Introduction.md

@@ -23,7 +23,7 @@ of several nations. Truncated formats often did not contain sufficient informati
  to identify the observations made by a particular ship or platform, 
  and these compact formats sometimes converted or encoded data in different ways. 
  For example, many observations do not have an identifier linking to the ship 
- (**ID**) or platform (**pt**), and for those that do have such identifiers 
+ (**ID**) or platform (**PT**), and for those that do have such identifiers 
  they may be different between data sources. The main types of duplicates are:
 
 *  Observations historically shared among national archives, 
@@ -37,10 +37,9 @@ likely to have different formats, precision, conversions and metadata.
 
 *  Planned redundancy, for example the ingestion of several near real time data streams. 
 
-There is already a protocol and other tools written in Python to read and 
-perform some quality control on the data and to identify duplicate observations 
-as described in [Freeman. *et al.,* (2017)](https://doi.org/10.1002/joc.4775). 
-However, the processing methods in this repository offer additional quality control
+The processing software used by ICOADS (https://icoads.noaa.gov/software/) is written in FORTRAN and includes code to translate software to the IMMA1 format [Smith. *et al.,* (2016)], to apply QC and flags, and to identify (and in earlier releases remove) reports likely to be duplicates [Freeman. *et al.,* (2017)](https://doi.org/10.1002/joc.4775). 
+
+The code in this repository offer additional quality control
 on the data, duplicate identification and linking of IDs between each pair of duplicate reports. 
 It also provides an identification of the best duplicate by assessing the track 
 (path in lat/lon) of the observation.