As Research Data Science Specialist, Jeremy Mikecz advises and collaborates with students and faculty conducting computational and data-intensive research in the arts, humanities, and social sciences. He is happy to help with finding, cleaning, analyzing, visualizing, and interpreting data (in all its forms). This help includes, but is not limited to:
- finding "data" - whether texts, quantitative tables, spatial information, or images - and brainstorming creative ways to explore and analyze this data
- compiling a text corpus and analyzing and visualizing patterns hidden within
- compiling, preparing, analyzing, and visualizing patterns within quantitative or qualitative datasets
- visualizing data using R, Python, or other tools.
- creating geospatial databases and maps in Arc or QGIS or creating qualitative and/or narrative maps in Inkscape or other platforms
- developing critical and humanistic techniques to explore and visualize gaps, errors, subjectivity, and imprecision in data (whether historical narratives or quantitative records)
To book an appointment with him, you may do so here: https://api.dartgo.org/jeremyappts (or you may email him directly).
For these consultations, Mikecz draws on his interdisciplinary background in history, geography, archaeology, the quantitative social sciences, and the digital and spatial humanities. In his own research, Mikecz applies and develops data visualization and cartographic techniques to interrogate historical narratives as well as to tell - narratively, visually, cartographically - alternative histories (with a focus on early colonial Latin America, especially the Andes). In this research, he has developed skills and experiences in GIS/geovisualization, narrative cartography, data visualization, and the analysis of quantitative, qualitative, spatial, and textual data. He does much of his work in QGIS, Python, R, Inkscape, and Oxygen (for XML encoding), but is always looking to learn new skills. Mikecz is especially interested in developing new methods and approaches to recover marginalized or hidden histories.