Tag Archives: data management

There’s been a Murder in SQL City!

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by Libby Cave
Detective faces board with files, a map and pictures connected with red string.

If you are interested in data or relational databases, then you have heard of SQL. SQL, or Structured Query Language, is designed to handle structured data in order to assist in data query, data manipulation, data definition and data access control. It is a very user-friendly language to learn with a simple code structure and minimal use of special characters. Because of this, SQL is the industry standard for database management, and this is reflected in the job market as there is a strong demand for employees with SQL skills.  

Enter SQL Murder Mystery

In an effort to promote the learning of this valuable language, Knight Labs, a specialized subsidiary of Northwestern University, created SQL Murder Mystery. Combining the known benefits of gamification and the popularity of whodunit detective work, SQL Murder Mystery aims to help SQL beginners become familiar with the language and have some fun with a normally dry subject. Players take on the role of a gumshoe detective tasked with solving a murder. The problem is you have misplaced the crime scene report and you now must dive into the police department’s database to find the clues. For true beginners with no experience, the website provides a walkthrough to help get players started. More experienced learners can jump right in and practice their skills. 

I’m on the case!

I have no experience with SQL but I am interested in database design and information retrieval, so I knew it was high time that I learn the basics. As a fan of both games and detective stories, SQL Murder Mystery seemed like a great place to start. Since I am a true beginner, I started with the walkthrough. As promised on the website, this walkthrough did not give me a complete, exhaustive introduction to SQL as a language, but instead gave me the tools needed to get started on the case. SQL as a language, relational databases and Entity Relationship Diagrams (ERD) were briefly explained in an approachable manner. In the walk through, I was introduced to vital SQL functions like “Select:, “Where”, wildcards, and “Between”. My one issue with the game was in the joining tables section. I learned later that the reason I was having issues was due to the tables each having columns with the same title, which is apparently a foundational SQL feature. The guide did not explain that this could be an issue and I had to do some digging on my own to find out how to fix it. It seems like the walkthrough should have anticipated this issue and mentioned it. That aside, By the end of the walkthrough, I could join tables, search for partial information matches, and search within ranges. With some common sense, the database’s ERD, and the new SQL coding skills, I was able to solve the crime! If users weren’t challenged enough with that task, there is an additional challenge that suggests users find the accomplice while only using 2 queries.

User interface of SQL Murder Mystery
Example of SQL Murder Mystery user interface

The Verdict is In

I really loved this game! It served as a great introduction to a language I had never used before but still managed to be really engaging. It reminded me of those escape room mystery boxes like Hunt a Killer that has users solve puzzles to get to a larger final solution. Anyone who loves logic puzzles or mysteries will enjoy this game, even if they have no experience with or even interest in coding or databases.  If you have some free time and a desire to explore a new skill, you should absolutely give SQL Murder Mystery a try!

Introducing Drop-In Consultation Hours at the Scholarly Commons!

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Do you have a burning question about data management, copyright, or even how to work Adobe Photoshop but do not have the time to set up an appointment? This semester, the Scholarly Commons is happy to introduce our new drop-in consultation hours! Each weekday, we will have an expert from a different scholarly subject have an open hour or two where you can bring any question you have about that’s expert’s specialty. These will all take place in room 220 in the Main Library in Group Room A (right next to the Scholarly Commons help desk). Here is more about each session:

 

Mondays 11 AM – 1 PM: Data Management with Sandi Caldrone

This is a photo of Sandi Caldrone, who works for Research Data Services and will be hosting the Monday consultation hours from 11 AM - 1 PMStarting us off, we have Sandi Caldrone from Research Data Services offering consultation hours on data management. Sandi can help with topics such as creating a data management plan, organizing/storing your data, data curation, and more. She can also help with questions around the Illinois Data Bank and the Dryad Repository.

 

 
 

Tuesdays 11 AM – 1 PM: GIS with Wenjie Wang

Next up, we have Wenjie Wang from the Scholarly Commons to offer consultation about Geographic Information Systems (GIS). Have a question about geocoding, geospatial analysis, or even where to locate GIS data? Wenjie can help! He can also answer any questions related to using ArcGIS or QGIS.

 
 

Wednesdays 11 AM – 12 PM: Copyright with Sara Benson

This is a photo of Copyright Librarian Sara Benson who will be hosting the Wednesday consultation hours from 11 AM - 12 PMDo you have questions relating to copyright and your dissertation, negotiating an author’s agreement, or seeking permission to include an image in your own work? Feel free to drop in during Copyright Librarian Sara Benson’s open copyright hours to discuss any copyright questions you may have.

 

 

 

Thursdays 1-3 PM: Qualitative Data Analysis with Jess Hagman

This is a photo of Jess Hagman, who works for the Social Science, Education, and Health Library and will be hosting the Thursday consultation hours from 1 PM - 3 PMJess Hagman from the Social Science, Health, and Education Library is here to help with questions related to performing qualitative data analysis (QDA). She can walk you through any stage of the qualitative data analysis process regardless of data or methodology. She can also assist in operating QDA software including NVivo, Atlas.ti, MAXQDA, Taguette, and many more! For more information, you can also visit the qualitative data analysis LibGuide.

 

 

 
 

Fridays 10 AM – 12 PM: Graphic Design and Multimedia with JP Goguen

To end the week, we have JP Goguen from the Scholarly/Media Commons with consultation hours related to graphic design and multimedia. Come to JP with any questions you may have about design or photo/video editing. You can also bring JP any questions related to software found on the Adobe Creative Cloud (such as Photoshop, InDesign, Premiere Pro, etc.).

 

Have another Scholarly Inquiry?

If there is another service you need help with, you are always welcome to stop by the Scholarly Commons help desk in room 220 of the Main Library between 10 AM – 6 PM Monday-Friday. From here, we can get you in contact with another specialist to guide you through your research inquiry. Whatever your question may be, we are happy to help you!

Introductions: What is Data Analysis, anyway?

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This post is part of a series where we introduce you to the various topics that we cover in the Scholarly Commons. Maybe you’re new to the field or you’re just to the point where you’re just too afraid to ask… Fear not! We are here to take it back to the basics!

So, what is Data Analysis, anyway?

Data analysis is the process of examining, cleaning, transforming, and modeling data in order to make discoveries and, in many cases, support decision making. One key part of the data analysis process is separating the signal (meaningful information you are trying to discover) from the noise (random, meaningless variation) in the data.

The form and methods of data analysis can vary widely, and some form of data analysis is present in nearly every academic field. Here are some examples of data analysis projects:

  • Taylor Arnold, Lauren Tilton, and Annie Berke in “Visual Style in Two Network Era Sitcoms” (2019) used large-scale facial recognition and image analysis to examine the centrality of characters in the 1960s sitcoms Bewitched and I Dream of Jeannie. They found that Samantha is the distinctive lead character of Bewitched, while Jeannie is positioned under the domination of Tony in I Dream of Jeannie.
  • Allen Kim, Charuta Pethe, Steven Skiena in “What time is it? Temporal Analysis of Novels” (2020) used the full text of 52,183 fiction books from Project Gutenberg and the HaithiTrust to examine the time of day that events in the book took place during. They found that events from 11pm to 1am became more common after 1880, which the authors attribute to the invention of electric lighting.
  • Wouter Haverals and Lindsey Geybels in “A digital inquiry into the age of the implied readership of the Harry Potter series” (2021) used various statistical methods to examine whether the Harry Potter books did in fact progressively become more mature and adult with successive books, as often believed by literature scholars and reviewers. While they did find that the text of the books implied a more advanced reader with later books, the change was perhaps not as large as would be expected.

How can Scholarly Commons help?

If all of this is new to you, don’t worry! The Scholarly Commons can help you get started.

Here are various aspects of our data services in the Scholarly Commons:

As always, if you’re interested in learning more about data analysis and how to support your own projects you can fill out a consultation request form, attend a Savvy Researcher Workshop, Live Chat with us on Ask a Librarian, or send us an email. We are always happy to help!