PRISM Glossary
Comprehensive definitions and guidance for using the Platform for Research Infrastructure Synergy Mapping
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Interaction Types
PRISM categorizes tool interactions into 11 distinct types. Understanding these helps you accurately describe how research tools connect and communicate.
API Integration
Definition:
Direct programmatic connection between tools using Application Programming Interfaces
When to use:
When tools communicate programmatically with structured data exchange
Example:
Technical Indicators:
Common Technologies:
- HTTP/HTTPS
- REST
- SOAP
- gRPC
Data Exchange
Definition:
Transfer of research data files or datasets between tools
When to use:
When the primary function is moving data content between systems
Example:
Technical Indicators:
Common Technologies:
- FTP
- SFTP
- rsync
- cloud storage APIs
Metadata Exchange
Definition:
Transfer of descriptive information about data without moving the data itself
When to use:
When exchanging descriptions, citations, or contextual information
Example:
Technical Indicators:
Common Technologies:
- OAI-PMH
- SWORD
- Dublin Core
- DataCite
File Format Conversion
Definition:
Transformation of data from one file format to another
When to use:
When format transformation is the primary interaction purpose
Example:
Technical Indicators:
Common Technologies:
- CSV
- JSON
- XML
- Parquet
- HDF5
- NetCDF
Workflow Integration
Definition:
Tools combined into multi-step research workflows or pipelines
When to use:
When tools are orchestrated together in a sequence
Example:
Technical Indicators:
Common Technologies:
- Airflow
- Nextflow
- Snakemake
- Galaxy
- Taverna
Plugin/Extension
Definition:
One tool extends functionality of another through add-ons or plugins
When to use:
When one tool adds features directly into another tool's interface
Example:
Technical Indicators:
Common Technologies:
- Browser extensions
- IDE plugins
- Office add-ins
Direct Database Connection
Definition:
Tools query or write to shared database infrastructure
When to use:
When tools share underlying data storage layer
Example:
Technical Indicators:
Common Technologies:
- PostgreSQL
- MySQL
- MongoDB
- Redis
- Elasticsearch
Web Service
Definition:
Tools interact via web-based service endpoints (may include APIs)
When to use:
For web-protocol-based interactions like HTTP, SOAP, OAI-PMH
Example:
Technical Indicators:
Common Technologies:
- HTTP
- SOAP
- XML-RPC
- OAI-PMH
Command Line Interface
Definition:
Tools invoked or controlled via terminal commands or scripts
When to use:
When interaction happens through shell commands or scripts
Example:
Technical Indicators:
Common Technologies:
- Batch processing
- Automation scripts
- HPC jobs
Import/Export
Definition:
Manual or semi-automated file-based data transfer between tools
When to use:
When users manually transfer files between systems
Example:
Technical Indicators:
Common Technologies:
- CSV
- Excel
- JSON
- XML
- text files
Other
Definition:
Interaction types not covered by standard categories
When to use:
When no other category fits; please describe in Technical Details
Example:
Technical Indicators:
Research Data Lifecycle Stages
The MaLDReTH model defines 12 stages in the research data lifecycle, representing the complete journey of research data from conception to reuse.
About the 12-Stage Model
This lifecycle model was developed by the MaLDReTH II RDA Working Group to provide a comprehensive framework for understanding research data workflows. The stages are sequential but can also be iterative and overlapping in practice.
1 CONCEPTUALISE
Definition: To formulate the initial research idea or hypothesis, and define the scope of the research project and the data component/requirements of that project.
Key Activities:
- Literature review
- Hypothesis formulation
- Research question development
- Defining data requirements
- Scope definition
Typical Outputs:
- Research questions
- Hypotheses
- Initial concepts
- Data requirements
Typical Tools:
Reference managers, Mind mapping tools, Literature databases, Ideation platforms
2 PLAN
Definition: To establish a structured strategic framework for management of the research project, outlining aims, objectives, methodologies, and resources required for data collection, management and analysis. Data management plans (DMP) should be established for this phase of the lifecycle.
Key Activities:
- Study design
- Protocol development
- Resource planning
- DMP creation
- Defining methodologies
- Resource identification
Typical Outputs:
- Data Management Plans
- Protocols
- Study designs
- Resource allocation plans
Typical Tools:
DMP tools, Project management, Protocol repositories, DMPTool, DMPonline
3 FUND
Definition: To identify and acquire financial resources to support the research project, including data collection, management, analysis, sharing, publishing and preservation.
Key Activities:
- Grant writing
- Budget planning
- Proposal submission
- Identifying funding sources
- Financial planning
Typical Outputs:
- Grant proposals
- Budgets
- Funding awards
- Financial plans
Typical Tools:
Grant management systems, Budget calculators, Proposal tools, Funding databases
4 COLLECT
Definition: To use predefined procedures, methodologies and instruments to acquire and store data that is reliable, fit for purpose and of sufficient quality to test the research hypothesis.
Key Activities:
- Experiments
- Surveys
- Observations
- Measurements
- Sampling
- Data acquisition
Typical Outputs:
- Raw data
- Observations
- Measurements
- Samples
- Experimental data
Typical Tools:
Lab instruments, Survey platforms, Sensors, Data loggers, Electronic lab notebooks
5 PROCESS
Definition: To make new and existing data analysis-ready. This may involve standardised pre-processing, cleaning, reformatting, structuring, filtering, and performing quality control checks on data. It may also involve the creation and definition of metadata for use during analysis, such as acquiring provenance from instruments and tools used during data collection.
Key Activities:
- Data cleaning
- Quality assurance
- Normalization
- Format conversion
- Metadata creation
- Filtering
- Structuring
Typical Outputs:
- Cleaned datasets
- Quality reports
- Processed data
- Metadata
- Analysis-ready data
Typical Tools:
Data cleaning tools, ETL platforms, Quality control software, OpenRefine, Data wrangling tools
6 ANALYSE
Definition: To derive insights, knowledge, and understanding from processed data. Data analysis involves iterative exploration and interpretation of experimental or computational results, often utilising mathematical models and formulae to investigate relationships between experimental variables. Distinct data analysis techniques and methodologies are applied according to the data type (quantitative vs qualitative).
Key Activities:
- Statistical tests
- Modeling
- Visualization
- Pattern discovery
- Iterative exploration
- Interpretation
Typical Outputs:
- Analysis results
- Statistical models
- Visualizations
- Insights
- Interpretations
Typical Tools:
R, Python, SPSS, MATLAB, Jupyter, Statistical software, Analysis platforms
7 STORE
Definition: To record data using technological media appropriate for processing and analysis whilst maintaining data integrity and security.
Key Activities:
- Active storage
- Backup
- Version control
- Collaboration
- Integrity maintenance
- Security management
Typical Outputs:
- Backed up data
- Version history
- Shared datasets
- Secure storage
Typical Tools:
Cloud storage, Version control, Lab servers, Collaborative platforms, Git, Institutional storage
8 PUBLISH
Definition: To release research data in published form for use by others with appropriate metadata for citation (including a unique persistent identifier) based on FAIR principles.
Key Activities:
- Paper writing
- Peer review
- Conference presentations
- Preprints
- Data publication
- Metadata creation
- DOI assignment
Typical Outputs:
- Publications
- Presentations
- Preprints
- Published datasets
- DOIs
Typical Tools:
Journal systems, Preprint servers, Writing tools, LaTeX, Data journals, Repository platforms
9 PRESERVE
Definition: To ensure the safety, integrity, and accessibility of data for as long as necessary so that data is as FAIR as possible. Data preservation is more than data storage and backup, since data can be stored and backed up without being preserved. Preservation should include curation activities such as data cleaning, validation, assigning preservation metadata, assigning representation information, and ensuring acceptable data structures and file formats. At a minimum, data and associated metadata should be published in a trustworthy digital repository and clearly cited in the accompanying journal article unless this is not possible (e.g. due to the privacy or safety concerns).
Key Activities:
- Archiving
- Format migration
- Metadata enrichment
- Curation
- Data cleaning
- Validation
- Format standardization
Typical Outputs:
- Archived datasets
- DOIs
- Preserved research outputs
- Preservation metadata
- Curated collections
Typical Tools:
Repositories, Archives, Preservation systems, Digital curation tools, Trustworthy repositories
10 SHARE
Definition: To make data available and accessible to humans and/or machines. Data may be shared with project collaborators or published to share it with the wider research community and society at large. Data sharing is not limited to open data or public data, and can be done during various stages of the research data lifecycle. At a minimum, data and associated metadata should be published in a trustworthy digital repository and clearly cited in the accompanying journal article.
Key Activities:
- Publishing datasets
- Access control
- License assignment
- Documentation
- Collaboration
- Community sharing
Typical Outputs:
- Shared datasets
- Data publications
- Access portals
- Collaborative workspaces
Typical Tools:
Data repositories, Institutional repositories, Figshare, Zenodo, Dryad, Sharing platforms
11 ACCESS
Definition: To control and manage data access by designated users and reusers. This may be in the form of publicly available published information. Necessary access control and authentication methods are applied.
Key Activities:
- Data discovery
- Search
- Download
- API access
- Access control
- Authentication management
Typical Outputs:
- Downloaded data
- Retrieved datasets
- Access logs
- Usage statistics
Typical Tools:
Data catalogs, Search engines, Repository interfaces, APIs, Access management systems
12 TRANSFORM
Definition: To create new data from the original, for example: (i) by migration into a different format; (ii) by creating a subset, by selection or query, to create newly derived results, perhaps for publication; or, (iii) combining or appending with other data.
Key Activities:
- Format conversion
- Subset creation
- Data integration
- Reanalysis
- Data migration
- Query and selection
Typical Outputs:
- Transformed data
- Subsets
- Integrated datasets
- New research
- Derived datasets
Typical Tools:
Conversion tools, Query systems, Integration platforms, Analysis tools, Data transformation pipelines
MaLDReTH Terminology
- MaLDReTH
- Mapping the Landscape of Digital Research Tools Harmonised. An RDA Working Group initiative focused on creating a comprehensive categorization schema for digital research tools.
- PRISM
- Platform for Research Infrastructure Synergy Mapping. This web application - a key output of the MaLDReTH II initiative.
- Exemplar Tool
- A representative tool within a category, demonstrating typical characteristics and capabilities. Currently PRISM contains 72 exemplar tools.
- Tool Category
- A classification group for similar tools within a lifecycle stage. Categories help organize tools by function and purpose.
- Tool Interaction
- A connection or integration between two research tools, describing how they communicate or work together. PRISM currently contains 0 documented interactions.
- Research Data Lifecycle (RDL)
- The 12-stage model describing the complete journey of research data from initial concept through to reuse and transformation.
- GORC
- Global Open Research Commons. An RDA initiative that PRISM contributes to, focused on improving interoperability and FAIR data practices.
Technical Terms
- API
- Application Programming Interface. A set of protocols for building software and enabling tool-to-tool communication.
- REST
- REpresentational State Transfer. An architectural style for web APIs using HTTP methods.
- OAuth
- Open Authorization. A standard for secure authorization and authentication between applications.
- DOI
- Digital Object Identifier. A persistent identifier for digital objects like datasets and publications.
- ORCID
- Open Researcher and Contributor ID. A unique identifier for researchers and scholars.
- FAIR
- Findable, Accessible, Interoperable, Reusable. Principles for scientific data management and stewardship.
- OAI-PMH
- Open Archives Initiative Protocol for Metadata Harvesting. A protocol for sharing metadata between repositories.
- JSON
- JavaScript Object Notation. A lightweight data format for API communication.
- CSV
- Comma-Separated Values. A simple file format for tabular data exchange.
- CLI
- Command Line Interface. Text-based interface for interacting with software via commands.
Contributing to PRISM
How to Add an Interaction
- Identify the tools: Determine the source and target tools involved
- Select interaction type: Review the definitions above to choose the most appropriate type
- Choose lifecycle stage: Identify which research stage this interaction supports
- Describe the interaction: Write 1-3 sentences explaining what happens and why it's useful
- Add technical details: Include protocols, APIs, or technologies used (optional but recommended)
- Provide examples: Share real-world use cases (optional but valuable)
What Makes a Good Interaction Description
- Clear and specific: Explain exactly what the interaction does
- Accurate categorization: Use the correct interaction type and lifecycle stage
- Technical depth: Include implementation details when known
- Real examples: Reference actual use cases or institutions
- Benefits and challenges: Help others understand trade-offs
Bulk Upload via CSV
For adding multiple interactions:
- Download the CSV template to see the format
- Prepare your data following the same structure
- Ensure tool names match existing tools in PRISM (or new tools will be created)
- Use the CSV upload page to submit your file
- Review the results and fix any errors reported
Frequently Asked Questions
- Contributing interaction data through PRISM
- Joining the MaLDReTH II working group
- Participating in RDA plenary sessions
- Providing feedback and suggestions
Ready to Contribute?
Use your new knowledge to help map the research infrastructure landscape