added: citation, reference to dataset, updated project version to 1.0.0

README.md

@@ -5,10 +5,12 @@ file into a Cloud Optimized GeoTIFF (COG) file.
 
 ## Table of Contents
 
-1. [Introduction](#how-does-it-work)
+1. [Introduction](#introduction)
 2. [Naming Scheme](#naming-scheme)
 3. [Development setup](#development-setup)
 4. [How to run](#how-to-run)
+5. [Funding](#funding)
+6. [Citation](#citation)
 
 ## Introduction
 
@@ -29,6 +31,10 @@ All functions used to obtain the data and generate the COGs is available in \
 
 Reprojection is done using `gdalwarp` and can be found in `reproject.sh`.
 
+This code has been used to create Cloud Optimized GeoTIFFs in this dataset:
+
+Premier, V., Ciapponi, N., Zvolenský, J., Zellner, P., & Marin, C. (2024). Daily Snow Water Equivalent (SWE) time-series for the Venosta Valley at 50 m spatial resolution - 2017 to 2022 (v0.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10686757
+
 ## Naming Scheme
 
 The naming scheme for the COGs is in accordance to the OEMC data management plan. \
@@ -135,3 +141,12 @@ A `logfile` is also generated in the main directory.
 - Read the [COG documentation](https://www.cogeo.org/)
 - Read the [STAC documentation](https://stacspec.org/)
 - Play some [FreeRice](https://freerice.com/) and win grains of rice for people in need.
+
+## Funding
+
+**OEMC – Open-Earth-Monitor Cyberinfrastructure 101059548**
+European Commision
+
+## Citation
+
+Zvolensky, Juraj. (2024). SWE maps to Cloud Optimized GeoTIFF (Version v1.0.0) [Software]. Available at https://gitlab.inf.unibz.it/REMSEN/oemc/swe-maps-cog

pyproject.toml

 [tool.poetry]
 name = "swe-maps-stac"
-version = "0.0.1"
+version = "1.0.0"
 description = "Convert NetCDF SWE maps to COGs and generate a STAC catalog"
 authors = ["Juraj Zvolensky <juraj.zvolensky@eurac.edu>"]
 readme = "README.md"