TLDR; This is my Open Conference Reflection to capture and share thoughts, observations and anticipated directional changes based on the experience of attending the International Digital Curation Conference (IDCC) 2026.
Personal IDCC26 takeaways:
- In RDM workflows, actively capture the 'why' for data retention early on for purposeful archiving and publication.
- Data curation & preservation serves a crucial but somewhat invisible role in research value chains in combatting research waste.
- (Digital) Archives can be experiential destinations in their own right.
A brief definition to establish the concept of an Open Conference Reflection: A conference reflection can be seen as a form of journaling, it becomes an artefact that expresses as auto-biographical narrative the authors self-described observations. It should be read as a subjective and biased experience of the author attending the conference.
Capturing thoughts before the conference and reflecting on these afterwards allows myself as author to more specifically observe any change or evolution in thinking around the topic(s) addressed at the conference.
Making the conference reflection open allows other attendees to contrast their own subjective experiences of the conference with the ones of the author. For people not attending the conference, it gives a (limited) possibility to also evolve their thinking on the topic(s) addressed at the conference by reading the reflection of the author.
This reflection was partially done in collaboration with AI (Claude Code). The following steps were performed with AI.
- Program PDF to raw text conversion.
- Thematic analysis of poster titles.
- Deep Research report on art world curation & RDM paralels.
- Generation of a conference-specific reflection template.
- Text formatting polish & spell checks.
- Formatting of mermaid graph.
- Adding emoji flairs.
This is a high level summary of the steps taken before, during and after the conference in preparing, capturing and reflecting on the conference experience.
- Pre-conference:
- Goal setting
- Curiosity-driven question formulation
- Theme identification
- Program session choices
- During conference
- Note-taking
- Concept identification
- After conference
- Notes processing
- Question reflections
- Report creation
- Sharing report
- Further develop curiosity on digital curation developments across institutions.
- Derive actions & inspiration for TU/e implementations.
In addressing the first goal, I state the following questions I am personally curious about:
- How do people think about the business model / value chain of data/digital curation?
- Whats our mental model for how researchers approach data re-use & sharing?
- Are there lessons the RDM/research data curation community can draw from other fields?
On the last question, Claude web chat with Deep Research mode was prompted to compile a report on what the RDM community can learn from Art World curation. Where curation and provenance tracking are established as key pillars of the 'Art World' as an industry.
See rdm-art-world-parallels-claude-deep-research-report.md for the full report created by Claude Code.
I read this AI-generated research report as warm-up exercise to activate my critical thinking. The ideas expressed within the report can potentially be interesting, however it mainly served as a resistance training warm-up exercise by not taking the output as a truth but by critically reflecting on the validity of such AI-facilitated collaging of texts.
To get an initial baseline impression of what to expect from the conference I first read the program in full myself. I observed quite some synergy in both the presentations part of the program but particularly also in the posters part. To accelerate getting a sense of overlapping themes, I used Claude Code to perform a thematic analysis of the poster titles.
The full program was downloaded as PDF from the conference website.
Claude Code was asked to convert this PDF into a markdown file, see program.md in the repository.
Then, Claude Code was asked to do a thematic analysis of the poster titles. This resulted in 7 AI-identified themes:
- Theme 1: Training, Education, and Workforce Development
- Theme 2: FAIR Data, Open Science, and Metadata Standards
- Theme 3: Institutional Change and Community Collaboration
- Theme 4: Data Management Plans — Design, Evaluation, and Harmonization
- Theme 5: Resilience, Digital Preservation, and Data Rescue
- Theme 6: Sensitive Data, Ethics, and Inclusive Research
- Theme 7: AI and Emerging Technologies in Data Curation
See thematic-analysis-by-claude.md for a full report created by Claude Code.
The program consisted of multiple parallel tracks where a choice needed to be made on which to attend. Below are the sessions chosen.
Full program: https://www.conftool.org/idcc2026/sessions.php
Day 1 (17 feb) 11:30 - 13:00 - Session A: AI/ML: Curation challenges and opportunities: I
14:00 - 15:00 - Session E: AI/ML in research support (presenting)
Day 2 (18 feb) 9:30 - 11:00 - Session G: Curating complex data
11:30 - 12:45 - Session M: AI/ML: Curation challenges and opportunities: II
13:45 - 14:45 - Session N: Data sovereignty and trusting people.
Together with TU/e colleague Davide Nardi we presented during Session E - AI/ML in research support the work of A Case to DMPs Pre-Filling.
Notes were taken in a physical notebook during presentations of themes, concepts and ideas presented.
Furthermore, I drew a high-level conceptual diagram of: Purpose -> Workflow -> Task(s).
flowchart LR
Goals --> Purpose
Meaning --> Purpose
Purpose --> Workflow --> Tasks
For each session I interpreted while listening what the workflow being described or addressed was by the speaker. What the higher level purpose was as mentioned,. If the purpose was not explicitly mentioned inferred, interpreted, or attempted to project a purpose onto the workflow by myself. I then also identified the specific task or tasks within the workflow that the presenter was addressing or improving.
The reflection template was generated with help of Claude Code.
I filled in the reflection template on the days after the conference.
A core concluding personal observation is that the RDM/Data Curation ecosystem has grown big enough that certain purposes have become institutionalised as dogma such as data must be 'fair', or 'open' but the local ability to articulate why this should be is sometimes lost, or even more deeply this dogma is assumed and there might be other approaches to data management that do not even get expressed. Some amount of dogmatic group think is not necessarily bad as it allows people and teams to focus on optimizing the workflow and task at hand, without continuously needing to reflect & restate the underlying assumptions. However, there does need to be some underlying why that brings all the participants and stakeholders involved in (research) data management together.