1 to 10 of 1,365 Results
Sep 4, 2026
de Vries, Nijs, 2026, "Architect interviews on the adoption of earth block masonry in Belgium, France, Germany, Luxembourg, the Netherlands and Switzerland (2024-2026)", https://doi.org/10.34934/DVN/LGYVBQ, Social Sciences and Digital Humanities Archive – SODHA, V2, UNF:6:/JovubzMuv9Q4rV4reAGEg== [fileUNF]
This dataset contains the underlying data for the study: Systemic problems constraining Earth Block Masonry diffusion in Western Europe: an architect-centred Innovation System approach. Since the number of EBM buildings in Western Europe is low, a comparison of the transcripts wi... |
MS Word - 2.9 MB -
MD5: 3ac5ee1f4d77bbdcdbcc9a455653baeb
Interview protocol |
MS Word - 17.6 KB -
MD5: 947c8bd11039fe671e72e53fbbca4dc4
Readme file |
Tabular Data - 5.4 KB - 30 Variables, 37 Observations - UNF:6:L5b6urQGjgdncMxbgvZvcg==
thematic analysis - results |
Aug 25, 2026
Zuzana Černáková; Fien Messens; Tess Dejaeghere; Julie M. Birkholz, 2026, "Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal", https://doi.org/10.34934/DVN/HNA7QO, Social Sciences and Digital Humanities Archive – SODHA, V1, UNF:6:MemhfA5Fl4s1tvLWf4VR2A== [fileUNF]
This dataset accompanies the article From nineteenth-century letters to entities: A Named Entity Recognition pipeline for French correspondence and its methodological lessons. It contains the input data, preprocessing scripts, analysis notebooks, evaluation outputs, and experimen... |
Jupyter Notebook - 465.1 KB -
MD5: 6062b90f470e79ad4062aa678de087d3
Preprocessing notebook that converts Label Studio JSON exports into the structured gold-standard corpus used throughout the experiments. The workflow merges annotation projects, extracts entity annotations, aligns character offsets, generates IOB labels, removes unsupported neste... |
Jupyter Notebook - 201.0 KB -
MD5: fafce906de349b467636197fa64a4d95
Implements Workflow 1 described in the article by evaluating the off-the-shelf spaCy fr_core_news_sm model using only the isolated Named Entity Recognition (NER) component. The notebook evaluates the model on the held-out test set without additional domain-specific training. |
Jupyter Notebook - 248.3 KB -
MD5: 4d33ecdf3831757bac0319676a57bb2a
Implements Workflow 1 using the larger fr_core_news_lg model. The notebook evaluates the isolated NER component on the historical correspondence corpus and compares its performance with the smaller spaCy model. |
Jupyter Notebook - 200.0 KB -
MD5: 2998c19d3448da4d9ed7a7776de6e9dc
Implements Workflow 2 by using the complete spaCy fr_core_news_sm pipeline, including all NLP components. The notebook assesses whether embedding the NER component within the full pipeline influences recognition performance. |
Jupyter Notebook - 255.7 KB -
MD5: 1e35b33c8202285ec93bb0d8508ec8ff
Implements Workflow 2 using the complete fr_core_news_lg pipeline. Results are compared with Workflow 1 to evaluate the effect of the full NLP pipeline on NER performance. |
