Social Sciences and Digital Humanities Archive

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Welcome to SODHA, the Belgian federal data archive for social sciences and the digital humanities!

Here you can find and deposit social science and digital humanities data in reusable form. Published datasets receive a DOI, making them citable like other types of publications. SODHA promotes open data by enabling reuse of research data and by safely preserving datasets in the long term.

SODHA is the Belgian service provider in the Consortium of European Social Science Data Archives (CESSDA) and is hosted by the State Archives of Belgium. SODHA was built with the help of DEMO (UCLouvain) and Interface Demography (VUB).

You can consult the SODHA Guide here, and you can read our policies here.

Want to learn more about SODHA? Consult our brochure or our presentation on the State Archives' website.

If you have any question, you can contact us at sodha@arch.be.

State Archives of Belgium

Belgian Archives for Social Sciences

Royal Library of Belgium

The Social Study

NICC-INCC

Royal Museums of Fine Arts

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81 to 90 of 4,202 Results

errors_camembertav2.tab Aug 25, 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 Tabular Data - 13.8 KB - 10 Variables, 159 Observations - UNF:6:dX1zhbyrPPk1DreziVEOHw== Prediction errors produced by the fine-tuned CamemBERTav2 model. Used for qualitative comparison with the other transformer-based NER models.
errors_camembert.tab Aug 25, 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 Tabular Data - 43.3 KB - 10 Variables, 529 Observations - UNF:6:LKJL6zjeIoC7+p0mefydwg== Prediction errors produced by the fine-tuned CamemBERT model. Documents incorrectly recognised, partially recognised, missed and spurious entities for qualitative error analysis.
errors_dalembert.tab Aug 25, 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 Tabular Data - 16.0 KB - 10 Variables, 185 Observations - UNF:6:+TqzKZgt8g2h9mleixtmLw== Prediction errors produced by the fine-tuned D'AlemBERT model. Documents representative recognition errors analysed in the accompanying study.
errors_europeana.tab Aug 25, 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 Tabular Data - 14.0 KB - 10 Variables, 161 Observations - UNF:6:r0qXPIXovg15bXXLwViguQ== Prediction errors produced by the fine-tuned Europeana BERT model. Supports the qualitative evaluation and comparison of transformer-based Named Entity Recognition models.
headline_counter_table.tab Aug 25, 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 Tabular Data - 1.0 KB - 12 Variables, 16 Observations - UNF:6:2raSn3MgN38lZjzDY60qZA== Table of the final evaluation results obtained on the held-out test set for all transformer-based models under different Nervaluate scenarios. Provides the principal performance metrics reported in the accompanying article and supporting the quantitative comparison of transformer...
headline_cv_summary.tab Aug 25, 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 Tabular Data - 206 B - 3 Variables, 4 Observations - UNF:6:LwsPP4NHhKrvsotyYYTR+w== Summary table of the cross-validation results for all transformer models, facilitating comparison of their overall performance during model selection.
headline_test_summary.csv Aug 25, 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 Comma Separated Values - 291 B - MD5: a6b7319263f4f7606f6483082613f61f Summary of the final evaluation results obtained on the held-out test set for all transformer-based models averaged across three different seeds.
hp_search.tab Aug 25, 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 Tabular Data - 4.4 KB - 6 Variables, 72 Observations - UNF:6:zu0B79FNEke0HpLSL1WIKw== Results of the hyperparameter optimisation experiments performed for the transformer-based Named Entity Recognition models. Records the tested hyperparameter combinations and their corresponding evaluation scores used to identify the optimal training configuration.
learning_curve.tab Aug 25, 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 Tabular Data - 186 B - 4 Variables, 4 Observations - UNF:6:fXKPhq8vFAp9d+v7Jlmpsg== Performance metrics recorded during model training across successive epochs. Used to analyse convergence behaviour, model learning dynamics and training stability.
length_stratified.tab Aug 25, 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 Tabular Data - 994 B - 5 Variables, 16 Observations - UNF:6:/TrpFwgbWWGaD2ZI1dFDKQ== Evaluation results stratified by document length. Used to assess the influence of letter length on Named Entity Recognition performance across the transformer-based models.

errors_camembertav2.tab

Aug 25, 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

Tabular Data - 13.8 KB - 10 Variables, 159 Observations -

Prediction errors produced by the fine-tuned CamemBERTav2 model. Used for qualitative comparison with the other transformer-based NER models.

errors_camembert.tab

Tabular Data - 43.3 KB - 10 Variables, 529 Observations -

Prediction errors produced by the fine-tuned CamemBERT model. Documents incorrectly recognised, partially recognised, missed and spurious entities for qualitative error analysis.

errors_dalembert.tab

Tabular Data - 16.0 KB - 10 Variables, 185 Observations -

Prediction errors produced by the fine-tuned D'AlemBERT model. Documents representative recognition errors analysed in the accompanying study.

errors_europeana.tab

Tabular Data - 14.0 KB - 10 Variables, 161 Observations -

Prediction errors produced by the fine-tuned Europeana BERT model. Supports the qualitative evaluation and comparison of transformer-based Named Entity Recognition models.

headline_counter_table.tab

Tabular Data - 1.0 KB - 12 Variables, 16 Observations -

Table of the final evaluation results obtained on the held-out test set for all transformer-based models under different Nervaluate scenarios. Provides the principal performance metrics reported in the accompanying article and supporting the quantitative comparison of transformer...

headline_cv_summary.tab

Tabular Data - 206 B - 3 Variables, 4 Observations -

Summary table of the cross-validation results for all transformer models, facilitating comparison of their overall performance during model selection.

headline_test_summary.csv

Comma Separated Values - 291 B -

Summary of the final evaluation results obtained on the held-out test set for all transformer-based models averaged across three different seeds.

hp_search.tab

Tabular Data - 4.4 KB - 6 Variables, 72 Observations -

Results of the hyperparameter optimisation experiments performed for the transformer-based Named Entity Recognition models. Records the tested hyperparameter combinations and their corresponding evaluation scores used to identify the optimal training configuration.

learning_curve.tab

Tabular Data - 186 B - 4 Variables, 4 Observations -

Performance metrics recorded during model training across successive epochs. Used to analyse convergence behaviour, model learning dynamics and training stability.

length_stratified.tab

Tabular Data - 994 B - 5 Variables, 16 Observations -

Evaluation results stratified by document length. Used to assess the influence of letter length on Named Entity Recognition performance across the transformer-based models.

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