Update README.md

README.md

@@ -11,25 +11,24 @@ We are a community of researchers from different countries and institutions inte
 
 
 <details>
-<summary>[Complete] Quantitative Analysis of Hugging Face Hub </summary>
+<summary><b>[Complete] Quantitative Analysis of Hugging Face Hub</b></summary>
     
 - Using activity data from Hugging Face Hub datasets, models and spaces, we ran a three-part quantitative analysis of development activity, finding that: 
     - activity is extremely imbalanced between repositories; with over 70% of models having 0 downloads, while 1% accounting for 99% of downloads
     - the community has a core-periphery structure, with a core of prolific developers while the large majority (89%) of developers are "islands", primarily working alone.  
 </details>
 
-
 <details>
-<summary>[Ongoing] Cartography of Collaboration in the Open Source AI Ecosystem </summary>
-
-- We are investigating how open source AI artefacts such as open datasets and open-weight models are built and who contributes to this process across the model building pipeline, including during the pre-release and post-release phases. 
-- Our research aims to map collaboration practices from pre-release to post-release across diverse organisational contexts, including individuals, grassroots initiatives, research institutes, startups, and large technology companies, and geographies. 
+<summary><b>[Ongoing] Model Tree Lineages from Base Models to Derived Models</b> </summary>
 
+- We are looking into what kinds of downstream activity are most prevalent for different kinds of base foundation models. 
+- What kinds of derivations (e.g. fine tuning, quantizing) are most popular?
 </details>
 
-<details>
-<summary>[Ongoing] Model Tree Lineages from Base Models to Derived Models </summary>
+<details open>
+<summary><b>[Ongoing] Cartography of Collaboration in the Open Source AI Ecosystem</b> </summary>
+
+- We are investigating how open source AI artefacts such as open datasets and open-weight models are built and who contributes to this process across the model building pipeline, including during the pre-release and post-release phases. 
+- Our research aims to map collaboration practices from pre-release to post-release across diverse organisational contexts, including individuals, grassroots initiatives, research institutes, startups, and large technology companies, and geographies. 
 
-- We are looking into what kinds of downstream activity are most prevalent for different kinds of base foundation models. 
-- What kinds of derivations (e.g. fine tuning, quantizing) are most popular?
 </details>