Added citation information to the paper and bib file for xarray

docs/paper.bib

@@ -88,6 +88,16 @@ @article{Harvey:1997
 	keywords     = {Comparing forecasts, Correlated forecast errors, Evaluation of forecasts, Non-normality},
 	abstract     = {Given two sources of forecasts of the same quantity, it is possible to compare prediction records. In particular, it can be useful to test the hypothesis of equal accuracy in forecast performance. We analyse the behaviour of two possible tests, and of modifications of these tests designed to circumvent shortcomings in the original formulations. As a result of this analysis, a recommendation for one particular testing approach is made for practical applications.}
 }
+@article{Hoyer:2017,
+  title         = {xarray: N-D labeled Arrays and Datasets in Python},
+  author        = {Hoyer, S. and Hamman, J.},
+  year          = 2017,
+  journal       = {Journal of Open Research Software},
+  volume        = 5,
+  number        = 1,
+  doi           = {https://doi.org/10.5334/jors.148},
+  keywords      = {Python, pandas, netCDF, multidimensional data, data handling, data analysis}
+}
 @Article{Diebold:1995,
 	title		 = {Comparing predictive accuracy},
 	author		 = {Diebold, Francis X and Mariano, Robert S},

docs/paper.md

@@ -26,7 +26,7 @@ bibliography: paper.bib
 
 `scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It primarily supports the geoscience and earth system science communities. It also has wide potential application in machine learning, and domains other than meteorology, geoscience and weather. 
 
-`scores` is focused on supporting xarray datatypes for earth system data. It also aims to be compatible with pandas and geopandas, and to work with NetCDF4, hdf5, Zarr and GRIB data sources among others. Scores is designed to utilise Dask for scaling and performance.
+`scores` is focused on supporting xarray [@Hoyer:2017] datatypes for earth system data. It also aims to be compatible with pandas and geopandas, and to work with NetCDF4, hdf5, Zarr and GRIB data sources among others. Scores is designed to utilise Dask for scaling and performance.
 
 All of the scores and metrics in this package have undergone a thorough statistical and scientific review. Every score has a companion Jupyter Notebook tutorial demonstrating its use in practice.