Data visualization literacy:

D’Ignazio, Catherine; Bhargava, Rahul

doi:10.2307/j.ctvzgb8c7.19

Cited by 23 publications

(29 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data literacy has been defined as the set of competencies and contexts associated with reading, working with and analysing data (D’lgnazio and Bhargava, 2015). Part of the difficulty, however, is that data literacies, along with expertise (Bassett, 2015), are also sites of struggle, built around the contextual knowledge and diverse experiences that shape relationships with data and its analysis.…”

Section: Why Study Data Capabilities In the Non-profit Sector?mentioning

confidence: 99%

“…Frank et al (2016) argue that a data literate citizen should have the ability to find, select, understand, interpret, evaluate and manipulate data in the context of the internet with confidence, and to appreciate the value of data. To their definition of data literacies as the ‘the ability to read, work with, analyse and argue with data’, D’lgnazio and Bhargava note important sensitivities in the era of big data including the need to understand algorithmic manipulations, and ability to weigh up consequences of data handling for already marginalised people (2015: 2). These are rarely the sole remit of individuals acting alone with or in relation to data.…”

Section: Defining Data Literacy Beyond Individual Competenciesmentioning

confidence: 99%

“…Although the concept of data literacy remains contested, it is useful for grounding and understanding data practices in their communicative settings, in relation to data's representational (communicative) and functional (computational) status (Poirier, 2021). Theoretical and practical work on data literacy generally seeks to improve individual competencies and critical awareness (D’Ignazio and Bhargava, 2015; Frank et al, 2016) as an aspect of contemporary ‘data citizenship’ (Carmi and Yates, 2020) and shifting big data practices. However, it is not always clear where or how this work should take place, and burgeoning corporate data literacy programs (e.g.…”

Section: Defining Data Literacy Beyond Individual Competenciesmentioning

confidence: 99%

“…We recognised early on that data literacy resources alone would not be enough to make a difference with the three organisations, or adequately address their specific context and situations. As noted in our review, there are extensive online resources for developing critical and operational data literacy (Sander, 2020; D’Ignazio and Bhargava, 2015), along with any number of open or paid online data science courses. However, post-interviews with participants reiterated that these were the least effective mode of capability development.…”

Section: Two Case Studies: Situating and Developing Data Literacy And...mentioning

confidence: 99%

See 3 more Smart Citations

Developing data capability with non-profit organisations using participatory methods

et al. 2022

View full text Add to dashboard Cite

In this paper, we explore the methodologies underpinning two participatory research collaborations with Australian non-profit organisations that aimed to build data capability and social benefit in data use. We suggest that studying and intervening in data practices in situ, that is, in organisational data settings expands opportunities for improving the social value of data. These situated and collaborative approaches not only address the ‘expertise lag’ for non-profits but also help to realign the potential social value of organisational data use. We explore the relationship between data literacy, data expertise and data capability to test the idea that collaborative work with non-profit organisations can be a practical step towards addressing data equity and generating data-driven social outcomes. Rather than adopting approaches to data literacy that focus on individuals – or ideal ‘data citizens’ – we target the organisation-wide data settings, goals and practices of the non-profit sector. We conclude that participatory methods can embed social value-generating data capability where it can be sustained at an organisational level, aligning with community needs to promote collaborative data action.

show abstract

Section: Why Study Data Capabilities In the Non-profit Sector?mentioning

confidence: 99%

Section: Defining Data Literacy Beyond Individual Competenciesmentioning

confidence: 99%

Section: Defining Data Literacy Beyond Individual Competenciesmentioning

confidence: 99%

Section: Two Case Studies: Situating and Developing Data Literacy And...mentioning

confidence: 99%

See 2 more Smart Citations

Developing data capability with non-profit organisations using participatory methods

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Geographical information permeates the written world, appearing as place names or place descriptions in texts including news articles, blog posts, social media content, historical documents, and scientific articles. Research on extracting geographical information from text has often focused on news articles [ 1 – 3 ] and social media content [ 4 – 6 ], with surprisingly limited attention being directed towards the increasing number of published scientific articles. Indeed, with each passing year, scientists face an ever-growing stack of scientific articles to sort through, read, understand, and build upon.…”

Section: Introductionmentioning

confidence: 99%

Extracting and modeling geographic information from scientific articles

Acheson

Purves

2021

PLoS ONE

View full text Add to dashboard Cite

Scientific articles often contain relevant geographic information such as where field work was performed or where patients were treated. Most often, this information appears in the full-text article contents as a description in natural language including place names, with no accompanying machine-readable geographic metadata. Automatically extracting this geographic information could help conduct meta-analyses, find geographical research gaps, and retrieve articles using spatial search criteria. Research on this problem is still in its infancy, with many works manually processing corpora for locations and few cross-domain studies. In this paper, we develop a fully automatic pipeline to extract and represent relevant locations from scientific articles, applying it to two varied corpora. We obtain good performance, with full pipeline precision of 0.84 for an environmental corpus, and 0.78 for a biomedical corpus. Our results can be visualized as simple global maps, allowing human annotators to both explore corpus patterns in space and triage results for downstream analysis. Future work should not only focus on improving individual pipeline components, but also be informed by user needs derived from the potential spatial analysis and exploration of such corpora.

show abstract

Designing equitable and inclusive visualizations: An underexplored facet of best practices for research and publishing

Schimpf

Beddoes

2021

J of Engineering Edu

View full text Add to dashboard Cite

Equitable and inclusive publishing practices for engineering education research have received increased attention in recent years. JEE editorials and guest editorials have raised awareness about multiple challenges, including the problematic Whiteness and maleness of much research and the need to make diversity the default condition (Pawley, 2017), racially biased citation patterns (Holly, 2020), and other general aspects of publishing ethics (Loui, 2016). There are also ongoing discussions in an engineering education journal editors' group about how to increase the inclusivity of our collective publishing practices. For instance, topics such as inclusive pronouns, positionality statements, and how to better involve scholars of color without overburdening them have been discussed. However, inclusive visualization practices have not yet received the same critical attention. Importantly, visualizations can play a number of key roles in manuscripts, such as synthesizing frameworks or literature (Eppler, 2006), showing relationships between core variables (Tufte, 1997), providing illustrative examples of focal phenomena (e.g., see Schimpf et al., 2020), or enabling comparisons of intervention outcomes (Gleicher et al., 2011). Thus, their influence has a wide reach. Just as other aspects of publishing can serve as mechanisms for either exclusion or inclusion, so too can our choices when designing visualizations.In this guest editorial, we highlight the heretofore unexamined topic of visualization to add to those ongoing efforts to increase the inclusivity of engineering education research publishing practices. The three inclusivity dimensions we discuss are (1) communicating to an interdisciplinary audience, (2) representation equity within visualizations, and (3) readers' physical dis/abilities and differences. In discussing these dimensions and how their associated design decisions can affect the inclusivity of engineering education research, we aim to raise awareness, provide reflective prompts for designing and reviewing visualizations, and ultimately decrease the unintentional use of exclusionary practices. These dimensions are not a definitive list but are intended to encourage a wider discussion within the community about inclusive visualization practices.Our first dimension of inclusivity involves communicating to an interdisciplinary audience. Engineering education is an interdisciplinary field that brings together scholars from engineering disciplines, education disciplines, and social science fields among others. While some types of complex visualizations (e.g., multivariate box plots or threedimensional bar graphs) may be standard or common in some of these fields, there are others that very rarely use any visualizations at all. Therefore, not all of the interdisciplinary contributors to engineering education research are equally familiar with all visualization approaches. As such, we need to ensure that visualizations are discernable to the full community so that they do not become inadvertent gatekeepers...

show abstract

Data visualization literacy:

Cited by 23 publications

References 0 publications

Developing data capability with non-profit organisations using participatory methods

Developing data capability with non-profit organisations using participatory methods

Extracting and modeling geographic information from scientific articles

Designing equitable and inclusive visualizations: An underexplored facet of best practices for research and publishing

Contact Info

Product

Resources

About