455 CARDS · 41 WEEKS · EVERY SOURCE THE PLAN NAMES
Every video, chapter, paper and repository in the programme, as one card each, with the time it costs and why it is there. Filter by how long you have.
171 cards shown. Stashes: LABLOOP-W00 Notebook (15) LABLOOP-W01 Notebook (18) LABLOOP-W02 Notebook (15) LABLOOP-W03 Notebook (19) LABLOOP-W04 Notebook (17) LABLOOP-W05 Notebook (16) LABLOOP-W06 Notebook (12) LABLOOP-W07 Notebook (12) LABLOOP-W08 Notebook (15) LABLOOP-W09 Notebook (10) LABLOOP-W10 Notebook (11) LABLOOP-W11 Notebook (12) LABLOOP-W12 Notebook (12) LABLOOP-W13 Notebook (13) LABLOOP-W14 Notebook (12) LABLOOP-W15 Notebook (10) LABLOOP-W16 Notebook (14) LABLOOP-W17 Notebook (13) LABLOOP-W18 Notebook (10) LABLOOP-W19 Notebook (10) LABLOOP-W20 Notebook (10) LABLOOP-W21 Notebook (13) LABLOOP-W22 Notebook (10) LABLOOP-W23 Notebook (9) LABLOOP-W24 Notebook (9) LABLOOP-W25 Notebook (12) LABLOOP-W26 Notebook (11) LABLOOP-W27 Notebook (10) LABLOOP-W28 Notebook (6) LABLOOP-W29 Notebook (7) LABLOOP-W30 Notebook (10) LABLOOP-W31 Notebook (8) LABLOOP-W32 Notebook (5) LABLOOP-W33 Notebook (13) LABLOOP-W34 Notebook (12) LABLOOP-W35 Notebook (10) LABLOOP-W36 Notebook (8) LABLOOP-W37 Notebook (7) LABLOOP-W38 Notebook (6) LABLOOP-W39 Notebook (6) LABLOOP-W40 Notebook (6)
Saturday sandbox
Good enough practices in scientific computing, Wilson et al., PLoS Computational Biology 2017. The repo habits you are setting up today, justified
In: LABLOOP-W00 Notebook
Saturday sandbox · after Elliot Hershberg, Century of Biology newsletter
carpentries-lab/good-enough-practices — the lesson version of R3, if you would rather do it than read it
In: LABLOOP-W00 Notebook
uv init batchlens && uv add numpy scipy pandas matplotlib; your environment manager for all 40 weeks
In: LABLOOP-W00 Notebook
ollama run qwen2.5:7b-instruct-q4_K_M "explain Monod growth in two sentences"; confirm it fits the 3050
In: LABLOOP-W00 Notebook
Read the folder structure in the README; copy only the ideas (data/raw never committed, reports/figures) into your skeletons
In: LABLOOP-W00 Notebook
Browse the "Guide for reproducible research" table of contents; save two checklists to PARKING.md
In: LABLOOP-W00 Notebook
Colab: notebook 02.00-Introduction-to-NumPy and 02.02-The-Basics-Of-NumPy-Arrays; re-do the 3Blue1Brown matrix-on-vector examples in NumPy
In: LABLOOP-W01 Notebook
Colab: lesson 1 notebook; skim, run the first cells. Linear algebra as computation, not proofs
In: LABLOOP-W01 Notebook
Copy the solve_ivp Lotka-Volterra example; replace predator-prey with biomass-substrate. This is 70% of your Monday task
In: LABLOOP-W01 Notebook
Read README and the docs page on growth-rate estimation; no hardware needed. Shows how real online signals look
In: LABLOOP-W01 Notebook
Saturday sandbox · after Steve Brunton
Deisenroth, Faisal & Ong, Mathematics for Machine Learning, ch. 10 "Dimensionality Reduction with PCA" — free PDF at mml-book.github.io
In: LABLOOP-W02 Notebook
Open IndPenSim_Summary_data_notebook_V3.ipynb in Colab; compare their variable list with your tidy table
In: LABLOOP-W02 Notebook
Colab: 03.08-Aggregation-and-Grouping and 05.09-Principal-Component-Analysis
In: LABLOOP-W02 Notebook
Run the gallery example "PCA example with Iris Data-set", then swap Iris for 20 IndPenSim batches unfolded
In: LABLOOP-W02 Notebook
melt your wide CSVs to long format, save as parquet; time both libraries on the 100 batches
In: LABLOOP-W02 Notebook
Saturday sandbox · after Stanford Online · Statistical Learning with Python
Nomikos P. & MacGregor J.F., "Multivariate SPC Charts for Monitoring Batch Processes", Technometrics 37(1):41-59, 1995. DOI 10.1080/00401706.1995.10485888 · author PDF mirror: three-mode.leidenuniv.nl/pdf/n/nomikosmacgregor1995tmet.pdf. The founding paper of batch MSPC — abstract and figures are enough. Cite it in week 08's report
In: LABLOOP-W03 Notebook
Saturday sandbox · after Kourti T. & MacGregor J.F., "Process analysis, monitoring and diagnosis, using multivariate proj…
salvadorgarciamunoz/pyphi — a pharma practitioner's PCA/PLS with T² and SPE. Niche, but written by someone who does this for a living
In: LABLOOP-W03 Notebook
Colab: Ch02-statlearn-lab.ipynb, then Ch03-linreg-lab.ipynb; redo the regression on two IndPenSim variables
In: LABLOOP-W03 Notebook
Its README shows Hotelling T² and SPE outlier detection on top of PCA; run the example, then compare its limits with yours (never ship its numbers without understanding them)
In: LABLOOP-W03 Notebook
Fit an OLS on two variables and read the summary table line by line with ISLP ch. 3 open
In: LABLOOP-W03 Notebook
Saturday sandbox · after The Turing Way — book.the-turing-way.org, chapters on "Version control"
carpentries-lab/good-enough-practices — the hands-on lesson behind week 00's R3
In: LABLOOP-W04 Notebook
Saturday sandbox · after carpentries-lab/good-enough-practices
Zenodo help — versioning and GitHub integration, if the DOI badge misbehaves
In: LABLOOP-W04 Notebook
Colab: Ch05-resample-lab.ipynb; then rewrite one cell using GroupKFold with batch as the group
In: LABLOOP-W04 Notebook
User guide "Cross-validation: evaluating estimator performance", section on group-wise splitters; run the visualization example "Visualizing cross-validation behavior"
In: LABLOOP-W04 Notebook
Read only the section on evaluation and "incremental tuning"; one page of habits you will reuse in weeks 8 and 17
In: LABLOOP-W04 Notebook
Compare your Makefile with its conventions; add make report FAKE=1
In: LABLOOP-W04 Notebook
Query OpenAlex for works on "fed-batch fermentation titer" per year; plot counts. Tells you the size of the ocean before you fish
In: LABLOOP-W05 Notebook
Search Semantic Scholar for "literature-derived fermentation dataset"; save the top 20 to RELATED.md with citation counts
In: LABLOOP-W05 Notebook
Parse one PMC OA XML into title, abstract, methods paragraphs; this becomes corpus/parse.py
In: LABLOOP-W05 Notebook
Read the README and run the quickstart example: extraction with source grounding (every value mapped to its exact span). This is the provenance idea FermLit needs, already implemented
In: LABLOOP-W05 Notebook
Colab: Ch08-baggboost-lab.ipynb (trees and boosting)
In: LABLOOP-W05 Notebook
Saturday sandbox · after 3Blue1Brown "Bayes theorem, the geometry of changing beliefs"
STRENDA guidelines (Standards for Reporting Enzymology Data) — search "STRENDA guidelines" (verify URL). A real "minimum information" standard; it shows what a reporting checklist looks like and will inspire your completeness analysis
In: LABLOOP-W06 Notebook
pip install label-studio, create a text-span project with 5 FermLit fields; annotate 2 papers to feel the difference from JSON-by-hand. Use it only if it speeds you up
In: LABLOOP-W06 Notebook
Lighter alternative for span annotation; compare in 15 minutes, pick one, never revisit
In: LABLOOP-W06 Notebook
Read the "human feedback on LLM outputs" quickstart; you will use this pattern in week 7 to correct agent outputs
In: LABLOOP-W06 Notebook
Colab: Statistical Rethinking chapter 2 notebook (grid approximation); do the globe-tossing update by hand, then in code
In: LABLOOP-W06 Notebook
Saturday sandbox
Anthropic Claude Cookbooks (github.com/anthropics/claude-cookbooks) — "extracting structured JSON" and "tool use" recipes
In: LABLOOP-W07 Notebook
Define your schema as a Pydantic model and let instructor enforce valid JSON from Claude and Ollama; kills 80% of parsing bugs
In: LABLOOP-W07 Notebook
Run an example in examples/notebooks with a local Ollama model; compare its grounded spans with your provenance sentences
In: LABLOOP-W07 Notebook
Tutorial on "information extraction" in the docs: define a signature, a metric (your F1), and let an optimizer improve the prompt. Only after your manual baseline exists
In: LABLOOP-W07 Notebook
Fit Michaelis-Menten to a small public dataset with confidence intervals on Km and Vmax
In: LABLOOP-W07 Notebook
Monday build block · Read it inside Monday's build block
Build-block reading · Luttmann et al. (2012), "Soft sensors in bioprocessing: a status report and recommendations", Biotechnology Journal 7(8):1040-1048. The reference framing for your soft-sensor section
In: LABLOOP-W08 Notebook
Saturday sandbox · after 3Blue1Brown Neural Networks ch. 3-4 ("Backpropagation, intuitively"
Stanford CS231n notes "Backpropagation, intuitions" (cs231n.github.io/optimization-2) — a classic written explanation
In: LABLOOP-W08 Notebook
Read engine.py (~100 lines); run demo.ipynb in Colab; then close it and rewrite from memory on Saturday
In: LABLOOP-W08 Notebook
Lecture notebooks under lectures/micrograd; follow along with the video
In: LABLOOP-W08 Notebook
Python quickstart regression; train on your unfolded features with batch-wise CV
In: LABLOOP-W08 Notebook
Colab: chapter 1 "The g-h filter". The intuition behind estimating a hidden variable from noisy sensors, which is what a soft sensor does
In: LABLOOP-W08 Notebook
Browse the model zoo for industrial soft sensors; note which baselines they compare, to justify your LightGBM vs PLS choice
In: LABLOOP-W08 Notebook
quarto create project manuscript; build a PDF with one figure regenerated from code
In: LABLOOP-W08 Notebook
Run makemore.py on a text file of 2,000 organism names extracted by FermLit; sample new "organisms" for fun
In: LABLOOP-W09 Notebook
lectures/makemore part 1 notebook in Colab
In: LABLOOP-W09 Notebook
Read the "Tutorial" page; model your batch extraction as an eval task with logged cost and tokens (the Vara telemetry idea, done by a government AI institute)
In: LABLOOP-W09 Notebook
Benchmark tokens/second of your local Qwen 7B on 20 papers; add the row to the cost table
In: LABLOOP-W09 Notebook
sklearn.metrics.cohen_kappa_score per field; gallery example "Gaussian process regression: basic introductory example"
In: LABLOOP-W10 Notebook
"Simple GP Regression" tutorial notebook in Colab; add predictive variance bands to one soft-sensor feature
In: LABLOOP-W10 Notebook
Compute alpha on the same annotations; compare with kappa and write one sentence on why they differ
In: LABLOOP-W10 Notebook
Build figure 1 (completeness by field) as a static, publication-ready image
In: LABLOOP-W10 Notebook
Tutorial closed_loop_botorch_only in Colab; replace the test function with your simulator's final titer as a function of 2 feed parameters
In: LABLOOP-W11 Notebook
"Getting started" BO loop in 10 lines; the higher-level alternative if BoTorch feels heavy
In: LABLOOP-W11 Notebook
Bio.Entrez to map 20 organism names to taxonomy IDs; cache results
In: LABLOOP-W11 Notebook
Create a private dataset repo, push a 100-row sample with a card; check the viewer renders
In: LABLOOP-W11 Notebook
Colab: examples/tutorial_1; then feed it X and S from your simulator and see whether it recovers a Monod-like term
In: LABLOOP-W12 Notebook
The book's Python notebooks for chapter 7 (SINDy) in Colab
In: LABLOOP-W12 Notebook
Read the README and model structure; a published example of "mechanistic + neural" for cell culture to compare against your "Monod + GBM"
In: LABLOOP-W12 Notebook
Optional: fit Monod parameters per batch with an optimizer; only if scipy.optimize struggles
In: LABLOOP-W12 Notebook
examples/ode_demo.py: learn a spiral's dynamics; read how a neural network replaces the right-hand side of an ODE
In: LABLOOP-W13 Notebook
Optional JAX alternative with excellent docs on neural ODEs
In: LABLOOP-W13 Notebook
Put the Paper 1 skeleton into a Quarto manuscript; make paper builds a PDF with placeholder figures
In: LABLOOP-W13 Notebook
Browse the datasets examples for pushing a dataset with a card from code
In: LABLOOP-W13 Notebook
statsmodels.stats.multitest.multipletests with fdr_bh on the journal comparison; report adjusted p-values
In: LABLOOP-W14 Notebook
Implement the fuzzy-field judge as a scorer; log judge vs human agreement on 50 pairs
In: LABLOOP-W14 Notebook
Read how tasks define metrics and few-shot prompts; borrow the discipline, not the tool
In: LABLOOP-W14 Notebook
Point it at your RELATED.md PDFs and ask "which of these papers report inter-annotator agreement?" to double-check your related-work claims (verify every answer by opening the paper)
In: LABLOOP-W14 Notebook
Run a biomedical NER model on 3 FermLit papers; compare what it finds (organisms, chemicals) with your agent's extraction. A classic baseline reviewers may ask about
In: LABLOOP-W15 Notebook
Parse one PDF-only paper into TEI XML; note how references and sections are recovered. Useful for the non-XML tail of the corpus
In: LABLOOP-W15 Notebook
Convert the same PDF with Docling; compare tables extraction with GROBID
In: LABLOOP-W15 Notebook
Final make paper; check figures regenerate from a clean clone
In: LABLOOP-W15 Notebook
Colab: examples/protein_language_modeling.ipynb — fine-tune/embed with ESM-2 through transformers; switch the model to facebook/esm2_t33_650M_UR50D in fp16 on your 3050
In: LABLOOP-W16 Notebook
Read the README's "ESM-2" and embedding extraction sections; the original reference
In: LABLOOP-W16 Notebook
Read only; the newer ESM C/ESM3 family. Parked for Half 2
In: LABLOOP-W16 Notebook
Colab: notebooks/meps-cqr.ipynb (conformalized quantile regression on tabular data); map it onto your soft sensor
In: LABLOOP-W16 Notebook
Regression example with SplitConformalRegressor (or the current API); get coverage on held-out batches in 20 lines
In: LABLOOP-W16 Notebook
Saturday sandbox · after Notin et al. (2023), "ProteinGym: large-scale benchmarks for protein fitness prediction and desi…
Andrew White, Deep Learning for Molecules and Materials — dmol.pub, chapter on "Deep learning on sequences" (with Colab buttons)
In: LABLOOP-W17 Notebook
Download the meltome splits; read the README on "mixed", "human" and "human_cell" splits; load them with pandas
In: LABLOOP-W17 Notebook
Browse the leaderboard page and the baselines list; note where ESM-2 sits
In: LABLOOP-W17 Notebook
Reuse protein_language_modeling.ipynb to cache mean-pooled embeddings to disk
In: LABLOOP-W17 Notebook
The curated reading list by Kevin Yang; pick 2 papers for PARKING.md, not more
In: LABLOOP-W17 Notebook
RidgeCV on embeddings → melting temperature; report Spearman correlation with a bootstrap interval
In: LABLOOP-W17 Notebook
Paper 2 manuscript project; cross-references for figures and equations
In: LABLOOP-W18 Notebook
Check the "interpretability" and "time series" sections for any widely used soft-sensor baseline you forgot to cite
In: LABLOOP-W18 Notebook
Re-read "scientific vs nuisance hyperparameters" and write two sentences in Methods on how you tuned fairly across arms
In: LABLOOP-W18 Notebook
Clean clone → make figs → compare hashes with your local figures
In: LABLOOP-W18 Notebook
Modern Statistics for Modern Biology sections on reporting and ISLP ch. 5.2 (bootstrap) re-read (40 min); apply bootstrap intervals across both papers
In: LABLOOP-W19 Notebook
PBMC 3k tutorial notebook (in the docs; scverse/scanpy-tutorials is archived, ★ 242) in Colab
In: LABLOOP-W19 Notebook
Run the quality-control notebook on the same data; compare cell counts after filtering
In: LABLOOP-W19 Notebook
scipy.stats.bootstrap on your key metrics; replace every naked number in both papers
In: LABLOOP-W19 Notebook
Turn the Paper 2 learning curve into a reactive notebook with a slider for number of batches; share it as the "try it yourself" link in the arXiv abstract
In: LABLOOP-W20 Notebook
Build the arXiv-ready LaTeX source from Quarto; check it compiles in arXiv's TeX Live
In: LABLOOP-W20 Notebook
Tag paper-2-v1.0 and mint a Zenodo DOI for the exact code used
In: LABLOOP-W20 Notebook
Run it on 50 Meltome sequences; compare its predictions with your ridge head. This is your most relevant external baseline
In: LABLOOP-W21 Notebook
Use their exact splits so your numbers are comparable with the paper
In: LABLOOP-W21 Notebook
EsmModel with torch_dtype=torch.float16; batch by sequence length to fit 6 GB
In: LABLOOP-W21 Notebook
Read the README; note ProtT5 as an alternative embedding for the ablation you might run in week 22
In: LABLOOP-W21 Notebook
Bio.SeqIO to read and length-filter FASTA; drop sequences > 1,022 residues or chunk them
In: LABLOOP-W21 Notebook
Run the "getting started" notebook; draw a DAG for "organism growth temperature → sequence composition → Tm" to see where confounding lives
In: LABLOOP-W22 Notebook
GroupKFold / StratifiedGroupKFold with sequence-cluster IDs
In: LABLOOP-W22 Notebook
Conformal intervals on the grouped folds; report coverage per fold
In: LABLOOP-W22 Notebook
mmseqs easy-cluster on Meltome FASTA at 30% identity; save cluster TSV
In: LABLOOP-W22 Notebook
Parse UniProt FASTA, compute length and composition features as a sanity baseline next to embeddings
In: LABLOOP-W23 Notebook
Colab: predict the structure of your top 2 candidates; look at them in 3D. Not used as a feature, only to build intuition and a figure
In: LABLOOP-W23 Notebook
Read the README on the AlphaFold Protein Structure Database; check if your candidates already have predicted structures there (no GPU needed)
In: LABLOOP-W23 Notebook
Browse "The Basic Tools of the Deep Life Sciences" tutorial list; one notebook on protein sequences, only if curious (Saturday)
In: LABLOOP-W23 Notebook
Build the analysis you pre-registered on simulated numbers: fit a thermal inactivation curve, get intervals; the code is frozen before real data arrives
In: LABLOOP-W24 Notebook
Power check by simulation: with n = 3 replicates per candidate, how big a difference can you detect? Write the answer into the plan
In: LABLOOP-W24 Notebook
Read the chapter on "Collaboration" and "Credit"; adapt its checklist for the authorship paragraph
In: LABLOOP-W24 Notebook
Saturday sandbox
Michael Clark, Mixed Models with R — m-clark.github.io/mixed-models-with-R (concepts transfer to Python)
In: LABLOOP-W25 Notebook
Saturday sandbox · after Michael Clark, Mixed Models with R
Bambi documentation (bambinos.github.io/bambi), Bayesian mixed models in one formula
In: LABLOOP-W25 Notebook
MixedLM example from the docs (random intercept per block); then fit one agridat trial
In: LABLOOP-W25 Notebook
Same trial as a Bayesian mixed model; compare intervals with MixedLM
In: LABLOOP-W25 Notebook
Pull 10 agridat CSVs by URL; this is your public data source
In: LABLOOP-W25 Notebook
Spreadsheet header → schema mapping with a Pydantic model, as in FermLit
In: LABLOOP-W25 Notebook
Read the "random intercepts" chapter for intuition
In: LABLOOP-W25 Notebook
Fit logistic and Gompertz to a public growth curve with parameter intervals
In: LABLOOP-W26 Notebook
curve_fit for 4PL; compare with lmfit output
In: LABLOOP-W26 Notebook
Read the README and one vignette to check your Python 4PL reproduces drc on the same data
In: LABLOOP-W26 Notebook
Look at how it estimates growth rate from optical density online; compare with your offline fit
In: LABLOOP-W26 Notebook
Record a 60-second demo of each running on public data (v4 Template C) and link both in the one-pager
In: LABLOOP-W27 Notebook
A "loop calculator" notebook: inputs (cycle length, cycles/year, burn/month, days saved) → months of runway; screenshot it for the one-pager
In: LABLOOP-W27 Notebook
Borrow a summarisation recipe to turn discovery-call notes into a structured scoping sheet (never with confidential data in a cloud model without consent)
In: LABLOOP-W27 Notebook
Keep them in a private business/ repo; version every proposal
In: LABLOOP-W28 Notebook
sha256sum every raw result file; commit the checksums, not confidential data if the partner prefers
In: LABLOOP-W28 Notebook
Compute cycle times from the client's timestamps (experiment start → decision) with group-bys
In: LABLOOP-W29 Notebook
If their records are PDFs, convert a sample locally (on-prem; no data leaves your machine)
In: LABLOOP-W29 Notebook
Run the relevant instrument on their sample in a private branch; never push client data
In: LABLOOP-W29 Notebook
Saturday sandbox
Vaux, Fidler & Cumming (2012), "Replicates and repeats—what is the difference and is it significant?", EMBO Reports 13:291-296
In: LABLOOP-W30 Notebook
Estimation plots (Gardner-Altman) for measured activity vs control; the library from Ho et al. 2019
In: LABLOOP-W30 Notebook
Strip plots with every replicate over the predicted ranking
In: LABLOOP-W30 Notebook
If you fit a small Bayesian model, plot posteriors per candidate
In: LABLOOP-W30 Notebook
Run the frozen week 24 analysis script on the real data, unchanged
In: LABLOOP-W30 Notebook
Paper 3 manuscript; export to .docx for a co-author who uses Word track changes
In: LABLOOP-W31 Notebook
make paper from a clean clone; tag paper-3-draft-1
In: LABLOOP-W31 Notebook
Check the Discussion's claims about prior work against the PDFs you cite (verify manually)
In: LABLOOP-W31 Notebook
Release enzymelens v1.0 with DOI; link from the preprint
In: LABLOOP-W32 Notebook
Interactive "candidate explorer" for the paper's supplementary material (public data only)
In: LABLOOP-W32 Notebook
Docker install per docs; create a team, a template, an experiment
In: LABLOOP-W33 Notebook
Create and update an experiment from Python; read the revision history eLabFTW keeps
In: LABLOOP-W33 Notebook
Convert 10 synthetic SOP PDFs; compare layout/table fidelity with the VLM
In: LABLOOP-W33 Notebook
Same 10 PDFs; note speed on the 3050
In: LABLOOP-W33 Notebook
Run a vision model (e.g., qwen2.5vl:3b or gemma3:4b) on one scanned page; measure seconds per page
In: LABLOOP-W33 Notebook
Compute context recall and faithfulness on 10 of your Q&A; compare with your human labels before trusting it
In: LABLOOP-W34 Notebook
Only as a reference implementation of chunking and citation; keep your own pipeline small
In: LABLOOP-W34 Notebook
Try page-image retrieval on 20 scanned SOP pages; compare recall@5 with text chunks
In: LABLOOP-W34 Notebook
Wrap the 60 Q&A as an eval with a model-graded scorer and your human labels
In: LABLOOP-W34 Notebook
Serve the embedding model locally on the mini PC for the on-prem story
In: LABLOOP-W34 Notebook
Saturday sandbox · after NIST Privacy Framework overview
Stanford HAI AI Index report, chapter on responsible AI (for context in M5)
In: LABLOOP-W35 Notebook
llama-bench on the mini PC and the 3050 for the same quantised model
In: LABLOOP-W35 Notebook
Same model via Ollama; record tokens/s and memory
In: LABLOOP-W35 Notebook
Add one row per converter to the benchmark (accuracy on tables, seconds/page)
In: LABLOOP-W35 Notebook
Optional OCR row if a GPU run fits; mark hardware requirements honestly
In: LABLOOP-W35 Notebook
Read the docs folder on paper format; open two recently accepted JOSS review issues for bioprocess or scientific tools to see what reviewers ask
In: LABLOOP-W36 Notebook
Check the JOSS checklist: license, tests run in CI, docs, contribution guide, example usage
In: LABLOOP-W36 Notebook
Export to the journal's LaTeX template where available
In: LABLOOP-W36 Notebook
Practise 5 questions aloud, record, compare with the book's guidance
In: LABLOOP-W37 Notebook
Skim the resources list for evaluation and deployment topics likely in a scientific-data interview
In: LABLOOP-W37 Notebook
A one-page interactive pilot dashboard for the weekly readout (client data stays on their machine)
In: LABLOOP-W37 Notebook
One live demo cell (the learning-curve slider) as the talk's "wow" moment; have a screenshot fallback
In: LABLOOP-W38 Notebook
Optional reveal.js slides from Markdown so figures regenerate from code
In: LABLOOP-W38 Notebook
Sample consolidated-report page from synthetic data: 5 companies, cycle time, readiness, days saved
In: LABLOOP-W39 Notebook
Generate the sample report figures reproducibly
In: LABLOOP-W39 Notebook
Script that counts blocks, lost weeks and gates from LOG.md and PROGRESS.md; the only dashboard you ever need
In: LABLOOP-W40 Notebook
One-page interactive "40 weeks" timeline for the public retro
In: LABLOOP-W40 Notebook