Evaluation

We look for exhibition projects that transform data into meaningful encounters — ensuring audiences don’t just see information, but experience relationships in tangible ways.

Everything is connected to everything else. — Barry Commoner

The Immersive Journey in the exhibition submission we are looking for:

• Clarity: Clearly connects data source, method, and visual or spatial form.
• Grounding: Anchors the work in a specific ecological, infrastructural, or lived context.
• Method: Explains how the data is gathered, generated, or interpreted — thoughtfully and ethically.
• Form: Translates data into an experiential medium that invites attention, interaction, or reflection.
• Insight: Reveals relationships, tensions, or patterns that help audiences notice something new.
• Feasibility: Demonstrates the capability to execute the work within space, time, and technical constraints.

Exhibition Evaluation Rubric

Exhibition proposals are reviewed through a curatorial process. Outcomes fall into three categories:

Strong: Can be accepted, with curatorial suggestions. The project aligns strongly with the exhibition vision and may require refinement through dialogue.

• We communicate with the proposer and share curatorial suggestions
• The proposer prepares visual mockups, sketches, or technical diagrams
• A curatorial review is conducted
• Production needs, technical requirements, and timeline are discussed
• The project is confirmed and moves to exhibition planning and communication

Maybe: Might be accepted, after vital revisions. The concept shows promise but requires clarification or strengthening.

• We communicate specific concerns or gaps
• Clarifications may be requested around experiential design, data methodology, technical feasibility, or timeline
• The proposer revises and resubmits with additional detail
• A second review is conducted
• If concerns are addressed, the project moves to acceptance
• If not, it may be declined with constructive feedback

Weak: Rejected. The proposal does not align sufficiently with the exhibition vision at this time.

• We send thoughtful feedback outlining strengths and gaps
• We clarify what did not align with exhibition vision
• Where appropriate, we encourage reapplication in future editions
• We aim to maintain a relationship with the artist

Criteria	Strong	Maybe	Weak
Proposal Clarity	Clearly articulates idea, data source, method, and experiential form. Connections are explicit. Timeline and technical requirements are realistic.	Concept is interesting but connections between data, method, and form need clarification. Some feasibility details are missing.	Description is vague or incomplete. It is unclear how data becomes experience or whether the project is feasible.
Experiential Quality	Creates a distinctive spatial, sensory, or interactive encounter. Form is thoughtfully matched to content.	Experiential dimension has potential but feels underdeveloped or conventional.	Reads as a static display with minimal consideration of audience encounter.
Ecological Relevance	Makes a compelling connection to ecology, climate, or lived environmental experience. Reveals meaningful relationships.	Ecological connection exists but feels broad or underdeveloped.	Ecological framing is superficial, unclear, or forced.
Data Approach	Data source is specific and ethically grounded. Method is appropriate and clearly described. Expands what counts as data in thoughtful ways.	Data source identified but methodology or ethics require clarification. Timeline for collection may be tight.	Data source is vague or problematic. No clear method. Ethical considerations not addressed.
Data Analysis & Visualisation	Strong synthesis between aesthetic expression and analytical rigour. Visualisation reveals non-obvious insight or deepens narrative meaning.	Conceptually sound but lacks novelty or depth in either visualisation or analysis.	Weak analytical grounding or aesthetic coherence. Insight is unclear or unconvincing.
Feasibility & Capability	Demonstrates clear ability to execute within timeline and technical constraints.	Shows potential but execution or timeline raises questions.	Insufficient evidence of capability. Timeline unrealistic or technically unfeasible.