[{"id":"dw_nominate_polarization","name":"US House Polarization (DW-NOMINATE)","legend_short":"DW-NOMINATE polarization","short_description":"Distance between Democratic and Republican House means on the first DW-NOMINATE dimension; 46th–118th Congress (1879–2023)","source":"Voteview / Lewis, Poole, Rosenthal, Boche, Rudkin, Sonnet (2026)","source_url":"https://voteview.com/articles/party_polarization","license":"freely available; project code MIT-licensed; no explicit data license","data_file":"/data/dw_nominate.csv","year_column":"year","value_column":"house_party_distance","value_units":"party-mean distance (d1)","color":"#0F1419","associated_cycle_id":"huntington","association_note":"Huntington's construct is recurring surges of creedal moralism - not interparty roll-call distance. This project pairs it with DW-NOMINATE polarization as an imperfect proxy chosen by us, not a measure Huntington proposed; it is the cleanest century-scale roll-call series available."},{"id":"us_tfp_growth","name":"US TFP growth (5-yr rolling)","legend_short":"TFP growth · 5-yr","short_description":"5-year centered rolling average of Fernald's utilization-adjusted US TFP growth, derived by this project from the annual `dtfp_util` column. Annual data begins 1948; the build script keeps clipped (asymmetric) windows at the boundaries rather than dropping rows, so the 1948 and 1949 endpoints are edge artifacts","source":"Fernald (2014), FRBSF Working Paper 2012-19","source_url":"https://www.frbsf.org/research-and-insights/data-and-indicators/total-factor-productivity-tfp/","license":"freely available; © FRBSF, no explicit reuse license","data_file":"/data/us_tfp_growth.csv","year_column":"year","value_column":"tfp_growth_5yr_avg_pct","value_units":"% per year","color":"#0891b2","associated_cycle_id":"kondratiev","association_note":"Kondratiev waves predict 50–60 year cycles of technological paradigm expansion and exhaustion. TFP growth is the most direct measurable output."},{"id":"wid_top1_wealth","name":"US Top 1% Wealth Share","legend_short":"Top 1% wealth share","short_description":"Share of total household wealth held by the top 1% of US adults. The modern Saez–Zucman series begins 1913; pre-1913 points (1820, 1850, 1880, 1900, 1910) are spliced from earlier historical sources via OWID/WID and have wider standard errors","source":"WID · World Inequality Database, retrieved via Our World in Data. 1913–present from Saez & Zucman (2016) / DINA; pre-1913 decadal points (1820, 1850, 1880, 1900, 1910) are WID interpolations sourced from earlier US wealth-distribution literature, not from Saez–Zucman directly","source_url":"https://wid.world/country/usa/","license":"CC BY 4.0","data_file":"/data/wid_top1_wealth.csv","year_column":"year","value_column":"top1_wealth_share_pct","value_units":"% of household wealth","color":"#5A1A1A","associated_cycle_id":"turchin","association_note":"Direct proxy for Turchin's elite-overproduction driver - when wealth concentrates, elite competition intensifies and instability follows."},{"id":"us_world_gdp_share","name":"US Share of World GDP","legend_short":"US share · world GDP","short_description":"US GDP as share of all-countries GDP in the Maddison Project Database (2011 PPP \\$); trimmed to 1870+ (earlier years have too-sparse country coverage, and the covered-country denominator keeps growing after 1870 - see methods). Upstream country estimates reach back to year 1; this project uses 1870–2022","source":"Maddison Project Database 2023 (Bolt & van Zanden, 2024, J. Econ. Surveys, DOI 10.1111/joes.12618)","source_url":"https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2023","license":"CC BY 4.0","data_file":"/data/us_world_gdp_share.csv","year_column":"year","value_column":"us_share_world_gdp_pct","value_units":"% of world GDP","color":"#0e7490","associated_cycle_id":"dalio","association_note":"Imperial-arc proxy. The data peaks at 1945 at ~32% of world GDP (war-production driven); Dalio's composite empire-score peaks ~1950 by his own statement, so the cycle and the data deliberately differ by ~5 years."},{"id":"vdem_libdem","name":"US Liberal Democracy Index (V-Dem)","legend_short":"V-Dem libdem index","short_description":"V-Dem liberal-democracy index for the US, 1789–2025, scale 0–1","source":"V-Dem Institute, Country-Year Dataset v16, March 2026 (retrieved via Our World in Data)","source_url":"https://v-dem.net/data/the-v-dem-dataset/","license":"CC BY-SA 4.0","data_file":"/data/vdem_libdem.csv","year_column":"year","value_column":"liberal_democracy_index","value_units":"index 0–1","color":"#4A4A4A","associated_cycle_id":"strauss_howe","association_note":"Generational-cycle theory predicts crisis lows that line up with stress on liberal-democratic institutions. V-Dem's recent US drop is the clearest empirical analogue to Strauss-Howe's 'Fourth Turning'."},{"id":"conflict_deaths","name":"Deaths in conventional wars (Project Mars, log)","legend_short":"Conflict deaths · log","short_description":"Natural log of (1 + deaths per 100,000) from Project Mars - log-transformed to keep WWI/WWII from flattening the rest of the series. Coverage 1800–2011; Project Mars covers conventional interstate and civil wars between states with differentiated militaries causing ≥500 deaths","source":"Our World in Data · Project Mars v1.1 (Lyall 2020)","source_url":"https://ourworldindata.org/grapher/deaths-in-wars-by-region-project-mars","license":"OWID chart CC BY 4.0; underlying Project Mars data Public Domain (Harvard Dataverse)","data_file":"/data/conflict_deaths.csv","year_column":"year","value_column":"deaths_per_100k","value_units":"deaths per 100,000","color":"#3D4250","associated_cycle_id":"khaldun","association_note":"Rough proxy for Khaldun-style state-breakdown intensity. Log-transformed because WWI/WWII spikes otherwise dominate; the transform reveals secular trend and lets the cycle pairing breathe.","transform":"log1p"},{"id":"stimson_policy_mood","name":"US Policy Mood (Stimson)","legend_short":"Policy Mood","short_description":"Stimson's Policy Mood index - composite measure of US public preference for liberal vs. conservative domestic policy, estimated from ~150 repeated survey items via the dyad-ratios algorithm; annual, 1952–2024","source":"James A. Stimson, Policy Mood data series (UNC), via Public Opinion in America (Westview, 2nd ed., 1999) and ongoing updates","source_url":"https://stimson.web.unc.edu/data/","license":"freely shared by author; no explicit reuse license","data_file":"/data/stimson_policy_mood.csv","year_column":"year","value_column":"mood","value_units":"index (higher = more liberal)","color":"#9C6B3D","associated_cycle_id":"schlesinger_jr","association_note":"The closest thing on this site to a direct measurement: an independently constructed index of mass preferences over the scope of domestic government, which is one component of Schlesinger Jr.'s public-purpose vs. private-interest rhythm, not the whole of it. Stimson's own reading of the series stresses shorter, thermostatic swings rather than a fixed ~30-year cycle; the pairing tests his data against Schlesinger's period, it does not report his endorsement of it. Coverage starts 1952."},{"id":"leading_power_gdp_share","name":"Leading Economy's Share of World GDP","legend_short":"Leading economy · GDP share","short_description":"The largest single economy's share of the summed GDP of the countries Maddison covers that year, with the leader named per year (Maddison Project Database 2023, 2011 PPP \\$); trimmed to 1870+, and the covered-country denominator grows over time - see provenance. The rule - largest economy - is mechanical, fixed without reference to Modelski's leadership succession","source":"Maddison Project Database 2023 (Bolt & van Zanden, 2024, J. Econ. Surveys, DOI 10.1111/joes.12618)","source_url":"https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2023","license":"CC BY 4.0","data_file":"/data/leading_power_gdp_share.csv","year_column":"year","value_column":"leading_power_gdp_share_pct","value_units":"% of world GDP","color":"#1F6F63","associated_cycle_id":"modelski","association_note":"Economic size as a deliberately imperfect correlate of world leadership - a proxy for a correlate of Modelski's naval/global-reach construct, not the construct itself. Mechanical by design (the largest economy per year, never hand-picking his hegemons), so its divergences show: under Maddison PPP the 1870–1881 leader is Qing China (not Britain, Modelski's naval leader) and China leads again from 2014. The series' all-time maximum, 31.6% in 1945, falls on the reference peak - a descriptive coincidence under a denominator whose coverage is thinnest early, not independent validation of the theory."},{"id":"perez_tech_diffusion","name":"US Technology-Diffusion Intensity (HATCH)","legend_short":"Tech diffusion · site-derived","short_description":"Experimental, site-derived composite from HATCH 2.0 national adoption series - not a measure published by the HATCH authors: the median, across ~105 US technology series, of within-technology z-scored 5-year log-changes in adoption; annual 1865–2023, with the contributing-technology count published per year","source":"HATCH - Extended Historical Adoption of Technology Dataset 2.0 (Greene & Nemet, U. Wisconsin–Madison), Zenodo, DOI 10.5281/zenodo.19579793","source_url":"https://zenodo.org/records/19579793","license":"CC BY 4.0","data_file":"/data/perez_tech_diffusion.csv","year_column":"year","value_column":"tech_diffusion_intensity","value_units":"z-score (median)","color":"#7C4D85","associated_cycle_id":"perez","association_note":"Perez's construct is economy-wide diffusion and deployment of a techno-economic paradigm - not asset prices - so a diffusion composite is the nearest measurable analogue public long-run data allows. Read it with its construction in view: each technology is standardized against its own full history, so S-curve maturity is built into the score, and the persistent negativity after the 1970s is substantially an artifact of that normalization and of an aging panel, not evidence that real diffusion slowed. The transform contains no Perez dates, period, or phase parameters (the build script is committed for audit), and the result is visible on the chart: no local peak at 2000."}]