− | The basis could be the receipts that users have added on prices. As all OFF data, any information should be backed up by image proofs. In the case for a store, this could be a receipt. The store information available on the receipt can be extracted to the taxonomy. What can be extracted depends on the receipt. In the example shown here, it is possible to extract the store name, the operator (useful for franchises), the address, phone number, siret, naf and tva. Hopefully this information is enough to unambiguously identify a store. | + | The basis could be the receipts that users have added on prices. As all OFF data, any information should be backed up by image proofs. In the case for a store, this could be a receipt. The store information available on the receipt can be extracted to the taxonomy. What can be extracted depends on the receipt. In the example shown here, it is possible to extract the store name, the operator (useful for franchises), the address, phone number, siret, naf and tva. Analysus of more receipts might reveal other country specific information. |
| The possible fields used follow the data structure used by [https://prices.openfoodfacts.org/ OFF Prices] and describes a single location: | | The possible fields used follow the data structure used by [https://prices.openfoodfacts.org/ OFF Prices] and describes a single location: |