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content/page/panels-2026.md

@@ -12,14 +12,14 @@ As we gather in Washington, D.C. for BOSC 2026, we stand at the literal and figu
 
 The current climate in the United States has seen federal agencies, which are the traditional engines of open research, reorienting under new mandates that prioritize "national interest" and "operational efficiency." While these shifts have created uncertainty, they have also demonstrated **the power of the community**; despite early threats of deep cuts, concerted advocacy has kept certain key research budgets remarkably stable. Resilience, therefore, is not just about surviving these shifts; it is about building a proactive presence in the rooms where these decisions are made. We will explore how these domestic tensions mirror global trends, from the rise of protectionist data policies to the decentralized alternatives emerging in the wake of federal volatility.
 
-Our discussion will move from high-level policy to the practical "how-to" of remaining principled and productive, addressing: the Advantage of Advocacy, Navigating Funding Fragility, Protecting the Digital Commons, and Institutional Fortification. 
+Our discussion will move from high-level policy to the practical "how-to" of remaining principled and productive, addressing: the Advantage of Advocacy, Navigating Funding Fragility, Protecting the Digital Commons, and Institutional Fortification.
 
 # Panelists
 
 ### To be named
 
 ### MODERATOR: Mónica Muñoz Torres
-Dr. Muñoz Torres is an Associate Professor at the University of Colorado Anschutz. Her work focuses on the critical challenge of developing the socio-technical foundations needed to realize the promise of artificial intelligence in biomedical sciences. Her expertise includes genomics, biocuration, knowledge representation, and data harmonization. She leads the NIH-funded Bridge to Artificial Intelligence (Bridge2AI)’s teams focused on Standards, Practices, and Quality Assessment. She is also Co-Lead of the Clinical & Phenotypic Data Capture Work Stream of the Global Alliance for Genomics and Health (GA4GH). 
+Dr. Muñoz Torres is an Associate Professor at the University of Colorado Anschutz. Her work focuses on the critical challenge of developing the socio-technical foundations needed to realize the promise of artificial intelligence in biomedical sciences. Her expertise includes genomics, biocuration, knowledge representation, and data harmonization. She leads the NIH-funded Bridge to Artificial Intelligence (Bridge2AI)’s teams focused on Standards, Practices, and Quality Assessment. She is also Co-Lead of the Clinical & Phenotypic Data Capture Work Stream of the Global Alliance for Genomics and Health (GA4GH).
 
 
 # Open Source in the Age of AI

content/posts/2026-03-30-open-source-in-the-age-of-AI.md

@@ -27,13 +27,13 @@ For example, here are some possible topics (but don't feel restricted to these):
 - Reuse: how can we encourage and facilitate reuse of tools and frameworks when AI makes it easy to code things up from scratch?
 - Evaluating open source projects: AI tools can generate thousands of lines of code in seconds. The most costly process is now verifying that code for scientific accuracy (https://arxiv.org/abs/2507.09089). What are some good approaches to address this?
 - Contribution guidelines: balancing scale and utility of AI-assisted development with community-building
-  - How should an open source project assess pull requests from AI agents? 
-  - Are zero-tolerance bans on submissions generated using AI reasonable? (e.g., https://medium.com/@livewyer/ai-disruption-to-open-source-software-oss-377f10be2d8a)  
+  - How should an open source project assess pull requests from AI agents?
+  - Are zero-tolerance bans on submissions generated using AI reasonable? (e.g., https://medium.com/@livewyer/ai-disruption-to-open-source-software-oss-377f10be2d8a)
   - How can humans and AI agents best work together?
 - Attribution and credit:
   - How should we recognize contributions in an age of AI-assisted commits?
   - Transparency:  Should there be mandatory requirements to disclose AI use, including models and prompts used?
-  - Human ownership: should authors always remain legally and ethically accountable for the outputs of their code? 
+  - Human ownership: should authors always remain legally and ethically accountable for the outputs of their code?
   - Licensing: do open source licenses still mean anything when coding agents can translate or reimplement code?
 - Sustainability: who does the long-term hard work of maintaining open source projects when AI does the "easy" work?
 - Credit for training data: part of what AI proposes is reusing existing human-coded work without crediting it. Can there be a way to fairly credit the contribution of an open source project to the (often non open-source) models?

hugo.yaml

@@ -84,7 +84,7 @@ menu:
     - name: 'Donate'
       parent: 'about'
       url: 'donate'
-      weight: 7    
+      weight: 7
     - name: 'News'
       url: 'blog'
       weight: 8