diff --git a/readme.md b/readme.md
index efa9e20..415a1c7 100644
--- a/readme.md
+++ b/readme.md
@@ -1,40 +1,17 @@
-# REFIT Loader
-> This project uses **Dask Dataframes** to ease and fasten the process of loading all the data of REFIT and provides functionalities to transform and manipulate the REFIT dataset for statistical analysis purpose.
+# NILM Datasets
+> This project uses **Dask Dataframes** to ease and fasten the process of loading and analyzing all the data of any publicly available NILM dataset and provides basic transformations like resampling, standardization and extracting activations by thresholding for statistical analysis purpose. Can be used further for splitting datasets into train, validation and test subsets for Energy Disaggregation task. 
 
 
-### About REFIT dataset
-An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study. Sci Data 4, 160122 (2017). <br />
-Murray, D., Stankovic, L. & Stankovic, V.  <br />
-
-For more detail information, visit the following links: <br />
-http://dx.doi.org/10.1038/sdata.2016.122 <br />
-https://rdcu.be/cMD9F <br />
-
-## Dependencies
-Ensure that the following dependencies are satisfied either in your current environment 
-```  
-  - python>=3.9.2
-  - numpy>=1.20.3
-  - pandas>=1.2.4
-  - dask>=2021.06.2
-  - scikit-learn>=1.1.2
-```
-or create a new environment using 'environment.yml'
-```
-conda create env --file=environment.yml
-conda activate refit_loader_env
-```
-
 ## Getting Started
-1) Install the refit-loader in your current target environment
+1) Install the refit-loader in your current target environment.
 ```
 pip install refit-loader
 ```
 
-2) [Download](#downloads) the refit dataset. Import the REFIT_Loader and pass the data path to the refit object.
+2) [Download](#downloads) any NILM dataset(s) and import the corresponding loader. Then, pass the data path of the data directory where the dataset is located. For instance,
 ```
 from refit_loader.data_loader import REFIT_Loader
-refit = REFIT_Loader(data_path='')
+refit = REFIT_Loader(data_path='data/refit/')
 ```
 3) Fetch the list of available appliances for selected houses.
 ```
@@ -52,12 +29,49 @@ house2 = refit.get_house_data(house=2)
 ```
 kettle = refit.get_appliance_data(appliance="Kettle", houses=[1,2,3])
 ```
-7) Take the reference from Refit_Analyzer to see how Refit_Loader can be accessed and how it's utilities can be used.
+7) Take the reference from NILM_Analyzer to see how Refit_Loader can be accessed and how it's utilities can be used.
 
 Reference Repository: <br />
 [Refit Analyzer](https://github.com/mahnoor-shahid/refit_analyzer) = REFIT analyzer is more like a user guide that uses REFIT Loader as a submodule and demonstrates how it can be used and how it can be useful with its utilities.
 
 
+## Dependencies
+Ensure that the following dependencies are satisfied either in your current environment 
+```  
+  - python>=3.9.2
+  - numpy>=1.20.3
+  - pandas>=1.2.4
+  - dask>=2021.06.2
+  - scikit-learn>=1.1.2
+```
+or create a new environment using 'environment.yml'
+```
+conda create env --file=environment.yml
+conda activate refit_loader_env
+```
+
+
+### Datasets Included
+REFIT [United Kingdom] <br />
+Murray, D., Stankovic, L. & Stankovic, V. An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study. Sci Data 4, 160122 (2017). https://doi.org/10.1038/sdata.2016.122 <br />
+
+UK-DALE [United Kingdom] <br />
+Kelly, J., Knottenbelt, W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci Data 2, 150007 (2015). https://doi.org/10.1038/sdata.2015.7 <br />
+
+GeLaP [Germany] <br />
+Wilhelm, S., Jakob, D., Kasbauer, J., Ahrens, D. (2022). GeLaP: German Labeled Dataset for Power Consumption. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_5 <br />
+
+DEDDIAG [Germany] <br />
+Wenninger, M., Maier, A. & Schmidt, J. DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. Sci Data 8, 176 (2021). https://doi.org/10.1038/s41597-021-00963-2 <br />
+
+AMPds [Canada] <br />
+S. Makonin, F. Popowich, L. Bartram, B. Gill and I. V. Bajić, "AMPds: A public dataset for load disaggregation and eco-feedback research," 2013 IEEE Electrical Power & Energy Conference, Halifax, NS, Canada, 2013, pp. 1-6, doi: 10.1109/EPEC.2013.6802949. <br />
+
+iAWE [India] <br />
+N. Batra, A. Singh, P. Singh, H. Dutta, V. Sarangan, M. Srivastava "Data Driven Energy Efficiency in Buildings"
+
+
+
 ## Repo Structure:
 This repository follows the below structure format:
 ```