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2021-10-07 | ISS

OpenRefine

Eine Einführung in 22.5 Minuten

https://digicademy.github.io/ISS2021_OpenRefine
CC-BY 4.0
Thomas Kollatz @kol_t
Digitale Akademie @ Akademie der Wissenschaften und Literatur | Mainz

OpenRefine

openRefine
  • working /w | cleaning messy data
  • transforming data from one format into another
  • enriching data with webservices and external data

schmutzige Daten säubern

CIL VII, 2
CIL VII 2
cil VII-2
CIL VII,2

☞ Facet ▸ Text facet

Faceting allows you to look for patterns and trends. Facets are essentially aspects or angles of data variance in a given column.

☞ Exploring facets

Tabellen reinigen

tabelle

Daten importieren: ISS_table.xls

Creating a facet on a column is a great way to look for inconsistencies in your data; clustering is a great way to fix those inconsistencies.

☞ Cluster and edit

You don’t need to understand the details behind each clustering method to apply them successfully to your data. The order in which these methods are presented in the interface and on this page is the order we recommend - starting with the most strict rules and moving to the most lax, which require more human supervision to apply correctly.

☞ Clustering methods

Datenabgleich (reconciliation)

Reconciliation is the process of matching your dataset with that of an external source. Datasets for comparison might be produced by libraries, archives, museums, academic organizations, scientific institutions, non-profits, or interest groups. You can also reconcile against user-edited data on Wikidata, or reconcile against a local dataset that you yourself supply.

☞ Reconciling 

Buber, Martin
Kafka, Franz
Wiese, Christian

☞ GND reconciliation for OpenRefine Align your data with the Integrated Authority File GND: 

https://lobid.org/gnd/reconcile/

☞ Export angereicherter Daten (jetzt noch weißer)

🎥 In 2 Minuten zur GND-ID

Informationen, Tutorials