Curatorial Statement
Making Sense of a Semester in Data Curation
Introduction
This digital exhibition represents my semester-long journey through the Curating Data course, where I have explored the complex relationship between data, knowledge, and curation practices. As a Cognitive Science student, I approached this course with a particular interest in how humans process, organize, and make sense of information.
Throughout this semester, I have engaged with various forms of data; from structured datasets to personal notes and creative assignments; transforming them into a cohesive exhibition that reflects my learning trajectory. This statement serves as both a guide to the exhibition and a reflection on my curatorial process.
Posthuman and Sociomaterial Reflections
Throughout the exhibition, I return to a central question: how human must the human in the loop be for curation to remain meaningful? This course has shown me that curation is never fully objective, yet it is also never fully human. My work sits within this tension and explores what happens when we consider curation as a sociomaterial process shaped by tools, infrastructures, and data itself.
While I organize, interpret, and frame the material, the technologies I use participate in the process too. Spreadsheets structure possibilities for sorting, Wikidata's schema shapes the limits of representation, and recommender systems produce classifications that emerge from model behavior rather than human intention alone. These interactions raise the question of whether curation can exist without a human presence or whether meaning always depends on some form of human interpretation.
At the same time, engaging with more-than-human elements highlights that my curatorial decisions are always co-produced. Instead of aiming for an impossible objectivity, I treat curation as a collaborative assemblage where humans, data, and technologies all contribute. This perspective reinforces the red thread running through the exhibition: understanding data curation as a shared practice in which the human is present, but never alone.
Theoretical Framework
Drawing from critical data studies (Dalton & Thatcher, 2014; Kitchin, 2022), this exhibition challenges the notion of "raw data" and explores how curation shapes knowledge. My approach is informed by several key theoretical perspectives that have guided my curatorial decisions.
Key Theoretical Concepts
Data Assemblages
Kitchin's concept of data as complex systems rather than simple collections
DIKW Hierarchy
Ackoff and Rowley's framework for understanding data-to-knowledge transformation
Posthuman Curating
Tyzlik-Carver's perspective on curation beyond human-centered approaches
Datastructuring
Flyverbom & Murray's concept of how data shapes organizational structures
Exhibition Structure
The exhibition is organized around four main themes that emerged from my semester's work, each representing a different facet of the data curation process:
Collecting
Exploring how real-world phenomena become data through selective capture and representation
Categorizing
Examining classification systems as forms of knowledge-making rather than neutral organization
Visualizing
Investigating how visual representations shape our understanding of data
Archiving
Considering preservation practices and their implications for future access
Each section of the exhibition contains assignments, notes, and reflections that demonstrate my engagement with these themes, creating a multi-layered exploration of data curation practices.
Critical Reflection
Reflecting on Okwui Enwezor's definition of curating as "participating and witnessing histories unfold," this exhibition documents my own unfolding understanding of data curation. The process of curating my semester's work has itself been an act of knowledge creation, forcing me to make explicit the connections between disparate elements of my learning.
"Curating is not just about selecting and arranging objects, but about creating relationships between them that generate new meanings and insights."
Through this exhibition, I've come to understand curation as an active, interpretive practice that shapes how knowledge is encountered and understood. The decisions I've made about what to include, how to categorize items, and what connections to highlight are not neutral; they reflect my particular perspective as a Cognitive Science student interested in human-information interaction.
Ethical Considerations in Data Curation
An essential dimension of curating data is recognizing the ethical responsibilities embedded in every curatorial decision. Data is never neutral; it reflects choices about inclusion, exclusion, representation, and power.
Core Ethical Principles
Responsibility & Care
Acknowledging the curator's role in shaping data presentation and interpretation
Privacy & GDPR
Ensuring compliance with data protection regulations and respecting privacy
Open Access & Attribution
Balancing openness with proper attribution and intellectual property rights
Reflexivity & Bias
Continuously examining assumptions and interpretive frameworks
Responsibility & Care
As a curator, I acknowledge my role in shaping how data is presented and interpreted. This includes a commitment to transparency in my methods and critical awareness of potential biases that inform data selection and framing. I approach data curation as a practice of care; care for the sources of data, for those represented within it, and for those who will encounter it in the future.
Privacy & GDPR Compliance
In accordance with the General Data Protection Regulation (GDPR), all data used in this exhibition either originates from open, non-personal sources or has been anonymized when necessary. Any personal or identifiable information encountered during coursework has been treated with confidentiality and not shared publicly. This reflects an understanding that ethical data practice begins with respect for privacy and consent.
Open Access & Attribution
This exhibition also aligns with the principles of open access and fair use, aiming to make knowledge accessible while respecting intellectual property. All third-party materials are properly cited, and open datasets are credited according to their licenses. My goal is to contribute to a culture of responsible sharing; balancing openness with accountability.
Reflexivity & Bias
Ethics in data curation extends beyond compliance to include reflexivity: continuously examining my own assumptions, disciplinary biases, and interpretive frameworks. By foregrounding these reflections, I seek to make my curatorial process as transparent and self-aware as possible.