docs: fix reference

publication/paper.bib

@@ -6,6 +6,26 @@ @misc{xmca_yefee
   	journal = {GitHub repository},
   	url = {https://github.com/Yefee/xMCA}
 }
+
+@article{dawson_eofs_2016,
+	title = {eofs: A Library for {EOF} Analysis of Meteorological, Oceanographic, and Climate Data},
+	volume = {4},
+	issn = {2049-9647},
+	url = {https://openresearchsoftware.metajnl.com/articles/10.5334/jors.122},
+	doi = {10.5334/jors.122},
+	shorttitle = {eofs},
+	abstract = {The eofs library provides a high-level Python interface for computing empirical orthogonal functions ({EOFs}) and related quantities, with a focus on correctness and ease of use. The library is implemented in a modular hierarchical fashion, allowing computations using plain arrays, or the inclusion of metadata. The software provides a convenient package for users wanting to perform {EOF} analysis in Python, and integrates with popular libraries from atmospheric and climate science. The software is available on Github.},
+	pages = {e14},
+	number = {1},
+	author = {Dawson, Andrew},
+	urldate = {2023-10-30},
+	date = {2016-04-26},
+	langid = {american},
+	note = {Number: 1
+Publisher: Ubiquity Press},
+	file = {Full Text PDF:/home/nrieger/Zotero/storage/RDYCWBQ4/Dawson - 2016 - eofs A Library for EOF Analysis of Meteorological.pdf:application/pdf},
+}
+
 @Manual{dask2016,
   title = {Dask: Library for dynamic task scheduling},
   author = {{Dask Development Team}},

publication/paper.md

@@ -40,7 +40,7 @@ reduction techniques like Empirical Orthogonal Functions (EOF) analysis-often
 called Principal Component Analysis (PCA) in other domains. 
 Integrating seamlessly with `xarray` objects [@hoyer2017xarray], `xeofs` 
 makes it easier to analyze large, labeled, multi-dimensional datasets.
-By harnessing `Dask`'s capabilities [@dask_2016], it scales computations efficiently
+By harnessing `Dask`'s capabilities [@dask2016], it scales computations efficiently
 across multiple cores or clusters, apt for extensive climate data applications.
 
 
@@ -62,7 +62,7 @@ of missing values and dimension coordinates, which can be cumbersome and prone t
 increasing the workload, especially for smaller-scale projects. Furthermore, the size 
 of climate datasets often necessitates out-of-memory processing.
 
-While `xMCA` [@xmca_yefee] and `eofs` [@dawson2016eofs] have addressed some of these 
+While `xMCA` [@xmca_yefee] and `eofs` [@dawson_eofs_2016] have addressed some of these 
 issues by offering analysis tools compatible with `xarray` and `Dask`, `xeofs` 
 expands on these by including a broader range of techniques such as 
 rotated [@kaiser_varimax_1958], complex/Hilbert [@rasmusson_biennial_1981], and