Explore duplicate institutions #189
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GRSciColl
Issues related to institutions, collections and staff
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MortenHofft
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GRSciColl
Examples of potential duplicatessplit across multiple comments as the text is too long 11 groups by title+code
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146 groups by code
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404 by title
332 fuzzy title match groups
51 by fuzzy title+city
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Same institutionSame code, different titles Different codes, same titles PCU Muséum National d'Histoire Naturelle Different institutionsSame code, different titles |
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To help decide if something is an institution or collection we can look at
To decide which institution code to use, we could look at
[+ Marie edit:]
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Thanks @MortenHofft! For each cluster, someone needs to decide:
Once everything is decided, here is what should happen:
This means that we would need 3 functions in the API:
To decide which institution should be the main one, we should use what Morten describes above:
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Good summary @ManonGros
In this case we need to decide what to do with the identifiers.
So marking the institution as |
MortenHofft
changed the title
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Looking in more details on the likely duplicate, then the wast majority (a guess 95%) is related to Index Herbariorum. Can that help us somehow? |
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What about the following? If an institution should in fact be a collection:
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Summary of our Skype conversation yesterday:
We ended up choosing option 1 for cases where we know this is a duplicate: either something that we have generated or the institution contacts us to delete the duplicate. We have a case for this. The Bishop museum contacted us to delete this duplicate "BISH" institution and keep only a "BISH" collection under the "BPBM" institution.
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Already implemented in /grscicoll/[institution|collection]/possibleDuplicates |
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We know we have duplicate institutions. But not how many. Let us try to identify the duplicates and discuss how to clean/merge them (if that is indeed preferred). First step is to get a feeling for how many duplicates there are and what types we see.
Glance and guess: we can delete 500 institutions (by merging them with existing)
Update: below examples only include institutions without a country (a mistake) - so 2500 institutions were ignored the cluster examples below.
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