[Paper] Update ref

paper/paper.md

@@ -53,7 +53,7 @@ Traja targets Python because of its popularity with data scientists. The library
 The software is structured into three parts. These provide functionality to transform, analyse and visualize trajectories. Full details are available at <https://traja.readthedocs.io/>.  The `trajectory` module provides analytical and preprocessing functionalities. The `models` subpackage provides both traditional and neural network-based tools to determine trajectory properties. The `plotting` module allows visualizing trajectories in various ways. 
 
 Data, e.g., x and y coordinates, are stored as one-dimensional labelled arrays as instances of the pandas native `Series` class. Further, subclassing the pandas `DataFrame` allows providing an API that mirrors the pandas API which is familiar to most data scientists, thus reducing the barrier for entry while providing methods and properties specific to trajectories for rapid prototyping.
-Traja depends on Matplotlib [@Hunter:2007] and Seaborn [@waskom2020seaborn] for plotting and NumPy [@harris2020array] for computation.
+Traja depends on Matplotlib [@Hunter:2007] and Seaborn [@Waskom2021] for plotting and NumPy [@harris2020array] for computation.
 
 ### Trajectory Data Sources
 Trajectory data as time series can be extracted from a wide range of sources, including video processing tools as described above, GPS sensors for large animals or via home cage floor sensors, as described in the section below. The methods presented here are implemented for orthogonal coordinates *(x, y)* primarily to track animal centroids, however with some modification they could be extended to work in 3-dimensions and with body part locations as inputs. Traja is thus positioned at the end of the data scientist's chain of tools with the hope of supporting prototyping novel data processing approaches. A sample dataset of jaguar movement [@morato_jaguar_2018] is provided in the `traja.dataset` subpackage.