- I tried a new technique to get some affiliations from Crossref using OpenRefine
- First I split them and clustered, resolving a few hundred clusters out of 1500 (!)
- Then I used a custom text facet with a few dozen CGIAR and other large affiliations to reduce the work
- Then I joined them with our affiliations, paying no attention to duplicates
- Then I deduped them using the Jython technique I learned in 2023-02
## 2024-03-06
- Peter sent me some more corrections for the authors that I had sent him in 2023-12
## 2024-03-08
- IFPRI sent me their 2023 records from CONTENTdm so I started working on those
- I found a way to match their ORCID identifiers in our list using Jython in OpenRefine:
```python
import re
with open(r"/tmp/cg-creator-identifier.txt",'r') as f :
orcid_ids = [orcid_id.strip() for orcid_id in f]
matched = False
for orcid_id in orcid_ids:
if re.search(r'.+: {}'.format(value), orcid_id):
matched = True
break
if matched:
return orcid_id
else:
return value
```
- I realized that [UNICEF was renamed to its current name in 1953](https://www.unicef.org/about-unicef/frequently-asked-questions#3) so I replaced all other variations in our vocabularies and metadata:
```sql
UPDATE metadatavalue SET text_value='United Nations Children''s Fund' WHERE dspace_object_id IN (SELECT uuid FROM item) AND text_value IN ('United Nations International Children''s Emergency Fund', 'United Nations International Children''s Emergency Fund', 'UNICEF');
```
- Note the use of two single quotes to escape the one in the name
