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-LOD
+# Linked Open Data
+
+The following Jupyter Notebooks showcase some of our work with Linked Open Data.
+
+## [Viewer Links from BIBFRAME](ViewerlinksFromBibframe.ipynb)
+
+Downloading data from the library services platform
+[Alma](https://knowledge.exlibrisgroup.com/Alma) and loading the results into a [pandas](https://pandas.pydata.org/)
+dataframe, we create an [xlsx file](ABOExamplesFromALMAextended.xlsx) with the catalogue metadata. Further we add links
+to the digital object and the barcodes of the physical objects to the file.
+
+## [Viewer Links from BIBFRAME with SRU](ViewerlinksFromBibframeWithSRU.ipynb)
+
+Very similar to the above, but this time we make an SRU-Query in Alma.
+
+## [Travelogues](Travelogues.ipynb)
+
+Working together with other institutions on the [Travelogues Project](http://www.travelogues-project.info/), we needed
+to extrect specific metadata from Alma, both catalogue data and LOD data.
+
+## [SACHA Statistik Collection](SACHA Statistik Collection.ipynb)
+
+Using [SPARQL](https://labs.onb.ac.at/en/tool/sparql/) and pandas we extract datasets with
+a specific subject heading. Making use of [SACHA](https://iiif.onb.ac.at/) we then create a collection of the data we
+queried.
+
+## [OAI-PMH](OAI-PMH.ipynb)
+
+We show how to use [Sickle](https://sickle.readthedocs.io/en/latest/) to first list all available sets and then count
+the number of entries in one specific set.
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