build with linkchecker

.github/workflows/deploy-docs.yml

@@ -24,7 +24,7 @@ jobs:
         shell: bash -l {0}
         run: |
           set -e
-          jupyter-book build jupyterbook
+          jupyter-book build jupyterbook --builder linkcheck
 
       - name: GitHub Pages action
         if: github.ref == 'refs/heads/main'

jupyterbook/content/code_gallery/data_access_notebooks/2016-10-12-fetching_data.ipynb

@@ -51,7 +51,7 @@
     "Updated: 2022-05-26\n",
     "\n",
     "In this post we will use `erddapy` to find and download data from the\n",
-    "[Center for Operational Oceanographic Products and Services (CO-OPS)](https://opendap.co-ops.nos.noaa.gov/erddap/) ERDDAP server.\n",
+    "[Center for Operational Oceanographic Products and Services (CO-OPS)](https://opendap.co-ops.nos.noaa.gov/erddap/index.html) ERDDAP server.\n",
     "\n",
     "Here we will fetch data during the [hurricane Matthew](https://en.wikipedia.org/wiki/Hurricane_Matthew) passage over the southeast states from 2016-10-05 to 2016-10-12. The first step is to instantiate the server object and then add the constraints for our query."
    ]
@@ -78,7 +78,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The geographical bounding box includes all the states in the [SECOORA](http://secoora.org/) region: Florida, Georgia, South and North Carolina."
+    "The geographical bounding box includes all the states in the [SECOORA](https://secoora.org/) region: Florida, Georgia, South and North Carolina."
    ]
   },
   {
@@ -1691,7 +1691,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

jupyterbook/content/code_gallery/data_access_notebooks/2016-11-15-glider_data_example.ipynb

@@ -50,7 +50,7 @@
     "\n",
     "Updated: 2022-05-25\n",
     "\n",
-    "In this notebook we demonstrate how to obtain and plot glider data using cf-xarray . We will explore data from the Rutgers University RU29 [Challenger](http://challenger.marine.rutgers.edu) glider that was launched from Ubatuba, Brazil on June 23, 2015 to travel across the Atlantic Ocean. After 282 days at sea, the Challenger was picked up off the coast of South Africa, on March 31, 2016. For more information on this ground breaking excusion see: [https://marine.rutgers.edu/main/announcements/the-challenger-glider-mission-south-atlantic-mission-complete](https://marine.rutgers.edu/main/announcements/the-challenger-glider-mission-south-atlantic-mission-complete)\n",
+    "In this notebook we demonstrate how to obtain and plot glider data using cf-xarray . We will explore data from the Rutgers University RU29 [Challenger](https://challenger.marine.rutgers.edu/) glider that was launched from Ubatuba, Brazil on June 23, 2015 to travel across the Atlantic Ocean. After 282 days at sea, the Challenger was picked up off the coast of South Africa, on March 31, 2016. For more information on this ground breaking excusion see: [https://marine.rutgers.edu/announcements/the-challenger-glider-mission-south-atlantic-mission-complete](https://marine.rutgers.edu/announcements/the-challenger-glider-mission-south-atlantic-mission-complete)\n",
     "\n",
     "Data collected from this glider mission are available on the IOOS Glider DAC THREDDS via OPeNDAP."
    ]
@@ -906,7 +906,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

jupyterbook/content/code_gallery/data_access_notebooks/2016-12-20-searching_glider_deployments.ipynb

@@ -50,9 +50,9 @@
     "\n",
     "Modified: 2023-09-04\n",
     "\n",
-    "IOOS provides an [`API`](https://en.wikipedia.org/wiki/Application_programming_interface) for getting information on all the glider deployments available in the [Glider DAC](https://gliders.ioos.us/index.html).\n",
+    "IOOS provides an [`API`](https://en.wikipedia.org/wiki/Application_programming_interface) for getting information on all the glider deployments available in the [Glider DAC](https://gliders.ioos.us/).\n",
     "\n",
-    "The raw JSON can be accessed at [https://data.ioos.us/gliders/providers/api/deployment](https://data.ioos.us/gliders/providers/api/deployment) and it is quite simple to parse it with Python.\n",
+    "The raw JSON can be accessed at [https://gliders.ioos.us/providers/api/deployment](https://gliders.ioos.us/providers/api/deployment) and it is quite simple to parse it with Python.\n",
     "\n",
     "First, lets check how many glider deployments exist in the Glider DAC."
    ]

jupyterbook/content/code_gallery/data_access_notebooks/2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb

@@ -52,7 +52,7 @@
     "\n",
     "Web Map Services are a great way to find data you may be looking for in a particular geographic area.\n",
     "\n",
-    "Suppose you are exploring the [IOOS Sensor Map](https://sensors.ioos.us/#map),\n",
+    "Suppose you are exploring the [IOOS Sensor Map](https://sensors.ioos.us/),\n",
     "you select Oxygen and click on the only returned value, the Moss Landing Marine Laboratories (MLML) station.\n",
     "\n",
     "![sensor_map.png](https://user-images.githubusercontent.com/950575/178321765-74ed0562-b942-4d97-af8b-85158bc6488c.png)\n",
@@ -460,7 +460,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

jupyterbook/content/code_gallery/data_access_notebooks/2017-06-12-NCEI_RA_archive_history.ipynb

@@ -50,7 +50,7 @@
     "\n",
     "Created: 2017-06-12\n",
     "\n",
-    "IOOS regional associations archive their non-federal observational data with NOAA's National Center for Environmental Information (NCEI). In this notebook we will use the [RESTful](https://github.com/Esri/geoportal-server/wiki/REST-API-Syntax) services of the [NCEI geoportal](https://www.ncei.noaa.gov/metadata/geoportal/#searchPanel) to collect metadata from the archive packages found in the NCEI archives. The metadata information are stored in [ISO 19115-2](https://wiki.earthdata.nasa.gov/display/NASAISO/ISO+19115-2) xml files which the NCEI geoportal uses for discovery of Archival Information Packages (AIPs). This example uses the ISO metadata records to display publication information as well as plot the time coverage of each AIP at NCEI which meets the search criteria.\n",
+    "IOOS regional associations archive their non-federal observational data with NOAA's National Center for Environmental Information (NCEI). In this notebook we will use the [RESTful](https://github.com/Esri/geoportal-server/wiki/REST-API-Syntax) services of the [NCEI geoportal](https://www.ncei.noaa.gov/metadata/geoportal) to collect metadata from the archive packages found in the NCEI archives. The metadata information are stored in [ISO 19115-2](https://wiki.earthdata.nasa.gov/display/NASAISO/ISO+19115-2) xml files which the NCEI geoportal uses for discovery of Archival Information Packages (AIPs). This example uses the ISO metadata records to display publication information as well as plot the time coverage of each AIP at NCEI which meets the search criteria.\n",
     "\n",
     "First we update the namespaces dictionary from owslib to include the appropriate namespace reference for gmi and gml.\n",
     "\n",
@@ -599,7 +599,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

jupyterbook/content/code_gallery/data_access_notebooks/2017-08-01-xtractoR.ipynb

@@ -221,7 +221,7 @@
     "For more information and example on the other routines see the full example from the documentation at [https://rmendels.github.io/Usingxtractomatic_3.4.0.nb.html](https://rmendels.github.io/Usingxtractomatic_3.4.0.nb.html)\n",
     "\n",
     "\n",
-    "PS: note that R and all the `xtractomatic` dependencies are already included in the [IOOS conda environment](http://ioos.github.io/notebooks_demos/other_resources/)."
+    "PS: note that R and all the `xtractomatic` dependencies are already included in the [IOOS conda environment](https://ioos.github.io/ioos_code_lab/content/ioos_installation_conda.html)."
    ]
   }
  ],
@@ -237,7 +237,7 @@
    "mimetype": "text/x-r-source",
    "name": "R",
    "pygments_lexer": "r",
-   "version": "4.0.5"
+   "version": "4.3.1"
   }
  },
  "nbformat": 4,

jupyterbook/content/code_gallery/data_access_notebooks/2017-09-09-hurricane_irma.ipynb

@@ -54,7 +54,7 @@
     "\n",
     "See https://tidesandcurrents.noaa.gov/quicklook/view.html?name=IRMA for the latest information on Irma.\n",
     "\n",
-    "1. http://www.nhc.noaa.gov/gis/\n",
+    "1. https://www.nhc.noaa.gov/gis/\n",
     "1. https://opendap.co-ops.nos.noaa.gov/ioos-dif-sos/\n",
     "\n",
     "First we have to download the National Hurricane Center (NHC) GIS 5 day predictions data for Irma.\n",
@@ -994,7 +994,7 @@
     "We can observe the sea level retreating around 10-Sep 9:00 and then a significant surge after 19:00.\n",
     "The lower winds at beginning of the surge is probably the eye of the hurricane.\n",
     "\n",
-    "For our interactive map we will use [`bokeh`](https://bokeh.pydata.org/en/latest) HTML plots instead of the usual raster [`matplotlib`](https://matplotlib.org) ones to enhance the user experience when exploring the graphs."
+    "For our interactive map we will use [`bokeh`](https://docs.bokeh.org/en/latest/) HTML plots instead of the usual raster [`matplotlib`](https://matplotlib.org) ones to enhance the user experience when exploring the graphs."
    ]
   },
   {

jupyterbook/content/code_gallery/data_access_notebooks/2018-02-20-obis.ipynb

@@ -48,7 +48,7 @@
     "\n",
     "Created: 2018-02-20\n",
     "\n",
-    "The [Ocean Biogeographic Information System (OBIS)](http://www.iobis.org) is an open-access data and information system for marine biodiversity for science, conservation and sustainable development.\n",
+    "The [Ocean Biogeographic Information System (OBIS)](https://www.obis.org/) is an open-access data and information system for marine biodiversity for science, conservation and sustainable development.\n",
     "\n",
     "In this example we will use R libraries [`obistools`](https://iobis.github.io/obistools) and [`robis`](https://iobis.github.io/robis) to search data regarding marine turtles occurrence in the South Atlantic Ocean.\n",
     "\n",
@@ -212,7 +212,7 @@
     "\n",
     "Now let us try to obtain the occurrence data for the South Atlantic. We will need a vector geometry for the ocean basin in the [well-known test (WKT)](https://en.wikipedia.org/wiki/Well-known_text) format to feed into the `robis` `occurrence` function.\n",
     "\n",
-    "In this example we converted a South Atlantic shapefile to WKT with geopandas, but one can also obtain geometries by simply drawing them on a map with [iobis maptool](http://iobis.org/maptool)."
+    "In this example we converted a South Atlantic shapefile to WKT with geopandas, but one can also obtain geometries by simply drawing them on a map with [iobis maptool](https://obis.org/maptool)."
    ]
   },
   {
@@ -1050,7 +1050,7 @@
    "source": [
     "One interesting feature of this map is *Dermochelys coriacea*'s migration between Brazilian and African shores.\n",
     "\n",
-    "More information on [*Dermochelys coriacea*](http://www.iucnredlist.org/details/6494/0) and the other Sea Turtles can be found in the species [IUCN red list](http://www.iucnredlist.org)."
+    "More information on [*Dermochelys coriacea*](https://www.iucnredlist.org/species/6494/43526147) and the other Sea Turtles can be found in the species [IUCN red list](https://www.iucnredlist.org/)."
    ]
   }
  ],
@@ -1070,7 +1070,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

jupyterbook/content/code_gallery/data_access_notebooks/2018-03-01-erddapy.ipynb

@@ -56,11 +56,11 @@
     "\n",
     "A typical ERDDAP RESTful URL looks like:\n",
     "\n",
-    "[https://data.ioos.us/gliders/erddap/tabledap/whoi_406-20160902T1700.mat?depth,latitude,longitude,salinity,temperature,time&time>=2016-07-10T00:00:00Z&time\\<=2017-02-10T00:00:00Z &latitude>=38.0&latitude\\<=41.0&longitude>=-72.0&longitude\\<=-69.0](https://data.ioos.us/gliders/erddap/tabledap/whoi_406-20160902T1700.mat?depth,latitude,longitude,salinity,temperature,time&time%3E=2016-07-10T00:00:00Z&time%3C=2017-02-10T00:00:00Z&latitude%3E=38.0&latitude%3C=41.0&longitude%3E=-72.0&longitude%3C=-69.0)\n",
+    "[https://gliders.ioos.us/gliders/erddap/tabledap/whoi_406-20160902T1700.mat?depth,latitude,longitude,salinity,temperature,time&time>=2016-07-10T00:00:00Z&time\\<=2017-02-10T00:00:00Z &latitude>=38.0&latitude\\<=41.0&longitude>=-72.0&longitude\\<=-69.0](https://gliders.ioos.us/erddap/tabledap/whoi_406-20160902T1700.mat?depth,latitude,longitude,salinity,temperature,time&time%3E=2016-07-10T00:00:00Z&time%3C=2017-02-10T00:00:00Z&latitude%3E=38.0&latitude%3C=41.0&longitude%3E=-72.0&longitude%3C=-69.0)\n",
     "\n",
     "Let's break it down to smaller parts:\n",
     "\n",
-    "- **server**: https://data.ioos.us/gliders/erddap/\n",
+    "- **server**: https://gliders.ioos.us/erddap/index.html\n",
     "- **protocol**: tabledap\n",
     "- **dataset_id**: whoi_406-20160902T1700\n",
     "- **response**: .mat\n",
@@ -872,7 +872,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One can build the proper variables programmatically, feed them in erddapy, and then build a service like [this notebook](https://mybinder.org/v2/gh/ioos/BioData-Training-Workshop/master?filepath=notebooks/ERDDAP_timeseries_explorer-IOOS.ipynb). However, erddapy is also designed for interactive work. One can explore interactively the ERDDAP server from Python.\n",
+    "One can build the proper variables programmatically, feed them in erddapy, and then build a service like [this notebook](https://github.com/ioos/BioData-Training-Workshop/blob/master/notebooks/ERDDAP_timeseries_explorer-IOOS.ipynb). However, erddapy is also designed for interactive work. One can explore interactively the ERDDAP server from Python.\n",
     "\n",
     "PS: Note that in this example below we did not feed any variables other than the server URL"
    ]
@@ -2003,7 +2003,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

jupyterbook/content/code_gallery/data_access_notebooks/2019-03-08-grids-temperature.ipynb

@@ -55,7 +55,7 @@
     "In this post we will demostrante how to leverage Python's libraries to plot\n",
     "horizontal temperature slices from a variety of ocean models with minimum specific code.\n",
     "\n",
-    "Be sure to check the [first post on the series](http://ioos.github.io/notebooks_demos/notebooks/2018-12-04-grids/)."
+    "Be sure to check the [first post on the series](https://ioos.github.io/ioos_code_lab/content/code_gallery/data_analysis_and_visualization_notebooks/2018-12-04-grids.html)."
    ]
   },
   {
@@ -497,7 +497,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

jupyterbook/content/code_gallery/data_access_notebooks/2019-05-27-hurricane_gis_part02.ipynb

@@ -56,7 +56,7 @@
     "[Sensor Observation Service (SOS)](https://opendap.co-ops.nos.noaa.gov/ioos-dif-sos/)\n",
     "along the hurricane track.\n",
     "\n",
-    "For the instructions on how to obtain the GIS data for Hurricane Michael please the the [first notebook in the series](https://ioos.github.io/notebooks_demos/notebooks/2019-02-26-hurricane_gis_part01/). The function below loads and extract the hurricane radii and points."
+    "For the instructions on how to obtain the GIS data for Hurricane Michael please the the [first notebook in the series](https://nbviewer.org/github/ioos/ioos_code_lab/blob/main/jupyterbook/content/code_gallery/data_access_notebooks/2019-02-26-hurricane_gis_part01.archived.ipynb). The function below loads and extract the hurricane radii and points."
    ]
   },
   {
@@ -159,7 +159,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The cell below is the main difference from what we did in [part 1](https://ioos.github.io/notebooks_demos/notebooks/2019-02-26-hurricane_gis_part01/), here we will use the bonding box and event dates with a [PyOOS collector](https://github.com/ioos/pyoos) to fetch all the data available in that scope."
+    "The cell below is the main difference from what we did in [part 1](https://nbviewer.org/github/ioos/ioos_code_lab/blob/main/jupyterbook/content/code_gallery/data_access_notebooks/2019-02-26-hurricane_gis_part01.archived.ipynb), here we will use the bonding box and event dates with a [PyOOS collector](https://github.com/ioos/pyoos) to fetch all the data available in that scope."
    ]
   },
   {
@@ -549,7 +549,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now that we have the data all that is left to do is to create interactive [Bokeh plots](https://bokeh.pydata.org/en/latest/),"
+    "Now that we have the data all that is left to do is to create interactive [Bokeh plots](https://docs.bokeh.org/en/latest/),"
    ]
   },
   {
@@ -801,7 +801,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

jupyterbook/content/code_gallery/data_access_notebooks/2021-10-19-multiple-erddap-search.ipynb

@@ -53,7 +53,7 @@
     "The Python interface allow the user to mix powerful variable handling and visualization with the query results.\n",
     "\n",
     "Let us explore an example\n",
-    "[based on this](https://nbviewer.jupyter.org/gist/rsignell-usgs/f2be18f9db07a3c2970d88576cd62b57)\n",
+    "[based on this](https://nbviewer.org/gist/rsignell-usgs/f2be18f9db07a3c2970d88576cd62b57)\n",
     "Rich Signell's gist where we search for salinity time-series data in a specific region and time span."
    ]
   },
@@ -1608,7 +1608,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.11.5"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

jupyterbook/content/code_gallery/data_access_notebooks/2022-11-23_pyobis_example.ipynb

@@ -1855,7 +1855,7 @@
    "source": [
     "Since the distribution of `n_occur` covers a wide range, we will present the concentration and histogram figures as log normal distributions.\n",
     "\n",
-    "So, let's combine the latitude and longitude histograms with the map to recreate this figure https://bbest.github.io/obis-lat-time-fig/#map,_hist,_time-series_combined."
+    "So, let's combine the latitude and longitude histograms with the map to recreate this figure [https://bbest.github.io/obis-lat-time-fig](https://bbest.github.io/obis-lat-time-fig)."
    ]
   },
   {
@@ -1980,7 +1980,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,