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Looking up journal-specific metrics is easiest when we use linking ISSN to fill the ISSN column in the personArticle table.
In actuality, this code for import from s3 (and its counterpart for DynamoDB) prefers e-ISSN even when there's a linking ISSN.
ReCiter-MachineLearning-Analysis/S3_AnalysisOutput_upload.py
Line 257 in 39306cb
For example, here's the output for szd3005 and 33476307.
{ "personIdentifier": "szd3005", "dateAdded": "2021-01-26T20:33:07.606+00:00", "dateUpdated": "2021-01-26T20:33:07.606+00:00", "reCiterArticleFeatures": [ { "pmid": 33476307, "journalTitleVerbose": "The Journal of clinical investigation", "issn": [ { "issntype": "Electronic", "issn": "1558-8238" }, { "issntype": "Linking", "issn": "0021-9738" } ],
To populate ReCiterDB, we're pulling electronic ISSN as opposed to linking ISSN. Unfortunately, this is quite common.
The relevant Python scripts should preferentially use linking ISSN whenever it exists.
The text was updated successfully, but these errors were encountered:
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Background
Looking up journal-specific metrics is easiest when we use linking ISSN to fill the ISSN column in the personArticle table.
Problem
In actuality, this code for import from s3 (and its counterpart for DynamoDB) prefers e-ISSN even when there's a linking ISSN.
ReCiter-MachineLearning-Analysis/S3_AnalysisOutput_upload.py
Line 257 in 39306cb
For example, here's the output for szd3005 and 33476307.
To populate ReCiterDB, we're pulling electronic ISSN as opposed to linking ISSN. Unfortunately, this is quite common.
Fix
The relevant Python scripts should preferentially use linking ISSN whenever it exists.
The text was updated successfully, but these errors were encountered: