# Inclusive Data Research Skills for Arts and Humanities/Data Visualization
## Definition(s)

Data is not just numbers but information (Gitelman, 2013)
What is data visualisation?
Data visualisation is the process of interpreting information through distinctive and contextually relevant concepts and translating the data into a visual narrative.
It connects readers to insights, knowledge and power, and it can generate new relevant questions.
Medium to
* Explore: analyse, engage, interact, reflect
* Discover: find insights, knowledge, ideas, connections, gaps
* Express: interpret, narrate, explain, guide attention, storytelling
Why is it valuable for A&H researchers?
* Creative Process Transforms research and collection of information into visual expression
* Product of that process / Outcome Presentation of the findings and presenting new questions

## Data

Data gathered in the course of qualitative research can vary dramatically depending on the subject of investigation. For example, data can be gathered through discussions, interviews, observations, photographs, painting, video and audio. This can pose both a challenge and an exciting creative opportunity for the qualitative researcher.
It is also important to remember that the interpretation of qualitative data can be subjective. It is therefore important to consider your own positionality and bias when embarking on data visualisations.

## Data visualisation in research

Data Visualisation is used in multiple ways within arts & humanities research, for numerous purposes. This can include visualisation of both qualitative and quantitative data, however within arts and humanities particular consideration is needed regarding visualisation of qualitative data. Whilst qualitative data can be translated into categorical or numerical data, there is a balance to be struck between simplifying the richness of a qualitative dataset and capturing its complexity for the purposes of visualisation. Within arts & humanities data visualisation is used for distinct purposes beyond graphical output of research outcomes and can inform processes of discovery and the research journey.
These purposes should not be seen as distinct, as they interact with each other, and also interact with other phases of the research journey including research outcomes.
Purposes of data visualisation include:
* Collecting: bringing together information from multiple data sources
* Synthesising: identifying connections between multiple data sources / different types of data into one visual array
* Exploring: Processes of data discovery including pattern identification, Comparison, Visualising change over time
* Analysis: Identifying sources of missing information / gaps in data eg who / what is not represented
* Experimentation: considering what is the best way to understand the data? How could it be communicated? What happens if the scale is changed or the data is configured differently?
* Narration: Using visualisation to explore what story the data may tell, particularly those that may be different from the stories we expect
* Displaying results
* Distilling - making choices about key themes / pulling out particular ideas
* Communication - using data to capture attention or share information in unusual and informative ways
* Inviting feedback - using visualisation as a means of engagement and capturing reflections / responses from an audience
* Generating further research questions - using data visualisation to inform and shape further stages of research
* Supporting interdisciplinary conversations - using data visualisation as a means of communicating across boundaries

## Data visualisation as an outcome

Data visualisation can be used to tell a story narrative; presenting information in a way which is accessible to the wider public. This is particularly important when exploring nuanced or complex questions or topics which a layperson may be unfamiliar with.
Benefits of data visualisation:
* Accessibility: Data visualisation can be used to connect with audiences who are unable or unwilling to read raw data
* Engagement: Data visualisation can gamify information and in doing so reach a larger audience
* Interaction: People can explore data at their own pace and seek out the information that interests them
* Empowerment: Making data more accessible can empower others to do their own research or take action within their own communities
When visualising your data, it is important to consider what your priorities are. If the goal is to make information as accessible as possible, this may affect how and where you present your data. Creating your own website may be a compelling idea but this requires continuous funding/resources. Will a website of your own design still be accessible in five years time? When possible, data can be made open to improve accessibility. You may want to consider sharing data you have collected on sites like Wikidata.
Visualising data can also be beneficial for the researcher by providing them with the opportunity to reflect on their research. Gaps in data can be identified and new questions can be raised.

## Improving data visualisation

Visualisation of data for un/underrepresented groups when the baseline for that data collection is ‘0’ might start from below the lowest data point to allow the visualisation of a lack of data. Missing data can be visualised by a showing of the lack of data, generally demonstrated through spatial or visual gaps and juxtapositions within data visualisations.
Qualitative data is often visualised through methods of quantifying qualitative information. We should find new ways of visualising or interacting with data through qualitative means. Chord, network, and Venn diagram charts may be a few ways of doing this effectively, but what are other innovative ways that we may view qualitative data? How might other modes of engagement be used to understand this data (i.e. sound, touch).
Something here is needed about using multiple visualisations to engage in varied interpretations.

## Information we collect

* Multiple auto/biographical statements (convergent narratives)
* Movement data
* Sound/music
* Historical (longitudinal)
* Crowdsourced

## Tools we're already using

Tools used to collect, code, analyse, explore, and share:
* augmented or virtual reality
* Recordings (images, videos, sound)
* Drawing - e.g. Notation (movement)
* Excel
* 3D
* AI
* Auto Ethnography
* Nvivo (textual analysis)

## Challenges within data visualisation

* Complexity of narrating and storytelling
* Subjectivity of Data
* Interpretation and Ambiguity
* Cultural Sensitivity
** Understanding de/colonial narratives of data
* Ethical Considerations
* Technical Limitations
* Digital Gap
** Urban vs. rural regions
** Generation-conflict
** Developed vs. developing countries
* Data Literacy
* Aesthetic Considerations
* Translate humanities research into the logic of database
* New tools, digital and analogue
* Accessibility (disability standards)

## See also

* Laura Knight, Design, Education and Research

## References

* ↑ Bumbuc, Ştefania (2016-06-01). "About Subjectivity in Qualitative Data Interpretation". International conference KNOWLEDGE-BASED ORGANIZATION. 22 (2): 419–424. doi:10.1515/kbo-2016-0072. ISSN 2451-3113.
* ↑ Kirk, Andy (2014-05-01). "Visualizing Zero: How to Show Something with Nothing". Harvard Business Review. ISSN 0017-8012. https://hbr.org/2014/05/visualizing-zero-how-to-show-something-with-nothing.