API

The public API is intentionally small. For per-dataset usage examples, see the Datasets catalog.

Dataset discovery

extracts.list_available_datasets()

Return the sorted list of dataset names known to this package.

Examples

>>> "barrett2020" in list_available_datasets()
True

Return type:

list[str]

extracts.list_available_tables(dataset, version=None)

Return the filenames registered for dataset at version.

Parameters:

• dataset (str)

• version (str | None)

Return type:

list[str]

extracts.fetch_path(dataset, table, version=None)

Return the local path of the downloaded raw file.

Parameters:

• dataset (str) – Dataset name from list_available_datasets.

• table (str) – Filename in the Zenodo registry. If no extension is given, .tsv is assumed.

• version (str, optional) – Zenodo version key (defaults to "latest").

Return type:

Path

Cache location

extracts.get_location()

Return the local cache root directory.

Resolves $EXTRACTS_DATA_DIR if set; otherwise falls back to the OS-appropriate cache from pooch.os_cache.

Return type:

Path

extracts.set_location(path)

Override the cache root via $EXTRACTS_DATA_DIR for this process.

Parameters:

path (str or Path) – New cache root. ~ is expanded and the path is resolved to absolute.

Return type:

None

Text and references

extracts.fetch_text(dataset, version=None, process=True)

Fetch the text.json file for a dataset.

Parameters:

• dataset (str) – Dataset name.

• version (str, optional) – Zenodo version key (defaults to "latest").

• process (bool, default True) – If True (default), return the file contents as a string. If False, return the local Path to the downloaded file.

Return type:

str | Path

extracts.fetch_reference(dataset, version=None, process=True)

Fetch the BibTeX reference.bib file for a dataset.

Parameters:

• dataset (str) – Dataset name.

• version (str, optional) – Zenodo version key (defaults to "latest").

• process (bool, default True) – If True (default), parse the BibTeX entry and return a dict with type, key, and fields keys. If False, return the local Path to the downloaded file.

Return type:

dict[str, Any] | Path

Table fetchers

extracts.fetch_barrett2020(table, version=None, process=True, **kwargs)

Fetch tables from Barrett (2020), Dreaming.

Citation

Barrett, 2020, Dreaming, Dreams about COVID-19 versus normative dreams: Trends by gender, doi:10.1037/drm0000149

Table captions

• table1 — Female Pandemic Survey Dreams Versus Hall and Van de Castle Female Normative Dreams.

• table2 — Male Pandemic Survey Dreams Versus Hall and Van de Castle Male Normative Dreams.

Notes

Table 2 has “male” in the column names, but Table 1 does not have “female” in the same respective location. Note that Table 1 is female-only values.

param table:

Name of desired table.

type table:

str

param version:

Zenodo version key (defaults to "latest").

type version:

str, optional

param process:

If True, return the processed (cached) DataFrame. If False, return the raw DataFrame loaded with pandas.read_table and **kwargs.

type process:

bool, default True

param **kwargs:

Forwarded to pandas.read_table when process=False.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_cariola2010(table, version=None, process=True, **kwargs)

Fetch tables from Cariola (2010), unpublished paper.

Citation

Cariola, 2010, unpublished paper, Assessing the latent linguistic structure of oral dream narratives, url:https://www.research.ed.ac.uk/en/publications/assessing-the-latent-linguistic-structure-of-oral-dream-narrative

Table captions

• table1 — Descriptive statistics of linguistic variables in orally elicited dream narratives.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_cariola2014(table, version=None, process=True, **kwargs)

Fetch tables from Cariola (2014), Imagin Cogn Pers.

Citation

Cariola, 2014, Imagin Cogn Pers, Lexical tendencies of high and low barrier personalities in narratives of everyday and dream memories, doi:10.2190/IC.34.2.d

Table captions

• table1 — Univariate Results of Body Boundary Imagery and LIWC Linguistic Variables of Low and High Barrier Personalities in Narratives of Everyday Memories.

• table2 — Univariate Results of Body Boundary Imagery and LIWC Linguistic Variables of Low and High Barrier Personalities in Narratives of Dream Memories.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_hawkins2017(table, version=None, process=True, **kwargs)

Fetch tables from Hawkins II & Boyd (2017), Dreaming.

Citation

Hawkins II & Boyd, 2017, Dreaming, Such stuff as dreams are made on: Dream language, LIWC norms, and personality correlates, Dreams about COVID-19 versus normative dreams: Trends by gender, doi:10.1037/drm0000049

Table captions

• table1 — Means and Standard Deviations (SDs) for the LIWC (2007) Linguistic Features of Dreams From Studies 1 to 3.

Notes

2007 Norms are a subset of the norms published in the LIWC2007 manual.

Ave. recent dream is UNWEIGHTED.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_mariani2023(table, version=None, process=True, **kwargs)

Fetch tables from Mariani et al. (2023), Psychoanal Psychol.

Citation

Mariani et al., 2023, Psychoanal Psychol, Referential processes in dreams: A brief report from a COVID-19 dreams analysis, doi:10.1037/pap0000420

Table captions

• table1 — ANOVA One Way Between Dreams’ Clusters and LIWC Text Analysis.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_mcnamara2015(table, version=None, process=True, **kwargs)

Fetch tables from McNamara et al. (2015), Dreaming.

Citation

McNamara et al., 2015, Dreaming, Aggression in nightmares and unpleasant dreams and in people reporting recurrent nightmares, doi:10.1037/a0039273

Table captions

• table1 — LIWC and Content Scale Means and SDs Across All Types of Dreams With LIWC Norms.

• table6 — Categorical Comparisons Between Nightmares That Woke A Dreamer Up to Nightmares Where the Dreamer Was Not Woken Up.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_meador2022(table, version=None, process=True, **kwargs)

Fetch tables from Meador et al. (2022), Appl Cognit Psychol.

Citation

Meador et al., 2022, Appl Cognit Psychol, Lexical tendencies of high and low barrier personalities in narratives of everyday and dream memories, doi:10.1002/acp.3976

Table captions

• table1 — Change in symptoms and language.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_niederhoffer2017(table, version=None, process=True, **kwargs)

Fetch tables from Niederhoffer et al. (2017), CLPsych.

Citation

Niederhoffer et al., 2017, CLPsych, In your wildest dreams: the language and psychological features of dreams, doi:10.18653/v1/W17-3102

PDF available at https://aclanthology.org/W17-3102.pdf.

Table captions

• table1 — Linguistic Processes Categories in LIWC2015.

• table2 — Top and Bottom Five dream Topics on CDI continuum.

• table3 — Most positively and negatively-correlated topics for each emotion.

• appendixA — Full list of LDA topics.

• appendixB — Sample dreams by CDI.

Notes

I corrected a typo in Table 2 (plave -> plane). The correct spelling is “plane”, as you can see it in the corresponding Topic in Appendix A.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_paquet2020(table, version=None, process=True, **kwargs)

Fetch tables from Paquet et al. (2020), Dreaming.

Citation

Paquet et al., 2020, Dreaming, A quantitative text analysis approach to describing posttrauma nightmares in a treatment-seeking population, doi:10.1037/drm0000128

Table captions

• table1 — Participant Demographics by Group.

• table2 — Psychological Diagnosis and Nightmare Qualities Experienced by Sample.

• table3 — Results Table of LIWC Variables.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

LIWC Psychometrics Manual fetchers

extracts.fetch_liwc1999(table, version=None, process=True, **kwargs)

Fetch tables from the LIWC1999 Psychometrics Manual.

Citation

LIWC1999 Psychometrics Manual, distributed on the LIWC website psychometrics manuals page.

Tables were extracted from the manual PDF and uploaded to Zenodo.

Table captions

• table1 — LIWC1999 category descriptions (judges, examples, word counts).

• table2 — Corpus summary statistics.

• table3 — Per-category means and standard deviations.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_liwc2001(table, version=None, process=True, **kwargs)

Fetch tables from the LIWC2001 Psychometrics Manual.

Citation

LIWC2001 Psychometrics Manual, distributed on the LIWC website psychometrics manuals page.

Tables were extracted from the manual PDF and uploaded to Zenodo. Content matches the LIWC1999 manual tables; deposit is kept separate so each manual version has its own DOI.

Table captions

• table1 — LIWC2001 category descriptions (judges, examples, word counts).

• table2 — Corpus summary statistics.

• table3 — Per-category means and standard deviations.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_liwc2007(table, version=None, process=True, **kwargs)

Fetch tables from the LIWC2007 Psychometrics Manual.

Citation

LIWC2007 Psychometrics Manual, distributed on the LIWC website psychometrics manuals page.

Tables were extracted from the manual PDF and uploaded to Zenodo.

Table captions

• table1 — LIWC2007 category descriptions with alpha (binary/raw).

• table2 — Corpus summary statistics.

• table3 — Per-corpus means and standard deviations.

• table4 — LIWC2007 vs LIWC2001 cross-version correlations.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_liwc2015(table, version=None, process=True, **kwargs)

Fetch tables from the LIWC2015 Psychometrics Manual.

Citation

LIWC2015 Psychometrics Manual, distributed on the LIWC website psychometrics manuals page.

Tables were extracted from the manual PDF and uploaded to Zenodo.

Table captions

• table1 — LIWC2015 category descriptions with internal consistency.

• table2 — Corpus summary statistics.

• table3 — Per-corpus means and standard deviations.

• table4 — LIWC2015 vs LIWC2007 cross-version correlations.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame

extracts.fetch_liwc22(table, version=None, process=True, **kwargs)

Fetch tables from the LIWC-22 Psychometrics Manual.

Citation

LIWC-22 Psychometrics Manual, distributed on the LIWC website psychometrics manuals page.

Tables were extracted from the manual PDF and uploaded to Zenodo.

Table captions

• table1 — Corpus word-count summary.

• table2 — Internal consistency per category.

• table3 — Per-corpus means and SDs (MultiIndex columns).

• table4 — LIWC-22 vs LIWC2015 cross-version correlations.

• tableA1 — Test-kitchen corpus appendix.

Parameters:

• table (str)

• version (str | None)

• process (bool)

• kwargs (Any)

Return type:

DataFrame