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Furthermore, it's almost always easier to fix dirty data before it goes into VIVO than to find and fix it after loading.
There's another important consideration – the benefit of fixing data at its source. Putting data into a VIVO makes that data much more discoverable through both searching and browsing. People will find errors that are most likely hidden in the source system of record – but if you fix them in VIVO, they won't be correct in the source system, and the next ingest runs the risk of overwriting changes.
There are many ways to clean data, but in the past few years Open Refine has emerged offering quite remarkable capabilities including the ability to develop changes interactively that can then be saved as scripts to run repeatedly on larger batches.
Matching against data already in VIVO
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The first data ingest from a clean slate is the easiest one. After the first time there's a high likelihood that updates will refer to many of the same people, organizations, events, courses, publications, etc. You don't want to introduce duplicates, so you need to check new information against what's already there.
Sometimes this is straightforward because you have a unique identifier to definitively distinguish new data from existing – your institutional identifier for people, but also potentially an ORCID iD, a DOI on a publication, or a FundRef registry identifier.
Often the matching is more challenging – is 'Smith, D' a match with Delores Smith or David Smith? Names will be misspelled, abbreviated, changed over time – and while a lot can be done with relatively simple matching algorithms, this remains an area of research, and of homegrown, ad-hoc solutions embedded in local workflows.
One way to check each new entry is to query VIVO, directly or through a derivative SPARQL endpoint using Fuseki or other tool, to see the identifier or name matches data already in VIVO. As VIVO scales, there can be performance reasons for extracting a list of names of people, organizations, and other common data types for ready reference during an ingest process. It's also sometimes possible to develop an algorithm for URI construction that is reliably repeatable and hence avoids the need to query – for example, if you embed the unique identifier for a person in their VIVO URI through some simple transformation, you should be able to predict a person's URI without querying VIVO. If that person has not yet been added to VIVO, adding them through a later ingest is not a problem as long as the same URI would have been generated. This requires that the identifier embedded in the URI is not private or sensitive (e.g., not the U.S. social security number) and will not change.
One caution here – it's important to think carefully about the default namespace you use for URIs in VIVO if you want linked data requests to work – please see A simple installation and look for the basic settings of the Vitro.defaultNamespace.
Doing further cleanup once in VIVO
VIVO's interactive editing can be helpful in fixing problems in relatively small datasets but it's important to remember that if data originate outside of VIVO and are not corrected at the source, subsequent updates will likely re-introduce the error.
It's also common to have discrepancies in the source data – for example, the naming conventions and identifiers used for departments in a personnel database vs. those used in a grants administration system. There is a command to merge two individuals in VIVO, specifying which URI to retain, but that will combine the statements associated with both, leading to duplicate labels and potentially other duplicates.
VIVO has only limited support for owl:sameAs reasoning due to the performance implications of having to query for all statements about more than one URI whenever rendering information about any one URI declared to be sameAs another.
Many VIVO installations have developed workflows for checking VIVO data, ranging from broken link checkers to nightly SPARQL queries to detect malformed data such as publications without authors, people without identifiers, 'orphaned' dates no longer referenced by any property statements, and so forth. These tools have been discussed on previous Apps and Tools Interest Group calls that have been recorded and uploaded to YouTube.
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