Mendeley Data Showcase

This dataset records the public positions of six U.S. labor unions and their paired industry associations on major federal climate policies and proposals between 1997 and 2024. It was constructed to test a theory of union-employer divergence in climate politics, examining whether and why unions systematically support climate policies their employers do not. The dataset includes 147 observations covering 38 distinct policies and proposals (26 core policies plus 12 iterations). Each observation represents a union–industry association–policy pairing and records actor positions (Support, Opposed, Neutral, or No Data), the stated motivations for union support, and key policy design features including labor standards, transition assistance provisions, and support for domestic manufacturing. Unions included are the United Mine Workers of America (UMWA), United Steelworkers (USW), United Auto Workers (UAW), Utility Workers Union of America (UWUA), and Laborers' International Union of North America (LIUNA), spanning fossil fuel extraction, manufacturing, automaking, utilities, and building and construction trades. Each union is paired with the industry association most representative of their members' employers. The dataset is accompanied by a README describing all variables and coding rules, and by replication code (Python/Jupyter) for analyses reported in the article.

This dataset contains mercury intrusion porosimetry (MIP) and X-ray Diffraction (XRD) data of Permian carbonaceous shale from Southwest China, collected under controlled oxalic acid corrosion conditions. It aims to support mechanistic studies on the coupled chemical-mechanical degradation of carbonaceous shale weak interlayers—critical geohazard drivers in the region—by linking mineral dissolution and microstructural evolution. The data were obtained from shale samples exposed to oxalic acid solutions at concentrations of 0.005, 0.011, and 0.268 mol/L (plus a distilled water control) for 7, 14, and 21 days, capturing changes in mineral composition (e.g., calcite, illite) and pore-structure parameters (e.g., pore size distribution). This dataset enables validation of multi-scale kinetic models and informs sustainable geohazard mitigation strategies by quantifying the link between acid corrosion and shear strength degradation.

An anonymized survey dataset (N = 310) examining managerial reliance on AI-generated consumer personas. The file includes raw Likert-scale measures, computed construct composites, and cleaning/selection indicators used in the reported analyses.

This dataset contains the variables used to analyze the relationship between corporate sustainability, environmental performance, and financial performance in the manufacturing industry in emerging markets. The data include indicators related to sustainability practices, environmental performance measures, and financial performance variables used in the empirical analysis. The dataset was compiled from secondary data sources and organized in a structured format to facilitate statistical analysis. It supports the study titled “Corporate Sustainability, Environmental and Financial Performance in Emerging Markets: An Empirical Approach from the Colombian Manufacturing Industry.” These data allow researchers to replicate the empirical analysis and further explore the relationship between sustainability practices and firm performance in the manufacturing sector.

In my research I discover the impact of changes in the Bank of Russia’s discount rate on stock returns for Russian public companies. The presented dataset covers the period from 2014 to 2025, contains stock quotes from 8 MOEX indices and 57 stocks, actual Bank of Russia’s discount rates on the day of the meeting of the Board of Directors of the Bank of Russia on the key rate.

Supplementary data

Participant Information Sheet and Consent Form used in GEOG336 in 2025. Uploaded as appendix to Klocker et al. (2026) "Between Pedagogy and Privacy: Developing Geography Students’ Qualitative Research Skills in the Context of Generative AI" (published in European Journal of Geography).

This data is used to examine the impact of microcredit on multidimensional poverty and vulnerability in India. This is a longitudinal data from the years 2016-19. By employing several identification strategies, we find that microcredit from MFIs/SHGs significantly reduces the multidimensional poverty and vulnerability among borrower households.

The Spell Error Corpus for Minang Language (SPEML) is a dataset designed to support spelling error detection and correction for Minangkabau, a low-resource language of Indonesia. The base corpus was derived from a publicly available Indonesian–Minangkabau parallel corpus in the minangNLP GitHub repository. From the aligned sentence pairs, only the Minangkabau column was extracted, yielding 16,372 sentences. The texts were preprocessed through normalization, cleaning, and deduplication, resulting in 16,334 unique Minangkabau sentences. SPEML contains 164,662 misspelled word forms organized into seven error categories. Spelling errors were generated using rule-based procedures covering character insertion (1–3 characters), character deletion (1–3 characters), character substitution (1–3 characters), character transposition (single adjacent swap), punctuation errors, real-word errors, and loanword errors, followed by expert validation for linguistic plausibility. This dataset can be reused to train, test, and benchmark NLP models, including spell checkers and language models, for Minangkabau spelling error detection and correction, as well as to evaluate methods by error type and edit length.