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Publication Year: 2026

61 to 70 of 1,365 Results

test_predictions_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 - 96.1 KB - 3 Variables, 17 Observations - UNF:6:47AUFPWKuqAoGujhisXcrQ== Named Entity Recognition predictions generated by the fine-tuned Europeana BERT model on the held-out test set. Contains predicted entity spans, labels and corresponding gold-standard annotations used for the quantitative and qualitative evaluation of model performance.
test_scores_per_seed.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 - 4.9 KB - 13 Variables, 48 Observations - UNF:6:BULzL9VsIcllUNnVwyPBzg== Evaluation results of the transformer-based Named Entity Recognition models across multiple random seeds. Reports the test-set performance of each training run and was used to assess the robustness and reproducibility of the experimental results by calculating mean performance ac...
test_set-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 - 117.5 KB - MD5: c079a2cd638f0797e2b2ec4e06348e29 Held-out test split used exclusively for final evaluation of the trained NER models.
test_set-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 - 6.8 MB - MD5: ebeb69df35b8eecb39e9ce2342040c8d The file contains manually annotated letter transcriptions and their token-level entity labels. For each letter, it includes the manuscript identifier, full transcription, tokenized text, annotated entities and entity types, character and token spans, remapped annotations, and IO...
training_set-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 - 730.5 KB - MD5: 8da5d01228c4818dddce6a97272b18be Held-out test partition of the gold-standard corpus used exclusively for final evaluation of the NER models after training.
training_set-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 - 24.8 MB - MD5: 050192c781468337fb7718810e8316b7 Python Pickle version of the held-out test dataset used in the evaluation pipeline.
[Embargo] THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10) Aug 25, 2026 - The Social Study The Social Study (TSS), 2026, "[Embargo] THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10)", https://doi.org/10.34934/DVN/GWJTLG, Social Sciences and Digital Humanities Archive – SODHA, V2, UNF:6:egi3tWo1b8iG17YrAWUdJQ== [fileUNF] The Social Study administered THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10) questionnaire on behalf of Magali Beylat (ULB), Olivier Klein (ULB), Vincent Yzerbyt (UCLouvain) between 1/6/2026 and 3/8/2026. The collect...
Codebooktssq26_10_EMBARGO_v1_1.html Aug 25, 2026 - [Embargo] THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10) HTML - 151.2 KB - MD5: 4bd1db669f5636982cc91867dd6c9ae6
tssq26_10_EMBARGO_v1_1-csv.tab Aug 25, 2026 - [Embargo] THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10) Tabular Data - 8.4 MB - 117 Variables, 4875 Observations - UNF:6:h3M9Yyxud9GjoCfzvM0/XQ== tssq26_10 embargo dataset v.1.1 csv format
tssq26_10_EMBARGO_v1_1-spss.tab Aug 25, 2026 - [Embargo] THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10) Tabular Data - 1.5 MB - 117 Variables, 4875 Observations - UNF:6:+ExpxAKXCmSC7y7ELdH5QA== tssq26_10 embargo dataset v.1.1 SPSS format

test_predictions_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 - 96.1 KB - 3 Variables, 17 Observations -

Named Entity Recognition predictions generated by the fine-tuned Europeana BERT model on the held-out test set. Contains predicted entity spans, labels and corresponding gold-standard annotations used for the quantitative and qualitative evaluation of model performance.

test_scores_per_seed.tab

Tabular Data - 4.9 KB - 13 Variables, 48 Observations -

Evaluation results of the transformer-based Named Entity Recognition models across multiple random seeds. Reports the test-set performance of each training run and was used to assess the robustness and reproducibility of the experimental results by calculating mean performance ac...

test_set-2.csv

Comma Separated Values - 117.5 KB -

Held-out test split used exclusively for final evaluation of the trained NER models.

test_set-2.pkl

Unknown - 6.8 MB -

The file contains manually annotated letter transcriptions and their token-level entity labels. For each letter, it includes the manuscript identifier, full transcription, tokenized text, annotated entities and entity types, character and token spans, remapped annotations, and IO...

training_set-2.csv

Comma Separated Values - 730.5 KB -

Held-out test partition of the gold-standard corpus used exclusively for final evaluation of the NER models after training.

training_set-2.pkl

Unknown - 24.8 MB -

Python Pickle version of the held-out test dataset used in the evaluation pipeline.

[Embargo] THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10)

Aug 25, 2026 - The Social Study

The Social Study (TSS), 2026, "[Embargo] THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10)", https://doi.org/10.34934/DVN/GWJTLG, Social Sciences and Digital Humanities Archive – SODHA, V2, UNF:6:egi3tWo1b8iG17YrAWUdJQ== [fileUNF]

The Social Study administered THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10) questionnaire on behalf of Magali Beylat (ULB), Olivier Klein (ULB), Vincent Yzerbyt (UCLouvain) between 1/6/2026 and 3/8/2026. The collect...

Codebooktssq26_10_EMBARGO_v1_1.html

Aug 25, 2026 - [Embargo] THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10)

HTML - 151.2 KB -

tssq26_10_EMBARGO_v1_1-csv.tab

Aug 25, 2026 - [Embargo] THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10)

Tabular Data - 8.4 MB - 117 Variables, 4875 Observations -

tssq26_10 embargo dataset v.1.1 csv format

tssq26_10_EMBARGO_v1_1-spss.tab

Aug 25, 2026 - [Embargo] THRIVE - Better understanding the experience of the COVID-19 pandemic to prepare for future crises (tssq26_10)

Tabular Data - 1.5 MB - 117 Variables, 4875 Observations -

tssq26_10 embargo dataset v.1.1 SPSS format

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