A blog about the dblp computer science bibliography

DTD update May 2023

(updated 2023-06-28) A few days ago, we discussed the new dataset publications in dblp. As a preparation for more and more detailed datasets we slightly modify the DTD that defines the structure of our XML data export. A quick reminder: you can download the dblp dataset as a single XML file. For more details please see our FAQ page. All modifications are additions or slight changes of data type. They should not affect most data imports. The new DTD can be used for older releases of the XML file. We will not add the new elements/attributes before May 29, 2023. All changes can be seen in our change log. If you have any questions please contact us. The changes Added Read more…

By Florian Reitz, ago

Dataset publications in dblp

Datasets and other research artifacts are a major topic in the scientific community in the recent years. Many ongoing projects focus on improving the standardization, publication and citation of these artifacts. Currently, the dblp team is involved in three of them: NFDI4DataScience, NFDIxCS, and Unknown Data. As part of these projects, we are happy to announce that datasets and artifacts have now been added as true “first-class citizens” to dblp, just like any other research contribution. To this end, we updated our internal tools to better support datasets as a type of publication. We started to index some dataset repositories, such as Zenodo and IEEE DataPort, with many more to come. A first batch about 3,700 data publications is already Read more…

By Benedikt Maria Beckermann, ago

Building the German Research Data Infrastructure NFDI – for and with Computer Science

On November 4, 2022, the Joint Science Conference (GWK) selected Schloss Dagstuhl – Leibniz Center for Informatics and the consortium NFDIxCS for federal and state funding within the German National Research Data Infrastructure (NFDI). The consortium will be funded in the double-digit millions of Euros and over a duration of five years. Together with its sister consortium NFDI4DataScience, this is already the second NFDI grant that has been awarded to Schloss Dagstuhl. The NFDI is a collaborative, nationwide network to systematically index, interconnect, and make openly available the valuable stock of data from science and research. Together with 16 other leading partner institutions, Schloss Dagstuhl aims to systematically manage research data in computer science. The main goal of NFDIxCS is to Read more…

By Marcel R. Ackermann, ago

Updates to the dblp RDF schema

In the six months since the release of the dblp RDF dump and its persistent snapshot releases, the RDF dump has been downloaded a total of about a thousand times. We are pleased to see that the community is interested in using our semantic data in their research and beyond. However, based on the feedback we have received and the experience we have had experimenting with query interfaces for the data, we have also noticed a number of shortcomings in our schema that we want to address. Therefore, as of September 9, 2022, we are changing a few details about the dblp RDF schema. These changes aim (in part) to make the schema more convenient in practice, e.g., in SPARQL Read more…

By Marcel R. Ackermann, ago

OpenAlex integration in dblp

For more than 12 years, the Microsoft Academic Search, later renamed to just Microsoft Academic and eventually to Microsoft Academic Graph (MAG), had been the software giant’s  scholarly bibliographic information service. Despite it being one of the most comprehensive collections across all scientific fields out there, Microsoft obviously never envisioned MAG to be a lasting infrastructure for the community, but rather a playground for certain internal research and technology projects. Consequently, when Microsoft decided to pull the plug and end MAG in 2021, it came to the dismay of many researchers who were relying on MAG as a data source. At this point, the fine people at OurResearch, who were already providing the very useful Unpaywall service, stepped in and Read more…

By Marcel R. Ackermann, ago

dblp in RDF

For more than 20 years, a full dump of all dblp records in our own XML format has been available as open data for download and reuse. These dump files have always been in high demand over the years (with 500+ downloads in February 2022 alone) and are used as a research dataset in numerous publications. For quite some time now, we have been asked to provide a full RDF dump as well. Snapshots of the dblp XML file have been converted to RDF before by members of the community, and there are still a number of those RDF files available on the Internet. However, the problem with these snapshots is that they are usually not updated once they are Read more…

By Marcel R. Ackermann, ago

Results and learnings from the dblp User Survey 2020/2021

From December 2020 to January 2021, we asked you to participate in an online survey in order to help us understand how researchers are using dblp, and how dblp and its features are perceived by the public. The response exceeded our expectations: We received the amazingly high number of 1046 responses to our survey in total, with 760 surveys fully answered and 286 surveys partially completed. This response provided us with a plethora of helpful remarks, constructive criticisms, novel ideas, and feature requests that will guide us in the development of dblp in the upcoming years. We would like to express our heartfelt gratitude for your kind support of dblp! In this blog post, we will share with you the Read more…

By Marcel R. Ackermann, ago

We are hiring!

Interested in working on the world’s most comprehensive open indexing service in computer science? We are currently looking for three additional members to join the dblp team. This includes the opportunity to qualify yourself for a doctorate in the context of the challenges arising from managing and operating a semantic research information infrastructure like dblp. For more information, please read our current job openings.

By Marcel R. Ackermann, ago

Schloss Dagstuhl becomes part of the National Research Data Infrastructure for Data Science and Artificial Intelligence

On July 2, 2021, the German Joint Science Conference (Gemeinsame Wissenschaftskonferenz, GWK) decided to fund the National Research Data Infrastructure (NFDI) consortium for Data Science and Artificial Intelligence (NFDI4DataScience) with an amount in the double-digit millions over a duration of five years. In this consortium, Schloss Dagstuhl has joined forces with numerous other leading research infrastructure providers in Germany. The NFDI is a collaborative, nationwide network to systematically index, interconnect, and make openly available the valuable stock of data from science and research. Dagstuhl’s renown research infrastructures – supporting research itself, the publication and dissemination of research results, and finding and reusing them – will be further developed, expanded, and integrated as part of NFDI. The results of this consortium Read more…

By Michael Gerke, ago