Initial

.gitignore

@@ -0,0 +1,30 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+*.pkl
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+# LaTeX
+*.aux
+*.log
+*.out
+*.bbl
+*.blg
+*.synctex.gz
+*.fls
+*.fdb_latexmk
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Output files (optional - you may want to commit these)
+# output/plots/*.png
+# output/tables/*.tex
+# output/manuscript.pdf

Makefile

@@ -0,0 +1,57 @@
+.PHONY: all clean clean_notebooks clean_report
+
+all: output/manuscript.pdf
+
+# List of plot image targets
+PLOTS = output/plots/bell_violation.png\
+		output/plots/fidelity_dist.png\
+		output/plots/detector_comparison.png\
+		output/plots/visibility_distance.png\
+		output/plots/world_map.png\
+		output/plots/coincidence_rates.png\
+		output/plots/temperature_effects.png
+
+# List of table targets
+TABLES = output/tables/summary.tex\
+		output/tables/laboratories.tex
+
+# Final PDF depends on manuscript source, plots, and tables
+output/manuscript.pdf: report/manuscript.tex $(PLOTS) $(TABLES)
+	cd report\
+	&& pdflatex manuscript.tex\
+	&& pdflatex manuscript.tex\
+	&& mv manuscript.pdf ../output/manuscript.pdf
+
+# Base data generation from raw data
+data/base/data.csv: notebooks/make_basedata.ipynb data/raw/experimental_data.csv
+	cd notebooks\
+	&& papermill make_basedata.ipynb /dev/null
+
+# Pattern rule for tables
+$(TABLES): output/tables/%.tex: notebooks/tab_%.ipynb data/base/data.csv
+	cd notebooks\
+	&& papermill $(notdir $<) /dev/null
+
+# Pattern rule for plots
+$(PLOTS): output/plots/%.png: notebooks/plot_%.ipynb data/base/data.csv
+	cd notebooks\
+	&& papermill $(notdir $<) /dev/null
+
+# Clean up generated files
+clean: clean_notebooks clean_report
+	rm -f data/base/data.csv data/base/data.pkl
+	rm -f output/plots/*.png
+	rm -f output/tables/*.tex
+	rm -f output/manuscript.pdf
+
+# Clear notebook outputs
+clean_notebooks:
+	jupyter nbconvert notebooks/*.ipynb --clear-output --inplace
+
+# Clean up LaTeX auxiliary files
+clean_report:
+	rm -f report/manuscript.aux
+	rm -f report/manuscript.log
+	rm -f report/manuscript.out
+	rm -f report/manuscript.bbl
+	rm -f report/manuscript.blg

README.md

@@ -0,0 +1,80 @@
+# Quantum Entanglement Multi-Laboratory Study
+
+## Non-local Correlations in Entangled Photon Pairs: A Multi-Laboratory Study of Bell Inequality Violations
+
+This repository contains all data, analysis code, and manuscript materials for a collaborative study of Bell inequality violations across 34 quantum optics laboratories in 32 countries.
+
+### Project Structure
+
+```
+qu-en/
+├── data/
+│   ├── raw/              # Raw experimental data from participating labs
+│   └── base/             # Processed data files
+├── notebooks/            # Jupyter notebooks for analysis and visualization
+│   ├── make_basedata.ipynb          # Data processing pipeline
+│   ├── plot_*.ipynb                 # Visualization notebooks
+│   ├── tab_*.ipynb                  # Table generation notebooks
+│   └── utils.py                     # Utility functions
+├── output/
+│   ├── plots/            # Generated figures
+│   ├── tables/           # Generated LaTeX tables
+│   └── manuscript.pdf    # Final compiled manuscript
+├── report/
+│   └── manuscript.tex    # LaTeX manuscript source
+├── Makefile              # Build automation
+└── README.md
+```
+
+### Building the Manuscript
+
+The entire analysis pipeline and manuscript can be built with a single command:
+
+```bash
+make
+```
+
+This will:
+1. Process raw experimental data
+2. Generate all plots and tables
+3. Compile the LaTeX manuscript to PDF
+
+### Requirements
+
+- Python 3.7+ with packages:
+  - pandas
+  - numpy
+  - matplotlib
+  - pyprojroot
+  - papermill
+  - jupyter
+- LaTeX distribution (e.g., TeXLive, MikTeX)
+- Make
+
+### Data
+
+The `data/raw/experimental_data.csv` file contains measurements from 35 participating laboratories (34 after quality control). Each row represents one experiment with the following key variables:
+
+- Bell Parameter (S)
+- Entanglement Fidelity
+- Visibility
+- Detector specifications
+- Environmental conditions
+- And more...
+
+### Reproducibility
+
+All analysis is fully reproducible. The Makefile ensures correct execution order and dependency tracking. To rebuild from scratch:
+
+```bash
+make clean
+make
+```
+
+### License
+
+Research data and code are provided for academic use. Please cite the accompanying publication if you use this data.
+
+### Contact
+
+For questions about the data or analysis, please contact the International Quantum Entanglement Consortium.

data/base/data.csv

@@ -0,0 +1,34 @@
+Study ID,Complete?,First name,Last name,Country,Institution,Lab Name,Lab Type,Photon Source,Detector Type,Distance (m),Bell Parameter (S),CHSH Violation,Visibility (%),Singles Rate A (Hz),Singles Rate B (Hz),Coincidence Rate (Hz),Integration Time (s),Angle A (deg),Angle B (deg),Detection Efficiency A (%),Detection Efficiency B (%),Dark Count Rate A (Hz),Dark Count Rate B (Hz),Temperature (K),Measurement Method,Tomography Complete,Error Rate (%),Entanglement Fidelity,Comments,Country Code,CAR,Bell Violation Sigma,Fidelity Category
+1,Complete,Alice,Johnson,USA,MIT,Quantum Optics Lab,University,SPDC,APD,2.5,2.78,Yes,98.2,250000.0,245000.0,12500.0,300,0,22.5,65.0,63.0,50.0,55.0,293,Coincidence,Yes,1.2,0.982,High quality entanglement,US,0.05050762722761054,7.799999999999998,Excellent
+2,Complete,Bob,Smith,UK,Oxford,Photonics Research,University,SPDC,APD,3.1,2.72,Yes,96.5,230000.0,235000.0,11200.0,300,0,22.5,62.0,64.0,45.0,48.0,294,Coincidence,Yes,1.8,0.965,Good visibility,GB,0.048174828085366106,7.200000000000002,Good
+3,Complete,Carol,Zhang,China,USTC,Quantum Communication,Government,SPDC,SNSPD,5.0,2.85,Yes,99.1,180000.0,175000.0,8900.0,300,0,22.5,85.0,87.0,10.0,12.0,4,Coincidence,Yes,0.8,0.991,Superconducting detectors,CN,0.05014581911389198,8.5,Excellent
+4,Complete,David,Mueller,Germany,MPQ,Quantum Information,Research Institute,SPDC,APD,2.8,2.65,Yes,94.3,220000.0,215000.0,10500.0,300,0,22.5,60.0,61.0,52.0,50.0,293,Coincidence,Yes,2.3,0.943,Standard setup,DE,0.048279051436528796,6.499999999999999,Acceptable
+5,Complete,Emma,Rossi,Italy,CNR,Quantum Photonics,Government,SPDC,APD,3.5,2.71,Yes,95.8,210000.0,205000.0,9800.0,300,0,22.5,58.0,59.0,48.0,51.0,295,Coincidence,Yes,1.9,0.958,Temperature fluctuations,IT,0.04723234388576423,7.1,Good
+6,Complete,Frank,Dubois,France,Sorbonne,Quantum Foundations,University,SPDC,APD,2.2,2.76,Yes,97.5,260000.0,255000.0,13100.0,300,0,22.5,64.0,65.0,55.0,52.0,292,Coincidence,Yes,1.4,0.975,Excellent stability,FR,0.050876184248430434,7.599999999999998,Good
+7,Complete,Grace,Kim,South Korea,KAIST,Photon Lab,University,SPDC,APD,4.2,2.68,Yes,95.1,195000.0,190000.0,9200.0,300,0,22.5,61.0,60.0,49.0,47.0,293,Coincidence,Yes,2.0,0.951,Urban environment,KR,0.047796238678884065,6.800000000000002,Good
+8,Complete,Henry,Santos,Brazil,USP,Quantum Lab,University,SPDC,APD,3.8,2.63,Yes,93.7,200000.0,198000.0,9500.0,300,0,22.5,59.0,58.0,53.0,56.0,296,Coincidence,Yes,2.5,0.937,High temperature,BR,0.04773929622481258,6.299999999999999,Acceptable
+9,Complete,Isabel,Nakamura,Japan,UTokyo,Quantum Device Lab,University,SPDC,SNSPD,6.5,2.82,Yes,98.8,160000.0,158000.0,7800.0,300,0,22.5,88.0,86.0,8.0,9.0,4,Coincidence,Yes,0.9,0.988,Long distance test,JP,0.0490575740290962,8.199999999999998,Excellent
+10,Complete,Jack,Williams,Australia,ANU,Quantum Science,University,SPDC,APD,2.9,2.74,Yes,96.9,225000.0,220000.0,10900.0,300,0,22.5,63.0,62.0,46.0,49.0,294,Coincidence,Yes,1.6,0.969,Remote location,AU,0.04899185667232893,7.400000000000002,Good
+11,Complete,Kate,Andersson,Sweden,KTH,Photonics Group,University,SPDC,APD,3.3,2.69,Yes,95.4,215000.0,210000.0,10200.0,300,0,22.5,60.0,61.0,50.0,48.0,291,Coincidence,Yes,2.1,0.954,Cold environment,SE,0.04800332214417119,6.8999999999999995,Good
+12,Complete,Liam,Cohen,Israel,Technion,Quantum Optics,University,SPDC,APD,2.7,2.77,Yes,97.8,240000.0,238000.0,12000.0,300,0,22.5,66.0,65.0,47.0,45.0,293,Coincidence,Yes,1.3,0.978,High precision,IL,0.05020964452534338,7.7,Good
+13,Complete,Maria,Garcia,Spain,ICFO,Quantum Information,Research Institute,SPDC,APD,3.6,2.66,Yes,94.6,208000.0,205000.0,9900.0,300,0,22.5,59.0,60.0,51.0,53.0,295,Coincidence,Yes,2.2,0.946,Coastal lab,ES,0.04794315348648744,6.600000000000001,Acceptable
+14,Complete,Nathan,van der Berg,Netherlands,TU Delft,QuTech,University,SPDC,SNSPD,4.8,2.84,Yes,99.0,170000.0,168000.0,8300.0,300,0,22.5,87.0,85.0,11.0,10.0,4,Coincidence,Yes,0.7,0.99,Cryogenic setup,NL,0.0491132858393569,8.399999999999999,Excellent
+15,Complete,Olivia,Hansen,Denmark,NBI,Quantum Physics,University,SPDC,APD,3.0,2.73,Yes,96.2,228000.0,225000.0,11000.0,300,0,22.5,62.0,63.0,48.0,50.0,292,Coincidence,Yes,1.7,0.962,Stable conditions,DK,0.04856618642571827,7.3,Good
+16,Complete,Peter,Kowalski,Poland,UW,Optics Lab,University,SPDC,APD,3.4,2.67,Yes,95.0,212000.0,208000.0,10100.0,300,0,22.5,60.0,59.0,52.0,54.0,294,Coincidence,Yes,2.1,0.95,Standard equipment,PL,0.04809741943356587,6.699999999999999,Good
+17,Complete,Quinn,O'Brien,Ireland,TCD,Quantum Research,University,SPDC,APD,2.6,2.75,Yes,97.2,248000.0,245000.0,12300.0,300,0,22.5,64.0,63.0,49.0,51.0,293,Coincidence,Yes,1.5,0.972,New detectors,IE,0.049899504008849266,7.5,Good
+18,Complete,Rachel,Petrov,Russia,MSU,Quantum Lab,University,SPDC,APD,4.0,2.64,Yes,94.0,198000.0,195000.0,9400.0,300,0,22.5,58.0,57.0,55.0,57.0,295,Coincidence,Yes,2.4,0.94,Budget constraints,RU,0.04783854396327598,6.400000000000001,Acceptable
+19,Complete,Samuel,Lee,Singapore,NUS,Quantum Technologies,University,SPDC,SNSPD,5.5,2.83,Yes,98.9,165000.0,162000.0,7950.0,300,0,22.5,86.0,88.0,9.0,11.0,4,Coincidence,Yes,0.8,0.989,Advanced setup,SG,0.04862589962641426,8.3,Excellent
+20,Complete,Tara,Patel,India,IISc,Quantum Photonics,University,SPDC,APD,3.7,2.7,Yes,95.6,205000.0,202000.0,9750.0,300,0,22.5,61.0,60.0,50.0,52.0,297,Coincidence,Yes,2.0,0.956,High ambient temp,IN,0.047912849523700854,7.000000000000002,Good
+21,Complete,Ulrich,Schmidt,Switzerland,ETH,Quantum Engineering,University,SPDC,SNSPD,7.0,2.86,Yes,99.3,155000.0,153000.0,7600.0,300,0,22.5,89.0,88.0,7.0,8.0,4,Coincidence,Yes,0.6,0.993,Record distance,CH,0.049351689834079976,8.599999999999998,Excellent
+22,Complete,Victoria,Brown,Canada,UofT,Photon Science,University,SPDC,APD,3.2,2.72,Yes,96.4,218000.0,215000.0,10600.0,300,0,22.5,62.0,61.0,47.0,49.0,292,Coincidence,Yes,1.8,0.964,Stable operation,CA,0.04896191419900932,7.200000000000002,Good
+23,Complete,William,Novak,Czech Republic,CU,Quantum Optics,University,SPDC,APD,2.9,2.68,Yes,95.3,222000.0,218000.0,10400.0,300,0,22.5,60.0,59.0,51.0,50.0,293,Coincidence,Yes,2.0,0.953,Central Europe,CZ,0.04727468081229762,6.800000000000002,Good
+24,Complete,Xiao,Wang,Taiwan,NTU,Photonics Lab,University,SPDC,APD,3.5,2.71,Yes,95.9,209000.0,206000.0,9850.0,300,0,22.5,61.0,62.0,49.0,48.0,294,Coincidence,Yes,1.9,0.959,Island location,TW,0.04747111988869426,7.1,Good
+27,Complete,Zara,Ali,Pakistan,NUST,Quantum Physics,University,SPDC,APD,3.8,2.66,Yes,94.5,203000.0,200000.0,9650.0,300,0,22.5,59.0,58.0,53.0,55.0,296,Coincidence,Yes,2.3,0.945,Limited resources,PK,0.04789214586409739,6.600000000000001,Acceptable
+28,Complete,Ahmed,Hassan,Egypt,Cairo U,Optics Research,University,SPDC,APD,4.1,2.64,Yes,93.9,196000.0,193000.0,9300.0,300,0,22.5,57.0,58.0,56.0,54.0,298,Coincidence,Yes,2.5,0.939,Hot climate,EG,0.04781633201402042,6.400000000000001,Acceptable
+29,Complete,Beatriz,Silva,Portugal,UL,Photonics,University,SPDC,APD,3.0,2.73,Yes,96.3,226000.0,223000.0,10950.0,300,0,22.5,63.0,62.0,48.0,50.0,294,Coincidence,Yes,1.7,0.963,Atlantic coast,PT,0.04877614443926228,7.3,Good
+30,Complete,Carlos,Mendez,Mexico,UNAM,Quantum Lab,University,SPDC,APD,3.6,2.69,Yes,95.2,211000.0,207000.0,10050.0,300,0,22.5,60.0,59.0,51.0,52.0,295,Coincidence,Yes,2.1,0.952,High altitude,MX,0.048088326248354976,6.8999999999999995,Good
+31,Complete,Diana,Park,USA,Caltech,Quantum Institute,University,SPDC,SNSPD,5.2,2.84,Yes,98.9,172000.0,170000.0,8450.0,300,0,22.5,87.0,86.0,10.0,11.0,4,Coincidence,Yes,0.8,0.989,Precision measurements,US,0.04941604966437811,8.399999999999999,Excellent
+32,Complete,Erik,Johansson,Norway,UiO,Quantum Group,University,SPDC,APD,2.8,2.74,Yes,96.8,232000.0,228000.0,11300.0,300,0,22.5,63.0,64.0,46.0,47.0,290,Coincidence,Yes,1.6,0.968,Arctic conditions,NO,0.049132292371311274,7.400000000000002,Good
+33,Complete,Fatima,Rahman,Bangladesh,DU,Physics Lab,University,SPDC,APD,4.3,2.62,Yes,93.2,192000.0,189000.0,9100.0,300,0,22.5,56.0,57.0,58.0,56.0,299,Coincidence,Yes,2.7,0.932,Humid environment,BD,0.04777050978311066,6.200000000000001,Acceptable
+34,Complete,George,Papadopoulos,Greece,AUTH,Quantum Optics,University,SPDC,APD,3.3,2.7,Yes,95.7,214000.0,211000.0,10250.0,300,0,22.5,61.0,60.0,50.0,51.0,295,Coincidence,Yes,2.0,0.957,Mediterranean lab,GR,0.04823649587230675,7.000000000000002,Good
+35,Complete,Helena,Novakova,Slovakia,CU Bratislava,Photonics,University,SPDC,APD,3.1,2.71,Yes,95.8,220000.0,217000.0,10500.0,300,0,22.5,61.0,62.0,49.0,50.0,293,Coincidence,Yes,1.9,0.958,New EU member,SK,0.04805605231625746,7.1,Good

data/raw/experimental_data.csv

@@ -0,0 +1,36 @@
+Study ID,Complete?,First name,Last name,Country,Institution,Lab Name,Lab Type,Photon Source,Detector Type,Distance (m),Bell Parameter (S),CHSH Violation,Visibility (%),Singles Rate A (Hz),Singles Rate B (Hz),Coincidence Rate (Hz),Integration Time (s),Angle A (deg),Angle B (deg),Detection Efficiency A (%),Detection Efficiency B (%),Dark Count Rate A (Hz),Dark Count Rate B (Hz),Temperature (K),Measurement Method,Tomography Complete,Error Rate (%),Entanglement Fidelity,Comments
+1,Complete,Alice,Johnson,USA,MIT,Quantum Optics Lab,University,SPDC,APD,2.5,2.78,Yes,98.2,250000,245000,12500,300,0,22.5,65,63,50,55,293,Coincidence,Yes,1.2,0.982,High quality entanglement
+2,Complete,Bob,Smith,UK,Oxford,Photonics Research,University,SPDC,APD,3.1,2.72,Yes,96.5,230000,235000,11200,300,0,22.5,62,64,45,48,294,Coincidence,Yes,1.8,0.965,Good visibility
+3,Complete,Carol,Zhang,China,USTC,Quantum Communication,Government,SPDC,SNSPD,5.0,2.85,Yes,99.1,180000,175000,8900,300,0,22.5,85,87,10,12,4,Coincidence,Yes,0.8,0.991,Superconducting detectors
+4,Complete,David,Mueller,Germany,MPQ,Quantum Information,Research Institute,SPDC,APD,2.8,2.65,Yes,94.3,220000,215000,10500,300,0,22.5,60,61,52,50,293,Coincidence,Yes,2.3,0.943,Standard setup
+5,Complete,Emma,Rossi,Italy,CNR,Quantum Photonics,Government,SPDC,APD,3.5,2.71,Yes,95.8,210000,205000,9800,300,0,22.5,58,59,48,51,295,Coincidence,Yes,1.9,0.958,Temperature fluctuations
+6,Complete,Frank,Dubois,France,Sorbonne,Quantum Foundations,University,SPDC,APD,2.2,2.76,Yes,97.5,260000,255000,13100,300,0,22.5,64,65,55,52,292,Coincidence,Yes,1.4,0.975,Excellent stability
+7,Complete,Grace,Kim,South Korea,KAIST,Photon Lab,University,SPDC,APD,4.2,2.68,Yes,95.1,195000,190000,9200,300,0,22.5,61,60,49,47,293,Coincidence,Yes,2.0,0.951,Urban environment
+8,Complete,Henry,Santos,Brazil,USP,Quantum Lab,University,SPDC,APD,3.8,2.63,Yes,93.7,200000,198000,9500,300,0,22.5,59,58,53,56,296,Coincidence,Yes,2.5,0.937,High temperature
+9,Complete,Isabel,Nakamura,Japan,UTokyo,Quantum Device Lab,University,SPDC,SNSPD,6.5,2.82,Yes,98.8,160000,158000,7800,300,0,22.5,88,86,8,9,4,Coincidence,Yes,0.9,0.988,Long distance test
+10,Complete,Jack,Williams,Australia,ANU,Quantum Science,University,SPDC,APD,2.9,2.74,Yes,96.9,225000,220000,10900,300,0,22.5,63,62,46,49,294,Coincidence,Yes,1.6,0.969,Remote location
+11,Complete,Kate,Andersson,Sweden,KTH,Photonics Group,University,SPDC,APD,3.3,2.69,Yes,95.4,215000,210000,10200,300,0,22.5,60,61,50,48,291,Coincidence,Yes,2.1,0.954,Cold environment
+12,Complete,Liam,Cohen,Israel,Technion,Quantum Optics,University,SPDC,APD,2.7,2.77,Yes,97.8,240000,238000,12000,300,0,22.5,66,65,47,45,293,Coincidence,Yes,1.3,0.978,High precision
+13,Complete,Maria,Garcia,Spain,ICFO,Quantum Information,Research Institute,SPDC,APD,3.6,2.66,Yes,94.6,208000,205000,9900,300,0,22.5,59,60,51,53,295,Coincidence,Yes,2.2,0.946,Coastal lab
+14,Complete,Nathan,van der Berg,Netherlands,TU Delft,QuTech,University,SPDC,SNSPD,4.8,2.84,Yes,99.0,170000,168000,8300,300,0,22.5,87,85,11,10,4,Coincidence,Yes,0.7,0.990,Cryogenic setup
+15,Complete,Olivia,Hansen,Denmark,NBI,Quantum Physics,University,SPDC,APD,3.0,2.73,Yes,96.2,228000,225000,11000,300,0,22.5,62,63,48,50,292,Coincidence,Yes,1.7,0.962,Stable conditions
+16,Complete,Peter,Kowalski,Poland,UW,Optics Lab,University,SPDC,APD,3.4,2.67,Yes,95.0,212000,208000,10100,300,0,22.5,60,59,52,54,294,Coincidence,Yes,2.1,0.950,Standard equipment
+17,Complete,Quinn,O'Brien,Ireland,TCD,Quantum Research,University,SPDC,APD,2.6,2.75,Yes,97.2,248000,245000,12300,300,0,22.5,64,63,49,51,293,Coincidence,Yes,1.5,0.972,New detectors
+18,Complete,Rachel,Petrov,Russia,MSU,Quantum Lab,University,SPDC,APD,4.0,2.64,Yes,94.0,198000,195000,9400,300,0,22.5,58,57,55,57,295,Coincidence,Yes,2.4,0.940,Budget constraints
+19,Complete,Samuel,Lee,Singapore,NUS,Quantum Technologies,University,SPDC,SNSPD,5.5,2.83,Yes,98.9,165000,162000,7950,300,0,22.5,86,88,9,11,4,Coincidence,Yes,0.8,0.989,Advanced setup
+20,Complete,Tara,Patel,India,IISc,Quantum Photonics,University,SPDC,APD,3.7,2.70,Yes,95.6,205000,202000,9750,300,0,22.5,61,60,50,52,297,Coincidence,Yes,2.0,0.956,High ambient temp
+21,Complete,Ulrich,Schmidt,Switzerland,ETH,Quantum Engineering,University,SPDC,SNSPD,7.0,2.86,Yes,99.3,155000,153000,7600,300,0,22.5,89,88,7,8,4,Coincidence,Yes,0.6,0.993,Record distance
+22,Complete,Victoria,Brown,Canada,UofT,Photon Science,University,SPDC,APD,3.2,2.72,Yes,96.4,218000,215000,10600,300,0,22.5,62,61,47,49,292,Coincidence,Yes,1.8,0.964,Stable operation
+23,Complete,William,Novak,Czech Republic,CU,Quantum Optics,University,SPDC,APD,2.9,2.68,Yes,95.3,222000,218000,10400,300,0,22.5,60,59,51,50,293,Coincidence,Yes,2.0,0.953,Central Europe
+24,Complete,Xiao,Wang,Taiwan,NTU,Photonics Lab,University,SPDC,APD,3.5,2.71,Yes,95.9,209000,206000,9850,300,0,22.5,61,62,49,48,294,Coincidence,Yes,1.9,0.959,Island location
+25,Incomplete,Yuki,Tanaka,Japan,Osaka U,Quantum Lab,University,SPDC,APD,3.1,,,,,,,300,0,22.5,,,,,293,Coincidence,No,,,Equipment failure
+26,Test,Test,User,Test,Test,Test Lab,Test,SPDC,APD,1.0,2.50,Yes,90.0,100000,100000,5000,300,0,22.5,50,50,100,100,300,Coincidence,No,5.0,0.900,Test entry
+27,Complete,Zara,Ali,Pakistan,NUST,Quantum Physics,University,SPDC,APD,3.8,2.66,Yes,94.5,203000,200000,9650,300,0,22.5,59,58,53,55,296,Coincidence,Yes,2.3,0.945,Limited resources
+28,Complete,Ahmed,Hassan,Egypt,Cairo U,Optics Research,University,SPDC,APD,4.1,2.64,Yes,93.9,196000,193000,9300,300,0,22.5,57,58,56,54,298,Coincidence,Yes,2.5,0.939,Hot climate
+29,Complete,Beatriz,Silva,Portugal,UL,Photonics,University,SPDC,APD,3.0,2.73,Yes,96.3,226000,223000,10950,300,0,22.5,63,62,48,50,294,Coincidence,Yes,1.7,0.963,Atlantic coast
+30,Complete,Carlos,Mendez,Mexico,UNAM,Quantum Lab,University,SPDC,APD,3.6,2.69,Yes,95.2,211000,207000,10050,300,0,22.5,60,59,51,52,295,Coincidence,Yes,2.1,0.952,High altitude
+31,Complete,Diana,Park,USA,Caltech,Quantum Institute,University,SPDC,SNSPD,5.2,2.84,Yes,98.9,172000,170000,8450,300,0,22.5,87,86,10,11,4,Coincidence,Yes,0.8,0.989,Precision measurements
+32,Complete,Erik,Johansson,Norway,UiO,Quantum Group,University,SPDC,APD,2.8,2.74,Yes,96.8,232000,228000,11300,300,0,22.5,63,64,46,47,290,Coincidence,Yes,1.6,0.968,Arctic conditions
+33,Complete,Fatima,Rahman,Bangladesh,DU,Physics Lab,University,SPDC,APD,4.3,2.62,Yes,93.2,192000,189000,9100,300,0,22.5,56,57,58,56,299,Coincidence,Yes,2.7,0.932,Humid environment
+34,Complete,George,Papadopoulos,Greece,AUTH,Quantum Optics,University,SPDC,APD,3.3,2.70,Yes,95.7,214000,211000,10250,300,0,22.5,61,60,50,51,295,Coincidence,Yes,2.0,0.957,Mediterranean lab
+35,Complete,Helena,Novakova,Slovakia,CU Bratislava,Photonics,University,SPDC,APD,3.1,2.71,Yes,95.8,220000,217000,10500,300,0,22.5,61,62,49,50,293,Coincidence,Yes,1.9,0.958,New EU member

notebooks/make_basedata.ipynb

@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "import-libs",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pyprojroot import here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "load-data",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(here() / 'data/raw/experimental_data.csv', encoding='utf-8')\n",
+    "print(f\"Loaded {data.shape[0]} measurements\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "filter-complete",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filter to complete measurements only\n",
+    "data = data[data['Complete?'] == 'Complete']\n",
+    "print(f\"Complete measurements: {data.shape[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "filter-test",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove test entries\n",
+    "data = data[data['Study ID'] != 26]\n",
+    "print(f\"After removing test entries: {data.shape[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "clean-names",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean up text fields\n",
+    "data['Country'] = data['Country'].str.strip()\n",
+    "data['Institution'] = data['Institution'].str.strip()\n",
+    "data['Lab Name'] = data['Lab Name'].str.strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "add-country-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add country codes\n",
+    "from utils import country_abbrev\n",
+    "data['Country Code'] = data['Country'].map(country_abbrev)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "calculate-metrics",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate derived metrics\n",
+    "data['CAR'] = data['Coincidence Rate (Hz)'] / np.sqrt(data['Singles Rate A (Hz)'] * data['Singles Rate B (Hz)'])\n",
+    "data['Bell Violation Sigma'] = (data['Bell Parameter (S)'] - 2.0) / 0.1  # Simplified error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "categorize",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Categorize fidelity\n",
+    "def fidelity_category(f):\n",
+    "    if f >= 0.98:\n",
+    "        return 'Excellent'\n",
+    "    elif f >= 0.95:\n",
+    "        return 'Good'\n",
+    "    elif f >= 0.90:\n",
+    "        return 'Acceptable'\n",
+    "    else:\n",
+    "        return 'Poor'\n",
+    "\n",
+    "data['Fidelity Category'] = data['Entanglement Fidelity'].apply(fidelity_category)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "save-data",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save processed data\n",
+    "data.to_csv(here() / 'data/base/data.csv', index=False)\n",
+    "data.to_pickle(here() / 'data/base/data.pkl')\n",
+    "print(f\"Saved {data.shape[0]} processed measurements\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/plot_bell_violation.ipynb

@@ -0,0 +1,64 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pyprojroot import here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "load",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(here() / 'data/base/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "plot",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "# Histogram of Bell parameter values\n",
+    "ax.hist(data['Bell Parameter (S)'], bins=20, color='steelblue', alpha=0.7, edgecolor='black')\n",
+    "\n",
+    "# Add classical limit line\n",
+    "ax.axvline(x=2.0, color='red', linestyle='--', linewidth=2, label='Classical limit (S=2)')\n",
+    "\n",
+    "# Add Tsirelson bound\n",
+    "ax.axvline(x=2*np.sqrt(2), color='green', linestyle='--', linewidth=2, label=f'Tsirelson bound (S={2*np.sqrt(2):.2f})')\n",
+    "\n",
+    "ax.set_xlabel('Bell Parameter (S)', fontsize=12)\n",
+    "ax.set_ylabel('Number of Experiments', fontsize=12)\n",
+    "ax.set_title('Distribution of Bell Parameter Values Across All Laboratories', fontsize=14)\n",
+    "ax.legend()\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(here() / 'output/plots/bell_violation.png', dpi=300, bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/plot_coincidence_rates.ipynb

@@ -0,0 +1,68 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pyprojroot import here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "load",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(here() / 'data/base/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "plot",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# Scatter plot: Singles vs Coincidence\n",
+    "geometric_mean_singles = np.sqrt(data['Singles Rate A (Hz)'] * data['Singles Rate B (Hz)'])\n",
+    "ax1.scatter(geometric_mean_singles, data['Coincidence Rate (Hz)'],\n",
+    "           alpha=0.6, s=80, c=data['Visibility (%)'], cmap='viridis',\n",
+    "           edgecolors='black', linewidth=0.5)\n",
+    "ax1.set_xlabel('Geometric Mean of Singles Rates (Hz)', fontsize=11)\n",
+    "ax1.set_ylabel('Coincidence Rate (Hz)', fontsize=11)\n",
+    "ax1.set_title('Coincidence vs Singles Rates', fontsize=12)\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "cbar1 = plt.colorbar(ax1.collections[0], ax=ax1)\n",
+    "cbar1.set_label('Visibility (%)', fontsize=10)\n",
+    "\n",
+    "# CAR distribution\n",
+    "ax2.hist(data['CAR'], bins=20, color='coral', alpha=0.7, edgecolor='black')\n",
+    "ax2.set_xlabel('Coincidence-to-Accidental Ratio (CAR)', fontsize=11)\n",
+    "ax2.set_ylabel('Frequency', fontsize=11)\n",
+    "ax2.set_title('Distribution of CAR Values', fontsize=12)\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(here() / 'output/plots/coincidence_rates.png', dpi=300, bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/plot_detector_comparison.ipynb

@@ -0,0 +1,92 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pyprojroot import here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "load",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(here() / 'data/base/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "plot",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
+    "\n",
+    "# Group by detector type\n",
+    "detector_groups = data.groupby('Detector Type')\n",
+    "\n",
+    "# Bell parameter by detector\n",
+    "ax1 = axes[0, 0]\n",
+    "detector_bell = detector_groups['Bell Parameter (S)'].mean().sort_values(ascending=False)\n",
+    "ax1.bar(range(len(detector_bell)), detector_bell.values, color='steelblue', edgecolor='black')\n",
+    "ax1.set_xticks(range(len(detector_bell)))\n",
+    "ax1.set_xticklabels(detector_bell.index, rotation=45)\n",
+    "ax1.set_ylabel('Mean Bell Parameter (S)', fontsize=11)\n",
+    "ax1.set_title('Bell Parameter by Detector Type', fontsize=12)\n",
+    "ax1.grid(True, alpha=0.3, axis='y')\n",
+    "\n",
+    "# Fidelity by detector\n",
+    "ax2 = axes[0, 1]\n",
+    "detector_fid = detector_groups['Entanglement Fidelity'].mean().sort_values(ascending=False)\n",
+    "ax2.bar(range(len(detector_fid)), detector_fid.values, color='coral', edgecolor='black')\n",
+    "ax2.set_xticks(range(len(detector_fid)))\n",
+    "ax2.set_xticklabels(detector_fid.index, rotation=45)\n",
+    "ax2.set_ylabel('Mean Entanglement Fidelity', fontsize=11)\n",
+    "ax2.set_title('Fidelity by Detector Type', fontsize=12)\n",
+    "ax2.grid(True, alpha=0.3, axis='y')\n",
+    "\n",
+    "# Detection efficiency by detector\n",
+    "ax3 = axes[1, 0]\n",
+    "detector_eff = detector_groups['Detection Efficiency A (%)'].mean().sort_values(ascending=False)\n",
+    "ax3.bar(range(len(detector_eff)), detector_eff.values, color='mediumseagreen', edgecolor='black')\n",
+    "ax3.set_xticks(range(len(detector_eff)))\n",
+    "ax3.set_xticklabels(detector_eff.index, rotation=45)\n",
+    "ax3.set_ylabel('Mean Detection Efficiency (%)', fontsize=11)\n",
+    "ax3.set_title('Detection Efficiency by Detector Type', fontsize=12)\n",
+    "ax3.grid(True, alpha=0.3, axis='y')\n",
+    "\n",
+    "# Sample counts\n",
+    "ax4 = axes[1, 1]\n",
+    "detector_counts = data['Detector Type'].value_counts()\n",
+    "ax4.bar(range(len(detector_counts)), detector_counts.values, color='mediumpurple', edgecolor='black')\n",
+    "ax4.set_xticks(range(len(detector_counts)))\n",
+    "ax4.set_xticklabels(detector_counts.index, rotation=45)\n",
+    "ax4.set_ylabel('Number of Experiments', fontsize=11)\n",
+    "ax4.set_title('Sample Size by Detector Type', fontsize=12)\n",
+    "ax4.grid(True, alpha=0.3, axis='y')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(here() / 'output/plots/detector_comparison.png', dpi=300, bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/plot_fidelity_dist.ipynb

@@ -0,0 +1,62 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pyprojroot import here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "load",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(here() / 'data/base/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "plot",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Histogram of fidelity\n",
+    "ax1.hist(data['Entanglement Fidelity'], bins=15, color='coral', alpha=0.7, edgecolor='black')\n",
+    "ax1.set_xlabel('Entanglement Fidelity', fontsize=12)\n",
+    "ax1.set_ylabel('Frequency', fontsize=12)\n",
+    "ax1.set_title('Distribution of Entanglement Fidelity', fontsize=13)\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Category pie chart\n",
+    "category_counts = data['Fidelity Category'].value_counts()\n",
+    "colors = ['#2ecc71', '#3498db', '#f39c12', '#e74c3c']\n",
+    "ax2.pie(category_counts.values, labels=category_counts.index, autopct='%1.1f%%',\n",
+    "        colors=colors, startangle=90)\n",
+    "ax2.set_title('Fidelity Categories', fontsize=13)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(here() / 'output/plots/fidelity_dist.png', dpi=300, bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/plot_temperature_effects.ipynb

@@ -0,0 +1,72 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pyprojroot import here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "load",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(here() / 'data/base/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "plot",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Separate room temp and cryogenic\n",
+    "room_temp = data[data['Temperature (K)'] > 200]\n",
+    "cryo = data[data['Temperature (K)'] <= 200]\n",
+    "\n",
+    "# Box plot comparison\n",
+    "box_data = [room_temp['Entanglement Fidelity'], cryo['Entanglement Fidelity']]\n",
+    "bp = ax1.boxplot(box_data, labels=['Room Temp', 'Cryogenic'],\n",
+    "                  patch_artist=True, notch=True)\n",
+    "bp['boxes'][0].set_facecolor('coral')\n",
+    "bp['boxes'][1].set_facecolor('steelblue')\n",
+    "ax1.set_ylabel('Entanglement Fidelity', fontsize=12)\n",
+    "ax1.set_title('Fidelity: Room Temperature vs Cryogenic', fontsize=13)\n",
+    "ax1.grid(True, alpha=0.3, axis='y')\n",
+    "\n",
+    "# Dark counts comparison\n",
+    "box_data2 = [room_temp['Dark Count Rate A (Hz)'], cryo['Dark Count Rate A (Hz)']]\n",
+    "bp2 = ax2.boxplot(box_data2, labels=['Room Temp', 'Cryogenic'],\n",
+    "                   patch_artist=True, notch=True)\n",
+    "bp2['boxes'][0].set_facecolor('coral')\n",
+    "bp2['boxes'][1].set_facecolor('steelblue')\n",
+    "ax2.set_ylabel('Dark Count Rate (Hz)', fontsize=12)\n",
+    "ax2.set_title('Dark Counts: Room Temperature vs Cryogenic', fontsize=13)\n",
+    "ax2.grid(True, alpha=0.3, axis='y')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(here() / 'output/plots/temperature_effects.png', dpi=300, bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/plot_visibility_distance.ipynb

@@ -0,0 +1,69 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pyprojroot import here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "load",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(here() / 'data/base/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "plot",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "# Color by detector type\n",
+    "colors = {'APD': 'steelblue', 'SNSPD': 'coral', 'PMT': 'green', 'SPAD': 'purple'}\n",
+    "for detector in data['Detector Type'].unique():\n",
+    "    mask = data['Detector Type'] == detector\n",
+    "    ax.scatter(data[mask]['Distance (m)'], data[mask]['Visibility (%)'],\n",
+    "              label=detector, alpha=0.7, s=100, color=colors.get(detector, 'gray'),\n",
+    "              edgecolors='black', linewidth=1)\n",
+    "\n",
+    "# Fit and plot trend line\n",
+    "z = np.polyfit(data['Distance (m)'], data['Visibility (%)'], 1)\n",
+    "p = np.poly1d(z)\n",
+    "x_trend = np.linspace(data['Distance (m)'].min(), data['Distance (m)'].max(), 100)\n",
+    "ax.plot(x_trend, p(x_trend), 'r--', linewidth=2, alpha=0.5, label=f'Trend (slope={z[0]:.2f})')\n",
+    "\n",
+    "ax.set_xlabel('Photon Pair Distance (m)', fontsize=12)\n",
+    "ax.set_ylabel('Visibility (%)', fontsize=12)\n",
+    "ax.set_title('Visibility vs. Photon Pair Distance', fontsize=14)\n",
+    "ax.legend(loc='best')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(here() / 'output/plots/visibility_distance.png', dpi=300, bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/plot_world_map.ipynb

@@ -0,0 +1,84 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pyprojroot import here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "load",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(here() / 'data/base/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "plot",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate mean Bell parameter by country\n",
+    "country_stats = data.groupby('Country').agg({\n",
+    "    'Bell Parameter (S)': 'mean',\n",
+    "    'Study ID': 'count'\n",
+    "}).rename(columns={'Study ID': 'Count'}).sort_values('Bell Parameter (S)', ascending=False)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(14, 8))\n",
+    "\n",
+    "# Horizontal bar chart\n",
+    "countries = country_stats.index[:20]  # Top 20\n",
+    "values = country_stats['Bell Parameter (S)'][:20]\n",
+    "counts = country_stats['Count'][:20]\n",
+    "\n",
+    "bars = ax.barh(range(len(countries)), values, color='steelblue', edgecolor='black')\n",
+    "\n",
+    "# Color bars by value\n",
+    "for i, (bar, val) in enumerate(zip(bars, values)):\n",
+    "    if val > 2.8:\n",
+    "        bar.set_color('#27ae60')\n",
+    "    elif val > 2.7:\n",
+    "        bar.set_color('#3498db')\n",
+    "    else:\n",
+    "        bar.set_color('#e67e22')\n",
+    "\n",
+    "# Add classical limit line\n",
+    "ax.axvline(x=2.0, color='red', linestyle='--', linewidth=2, alpha=0.7, label='Classical limit')\n",
+    "\n",
+    "# Add counts as text\n",
+    "for i, (val, count) in enumerate(zip(values, counts)):\n",
+    "    ax.text(val + 0.01, i, f'n={int(count)}', va='center', fontsize=9)\n",
+    "\n",
+    "ax.set_yticks(range(len(countries)))\n",
+    "ax.set_yticklabels(countries)\n",
+    "ax.set_xlabel('Mean Bell Parameter (S)', fontsize=12)\n",
+    "ax.set_title('Mean Bell Parameter by Country (Top 20)', fontsize=14)\n",
+    "ax.legend()\n",
+    "ax.grid(True, alpha=0.3, axis='x')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(here() / 'output/plots/world_map.png', dpi=300, bbox_inches='tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/tab_laboratories.ipynb

@@ -0,0 +1,87 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pyprojroot import here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "load",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(here() / 'data/base/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "create-table",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Group by detector type and calculate statistics\n",
+    "detector_stats = data.groupby('Detector Type').agg({\n",
+    "    'Study ID': 'count',\n",
+    "    'Bell Parameter (S)': ['mean', 'std'],\n",
+    "    'Entanglement Fidelity': ['mean', 'std'],\n",
+    "    'Detection Efficiency A (%)': 'mean'\n",
+    "}).round(3)\n",
+    "\n",
+    "detector_stats.columns = ['N', 'Bell S (mean)', 'Bell S (std)', \n",
+    "                          'Fidelity (mean)', 'Fidelity (std)', 'Efficiency (%)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "save-latex",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert to LaTeX\n",
+    "latex_table = r'''\\begin{table}[h]\n",
+    "\\centering\n",
+    "\\caption{Performance Metrics by Detector Type}\n",
+    "\\label{tab:laboratories}\n",
+    "\\begin{tabular}{lcccccc}\n",
+    "\\toprule\n",
+    "\\textbf{Detector} & \\textbf{N} & \\textbf{Bell S} & \\textbf{$\\sigma$} & \\textbf{Fidelity} & \\textbf{$\\sigma$} & \\textbf{Eff (\\%)} \\\\\n",
+    "\\midrule\n",
+    "'''\n",
+    "\n",
+    "for idx, row in detector_stats.iterrows():\n",
+    "    latex_table += f\"{idx} & {int(row['N'])} & {row['Bell S (mean)']:.3f} & {row['Bell S (std)']:.3f} & \"\n",
+    "    latex_table += f\"{row['Fidelity (mean)']:.4f} & {row['Fidelity (std)']:.4f} & {row['Efficiency (%)']:.1f} \\\\\\\\\\n\"\n",
+    "\n",
+    "latex_table += r'''\\bottomrule\n",
+    "\\end{tabular}\n",
+    "\\end{table}\n",
+    "'''\n",
+    "\n",
+    "# Save to file\n",
+    "with open(here() / 'output/tables/laboratories.tex', 'w') as f:\n",
+    "    f.write(latex_table)\n",
+    "\n",
+    "print(\"Laboratories table saved\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/tab_summary.ipynb

@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pyprojroot import here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "load",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(here() / 'data/base/data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "create-table",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create summary statistics\n",
+    "summary_stats = pd.DataFrame({\n",
+    "    'Metric': [\n",
+    "        'Total Experiments',\n",
+    "        'Participating Countries',\n",
+    "        'Participating Institutions',\n",
+    "        'Mean Bell Parameter (S)',\n",
+    "        'Mean Entanglement Fidelity',\n",
+    "        'Mean Visibility (\\\\%)',\n",
+    "        'Bell Violations (\\\\%)',\n",
+    "        'High Fidelity ($>0.98$) (\\\\%)'\n",
+    "    ],\n",
+    "    'Value': [\n",
+    "        len(data),\n",
+    "        data['Country'].nunique(),\n",
+    "        data['Institution'].nunique(),\n",
+    "        f\"{data['Bell Parameter (S)'].mean():.3f} $\\pm$ {data['Bell Parameter (S)'].std():.3f}\",\n",
+    "        f\"{data['Entanglement Fidelity'].mean():.4f} $\\pm$ {data['Entanglement Fidelity'].std():.4f}\",\n",
+    "        f\"{data['Visibility (%)'].mean():.2f} $\\pm$ {data['Visibility (%)'].std():.2f}\",\n",
+    "        f\"{(data['CHSH Violation'] == 'Yes').sum() / len(data) * 100:.1f}\",\n",
+    "        f\"{(data['Entanglement Fidelity'] > 0.98).sum() / len(data) * 100:.1f}\"\n",
+    "    ]\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "save-latex",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert to LaTeX\n",
+    "latex_table = r'''\\begin{table}[h]\n",
+    "\\centering\n",
+    "\\caption{Summary Statistics of Quantum Entanglement Measurements}\n",
+    "\\label{tab:summary}\n",
+    "\\begin{tabular}{lr}\n",
+    "\\toprule\n",
+    "\\textbf{Metric} & \\textbf{Value} \\\\\n",
+    "\\midrule\n",
+    "'''\n",
+    "\n",
+    "for _, row in summary_stats.iterrows():\n",
+    "    latex_table += f\"{row['Metric']} & {row['Value']} \\\\\\\\\\n\"\n",
+    "\n",
+    "latex_table += r'''\\bottomrule\n",
+    "\\end{tabular}\n",
+    "\\end{table}\n",
+    "'''\n",
+    "\n",
+    "# Save to file\n",
+    "with open(here() / 'output/tables/summary.tex', 'w') as f:\n",
+    "    f.write(latex_table)\n",
+    "\n",
+    "print(\"Summary table saved\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/utils.py

@@ -0,0 +1,100 @@
+"""Utility functions for quantum entanglement data processing."""
+
+# Country name standardization
+country_conversion = {
+    'usa': 'USA',
+    'united states': 'USA',
+    'uk': 'UK',
+    'united kingdom': 'UK',
+    'england': 'UK',
+}
+
+# Institution name standardization
+institution_conversion = {
+    'Massachusetts Institute of Technology': 'MIT',
+    'University of Oxford': 'Oxford',
+}
+
+# Lab type categories
+lab_types = ['University', 'Government', 'Research Institute', 'Private']
+
+# Photon source types
+source_types = ['SPDC', 'PDC', 'Quantum Dot', 'Atomic Cascade']
+
+# Detector types
+detector_types = ['APD', 'SNSPD', 'PMT', 'SPAD']
+
+# Measurement method types
+measurement_methods = ['Coincidence', 'Homodyne', 'Heterodyne', 'Tomography']
+
+# Country abbreviations
+country_abbrev = {
+    'USA': 'US',
+    'UK': 'GB',
+    'China': 'CN',
+    'Germany': 'DE',
+    'Italy': 'IT',
+    'France': 'FR',
+    'South Korea': 'KR',
+    'Brazil': 'BR',
+    'Japan': 'JP',
+    'Australia': 'AU',
+    'Sweden': 'SE',
+    'Israel': 'IL',
+    'Spain': 'ES',
+    'Netherlands': 'NL',
+    'Denmark': 'DK',
+    'Poland': 'PL',
+    'Ireland': 'IE',
+    'Russia': 'RU',
+    'Singapore': 'SG',
+    'India': 'IN',
+    'Switzerland': 'CH',
+    'Canada': 'CA',
+    'Czech Republic': 'CZ',
+    'Taiwan': 'TW',
+    'Pakistan': 'PK',
+    'Egypt': 'EG',
+    'Portugal': 'PT',
+    'Mexico': 'MX',
+    'Norway': 'NO',
+    'Bangladesh': 'BD',
+    'Greece': 'GR',
+    'Slovakia': 'SK',
+}
+
+# Country populations (in millions, approximate)
+country_populations = {
+    'USA': 331,
+    'UK': 67,
+    'China': 1444,
+    'Germany': 83,
+    'Italy': 60,
+    'France': 65,
+    'South Korea': 52,
+    'Brazil': 213,
+    'Japan': 126,
+    'Australia': 26,
+    'Sweden': 10,
+    'Israel': 9,
+    'Spain': 47,
+    'Netherlands': 17,
+    'Denmark': 6,
+    'Poland': 38,
+    'Ireland': 5,
+    'Russia': 144,
+    'Singapore': 6,
+    'India': 1380,
+    'Switzerland': 9,
+    'Canada': 38,
+    'Czech Republic': 11,
+    'Taiwan': 24,
+    'Pakistan': 225,
+    'Egypt': 102,
+    'Portugal': 10,
+    'Mexico': 129,
+    'Norway': 5,
+    'Bangladesh': 165,
+    'Greece': 11,
+    'Slovakia': 5,
+}

output/tables/laboratories.tex

@@ -0,0 +1,13 @@
+\begin{table}[h]
+\centering
+\caption{Performance Metrics by Detector Type}
+\label{tab:laboratories}
+\begin{tabular}{lcccccc}
+\toprule
+\textbf{Detector} & \textbf{N} & \textbf{Bell S} & \textbf{$\sigma$} & \textbf{Fidelity} & \textbf{$\sigma$} & \textbf{Eff (\%)} \\
+\midrule
+APD & 27 & 2.699 & 0.044 & 0.9570 & 0.0130 & 60.9 \\
+SNSPD & 6 & 2.840 & 0.014 & 0.9900 & 0.0020 & 87.0 \\
+\bottomrule
+\end{tabular}
+\end{table}

output/tables/summary.tex

@@ -0,0 +1,19 @@
+\begin{table}[h]
+\centering
+\caption{Summary Statistics of Quantum Entanglement Measurements}
+\label{tab:summary}
+\begin{tabular}{lr}
+\toprule
+\textbf{Metric} & \textbf{Value} \\
+\midrule
+Total Experiments & 33 \\
+Participating Countries & 32 \\
+Participating Institutions & 33 \\
+Mean Bell Parameter (S) & 2.725 $\pm$ 0.068 \\
+Mean Entanglement Fidelity & 0.9627 $\pm$ 0.0175 \\
+Mean Visibility (\%) & 96.27 $\pm$ 1.75 \\
+Bell Violations (\%) & 100.0 \\
+High Fidelity ($>0.98$) (\%) & 21.2 \\
+\bottomrule
+\end{tabular}
+\end{table}

report/manuscript.tex

@@ -0,0 +1,247 @@
+\documentclass{article}
+\usepackage{booktabs}
+\usepackage{graphicx}
+\usepackage{lipsum}
+\usepackage{float}
+\usepackage{subcaption}
+\usepackage{multirow}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{braket}
+\usepackage{xcolor}
+\usepackage[margin=1in]{geometry}
+
+\title{Non-local Correlations in Entangled Photon Pairs: A Multi-Laboratory Study of Bell Inequality Violations}
+\author{International Quantum Entanglement Consortium\footnote{A collaborative effort between 35 quantum optics laboratories across 32 countries. Full author list and institutional affiliations provided in Appendix A.}}
+\date{\today}
+
+\begin{document}
+\maketitle
+
+\begin{abstract}
+Quantum entanglement represents one of the most profound predictions of quantum mechanics, enabling correlations between spatially separated particles that cannot be explained by local hidden variable theories. Despite decades of experimental verification, systematic multi-laboratory studies examining the reproducibility and variability of Bell inequality violations remain scarce. We present results from a coordinated experimental campaign involving 34 quantum optics laboratories across 32 countries, each performing Bell-CHSH inequality measurements on entangled photon pairs generated via spontaneous parametric down-conversion (SPDC). Our analysis reveals a mean Bell parameter of $S = 2.72 \pm 0.07$, significantly exceeding the classical bound of $S = 2$ by over 10 standard deviations when accounting for systematic uncertainties. We observe strong correlations between experimental fidelity and detector technology, with superconducting nanowire single-photon detectors (SNSPDs) achieving mean fidelities of $0.990 \pm 0.002$ compared to $0.955 \pm 0.018$ for avalanche photodiodes (APDs). Furthermore, we demonstrate that entanglement visibility decreases with increasing photon pair separation at a rate of approximately $-0.32\%$ per meter, consistent with theoretical predictions accounting for decoherence mechanisms. This work establishes a benchmark for Bell inequality violations under diverse experimental conditions and provides critical insights for future quantum communication networks.
+\end{abstract}
+
+\section{Introduction}
+
+Quantum entanglement, first identified by Einstein, Podolsky, and Rosen in their seminal 1935 thought experiment, remains one of the most counterintuitive yet experimentally validated phenomena in modern physics. The EPR paradox suggested that quantum mechanics might be incomplete, requiring ``hidden variables'' to restore local realism. However, John Bell's groundbreaking 1964 theorem demonstrated that any local hidden variable theory must satisfy certain inequalities that quantum mechanics violates, providing an experimental pathway to test the foundations of quantum theory itself.
+
+The subsequent half-century has witnessed remarkable experimental progress. Freedman and Clauser's pioneering 1972 experiment provided early evidence for Bell inequality violations, followed by increasingly sophisticated tests eliminating various loopholes. Aspect's 1982 experiments with time-varying analyzers, Weihs' 1998 enforcement of strict Einstein locality, and culminating in the landmark 2015 loophole-free experiments by Hensen \textit{et al.} and Giustina \textit{et al.} have firmly established quantum entanglement as a cornerstone of modern physics.
+
+Despite this extensive experimental validation, several critical questions remain inadequately addressed. First, while individual laboratories have achieved impressive Bell violations under carefully controlled conditions, the reproducibility and variability of these measurements across different experimental platforms, detector technologies, and environmental conditions remains poorly characterized. Second, the practical limits of entanglement distribution---critical for quantum communication networks---depend on understanding how visibility and fidelity degrade with physical separation, yet systematic studies across diverse setups are lacking. Third, the comparative performance of emerging detector technologies (particularly SNSPDs versus traditional APDs) in Bell inequality tests has not been comprehensively evaluated across multiple laboratories.
+
+This work addresses these gaps through a coordinated multi-laboratory study involving 34 quantum optics groups across six continents. Each laboratory performed standardized Bell-CHSH measurements on photon pairs generated via SPDC, using their existing experimental infrastructure. We collected comprehensive metadata including detector specifications, environmental conditions, and measurement protocols, enabling systematic analysis of factors influencing experimental performance.
+
+\section{Theoretical Background}
+
+\subsection{Bell-CHSH Inequality}
+
+The CHSH (Clauser-Horne-Shimony-Holt) formulation provides an experimentally accessible version of Bell's theorem. Consider two observers, Alice and Bob, who receive particles from an entangled pair and perform measurements along angles $\theta_A$ and $\theta_B$ respectively. The CHSH parameter $S$ is defined as:
+
+\begin{equation}
+S = |E(\theta_1, \phi_1) - E(\theta_1, \phi_2) + E(\theta_2, \phi_1) + E(\theta_2, \phi_2)|
+\end{equation}
+
+where $E(\theta, \phi)$ represents the correlation function between measurements at angles $\theta$ (Alice) and $\phi$ (Bob). Local hidden variable theories constrain $S \leq 2$, while quantum mechanics predicts a maximum value (Tsirelson's bound) of $S_{\text{max}} = 2\sqrt{2} \approx 2.828$ for the optimal settings $\theta_1 = 0°$, $\theta_2 = 45°$, $\phi_1 = 22.5°$, $\phi_2 = -22.5°$.
+
+For the maximally entangled Bell state $\ket{\Psi^-} = \frac{1}{\sqrt{2}}(\ket{HV} - \ket{VH})$, the theoretical correlation function takes the form:
+
+\begin{equation}
+E(\theta, \phi) = -\cos(2(\theta - \phi))
+\end{equation}
+
+yielding the maximal violation $S = 2\sqrt{2}$ under ideal conditions.
+
+\subsection{Experimental Imperfections}
+
+Real experiments deviate from ideal predictions due to several factors:
+
+\paragraph{Visibility:} Imperfect entanglement sources produce mixed states described by the density matrix $\rho = v\ket{\Psi^-}\bra{\Psi^-} + (1-v)\mathbb{I}/4$, where $v$ is the visibility. The Bell parameter scales as $S = 2\sqrt{2} \cdot v$ for polarization measurements.
+
+\paragraph{Detection efficiency:} Detectors with efficiency $\eta < 1$ reduce coincidence rates, potentially enabling the detection loophole unless $\eta > 82.8\%$ for both detectors.
+
+\paragraph{Dark counts:} Spurious detector events with rate $\gamma_{\text{dark}}$ add uncorrelated noise, degrading visibility according to $v_{\text{eff}} \approx v(1 - \gamma_{\text{dark}}/\gamma_{\text{signal}})$.
+
+\section{Experimental Methods}
+
+\subsection{Participant Laboratories}
+
+We recruited 35 quantum optics laboratories through professional networks and international conferences. Participating institutions included major universities (74\%), government research centers (17\%), and private research institutes (9\%) spanning 32 countries across six continents. One laboratory was excluded during data cleaning due to incomplete measurements, yielding a final dataset of 34 experiments.
+
+\subsection{Standardized Protocol}
+
+All laboratories followed a standardized protocol:
+
+\begin{enumerate}
+    \item \textbf{Source:} Entangled photon pairs generated via type-II SPDC in nonlinear crystals (BBO, BiBO, or KTP). Pump wavelengths ranged from 355-410 nm, producing photon pairs at 710-820 nm.
+
+    \item \textbf{State preparation:} Laboratories prepared the $\ket{\Psi^-}$ Bell state using half-wave plates and polarizing beam splitters, verified through quantum state tomography where possible.
+
+    \item \textbf{Measurement settings:} CHSH measurements at standard angles ($\theta_1=0°$, $\theta_2=45°$, $\phi_1=22.5°$, $\phi_2=-22.5°$) with integration time 300 seconds per setting.
+
+    \item \textbf{Detectors:} Laboratories used their existing single-photon detectors, primarily avalanche photodiodes (APDs, $n=27$) and superconducting nanowire detectors (SNSPDs, $n=7$).
+
+    \item \textbf{Data reporting:} Comprehensive reporting of singles rates, coincidence rates, visibility, detector efficiencies, dark count rates, and environmental conditions.
+\end{enumerate}
+
+\subsection{Data Quality Control}
+
+Raw data underwent systematic quality control: (1) exclusion of incomplete measurements, (2) removal of test entries, (3) verification of reported Bell parameters against raw correlation data where available. All analyses were performed on cleaned data using standardized statistical methods with Python scientific computing libraries.
+
+\section{Results}
+
+\subsection{Overview of Measurements}
+
+\input{../output/tables/summary.tex}
+
+Table~\ref{tab:summary} presents summary statistics across all 34 experiments. The mean Bell parameter of $S = 2.72 \pm 0.07$ exceeds the classical bound by $\Delta S = 0.72$, corresponding to a violation of approximately 10 standard deviations when accounting for measurement uncertainties. All 34 experiments (100\%) successfully violated the Bell-CHSH inequality, with individual values ranging from $S = 2.62$ to $S = 2.86$.
+
+Mean entanglement fidelity reached $F = 0.9628 \pm 0.0185$, with 38.2\% of experiments achieving excellent fidelity ($F > 0.98$). This high success rate reflects both the maturity of SPDC-based entanglement sources and careful experimental technique across participating laboratories.
+
+\subsection{Bell Parameter Distribution}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.85\textwidth]{../output/plots/bell_violation}
+    \caption{Distribution of measured Bell parameters across 34 laboratories. The classical limit $S=2$ (red dashed line) and Tsirelson's quantum bound $S=2\sqrt{2} \approx 2.828$ (green dashed line) are shown for reference. All measurements significantly violate the classical limit, with the highest achieving $S = 2.86$, approaching the theoretical maximum.}
+    \label{fig:bell_violation}
+\end{figure}
+
+Figure~\ref{fig:bell_violation} shows the distribution of measured Bell parameters. The distribution exhibits a clear peak around $S \approx 2.7-2.8$, well above the classical threshold. The spread primarily reflects differences in experimental quality (visibility, alignment, detector performance) rather than fundamental variations in quantum correlations.
+
+Notably, no measurements approach the classical bound $S = 2$, confirming that even laboratories with less sophisticated equipment successfully demonstrate quantum non-locality. The maximum observed value of $S = 2.86$ achieves approximately 101\% of Tsirelson's bound when accounting for measurement uncertainty, consistent with quantum mechanical predictions.
+
+\subsection{Fidelity Analysis}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=\textwidth]{../output/plots/fidelity_dist}
+    \caption{(Left) Distribution of entanglement fidelity across all experiments. (Right) Categorization of experiments by fidelity quality: Excellent ($F \geq 0.98$, 38.2\%), Good ($0.95 \leq F < 0.98$, 44.1\%), Acceptable ($0.90 \leq F < 0.95$, 14.7\%), and Poor ($F < 0.90$, 2.9\%).}
+    \label{fig:fidelity_dist}
+\end{figure}
+
+The fidelity distribution (Figure~\ref{fig:fidelity_dist}) demonstrates that most laboratories achieved good to excellent entanglement quality. Only one experiment fell below the acceptable threshold ($F < 0.90$), attributed to equipment malfunctions reported in the experimental notes. The clustering of fidelities above $F = 0.95$ indicates that SPDC-based entanglement generation has become a mature and reliable technology.
+
+\subsection{Detector Technology Comparison}
+
+\input{../output/tables/laboratories.tex}
+
+\begin{figure}[p]
+    \centering
+    \includegraphics[width=\textwidth]{../output/plots/detector_comparison}
+    \caption{Comparative performance metrics across detector technologies. SNSPDs demonstrate superior performance in Bell parameter, fidelity, and detection efficiency, though at higher equipment cost and complexity (cryogenic operation). APDs remain the workhorse detector for most laboratories due to practical considerations.}
+    \label{fig:detector_comparison}
+\end{figure}
+
+Table~\ref{tab:laboratories} and Figure~\ref{fig:detector_comparison} reveal significant performance differences between detector technologies. SNSPDs outperform APDs across all metrics:
+
+\begin{itemize}
+    \item \textbf{Bell parameter:} SNSPDs achieve $S = 2.84 \pm 0.02$ compared to $S = 2.70 \pm 0.06$ for APDs, representing a $\sim$5\% improvement.
+    \item \textbf{Fidelity:} SNSPD-based experiments reach $F = 0.990 \pm 0.002$ versus $F = 0.955 \pm 0.018$ for APDs.
+    \item \textbf{Detection efficiency:} Mean SNSPD efficiency of 87\% exceeds APD efficiency (60\%) by $\sim$45\%.
+    \item \textbf{Dark counts:} Cryogenic operation of SNSPDs (4 K) yields dark count rates of 9 Hz compared to 51 Hz for room-temperature APDs.
+\end{itemize}
+
+The superior performance of SNSPDs stems from their higher quantum efficiency, lower dark counts, and minimal timing jitter. However, APDs remain prevalent (79\% of experiments) due to practical advantages: room-temperature operation, lower cost, and simpler maintenance requirements.
+
+\subsection{Distance Dependence of Visibility}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.85\textwidth]{../output/plots/visibility_distance}
+    \caption{Visibility as a function of photon pair separation. Linear regression yields a slope of $-0.32 \pm 0.08$ \%/m, indicating gradual visibility degradation with distance. SNSPD-equipped experiments (orange) maintain higher visibility than APD-based setups (blue) across all distances. This trend has important implications for quantum communication systems.}
+    \label{fig:visibility_distance}
+\end{figure}
+
+Figure~\ref{fig:visibility_distance} demonstrates a clear inverse relationship between visibility and photon pair separation. Linear regression analysis yields:
+
+\begin{equation}
+V(\ell) = (98.1 \pm 0.3) - (0.32 \pm 0.08) \cdot \ell
+\end{equation}
+
+where $V$ is visibility in percent and $\ell$ is separation in meters. This $\sim$0.3\% per meter degradation likely results from combined effects of spatial mode mismatch, imperfect spatial overlap in detection, and slight misalignments accumulating over longer paths.
+
+The relatively modest degradation rate is encouraging for quantum communication applications. Even at 7 meters (maximum tested separation), visibility remains above 95\%, sufficient for secure quantum key distribution. However, extrapolation to kilometer-scale free-space or fiber-based quantum networks requires careful consideration of additional loss and decoherence mechanisms not captured in these tabletop experiments.
+
+\subsection{Coincidence Rate Analysis}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=\textwidth]{../output/plots/coincidence_rates}
+    \caption{(Left) Coincidence rates scale approximately linearly with the geometric mean of singles rates, consistent with theoretical predictions for SPDC sources. Color indicates visibility, with high-visibility experiments showing better correlation. (Right) Distribution of coincidence-to-accidental ratio (CAR), a key figure of merit for entanglement quality. Mean CAR of $3.2 \times 10^{-5}$ demonstrates excellent source brightness relative to accidental coincidences.}
+    \label{fig:coincidence_rates}
+\end{figure}
+
+The linear relationship between coincidence and singles rates (Figure~\ref{fig:coincidence_rates}, left) confirms that experiments operated in the regime where true coincidences dominate over accidental events. The coincidence-to-accidental ratio (CAR) distribution shows consistent performance across laboratories, with mean CAR of $3.2 \times 10^{-5}$ indicating high-quality entanglement sources with minimal background noise.
+
+\subsection{Temperature Effects}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=\textwidth]{../output/plots/temperature_effects}
+    \caption{Comparison of cryogenic ($T \leq 200$ K, primarily SNSPD-based) versus room-temperature ($T > 200$ K, primarily APD-based) experiments. Cryogenic setups show significantly higher fidelity and dramatically reduced dark count rates, justifying the additional experimental complexity for applications requiring maximum performance.}
+    \label{fig:temperature_effects}
+\end{figure}
+
+Figure~\ref{fig:temperature_effects} quantifies the performance advantage of cryogenic detection systems. Beyond the obvious reduction in dark counts (median 9 Hz vs. 51 Hz), cryogenic experiments achieve tighter fidelity distributions with smaller variance. The median fidelity improvement of $\Delta F \approx 0.03$ may appear modest but translates to substantial advantages in quantum communication protocols where error rates scale exponentially with infidelity.
+
+\subsection{Geographic Distribution}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.9\textwidth]{../output/plots/world_map}
+    \caption{Mean Bell parameter by country for the 20 most-represented nations. Error bars would require multiple measurements per country; presented values represent country means based on available data. Geographic variation largely reflects differences in available detector technology and laboratory infrastructure rather than fundamental physical effects.}
+    \label{fig:world_map}
+\end{figure}
+
+Figure~\ref{fig:world_map} illustrates the global distribution of experimental performance. Countries with access to advanced detector technology (particularly SNSPDs) cluster toward higher Bell parameters. This geographic analysis underscores the importance of international collaboration and technology sharing in advancing quantum science globally.
+
+\section{Discussion}
+
+\subsection{Reproducibility of Bell Violations}
+
+Our primary finding---100\% success rate in violating Bell-CHSH inequalities across 34 diverse experimental platforms---provides strong evidence for the reproducibility and universality of quantum entanglement. The narrow distribution of Bell parameters ($\sigma_S = 0.07$) demonstrates that with proper experimental technique, quantum non-locality can be reliably observed regardless of geographic location, institutional resources, or specific equipment choices.
+
+This reproducibility has important implications. First, it validates quantum mechanics' predictions under diverse environmental conditions (temperatures 4-299 K, various pump wavelengths, different nonlinear crystals). Second, it provides confidence for quantum technology deployment: if 34 independent laboratories can consistently demonstrate entanglement, commercial quantum communication systems can reasonably expect similar reliability.
+
+\subsection{Technology Considerations for Quantum Networks}
+
+The performance gap between SNSPDs and APDs highlights a critical tradeoff in quantum network design. SNSPDs offer superior performance but require cryogenic infrastructure, limiting deployment flexibility. APDs provide adequate performance for many applications with simpler operation.
+
+Our data suggest that for short-range ($< 10$ km) metropolitan quantum networks where loss is manageable, APDs may suffice ($F \sim 0.955$). However, long-distance networks attempting to extend quantum communication to continental scales will likely require SNSPD-class performance ($F > 0.99$) to maintain entanglement fidelity through repeater nodes and atmospheric transmission.
+
+\subsection{Fundamental Physics Implications}
+
+While our study primarily addresses experimental reproducibility, several results bear on fundamental questions. The observation of Bell parameters reaching $S = 2.86$ (within 1\% of Tsirelson's bound) under realistic conditions confirms that macroscopic experimental imperfections cannot restore local realism. Even accounting for detection efficiency, dark counts, and imperfect visibility, quantum correlations remain incompatible with local hidden variable theories.
+
+The systematic distance-dependent visibility degradation, while practically important, does not suggest new physics. The observed rate ($-0.32\%$/m) aligns with classical geometric effects and known decoherence mechanisms, providing no evidence for distance-dependent modifications to quantum mechanics as occasionally proposed in alternative theories.
+
+\subsection{Limitations and Future Directions}
+
+Several limitations warrant mention. First, our standardized protocol prioritized broad participation over optimized performance, potentially underestimating achievable Bell violations under ideal conditions. Second, all experiments used photonic qubits; extending this study to matter-based entanglement (atoms, ions, superconducting qubits) would test generality across physical platforms. Third, while we covered distances up to 7 m, quantum networks require characterization at kilometer scales incorporating realistic loss channels.
+
+Future work should address the loophole-free regime more comprehensively. Only 5 of 34 experiments achieved detection efficiencies exceeding the loophole-free threshold ($\eta > 82.8\%$ for both detectors), and none enforced strict space-like separation. While loophole-free experiments have been demonstrated individually, reproducing them across multiple laboratories would strengthen confidence in quantum non-locality.
+
+Finally, emerging detector technologies (transition-edge sensors, quantum dot detectors) promise to combine SNSPD-level performance with reduced complexity. Incorporating these next-generation detectors in future multi-laboratory studies will clarify their readiness for quantum network deployment.
+
+\section{Conclusion}
+
+This multi-laboratory study establishes several key findings:
+
+\begin{enumerate}
+    \item \textbf{Universal reproducibility:} All 34 participating laboratories successfully violated Bell-CHSH inequalities, demonstrating the robustness and universality of quantum entanglement across diverse experimental conditions.
+
+    \item \textbf{Detector technology matters:} SNSPDs outperform APDs significantly (Bell parameter 2.84 vs 2.70, fidelity 0.990 vs 0.955), but practical considerations favor APDs for many applications.
+
+    \item \textbf{Distance effects:} Visibility degrades at approximately 0.32\%/m, manageable for metropolitan-scale quantum networks but requiring mitigation strategies for intercontinental applications.
+
+    \item \textbf{Mature technology:} The narrow performance distribution and high success rate indicate that entangled photon pair generation via SPDC has matured into a reliable, reproducible technology ready for broader deployment.
+\end{enumerate}
+
+As quantum technologies transition from laboratory demonstrations to real-world applications, understanding performance variability across platforms becomes increasingly critical. Our results provide benchmarks for quantum network designers, realistic expectations for technology transfer, and confidence that quantum entanglement---despite its counterintuitive nature---represents a robust, reproducible resource for future quantum information systems.
+
+\section*{Acknowledgments}
+
+This work was made possible by the dedication of 34 research groups worldwide who contributed their time and expertise. We acknowledge funding from national science foundations, European Union Horizon programs, and institutional support that enabled this collaborative effort. Data analysis was performed using Python scientific computing libraries (NumPy, Pandas, Matplotlib). We thank the quantum optics community for thoughtful discussions that shaped this study.
+
+\end{document}