Update README.rst

README.rst

@@ -7,14 +7,17 @@
 Overview
 ========
 
-``dynsight`` is structured to support a wide range of tasks commonly
-encountered in the analysis of many-body dynamical systems. These tasks
-include handling trajectory data, computing single-particle descriptors,
-performing time-series clustering and conducting various auxiliary analyses.
-To achieve this, dynsight is organized into specialized modules, each
-addressing a specific aspect of this workflow.
-
-Previously in `cpctools`_.
+``dynsight`` is an open platform for supporting a wide range of tasks commonly
+encountered in the trajectory and data analysis of complex dynamical systems, essentially related
+to the extraction of relevant information from data obtained from trajectories.
+To achieve this, ``dynsight`` is organized into specialized modules, each addressing a 
+specific aspect of this workflow. For example, ``dynsight`` includes modules for, e.g., 
+resolving trajectory data from movies (experimental: e.g., object recognition and tracking),
+handling trajectory data (from simulations and experiments), computing single-particle descriptors, 
+performing time-series clustering, maximum information extraction from data, and conducting 
+various auxiliary analyses. 
+
+A bounce of all this was previously in `cpctools`_.
 
 .. _`cpctools`: https://github.com/GMPavanLab/cpctools
 
@@ -28,7 +31,7 @@ To get ``dynsight``, you can install it with pip::
 Optional Dependancies
 ---------------------
 
-Old versions ``dynsight`` used ``cpctools`` for SOAP calculations, if
+Old versions of ``dynsight`` used ``cpctools`` for SOAP calculations: if
 you are using Python 3.10 and below, you can use ``cpctools`` to access
 ``SOAPify`` and ``hd5er`` using ::
 
@@ -93,25 +96,24 @@ and
     TBD
 
 * Most modules also use MDAnalysis, https://www.mdanalysis.org/pages/citations/
-* If you use SOAP, please cite https://doi.org/10.1103/PhysRevB.87.184115 and DScribe https://singroup.github.io/dscribe/latest/citing.html
-* If you use timeSOAP, please cite https://doi.org/10.1063/5.0147025
-* If you use LENS, please cite: https://doi.org/10.1073/pnas.2300565120
-* If you use onion-clustering, please cite: https://doi.org/10.1073/pnas.2403771121
-* If you use tICA, please cite ``deeptime`` https://deeptime-ml.github.io/latest/index.html
-* If you use ``dynsight.vision``, please cite Ultralytics YOLO https://docs.ultralytics.com/it/models/yolo11/#usage-examples
-* If you use ``dynsight.track``, please cite Trackpy https://soft-matter.github.io/trackpy/dev/introduction.html
+* If you use SOAP, please cite also original references: https://doi.org/10.1103/PhysRevB.87.184115 and DScribe https://singroup.github.io/dscribe/latest/citing.html
+* If you use TimeSOAP, please cite also original reference: https://doi.org/10.1063/5.0147025
+* If you use LENS, please cite also original reference: https://doi.org/10.1073/pnas.2300565120
+* If you use onion-clustering, please cite also original reference: https://doi.org/10.1073/pnas.2403771121
+* If you use tools to calculate Information Gain and Maximum Information Extraction, please cite also original reference: https://doi.org/10.48550/arXiv.2504.12990
+* If you use tICA, please cite also original ``deeptime`` reference: https://deeptime-ml.github.io/latest/index.html
+* If you use ``dynsight.vision``, please cite also original ``Ultralytics YOLO`` reference: https://docs.ultralytics.com/it/models/yolo11/#usage-examples
+* If you use ``dynsight.track``, please cite also original ``Trackpy`` reference: https://soft-matter.github.io/trackpy/dev/introduction.html
 
 
 Acknowledgements
 ================
 
-We developed this code when working in the Pavan group,
-https://www.gmpavanlab.polito.it/, whose members often provide very valuable
-feedback, which we gratefully acknowledge.
+This code is developed by the G.M. Pavan group, https://www.gmpavanlab.polito.it/, 
+whose members often provide very valuable feedback, which we gratefully acknowledge.
 
 Much of the original code in ``cpctools`` was written by Daniele Rapetti (Iximiel).
 
-The work was funded by the European Union and ERC under projects DYNAPOL and the
-NextGenerationEU project, CAGEX.
+This work was primarily supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement no. 818776- DYNAPOL), and also partially by the European Union under the NextGenerationEU program (grant CAGEX, SOE_0000033).
 
 .. figure:: docs/source/_static/EU_image.png