Call for Papers

Trust through Transparency

Trust and transparency also drive preservation and curation efforts. From archival notions of provenance through to FAIR, the TRUST principles, and formal certification standards like CoreTrustSeal, the ability to have faith in an organisation to preserve data and have confidence that what we get is what we think we are getting is central to our communities.

But there is a cost to transparency. Not only in achieving and maintaining trustworthy standards, but in the labour of documenting and preserving the tools of transparency - from software and code through to data itself. Significant effort is needed to foster the required change in research culture to ensure that researchers and support staff have the skills and tools they need to work transparently and to ensure that they have confidence in the infrastructures they use to do so.   

There are also significant challenges: how do we connect universal standards of transparency and trust to disciplinary norms and legal issues around data protection and ethical concerns over reuse? And, furthermore, how do we connect disciplinary norms to the adoption of FAIR principles. 

How then can transparency be used to promote trustworthiness? How do we ensure trustworthiness is possible for all our audiences? When does transparency conflict with career incentives not to be transparent, for example, when data cannot be open or when commercial sensitivities are a factor? 

Papers are invited, but not limited, to address one or multiple themes in the broad scope of trust and transparency: 

Transparency and trust in research systems:  

• Trust and Transparency in discarding data: when and how it is de-acquisitioned. What can we learn and adapt from records management and archiving?  
• Transparency and trust in relation to the environmental sustainability of digital curation, including the effects on:  

• Appraisal and selection criteria
• Curation activities during ingest
• Approaches to long-term preservation
• Mechanisms for re-use

• Work on automating curation processes  
• Transparency in terms of the metaphors used to frame curation. 

• What aspects of practice are hidden by the ‘lifecycle’ metaphor? 
• What do ‘ecosystems’ fail to clarify? 
• What alternative metaphors can usefully frame curation models?  

• Intellectual Property Rights, legal and policy issues around AI training data collection and use: Negotiating national and regional sovereignty issues.  
• Trust and transparency between universal standards and expectations and that of context and discipline specific adaptations. Are there cases where they do not apply?  
• Building trust and transparency into research and curation techniques, costs, and workflows. 

Transparency and trust in research practices: 

• Transparency as a means of demonstrating research integrity  
• Data Management Plans to provide transparency from the outset  
• Sharing outputs earlier using Open Research platforms  
• Balancing transparency against privacy, especially in health and sharing of genomic data.  
• Artificial Intelligence (AI) and Machine Learning. How can we trust what we cannot explain?  
• Transparency in AI for data re-use - automated data collection, cleaning, analysis, and visualisation  
• Transparency and Large Language Models (LLMs) for AI. Documenting corpora on which models are trained and fine-tuned. How do we stop it getting worse as we move to distributed, smaller scale models?  

Transparency and trust in people:

• Trust in skills acquisition. 
• What skills are needed for researchers and data stewards to make their practices transparent? 
• How do we know researchers and data stewards have the skills they need?  
• Transparency in data sovereignty: trusting individuals to have power over their own data and its (re)use.  
• Relationships between transparency movements in Open and Citizen Science and trust in digital curation.

How to submit

The Submissions page will provide details on proposal submissions, key dates and templates.

Papers are invited, but not limited, to address one or multiple themes in the broad scope of trust and transparency.