Roadmap

Strategy

Purpose

• Migrate data from or to Dataverse via it's API
• Process Dataverse metadata: manipulate, export, import, convert,
• Connect other software to Dataverse: Microservices, Jupyter notebooks,
• Save time:
  • 1 Dataverse:
  • 1 Dataset:
  • 1 Datafile:
• Better understanding of your metadata and data processes, requirements and practices through a clearly structured workflow.
• Microservices
• Validate Metadata for Dataverse validity
• Enrich Dataverse metadata

Scope

• API wrapper
• metadata data model: Dataverse, Dataset, Datafile
• connectors: Dataverse Upload JSON, Dataverse Download JSON, CSV, DDI XML, GESIS DSpace JSON, BagIt etc.

Target Audience

People who work with Dataverse:

• DevOps
• Developers
• SysAdmins
• Technicans

Personas:

• DevOps engineer who upgrades Dataverse instance
• DevOps engineer / SysAdmin, who needs to setup new Dataverse
• Technician, who should migrate data from any source into Dataverse together with other team members
• Dataverse developer, who wants to test installation

Future

• pipeline
• oaistree
• pdvtree

Solutions

• Tests with pytest, tox on travis-ci
• Test coverage at coveralls.io
• Online Documentation with Sphinx
• Virtual environment with Python 3 venv
• Publishing and issue tracking at GitHub. Integration with coveralls.io and Travis-CI.
• Dataverse Version 4.18.1 for development and testing.

Strength

• Easy extendible
• open source
• tested and documented
• upload data and metadata automatically

Funding

Income streams

• data migrations:
  • from Nesstar to Dataverse
  • from DSpace to Dataverse

Possible Sources:

• EU projects
• Instutitions: IQSS, ODUM, DANS, CDCC??

2020

Goals

• Make next level in Python programming and structurally improve the repo: Read and apply the 2 Python books.
• Stabilize existing functionality
• Add features which are requested and/or with high impact.
• Learn from others and implement existing functionalities / code

Big Ideas

• Microservice: Test and vallidate data submissions
• Microservice: Test prepared data before data access on Dataverse and preservation
• Microservice: Alternatives Frontend: Daten via pyDataverse von Instanz holen und anders aufbereiten.

Future

Possible activities

• Write paper about pyDataverse
• Collect and share information about usage of module: screenshots, stories, use-cases.
• Code-Sprint / Hackathon
• Present: Conferences, other CESSDA Service Providers
• Webinar
• GUI
• Database Connectors: SQLAlchemy, OAI-PMH