Merge pull request #150 from OBF/panels-26

New Panels 2026 page

changes to the other two files just remove spaces at the ends of a few lines - not sure why that was done

Author: nlharris

content/page/panels-2026.md

@@ -0,0 +1,38 @@
+---
+author: monicacecilia
+date: "2026-03-31T18:50:23+00:00"
+title: BOSC 2026 Panels
+url: /events/bosc-2026/panels/
+bosc: yes
+
+---
+# Policies and Strategies for Resilient Open Science
+
+As we gather in Washington, D.C. for BOSC 2026, we stand at the literal and figurative crossroads of the policies that dictate the pulse of our field. For years, the open science community has operated under a steady wind of progress, but the past year has shifted the weather. We have moved from an era of "open by default" to an environment where the infrastructure of open science—funding, federal data repositories, and international collaborations—is increasingly under scrutiny. This panel moves beyond theoretical support for open science to focus on the strategies of resilience and active advocacy required to navigate a landscape that is being rapidly redefined.
+
+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.
+
+# 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).
+
+
+# Open Source in the Age of AI
+
+In the rapidly evolving landscape of bioinformatics, open-source software (OSS) has long served as the engine of collaborative innovation and reproducible science. However, the emergence of generative AI represents a fundamental shift in how code is authored, maintained, and shared. This panel will examine the "elephant in the room": whether generative AI is a powerful advantage for open-source communities or a complex hindrance to their long-term health. We will explore how AI tools—which can generate thousands of lines of code in seconds—challenge our traditional notions of contribution, reuse, and the very value of human-authored frameworks.
+
+The imperative to balance the scale of AI-assisted development with the core values of community-building has never been more acute. While AI makes it easier than ever to code solutions from scratch, it raises critical questions about the sustainability of existing projects and the scientific accuracy of machine-generated outputs. As the cost of software development shifts from writing code to the rigorous verification of that code for scientific integrity, we must determine how our communities can adapt. From assessing pull requests submitted by AI agents to debating the merits of zero-tolerance bans on AI-generated submissions, the open-source community is at a crossroads in defining how humans and AI agents can best work together.
+
+This panel will bring together a variety of perspectives to address pressing questions on the future of open science in an AI-driven world. We will discuss practical strategies and address topics including: Attribution and Accountability, Transparency and Licensing, Sustainability and Credit, and the Future of Open Data.
+
+# Panelists
+
+### To be named
+
+
+### MODERATOR: Jason Williams

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