diff --git a/README.md b/README.md
index b8ca677..5b4f5fc 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,10 @@
 # geospatial-random-forest
 
-> The `geospatial-rf` library provides a series of functions and wrappers to assist with random forest applications in a spatial context
-> It was developed to assist in the classification of exposed rock features from multi-variate datasets, but has wider applications
-> A full methodology for the code is being published and is currently under review
+* The `geospatial-rf` library provides a series of functions and wrappers to assist with random forest applications in a spatial context
+* It was developed to assist in the classification of exposed rock features from multi-variate datasets, but has wider applications
+* A full methodology for the code is being published and is currently under review
     - Williams et al., 2023. 
-> The underlying codebase is being made publically available to support others undertaking or experimenting with simialr approaches
-
-* **Name**: `geospatial-rf`
-* **Source**: [geospatial-rf](https://kwvmxgit.ad.nerc.ac.uk/data-science/geospatial-random-forest/-/tree/method-paper-RELEASE) 
+* The underlying codebase is being made publically available to support others undertaking or experimenting with simialr approaches
 
 ## Overview
 
@@ -15,12 +12,18 @@
 
 To assist users of this library, a worked example implementation is provided that demonstrates how rock/presence absence can be predicted using geospatial (x,y) terrain derivative information, considering a training dataset that also denotes rock presence and absence. In addition to the example provided, scripts are provided for the full data pipeline from data processing to results visualisation. Note that due to the varying nature of geospatial datasets and the possible applications of this repository, some modification is required to run these scripts for differen applciations.
 
-This code is provided to supplement the publication on the development and use of this code for the purposes of predicting geological rock exposure - please refer to Williams et al., 2023 :zap: hyperlink to be added to submission/publication :zap:
+This code is provided to supplement the publication on the development and use of this code for the purposes of predicting geological rock exposure - please refer to Williams et al., 2023 :zap: hyperlink to be added to publication :zap:
 
 ## License info
 
 [LGPL-3.0 license](./license.md)
 
+## Maintenance
+
+* Contributions to the code, including extensions are welcome and merge requests can be made where appropriate (though consider below maintenance)
+* No maintenance support is intended for the external code release therefore interaction will be limited
+* No major updates are intended to this code
+
 ---
 
 # Installation guidance
@@ -70,12 +73,10 @@ Java HotSpot(TM) 64-Bit Server VM (build 25.361-b09, mixed mode)
 
 Some info below quoted from the H2O installation page on Java requirements - see [here](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/welcome.html#java-requirements):
 
-> H2O runs on Java. To build H2O or run H2O tests, the 64-bit JDK is required. To run the H2O binary using either the command line, R, or Python packages, only 64-bit JRE is required.
-> H2O supports the following versions of Java:
->
->    Java SE 17, 16, 15, 14, 13, 12, 11, 10, 9, 8
->
-> Click [here](https://jdk.java.net/archive/) to download the latest supported version.
+* H2O runs on Java. To build H2O or run H2O tests, the 64-bit JDK is required. To run the H2O binary using either the command line, R, or Python packages, only 64-bit JRE is required.
+* H2O supports the following versions of Java:
+		* Java SE 17, 16, 15, 14, 13, 12, 11, 10, 9, 8
+* Click [here](https://jdk.java.net/archive/) to download the latest supported version.
 
 ## Getting version-specific h2o installed