Add reference to example data on zenodo and improves the manual a bit

Example data was needed for submission

README.md

@@ -11,7 +11,7 @@ Features
 
 * Manual tracking/correction with live feedback.
 * Automated cell center detection using a convolutional neural network based on U-net.
-* Automated and accurate predictions link and division probabilities using convolutional neural networks. 
+* Automated and accurate predictions of link and division probabilities using convolutional neural networks. 
 * Automatically finding the most likely tracking solution using a min-cost flow solver ([Haubold, 2016])
 * High confidence, context-aware, error probabilities for every link in a track to indicate the tracking quality. 
 * Supports [TIFF files, TIFF series, Leica LIF files, Imaris IMS files, Zeiss CZI files and NIKON nd2 files](https://jvzonlab.github.io/OrganoidTracker/IMAGE_FORMATS.html).
@@ -57,6 +57,11 @@ Pre-trained neural networks
 * [Network trained for OrganoidTracker 2 (C elegans)](https://doi.org/10.5281/zenodo.13912686) - trained using C elegans confocal data from the Cell Tracking Challenge
 * [Network trained for OrganoidTracker 2 (organoid)](https://zenodo.org/records/13946119) - trained using our manually annotated intestinal organoid data
 
+Example data
+------------
+Example intestinal organoid data is available with associated automated tracking results and can be used to try out the software. We have included the settings and executables used to generate the tracking results.
+* [Example data organoid](https://zenodo.org/records/13982844) - 50 frames of intestinal organoid data
+
 API
 ---
 You can also use OrganoidTracker as a library to write your own scripts. All public functions in OrganoidTracker have docstrings to explain what they are doing. As a starting point for using the API, see the [API] page.

manuals/AUTOMATIC_TRACKING.md

@@ -4,9 +4,9 @@
 The intended workflow is as follows:
 
 1. Obtain nucleus positions (for now this is done using an external program)
-2. Obtain division scores.
-3. Obtain linking scores.
-4. Link the cells of different time points together
+2. Obtain division probabilities.
+3. Obtain linking probabilities.
+4. Link the cell detections at different time points together
 5. Calculate error probabilities
 6. Manually correct warnings
 7. OR Filter and do automated analysis
@@ -58,7 +58,7 @@ Now that we have the tracks we can compute error rates through marginalization.
 
 In the `organoid_tracker.ini` file you might need to change the so-called 'temperature'. This accounts for the amount of shared information between the individual neural network predictions. If your data is similar to the data the neural networks are trained on you can use the temperature associated with them (1.5 for our own models). If you have trained your own models you have to calibrate the marginalization procedure to get a temperature ([See here for how to calibrate](./CALIBRATE_MARGINALIZATION.md). It is good to note that this temperature is generally able to absorb any miscalibration of the neural network outputs as well. 
 
-This step also will you dataset where all low-confidence links are filtered out. The threshold can be set in the `organoid_tracker.ini` file.
+This step also gives you dataset where all low-confidence links are filtered out. The threshold can be set in the `organoid_tracker.ini` file.
 
 Step 6: Manually correct warnings
 ---------------------------------
@@ -86,7 +86,7 @@ Often correcting mistakes below a certain threshold also fixes a few mistakes th
 ## What to do if my results aren't good?
 That's a difficult problem! You have a few options:
 
-* Improve image quality. The images should be good enough that it is straightforward to track the nuclei by hand.
+* Improve image quality. The images should be good enough that it is straightforward to track the nuclei by hand in the large majority of cases.
 * Change some settings. The generated `organoid_tracker.ini` files contain an explanation for each of the settings.
 * [Retrain the neural networks for your data.](TRAINING_THE_NETWORK.md)
 * [Replace parts of the tracker with custom code](CUSTOM_TRACKING_SCRIPTS.md)
@@ -97,4 +97,4 @@ Step 7: Automated analysis
 In step 5 a set of filtered high-confidence tracks is produced. You can choose to analyze these further. Generally for many application, manual corrections are not needed. 
 * If you want to quantify a fluorescence reporter you can take long tracks (`Edit` -> `Manually change data` -> `Batch deletion` -> `Delete short lineages`) and work from there.
 * If you want to quantify tissue flows flawless trajectories are often overkill. Setting a lower probability threshold should still give you good enough data. 
-* Analyzing cell cycle dynamics should be done using survival analysis ([See here for how to do this](./SURVIVAL_ANALYSIS.md)) anyway and this can deal with cells that are lost to follow up. 
+* Analyzing cell cycle dynamics should be done using survival analysis ([See here for how to do this](./SURVIVAL_ANALYSIS.md)) anyway and this can deal with cells that are lost to follow up. So it is not a problem to use the uncorrected filtered data.

manuals/index.md

@@ -5,7 +5,7 @@ Welcome to the OrganoidTracker manual! This manual will tell you how to do manua
 Tutorials
 ---------
 * [Tutorial for manual tracking and error correction](MANUAL_TRACKING.md)
-* [Tutorial for semi-automated tracking](AUTOMATIC_TRACKING.md)
+* [Tutorial for (semi-)automated tracking](AUTOMATIC_TRACKING.md)
 * [Tutorial for writing a plugin for OrganoidTracker](PLUGIN_TUTORIAL.md)
 * [Tutorial for training a neural network](TRAINING_THE_NETWORK.md)
 * [Tutorial for using the segmentation editor](SEGMENTATION_EDITOR.md)