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.
Mathieu Jacomy and Anders Munk, TANT Lab & Public Data Lab
6 minutes read
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.
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.
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.
While in an image-saturated society, methods for visual analysis gain urgency, this special issue explores visual ways to study online images. The proposition we make is to stay as close to the material as possible. How to approach the visual with the visual? What type of images may one design to make sense of, reshape, and reanimate online image collections? The special issue also touches upon the role that algorithmic tools, including machine vision, can play in such research efforts. Which kinds of collaborations between humans and machines can we envision to better grasp and critically interrogate the dynamics of today’s digital visual culture?
The articles (available both in English and in Spanish) touch on the diversity of formats and uses of online images, focusing on collection and visual interpretation methods. Other themes touched by this issue are image machine co-creation processes and their ethics, participatory actions for image production and analysis, and feminist approaches to digital visual work.
Further information about the issue can be found in our introduction. Following is the complete list of contributions (with links) and authors (some from the Public Data Lab).
The book provides a wide-ranging collection of perspectives on how data journalism is done around the world. It is published a decade after the first edition (available in 14 languages) began life as a collaborative draft at the Mozilla Festival 2011 in London.
The new edition, with 54 chapters from 74 leading researchers and practitioners of data journalism, gives a “behind the scenes” look at the social lives of datasets, data infrastructures, and data stories in newsrooms, media organizations, startups, civil society organizations and beyond.
The book includes chapters by leading researchers around the world and from practitioners at organisations including Al Jazeera, BBC, BuzzFeed News, Der Spiegel, eldiario.es, The Engine Room, Global Witness, Google News Lab, Guardian, the International Consortium of Investigative Journalists (ICIJ), La Nacion, NOS, OjoPúblico, Rappler, United Nations Development Programme and the Washington Post.
An online preview of various chapters from book was launched in collaboration with the European Journalism Centre and the Google News Initiative and can be found here.
Further background about the book can be found in our introduction. Following is the full table of contents and some quotes about the book. We’ll be organising various activities around the book in coming months, which you can follow with the #ddjbook hashtag on Twitter.