fix

Author: JensKrumsieck

paper/paper.md

@@ -57,7 +57,7 @@ Its design emphasizes flexibility and machine readability but its verbose YAML-b
 SciWIn-Client addresses this gap by provinding an intuitive command-line interface that automates CWL generation and management. It translates typical research computing tasks into structured, version-controlled workflow definitions, effectively allowing scientists to “write CWL by doing science.”
 
 # Features and Implementation
-SciWIn-Client (short: `s4n`) is implemented in the Rust programming language, chosen for its high performance, strong type safety, and robust error handling—qualities essential in scientific software. Git integration provides built-in version control and interoperability with research data management frameworks such as  DataPLANTs ARC [@dataplant2025ARCSpec, @Weil2023PLANTdataHUB] format which can be viewd as a Git-based implementation of the RO-Crate standard [@SoilandReyes2022ROCrate].
+SciWIn-Client (short: `s4n`) is implemented in the Rust programming language, chosen for its high performance, strong type safety, and robust error handling—qualities essential in scientific software. Git integration provides built-in version control and interoperability with research data management frameworks such as  DataPLANTs ARC [@dataplant2025ARCSpec][@Weil2023PLANTdataHUB] format which can be viewd as a Git-based implementation of the RO-Crate standard [@SoilandReyes2022ROCrate].
 
 ## Creation of CWL Files
 A central concept of the tool is the automation of CWL generation. When users invoke a command or script using the `s4n create` prefix SciWIn-Client analyzes the command-line inputs and execution to identify `inputs`, `baseCommand` and `requirements` metadata and creates a CWL CommandLineTool. SciWIn-Client uses Git in background  a version-controlled environment for tracking changes and support this process. However most importantly Git serves information of changed files to create the  `outputs`-Section of the CWL CommandLineTool. While the system can automatically infer inputs and outputs, users also have the option to define them explicitly. Users can specify a container image pulled from Docker Hub or provide paths to local Dockerfiles to ensure consistent, reproducible execution environments across different systems.