TABLE IV:

Community needs and actions for advancing open science.

Category	Actions	Key Concepts
Guidance	• Provide community guidance on sharing methodologies, datatypes (raw, processed). • Standardize required and recommended metadata types. • Select and unify ontologies for metadata standardization. • Define essential provenance information for shared data.	Provenance, shared methodologies, standardized metadata, unified ontologies
Tool Development	• Enhance tools for data compression, conversion, sharing, and analysis. • Develop cloud-based data access and analysis solutions. • Establish benchmarking platforms for model and theory evaluation. • Develop platforms for tool comparison. • Support large-scale data pooling and annotation. • Simplify metadata entry through user-friendly interfaces. • Improve automated metadata capture tools. • Enable on-the-fly data annotation of anomalies during experiments. • Improve ability to detect and filter anomalous data.	Cloud solutions, data compression, data pooling, metadata entry, tool benchmarking
Research	• Improve models for understanding complex data. • Create benchmarks and metrics for model evaluation. • Develop data quality assurance metrics. • Innovate on automated data labeling for enhanced data reuse.	Advanced model zoo, automated data labeling, data quality metrics, model benchmarks
Databases	• Maintain centralized databases for datasets, methodologies, and tools. • Facilitate community feedback mechanisms for shared resources.	Centralized databases
Knowledge & Education	• Create knowledge graphs for describing entities and their relationships, and for linking disparate databases. • Continue to develop online resources and training for data processing and analysis tools.	Knowledge graphs, online resources, training workshops
Funding & Incentives	• Support community engagement and multi-laboratory collaborations. • Fund technical personnel for open-source software maintenance. • Fund the creation of core facilities in research institutions that provide centralized technical expertise to individual laboratories. • Encourage and facilitate adoption of new technologies and open science practices. • Invest in scaling data storage solutions.	Community engagement, core facilities, multi-laboratory collaboration, open-source support