diff --git a/README.md b/README.md
index 61f8b4d..3d82647 100644
--- a/README.md
+++ b/README.md
@@ -1,17 +1,34 @@
 # azint: Azimuthal Integration
 
-azint is a python library for azimuthal integration of area detectors. The azimuthal integration is transformed into a sparse matrix vector multiplication for performance. Pixel splitting is done by subdividing each pixels into subpixels and assigning bins and weights for the individual subpixels. This can increase the precision of the transformation but also introduces correlation between neighboring bins.
+[![DOI](https://zenodo.org/badge/315677387.svg)](https://doi.org/10.5281/zenodo.16480726)
 
+**azint** is a Python library for efficient azimuthal integration of area detector data.  
+The integration process is transformed into a sparse matrix–vector multiplication for performance.  
+
+For increased precision, pixel splitting is done by subdividing each pixel into subpixels, assigning bins and weights to each.  
+This improves the accuracy of the transformation but may introduce correlation between neighboring bins.
 
 ---
-**Documentation**: <a href="https://maxiv-science.github.io/azint" target="_blank">https://maxiv-science.github.io/azint</a>
+
+## 📖 Documentation
+
+Full documentation is available at:  
+👉 [https://maxiv-science.github.io/azint](https://maxiv-science.github.io/azint)
 
 ---
-## Installation
-``` bash
+
+## 💾 Installation
+
+Install via `conda` from the MAX IV channel:
+
+```bash
 conda install -c maxiv azint
 ```
 
 ---
-## NXazint Output
-To write integration results in the [NeXus](https://www.nexusformat.org/) format, you can use the [azint_writer](https://github.com/maxiv-science/azint_writer) package together with azint.
\ No newline at end of file
+## 📤 NXazint Output
+
+To write azimuthal integration results in the [NeXus](https://www.nexusformat.org/) data format,  
+you can use the [`azint_writer`](https://github.com/maxiv-science/azint_writer) package in combination with `azint`.
+
+This enables standardized output for integration results, compatible with NeXus-based data workflows and analysis tools.
diff --git a/docs/index.md b/docs/index.md
index ec0e0e8..9f9dd3b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -29,36 +29,54 @@ conda install -c maxiv azint
 
 ## Getting started
 azint is using poni files from [pyFAI](https://pyfai.readthedocs.io) to setup the azimuthal integrator
+([link](https://zenodo.org/records/15744977?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjQxMTA4MjgzLWQ5ODUtNGE3MS04MGU4LTI4MzgwYzAwNDNlYiIsImRhdGEiOnt9LCJyYW5kb20iOiIzOTI4ZmM1YzRhODgwODI3ZDU0ZGVjYTYxNmViNTg0NyJ9.lt_pXKDYR6t29tGKqcm6huHzvgeqlwc5U5I9TXJ5-LQlL865aGLQE7B6-h6ZS7PLQ7yEGf6M3jV1HacXVpSPiA) to file examples and output)
 ``` python
-import fabio
 import numpy as np
 from azint import AzimuthalIntegrator
 
-img = fabio.open('Eiger4M_Al2O3_13.45keV.edf').data
-mask = fabio.open('mask.tif').data
-# 1D integration
-ai = AzimuthalIntegrator('test.poni', 
-                          4, 
-                          2000,
-                          unit='q',
-                          solid_angle=True,
-                          mask=mask) 
-I, error = ai.integrate(img)
+# ------------------------------
+# Load detector image and setup
+# ------------------------------
+
+# Path to the input HDF5 file containing detector data
+h5name = "scan-1737_pilatus.h5"
+h = h5py.File(h5name, 'r')
+
+# Extract a single image frame from the dataset
+img = h['/entry/instrument/pilatus/data'][10]
+
+# Path to the detector calibration file
+poni = 'Si_135mm.poni'
+
+# Path to the mask file for bad pixels or beamstop
+mask = 'hot_px_bs_mask.npy'
+
+
+# ------------------------------
+# Azimuthal integration settings
+# ------------------------------
+
+config = {
+    'poni': poni,                     # Detector geometry calibration file
+    'mask': mask,                     # Mask to ignore hot/dead pixels
+    'radial_bins': 3000,              # Number of radial bins
+    'azimuth_bins': 180,              # Number of azimuthal bins (set to None for 1D only)
+    'n_splitting': 21,                # Pixel subdivision for integration precision
+    'error_model': 'poisson',         # Error model for propagation
+    'solid_angle': True,              # Apply solid angle correction
+    'polarization_factor': 0.965,     # Correction for polarization effects
+    'normalized': True,               # Normalize intensity values
+    'unit': '2th',                    # Output units (e.g., '2th' for 2θ)
+}
+
+ai = AzimuthalIntegrator(**config)
+I, errors_1d, I_2d, errors_2d = ai.integrate(img)
 
 import matplotlib.pyplot as plt
 plt.figure()
 plt.plot(ai.radial_axis, I)
 
-# 2D integration
-ai = AzimuthalIntegrator('test.poni', 
-                          4, 
-                          512, 
-                          180, 
-                          unit='q',
-                          solid_angle=True,
-                          mask=mask) 
-I, error = ai.integrate(img)
 plt.figure()
-plt.imshow(I)
+plt.imshow(I_2d)
 
 ```