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2 changes: 2 additions & 0 deletions README.rst
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Expand Up @@ -11,6 +11,8 @@ xarray: N-D labeled arrays and datasets
:target: https://pypi.python.org/pypi/xarray/
.. image:: https://badges.gitter.im/Join%20Chat.svg
:target: https://gitter.im/pydata/xarray
.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.59499.svg
:target: http://dx.doi.org/10.5281/zenodo.59499

**xarray** (formerly **xray**) is an open source project and Python package that aims to bring the
labeled data power of pandas_ to the physical sciences, by providing
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38 changes: 38 additions & 0 deletions doc/faq.rst
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Expand Up @@ -132,3 +132,41 @@ pandas) make it a faster and more flexible data analysis tool. That said, Iris
and CDAT have some great domain specific functionality, and we would love to
have support for converting their native objects to and from xarray (see
:issue:`37` and :issue:`133`)

How should I cite xarray?
-------------------------

If you are using xarray and would like to cite it in academic publication, we
would certainly appreciate it. We recommend two citations.

1. At a minimum, we recommend citing the xarray overview journal article,
to be submitted to the Journal of Open Research Software.

- Hoyer, S., Hamman, J. (In preparation). Xarray: N-D labeled arrays and
datasets in Python. Journal of Open Research Software.

Here’s an example of a BibTeX entry::

@article{hoyer2016xarray,
title = {xarray: {N-D} labeled arrays and datasets in {Python}},
author = {Hoyer, S. and J. Hamman},
journal = {in prep, J. Open Res. Software},
year = {2016}
}

2. You may also want to cite a specific version of the xarray package. We
provide a `Zenodo`__ citation and DOI for this purpose.

- Hoyer, S. et al.. (2016). xarray: v0.8.0. Zenodo. 10.5281/zenodo.59499

An example BibTeX entry::

@misc{xarray_v0_8_0,
author = {Stephan Hoyer and Clark Fitzgerald and Joe Hamman and others},
title = {xarray: v0.8.0},
month = aug,
year = 2016,
doi = {10.5281/zenodo.59499},
url = {http://dx.doi.org/10.5281/zenodo.59499}
}
__ https://zenodo.org