescope
  • Zooniverse

External links

  • Astronomical Data Analysis Software and Systems (ADASS)
  • Astrostatistics and Astroinformatics Portal
  • Cosmostatistics Initiative (COIN)
  • Astroinformatics and Astrostatistics Commission of the International Astronomical Union


  1. ^ Jump up to:c Borne, Kirk. “Astroinformatics: Data-Oriented Astronomy Research and Education” . Journal of Earth Science Informatics, June 2010, Volume 3, Issue 1, pp 5-17 . Springer Link, Netherlands . Retrieved 11 January 2016.
  2. ^ Jump up to:b Astroinformatics and digitization of astronomical heritage , Nikolay Kirov. The fifth SEEDI International Conference Digitization of cultural and scientific heritage, May 19-20, 2010, Sarajevo. Retrieved 1 November 2012.
  3. Jump up^ Borne, Kirk. “Science User Scenarios for a Virtual Observatory Design Reference Mission: Science Requirements for Data Mining”. arXiv : astro-ph / 0008307  .
  4. Jump up^ Borne, Kirk. “Scientific Data Mining in Astronomy” . CRC Press, pp. 91-114 . Taylor & Francis Group . Retrieved 11 January 2016 .
  5. Jump up^ Borne, Kirk. “Distributed Data Mining in the National Virtual Observatory”. SPIE Digital Library . SPIE . Retrieved 11 January 2016 .
  6. ^ Jump up to:b terminal, Kirk. “Virtual Observations, Data Mining, and Astroinformatics” . Planets, Stars and Stellar Systems, Volume 2: Astronomical Techniques, Software, and Data, pp . 403-443 . Springer Link, Netherlands . Retrieved 11 January 2015 .
  7. Jump up^ Laurino, O .; et al. Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation . Monthly Notices of the Royal Astronomical Society, vol.418, pp. 2165-2195 . Oxford Journals . Retrieved 12 January 2016 .
  8. Jump up^ Borne, Kirk. “Astroinformatics: A 21st Century Approach to Astronomy” . Astrophysics Data System . SAO / NASA. Bibcode : 2009astro2010P … 6B . arXiv : 0909.3892  . Retrieved 11 January 2016 .
  9. Jump up^ ” ‘ Online Science ‘ ” . Talks by Jim Gray . Microsoft Research . Retrieved 11 January 2015 .
  10. Jump up^ “Jim Gray eScience Award” . Microsoft Research .
  11. Jump up^ Astroinformatics in Canada, Nicholas M. Ball, David Schade. Retrieved 1 November 2012.
  12. Jump up^ ” ‘ Astroinformatics’ helps Astronomers explore the sky” . . Heidelberg University . Retrieved 11 January 2015 .
  13. Jump up^ “The Fourth Paradigm: Data-Intensive Scientific Discovery” . Microsoft Research .
  14. Jump up^ Ball, NM; Brunner, RJ “Data Mining and Machine Learning in Astronomy”. International Journal of Modern Physics D . World Scientific Publishing . Retrieved 12 January 2016 .
  15. Jump up^ Borne, Kirk. “The LSST Data Mining Research Agenda” . Classification and Discovery in Large Astronomical Surveys, pp.347-351 . American Institute of Physics . Retrieved 12 January 2016 .
  16. Jump up^ Ivezić, Ž .; et al. “Parametrization and Classification of 20 Billion LSST Objects” . Classification and Discovery in Large Astronomical Surveys, pp.359-365 . American Institute of Physics . Retrieved 12 January 2016 .
  17. Jump up^ Borne, Kirk. “Collaborative Annotation for Scientific Data Discovery and Reuse” . Bulletin of the ASIS & T . American Society for Information Science and Technology . Retrieved 11 January 2016 .
  18. Jump up^ Borne, Kirk. “Astroinformatics in a Nutshell” . . The Astrostatistics and Astroinformatics Portal, Penn State University . Retrieved 11 January 2016 .
  19. Jump up^ Feigelson, Eric. “Astrostatistics in a Nutshell” . . The Astrostatistics and Astroinformatics Portal, Penn State University . Retrieved 11 January 2016 .
  20. Jump up^ Feigelson, E .; Ivezić, Ž .; Hilbe, J .; Borne, K. “New Organizations to Support Astroinformatics and Astrostatistics”. arXiv : 1301.3069  .
  21. Jump up^ Borne, Kirk. “The Revolution in Astronomy Education: Data Science for the Masses” . Astrophysics Data System . SAO / NASA. Bibcode :2009astro2010P … 7B . arXiv : 0909.3895  . Retrieved 11 January 2016 .
  22. Jump up^ “Using Data in the Classroom” . Science Education Resource Center at Carleton College . National Science Digital Library . Retrieved 11 January2016 .
  23. Jump up^ Borne, Kirk. Astroinformatics: Data-Oriented Astronomy (PDF) . George Mason University, USA . Retrieved January 21, 2015 .