arXiv subjects

Charles Berret

Publications and source records attributed to Charles Berret.

4 recordsLinked to original sources

arXiv2026

OpenRoundup: Multi-Table Data Wrangling Through Interactive Visualization

Data journalists routinely integrate records across multiple independently published sources to support accountability reporting, yet no existing interactive wrangling tool treats the collection of tables -- rather than the single table -- as its primary unit of work. We present OpenRoundup, an open-source, browser-based system that enables data journalists to consolidate multiple tables into a single analysis-ready output without writing code. The interface comprises five coordinated panels that implement a schema-first, values-on-demand paradigm with live schema previews, ambient data quality alerts, and a recursive treemap visualization of the evolving operation tree. A client-only architecture powered by DuckDB-WASM runs in the browser, providing strong data privacy guarantees suited to sensitive journalism data. The system introduces two conceptual contributions: eager table consolidation, in which a composite table is assembled early in the wrangling phase via interactive, incremental assembly of multiple source tables; and a declarative vocabulary for table consolidation consisting of two operations, Stack and Pack. We evaluate the system through a replication study in which the authors reproduce 17 published journalist programming workflows using only the interface, and a deployment study with four professional data journalists. The replication study demonstrates expressive coverage of real-world consolidation tasks. The deployment study confirms utility for practitioners who understand joins conceptually but lack the programming skills to execute them, and surfaces an unanticipated secondary value for data journalism education.

cs.HC↗

arXiv2025

Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science

The work involved in gathering, wrangling, cleaning, and otherwise preparing data for analysis is often the most time consuming and tedious aspect of data work. Although many studies describe data preparation within the context of data science workflows, there has been little research on data preparation in data journalism. We address this gap with a hybrid form of thematic analysis that combines deductive codes derived from existing accounts of data science workflows and inductive codes arising from an interview study with 36 professional data journalists. We extend a previous model of data science work to incorporate detailed activities of data preparation. We synthesize 60 dirty data issues from 16 taxonomies on dirty data and our interview data, and we provide a novel taxonomy to characterize these dirty data issues as discrepancies between mental models. We also identify four challenges faced by journalists: diachronic, regional, fragmented, and disparate data sources.

cs.HC↗

arXiv2022

Iceberg Sensemaking: A Process Model for Critical Data Analysis and Visualization

We offer a new model of the sensemaking process for data analysis and visualization. Whereas past sensemaking models have been grounded in positivist assumptions about the nature of knowledge, we reframe data sensemaking in critical, humanistic terms by approaching it through an interpretivist lens. Our three-phase process model uses the analogy of an iceberg, where data is the visible tip of underlying schemas. In the Add phase, the analyst acquires data, incorporates explicit schemas from the data, and absorbs the tacit schemas of both data and people. In the Check phase, the analyst interprets the data with respect to the current schemas and evaluates whether the schemas match the data. In the Refine phase, the analyst considers the role of power, articulates what was tacit into explicitly stated schemas, updates data, and formulates findings. Our model has four important distinguishing features: Tacit and Explicit Schemas, Schemas First and Always, Data as a Schematic Artifact, and Schematic Multiplicity. We compare the roles of schemas in past sensemaking models and draw conceptual distinctions based on a historical review of schemas in different academic traditions. We validate the descriptive and prescriptive power of our model through four analysis scenarios: noticing uncollected data, learning to wrangle data, downplaying inconvenient data, and measuring with sensors. We conclude by discussing the value of interpretivism, the virtue of epistemic humility, and the pluralism this sensemaking model can foster.

cs.HC↗

arXiv2020

Table Scraps: An Actionable Framework for Multi-Table Data Wrangling From An Artifact Study of Computational Journalism

For the many journalists who use data and computation to report the news, data wrangling is an integral part of their work.Despite an abundance of literature on data wrangling in the context of enterprise data analysis, little is known about the specific operations, processes, and pain points journalists encounter while performing this tedious, time-consuming task. To better understand the needs of this user group, we conduct a technical observation study of 50 public repositories of data and analysis code authored by 33 professional journalists at 26 news organizations. We develop two detailed and cross-cutting taxonomies of data wrangling in computational journalism, for actions and for processes. We observe the extensive use of multiple tables, a notable gap in previous wrangling analyses. We develop a concise, actionable framework for general multi-table data wrangling that includes wrangling operations documented in our taxonomy that are without clear parallels in other work. This framework, the first to incorporate tablesas first-class objects, will support future interactive wrangling tools for both computational journalism and general-purpose use. We assess the generative and descriptive power of our framework through discussion of its relationship to our set of taxonomies.

cs.HC↗