Projects

Cooking as Conversation: Embracing Embodied Knowledge and Oral History in Culinary Information Systems

Project Description:  This paper examines how culinary information has been disseminated through written and oral communication. It analyzes the structure of instructional food information such as cookbooks and recipes, presents on how current food media is invigorating oral history in the kitchen, and concludes with imperatives to develop culinary information systems that faithfully represent the cultures from which the recipes originate. I argue that the primary way culinary information is transmitted is through embodied experiences and conversations between cooks and that this should be considered when creating culinary resources. In addition to the paper, I presented this research at INFOSHOW ‘23, where I framed my arguments within the context of my personal experience cooking my grandfather’s pierogi recipe. 

Project Elements: Paper and presentation

Methods: This project began with the formulation of a research question. I knew that I wanted to study culinary information, and had been particularly interested in theories of embodied learning and information seeking behaviors studied by scholars such as Marcia Bates and Jenna Hartel. Recognizing that cooking knowledge takes the shape of many forms, I consulted  a variety of sources including academic food studies journals, independently published zines, food blogs, cookbooks, personal recipes, and social media posts on YouTube and Instagram. Throughout my research, I also considered my prior experiences. With my established thesis in mind, I returned to the resources I use the most in the kitchen and evaluated them within the context of how they teach their readers. This process also led me to revisit food media such as YouTube videos by Kenji Lopez-Alt. These explorations in media beyond traditional print sources greatly informed my central thesis.

My Role:  I am the sole creator of this project. 

Learning Objective(s) Achieved: Research

Rationale: Through this project, I experienced the full lifecycle of a professional research endeavor, including the initial steps of exploratory research, thesis development, consulting and evaluating  a variety of scholarly sources, and formulating an argument. The research I produced contributes to the field of library science by investigating users’ information seeking behavior outside of traditional learning and instruction environments.

Metadata Application Profile: Voices from the Food Revolution

Project Description: The goal of this project was to design a metadata application profile and digital collection for oral history resources from Voices from the Food Revolution: People Who Changed The Way Americans Eat -- An oral history project conducted by Judith Weinrau, held by the NYU Fales Library. Resources were previously handled inconsistently between two separate websites and our goal was to standardize the collection’s metadata in order to facilitate more comprehensive discovery and searching by the collection’s users. The final products of the project are a manual that contains a domain model, an element set with definitions and crosswalks, and cataloging instructions, and a digital collection site hosted by Collection Builder. 

Methods: Working on this project was a multistep, iterative process. First, we established the functional requirements that our digital collection needed to fulfill and the project principles that we valued. Next, we designed a domain model to conceptually map the properties of our resources. After that, we created our element set, which included definitions, use requirements, and value constraints. We then designed a metadata entry mechanism and wrote cataloging instructions. Finally, we crosswalked our element set to other widely used metadata schemas. All of our work was compiled into a complete manual, and the digital resources were uploaded to a Collection Builder site. 

My Role: This was a collaborative project completed with Steve Beck and Candace Hernanez. All work performed on the establishment of the functional requirements, the domain model, element set, cataloging instructions, and final manual was shared equally among team members. In addition, I took the lead on building our metadata entry form and adapting our cataloging instructions to it. I also edited the configuration files for our Collection Builder site using Github. 

Learning Objective Achieved: Foundations of Library and Information Science

Rationale: Often, the first point of access users have with a library collection is through its catalog. Through this project, I gained direct experience managing and facilitating access to resources in a digital collection. I have a comprehensive understanding of how the design of metadata elements in a catalog record directly contributes to a resource’s accessibility or inaccessibility. I gained foundational skills required for cataloging such as authority control and subject classification, and also became familiar with prominent metadata schemas such as PBCore, VRA Core, Dublin Core, and PREMIS. 

Learning Objective Achieved: User Centered Services

Rationale: A central component of this project involved considering and designing our metadata application profile to meet the needs of multiple user groups. This included not only the users of our digital collection, but also our cataloguers who would be creating the metadata and maintaining this application profile in the future. I learned that it is important that librarians, as information maintainers, also be considered in the design of information systems. To consider our collection user’s needs, we ensured that the element set and the front-end configuration of our digital collection provided ways to browse, search, and sort the resources as established in our functional requirements (ie. the user needs). To consider our catalogers’ needs, I learned the importance of clear and efficient language, and concrete examples while creating our documentation manual. 

Read our Manual:

How New York City Eats: Mapping the City's Landscape of Restaurants, Bars, and Cafes 

Project Description: This project is an interactive story map built using ArcGIS that examines how the landscape of restaurants, bars, and cafes reflects the broader demographic, social, and economic characteristics of New York City. The project understands these establishments as important place-making sites where social rituals, personal tastes, community identity, and collective nostalgia are expressed. The first group of maps visualize the density of restaurants throughout the city and the most prevalent types of food items, cuisines, and establishment types in each of the city's neighborhoods. The final map functions as an exploratory tool for studying restaurant openings and closures over time, alongside relevant restaurant news and reviews. Through this research, I explore how communities may be represented by their surrounding food establishments and how this may be shifting as American dining evolves.

Project Components: An ArcGIS Online story map, and a Jupyter notebook file containing scripts and my methodology. 

Methods: I began this project by establishing my primary research questions and by brainstorming what data sources would best facilitate my research. After exploring multiple sources, I found that Yelp provided the most comprehensive dataset of both operational and closed restaurants in New York. I developed a workflow using Python to compile the data from the Yelp Fusion API. I also wanted to include additional narrative information about restaurants in the city, which I found by collecting restaurant news articles from Eater.com and Gayot.com. After preparing and cleaning my compiled data, I built the maps using ArcGIS and designed a story map featuring my analysis. Throughout the entire project, I also performed scholarly research to better understand how these restaurants are situated within contemporary political, economic, and social contexts. My analysis focusing on the ethnic distribution of restaurants throughout the city is especially influenced by the work of Krishnendu Ray and Ran Mei. 

My Role: I am the sole creator of this project.

Learning Objective Achieved: Technology

Rationale: This project utilized several advanced technological skills, including scripting using Python and geographic information visualization and analysis using GeoPandas and ArcGIS. I effectively used Python scripts to retrieve the bulk of my data from the Yelp Fusion API. This involved several rounds of API calls in which I first searched for all operating bars, restaurants, and cafes throughout the city’s five boroughs, and then performed additional API calls to retrieve all businesses and their details that have previously existed at each of these establishments’ addresses. Furthermore, I also utilized my knowledge of HTML as I parsed and scraped information from restaurant news websites. After data collection was complete, I used Python and OpenRefine to clean and prepare my data. In order to build the maps that summarize restaurant characteristics by neighborhood, I used the Pandas and GeoPandas Python libraries to perform spatial aggregation. 

Overall, this project reinforced not only my technical skills in geographic information systems, but also provided me with creative problem solving and troubleshooting skills as I built a large dataset from the ground up. Additionally, I learned the importance of maintaining and creating documentation to preserve my code for future reference by both myself and others. My full methodology can be found on Github. 

Learning Objective Achieved: Ethical/Creative/Critical Practice

Rationale: This project has been informed by the critical theory of Data Feminism, as proposed by Catherine D'Ignazio and Lauren F. Klein. In agreement with the principles they establish, this project understands that data is not neutral and ultimately reflects the biases of its creator. Furthermore, given that data are produced within power imbalances, context is crucial  for conducting an ethical analysis.

I particularly felt this while performing my analysis of restaurants by ethnicity throughout the city. By nature, classification attempts to contain fluid identities into fixed boxes, but the reality of food and cultural identities are far messier. I found that while my visualizations were helpful at painting a broad picture about dining in the city, some nuance was lost and treatment of some ethnic identities felt reductionist.This likely results from the crowd-sourced Yelp classifications which may come from a user-base that is a product of a biased society that may lack the language for describing certain cultural identities. By engaging with scholars, like Ray and Mei, who have studied the “ethnic procession” of American dining, it was my hope that this context paints a more holistic picture of the reality of the city’s food landscape. Finally, it was also important that my narrative acknowledge limits of the data, and acknowledge that more work needs to be done before drawing certain conclusions.