This preprint introduces Cognitive Digital Shadows (CDS), a corpus of LLM responses on four social topics: vaccines and health, fake social-media content, the gender gap in science and stereotype threat in STEM. Records are generated either in AI-assistant mode or under a synthetic persona built from age, gender, sexuality, work, education, city, migration, religion, social-media use, psychological labels and OCEAN scores. Its useful contribution is infrastructural: it preserves prompts, selections and responses, releases derived textual networks and supplies a Colab notebook for filtering profiles and visualizing semantic and emotional networks. Validation through textual forma mentis networks asks only whether words taken from the prompted topic are frequent or structurally different from other words. The paper correctly states that this does not validate truth, quality, persona realism or faithfulness to any human population. Full audit of the public commit finds 226,571 valid JSON records rather than an identifiable 190,000-record subset: 133,219 are human mode and 93,352 AI mode, with the four topics nearly balanced. However, no human persona contains the stated 17 attributes because biological_sex is absent from all 133,219. Only 128,676 opinions, 56.79%, satisfy the promised 250-500 word range; 94,836 are shorter and 3,059 longer. In addition, 2,415 GPT-oss records use temperature 0.0 although the text says every simulation uses 0.7. The 19 analysis CSVs contain 226,924 names: 560 have no raw JSON and 207 released JSONs are omitted, so raw-to-analysis lineage does not close. Several directories mix versions or families: Claude Sonnet 4.5 contains two Claude 3.5 outputs; Mistral Small combines mistral-small-latest and mistral-large-2512; and Phi-4-mini-instruct actually contains Phi-4-mini-reasoning. Released code also cannot regenerate the corpus: both ROLE_MODES entries are fixed to llm, biological sex is omitted, current_marital_status is written where the parser reads marital_status, only one Qwen configuration is covered, no random seed is preserved, and the reasoning-summary description is contradicted by a prompt requesting full step-by-step derivations. Topic-anchoring tests produce tiny p-values from flattened, dependent observations; significance does not imply higher centrality, and the executed LiquidAI notebook has topic-keyword medians equal to or below other words for all four reasoning-summary tests. The dashboard is a notebook rather than a hosted application: it requires Colab, unpinned dependencies, a mounted Drive and a mutable 773 MB parquet. Zenodo preserves only the 76 kB v3 notebook; the repository has no global license, tests, CI or locked environment. CDS remains a large resource for studying conditioned outputs and designing future audits, but it should be cited as a synthetic collection with material schema and reproducibility drift, never as a survey, population simulator or evidence of real-world group beliefs.
Research question
How can a traceable corpus be constructed and made available to compare the discourse generated by different LLMs under assistant roles and synthetic configurations of personality and sociodemographics on sensitive social topics?