Student projects

This page lists the projects currently open at the Laboratory for the History of Science and Technology (LHST). If you are interested in working on one of the projects listed below, please contact directly Prof. Baudry by email.

The project descriptions are only brief outlines and we are in general flexible about the particulars.

The project will investigate how digital tools can be used to study the dynamics of innovation in science and technology, from the eighteenth century to today. Innovation—the production of the new—is often said to be radical and path-breaking; yet, what can history teach us about the actual rhythms of innovation? Looking at past centuries of technological development, do we see continuity or discontinuity? Can we identify waves of innovation (and imitation)? How new is the new and through what kinds of textual and pictorial strategies are innovations constructed as new? To answer these questions, you will use text mining techniques to build a corpus of historical patents, which you will then analyze using statistical methods (regressions, possibly multiple component analysis) and/or NLP tools.

Project type: semester project or master’s thesis.

Prerequisites: prior experience in text mining; solid data analysis skills; knowledge in NLP and computational linguistics a plus.

 

In recent years, network analysis has become a classic method to understand the social construction of knowledge. With the increasing digitization of historical archives, it has become possible to pursue these efforts on a much larger scale. This project proposes to reconstruct the social network of Vincenzio Viviani, the last disciple of Galileo, by analyzing his correspondence computationally. You will scrape the metadata of Viviani’s digitized archive and build a database of his letters in order to map and visualize the social dynamic of his scientific life.

Project type: semester project or master’s thesis.

Prerequisites: strong skills in web scraping and databases; working knowledge of data visualization; prior knowledge in network analysis software a plus.

Over the past decades, the development of online scientific platforms like arXiv.org or JSTOR radically changed the way researchers access, browse, or read scientific articles. Yet, while observing the behavior of researchers in libraries and laboratories has become commonplace in the humanities, the computational study of digital research practices is only in its early days. This project aims at documenting the behavior of scientists on online platforms by making sense of the digital traces they generate while navigating. You will perform data mining on the browsing logs of selected platforms and make sense of user experience by identifying patterns through cluster analysis.

Project type: semester project or master’s thesis.

Prerequisites: solid skills in data cleansing and enriching; prior experience in unsupervised machine learning (notably non-Euclidean vector space models, PCA, and pattern recognition through cluster analysis).

L’objectif du projet est d’étudier la construction de la figure du savant du 18e siècle à nos jours. D’abord relégué au bas des hiérarchies culturelles, loin derrière le guerrier, le saint ou le chef, quand et comment le savant est-il devenu, lui aussi, un « grand homme » ? D’où vient la gloire du savant et comment peut-on expliquer la grandeur de la science en tant qu’activité ? Pour répondre à cette question, vous constituerez et analyserez, avec des outils de NLP et de linguistique computationnelle, un corpus d’éloges funèbres de savants ayant fait partie de l’Académie des sciences de Paris, depuis sa création en 1699 jusqu’à aujourd’hui.

Project type: semester project or master’s thesis.

Prerequisites: knowledge of French; solid skills in NLP;  knowledge in computational linguistics a plus

General Semantic Knowledge Bases (Wikidata, DBpedia) convert human knowledge (e.g. Wikipedia articles) into structured content which is machine readable and queryable. The goal of this project is to create a specialized Semantic Knowledge Base which is suited for the research methods of historians. Can one use the available semantic web technologies to encode not only simple statements of fact, but also complex claims arising from historical interpretations? For this project, you will design an ontology appropriate for encoding a heterogeneous set of information about a historical artefact (the Hipp Chronoscope). The data will be modelled as semantic triples (RDF) and queried through SPARQL. The project will give you the opportunity to learn about semantic web technologies even if you are not familiar with them.

Project type: semester project or master’s thesis.

Prerequisites: knowledge about database concepts and SQL.

This project will investigate how digital tools can be used to recreate the historical setting in which scientific objects and instruments were used. Can we build narratives and displays of science that go beyond selecting and contemplating individual objects in isolation? Can we visualize the role played by the setting in shaping the outcomes of an experiment? The project will map out the space of the experiment (e.g. the workbench, the lab-room etc.), the spatial relations between the objects and people involved in the experiment, and the temporal sequence of events. You will be responsible for designing the visualization of a past scientific experiment such that the role of the historical setting is made conspicuous. You are free to choose the graphical environment best suited for the project goals.

Project type: semester project or master’s thesis.

Prerequisites: prior experience in graphic design or 3D modelling.

This project will investigate how machine vision algorithms could be applied by historians to automate the analysis of historical images. Are the available datasets and algorithms suitable for the research goals and methods of historians? What are the kind of historical questions best suited to machine vision algorithms? You will be responsible for constructing a dataset of historical scientific images adequate for machine learning by automating a process which extracts images and their textual description from pdf scans of 19th century science textbooks. You will then use the dataset to test algorithms and extract visual patterns and features.

Project type: semester project or master’s thesis.

Prerequisites: prior experience in machine learning and pattern recognition.

Savoirs is a digital platform that offers its users to navigate a collection of essays in the anthropology and history of knowledge. Built around three major indexing tools — a chronology, a conceptual thesaurus, and geographical localisation —, the platform’s recommendation algorithms aim at enabling readers to fine tune step-by-step the settings of their queries.

This project proposes to develop new and innovative metrics to vectorize Savoirs’s collection of essays within a conceptual space. Making sense of the thesaurus as a graph weighing the XML-tagged essays, you will be asked to design and benchmark against each other a variety of vector space models accounting for Savoirs’s ontology.

Project type: semester projects or master’s theses.

Prerequisites: solid skills in Python; working knowledge in data analysis for digital humanities; prior experience in web implementation a plus.

Savoirs is a digital platform that offers its users to navigate a collection of essays in the anthropology and history of knowledge. Built around three major indexing tools — a chronology, a conceptual thesaurus, and geographical localisation —, the platform’s recommendation algorithms aim at enabling readers to fine tune step-by-step the settings of their queries.

In an effort to open the black boxes of algorithms for the users to tweak them, this project proposes to specify and design features to enrich user experience. You will be asked to imagine technical solutions that enable users to manually tune the parameters of the search algorithms, to estimate the costs in computational resources involved, and eventually to draft scopes of work for potential tenders.

Project type: semester projects or master’s theses.

Prerequisites: solid skills in Python; working knowledge in data analysis for digital humanities; prior experience in web implementation a plus.