[MUSIC] Welcome back everyone. In this lesson you'll learn how to document your workflows and data processes, and you'll learn how to view and edit metadata and ArcGIS in order to make your data reusable. Before we dive into the concept of metadata, I want to play out a common scenario in your minds. You receive a complex feature class with thousands of polygons and a few dozen attributes in it from a coworker. These attributes have names like muaggat, M-U-A-G-G-A-T, and huck 12, and values like 180204010305. Or two-letter abbreviations for concepts, like CA or something like that. What would be your first instinct in a situation like this? Wouldn't you want to look up what these fields and values mean? That's the concept behind metadata. Imagine that that information isn't there for you to look up and you don't what those values mean. That diminishes the value of the data, doesn't it? So what is metadata? To start with, let's deconstruct the word and look at the first part, meta. Meta means something that refers to itself. So when we talk about metadata, we mean data referring to itself or data. If that seems a little circular, that's because it's supposed to be. We are writing data that describes our data. Photographers, software developers, and creative professionals of all types will be familiar with metadata in their various disciplines. For photos, metadata includes things like what camera captured the image, the date and time the image was taken, and critical information about the exposure of the image. It's no different for our spatial data, except that we keep track of different information about our spatial data such as its extent, instruments that captured the data, workflows that the data were taken through, and other information that's relevant to our product. We can also author metadata directly from ArcGIS. For a quick example, let's take a look at the attribute table. Notice these columns with abbreviations that seem very specific to this dataset and a few values that we might recognize, but mostly quite a lot of names and values that won't be understandable without some additional information explaining what they mean. To find out, we can look inside the metadata by going to the item description section, accessible by right clicking on a layer in the table of contents. And a window will pop up describing the data set, its sources, its fields and values, and so on. So long as the author filled that information in. Now let's talk for a moment about what good metadata should contain. In general, metadata should include any information required to effectively interpret or use the data. To be more specific, metadata should describe the data sources or the data collection instruments. So if I'm collecting points on a GPS, I should put in the metadata the model of the GPS that collected it and the date and time the data was collected, as well as the person who collected the data, at the very least. I should also describe important individuals connected to this data in the contact information, so if someone who receives this dataset has a question, they know who to reach out to for an answer. Metadata should also define any fields in the data and their potential values and what they mean. Abbreviations should be well-defined, as well as anything specific or proprietary to this dataset. Again, metadata should include any information required to effectively interpret or use the data. That doesn't mean just the list of information I just gave you, because your project may be different and have other information necessary to interpret or use your data. As part of this, your metadata should also describe the actual meaning of the data in addition to the basics of the data. In the end, you're really trying to help someone else or even yourself in the future after you've forgotten the answers to work with the data and interpret the data. You should record in your metadata anything necessary to make that possible. One important aspect of this is your choice of metadata type. There are numerous standards that you can use for metadata. The basic one that ArcGIS uses by default has just a few options for what to fill in that allow you to describe your data. That's fine, but it's often easy to forget important things when using that format, because it doesn't really guide you through the process very much. Instead I like to use the FGDC CSDGM metadata format. I know that's a mouthful. FGDC stands for Federal Geographic Data Committee, and they are a group responsible for coordinating all things spatial in the Federal Government of the United States. The metadata standard described by the FGDC is robust and explicit. It forces me to fill in all the important information I need in order to have complete metadata. To switch ArcGIS into this mode, I need to go into the options area, either in ARC map or ARC catalog. If I go to the metadata tab, I can select the metadata style from the drop down. The default is item description, but I'll select FDGC CSDGM now. If I go edit a dataset's metadata now, I get numerous options for information that could be added to my data. I can include citations, topics and keywords, use constraints, points of contact, information about data quality and lineage, explicit information about the fields and the dataset and their values. And ArcGIS will even attach a history of geoprocessing for me, to help provide context for how the dataset was created. Just like you would expect based on things like photos, some information is automatically populated in your metadata. Basic information about your fields, your projection, the extent of the data, etc, are all populated automatically based upon the spatial information that ArcGIS can read for you. I find this very useful and it's a great way to document your data. It's not the only way though, and you can use the other styles to document your data, depending upon your needs. To wrap this up, let's take one more look at that dataset that I showed before with the mystery fields. Remember how the fields had these two character abbreviations and sometimes even multiple abbreviations in a single field? Let's see what they mean by looking in the metadata. If I open it up and go to the section with the information on the dataset's fields or attributes, I can find the field HU 12 MOD, which was one of the fields with these codes. And then expand out a list of definitions of the two digit codes, imagine that. I can see a full description of the field and then information of each individual code. This is how good metadata should be. Without it, I wouldn't understand that field so it wouldn't be useful to me. It wouldn't even have the opportunity to be useful to me. With it, I can use that field. Okay, that's it for this lesson. Remember that documenting your data and workflows with metadata is important and helps you share your data. If you are thinking, I'll just remember what I did and I don't need to make metadata, it's not true. You will find out frequently if you do that, how much you need those details written down. Even if you don't share your datasets with other people, you'll forget critical information. Now that we know how to create metadata, in the next lesson, I'll show you how to start sharing your data. See you then.