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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31 to 40 of 4,336 Results

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.
length_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 - 533 B - 5 Variables, 9 Observations - UNF:6:XKylVdddSgIVxmbYCdrnFg== Summary statistics describing the distribution of document subword lengths in the evaluation corpus splitsand their relationship to model performance. Used to assess the number of letters affected by preprocessing.
navez_gs_preprocessed-2.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 - 1015.3 KB - MD5: 0469ba07b6aa2663770de059d36ef03f Preprocessed version of the manually annotated corpus after cleaning and conversion from Label Studio. Contains token- and entity-level information used for model training and evaluation.
navez_gs_preprocessed-2.pkl 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 Unknown - 33.0 MB - MD5: 3a11affcc28d1dc3ac86f185c3f089a2 Python pickle version of the preprocessed gold-standard corpus, preserving data structures used directly in the notebooks.

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 -

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.

length_summary.tab

Tabular Data - 533 B - 5 Variables, 9 Observations -

Summary statistics describing the distribution of document subword lengths in the evaluation corpus splitsand their relationship to model performance. Used to assess the number of letters affected by preprocessing.

navez_gs_preprocessed-2.csv

Comma Separated Values - 1015.3 KB -

Preprocessed version of the manually annotated corpus after cleaning and conversion from Label Studio. Contains token- and entity-level information used for model training and evaluation.

navez_gs_preprocessed-2.pkl

Unknown - 33.0 MB -

Python pickle version of the preprocessed gold-standard corpus, preserving data structures used directly in the notebooks.

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