Mapping Academic- Reproducibility Indicators

Author: PetrosStav

indicator_templates/quarto/2_academic_impact/use_of_code_in_research.qmd

@@ -33,6 +33,10 @@ One challenge is that we are typically interested in the use of “research soft
 
 This indicator can be useful to provide a more comprehensive view of the impact of the contributions by researchers. Some researchers might be more involved in publishing, whereas others might be more involved in developing and maintaining research software (and possibly a myriad other activities).
 
+### Connections to Reproducibility Indicators
+
+This indicator focuses on identifying and measuring the presence and contribution of code or software within research activities, providing insight into how these tools support the research process itself. In contrast, reproducibility-focused indicators such as [Reuse of Code in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/5_reproducibility/reuse_of_code_in_research.html) examine the extent to which code or software is adopted and utilized in subsequent studies, reflecting its broader applicability, reusability and role in reproducibility. Additionally, the [Impact of Open Code in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/5_reproducibility/impact_of_open_code_in_research.html) highlights the value of openly shared code or software in fostering transparency, collaboration, and validation across the scientific community.
+
 ## Metrics
 
 Most research software is not properly indexed. There are initiatives to have research software properly indexed and identified, such as the [Research Software Directory,](https://research-software-directory.org/) but these are far from comprehensive at the moment. Many repositories support uploading research software. For instance, Zenodo currently holds about 116,000 records of research software. However, there are also reports of the absence of support for including research software in repositories [@carlin2023].

indicator_templates/quarto/2_academic_impact/use_of_data_in_research.qmd

@@ -33,6 +33,10 @@ The open research data (ORD) movement within open science has advocated to make
 
 Nevertheless, this document attempts to summarize what indicators can be used to approximate data use in research.
 
+### Connections to Reproducibility Indicators
+
+This indicator focuses on identifying and measuring how data is utilized in research activities, providing insight into its contribution to academic outputs and innovation. In contrast, the [Reuse of Data in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/5_reproducibility/reuse_of_data_in_research.html) examines the extent to which existing datasets are adopted for subsequent studies, emphasizing reusability and reproducibility. Additionally, the [Impact of Open Data in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/5_reproducibility/impact_of_open_data_in_research.html) highlights the broader effects of openly sharing data, fostering transparency, and driving advancements across scientific communities.
+
 ## Metrics
 
 ### Number (Avg.) of times data is cited/mentioned in publications

indicator_templates/quarto/5_reproducibility/impact_of_open_code_in_research.qmd

@@ -32,6 +32,10 @@ The impact of Open Code in research aims to capture the effect of making researc
 
 This indicator can be used to assess the level of openness and accessibility of research code within a specific scientific community or field and to identify potential barriers or incentives for the adoption of Open Code practices. It can also be used to track the reuse and subsequent impact related to reproducibility of Open Code, as well as to evaluate the effectiveness of policies and initiatives promoting Open Code practices.
 
+### Connections to Academic Indicators
+
+This indicator examines the broader effects of making code or software openly accessible, focusing on its role in fostering transparency, collaboration, and reproducibility across the scientific community. This builds upon the [Use of Code in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/2_academic_impact/use_of_code_in_research.html), which assesses the initial incorporation of code or software into research, and the [Reuse of Code in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/5_reproducibility/reuse_of_code_in_research.html), which measures the extent to which existing code or software is adopted in subsequent studies. Together, these indicators provide a comprehensive view of how code or software contributes to research outputs, reusability, reproducibility, and the wider adoption of Open Code practices.
+
 ## Metrics
 
 ### NCI for publications that have introduced Open Code

indicator_templates/quarto/5_reproducibility/impact_of_open_data_in_research.qmd

@@ -32,6 +32,10 @@ The impact of Open Data in research aims to capture the effect of making researc
 
 The indicator can be used to assess the level of openness and accessibility of research data within a specific scientific community or field, and to identify potential barriers or incentives for the adoption of Open Data practices.
 
+### Connections to Academic Indicators
+
+This indicator examines the broader effects of making research data openly accessible, focusing on how transparency and accessibility enhance reproducibility, collaboration, and innovation within the scientific community. This builds upon the [Use of Data in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/2_academic_impact/use_of_data_in_research.html), which evaluates how data is initially utilized within research activities, and the [Reuse of Data in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/5_reproducibility/reuse_of_data_in_research.html), which measures the extent to which datasets are adopted in subsequent studies. Together, these indicators provide a comprehensive view of how data contributes to scientific outputs, reusability and reproducibility.
+
 ## Metrics
 
 ### NCI for publications that have introduced Open Datasets

indicator_templates/quarto/5_reproducibility/reuse_of_code_in_research.qmd

@@ -30,6 +30,10 @@ affiliations:
 
 The reuse of code or software in research refers to the practice of utilising existing code or software to develop new research tools, methods, or applications. It is becoming increasingly important in various scientific fields, including computer science, engineering, and data analysis, because it directly contributes to scientific reproducibility by enabling other researchers to validate the findings without the need to recreate the software or tools from scratch. Additionally, it is an indicator of research quality, as repeated use of code or software often signals robustness and reliability. Furthermore, a high percentage of research projects reusing code within a particular field could be an indication of strong collaboration and trust within the scientific community. This indicator aims to capture the extent to which researchers engage in the reuse of code or software in their research by quantifying the number and proportion of studies that utilise existing code or software. The indicator can be used to assess the level of collaboration and sharing of resources within a specific scientific community or field and to identify potential barriers or incentives for the reuse of code or software in research. Additionally, it can serve as a measure of the quality and reliability of research, as the reuse of code or software can increase the transparency, replicability, and scalability of research findings.
 
+### Connections to Academic Indicators
+
+This indicator emphasizes the adoption and utilization of existing code or software in subsequent studies, focusing on its role in enhancing reproducibility, collaboration, and research quality. In contrast, the [Use of Code in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/2_academic_impact/use_of_code_in_research.html) measures the initial incorporation of code or software into research activities, providing insights into its contribution to the research process itself. Furthermore, the [Impact of Open Code in Research](https://handbook.pathos-project.eu/indicator_templates/quarto/5_reproducibility/impact_of_open_code_in_research.html) extends this perspective by evaluating the broader effects of making code or software openly accessible, fostering transparency, and driving innovation across the scientific community.
+
 ## Metrics
 
 ### Number of code/software reused in publications

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