Datasets:

MirCCus
/

MetaLingCorpus

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
id: int64
Dublin Core:Contributor: string
Dublin Core:Coverage: string
Dublin Core:Creator: string
Dublin Core:Date: double
Dublin Core:Description: string
Dublin Core:Identifier: string
Dublin Core:Is Version Of: string
Dublin Core:Language: string
Dublin Core:Source: string
Dublin Core:Subject: string
Dublin Core:Title: string
Dublin Core:Type: string
featured: bool
items_count: int64
owner_id: int64
vs
id: int64
Text: string
exhibit_id: int64
order: int64
slug: string
title: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              id: int64
              Dublin Core:Contributor: string
              Dublin Core:Coverage: string
              Dublin Core:Creator: string
              Dublin Core:Date: double
              Dublin Core:Description: string
              Dublin Core:Identifier: string
              Dublin Core:Is Version Of: string
              Dublin Core:Language: string
              Dublin Core:Source: string
              Dublin Core:Subject: string
              Dublin Core:Title: string
              Dublin Core:Type: string
              featured: bool
              items_count: int64
              owner_id: int64
              vs
              id: int64
              Text: string
              exhibit_id: int64
              order: int64
              slug: string
              title: string

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Dataset Card for MetaLing Corpus

Dataset Summary

The MetaLing Corpus is a historical corpus focused on the metalanguage of English linguistics from the 16th to the 18th century. It collects texts that describe, discuss, and conceptualise language before the emergence of linguistics as a formal academic discipline.

The corpus brings together a wide range of early modern texts that reflect how language was analysed, classified, and explained in grammatical, pedagogical, and descriptive works. It is designed as an open-access resource to support research in historical linguistics, corpus linguistics, and the history of linguistic thought.

Dataset Description

MetaLing Corpus was created within the PRIN 2022 research project “MetaLing Corpus: Creating a corpus of English linguistics metalanguage from the 16th to the 18th century”.
The project is funded by the Italian Ministry of University and Research (MUR) with support from the European Union through the NextGenerationEU programme (PNRR Mission 4, Component 2, Investment 1.1; Project reference: 202233C93X).

The main objective of the corpus is to document and analyse the terminology, conceptual frameworks, and discursive strategies used to talk about language in early modern English texts. By focusing on metalanguage, the corpus provides insight into how linguistic knowledge was constructed and transmitted prior to modern linguistic theory.

Uses

Direct Use

The dataset is intended for:

diachronic analysis of linguistic terminology and concepts
research on the history of English linguistics
corpus-based studies of metalinguistic discourse
lexicographic and terminological research
teaching and training in historical linguistics and digital humanities

Dataset Creation

Curation Rationale

Historical English linguistics lacks large, structured, and machine-readable resources dedicated specifically to metalanguage. MetaLing Corpus was created to address this gap by systematically collecting and processing texts that explicitly reflect on language, its structure, and its use during the early modern period.

Source Data

The source material consists of early modern English texts such as grammar books, language manuals, pedagogical treatises, and other writings that include explicit discussion of linguistic phenomena. The texts were selected to be representative of different genres and approaches to language description between approximately 1500 and 1700.

Data Collection and Processing

Texts were collected through archival research and digital sources. The processing pipeline includes text cleaning, normalization, tokenisation, and computational analysis using standard corpus-linguistic tools and Python-based libraries. Particular attention was paid to preserving historical language features while ensuring usability for computational analysis.

Annotations

The corpus may include analytical or descriptive metadata related to linguistic categories, genres, or thematic areas. Any annotations are produced by domain experts following shared methodological guidelines.

Personal and Sensitive Information

The dataset contains historical texts only. It does not include personal, sensitive, or private data relating to living individuals.

Bias, Risks, and Limitations

The corpus reflects the linguistic, cultural, and ideological perspectives of early modern authors, which may include biases related to gender, class, geography, or language prestige. Coverage is limited to texts that have survived and are accessible, and therefore cannot be considered fully representative of all linguistic practices of the period.

Recommendations

Users are encouraged to take into account the historical and sociocultural context of the texts when interpreting results. The corpus should be used with awareness of its temporal scope, genre selection, and inherent historical biases.

Citation

If you use MetaLing Corpus, please cite the project and relevant publications, for example:

Andreani, A., & Russo, D. (2023). Building a Corpus of the Metalanguage of English Linguistics 1500–1700: Methodological Issues. Linguistica e Filologia, 43, 151–174.

Dataset Curators

The MetaLing Corpus is curated within the PRIN 2022 project by:

Angela Andreani, University of Milan
Daniel Russo, University of Insubria

The project involves collaboration with researchers specialising in historical linguistics, corpus linguistics, and the history of linguistic thought.

Dataset Card Contact

For information about the dataset, please refer to the MetaLing Corpus project website or contact the project coordinators through their institutional affiliations.

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