small description changes

README.md

@@ -104,7 +104,7 @@ See [DATABASE](DATABASE.md) for a step-by-step walkthrough.
 
 ## Limitations
 
-OnSIDES is **strictly** intended for research purposes. The adverse drug event term extraction method is far from perfect - some side effects will be missed and some predicted as true adverse events will be incorrect. 
+OnSIDES is **strictly** intended for academic research purposes. The adverse drug event term extraction method is far from perfect - some side effects will be missed and some predicted as true adverse events will be incorrect. 
 
 **Patients/healthcare professionals seeking health information should not trust or use this data, and instead refer to the information available from their regions' respective drug regulatory agencies, such as the [FDA](https://www.fda.gov/) (USA), [EMA](https://www.ema.europa.eu/en) (EU), [MHRA](https://www.gov.uk/government/organisations/medicines-and-healthcare-products-regulatory-agency) (UK), [PMDA](https://www.pmda.go.jp/english/) (Japan) and consult their healthcare providers for information.**
 

onsides_intl/ONSIDES_INTL.md

@@ -1,6 +1,6 @@
 # OnSIDES-INTL 
 
-Here, we generate databases mirroring the OnSIDES database (which extracts ADE data from US FDA SPL drug labels) from UK (EMC), EU (EMA), and Japan (PMDA) drug labels. We have also generated uniformly processed drug label text data from these drug labels that can be used as raw, structured data to train a myriad of machine learning models. 
+Here, we generate databases mirroring the OnSIDES database (which extracts ADE data from US FDA SPL drug labels) from UK (EMC), EU (EMA), and Japan (PMDA) drug labels.
 
 While we follow a similar ADE extraction/prediction philosophy to OnSIDES, as the raw label are formatted in a slightly different manner for each nation/region, the technical workflow is slightly adjusted to each nation. The detailed methods are described in [DATABASE_INTL](./DATABASE_INTL.md).