add trailing slashes to links in docs

Author: normanrz

docs/data/concepts.md

@@ -105,7 +105,7 @@ The term "magnifications" is used synonymously for mags throughout the UI.
 At the moment, WEBKNOSSOS guarantees correct rendering of data with non-uniform mag factors only if the z-component between two mags changes by a factor of 1 or 2.
 
 Most users do not create these metadata files manually.
-When using the [WEBKNOSSOS CLI](https://docs.webknossos.org/cli), a metadata file is automatically generated. Alternatively, you can create and edit WEBKNOSSOS datasets using the [WEBKNOSSOS Python library](https://github.com/scalableminds/webknossos-libs/).
+When using the [WEBKNOSSOS CLI](https://docs.webknossos.org/cli/), a metadata file is automatically generated. Alternatively, you can create and edit WEBKNOSSOS datasets using the [WEBKNOSSOS Python library](https://github.com/scalableminds/webknossos-libs/).
 During the data import process, WEBKNOSSOS will ask for the necessary properties.
 
 [See below for the full specification](#dataset-metadata-specification).

docs/data/export_python.md

@@ -1,5 +1,5 @@
 ## Data Export through Python
 
-The [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/index.html) makes it very easy to download/upload any of your organization's datasets, and annotations. For detailed info, please refer to the `webknossos-libs` [documentation website for guides and tutorials](https://docs.webknossos.org/webknossos-py/index.html).
+The [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/) makes it very easy to download/upload any of your organization's datasets, and annotations. For detailed info, please refer to the `webknossos-libs` [documentation website for guides and tutorials](https://docs.webknossos.org/webknossos-py/).
 
 There are also quick start instructions for Python download available directly from the [WEBKNOSSOS UI](./export_ui.md).

docs/data/image_stacks.md

@@ -54,8 +54,8 @@ WEBKNOSSOS understands most modern bio-imaging file formats and uses the [BioFor
 
 You can manually convert image stacks through:
 
-- [WEBKNOSSOS CLI](https://docs.webknossos.org/cli)
-- [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py)
+- [WEBKNOSSOS CLI](https://docs.webknossos.org/cli/)
+- [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/)
 
 ### Conversion with CLI
 You can easily convert image stacks manually with the WEBKNOSSOS CLI.
@@ -75,11 +75,11 @@ This snippet converts an image stack that is located in directory called `data/s
 It will create a so called `color` layer containing your raw greyscale/color image.
 The supplied `--voxel-size` is specified in nanometers.
 
-Read the full documentation at [WEBKNOSSOS CLI](https://docs.webknossos.org/cli).
+Read the full documentation at [WEBKNOSSOS CLI](https://docs.webknossos.org/cli/).
 
 ### Conversion with Python
 
-You can use the free [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py) to convert image stacks to Zarr v3 or integrate the conversion as part of an existing workflow. 
+You can use the free [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/) to convert image stacks to Zarr v3 or integrate the conversion as part of an existing workflow. 
 
 ```python
 import webknossos as wk

docs/data/index.md

@@ -12,6 +12,6 @@ WEBKNOSSOS natively supports loading and streaming data in the following formats
 
 The Zarr3 format is used for all internal voxel data representations - both for the raw (microscopy) image datasets and segmentations. Skeleton annotations are saved as NML files. 
 
-Any dataset uploaded to webknossos.org will automatically be converted to Zarr3 on upload - given its source file format is supported by WEBKNOSSOS. Alternatively, you can manually convert your datasets using the [WEBKNOSSOS CLI tool](https://docs.webknossos.org/cli) or use a custom script based on the [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/index.html).
+Any dataset uploaded to webknossos.org will automatically be converted to Zarr3 on upload - given its source file format is supported by WEBKNOSSOS. Alternatively, you can manually convert your datasets using the [WEBKNOSSOS CLI tool](https://docs.webknossos.org/cli/) or use a custom script based on the [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/).
 
 Read more about uploading and configuring datasets on the [datasets page](../datasets/settings.md).

docs/data/upload_python.md

@@ -1,2 +1,2 @@
 ### Uploading through Python
-For those wishing to automate dataset upload or to do it programmatically, check out the WEBKNOSSOS [Python library](https://docs.webknossos.org/webknossos-py). You can create, manage and upload datasets with the Python lib. 
+For those wishing to automate dataset upload or to do it programmatically, check out the WEBKNOSSOS [Python library](https://docs.webknossos.org/webknossos-py/). You can create, manage and upload datasets with the Python lib. 

docs/data/upload_ui.md

@@ -21,8 +21,8 @@ Depending on the size of the dataset, the conversion will take some time.
 You can check the progress at the [`Jobs`](../automation/jobs.md) page or the "Datasets" tab in the dashboard.
 WEBKNOSSOS will also send you an email notification.
 - Convert your data manually to WKW. For this, we provide the following software tools and libraries:
-    - The [WEBKNOSSOS CLI](https://docs.webknossos.org/cli) is a CLI tool that can convert many formats to WKW. 
-    - For other file formats, the [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/index.html) can be an option for custom scripting.
+    - The [WEBKNOSSOS CLI](https://docs.webknossos.org/cli/) is a CLI tool that can convert many formats to WKW. 
+    - For other file formats, the [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/) can be an option for custom scripting.
 
 In particular, the following file formats are supported for uploading (and conversion):
 

docs/data/zarr.md

@@ -110,7 +110,7 @@ For OME-Zarr (v0.5) datasets, the structure is slightly different (See [OME-Zarr
 
 ## Conversion to Zarr
 
-You can easily convert image stacks manually with the [WEBKNOSSOS CLI](https://docs.webknossos.org/cli).
+You can easily convert image stacks manually with the [WEBKNOSSOS CLI](https://docs.webknossos.org/cli/).
 The CLI tool expects a single file or all image files in a single folder with numbered file names.
 After installing, you can convert image stacks to Zarr datasets with the following command:
 
@@ -132,11 +132,11 @@ This example will create a sharded Zarr v3 dataset with a voxel size of (11.24,
 A maximum of 4 parallel jobs will be used to parallelize the conversion, compression and downsampling.
 Using the `--data-format zarr` argument will produce unsharded Zarr v2 datasets.
 
-Read the full documentation at [WEBKNOSSOS CLI](https://docs.webknossos.org/cli).
+Read the full documentation at [WEBKNOSSOS CLI](https://docs.webknossos.org/cli/).
 
 ### Conversion with Python
 
-You can use the free [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py) to convert image stacks to Zarr3 or integrate the conversion as part of an existing workflow. 
+You can use the free [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/) to convert image stacks to Zarr3 or integrate the conversion as part of an existing workflow. 
 
 ```python
 import webknossos as wk

docs/faq.md

@@ -27,13 +27,13 @@ We have years of experience with automated machine learning analysis and [offer
 We are also always interested in new collaborations. 
 Get in touch if you want to work together on a project resulting in new classifiers.
 
-WEBKNOSSOS does not allow you to run custom machine learning models on your data yet. As a work-around you can download your annotations from WEBKNOSSOS - either manually or scripted [through our Python library](https://docs.webknossos.org/webknossos-py) - and do your ML analysis offline and use WEBKNOSSOS to inspect the results. 
+WEBKNOSSOS does not allow you to run custom machine learning models on your data yet. As a work-around you can download your annotations from WEBKNOSSOS - either manually or scripted [through our Python library](https://docs.webknossos.org/webknossos-py/) - and do your ML analysis offline and use WEBKNOSSOS to inspect the results. 
 
 ## How can I use my dataset with WEBKNOSSOS?
 
 WEBKNOSSOS supports [WKW (optimized), OME-Zarr (NGFF), Neuroglancer Precomputed, N5, KNOSSOS cubes](./data/index.md), and image stacks (converted on upload). You can also connect to Neuroglancer Precomputed, N5, and Zarr datasets hosted in the cloud (Google Cloud Storage, AWS S3).
 
-Smaller datasets (up to multiple GB) can be uploaded directly through the web interface. For larger datasets, we recommend converting them to the standard WKW format using the [WEBKNOSSOS CLI](https://docs.webknossos.org/cli) CLI tool and uploading it via the [WEBKNOSSOS Python package](https://docs.webknossos.org/webknossos-py/examples/upload_image_data.html).
+Smaller datasets (up to multiple GB) can be uploaded directly through the web interface. For larger datasets, we recommend converting them to the standard WKW format using the [WEBKNOSSOS CLI](https://docs.webknossos.org/cli/) CLI tool and uploading it via the [WEBKNOSSOS Python package](https://docs.webknossos.org/webknossos-py/examples/upload_image_data.html).
 
 ## Can I host the WEBKNOSSOS data in my own compute cluster (on-premise installation)?
 
@@ -53,7 +53,7 @@ For example, the WEBKNOSSOS main component could be hosted on commercial cloud i
 ## Can I further analyze my annotations outside of WEBKNOSSOS with Python/MATLAB?
 Yes, you can. WEBKNOSSOS allows the download and export of skeleton annotations as NML files and segmentations/volume data as binary/wkw files.
 
-Use our free [Python library](https://docs.webknossos.org/webknossos-py) to work with the WEBKNOSSOS standard formats.
+Use our free [Python library](https://docs.webknossos.org/webknossos-py/) to work with the WEBKNOSSOS standard formats.
 
 ## Newly registered users don't show up
 

docs/index.md

@@ -26,7 +26,7 @@ WEBKNOSSOS is also a platform for [showcasing datasets](https://webknossos.org)
 - Supports Time Series datasets
 - [3D Mesh Visualization](./meshes/index.md)
 - [Integrated Synapse and Connectome Viewer](./connectome_viewer.md)
-- [Python library with documentation for API access and integration in custom analysis workflows](https://docs.webknossos.org/webknossos-py/index.html)
+- [Python library with documentation for API access and integration in custom analysis workflows](https://docs.webknossos.org/webknossos-py/)
 - [Frontend API for user scripting](https://webknossos.org/assets/docs/frontend-api/index.html)
 - Developed as an open-source project with [automated testing](https://circleci.com/gh/scalableminds/webknossos)
 - [Deployable with Docker for production and development](https://hub.docker.com/r/scalableminds/webknossos/)

docs/skeleton_annotation/import_export.md

@@ -23,7 +23,7 @@ Importing a skeleton annotation can be achieved using one of two ways:
 
 ![Skeletons can be imported by drag and drop in the annotation view or from the dashboard](../images/tracing_ui_import.jpeg)
 
-If you are looking to import/export annotations through Python code, check out our [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py).
+If you are looking to import/export annotations through Python code, check out our [WEBKNOSSOS Python library](https://docs.webknossos.org/webknossos-py/).
 
 ## Merging skeleton annotations