added JOSS files

.github/workflows/draft-pdf.yml

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+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper.pdf

paper.bib

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+@software{vispy,
+  author       = {Luke Campagnola and
+                  Eric Larson and
+                  Almar Klein and
+                  David Hoese and
+                  Siddharth and
+                  Cyrille Rossant and
+                  Adam Griffiths and
+                  Nicolas P. Rougier and
+                  asnt and
+                  Kai Mühlbauer and
+                  Alexander Taylor and
+                  MSS and
+                  Talley Lambert and
+                  sylm21 and
+                  Alex J. Champandard and
+                  Max Hunter and
+                  Thomas Robitaille and
+                  Mustafa Furkan Kaptan and
+                  Elliott Sales de Andrade and
+                  Karl Czajkowski and
+                  Lorenzo Gaifas and
+                  Alessandro Bacchini and
+                  Guillaume Favelier and
+                  Etienne Combrisson and
+                  ThenTech and
+                  fschill and
+                  Mark Harfouche and
+                  Michael Aye and
+                  Casper van Elteren and
+                  Cedric GESTES},
+  title        = {vispy/vispy: Version 0.11.0},
+  month        = jul,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {v0.11.0},
+  doi          = {10.5281/zenodo.6795163},
+  url          = {https://doi.org/10.5281/zenodo.6795163}
+}
+
+
+
+@article{bento,
+  abstract = {The study of naturalistic social behavior requires quantification of animals' interactions. This is generally done through manual annotation---a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARS's annotations to human annotations and find that MARS's pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable.},
+  article_type = {journal},
+  author = {Segalin, Cristina and Williams, Jalani and Karigo, Tomomi and Hui, May and Zelikowsky, Moriel and Sun, Jennifer J and Perona, Pietro and Anderson, David J and Kennedy, Ann},
+  citation = {eLife 2021;10:e63720},
+  date-modified = {2022-10-05 13:18:49 -0400},
+  doi = {10.7554/eLife.63720},
+  editor = {Berman, Gordon J and Wassum, Kate M and Gal, Asaf},
+  issn = {2050-084X},
+  journal = {eLife},
+  keywords = {social behavior, pose estimation, machine learning, computer vision, microendoscopic imaging, software},
+  month = {nov},
+  pages = {e63720},
+  pub_date = {2021-11-30},
+  publisher = {eLife Sciences Publications, Ltd},
+  title = {The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice},
+  url = {https://doi.org/10.7554/eLife.63720},
+  volume = 10,
+  year = 2021,
+  bdsk-url-1 = {https://doi.org/10.7554/eLife.63720}}
+
+
+@misc{rastermap,
+  author = {C. Stringer and M. Pachitariu},
+  title = {rastermap},
+  year = {2020},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  url = {https://github.com/MouseLand/rastermap}
+}
+
+@misc{vidio,
+  author = {J. Bohnslav},
+  title = {VidIO: simple, performant video reading and writing in python},
+  year = {2020},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  url = {https://github.com/jbohnslav/vidio}
+}
+

paper.md

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+---
+title: 'Systems Neuro Browser (SNUB)'
+tags:
+  - Python
+  - neuroscience
+  - animal behavior
+  - graphical user interface
+authors:
+  - name: Caleb Weinreb
+    orcid: 0000-0001-6100-6084
+    affiliation: 1
+  - name: Sandeep Robert Datta
+    orcid: 0000-0002-8068-3862
+    affiliation: 1
+affiliations:
+ - name: Department of Neurobiology, Harvard University, Boston, MA
+   index: 1
+date: 5 October 2022
+bibliography: paper.bib
+
+---
+
+# Summary
+
+SNUB is a tool for exploring time-series data, such as neural 
+recordings, behavioral videos, temperature, movement or other sensor signals, 
+and any higher-level annotations derived from such data. The interface is 
+divided into windows that each show a different data stream and all synchonize 
+to a common timeline. The linked data views allow users to quickly inspect 
+the relationships between different phenomena, such as the behaviors that 
+occur during a particular pattern of neural activity (\autoref{fig:screenshot}). 
+
+![Screenshot from SNUB.\label{fig:screenshot}](docs/media/screenshot.png)
+
+We provide dedicated widgets and loading functions for exploring
+raw video, 3D animal pose, behavior annotations, electrophysiology recordings,
+and calcium imaging data - either as a raster or as a super-position of 
+labeled regions of interest (ROIs). More broadly, SNUB can dislay any data
+that takes the form of a heatmap, scatter plot, video, or collection of 
+named temporally-varying signals. 
+
+In addition to the front-end GUI, we include a library of functions for
+ingesting raw data and saving it to a format that is readable by the SNUB
+viewer. The following code, for example, creates a project with paired 
+electrophysiology and video data.
+
+```
+import snub.io
+
+project_directory = 'path/to/new/project'
+
+snub.io.create_project(project_directory, duration=1800)
+snub.io.add_video(project_directory, 'path/to/my_video.avi', name='IR_camera')
+snub.io.add_splikeplot(project_directory, 'my_ephys_data', spike_times, spike_labels)
+```
+
+SNUB is a flexible general-purpose tool that complements more specialized 
+packages such as rastermap [@rastermap] or Bento [@bento]. The rastermap user 
+interface, for example, is hard-coded for the display of neural activity 
+rasters, ROIs and 2D embeddings of neural activity. Bento is hard-coded for 
+the display of neural activity rasters, behavioral videos and behavioral 
+annotations. SNUB can reproduce either of these configurations and is 
+especially useful when one wishes to include additional types of data
+or more directly customize the way that data is rendered.
+
+The graphics in SNUB are powered by vispy [@vispy]. SNUB includes wrappers
+for several dimensionality reduction methods, including rastermap [@rastermap]
+for ordering raster plots and UMAP [@umap] for 2D scatter plots. Fast video
+loading is enabled by vidio [@vidio].
+
+# Acknowledgements
+
+We are grateful to Mohammed Osman for initial contributions to the 3D keypoint
+visualization tool. CW is a Fellow of The Jane Coffin Childs Memorial Fund for 
+Medical Research. SRD ... 
+
+# References