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Abstract. There are few multi-decadal observations of atmospheric aerosols worldwide. This study applies global hourly visibility (Vis) observations at more than 3000 stations to investigate historical trends in atmospheric haze over 1945-1996 for the US, and over 1973-2013 for Europe and eastern Asia. A comprehensive data screening and processing framework is developed and applied to minimize uncertainties and construct monthly statistics of inverse visibility (1/Vis). This data processing includes removal of relatively clean cases with high uncertainty, and change point detection to identify and separate methodological discontinuities such as the introduction of instrumentation. Although the relation between 1/Vis and atmospheric extinction coefficient (b ext ) varies across different stations, spatially coherent trends of the screened 1/Vis data exhibit consistency with the temporal evolution of collocated aerosol measurements, including the b ext trend of −2.4 % yr −1 (95 % CI: −3.7, −1.1 % yr −1 ) vs. 1/Vis trend of −1.6 % yr −1 (95 % CI: −2.4, −0.8 % yr −1 ) over the US for 1989-1996, and the fine aerosol mass (PM 2.5 ) trend of −5.8 % yr −1 (95 % CI: −7.8, 1973-2008 (r ∼ 0.9). Consistent "reversal points" from increasing to decreasing in SO 2 emission data are also captured by the regional 1/Vis time series (e.g., late 1970s for the eastern US, early 1980s for western Europe, late 1980s for eastern Europe, and mid 2000s for China). The consistency of 1/Vis trends with other in situ measurements and emission data demonstrates promise in applying these quality assured 1/Vis data for historical air quality studies.

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The meta-analysis dataset presented is a convenience sample from 218 separate studies of agricultural technology adoption in Africa, Asia, and Latin America. Each study uses survey data to estimate a form of multiple regression of adoption of a technology (dependent variable) with a diverse array of predictor variables. Fifteen predictor variable categories are included in this dataset: Age, education, gender, household size, farming experience, land size, soil fertility, land slope, distance to inputs/outputs, access to credit, land tenure, livestock ownership, non-farm income, access to extension, and organization membership. Data have been cleaned and transformed to common units. A total of 384 statistical models are recorded, with a total of 2875 effect size estimates.