Update reuse_of_data_in_research.qmd - mapping to academic indicators

indicator_templates/quarto/5_reproducibility/reuse_of_data_in_research.qmd

@@ -30,6 +30,10 @@ affiliations:
 
 The reuse of data in research refers to the practice of utilizing existing data sets for new research questions. It is a common practice in various scientific fields, and it can lead to increased scientific efficiency, reduced costs, and enhanced scientific collaborations. Additionally, the reuse of well-documented data can serve as an independent verification of original findings, thereby enhancing the reproducibility of research. This indicator aims to capture the extent to which researchers engage in the reuse of data in their research, by quantifying the number and proportion of studies that utilize previously collected data. The indicator can be used to assess the level of scientific collaboration and sharing of data within a specific scientific community or field, and to identify potential barriers or incentives for the reuse of data in research. Additionally, it can serve as a measure of the quality and reliability of research, as the reuse of data can increase the transparency, validity, and replicability of research findings.
 
+### Connections to Academic Indicators
+
+This indicator emphasizes the adoption and utilization of existing datasets for new research purposes, highlighting its role in enhancing reusability, reproducibility, collaboration, and research efficiency. In contrast, the [Use of Data in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/2_academic_impact/use_of_data_in_research.html) focuses on the initial incorporation of data into research activities and its contributions to academic outputs. Furthermore, the [Impact of Open Data in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/5_reproducibility/impact_of_open_data_in_research.html) extends this perspective by evaluating how openly shared datasets foster transparency, accessibility, and innovation across the scientific community.
+
 ## Metrics
 
 ### Number of datasets reused in publications
@@ -122,4 +126,4 @@ One limitation of this methodology is that it may not capture all instances of d
 
 To measure the proposed metric, DataSeer.ai can scan the body of text in research articles and identify instances of dataset reuse.
 
-However, it is important to note that DataSeer.ai's ability to determine actual data reuse may depend on the explicitness of the authors' writing about their data usage, thus not capturing all instances of dataset reuse if they are not explicitly mentioned in the text. Moreover, the machine learning algorithms used by the tool may not always accurately classify whether a dataset has been reused and may require manual validation.
+However, it is important to note that DataSeer.ai's ability to determine actual data reuse may depend on the explicitness of the authors' writing about their data usage, thus not capturing all instances of dataset reuse if they are not explicitly mentioned in the text. Moreover, the machine learning algorithms used by the tool may not always accurately classify whether a dataset has been reused and may require manual validation.