The hectares of habitat protected and the number of adults and children fed in one year were calculated for each of the six crop types for Canada and United States. The calculations were based on the 50th centile of the cumulative frequency distributions of change in crop yield due to pesticide treatment for each crop type. An editable interactive table was created using Microsoft Excel that would allow individuals to determine how pesticide treatment in their selected jurisdiction (province in Canada or state in the United States) and crop translates into habitat saved, calories produced, and mouths fed. This table allows the user to choose the country (Canada or United States), whether to include the organic agriculture correction factor, their state or province of interest, crop, and whether a young child, adolescent child, adult women, or adult man is being fed. The table will then calculate the hectares of habitat saved, added number of calories produced (kcal), the number of individual fed in one day, and the number of individual fed in one year. Due to the variability in yield results between crops and studies, the Excel user form allows individuals to set whichever yield increase they anticipate observing or use the 50th centile of yield increase from the cumulative frequency distribution for each crop.

You can also access an API version of this dataset.

TMS

(traffic monitoring system) daily-updated traffic counts API

Important note: due to the size of this dataset, you won't be able to open it fully in Excel. Use notepad / R / any software package which can open more than a million rows.

Data reuse caveats: as per license.

Data quality

statement: please read the accompanying user manual, explaining:

how

 this data is collected identification 

 of count stations traffic 

 monitoring technology monitoring 

 hierarchy and conventions typical 

 survey specification data 

 calculation TMS 

 operation. 

Traffic

monitoring for state highways: user manual

[PDF 465 KB]

The data is at daily granularity. However, the actual update

frequency of the data depends on the contract the site falls within. For telemetry

sites it's once a week on a Wednesday. Some regional sites are fortnightly, and

some monthly or quarterly. Some are only 4 weeks a year, with timing depending

on contractors’ programme of work.

Data quality caveats: you must use this data in

conjunction with the user manual and the following caveats.

The

 road sensors used in data collection are subject to both technical errors and 

 environmental interference.Data 

 is compiled from a variety of sources. Accuracy may vary and the data 

 should only be used as a guide.As 

 not all road sections are monitored, a direct calculation of Vehicle 

 Kilometres Travelled (VKT) for a region is not possible.Data 

 is sourced from Waka Kotahi New Zealand Transport Agency TMS data.For 

 sites that use dual loops classification is by length. Vehicles with a length of less than 5.5m are 

 classed as light vehicles. Vehicles over 11m long are classed as heavy 

 vehicles. Vehicles between 5.5 and 11m are split 50:50 into light and 

 heavy.In September 2022, the National Telemetry contract was handed to a new contractor. During the handover process, due to some missing documents and aged technology, 40 of the 96 national telemetry traffic count sites went offline. Current contractor has continued to upload data from all active sites and have gradually worked to bring most offline sites back online. Please note and account for possible gaps in data from National Telemetry Sites. 

The NZTA Vehicle

Classification Relationships diagram below shows the length classification (typically dual loops) and axle classification (typically pneumatic tube counts),

and how these map to the Monetised benefits and costs manual, table A37,

page 254.

Monetised benefits and costs manual [PDF 9 MB]

For the full TMS

classification schema see Appendix A of the traffic counting manual vehicle

classification scheme (NZTA 2011), below.

Traffic monitoring for state highways: user manual [PDF 465 KB]

State highway traffic monitoring (map)

State highway traffic monitoring sites

All Supplementary Tables are provided in one Excel sheet. If you are loading these tables into R or python, you must remove the top rows, which contain a title, and sometimes a description of the fields.Table ST1: Descriptive statistics of taxa. Various descriptive statistics for subgenera of the genus Lactobacillus and genera detected in this study: number of ASVs within the (sub)genus (n_asvs), prevalence (occurrence), average relative abundance (mean_rel_abundance), frequency of being the most abundant taxon and greater than 0% abundant (top_and_gt0p), same as previous but greater than 30% abundant (top_and_gt30p), same as previous but greater than 50% abundant (top_and_gt50p), the previous three measures but in terms of relative frequencies (top_and_gtXp_rel).Table ST2: Table of the number of Isala participants per VALENCIA subCST.Table ST3: Taxa-taxa correlation network generated from sparcc for the Isala dataset. Each cell indicates the compositionality aware correlation between two taxa.Table ST4: Association tests between participant characteristics and their vaginal microbiome. Results of statistical tests for each tested questionnaire response. Results of association tests are provided for beta-diversity (Adonis tests), alpha-diversity, CSTs, eigentaxa and individual taxa. In addition to effect sizes, test statistics and p-values, the number of participants in each condition is provided.Table ST5: Supplementary meta-data for the Isala samples used in this study. Each ENA sample ID is linked to a participant’s age, whether intercourse occurred in the last 24 hours, technical covariates, and CST annotations. This file can be used in combination with the code available on github.Table ST6: Results of the PERMANOVA (Adonis2) tests between technical factors and the vaginal microbiome.Table ST7: Count data per (sub)genus per sample. Linked by identified to the meta data provided on EGA and Table ST5. This file can be used in combination with the code available on github.Table ST8: Relative abundance data per sample. Linked by identified to the meta data provided on EGA and Table ST5.Table ST9: Taxa classification specification per (sub)genus specified in Tables ST7 and ST8. This file can be used in combination with the code available on github.Table ST10: Count data per ASV per sample. Linked by identified to the meta data provided on EGA and Table ST3. This file can be used in combination with the code available on github.Table ST11: Taxa classification specification per ASV specified in Table ST10. This file can be used in combination with the code available on github.