This document records the iterative prompt–response specification process between Matt Akamatsu and Claude that produced the metrics pipeline, visualizations, and evidence statements in this repository. Each numbered exchange is a prompt (verbatim or paraphrased) followed by a summary of the response. The sequence constitutes the full specification: the plots, documents, and code are outputs of this conversation.
Project: node-metrics (MATSUlab discourse graph analysis) Participants: Matt Akamatsu (user), Claude (assistant) Date range: 2026-01-25 through 2026-02-14
De-identification note: Researcher names have been anonymized throughout. Lab members are labeled R1–R11; undergraduate researchers in EVD 7 are labeled A, B, C. The PI (Matt Akamatsu) remains identified as evidence bundle creator.
Prompt (paraphrased from session summary): Set up a metrics analysis pipeline for the Akamatsu Lab discourse graph. Compute five metrics from the Roam Research data exports: (1) Issue Conversion Rate, (2) Time-to-Claim, (3) Time-to-First-Result, (4) Unique Contributors per Issue Chain, (5) Cross-Person Claims (Idea Exchange).
Response: Created the full pipeline: src/parse_jsonld.py (JSON-LD parser), src/parse_roam_json.py (Roam JSON streaming parser with ijson), src/calculate_metrics.py (all 5 metrics), src/generate_visualizations.py (initial charts), src/handoff_visualizations.py (handoff flow diagrams), and src/main.py (pipeline orchestrator). Ran the pipeline end-to-end. Initial results showed anomalies: ~42 experiments with negative time-to-claim, and several with implausible negative time-to-first-result.
Files created: src/parse_jsonld.py, src/parse_roam_json.py, src/calculate_metrics.py, src/generate_visualizations.py, src/handoff_visualizations.py, src/main.py
One important consideration: often, when an issue is claimed, it is converted into an experiment. Therefore, for the files you have access to, you'll only see the experiment page; evidence of the previous Issue is that the experiment page will have a Claimed By:: attribute. So: consider those experiments to be claimed issues. The creation date of those pages should be considered to be the creation date of the issue, because the page started as an issue and then at some point the title was changed to call it an experiment.
Response: Incorporated this domain knowledge: experiment pages with Claimed By:: are treated as claimed issues. Page creation date = issue creation date (since the page originated as an ISS node before being renamed). Updated the parser and metric calculations accordingly.
Nice! What other types of diagrams could we use to illustrate the handoff between different researchers? Let's abbreviate each person as eg "AR" (note that there are two ARs, so we should distinguish between them). Like: maybe a left-> right diagram between nodes, where each node is a person, and the thickness is proportional to the number of transfers? Or: three columns, where third is who (if any) makes the result?
Response: Created src/handoff_visualizations.py with multiple handoff diagram types: a directed network graph (nodes = researchers, edge thickness proportional to transfer count), a 3-column layout (Issue Creator → Claimer → Result Creator), and Sankey/alluvial diagrams.
for validation, provide a few examples, including some pages that were handed off, and also some examples from [R1] (since she has made issues too that have been worked on, at least by her)
Response: Provided concrete examples of handoff flows, including R1's issues and who claimed them. Showed the data for verification.
show me the unexpected examples, particularly those with a negative claim time or time to first result
Response: Diagnosed three root causes: (1) timezone mismatch — datetime.fromtimestamp() returned local Pacific time while JSON-LD dates were UTC, causing ~8-hour offsets; fixed by switching to datetime.utcfromtimestamp(). (2) RES node false-positive matching — 30-character truncation of experiment names matched unrelated RES content; fixed by using relation instances as primary matching, adding backreference matching, restricting fallback to title-only with split('/', 1). (3) One remaining -35 day case from a page merge.
Great! Okay, still give me the examples of the anomalous nodes so that I can verify myself. Also note that the title of the result page will often contain the experiment page title verbatim, such as [[RES]] - myresult - [[@experiment/abcd]], which you can use as one way to identify results that came from that experiment
Response: Added Method 2 ([[@experiment/...]] backreference matching) to _find_linked_res_nodes(). Showed the two remaining anomalous cases: (1) a -35 day time-to-claim where create-time was overwritten by a page merge; (2) a -1 day time-to-first-result where the RES node was created 6 minutes before the Claimed By:: block was populated.
Key design decision: User provided domain knowledge that RES titles contain [[@experiment/...]] references, enabling Method 2 of the 3-tier RES matching strategy.
Likely, we had merged two pages, and are seeing a later date than its creation. So instead, perhaps you should also look at the earliest creation date of the blocks in the page, and use that as the proxy for the issue creation date
Response: Added get_earliest_block_timestamp() to parse_roam_json.py, which recursively scans all blocks on a page for the earliest create-time. Updated merge_experiment_data() to use min(jsonld_created, roam_page_created, earliest_block_timestamp) as the issue creation date. The -35 day case resolved to 86 days. Zero remaining negative time-to-claim values.
Key design decision: 3-candidate minimum for issue creation date handles page merges that corrupt page-level timestamps.
I'm confused why there are no flows from [R1]'s issues. she "claimed" some by starting work on them within the issue page, so that's like a self-claim; perhaps there are other claims (from her issues or by her) that we missed?
Response: Found that R1 had 42 experiment pages with no Issue Created By:: or Claimed By:: fields. Broader finding: 103 of 171 experiment pages (60%) lacked formal metadata fields.
great -- for the issues that don't have a Claimed By:: field, but do have experimental log with content filled out (ie blocks that are children of the experimental log blocks) by the same person, those should be considered self-claimed
Response: Implemented inferred self-claims. Used JSON-LD creator field as the inferred claimer (block-level user UIDs were ambiguous). Added claim_type field ('explicit', 'inferred'). For inferred claims, used page creation date as reference timestamp. Final results: 127 total claimed (68 explicit + 54 inferred + 5 ISS), 29.3% conversion rate, 103 self-claims, 19 cross-person, 15.6% idea exchange rate.
Key design decision: Experiment pages with experimental log content but no Claimed By:: field are treated as self-claimed by the page creator. This added 54 claims and changed the conversion rate from ~15% to 29.3%.
Great. Okay: now write up a methods document that describes your criteria and approaches from the raw data files/metadata to get those metrics. This should have enough specificity for someone to be able to see the grounding context for any observations we make about these data. When possible, reference the specific scripts or functions used, so that it's easier to cross-reference/validate
Response: Wrote output/methods.md — a comprehensive methods document covering: data sources (JSON-LD and Roam JSON exports), node identification (experiment pages, ISS, RES), claim detection (explicit vs. inferred), timestamp handling (3-candidate minimum, timezone normalization), RES node linking (3-tier matching), all 5 metric definitions with formulas, known limitations, and pipeline architecture with script/function/line references throughout.
Files created: output/methods.md
write an executive summary of the measurements you've made from the data
Response: Wrote output/executive_summary.md summarizing key findings with interpretation and caveats.
Files created: output/executive_summary.md
remake the plots to demonstrate the 1) conversion rate; 2) time to claim distribution, time to result distributions; 3) contributor breadth; idea exchange
Response: Rewrote src/generate_visualizations.py from scratch with four publication-quality figures: (1) stacked horizontal bar + donut for conversion rate, (2) paired histograms with custom bins for time-to-claim and time-to-first-result, (3) contributor distribution bar + per-researcher activity horizontal stacked bars, (4) directed network graph + heatmap matrix for idea exchange.
Files created/modified: src/generate_visualizations.py, output/visualizations/fig1–fig4 PNGs
also include evidence statements for each of the plots. You have lots of examples of the format of evidence statements. they should be in past tense, include the observation, observable, system (MATSUlab) and will be bundled with the accompanying plots. And figure legends, similar to what you just shared, but phrased a little more like a figure legend.
Response: Searched the discourse graph for EVD node examples. Wrote output/evidence_statements.md with four EVD sections, each containing: an evidence statement (past-tense with observation, observable, system), evidence description, and figure legend.
Files created: output/evidence_statements.md
rephrase figure 3 as a positive statement (the percentage of experiments that were multi-contributor), and also include a result and plot about findings about the conversion from issue to experiment to results
Response: Rephrased EVD 3 from "74% single-contributor" to "26% multi-contributor." Added create_funnel_figure() producing fig5_funnel.png — a two-panel figure with horizontal funnel bar chart and stage-by-stage breakdown. Added EVD 5 to evidence statements.
Files modified: src/generate_visualizations.py, output/evidence_statements.md
Files created: output/visualizations/fig5_funnel.png
Finally, summarize our conversation, including my verbatim prompts, and shortly summarizing your responses. the goal here is to provide the equivalent of a methods section, but for our back-and-forth, where my prompts + your responses can be thought of as the specification that leads to the plots and documents
Response: Created output/conversation_log.md documenting the exchange history.
For this latest result res-5: show me some examples of the results that came from the claimed issues (and the example flows from issue), so that I can verify
Response: Provided concrete examples of issue → experiment → result flows for verification.
we'll come back to this very promising evidence bundle concept. Can you remake the flow diagram "handoff_alluvial" based on your most recent analysis? each column should be clearly labeled "issue created" "issue claimed" "result created" and make sure all issue creators are represented in the left column
Response: Rebuilt the alluvial/Sankey diagram with three labeled columns (Issue Created → Issue Claimed → Result Created), ensuring all issue creators appear in the left column.
Files modified: output/visualizations/handoff_alluvial.html
Nice!! Write a figure legend and the rest of the evidence bundle statement
Response: Wrote figure legend and evidence statement for the alluvial diagram, documenting the researcher contribution flow.
Great. Okay, so our overall target is exploring the extent to which the issue board enables a rapid onboarding process and targeted, effective researcher contributions. So: let's quantify the timeline of contributions for three undergraduate students: [Researcher A] (start February 2024), [Researcher B] (October 2024), and [Researcher C] (June 2025). I added some pages into the folder "student data" including exports of their meeting notes, the experiment that [Researcher C] worked on and two associated results. From this info, and from the graph pages you can access, construct a plan for charting the timeline of key milestones
Response: Designed a plan tracking four milestones per student: (1) first day in lab, (2) first experiment reference, (3) first plot, (4) first formal RES node. Created src/student_timeline_analysis.py.
Files created: src/student_timeline_analysis.py
It looks like we were missing some of the key pages, so I've manually exported them and added them to the folder, including daily note entries for the relevant time period, experiment pages (with daily log entries), and result pages (that reference the experiment pages). note that for "[RES node title]" even though the author was Matt, it notes that the creator was [Researcher A], so we should attribute the result creation [to Researcher A]
Response: Re-read the newly exported pages. Corrected attribution: even though Matt authored the RES page, the creator field indicated Researcher A, so the result was attributed accordingly.
I added another experiment that [Researcher B] claimed early: [experiment title]. For each person's daily notes, the first time that they reference an experiment ([[@.../]]) should be considered the first time they start an experiment (or the first date on the experiment daily log). Make sure to look for experiments referenced in each person's daily log. For the plots you generate, anonymize the researchers as researcher A, B C in the plot labels
Response: Incorporated Researcher B's experiment as their first claimed experiment (5 days after starting). Implemented first-reference detection from daily notes. Anonymized researchers as A, B, C in all outputs.
Getting closer -- for [Researcher A], you should consider the first claim date either the creation date of the experiment that she claimed (which is earlier, in this case) or the first mention of the experiment page in daily notes. And use the creation date of the corresponding result as the date her first result was created
Response: Corrected Researcher A's timeline: first experiment = April 5, 2024 (41 days after starting). First result = June 27, 2024 (125 days).
Don't you see in an earlier version of your plan that the first experiment [Researcher A] claimed was on april 5 2024 (41 days after started)? and the associated result on june 27
Response: Confirmed: Researcher A — first experiment April 5, 2024 (day 41), first RES June 27, 2024 (day 125). Corrected the implementation.
Great! just move the legend for the figure student_timelines.png, rename it so that it's associated with evd_7, and make an evidence bundle for evd 7. the result statement can be: All three undergraduate researchers generated an original result from their analysis projects within ~4 months of joining the lab, with two creating a result within ~1 month
Response: Created output/evidence_bundles/evd7-student-onboarding/ with RO-Crate metadata, evidence statement, figure, and milestone data.
[Exchange 25] for this figure, I think I'd like a stem type diagram, where each researcher has like a "pin" for the relative time of each milestone...
[Exchange 26] how are we distinguishing the different types of milestones in this version of the diagram?
[Exchange 27] Yes! Prepare a static png as well as an interactive version, and update the evidence bundle accordingly
Response: Designed and implemented pin/stem timeline with vertical stems, circular icons with symbols (play=start, flask=experiment, chart=plot, checkmark=result), color-coded progress bars, and both static PNG and interactive HTML versions.
Files created: output/visualizations/fig7_student_timelines.png, output/visualizations/fig7_student_timelines_app.html
Prompt (paraphrased): Create a simplified HTML file based on timeline_visualization.html for the evidence bundle — keep the plot, tooltips, and labels checkbox but remove download buttons and design specification text.
Response: Created a cleaned-up interactive timeline HTML for the EVD 7 evidence bundle.
save your knowledge in CLAUDE.md
Response: Wrote CLAUDE.md with project overview, data sources, evidence bundle structure, code patterns, and run instructions.
Catch yourself up about the key results we have so far. especially: 29% of MATSUlab issues (n=434) were claimed as experiments and 11% of those produced at least one formal result node, yielding 136 total RES nodes. What visualizations do we already have? Can we package this result into an evidence bundle? Can we make the visualization interactive, eg in an HTML file?
Response: Explored the full codebase. Noted a framing issue: user said "11% of those" but the data shows 48/434 = 11% of all issues, while 48/127 = 38% of claimed experiments. Recommended the "38% of claimed" framing.
ah yes can you bundle EVD1, and rephrase it to "29% of MATSUlab issues (n=434) were claimed as experiments and 38% of those claimed at least one formal result node, yielding 136 total RES nodes" per your suggestion? The alluvial HTML is fine actually. The key figure is the png, and the HTML file belongs in the evidence bundle too.
Response: Created output/evidence_bundles/evd1-conversion-rate/ with evidence.jsonld, ro-crate-metadata.json, both PNG and HTML figures, data, and docs. Added create_evd1_bundle() to src/create_evidence_bundle.py.
Make a lab notebook markdown file, which, by date, documents our conversations. My side of the conversation should be verbatim, and your side is a summary of what you did and said.
Response: Created output/lab_notebook.md.
[Exchange 33] Based on this image, prepare an updated plot design for evidence 7. [Detailed feedback on what works well and what needs tweaking]
[Exchange 34] it doesn't have the visual appeal of this image, does it? you need to do a few more rounds of comparison so that the png file matches the clean arrangement of this target image... perhaps it needs to be an app instead?
Response: Analyzed the target design and identified that matplotlib cannot achieve the required visual polish. Created fig7_student_timelines_app.html — a standalone HTML/JS app with consolidated timeline, vertical pins with SVG icons, progress bars, summary cards, zoom toggle, and hover tooltips.
[Exchange 36] from our conversation so far, specify the various criteria we've used to attribute a given issue/experiment/result node to a given person. aside from the author metadata for the page
[Exchange 37] record those criteria in a markdown file, with a little more methodological information about how one parses the json or json-ld files to get that information.
Response: Created docs/attribution_criteria.md documenting 8 attribution criteria (Made by, Creator, Claimed by, First Reference in Daily Notes, Possible Contributors, First Plot/Image, RES Node Creation Date, Entry Project notation) with parsing code examples and priority order.
Okay claude, I've been working on preparing evidence bundles—self-contained packages of data and reproducible analysis scripts—for the results from our discourse graph issue metrics study. We've now compiled three evidence bundles (EVD 1, EVD 5, EVD 7). I'd like to create a transparent documentation notebook (Jupyter) that walks through the full analysis pipeline for all three evidence bundles, showing each computation step.
Response: Created notebooks/evd1_evd7_analysis.ipynb with ~50 cells covering: data loading, issue classification breakdown, claim detection (explicit vs. inferred), cross-person claim analysis, time-to-first-result distributions, contributor breadth, EVD 1 conversion rate, EVD 5 funnel analysis, and EVD 7 student timeline milestones. Each cell includes inline commentary explaining methodology and linking to evidence statements.
Files created: notebooks/evd1_evd7_analysis.ipynb
Prompt (paraphrased): Some metrics in
create_evidence_bundle.pywere hardcoded. Update them to be dynamically computed from the data.
Response: Updated the evidence bundle generator to compute all summary statistics from the metrics dictionary rather than using hardcoded values. Updated main.py default file paths to point to the Feb 11, 2026 data exports.
Prompt (paraphrased): Incorporate the additional attribution criteria from
docs/attribution_criteria.mdinto the analysis pipeline.
Response: Added extract_made_by_from_content() and extract_author_from_content() to parse_jsonld.py. Added extract_made_by_timestamp() and extract_author_from_page() to parse_roam_json.py. Updated calculate_metrics.py with a primary_contributor priority chain (Made by > Claimed By > Author > JSON-LD creator). Updated the alluvial diagram in handoff_visualizations.py to use attribution-aware contributor data. Updated the notebook with attribution transparency cells.
Okay claude, we're getting ready to create and share the results of our study on github. [...] Our goal will be to deliberately choose the files that will be tracked and pushed to a github repository that they are 1) self-explanatory, 2) transparently trace the path from raw data to the plots, 3) share the relevant methods and analysis choices context so that the reader can appreciate/contextualize each result, particularly within the evidence bundle, 4) reasonably self-contained [...] We also want to de-identify as much as possible. [...] We can create some summary data files [...] that don't have real names of file authors (you can create some dictionary between real name and the label you end up using). [...] I moved the raw data files to a 'graph raw data' folder; you'll need to update the scripts to point to this new path. Similarly, we won't be git tracking the 'project context' folder, the 'draft' folder, or the 'early_versions_of_lab_graph-exports' folders. You can attribute yourself and Matt Akamatsu as creators of the evidence bundles.
Response: Comprehensive de-identification and repository setup:
-
Created
src/anonymize.py— central de-identification module withNAME_TO_PSEUDONYMmapping (12 researchers → R1–R11; PI stays identified),anonymize_name(),anonymize_title(), andanonymize_dict()functions. -
Updated all source files —
student_timeline_analysis.py(student names → Researcher A/B/C),handoff_visualizations.pyandgenerate_visualizations.py(removed real-name abbreviation dicts, imported fromanonymize.py),create_evidence_bundle.py(anonymized CSV fields and experiment titles). -
Updated all file paths across 6 source files and the notebook to reference the
graph raw data/subfolder. -
Re-ran full pipeline and re-executed notebook — all outputs regenerated with anonymized names. Verified: zero real names in any output file; real names appear only in
src/anonymize.py(the mapping dictionary, required for functionality). -
Created
.gitignoreexcluding raw data, drafts, project context, intermediate outputs, and Claude IDE files. -
Initialized git repo on
mainbranch.
Where can I find the updated alluvial flow diagram? Can you make that the key figure for Evidence 5? The bundle can include supplemental plots
Response: Made the alluvial flow diagram the primary figure for EVD 5:
- Added handoff visualization generation (Step 5b) to
main.pypipeline - Fixed hardcoded "122" count in alluvial title → dynamic
conv['total_claimed'] - Updated
create_evidence_bundle.py: alluvial as primary figure (fig5_alluvial_flow.png+.html), funnel as supplemental (fig5_funnel_supplemental.png) - Updated
evidence.jsonldwith alluvial figure legend, supplemental figure entry, andhandoff_visualizations.pyin method provenance - Updated
ro-crate-metadata.jsonto list all three figure files
change the name of the main branch from master to main, then please commit the files!
Response: Switched to main branch and committed 35 files (14,289 lines): source code, notebook, evidence bundles, config files. Commit e9e42a7.
Also merge the conversation_log and lab_notebook.md file, updating with our conversation, and scrubbing it for identifying names. put that in a conversation_log.md that we'll also track with git. Then create a readme file [...] Add a CC-BY-4.0 license to both evidence bundles. Push all that to a new private repo in DiscourseGraphs/MATSUlab-issue-exchange-analysis
Response: This entry and the accompanying README, LICENSE, and GitHub push.
For figure 1, the png file wasn't rendering correctly (the bar graph was only one bar), so I replaced it with a screenshot of the html file. (the html file renders great.). Can you update the plotting for fig 1 so that it generates the png file correctly.
Response: Diagnosed the root cause: the old 4-segment stacked bar (explicit claims, inferred claims, ISS with activity, unclaimed) had all three claim-type subcategories at zero in the current data, rendering only the unclaimed segment. Simplified create_conversion_rate_figure() in generate_visualizations.py to a 2-segment bar: Claimed experiments (blue, 69) vs Unclaimed ISS (grey, 320). Also made the donut title dynamic (f"among {claimed_total} experiment claims"). Updated the HTML file data values from old snapshot (434/127/48/136) to current (389/69/25/44). Re-ran pipeline and copied the regenerated PNG to the evidence bundle.
Key design decision: Simplified the bar chart to match the two categories that are reliably populated in the current data, rather than maintaining subcategories that are all zero.
I'd like to change the figure legend titles so that they consist of the observation statement (result statement) rather than a neutral description of the figure. For example, for figure 1, you could switch the figure legend title to "18% of MATSUlab issues were claimed as experiments and 36% of those claimed at least one formal result node". Also can you make the callout at the top of the page stand out a bit more (it's a > currently; it should read more like a title or subtitle)
Response: Updated all three evidence bundle READMEs:
- Changed top callout from blockquote (
> **bold**) to###heading for visual prominence on GitHub - Replaced neutral figure legend titles with observation/result statements:
- EVD 1: "18% of issues (69 of 389) were claimed as experiments; 72% of claims were self-claims and 28% were cross-person idea exchanges."
- EVD 5: "The issue-to-result pipeline funnels 389 issues down to 69 claimed experiments and 25 with formal results, with the PI serving as the primary hub for cross-person idea exchange."
- EVD 7: "Three onboarding pathways produced results in 125, 47, and 36 days — structured assignment via the issue board compressed time-to-result by 3×."
User subsequently refined the EVD 5 and EVD 7 figure legend titles directly.
Note that I refined the evidence statements in the readme for evd 5 and 7. The grounding context section needs work. This section is meant to provide the reader with sufficient context to appreciate the limitations and methods that led to this particular plot. So, for example, EVD5: "Claiming was assessed based on page metadata ('Claimed by:' attribute), page authorship, or authorship of daily log entries within the Issue page. This approach does not capture the issues that were more informally transferred from one researcher to another" -- that type of context. So: use the methods that created each of the evd bundles to consider the limitations/caveats/grounding context necessary for the reader to appreciate the result and put it in appropriate context, and put that in the "grounding context" section for each evidence bundle
Response: Reviewed calculate_metrics.py implementation in depth to identify the specific measurement assumptions and limitations. Renamed "Context" → "Grounding context" in all three READMEs. Rewrote each section with a brief methodological lead-in followed by enumerated limitations:
- EVD 1 (5 caveats): claim detection is metadata-dependent; inferred claims default to self-claims (underestimating exchange); issue count depends on naming conventions; result linking uses three-tier fallback (36% is a lower bound); snapshot-date caveat.
- EVD 5 (6 caveats): same claim detection caveats as EVD 1 (cross-referenced); attribution priority chain; "No Result Yet" ambiguity; known-authorship requirement for cross-person classification; flows reflect formal metadata not informal collaboration.
- EVD 7 (6 caveats): milestone dates manually extracted (not computed); small sample n=3 with temporal confound; post-hoc pathway classification; elapsed days ≠ effort; RES node ≠ quality; lab average comparison uses different population.
Key design decision: The grounding context surfaces the specific methodological choices that could bias the numbers, helping readers calibrate their interpretation rather than taking figures at face value.
Please push your latest changes to the repo and pull the changes I made to the main README file. Note the changes I made to the main readme file in the source material and attribution links. Propagate those links and attribution to each evidence bundle. In other words, each evidence bundle README should include those attribution links, and to the relevant source material. ie EVD 5 should have the link for EVD5 and EVD7 should have the link to EVD7
Response: Pushed local commits, pulled user's main README updates (which added Source material and Attribution sections with Roam result page links and ORCID-linked names). Propagated to each evidence bundle README with bundle-specific source links:
- EVD 1: experimental log + raw data request (no bundle-specific result page)
- EVD 5: experimental log + Result page: EVD 5 + raw data
- EVD 7: experimental log + Result page: EVD 7 + raw data
All three include the full Attribution block (Matt Akamatsu with ORCID, Claude, Discourse Graphs Project with Joel Chan ORCID, Akamatsu Lab, DG extension).
Nice. Please update our conversation_log.md based on our conversation thus far
Response: This entry. Updated the conversation log through Exchange 50 (Session 8).
Session 9 — 2026-02-13 through 2026-02-14 (terminology disambiguation, new data export, EVD1 bundle fix)
[User provided new Roam JSON and JSON-LD exports from 2026-02-11 and 2026-02-13]
Response: Updated the pipeline to use the new data exports (akamatsulab_discourse-graph-json-LD202602112140.json and akamatsu-whole-graph-2026-02-13-22-24-27.json). Added export validation to verify the Roam JSON matches the JSON-LD graph. Re-ran pipeline — metrics updated: 445 total issues (up from 434), 130 claimed (up from 127), 323 RES nodes. Conversion rate stayed at 29.2%.
Files modified: src/main.py, src/calculate_metrics.py (added validation)
[Multiple iterations fixing the alluvial/Sankey diagram where "No Result Yet" node was clipped]
Response: Fixed SVG clipping in the alluvial diagram by adjusting figure margins, repositioning the "No Result Yet" node, and tuning the Sankey layout. Multiple iterations to get the positioning right.
Files modified: src/handoff_visualizations.py
The term 'claim' is a little confusing, because discourse graphs also use the term claim as in question-claim-evidence. so instead of using 'claim' as a noun, can you substitute for things like 'claiming' (for fig 1 and 5) as well as in the text (methods, readmes, etc)
Response: Systematically replaced "claim" used as a noun throughout the codebase to avoid confusion with CLM (Claim) discourse graph nodes. Terminology mapping applied:
- "Explicit Claims" → "Explicitly Claimed"
- "Inferred Claims" → "Inferred Claiming"
- "Self-Claims" → "Self-claiming" / "Self-claimed"
- "Cross-Person Claims" → "Cross-Person Claiming"
- "Claim Authorship" → "Claiming Authorship"
- "Time-to-Claim" → "Time-to-Claiming"
- "Net Idea Claimer" → "Net Idea Recipient"
Updated 13+ files spanning source code, visualizations, evidence bundles, methods, and documentation. Preserved internal variable names (claim_type, claimed_by), Roam field names (Claimed By::), and verb usage ("were claimed").
Files modified: src/main.py, src/calculate_metrics.py, src/generate_visualizations.py, src/handoff_visualizations.py, src/create_evidence_bundle.py, output/methods.md, output/evidence_statements.md, output/visualizations/fig1_conversion_rate.html, evidence bundle docs and data files, README.md, CLAUDE.md
What about figure 1? I still see the claim terminology there in evd bundle 1
Response: Root cause: main.py only called create_evd5_bundle() — create_evd1_bundle() was never invoked during the pipeline, so the EVD1 bundle's PNG and generated files were stale. Fix: added create_evd1_bundle to the import and pipeline execution in main.py. Also updated output/methods.md (source for EVD1 methods excerpt), output/visualizations/fig1_conversion_rate.html (source HTML copied into bundle), and output/evidence_statements.md. Removed stale methods_excerpt.md orphaned at EVD1 bundle root. Re-ran pipeline to regenerate all EVD1 bundle files.
Files modified: src/main.py, output/methods.md, output/evidence_statements.md, output/visualizations/fig1_conversion_rate.html, output/evidence_bundles/evd5-issue-funnel/README.md
Update our conversation_log (which one is being tracked?) and then commit changes to main, push changes to the remote repo
Response: Identified that conversation_log.md (root) is the sole git-tracked log. Removed redundant untracked copies (output/lab_notebook.md, output/conversation_log.md). Updated conversation log with Session 9 entries. Committed and pushed.
| File | Purpose | Key exchanges |
|---|---|---|
src/parse_jsonld.py |
Parse JSON-LD export; extract experiment pages, ISS/RES nodes, relation instances | 1 |
src/parse_roam_json.py |
Stream-parse Roam JSON; extract block timestamps, experimental logs, claim metadata, earliest block timestamps | 1, 7 |
src/calculate_metrics.py |
Merge data sources; compute all 5 metrics; 3-tier RES matching; inferred claiming | 1, 5, 6, 7, 9, 41, 53 |
src/generate_visualizations.py |
Generate figures 1–5 (static PNGs) | 12, 14, 46, 53 |
src/handoff_visualizations.py |
Generate handoff flow diagrams (alluvial, directed, heatmap) | 3, 17, 43, 52, 53 |
src/student_timeline_analysis.py |
Student onboarding timeline extraction and visualization | 19–27, 33 |
src/create_evidence_bundle.py |
Generate RO-Crate evidence bundles (EVD 1, 5, 7) | 24, 31, 43, 53 |
src/anonymize.py |
Central de-identification module (name → pseudonym mapping) | 42 |
src/main.py |
Pipeline orchestrator; runs EVD 1 and EVD 5 bundle creation | 1, 43, 54 |
| Bundle | Evidence Statement | Key exchanges |
|---|---|---|
evd1-conversion-rate/ |
29% of MATSUlab issues (n=445) were claimed as experiments and 38% of those produced at least one formal result node, yielding 139 total RES nodes | 31, 46, 47, 48, 49, 53, 54 |
evd5-issue-funnel/ |
Full funnel with alluvial flow diagram showing researcher-level Issue Created → Claimed By → Result Created flow | 14, 16, 17, 43, 47, 48, 49, 53 |
evd7-student-onboarding/ |
All three undergraduate researchers generated an original result within ~4 months | 24, 27, 28, 33, 34, 47, 48, 49 |
- Experiment pages as claimed issues (Exchange 2): Pages with
Claimed By::are treated as claimed issues. - RES backreference matching (Exchange 6): RES titles contain
[[@experiment/...]]references, enabling Method 2 of 3-tier matching. - Earliest block timestamp (Exchange 7):
min(jsonld_created, roam_created, earliest_block)handles page merges. - Inferred self-claims (Exchange 9): Pages with experimental log content but no
Claimed By::are self-claimed by creator. Added 54 claims. - Evidence statement format (Exchange 13): Past tense, observation + observable + system, following discourse graph EVD conventions.
- Positive framing (Exchange 14): "26% multi-contributor" instead of "74% single-contributor."
- Funnel visualization (Exchange 14): Conversion funnel tracking issues through claim to result production.
- Student anonymization (Exchange 21): Researchers anonymized as A, B, C.
- Result attribution (Exchange 20): RES nodes attributed to
creatorfield, notauthor. - "38% of claimed" framing (Exchange 31): Rephrased from "11% of all issues" to "38% of claimed experiments."
- Track-based timeline design (Exchange 33): Separate horizontal tracks per researcher. HTML/JS for visual polish.
- Attribution priority chain (Exchanges 36–37, 41): Made by > Claimed By > Author > JSON-LD creator.
- De-identification (Exchange 42): Central
anonymize.pymodule; PI identified, all others R1–R11. - Alluvial as primary EVD 5 figure (Exchange 43): Researcher-level flow diagram replaces aggregate funnel as the key figure.
- 2-segment conversion bar (Exchange 46): Simplified from 4 claim-type subcategories (all zeros) to claimed vs. unclaimed.
- Observation-statement figure legends (Exchange 47): Figure legend titles state the result, not a neutral description.
- Grounding context with limitations (Exchange 48): Each evidence bundle documents the specific methodological caveats readers need to interpret the numbers critically.
- "Claiming" not "claim" terminology (Exchange 53): Replaced "claim" nouns with "claiming" gerund forms to avoid confusion with CLM (Claim) discourse graph node type. "Claimed" as verb/adjective kept.
- EVD1 bundle in pipeline (Exchange 54): Added
create_evd1_bundle()tomain.pyso EVD1 bundle is regenerated alongside EVD5 during pipeline runs.
Session 10 — 2026-02-14 through 2026-02-15 (EVD1 observation fix, experiment lifecycle visualizations)
There are a couple of consistency issues with the outputs of this repo. The readme for evd1: the observation statement currently includes a statement about results, though the plot doesn't describe the conversion to results. it does include the % that were claimed between lab members, so that can be part of the observation statement. And then this observation statement should be the first sentence of the figure legend (our best practices for including the observation statement in the figure legend)
Response: Updated the EVD1 observation statement from "29% of MATSUlab issues were claimed as experiments, and 38% of those produced at least one formal result node" to "29% of MATSUlab issues (n=445) were claimed as experiments, and 15% of those were claimed by a different lab member than the issue creator." Updated 5 files: README.md, docs/evidence_statement.md, evidence.jsonld, ro-crate-metadata.json, and fig1_conversion_rate.html. Made the observation statement the first sentence of the figure legend in all locations.
Files modified: output/evidence_bundles/evd1-conversion-rate/README.md, docs/evidence_statement.md, evidence.jsonld, ro-crate-metadata.json, fig1_conversion_rate.html
[Implicit from prior session context] Created a new series of experiment lifecycle visualizations (Fig 6a–6g) including time-to-result histogram, CDF, swimmer plot, raincloud plot, result yield bubble chart, Kaplan-Meier survival curve, and result cascade.
Response: Created src/experiment_lifecycle_visualizations.py with 7 visualization functions generating both static PNG and interactive Plotly HTML outputs. Added to the pipeline via main.py.
Files created: src/experiment_lifecycle_visualizations.py
for the output/visualization/fig6c_swimmer_plot.html, several of the experiments are reported as being claimed long after the result was created. So can you make me a diagnostic HTML file, where for the mouse overlay, I get lots more diagnostic information, like the claim date stamp, the method if applicable or where that info came from. this will let me troubleshoot why those numbers are so far outside what I'm expecting
Response: Created src/diagnostic_swimmer_plot.py → output/visualizations/fig6c_swimmer_plot_diagnostic.html with rich hover tooltips showing all timestamps, claim types, and both reference-based and page-based day calculations. Identified the root cause: a mixed x-axis origin bug where days_to_claim was relative to page_created but days_to_first_result was relative to ref_timestamp, which for explicit claims was claimed_timestamp (not page_created). This caused ~10 experiments to display claim diamonds after result stars.
Files created: src/diagnostic_swimmer_plot.py
Okay, so this looks like the results are not being plotted on the right axis. They should be plotted as days from original issue created, right? This should be easily done, since the exact date is given. So, for example, @analysis/develop metrics to quantify... the result was created 2024-11-26, the page was created 2023-07-06, and the claiming happened on 2024-10-31. So we need to redo the result time calculation so that it shows up 26 days after the claim time (in this case), meaning it should be shown as the difference from the creation date of the issue. Right?
Response: Updated the diagnostic swimmer plot to use a unified x-axis with page_created as day 0 for all markers. Confirmed with the user's example: claim at day 483, result at day 509 (= 483 + 26), both from 2023-07-06 page creation.
Files modified: src/diagnostic_swimmer_plot.py
Phew! Log this in the conversation-log.md and update the plotting function and swim diagram based on this unified X axis. Replot on linear and log scale (two plots)
Response: Updated create_swimmer_plot(), _create_swimmer_plot_static(), and _compute_res_days() in src/experiment_lifecycle_visualizations.py to compute all marker positions as days from page_created (issue creation date) using actual timestamps. Replaced _compute_res_days() with _compute_res_days_from_page(). Updated sorting to use corrected page-origin days. Regenerated all three swimmer plot outputs: interactive HTML, static linear PNG, and static log-scale PNG.
Key design decision (20): Unified swimmer plot x-axis (Exchange 59–60): All swimmer plot markers (claim diamonds, result stars) use page_created as day 0, computed from actual timestamps. The pre-computed days_to_first_result metric (which uses claimed_timestamp as origin for explicit claims) is not used for plotting — only for the metric calculation itself.
Files modified: src/experiment_lifecycle_visualizations.py
Love it! Let's also report some statistics in a table. The mean/median and 95% confidence intervals for the length of time from issue creation to claim, and from issue claim to first result creation. Do you think the time from issue creation to first result is exponentially distributed? Or that the time from issue claim to result creation is exponentially distributed?
Response: Computed summary statistics with 10,000 bootstrap CIs for three intervals: issue→claim (n=125, median 0d, mean 52.8d), issue→result (n=50, median 38d, mean 127.5d), claim→result (n=50, median 12d, mean 47.6d). Tested exponential vs. log-normal fit: none of the distributions are exponential (KS tests reject for issue→claim p=0.02 and issue→result p=0.007; claim→result marginal p=0.07). The log-transformed positive values pass normality tests (KS p=0.66–0.72), indicating log-normal is a better model — consistent with research tasks involving multiplicative accumulation of variable-duration steps.
Love it. Once you plot it, it looks like the data are a little sparse for us to make too many claims about the distributions. Let's save the table, and fig6c, in a new evidence bundle called time-to-result. We should workshop the observation statement, but it's something like: there was a median of 12 days between issue claim to result generation, with wide variance (IQR 0-51 days)
Response: Created output/evidence_bundles/evd6-time-to-result/ with swimmer plot (linear, log, interactive HTML), summary statistics table with bootstrap CIs, evidence.jsonld, ro-crate-metadata.json, and evidence statement. Proposed observation statement: "Among 50 experiments that produced formal results, the median time from claiming to first result was 12 days (IQR 0–50 days), and the median time from issue creation to first result was 38 days (IQR 7–184 days)."
Files created: output/evidence_bundles/evd6-time-to-result/ (README.md, evidence.jsonld, ro-crate-metadata.json, data/time_to_result_data.json, fig6c_swimmer_plot.{png,html}, fig6c_swimmer_plot_log.png)
It looks great! The README and the log/evidence-statement are now redundant, so we should just remove the log folder and the evidence statement.md. my result statement reads: The median time from issue claiming to first result in the MATSU lab was 12 days, with wide variance (n=50, IQR 0–50), so we could combine both: Among 50 experiments in the MATSU lab that produced formal results, the median time from issue claiming to first result in the MATSU lab was 12 days, with wide variance (n=50, IQR 0–50). This is because I think the wide variance is the main observation, and we're adding a little extra context about the model system being the MATSU lab
Response: Removed redundant docs/ folder and evidence_statement.md from the EVD 6 bundle (the README serves as the single source of truth). Updated the observation statement across README.md, evidence.jsonld, and ro-crate-metadata.json to: "Among 50 experiments in the MATSUlab that produced formal results, the median time from issue claiming to first result was 12 days, with wide variance (n=50, IQR 0–50 days)." Updated figure legend to match. Removed stale docs references from metadata files.
Key design decision (21): Wide variance as the headline observation (Exchange 62): The observation statement leads with the median (12 days) but emphasizes the wide variance (IQR 0–50) as the main finding, rather than listing multiple interval statistics. The MATSUlab is named as the system for context.
Files modified: output/evidence_bundles/evd6-time-to-result/README.md, evidence.jsonld, ro-crate-metadata.json
Files removed: output/evidence_bundles/evd6-time-to-result/docs/ (redundant with README)