Social Sciences and Digital Humanities Archive

Metrics

66,593 Downloads

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Access: Public

11 to 20 of 3,896 Results

tssq25_1_SUF_v2.0_SPSS.tab Aug 28, 2026 - rEUsilience - Family Dynamics and Labour Market Risks (tssq25_1) Tabular Data - 1.6 MB - 224 Variables, 2254 Observations - UNF:6:qb1kur9VxUc6vdWn1vPtfw== SPSS export of the dataset v 2.0
tssq25_1_SUF_v2.0_STATA.tab Aug 28, 2026 - rEUsilience - Family Dynamics and Labour Market Risks (tssq25_1) Tabular Data - 1.9 MB - 224 Variables, 2254 Observations - UNF:6:qb1kur9VxUc6vdWn1vPtfw== Stata export of the dataset v 2.0
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
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.

tssq25_1_SUF_v2.0_SPSS.tab

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

Tabular Data - 1.6 MB - 224 Variables, 2254 Observations -

SPSS export of the dataset v 2.0

tssq25_1_SUF_v2.0_STATA.tab

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

Tabular Data - 1.9 MB - 224 Variables, 2254 Observations -

Stata export of the dataset v 2.0

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

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.

Add Data

Share Dataverse

Link Dataverse

Reset Modifications