The increasing reliance on data-driven research underscores the need for accessible datasets, particularly in the medical domain.However, raw datasets are frequently unavailable due to privacy constraints and ethical considerations, which complicates reproducibility, meta-analyses, and large-scale data-driven research.Text2Tabular addresses this challenge by reconstructing research datasets from scientific publications using advanced natural language processing and statistical modeling.Our key contributions include: (1) a unified framework combining Large Language Model driven information extraction with copula-based distribution modeling, (2) novel integration of statistical test results as distribution constraints through constrained Markov Chain Monte Carlo refinement, and (3) an own comprehensive benchmark comprising real scientific publications with corresponding raw datasets for evaluating our literaturebased data reconstruction.Evaluation on both benchmark datasets and our curated collection demonstrates strong performance in Trainon-Synthetic-Test-on-Real (TSTR) evaluations, alongside accurate replication of descriptive statistics showing that Text2Tabular preserves the statistical properties and multivariate relationships of the original datasets.Text2Tabular facilitates scientific progress by enabling immediate access to realistic, domain-specific synthetic data, thus, improving data accessibility, and mitigating data scarcity in fields with limited real-world data.