1,720,964 research outputs found
JSON Schema for the changelog PID
No full textThis is the JSON schema of the value in a changelog PID. This schema allows users to describe the changes the made to metadata of a PID
Building a Graph Database for Digital Humanities Scientists
No full textGraph database has developed rapidly and plays an important role in research nowadays. It helps scientists in various ways, e.g., finding related works, exploring works in a research area, or gaining knowledge from connections between different nodes. There are already some graph databases for research available on the Internet. However, they do not meet the needs of Digital Humanities (DH) scientists, who mainly work with historical data. Therefore, we create a graph database specifically for DH scientists. This database is part of MINE, a service that facilitates data acquisition and big data analysis
System Architecture for the integration, processing, and publishing of many heterogenous text resources
No full textThis poster is used in the Hekksagon conference
A Graph Database for Persistent Identifiers
No full textMaster Thesis - A Graph Database for Persistent Identifier
LWDA 2019 Poster
No full textThis is the poster which is used in the LWDA Conference 2019 (https://pages.cms.hu-berlin.de/ipa/lwda2019/)
Using a Workflow Management Platform in Textual Data Management
No full textAbstract The paper gives a brief introduction about the workflow management platform, Flowable, and how it is used for textual-data management. It is relatively new with its first release on 13 October, 2016. Despite the short time on the market, it seems to be quickly well-noticed with 4.6 thousand stars on GitHub at the moment. The focus of our project is to build a platform for text analysis on a large scale by including many different text resources. Currently, we have successfully connected to four different text resources and obtained more than one million works. Some resources are dynamic, which means that they might add more data or modify their current data. Therefore, it is necessary to keep data, both the metadata and the raw data, from our side up to date with the resources. In addition, to comply with FAIR principles, each work is assigned a persistent identifier (PID) and indexed for searching purposes. In the last step, we perform some standard analyses on the data to enhance our search engine and to generate a knowledge graph. End-users can utilize our platform to search on our data or get access to the knowledge graph. Furthermore, they can submit their code for their analyses to the system. The code will be executed on a High-Performance Cluster (HPC) and users can receive the results later on. In this case, Flowable can take advantage of PIDs for digital objects identification and management to facilitate the communication with the HPC system. As one may already notice, the whole process can be expressed as a workflow. A workflow, including error handling and notification, has been created and deployed. Workflow execution can be triggered manually or after predefined time intervals. According to our evaluation, the Flowable platform proves to be powerful and flexible. Further usage of the platform is already planned or implemented for many of our projects
Chemical mixture risk drivers and their heterogeneity in European freshwaters
No full texthttp://dx.doi.org/10.13039/501100009318 Helmholtz Association of German Research Centreshttp://dx.doi.org/10.13039/100020655 European Health and Digital Executive Agencyhttp://dx.doi.org/10.13039/100018693 Horizon Europ
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