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.
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.
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.
