What is your educational and/or professional background?
My undergraduate degree was in Library and Information Science at the University of Ilorin in Nigeria, West Africa. In my prior experience, I worked in a research library where I was able to help researchers and students get access to the right information, I absolutely loved doing this and then I went on to work for Scholars Academy where I gathered some data analytics experience and helped students and researchers with data related questions.
What led you to your field?
One key factor that influenced my choice of this field is my ardent love for helping people. Connecting people to the information they are looking for has always been something I enjoyed. Then I realized the philosophies that libraries represent for the people in their communities and how they influence the success of people, which can, in turn, birth a strong nation; all of these are what drove me to the field.
What are your research interests?
While I have a broad interest, I am particularly interested in information organization and management, digital libraries, data, and learning analytics.
What is your specialty within the Scholarly Commons?
I will be focusing on the data side of things at the Scholarly Commons such as data analysis and data visualization.
Describe a favorite project you’ve worked on.
This is a hard one because I have enjoyed all the projects I have worked on, particularly the one where I created a small database for a particular library collection. The library had a handwritten manual inventory book used to locate items. To save the amount of time in locating items; I designed a simple inventory database to make access to information faster and easier.
What Scholarly Commons resource are you most excited to learn about?
I am really looking forward to learning about room 308 studio booths, I have always loved music and so maybe I will get to record my imaginary music album in it – haha!
What do you hope to do after graduation?
While I am still undecided on what I would like to do after graduation, I am really interested in data librarianship and working in the academic sphere.
Coming from a background in the humanities, I have recently developed an interest in data analysis but am just learning how to code. While I have been working to remedy that, one of my professors showed me this program known as Orange. Created in 1996, Orange is primarily designed to help researchers through the data analysis process, whether that is by applying machine learning methods or visualizing data. It is an open-source program (meaning you can download it for free!) and uses a graphical user interface (GUI) that allows the user to perform their analysis by matching icons to one another instead of having to write code.
How it Works
Orange works by using a series of icons known as widgets to perform the various functions that a user would otherwise need to manually code if they were using a program such as Python or R. Each widget appears as a bubble that can be moved around the interface. Widgets are divided into various categories based on the different steps in the analysis process. You can draw lines between the widgets to create a sequence, which will determine the process for how that data is analyzed (which is also known as a workflow). In its current state, Orange contains 96 widgets, each with different customizable and interactive components, so there are many opportunities for performing different types of basic data analysis with this software.
To demonstrate, I will use a dataset about the nutrition facts in specific foods (courtesy of Kaggle) to see how accurately a machine learner can predict the food group a given item falls in based on its nutrients. The following diagram is the workflow I designed to analyze this data:
On the left side of the screen are different tabs that each contain a series of widgets related to the task at hand. By clicking on the specific widgets, a pop-up window appears that allows you to interact with the widget. In this particular workflow, the “file” widget is where I can upload the file I want to analyze (there are a lot of different formats you can upload too; in this case, I uploaded an Excel spreadsheet). From there, I chose the machine learning method that I wanted to use to classify the data. The third widget tests the data using the classification method, and compares it to the original data. Finally, the results are visualized through the “confusion matrix” widget to show which cases the machine learner accurately predicted and which ones it got wrong.
The Limitations
While Orange is a helpful tool for those without a coding background, this system also presents some limitations when it comes to performing certain types of data analysis. One way Orange tries to reconcile this is by providing a widget where the user can insert some Python script into the workflow. While this feature may be helpful for those with a coding background, it would not really impact those who do not have a coding background, thereby limiting the ways they can analyze data.
Additionally, although Orange can visualize data, there are not many features that allow users to adjust the visualization’s appearance. Such limitations may require exporting the data and using another tool to create a more accessible or visually appealing data visualization, but for now, Orange is quite limited in this capacity. As a result, Orange is an incredibly useful tool for basic data visualization but struggles with more advanced types of data science work that may require using other tools or programming to accomplish.
Final Remarks
If you are looking to get involved in data analysis but are just starting to develop an interest in coding, then Orange is a great tool to use. Unlike most data analysis programs, the user-designed interface of Orange makes it easy to perform basic types of data analysis through its widgets. It is far from perfect though, and a lack of a coding background is going to limit the ways you can analyze and visualize your data. Nevertheless, Orange can be an incredibly useful tool if you are just starting to learn how to code and looking to understand the basics of data science!
What is your educational and/or professional background?
I graduated from the University of Illinois at Urbana-Champaign with a bachelor’s in History and Cognitive Psychology and a minor in Music. While an undergrad, I worked at the Music and Performing Arts Library (MPAL) on campus, which is where I developed my interest in librarianship. While working there, I was responsible for the circulation desk, reference desk, shelving, and had some experience with special collections.
What led you to your field?
I actually never thought much about librarianship until I reached my undergraduate years. A combination of working at MPAL, doing research for history courses, and a love for learning made me realize that a Library and Information Science (LIS) pathway was a perfect fit for me. I reached this thought in my freshman year of college, so over the course of 4 years, that passion did not change!
What is your specialty within the Scholarly Commons?
I am one of the weekend and evening shift supervisors. I am responsible for supervising the undergraduate student assistants and ensuring the smooth operation of the Scholarly Commons and loanable tech during times when full-time staff are not working. I also do some work on our LibGuides, have created signs for the 220 space, and am hoping to pursue more data or audio/visual (AV)-related projects.
What Scholarly Commons resource are you most excited to learn about?
I am really excited for the A/V booths! Since I have a background in music, I spent a lot of time in studios and recording spaces. I love being in them and being able to have a space to perform and record music. I would love to set up my equipment one day when I am not working and just spend some time in there!
What do you hope to do after graduation?
I am mostly undecided as to what I want to do after graduation! However, I am very interested in archives, special collections, and museums. I am hoping to explore courses relating to these and pursue whichever field that I think I will enjoy most!
It’s Open Access Week! Every year this international event brings the academic community together to discuss the benefits of free and immediate access to information, especially scholarly resources.
This week, I’ll be sharing open (and semi-open) resources for artists. When I’m not at the library desk, I like to draw, and I’m always on the hunt for high quality reference images. When learning how to draw people, you’ll often have to figure out a pose without the help of live models. References, however, are not always free or easy to find. Here some of the resources that I’ve found helpful over the years.
Provides both nude and clothed photos for study. Artists can start a drawing session by choosing the kinds of models, and the time intervals between photos. There are also posts here that give advice for improving your technique.
This collection of motion images provides rapid sequence photographs of athletes and dancers. These images are a good way to study how the human body moves. Most of this content is only available with a subscription, but there are some free sequences. When browsing a section, click the “free” tab on the right-hand side of the page.
This stock photo collection has models with plenty of different body types. There are some fun poses in here: from fantasy to action, to sci-fi settings. All models are wearing clothing or flesh-tone bodysuits, so no need to worry about using it in a public space.
Provides a variety of photos in timed study sessions. You can choose to practice bodies, hands, feet, heads, or animals and structures. It’s a good tool for warm-up drawing with no fuss.
Okay, so this one is from the 40’s and it shows; the majority of nude female figures are still sporting high heels. However, Loomis still offers many helpful tips. It contains an exhaustive instruction of perspective, musculature, the mechanics of motion, shading and lighting as well as exercises for practice.
Practicing with the gesture technique can help you break out of “stiff” poses and figure out how to imbue your figures with character and expression. This guide contains an overview of gesture, videos of instruction, and a list of books on gesture.
A good fashion reference site that showcases clothing through time and around the world. The information here gives context for clothing, bios of fashion icons, overviews of fashion movements, and the history of clothing items. It’s a good tool to inspire clothing design for the people and characters you draw.
You’ll have to create a free account on the Internet Archive to view this one. It’s a collection of costume plates from the 19th century. There are later editions of this book available, but this edition still contains original clothing pattern drafts.
This website provides free tutorials and podcasts on drawing topics with a focus on human figures. Sign up for the free “fresh eyes” drawing challenge, a ten-day course that teaches students to identify gesture and structure of the form.
This resource isn’t human-figure specific but these videos are great resources for learning how to draw and design. Try “EP 30: Character Silhouettes” to buff up your character illustration skills. This channel is especially good for creatives interested in comics or illustration.
Muddy Colors posts helpful tips on all kinds of art topics from over 20 practicing artists. The site hosts paid classes from their contributing artists, but there is plenty of free advice here too.
An independent website that showcases concept art from animation, games, and comics. There’s a little bit of everything here. I’d recommend checking out their visual library. There are anatomical references, character/creature design references, vehicles, props, and lighting/color tutorials.
The New York Met Gallery offers 609 publications of art, photography, sculpture and more, all free for download. This is an excellent place to find inspiration.
What is your educational and/or professional background?
I went to Elon University for my undergraduate degree, where I majored in psychology with minors in teaching & learning and literature. While at Elon, I worked at their library which cemented my desire to work at a library. Other professional experiences have included working at local bookstores during the summer.
What led you to your field?
I grew up going to my local public library. I have always loved reading and knew I could not picture a future where I did not spend my workday surrounded by books. I enjoy doing research and helping patrons find what they are looking for.
What is your specialty within the Scholarly Commons?
I am a shift supervisor at Scholarly Commons. I supervise student assistants during weekend and evening hours. I also work on various projects within the department including working on LibGuides and scheduling.
What Scholarly Commons resource are you most excited to learn about?
I am most excited about learning about all the loanable technology that Scholarly Commons has to offer! It has been interesting getting to see the different options for equipment to check out and to learn the difference between them.
What do you hope to do after graduation?
I am still undecided on what kind of library I would like to work at after graduation. Right now, I am considering working at either an academic, community college or public library.
