Outline

Crowdsourcing of PSI

Management summary

Challenge

Many institutions lack resources necessary to manually go through large collections of unstructured data that has been created over the years.

Solution

By engaging external communities to collaborate on this data it is possible to create more detailed machine readable data supporting a wider range of re-use cases.

Why is this a Best Practice?

• Crowd sourcing can be an efficient way to increase quality and availability of machine readable data, particular in cultural heritage institutions.
• On a policy level, identifying community crowd sourcing projects outside government institutions can also be an indicator of valuable datasets that should be prioritized for open release.

Links to the PSI Directive

Data and Metadata

Why is there a need for this Best Practice?

• So that there would be more machine-readable open data supporting a wider range of use-cases in services and applications
• Engaged communities / social engagement

What do you need for this Best Practice?

• Identify the exact need first and then seek groups able to support solving that need via crowd sourcing.
• Think of crowd sourcing as another tool to create/improve data sets and think about the phases of your data collection project and where crowd sourcing could best fit in
• Involve stakeholders who could benefit from a free source of certain data sets and have them provide funding in order to sustain crowd sourcing efforts
• The tasks have to be really small tasks
• Utilize gamification approach
• Use crowdsourcing without the users knowledge e.g. captcha systems to solve micro tasks.

Different tests can be undertaken:

• Is the crowd sourced data being used by third parties?
• Is the crowd sourced data as complete as an already existing official source of the same data?
• Is the crowd sourced data being updated by volunteers?
• Often quite short. In the case of Share-PSI BPs, it's likely that all tests will need to be carried out by people rather than machines but if something is machine testable, that's often more precise.

Applicability to other Member States

The approach is applicable to any Member State.

Contact info

Missing