Restore giscus comments on all posts

_config.yml

@@ -21,6 +21,26 @@ twitter_username: username
 github_username: diff-use
 minimal_mistakes_skin: default
 search: true
+repository: diff-use/diff-use.github.io
+# Pin these explicitly. Setting `repository` activates jekyll-github-metadata,
+# which otherwise infers a wrong baseurl (/pages/diff-use) and breaks all links.
+url: "https://diffuse.science"
+baseurl: ""
+
+# Comments (GitHub Discussions via giscus)
+comments:
+  provider: "giscus"
+  giscus:
+    repo_id: "R_kgDOPO07gg"
+    category_name: "General"
+    category_id: "DIC_kwDOPO07gs4CtV5I"
+    discussion_term: "title"
+    reactions_enabled: "1"
+    emit_metadata: "0"
+    input_position: "bottom"
+    theme: "light"
+    strict: "0"
+    lang: "en"
 
 # Build settings
 markdown: kramdown
@@ -84,7 +104,7 @@ defaults:
       layout: single
       author_profile: true
       read_time: true
-      comments: false
+      comments: true
       share: true
       related: true
   # _pages

_posts/2025-07-22-new-website.md

@@ -11,5 +11,3 @@ comments: true
 ---
 
 Our new website is hosted by Github Pages, and its source code can be viewed [here](https://github.com/diff-use/diff-use.github.io/).
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-{% include github-comments.html %}

_posts/2025-07-29-davinci.md

@@ -19,6 +19,3 @@ The pattern on the left side corresponds to a particular 2-state molecular motio
 The two diffuse patterns are similar, but clearly different. A major goal of the diffUSE project is to get the weak signal of diffuse scatter measured clearly enough to be analyzed unambiguously in this way.
 
 [Script](https://github.com/jmholton/UnTangle/blob/main/confpdb2diffusemtz.csh) for converting a two-conformer pdb file into diffuse scatter data.
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_posts/2025-07-29-ligands.md

@@ -18,5 +18,3 @@ The authors distill these findings into a set of very conservative lessons: prio
 3. **Input to machine learning to predict protein ligand complexes**: Curating only “gold-standard” data is going to be incredibly limiting. Disagreements between models and experimental data are opportunities to improve algorithms and better understand real-world uncertainty. I doubt that many of the structures that will be deposited by the [OpenBind consortium](https://openbind.uk/) will pass these filters.
 
 Real experimental data is messy, full of alternate conformations, unexpected chemistries, and crystallization “oddities”. Filtering exclusively for perfection may feel safe, but it also limits discovery. Even though using coordinates of the partial occupancy ligands and static alternative conformations will improve things, I'm hoping that the ML for structural biology field will increasingly embrace the mess of experimental data more directly. I’ve written about this before from [conceptual](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10947451), [practical](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12052810), and [policy](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11220883) perspectives. While this trio of papers represents a tremendous teaching text that guides the reader through many of the complexities of protein-ligand data sets, I disagree with the jeremiads at the end of these papers about the potential for misuse. I truly wish there were more careful papers like this out there.   
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_posts/2025-07-30-wetfeet.md

@@ -32,5 +32,3 @@ Team diffUSE convened at Astera HQ in Emeryville, CA on June 23-24, 2025. The wh
 We were aided in our quest by Galen Correy (Fraser Lab at UCSF) who donated a tray full of beautiful SARS-CoV-2 Nsp3 Macrodomain crystals. Galen and collaborators have done some amazing crystallography with this system: check out their [ligand-screening campaign](https://fraserlab.com/macrodomain/) and [neutron diffraction experiments](https://pmc.ncbi.nlm.nih.gov/articles/PMC9140965/). As we discovered at ALS that afternoon, it also has beautiful diffuse scattering!
 
 The diffUSE collaboration has an ambitious open science policy. In the logbook, you can find our [beamtime notes](https://diffuse.science/logbook/beamtime/20250624-als/) and read a [preliminary analysis of the diffuse scattering](https://diffuse.science/logbook/20250624-als831-macrodomain-analysis/).
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-{% include github-comments.html %}

_posts/2025-08-05-jobs.md

@@ -21,5 +21,3 @@ Description: The Diffuse Project is seeking a Machine Learning Infrastructure En
 
 3) Software Engineer
 Description: The Diffuse Project is seeking a Software Engineer to join a multidisciplinary team working to expand the frontier of structural biology by developing methods to capture protein motion. We are assembling a team to develop the process for collecting and interpreting this data from data collection to the final interpretation and scientific impact. You will develop open-source software products to process experimental structural biology data and to manipulate protein structural models. We are particularly interested in product minded applicants who have worked to build products for scientists or other disciplines where a close interface with your users was critical. [Apply here](https://jobs.ashbyhq.com/astera/18327a4b-acfd-46ac-8059-aa06304b0cb5)
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-{% include github-comments.html %}

_posts/2025-08-05-lets-dance.md

@@ -42,5 +42,3 @@ The simulated electron density in the center panel shows the average picture. It
 *Methods*
 
 The [lunus](http://github.com.lanl/lunus) repository has some examples of how to [prepare](https://github.com/lanl/lunus/tree/master/examples/tutorials/crystalline_MD_prep) crystalline MD simulations and use them to analyze [Bragg](https://github.com/lanl/lunus/tree/master/examples/tutorials/crystalline_MD_analysis_bragg) and [diffuse](https://github.com/lanl/lunus/tree/master/examples/tutorials/crystalline_MD_analysis_diffuse) data. 
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-{% include github-comments.html %}

_posts/2025-08-12-encoding.md

@@ -20,7 +20,3 @@ Exciting, much of this information on dynamics is already encoded in the raw exp
 [As we have written about before](https://pmc.ncbi.nlm.nih.gov/articles/PMC11220883/), we envision a hierarchical, ensemble-aware encoding framework designed to capture the full complexity of macromolecular dynamics. This includes distinguishing between different sources of heterogeneity, such as conformational changes versus compositional variation, and representing them in a nested structure that reflects the true physical states. Beyond encoding a single model, this would enable searches based on dynamic properties—for example, identifying all proteins where a particular loop adopts multiple conformations or where ligand binding alters flexibility in a neighboring site. To enable AI co-driven discovery, these representations must serve both human reasoning and machine learning, which means encoding at the individual model level and making this data practical for researchers to access, adapt, and integrate into their pipelines. Such a system would not only help scientists interpret complex structures but also establish community benchmarks, uncover systematic errors, and accelerate method development.
 
 Our goal is to re-engineer the encoding and infrastructure of structural biology to embrace dynamics and set the stage for the next revolution, one where machine learning models not only predict what a protein looks like but also how it moves and functions.
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_posts/2025-08-12-modeling.md

@@ -16,6 +16,3 @@ At present, we often model ensembles separately from Bragg peaks, the sharp, wel
 Our approach begins by improving how experimental data is integrated into modeling, building tools that incorporate both Bragg and diffuse data into optimization and machine learning loss functions and validation metrics, and improving algorithms for ensemble modeling directly from Bragg data. We are also moving towards developing machine learning algorithms that train directly on experimental data rather than using it only in the loss function.
 
 Ultimately, we envision a representation learning framework that dissolves the boundaries between experimental modalities, bringing Bragg, diffuse, and other structural data types into a single, shared space. Within this unified representation, molecular dynamics simulations informed by diffuse data will flow seamlessly into Bragg-based training and inference, allowing the strengths of each approach to amplify the other. By enabling AI models to learn jointly from heterogeneous datasets, we can unlock new levels of predictive accuracy, reveal hidden relationships between data types, and open the door to true cross-modality discovery in structural biology.
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-{% include github-comments.html %}

_posts/2025-08-13-diffuse-blog-post.md

@@ -18,6 +18,3 @@ We just launched The Diffuse Initiative, a new project to experimentally study p
 As a scientist and funder, this is near and dear to my heart. I wrote up some of our thinking here: [https://seemay.substack.com/p/from-systems-operators-to-systems](https://seemay.substack.com/p/from-systems-operators-to-systems)
 
 ![Protein in motion](/assets/images/posts/20250813_seemay_substack.png)
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_posts/2025-09-10-diffuse-shipping.md

@@ -26,5 +26,3 @@ By shipping both frozen proteins and a crystallization plate, we aim to determin
 The next steps involve the Ando Lab assessing the quality of the shipped crystals to determine whether crystallization plates are a feasible option for sample transport. The Ando Lab will also use the shipped protein to grow new crystals. While we anticipate crystallization to be largely reproducible, some optimization may be necessary to produce crystals of sufficient size for diffuse scattering experiments, especially if plate formats and drop sizes differ from our established methods.
 
 The Fraser Lab remains available to provide troubleshooting support and to ship additional protein as needed. Once we establish confidence in our shipping methods and crystal growth reproducibility, we will also prepare shipments of ligands to conduct soaking experiments.
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_posts/2025-09-10-windows.md

@@ -45,5 +45,3 @@ This reminds me of a big caveat that I'm not sure this work addressed: self-abso
 Another important principle to remembner is that the scattering cross section of an atom is a fixed quantity. It is independent of the structure of the material it is in: crystal, amorphous, gas, or otherwise. A given number of oxygen atoms in the beam scatters a knowable number of photons. The only thing the structure of the material does is push those photons around on the detector.  So, at the end of the day, the only way a window can be "X-ray transparent" is to be thin. And light.  And it would be great if it is also cheap and easy to work with.
 
 Looking forward to everyone else's thoughts and to learning how to do this right!
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_posts/2025-10-20-3-2-1-contact.md

@@ -72,5 +72,3 @@ We now have our first assessment of the agreement of MD simulations with diffuse
 ---
 
 *This post was initially drafted in ChatGPT based on a Slack exchange between Steve Meisburger and Michael Wall, and was rewritten and posted by Michael Wall on October 20, 2025.*
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_posts/2025-11-15-in_the_cloud.md

@@ -41,6 +41,3 @@ Thinking about (2), I contacted James Fraser to chat about what to do for the ne
 What will the MD simulation of diffuse scattering from crystals of Mac1 in complex with ADPr look like? Probably a lot like the ones we've done already, with some small changes. We're planning to analyze the differences and find out what happens to the dynamics when different ligands bind. But we don't really know yet what we'll see. These moments of suspense are very common in science, but they're absent from the stories we usually tell in the literature. The open science model we're using on the diffUSE project enables us to document these periods of uncertainty as a part of the public narrative of the project. It feels kind of liberating.
 
 ---
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-{% include github-comments.html %}

_posts/2026-02-02-allhands.md

@@ -433,6 +433,3 @@ Special thanks to Astera for hosting the retreat in Emeryville.
 </table>
 
 ---
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-{% include github-comments.html %}

_posts/2026-02-22-shake-it-up.md

@@ -49,6 +49,3 @@ Along the way we encountered a common pitfall in making these kinds of compariso
 The next step is to compare both of these simulations with data recently collected at CHESS (see [logbook](https://diffuse.science/logbook/beamtime/20251105-chess/)), in one of a series of diffUSE beam times that are expected to yield a large number of datasets. These runs already have revealed that diffuse data are [reproducible between CHESS and ALS beamlines]({% post_url 2026-02-02-allhands %}). Data from Mac1 +/- ADPr are now in the processing pipeline; we're eager to see how Mac1 diffuse scattering changes upon ligand binding, and whether MD simulations can help explain what we see.
 
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