to analyze. To work collaboratively, it is essential to find balance between rigidity
and flexibility.
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Combining different interests and purposes: every member of the network needs to
understand their role, as well as the general purpose of the network and how it
helps members' specific purposes.

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Information accountability: from the external point of view, the data belongs to the
network, but inwards each one is responsible for the data they provide, in spite of
the filters it passes before being recorded in the database.

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Working collaboratively with structured data requires many previous agreements.
Planning tasks increase when networking.

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Culturally sensitive work is mandatory when collecting and managing data from
different places. The same questions might not have the same interpretations
everywhere. The language is only the first obstacle.

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More people working with the data always entail more concerns about sensitive
information.

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Collective work demands commitment from every network member.

Recommendations for collaborative work
with data
Based on the challenges previously identified, it is possible to issue some
recommendations that can improve our work as a network.

Consistent standards
Having a common dataset involves unifying data collection and processing methodologies
and tools. In the data collection process, the survey or interview form should be
standardized and the personnel in charge of gathering the data must be trained to
minimize personal subjectivity. The questions and possible answers should be presented in
such a way that reduces ambiguity and additional interpretations.

We also need to define standards regarding data quality and verify that those are fulfilled
before recording the data in the database. On this basis, we can identify some mandatory
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