Minor modification in ref to figure

joss/paper.md

@@ -40,7 +40,7 @@ MRdataset uses modular classes to read various dataset formats to produce a comm
 
 Medical imaging data is shared in various file formats (such as DICOM, NIfTI, Analyze) and organized differently for distinct study designs, and data capturing platforms (such as XNAT). The lack of standardized interfaces adds unnecessary complexity to the otherwise straightforward task of retrieving imaging sequences. This leads to significant inefficiencies and delays, hindering the seamless flow between data acquisition and analysis. MRdataset includes separate modules for handling each format (e.g., DICOM, NifTi) and the user can simply specify the format while reading the dataset. MRdataset provides a canonical data structure to offer researchers with a consistent API, regardless of the underlying file format or dataset structure on disk, thereby acting as a unified interface across various dataset formats (\autoref{fig:hierarchy}).
 
-Prior works [@Yarkoni2019;@brett_2023_7795644] have acknowledged the complexity of working with large scale, heterogeneous datasets. However, there has been limited emphasis on providing a unified interface for accessing various subjects and sequences without reformatting, that goes beyond one file format and dataset structure. It is important to note that in the normal course of a typical research project that involves many open datasets, importing, reformatting and validation of datasets typically occurs years after the data acquisition process has been completed. Relying on a specialized tool designed exclusively for a specific format is not practical. A unified interface helps decouple the analyses from the constraints and idiosyncrasies of various formats. Besides being user-friendly for accessing multiple formats, MRdataset enables direct access to sessions/subjects to facilitate sequence-level and subject-level analyses. MRdataset captures the broader perspective of reading the dataset as a whole and provides intuitive methods for horizontal traversal (across subjects for a given sequence) and vertical traversal (across multiple sequences for a given subject) as shown in \autoref{fig:schematic}. \autoref{fig:loops} shows an example of a script accessing subjects and sequences from a DICOM dataset.
+Prior works [@Yarkoni2019;@brett_2023_7795644] have acknowledged the complexity of working with large scale, heterogeneous datasets. However, there has been limited emphasis on providing a unified interface for accessing various subjects and sequences without reformatting, that goes beyond one file format and dataset structure. It is important to note that in the normal course of a typical research project that involves many open datasets, importing, reformatting and validation of datasets typically occurs years after the data acquisition process has been completed. Relying on a specialized tool designed exclusively for a specific format is not practical. A unified interface helps decouple the analyses from the constraints and idiosyncrasies of various formats. Besides being user-friendly for accessing multiple formats, MRdataset enables direct access to sessions/subjects to facilitate sequence-level and subject-level analyses. MRdataset captures the broader perspective of reading the dataset as a whole and provides intuitive methods for horizontal traversal (across $N$ subjects, and $S$ sites for a given sequence) and vertical traversal (across multiple sequences for a given subject within a session) as shown in \autoref{fig:schematic}. \autoref{fig:loops} shows an example of a script accessing subjects and sequences from a DICOM dataset.
 
 ![ Illustration of horizontal and vertical traversal for accessing subjects/sequences of a medical imaging dataset\label{fig:schematic} ](Slide8.png)