The climate-disease relationship is complex, where multiple driver-pressure factors and interactions combine with climate change in determining the occurrence geographies and timings of various infectious diseases. However, studies often focus on just some selected factor(s) and interaction(s). Such focus choices may limit and bias our understanding and predictive capability of disease sensitivity to climate and other driver-pressure changes. To assess these research choices and identify possible remaining key gaps and biases, a scoping review is applied to climate-disease related publications for Lyme disease and cryptosporidiosis. This dataset includes two excel files for Lyme disease (“literature_search_LD_raw.xls”) and cryptosporidiosis (“literature_search_CY_raw.xls”) respectively, covering all the relevant information for further analysis. Each excel file contains three sheets named “Search”, “Filter”, and “Quantitative”. Contents of each sheet is explained as follows. 1. “Search” Sheet “Search” listed all the publications found from the literature searches, including the information of publication year, title, DOI, and Authors. The searches were performed in Web of Science™ (WoS) and considered publications from 1 January 2000 to 10 February 2022. Search terms were (('borreliosis' OR 'Lyme disease') AND ('climate' OR 'climate change' OR 'climate variability')) for Lyme disease, ((‘cryptosporidiosis’ OR ‘cryptosporidium’ OR ‘crypto.’) AND (‘climate’ OR ‘climate change’ OR ‘climate variability’)) for cryptosporidiosis. The search yielded 555 publication results for Lyme disease and 185 for cryptosporidiosis. 2. “Filter” Sheet “Filter” listed the inclusions and exclusions for searched publications, and categories each included publication belonging to. Excluded articles (marked as blank in column “Included”) are ones that: (i) do not consider both climate and disease; (ii) are for Lyme disease, but not about the Ixodes transmission of the Borrelia pathogen; (iii) are not written in English; and (iv) are not full-text open access. Included articles (marked as “x” in column “Included”) are further classified into following categories based on their focus: for Lyme disease, categories of publications include reviews (mentioning), reviews (specifically discussing), public awareness, mitigation, survey (implications), investigations, projections, and others; for cryptosporidiosis, categories include reviews (mentioning), reviews (specifically discussing), survey (implications), investigations, projections, and others. Articles belonging to any categories are marked as “x”. 3. “Quantitative” Sheet “Quantitative” lists further information extracted from quantitative studies which are articles under category of investigations and projections in sheet “filter”. Further extracted information is from the methods section of each study or from the full text if necessary. The information includes transmission components, study region (if applicable), category of investigations or projections, included driver-pressure factors, and methods. Transmission components are reproduction host, transmission host, vector, and human for Lyme disease; animal reservoir, environmental reservoir, and human for cryptosporidiosis. Study region considers specific countries as the smallest scale for spatial resolution, so that smaller than whole-country study sites were counted as studies of the associated countries. Included driver-pressure factors are categorized as shown in Table 1, including main categories and their covered variables. Methods include laboratory/field experimentation/observation, statistical analysis, mechanistic modelling, and synthesis/meta-analysis. Table 1. Categories of factors and variables within each factor category studied in the quantitative publications. Category Lyme disease Cryptosporidiosis Variable Sub-variable Variable Sub-variable Climate Temperature Temperature Precipitation Precipitation Air humidity Air humidity Wind speed Wind speed Solar radiation Solar radiation Cloud cover Extreme weather Heavy rainfall, storms, extreme heat Climate variability The North Atlantic Oscillation (NAO) Climate variability The Indian Ocean Dipole (IOD), The El Niño-Southern Oscillation (ENSO) Air pressure Land factors Ground properties Temperature, soil features, litter depth Ground properties Soil features Vegetation Density, height, vegetation type, forest type, vegetation water content, the Normalized Difference Vegetation Index (NDVI), beech tree production, pine masting, growing stage Vegetation Vegetation type, the Normalized Difference Vegetation Index (NDVI) Land use Urban, agriculture, green cover, grazing, landscape fragmentation Land use Urban area, agriculture area Terrestrial water factors Surface water Distance to coast, proximity to water source, river length, water-covered area Stream flow and quality aspects Turbidity, flow rate, aquifer type, pH, electrical conductivity, water level Snow cover Snow cover Soil moisture Soil moisture Extreme hydrological events Long-term drought index Extreme hydrological events Long droughts, flood frequency, flood extent (area), flood history (event) Evapotranspiration Potential evapotranspiration, actual evapotranspiration Water resource facilities Water supply, water treatment Socioeconomics Population density, awareness, behavior Urbanization, population density, economic development, health care facilities, human behavior, age structure Other Disease seasonality Disease seasonality Location-related features Latitude, longitude, altitude, day length, slope Location-related features Latitude, longitude, altitude Wildfires Biodiversity

This work was supported by the Nordforsk Centre of Excellence CLINF (grant number 76413).