Article on “Engaged research-led teaching: composing collective inquiry with digital methods and data”

A new article on “Engaged research-led teaching: composing collective inquiry with digital methods and data” co-authored by Jonathan GrayLiliana BounegruRichard RogersTommaso VenturiniDonato RicciAxel MeunierMichele MauriSabine NiedererNatalia Sánchez-QuerubínMarc TutersLucy Kimbell and Anders Kristian Munk has just been published in Digital Culture & Education.

The article is available here, and the abstract is as follows:

This article examines the organisation of collaborative digital methods and data projects in the context of engaged research-led teaching in the humanities. Drawing on interviews, field notes, projects and practices from across eight research groups associated with the Public Data Lab (publicdatalab.org), it provides considerations for those interested in undertaking such projects, organised around four areas: composing (1) problems and questions; (2) collectives of inquiry; (3) learning devices and infrastructures; and (4) vernacular, boundary and experimental outputs. Informed by constructivist approaches to learning and pragmatist approaches to collective inquiry, these considerations aim to support teaching and learning through digital projects which surface and reflect on the questions, problems, formats, data, methods, materials and means through which they are produced.

Make a deal with Gephisto

Mathieu Jacomy and Anders Munk, TANT Lab & Public Data Lab

6 minutes read

Make a deal with Gephisto

Gephisto is Gephi in one click. You give it network data, and it gives you a visualization. No settings. No skills needed. The dream! With a twist.

Gephisto produces visualizations such as the one above. It exists as a website, and you can just try it below. It includes test networks, you don’t even need one. Do it! Try it, and come back here. Then we talk about it.

https://jacomyma.github.io/gephisto/

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Introducing Memespector-GUI: A Graphical User Interface Client for Computer Vision APIs

In this post Jason Chao, PhD candidate at the University of Siegen, introduces Memespector-GUI, a tool for doing research with and about data from computer vision APIs.

In recent years, tech companies started to offer computer vision capabilities through Application Programming Interfaces (APIs). Big names in the cloud industry have integrated computer vision services in their artificial intelligence (AI) products. These computer vision APIs are designed for software developers to integrate into their products and services. Indeed, your images may have been processed by these APIs unbeknownst to you. The operations and outputs of computer vision APIs are not usually presented directly to end-users.

The open-source Memespector-GUI tool aims to support investigations both with and about computer vision APIs by enabling users to repurpose, incorporate, audit and/or critically examine their outputs in the context of social and cultural research.

What kinds of outputs do these computer vision APIs produce? The specifications and the affordances of these APIs vary from platform to platform. As an example here is a quick walkthrough of some of the features of Google Vision API…

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Using ObservableHQ notebooks for gathering and transforming data in digital research

We’ve recently been experimenting with the use of ObservableHQ notebooks for gathering and transforming data in the context of digital research. This post walks through a few recent examples of notebooks from recent Public Data Lab projects.

In one project we wanted to use the CrowdTangle “Links” API to fetch data about how certain web pages were shared online and across different platforms. After gaining access to relevant end points, we could adopt different means to call the APIs and retrieve data: such as using something like Postman (a general-purpose interface to call endpoints), or writing custom scripts (for example in Python or Javascript).

Code notebooks are a third option that lies somewhere in between these options. Designed for programmers, notebooks allow for iterative manipulation and experimentation with code whilst keeping track of creative processes by commenting on the thinking behind each step.

Notebooks allow us to both write and run custom scripts as well as creating simple interfaces for those who may not code. Thus we can use them to help researchers, students and external collaborators to collect data, making it easier to call APIs, setting parameters, or perform manipulations.

ObservableHQ is one solution for writing programming notebooks, it runs in the browser and is oriented towards data and visualisations (“We believe thinking with data is an essential skill for the future”). Hence, we thought it could be a good starting point for what we wanted to do.

Screen capture of a notebook
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