Merge pull request #6 from Kaszanas/draft_paper

[PR] Fixing Enumeration in Markdown

Author: Kaszanas

paper.md

@@ -76,16 +76,7 @@ Our software consists of multiple modules that the user can match to their speci
 
 ## DatasetPreparator
 
-The "DatasetPreparator" [@Bialecki_2022_SC2DatasetPreparator] submodule is a set of scripts that ease the process of working with major collections of raw data (replaypacks/datasets). A full list of scripts is as follows:
-- "directory_flattener.py"; flattens the nested directory structure of the replaypacks,
-- "directory_packager.py"; packages all of the directories in the specified input directory,
-- "file_renamer.py"; renames the files in the directory to follow a specific naming convention (e.g., to match the dataset schema),
-- "json_merger.py"; merges two JSON files into one,
-- "processed_mapping_copier.py"; copies the auxiliary files generated by directory_flattener.py to matching output directories. Built specifically to prepare the SC2EGSet prior to packaging,
-- "sc2_map_downloader.py"; wraps "SC2InfoExtractorGo" to run the map downloading step,
-- "sc2egset_replaypack_processor.py"; wraps "SC2InfoExtractorGo" to run the replaypack processing step on multiple directories at once,
-- "sc2egset_pipeline.py"; wraps the entire processing pipeline used to obtain the "SC2ReSet" and "SC2EGSet" datasets,
-- "sc2reset_replaypack_downloader.py"; downloads the raw (flattened) replaypacks of "SC2ReSet" [@Bialecki2022ReSetZenodo] for users that wish to use their own tools for data processing.
+The "DatasetPreparator" [@Bialecki_2022_SC2DatasetPreparator] submodule is a set of scripts that ease the process of working with major collections of raw data (replaypacks/datasets). A full list of scripts is as follows: (1) "directory_flattener.py"; flattens the nested directory structure of the replaypacks, (2) "directory_packager.py"; packages all of the directories in the specified input directory, (3) "file_renamer.py"; renames the files in the directory to follow a specific naming convention (e.g., to match the dataset schema), (4) "json_merger.py"; merges two JSON files into one, (5) "processed_mapping_copier.py"; copies the auxiliary files generated by directory_flattener.py to matching output directories. Built specifically to prepare the SC2EGSet prior to packaging, (6) "sc2_map_downloader.py"; wraps "SC2InfoExtractorGo" to run the map downloading step, (7) "sc2egset_replaypack_processor.py"; wraps "SC2InfoExtractorGo" to run the replaypack processing step on multiple directories at once, (8) "sc2egset_pipeline.py"; wraps the entire processing pipeline used to obtain the "SC2ReSet" and "SC2EGSet" datasets, (9) "sc2reset_replaypack_downloader.py"; downloads the raw (flattened) replaypacks of "SC2ReSet" [@Bialecki2022ReSetZenodo] for users that wish to use their own tools for data processing.
 
 In the context of our work, this submodule is responsible for preparing directory structure, execution of "SC2InfoExtractorGo" on the data, and packaging the dataset for hosting. Finally, the current capabilities include downloading the raw replaypacks of "SC2ReSet" [@Bialecki2022ReSetZenodo] for ease of "SC2EGSet" Dataset reproduction [@Bialecki2023EGSetZenodo].