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FosSahul 2.0 database and R code accompanying manuscript "FosSahul 2.0, an updated database for the Late Quaternary fossil records of Sahul" submitted to Scientific Data. Excel files: FosSahul2.0.csv: FosSahul database collating non-human vertebrate megafauna fossil records for the Sahul region. Note that location data have been rounded to one degree decimal and might not reflect the exact location of the fossil record. For more information on precise locations, contact the authors.FosSahul2.0_metadata.xlsx: Column description and further detail on the FosSahul 2.0 database.CalibratedC14Dates_FosSahul.csv: Calibrated radiocarbon dates for FosSahul 2.0. Needed for the calculation of the biodiversity index.TimeBins.csv: Time bins needed for the calculation of the biodiversity index. R-scripts: FosSahul_Rating.R: Quality-rating algorithm for the FosSahul database.FosSahul_Data import.R: Data import script necessary for the calculation of the biodiversity index.FosSahul_Biodiversity_index_calculation.R: Code for the calculation of the biodiversity index.

FosSahul 2.0 database and R code accompanying manuscript "FosSahul 2.0, an updated database for the Late Quaternary fossil records of Sahul" submitted to Scientific Data. Excel files: FosSahul2.0.csv: FosSahul database collating non-human vertebrate megafauna fossil records for the Sahul region. Note that location data have been rounded to one degree decimal and might not reflect the exact location of the fossil record. For more information on precise locations, contact the authors.FosSahul2.0_metadata.xlsx: Column description and further detail on the FosSahul 2.0 database.CalibratedC14Dates_FosSahul.csv: Calibrated radiocarbon dates for FosSahul 2.0. Needed for the calculation of the biodiversity index.TimeBins.csv: Time bins needed for the calculation of the biodiversity index. R-scripts: FosSahul_Rating.R: Quality-rating algorithm for the FosSahul database.FosSahul_Data import.R: Data import script necessary for the calculation of the biodiversity index.FosSahul_Biodiversity_index_calculation.R: Code for the calculation of the biodiversity index.

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Supplemental material, SOM_microfossil_counts for Early Holocene phytolith records for three shell midden sites, Yongjiang River, Guangxi Province, China by Yekun Zhang, Shengmin Huang, Weiju Chen, Fang Qin, Xiaodong Pu, Wenheng Wei, Miaomiao Huang, Rachel Wood and Tim Denham in The Holocene

Supplemental material, SOM_microfossil_counts for Early Holocene phytolith records for three shell midden sites, Yongjiang River, Guangxi Province, China by Yekun Zhang, Shengmin Huang, Weiju Chen, Fang Qin, Xiaodong Pu, Wenheng Wei, Miaomiao Huang, Rachel Wood and Tim Denham in The Holocene

FosSahul 2.0 database and R code accompanying manuscript "FosSahul 2.0, an updated database for the Late Quaternary fossil records of Sahul" submitted to Scientific Data. Excel files: FosSahul2.0.csv: FosSahul database collating non-human vertebrate megafauna fossil records for the Sahul region. Note that location data have been rounded to one degree decimal and might not reflect the exact location of the fossil record. For more information on precise locations, contact the authors.FosSahul2.0_metadata.xlsx: Column description and further detail on the FosSahul 2.0 database.CalibratedC14Dates_FosSahul.csv: Calibrated radiocarbon dates for FosSahul 2.0. Needed for the calculation of the biodiversity index.TimeBins.csv: Time bins needed for the calculation of the biodiversity index. R-scripts: FosSahul_Rating.R: Quality-rating algorithm for the FosSahul database.FosSahul_Data import.R: Data import script necessary for the calculation of the biodiversity index.FosSahul_Biodiversity_index_calculation.R: Code for the calculation of the biodiversity index.

FosSahul 2.0 database and R code accompanying manuscript "FosSahul 2.0, an updated database for the Late Quaternary fossil records of Sahul" submitted to Scientific Data. Excel files: FosSahul2.0.csv: FosSahul database collating non-human vertebrate megafauna fossil records for the Sahul region. Note that location data have been rounded to one degree decimal and might not reflect the exact location of the fossil record. For more information on precise locations, contact the authors.FosSahul2.0_metadata.xlsx: Column description and further detail on the FosSahul 2.0 database.CalibratedC14Dates_FosSahul.csv: Calibrated radiocarbon dates for FosSahul 2.0. Needed for the calculation of the biodiversity index.TimeBins.csv: Time bins needed for the calculation of the biodiversity index. R-scripts: FosSahul_Rating.R: Quality-rating algorithm for the FosSahul database.FosSahul_Data import.R: Data import script necessary for the calculation of the biodiversity index.FosSahul_Biodiversity_index_calculation.R: Code for the calculation of the biodiversity index.

Abstract Aims: to identify the prevalence of metabolic syndrome (MetS) and associated risk factors in children. Methods: a total of 1,480 Brazilian children aged 6-10 years old (52.2% girls) participated in this population-based, epidemiological cross-sectional study. The inclusion criteria were children born between the years 2001 and 2006, of both sexes, who did not use remedy, were not on a calorie restriction diet, and who respected the 12-hour fast for blood collection. Anthropometric measurements, blood pressure, blood collection, and completion of the Previous Day Food Questionnaire and interview using a Physical Activity List were held at school. Parents were asked to complete a questionnaire on socioeconomic status and lifestyle habits of their child. Chi-square test compared proportions and factors associated with MetS were identified using Poisson Regression. Results: Girls had significantly higher MetS prevalence compared with boys (12.6% vs. 8.5%, p=0.046). After multivariable analysis, body fat percentage (p=0.001), fat mass (p<0.001), lean body mass (p< 0.001) and sedentary behavior (p= 0.050) were positively associated with MetS. Conclusions: Modifiable factors such as body fat percentage, fat mass, lean body mass and sedentary behavior were associated with MetS in children. Thus, interventions targeted for weight management, and adopting healthy habits such as reducing time in front of TV/computer/video game need to be part of the lifestyle of children.

Abstract Aims: to identify the prevalence of metabolic syndrome (MetS) and associated risk factors in children. Methods: a total of 1,480 Brazilian children aged 6-10 years old (52.2% girls) participated in this population-based, epidemiological cross-sectional study. The inclusion criteria were children born between the years 2001 and 2006, of both sexes, who did not use remedy, were not on a calorie restriction diet, and who respected the 12-hour fast for blood collection. Anthropometric measurements, blood pressure, blood collection, and completion of the Previous Day Food Questionnaire and interview using a Physical Activity List were held at school. Parents were asked to complete a questionnaire on socioeconomic status and lifestyle habits of their child. Chi-square test compared proportions and factors associated with MetS were identified using Poisson Regression. Results: Girls had significantly higher MetS prevalence compared with boys (12.6% vs. 8.5%, p=0.046). After multivariable analysis, body fat percentage (p=0.001), fat mass (p<0.001), lean body mass (p< 0.001) and sedentary behavior (p= 0.050) were positively associated with MetS. Conclusions: Modifiable factors such as body fat percentage, fat mass, lean body mass and sedentary behavior were associated with MetS in children. Thus, interventions targeted for weight management, and adopting healthy habits such as reducing time in front of TV/computer/video game need to be part of the lifestyle of children.