Symbiota Collections of Arthropods Network (SCAN): A Data Portal Built to Visualize, Manipulate, and Export Species Occurrences

Taxon Search
The Symbiota Collections of Arthropods Network (SCAN) serves specimen occurrence records and images from over 80 North American arthropod collections for all arthropod taxa. The focus is on North America but global in scope. SCAN is built on Symbiota, a web-based collections database system that is used for other taxonomic data portals, including (Symbiota Portals). SCAN is the primary repository for occurrence data produced by the three continuing Thematic Collections Networks (TCNs), the Southwest Collections of Arthropods Network (SCAN TCN), the Lepidoptera of North America Network (LepNet TCN), and arthropod data produced by InvertEBase TCN. InvertEBase serves occurrence data for mollusk and other non-arthropod taxa. We also host observational data, the largest data provider is iNaturalist. Each collection is primarily responsible for their data and we have structured the database to make it easy to include collections of interest when querying the database.
Important features of all Symbiota portals include:
  1. Easy web-based data entry.
  2. Download entire datasets in two clicks.
  3. Map georeferenced records in two clicks.
  4. Upload high-resolution images & create species profile pages.
  5. Design custom species lists for any locality at multiple scales.
  6. Develop educational games with data.
  7. Create taxonomic keys.
The key organizational feature is that each museum or project is listed as a separate collection, so that one database group does not interfere with another. End users can select all "collections", or just a subset. This website is the central data portal for SCAN; all other project information can be found on the LepNet WordPress site, including How-To-Guides and network updates.
SCAN currently serves over 12.5 million records for 149,500 species, including 756,607 specimens imaged (4/12/17).
The National Science Foundation
This project made possible by National Science Foundation Award EF 1207371
iDigBio