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 4,407 Results

tssq25_1_UserGuide_v2.0.pdf Aug 28, 2026 - rEUsilience - Family Dynamics and Labour Market Risks (tssq25_1) Adobe PDF - 355.2 KB - MD5: 86278cc92a3598d82e6eb8b5586fc2d7 Data user guide v 2.0
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 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...
00_navez_gold_standard_preprocessing_json2df.ipynb 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 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...
01a_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_sm.ipynb 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 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.
01b_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_lg.ipynb 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 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.
02a_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_sm.ipynb 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 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.
02b_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_lg.ipynb 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 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.
03_navez_NER_spacy_custom-trained_dk.ipynb 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 Jupyter Notebook - 575.5 KB - MD5: 5b3ef0c547261ba0b3323ce0fd2ca50b Implements Workflow 3 described in the article. The notebook fine-tunes fr_core_news_lg on the Navez gold-standard corpus by extending the default spaCy model with the domain-specific entity types ART, EXH, GRP and LETT, in addition to the standard PER, LOC and ORG categories. It...
best_hps.json 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 JSON - 474 B - MD5: a34a656006d26bd49168cb552f29955f JSON file containing the final hyperparameter configuration selected for each transformer model (CamemBERT, CamemBERTav2, D'AlemBERT and Europeana BERT) after hyperparameter optimisation.
combined_eval_scores3.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 - 1.4 KB - 13 Variables, 24 Observations - UNF:6:IHdDRnEBZ4B81/KMQBqVKA== Consolidated spreadsheet containing the evaluation scores of all experiments performed in this study. Facilitates comparison between off-the-shelf spaCy models, the custom-trained spaCy model, and the transformer-based models across different evaluation scenarios and entity types...

tssq25_1_UserGuide_v2.0.pdf

Aug 28, 2026 - rEUsilience - Family Dynamics and Labour Market Risks (tssq25_1)

Adobe PDF - 355.2 KB -

Data user guide v 2.0

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

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...

00_navez_gold_standard_preprocessing_json2df.ipynb

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

Jupyter Notebook - 465.1 KB -

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...

01a_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_sm.ipynb

Jupyter Notebook - 201.0 KB -

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.

01b_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_lg.ipynb

Jupyter Notebook - 248.3 KB -

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.

02a_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_sm.ipynb

Jupyter Notebook - 200.0 KB -

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.

02b_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_lg.ipynb

Jupyter Notebook - 255.7 KB -

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.

03_navez_NER_spacy_custom-trained_dk.ipynb

Jupyter Notebook - 575.5 KB -

Implements Workflow 3 described in the article. The notebook fine-tunes fr_core_news_lg on the Navez gold-standard corpus by extending the default spaCy model with the domain-specific entity types ART, EXH, GRP and LETT, in addition to the standard PER, LOC and ORG categories. It...

best_hps.json

JSON - 474 B -

JSON file containing the final hyperparameter configuration selected for each transformer model (CamemBERT, CamemBERTav2, D'AlemBERT and Europeana BERT) after hyperparameter optimisation.

combined_eval_scores3.tab

Tabular Data - 1.4 KB - 13 Variables, 24 Observations -

Consolidated spreadsheet containing the evaluation scores of all experiments performed in this study. Facilitates comparison between off-the-shelf spaCy models, the custom-trained spaCy model, and the transformer-based models across different evaluation scenarios and entity types...

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