The digital universe is booming, especially in terms of the amount of

metadata and user-generated data available. This raises serious data

management challenges, including the identification, amongst all such

data, of the particular data pieces relevant to a specific purpose and

the observation of the lifecycle of those data entities. Finer grain

challenges include evolution and versioning and the impact that change

and non availability of resources may have on depending applications,

causing decay and eventually malfunction. In this talk, we focus on

these challenges with special emphasis on the preservation and reuse

of scientific workflows in data-intensive research. We introduce the

concept of workflow-centric Research Object (RO) as the means to

identify and structure the relevant resources for the execution of

workflows and to ensure the replicability of their results, addressing

data as first-class citizens. We also analyze the main reasons for

workflow (and therefore RO) decay in this particular domain and

propose methods and tools for its prevention. Finally, we reflect on

the lessons learnt and the potential use of these concepts in other

data-intensive domains.

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