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A Python library for calculating a large variety of metrics from text

The aim of this project is to create a library in Python based on a taxonomy of commonly used concepts, so that it can be used for artificial intelligence (AI) improvement. If you use this software, please cite it using the metadata from this file.

Materials science experiments involve complex data that are often very heterogeneous and challenging to reproduce. This was observed, for example, in a previous study on harnessing lightweight design potentials via the Materials Data Space [3] for which the data from materials sciences engineering experiments were generated using linked open data principles [1,2], e.g., Resource Description Framework (RDF) as the standard model for data interchange on the Web. However, detailed knowledge of formulating questions in the query language SPARQL is necessary to query the data. A lack of knowledge in SPARQL to query data was observed by domain experts in materials science. With this work, we aim to develop NaturalMSEQueries an approach for the material science domain expert where instead of SPARQL queries, the user can develop expressions in natural language, e.g., English, to query the data. This will significantly improve the usability of Semantic Web approaches in materials science and lower the adoption threshold of the methods for the domain experts. We plan to evaluate our approach, with varying amounts of data, from different sources. Furthermore, we want to compare with synthetic data to assess the quality of the implementation of our approach. References [1] T Berners-Lee, J Hendler, O Lassila - Scientific American, 2001, 284, 34–43. [2] RDF specification. 2023. available at: https://www.w3.org/RDF/ [3] Huschka M, Dlugosch M, Friedmann V, Trelles EG, Hoschke K, Klotz UE, Patil S, Preußner J, Schweizer C, Tiberto D. The “AluTrace” Use Case: Harnessing Lightweight Design Potentials via the Materials Data Space®.

Shiny app to visualize the effects of changing sedimentation rates on formation of fossil accumulations. If you use this software, please cite it using the metadata from this file.

Current version: v1.0.0 Examples of exploring the different data sources that are aggregated into Trove from institutional contributors. For more information see the Trove contributors section of the GLAM Workbench. Notebook topics Create a flat list of organisations contributing metadata to Trove – converts the nested data available from the /contributor endpoint into a single flat list of contributors Get the number of records from each contributor by zone and format – gets the number of records contributed by each organisation, aggregated by zone and format Data See this repository for weekly harvests using code from the above notebooks. See the GLAM Workbench for more details.

This is the first release of two latex scripts used to create detailed catalogues of archaeological artefacts, here pottery sherds, including photographs and descriptions. These two examples make one entry on one page. These scripts can be used with any latex editor. The resulting entry can be previewed in the two PDF files.

convert a table of letters into CMI format

A cross-platform C++ library and toolkit for the recovery and restoration of damaged cultural artifacts.

This experimental span categorization model was designed solely for research purposes and was trained to detect names of people, organizations, concentration camps, ghettos, and dates in Holocaust-related texts. Please note that this model may produce false positive and false negative errors. The model was trained using spaCy (v3.5.0 - https://spacy.io/) on a very small corpus (196 documents that were split into train/test datasets) consisting of annotated early Holocaust testimonies (https://early-testimony.ehri-project.eu/) and diplomatic reports (https://diplomatic-reports.ehri-project.eu/). In addition to the 'spancat' pipeline component which predicts what labels should be assigned to different spans of texts, the processing pipeline also includes a series of rule-based components to compare and match the input text with entries in controlled vocabularies created by EHRI. These components are: 'ehri-camps', 'ehri-ghettos', 'ehri-personalities', 'ehri-corporate-bodies', 'ehri-terms', 'ehri-camps-fuzzy', 'ehri-ghettos-fuzzy', 'ehri-personalities-fuzzy', 'ehri-corporate-bodies-fuzzy', 'ehri-terms-fuzzy'. The pipeline components that include the word 'fuzzy' in their names support fuzzy matching, which means they can identify matches even if the input text contains minor variations such as small spelling mistakes. For fuzzy matching, a Levenshtein edit distance of one is used. Users may choose to activate or disable any components based on the needs of their projects. Please keep in mind that while this model may be useful for research purposes, it should not be relied upon for accurate results in production environments, and a human expert should carefully evaluate any output.

Data and R script accompanying article in Journal of Archaeological Method and Theory. Estimation of fractal dimension and lacunarity on empirical (Linear Pottery and Trypillia/European Neolithic) and simulated settlement plans through box-counting and gliding box-algorithms. File includes binary images of all plans that are analysed in the article (N=70). Written as part of the author's PhD thesis at the University of Oslo. See README file (included) and main article for further description and details.