update formating

paper.md

@@ -65,13 +65,13 @@ Nonetheless, the FAOSTAT data employed within `gcamdata` has traditionally invol
 ## Key functions 
 
 Here we describe key functions included in **`gcamfaostat (v1.0.0)`** focusing on the data [preparing](https://jgcri.github.io/gcamfaostat/articles/vignette_preparing_data.html) and [processing](https://jgcri.github.io/gcamfaostat/articles/vignette_processing_flow.html). More details about functions and examples for
-[data tracing](https://jgcri.github.io/gcamfaostat/articles/vignette_preparing_data.html#generate-the-metadata-for-the-gcamfaostat-input-data), [visualization](https://jgcri.github.io/gcamfaostat/articles/vignette_visualization.html) and [other cabilities](https://jgcri.github.io/gcamfaostat/articles/vignette_use_cases.html) are illustrated in the online [**User Guide**](https://jgcri.github.io/gcamfaostat/index.html). 
+[data tracing](https://jgcri.github.io/gcamfaostat/articles/vignette_preparing_data.html#generate-the-metadata-for-the-gcamfaostat-input-data), [visualization](https://jgcri.github.io/gcamfaostat/articles/vignette_visualization.html) and [other cabilities](https://jgcri.github.io/gcamfaostat/articles/vignette_use_cases.html) are illustrated in the online [User Guide](https://jgcri.github.io/gcamfaostat/index.html). 
 
 The architecture of **`gcamfaostat`** processing modules is depicted in \autoref{fig:Fig2}. This framework currently comprises eight preprocessing modules and nine processing and synthesizing modules, generating twelve output files tailored for
 [GCAM v7.0](https://github.com/JGCRI/gcam-core/releases/tag/gcam-v7.0). Each module is essentially an `R` function with well-defined inputs and outputs. 
 
 
-Note that by default, the preprocessed FAOSTAT data, i.e., outputs of the `xfaostat_L101_*` modules, have been stored in the [`Prebuilt Data`](https://github.com/JGCRI/gcamfaostat/blob/main/data/PREBUILT_DATA.rda) of the package. **`gcamfaostat`** includes a function to generate metadata ([`gcamfaostat_metadata`](https://jgcri.github.io/gcamfaostat/reference/gcamfaostat_metadata.html)). It accesses both the latest FAOSTAT metadata and local data information and returns a [summary table] (see [example online](https://jgcri.github.io/gcamfaostat/articles/vignette_preparing_data.html#generate-the-metadata-for-the-gcamfaostat-input-data)) including the dataset information needed for **`gcamfaostat`**. There are also functions to download FAOSTAT raw data from either a remote archive [(`FF_download_RemoteArchive`)](https://jgcri.github.io/gcamfaostat/reference/FF_download_RemoteArchive.html) or directly from FAOSTAT [(`FF_download_FAOSTAT`)](https://jgcri.github.io/gcamfaostat/reference/FF_download_FAOSTAT.html).  
+Note that by default, the preprocessed FAOSTAT data, i.e., outputs of the `xfaostat_L101_*` modules, have been stored in the [`Prebuilt Data`](https://github.com/JGCRI/gcamfaostat/blob/main/data/PREBUILT_DATA.rda) of the package. **`gcamfaostat`** includes a function to generate metadata ([`gcamfaostat_metadata`](https://jgcri.github.io/gcamfaostat/reference/gcamfaostat_metadata.html)). It accesses both the latest FAOSTAT metadata and local data information and returns a [summary table](https://jgcri.github.io/gcamfaostat/articles/vignette_preparing_data.html#generate-the-metadata-for-the-gcamfaostat-input-data)) including the dataset information needed for **`gcamfaostat`**. There are also functions to download FAOSTAT raw data from either a remote archive ([`FF_download_RemoteArchive`](https://jgcri.github.io/gcamfaostat/reference/FF_download_RemoteArchive.html)) or directly from FAOSTAT ([`FF_download_FAOSTAT`](https://jgcri.github.io/gcamfaostat/reference/FF_download_FAOSTAT.html)).  
 
 To showcase the flexibility and expandability of our package, we also incorporated two AgLU modules (from `gcamdata`) that exemplify the data aggregation processes, e.g., across regions, sectors, and time. More importantly, the [`driver_drake()`](https://jgcri.github.io/gcamfaostat/reference/driver_drake.html) function plays a pivotal role by executing all available data processing modules, thereby generating both intermediate and final outputs, which are vital components of our comprehensive data processing pipeline. The function was inherited from `gcamdata` and it uses the drake [@Landau2018] pipeline framework, which simplifies module updates, data tracing, and results visualization process. 
 

vignettes/references.bib

@@ -288,7 +288,7 @@ @software{bond_lamberty_2023
 }
 
 @article{zhao_cmp360,
-  title={Core Model Proposal\# 360: GCAM agriculture and land use (AgLU) data and method updates: connecting land hectares to food calories},
+  title={Core {M}odel {P}roposal 360: GCAM agriculture and land use (AgLU) data and method updates: connecting land hectares to food calories},
   author={Zhao, Xin and Wise, Marshall},
   number={PNNL-34313},
   year={2023},
@@ -307,7 +307,7 @@ @article{calvin2019gcam
   DOI={10.5194/gmd-12-677-2019}
 }
 @article{bond2019gcamdata,
-  title={gcamdata: An R package for preparation, synthesis, and tracking of input data for the GCAM integrated human-earth systems model},
+  title={{g}camdata: An R package for preparation, synthesis, and tracking of input data for the GCAM integrated human-earth systems model},
   author={Bond-Lamberty, Ben and Dorheim, Kalyn and Cui, Ryna and Horowitz, Russell and Snyder, Abigail and Calvin, Katherine and Feng, Leyang and Hoesly, Rachel and Horing, Jill and Kyle, G Page and others},
   journal={Journal of Open Research Software},
   doi={10.5334/jors.232},