md ` ## scrapping the advanced dataset [This dataset](https://nzhistory.govt.nz/politics/womens-suffrage/petition ) was used because it had better references to tge signotories than the [xls document](https://nzhistory.govt.nz/politics/womens-suffrage/about-the-petition). Still [the XLS document](https://docs.google.com/spreadsheets/d/1eOvHYjFF21IWs0Xoj9Ecw-sazVO-3HqVaevAMgomFVM/edit#gid=1295061349) No direct links to the images of the signatures but includes a column for *Mrs, Miss, Mr.* So scraped the 600 pages. [Scrapper Code can be seen here.](https://gist.github.com/hellonearthis/e1a418e7e84399a92b8177cfb92bda81) #### example of one entry in the table. ~~~html 1 Carpenter Mary J.

Yaldhurst

Christchurch

more… ~~~ From the class I could extract the type do data. *(but I went for a quick hardcoded option as it's a run one time script)* - views-field-field-original-sheet-no -- *Link to what page the signature appeared on* - views-field-field-surname-0 - views-field-field-given-names - views-field-field-original-address - views-field-field-consistent-town-subutb - views-field-field-consistent-city-region -- *can be null* - views-field-view-node -- *contains a url to an image* baseURI https://nzhistory.govt.nz/politics/womens-suffrage/ url looks like this: https://nzhistory.govt.nz/politics/womens-suffrage/petition?page=0 (page 0 -> 599) selectors are: ~~~js document.querySelectorAll('div.view-content > table > tbody > tr' Array.from(document.querySelectorAll('div.view-content > table > tbody > tr')).map( (d) => d.innerText) Array.from(document.querySelectorAll('div.view-content > table > tbody > tr')).map( (d) => d.textContent) ~~~ There are about 40 entries per page. **Thanks to Shirley Wu for the help on how to build the hierarchical data structure. ** `