Add repo-actions on pages without headers

vtraag · vtraag · commit cc524ea62eb0 · 2025-01-15T10:39:24.000+01:00
Based on quarto-dev/quarto-cli#691 (comment)
diff --git a/sections/1_open_science/introduction_open_science.qmd b/sections/1_open_science/introduction_open_science.qmd
@@ -13,6 +13,8 @@ affiliations:
   country: the Netherlands
 
 title: Introduction to Open Science
+
+repo-actions: true
 ---
 
 ::: {.callout collapse="true"}
diff --git a/sections/2_academic_impact/introduction_academic_impact.qmd b/sections/2_academic_impact/introduction_academic_impact.qmd
@@ -13,18 +13,16 @@ affiliations:
   country: the Netherlands
 
 title: Introduction to academic impact
----
 
+repo-actions: true
+---
 
 ::: {.callout collapse="true"}
-
-
 # History
 
 | Version | Revision date | Revision    | Author     |
 |---------|---------------|-------------|------------|
 | 1.0     | 2024-12-06    | First draft | V.A. Traag |
-
 :::
 
 Of the various areas of impact (academic, societal and economic) of Open Science, academic impact is probably the most well-established and researched impact area. In particular, indicators of [citation impact](citation_impact.qmd) and [collaborations](collaboration_intensity.qmd) have a long history and a long development and are well-established. At the same time, there are also more recently developed indicators that are becoming relevant in this area, such as the [reuse of data](reuse_of_data_in_research.qmd) and [code](reuse_of_code_in_research.qmd) in research, and are still actively being developed. The rise of reference managers enabled tracking [readership](academic_readership.qmd) instead of only citations, making it possible to distinguish between papers that are read widely and papers that are cited widely. At the same time, aspects of academic impact such as “quality”, [“novelty” or “interdisciplinarity”](interdisciplinarity.qmd) remain complicated to develop robust indicators for based on existing data sources, thus requiring more in-depth manual assessments.
diff --git a/sections/3_societal_impact/introduction_societal_impact.qmd b/sections/3_societal_impact/introduction_societal_impact.qmd
@@ -18,8 +18,9 @@ affiliations:
   country: Switzerland 
 
 title: Introduction to societal impact
----
 
+repo-actions: true
+---
 
 ::: {.callout collapse="true"}
 # History
diff --git a/sections/4_economic_impact/introduction_economic_impact.qmd b/sections/4_economic_impact/introduction_economic_impact.qmd
@@ -12,9 +12,11 @@ affiliations:
   country: Italy
 
 title: Introduction to Economic Impact
----
 
 
+repo-actions: true
+---
+
 ::: {.callout collapse="true"}
 # History
 
diff --git a/sections/5_reproducibility/introduction_reproducibility.qmd b/sections/5_reproducibility/introduction_reproducibility.qmd
@@ -15,40 +15,42 @@ affiliations:
   city: Athena
   country: Greece
 
-title: Introduction to Reproducibility 
----
+title: Introduction to Reproducibility
+
 
+repo-actions: true
+---
 
 Large-scale computation and the rise of data-driven methodologies have transformed the way scientific research is conducted in many disciplines. Open Science with its overarching goals of sharing research outcomes (resources, methods, or tools) as well as the flow of the actual research processes has become a key enabler for scientific discovery and faster knowledge spillover, contributing or leading those major shifts in science.
 
 In the backdrop of these changes, reproducibility and replicability have raised critical concerns about the development and evolution of science and the way we generate reliable knowledge. Open Science could streamline the requisite processes addressing reproducibility challenges and accelerate the uptake of good practices about research integrity.
 
 In PathOS, reproducibility refers strictly to computational reproducibility and computational non-reproducibility. Concretely, we define reproducibility as a continuous, “ongoing” process, ranging from
 
-- systematic efforts to regenerate/reproduce computationally a previous study,
-- studies that re-use or build upon or expand (part of) the research outputs of a previous study,
-- studies that verify or confirm the results of a previous study by collecting and analysing new data,
-- studies that provide evidence that support or refute scientific claims and inferences from a previous study, to
-- studies conducting meta-analyses and research synthesis by consolidating, evaluating, interpreting, and contextualizing findings from previous studies on a particular topic.
+-   systematic efforts to regenerate/reproduce computationally a previous study,
+-   studies that re-use or build upon or expand (part of) the research outputs of a previous study,
+-   studies that verify or confirm the results of a previous study by collecting and analysing new data,
+-   studies that provide evidence that support or refute scientific claims and inferences from a previous study, to
+-   studies conducting meta-analyses and research synthesis by consolidating, evaluating, interpreting, and contextualizing findings from previous studies on a particular topic.
 
 In the following sections, we delve into the following aspects in the intersection of OS and reproducibility providing relevant indicators (summarized in the table below) while keeping a pragmatic approach to what is feasible in terms of measuring, monitoring, and evaluating reproducibility.
 
-+--------------------------------------------------------------------+----------------------------------------------------+
-| Reproducibility Aspect                                             | Relevant Indicators Provided                       |
-+====================================================================+====================================================+
-| Availability and transparency of research outputs to other studies | - Reuse of Code in research                        |
-|                                                                    | - Reuse of Data in research                        |
-+--------------------------------------------------------------------+----------------------------------------------------+
-| Verification                                                       | - Consistency in reported numbers                  |
-+--------------------------------------------------------------------+----------------------------------------------------+
-| Coherence of the approach                                          | - Pre-registration of method/protocol              |
-+--------------------------------------------------------------------+----------------------------------------------------+
-| Reviews and checks on reproducibility of OS research               | - Levels of replication found                      |
-|                                                                    | - Polarity of publications                         |
-+--------------------------------------------------------------------+----------------------------------------------------+
-| Integrity of OS datasets, code and methods                         | - Impact of Open Code in research                  |
-|                                                                    | - Impact of Open Data in research                  |
-|                                                                    | - Inclusion in systematic reviews or meta-analyses |
-+--------------------------------------------------------------------+----------------------------------------------------+
-
-: Reproducibility aspects and indicators {#tbl-reproducibility}
++--------------------------------------------------------------------+------------------------------------------------------+
+| Reproducibility Aspect                                             | Relevant Indicators Provided                         |
++====================================================================+======================================================+
+| Availability and transparency of research outputs to other studies | -   Reuse of Code in research                        |
+|                                                                    | -   Reuse of Data in research                        |
++--------------------------------------------------------------------+------------------------------------------------------+
+| Verification                                                       | -   Consistency in reported numbers                  |
++--------------------------------------------------------------------+------------------------------------------------------+
+| Coherence of the approach                                          | -   Pre-registration of method/protocol              |
++--------------------------------------------------------------------+------------------------------------------------------+
+| Reviews and checks on reproducibility of OS research               | -   Levels of replication found                      |
+|                                                                    | -   Polarity of publications                         |
++--------------------------------------------------------------------+------------------------------------------------------+
+| Integrity of OS datasets, code and methods                         | -   Impact of Open Code in research                  |
+|                                                                    | -   Impact of Open Data in research                  |
+|                                                                    | -   Inclusion in systematic reviews or meta-analyses |
++--------------------------------------------------------------------+------------------------------------------------------+
+
+: Reproducibility aspects and indicators {#tbl-reproducibility}
