Twenty Things to Know about Uexplore/Dexter and the MCDC Data Archive
This document provides some vital things you should know if you are interested in using the Uexplore and Dexter
web tools to access the contents of the MCDC/OSEDA public data archive.
- You may not really want to. The data archive can be quite complex, and the interface provided to access it is aimed at a somewhat sophisticated user. If you are a total novice when it comes to accessing the kind of data you are looking for, or if you are not sure what kind of data you are looking for and just want to do do keyword searches, then you may be disappointed. The archive is full of cryptic jargonish names like sf32000x, acspums and beareis that will alienate and even offend some users. While the archive does usually provide descriptions of these terms it may require more time and effort to actually read the explanations than many users will want to expend. Most of the people who find these tools to be useful and become repeat users are those who are involved with using data and doing data queries as a regular part of their jobs. In many cases, the people who use these tools are data intermediaries who are doing rsearch on behalf of someone else.
You can use these tools in a lot of different ways, and with a lot of different levels of investment in learning how it works. You can be provided with links that will take you to where you need to go to get what you need, without your having to study the map. But to do that, you may have to be willing to ask for help.
- Using these tools is like doing research at the library. The Data Archive is like the book collection. Just as there are many kinds of books at the library covering many subjects (history, art, science, religion, etc) there are many kinds of data sets and files in the archive covering many subjects (general demographic information, current and historical data, economic data, geographic information, social and economic indicators, etc.) You don't have to know about all the books in the library if all you want to study is the Civil War. You don't have to know about everything in the Data Archive if all you want to study is the latest population estimates for the counties in your state. Just as there are electronic catalogs to help you locate books in the library there are tools available to assist you in your search for data in the archive. But just as it is very helpful to seek the assistance of a good librarian when you are looking for a book about a very specific subject, it can be helpful to seek the assistance of someone who understands what is available in the Data Archive. That is why we provide all those feedback buttons at the bottom of most of our pages.
- Dexter is not as hard as it looks. It has some tricky details and the Advanced Options section is a bit daunting but the important thing to remember is that most uses of the application are done with rather minimal input from the user. The only 1 of the 5 form sections that requires you to enter something is the Variables selection section. And if you wish you can just hit the checkbox that says let me have all the variables. There are lots of data sets that can be turned into Excel files with very little effort. What we are talking about here is simply the portion of the query that involves using the Dexter software to perform the extraction. The other aspects of the query, which involve things such as determining where in the database can I find such data and what do I do with it after I get it and just what is the meaning of the data I have extracted are often considerably more difficult.
- Many of the data sets in the archive are larger than what you may need. They may contain data for the whole country when all you want is your state; or a data set may have data for several different kinds of geographies (states, counties, places, etc.) but you only need data for one kind. This kind of thing is exactly what Dexter can be good for. It requires defining a data filter in Section II of the Dexter query form. Coding such a filter requires that you define conditions that you want to require in order that a row of data be kept on your output. If, for example, the data set has ....