ARCH collection files provide a glimpse of the domains present in a web archive collection. These may be especially useful in conjunction with other ARCH datasets. Learn more...

ARCH network files represent how websites or domains have linked to each other over time, as well as how hyperlinked images have been used. Learn more...

ARCH full text datasets present the full text of the sites in web archives on one line each, along with accompanying metadata and other web elements including HTML, CSS, JS, JSON, and XML. Learn more...

ARCH file format datasets contain information on certain types of binary files found within web archive collections, including PDFs, audio, images, PowerPoints, spreadsheets, video, and Word documents. Learn more...

To create a dataset, you can either select “Generate a new dataset," which appears below the collection overview dashboard, or you can click on the Generate Datasets tab. Each category can be expanded to brows and select datasets you wish to generate.

NB: Each dataset presents two options. We strongly recommend running an Example Job first, especially when working with large collections (100GB+). This generates a small dataset, which should be reviewed to ensure the analysis produces the desired kind of data. Accordingly, it also generates data far more quickly than does the Generate Dataset option.

• Generate Example Dataset: Generates a small dataset using 100 relevant records from the collection.
• Generate Dataset: Generates a large dataset using all of the records in your collection.

Once Generate Dataset is initiated, the button will change to Running. You can generate multiple datasets at a time and can navigate away from ARCH while the datasets are being processed. When the dataset is ready, click either the View Example Dataset or View Dataset button. You ccan also view the datasets' status by returning to the Job Summary page.

Remember that analysis is not instant. The larger the collection, the more time needed to process and create the dataset. Some example jobs may complete in a few minutes, while some full jobs may take days to complete.  Additionally, keep in mind that multiple ARCH users can be generating datasets simultaneously, which can impact processing times.

You will receive an email when your job is complete and the dataset is ready to use. The email will be sent to the address tied to your Archive-It account.

Each generated dataset has an accompanying results page, which provides a variety of features that allow for further interaction with the datasets. Where possible, visualizations, charts, and graphs are present to visually summarize the contents of their downloadable files:

For example, a domain graph dataset (above) presents an interactive network graph in which you can see nodes (domains) and edges (links) among the 100 top domain nodes with the greatest number of inbound and outbound links in a collection.

Note that these are designed to provide a quick overview of the collection. For in-depth examination, you will need to download the datasets and work with additional analysis tools.

Some dataset visualizations may have limited data to graph, so don't be alarmed if your network graph only shows one or two nodes. Graphs provided through ARCH are determined by the data available in the web archive collection. Unfortunately, not all web collections are conducive to graphics elements such as charts and graphs.

The most important part of this page is the ability to download your dataset.

The ARCH interface provides basic information about the generated dataset, including the file size and result count, which provides a number of lines found within the dataset file.

Most users will download files directly through the web browser. This method is best for a few, small files (less than 2GB), and/or one-time downloads. Simply click on the download icon. Just a reminder, you may need to adjust your browser settings to allow for this download.

Downloading the WAT, WANE, and LGA datasets is different as they require use of WASAPI Data Transfer APIs. Follow the download instructions as found on those dataset pages.

However, larger datasets and computational workflows may instead require using a command line application such as wget or curl. To do so, right click on the download icon, click copy link, and paste into your command line interface using wget, curl, or the network transfer utility of your choosing.

NB: There is a known bug in more recent versions of macOS that interfere with opening some of our datasets downloaded through the browser. If you are on macOS, you will see a warning asking you to use The Unarchiver to open the ZIP files.

ARCH also provides a preview of up to 100 lines of data from the csv file. The preview offers researchers a glimpse of the content found in the csv without having to download and open.

The example below identifies the preview of the extract domain graph dataset.

In the event that a collection has run additional crawls, you can re-run the dataset to include new records. When a dataset job is re-run, the existing derivatives will be permanently deleted and replaced with the most recently run job.

The Help widget, located at the bottom of the ARCH interface, can be used to search terms and concepts throughout Archive-It documentation, including information related directly to ARCH.

Working with web archives at scale can be challenging. While ARCH provides users with a starting point by generating datasets, external tools will be needed to further explore and investigate web archive collections.

The following tools provide foundational knowledge, skills, and processes necessary for working with large datasets computationally. A dedicated tools section is provided on each dataset information page in this documentation to share further details around suggested tools and techniques.

Command Line

The command-line interface can seem daunting, but it is especially helpful for conducting analysis. The Programming Historian has published peer-reviewed tutorials (in four languages) which focus on digital tools and techniques to help support digital humanities research.

For command line information, we recommend the following two tutorials:

• Introduction to the Windows Command Line with PowerShell (for Windows)
• Introduction to the Bash Command Line (for Mac/Linux)

OpenRefine

While there are several methods, data cleaning is an important aspect of the research process. OpenRefine is an open-source tool for working with messy data: from cleaning to transforming formats to extending it with web services and external data.

• Programming Historian, Cleaning Data with OpenRefine

Python

Widely adopted in many fields, the Python programming language is foundational in data science, particularly helpful for data mining, processing, analytics and visualization. We have created an example Python notebook for working with ARCH datasets, and we suggest the following resources for getting started with entry-level Python uses:

• Programming Historian, Python Introduction and Installation
• Melanie Walsh, Introduction to Cultural Analytics and Python