Update README.md

README.md

@@ -33,12 +33,15 @@ A key input to legal understanding projects is caselaw -- the published, precede
 United States caselaw is collected and published as open data by [CourtListener](https://www.courtlistener.com/), which maintains scrapers to aggregate data from
 a wide range of public sources.
 
-COLD Cases reformats CourtListener's [bulk data](https://www.courtlistener.com/help/api/bulk-data) so that all of the semantic information about each legal decision
-(the authors and text of majority and dissenting opinions; head matter; and substantive metadata) is encoded in a single JSON object per decision, with extraneous
-data removed. By consolidating the data engineering for preprocessing caselaw in an
-[open source](https://github.com/harvard-lil/cold-cases-export)
-pipeline maintained by the Harvard Law School Library, we ensure
-that downstream machine learning and natural language processing projects can use consistent, high quality representations of cases for legal understanding tasks.
+A key input to legal computation tasks is caselaw -- the published, precedential decisions of judges deciding legal disputes and explaining their reasoning.
+United States caselaw is collected and published as open data by CourtListener, which maintains scrapers to aggregate data from a wide range of public sources,
+[including the Library Innovation Lab’s Caselaw Access Project].
+
+COLD Cases reformats CourtListener's [bulk data](https://www.courtlistener.com/help/api/bulk-data) so that all of the semantic information about each legal decision 
+(the authors and text of majority and dissenting opinions; head matter; and substantive metadata) is encoded in a single JSON object per decision, 
+with extraneous data removed. Serving in the traditional role of libraries as a standardization steward, the Harvard Library Innovation Lab is maintaining
+this [open source](https://github.com/harvard-lil/cold-cases-export) pipeline to consolidate the data engineering for preprocessing caselaw so downstream machine
+learning and natural language processing projects can use consistent, high quality representations of cases for legal understanding tasks.
 
 Prepared by the [Harvard Library Innovation Lab](https://lil.law.harvard.edu) in collaboration with the [Free Law Project](https://free.law/).