Modern research and conservation efforts require high quality data and ideally lots of it. There is thus a critical need to bring occurrence data together and normalize, georeference (so they can be mapped), correct errors, and provide them to the world. Before this project (<2006), museum data for Texas' fish were only available from many disparate and often hard to find sources, located in several countries and managed in various incompatible databases. Some of these museums lacked a digital record of their collections, having paper ledgers only. Many are small museums that did not offer their data online (although this is now changing quickly). Some had no catalog at all, except what is recorded on jar labels. Museums that did provide data varied considerably in how the managed data. Many rarely updated their databases as taxonomy changed or examine specimens as new information is learned. Spelling mistakes and other typographical errors are common among all data fields in most museums. These problems make useful queries difficult to impossible.
Thus the highest quality data about where and when fish occur in Texas were largely inaccessible and not often very useful when they were. Anyone who did access them (what they could find) for a specific research problem, perhaps for a specific species, had to clean them up themselves - a process that has been done many times over the years with various levels of completeness. Those efforts have been sporadic and not usually done in ways that correct data at the source so future users can benefit. At the beginning of this project, to our knowledge, no one had tried to bring the data together into a single normalized database where all of the data could be queried together the way the Fishes of Texas project has now done.
These are some of the things we've done to fill this need:
- find data
- data entry (when needed)
- re-formatting (normalizing)
- compile data
- georeference locations (apply spatial coordinates)
- synonymizing taxa, collector names
- detect errors (usually via visualization on a map)
- verify/correct determinations
- verify data against ledgers, labels, and fieldnotes
- research manuscripts and other documents that can improve data quality
- photograph specimens
- photograph field notes and jar labels
- preserve original data
- publish data (including useful summaries)
- publish research products (models, conservation areas)