Tools

Data, code, reproducible research methods, and responsible AI-supported academic workflows for applied economics, teaching, and research infrastructure.

This section documents Prof. Osman Gulseven’s computational profile, data-code resources, and experimental academic tools for reproducible research and teaching support. Public resources are added only after data sources, licensing, confidentiality, and usage rights are verified. Coursebook sites such as NREC4107 Applied Agricultural Econometrics, NREC4230 Agricultural Finance and Risk, and NREC4410 International Agricultural Trade are examples of open teaching resources built with Quarto.

Technical profile

Prof. Gulseven’s technical profile combines reproducible academic workflows, applied econometric modeling, visual communication, and cautious academic software development.

Programming and reproducible workflows

Python R Quarto LaTeX Git GitHub Google Colab TINA

Econometrics and modeling

OLS panel data PPML gravity models hedonic models WTP analysis time series wavelet coherence quantile regression

Visualization and communication

data visualization dashboards policy charts teaching graphics reproducible tables

Academic tools and public resources

ScholarDoc ScholarTeX Google Drive public folder

Academic software and responsible AI-supported workflows

ScholarDoc

Prototype academic software project for structured academic writing and document preparation workflows, including manuscript-style sections, equations, tables, and citation-oriented organization.

Open ScholarDoc

ScholarTeX

Prototype academic software project for browser-based LaTeX, Beamer, references, multi-file projects, and teaching-material workflows.

Open ScholarTeX

These experimental academic tools are described as research infrastructure and teaching support rather than finished commercial platforms. They can support organization, editing, formatting, coding assistance, visualization, and document preparation, but they do not replace scholarly judgment, source verification, citation checking, authorship responsibility, or institutional review requirements.

Tool Main purpose Typical users Main outputs
ScholarDoc AI-supported academic document preparation Researchers, students, instructors Structured academic documents
ScholarTeX Browser-based LaTeX and Beamer editing/compilation Researchers, instructors, LaTeX learners Articles, slides, handouts, PDF outputs

Public presentations and downloads

Selected presentations, teaching materials, and public documents are shared through an external Google Drive folder. Only files intentionally made public are placed there.

Open public Google Drive folder

Method library

Trade and policy models

PPML gravity estimation, GPML, structural gravity examples, CEPA and FTA simulation notes, non-tariff-measure templates, and TINA teaching exercises.

Food and price analytics

Wavelet coherence, price-transmission analysis, time-series visualization, commodity-market indicators, and Oman food-security teaching examples.

Consumer and resource economics

Hedonic demand modeling, willingness-to-pay analysis, non-market valuation, adoption models, survey-based examples, and sustainability indicators.

Finance and risk

Portfolio analysis, quantile regression, volatility analysis, time-series decomposition, and financial-market examples for research and teaching.

Planned data, code, and teaching resources

  • R templates for econometric modeling and visualization.
  • Python notebooks for applied data visualization and teaching.
  • Google Colab notebooks for student exercises.
  • TINA simulation note templates for trade-policy scenarios.
  • Quarto templates for reproducible research reports, including teaching patterns used in the NREC4107, NREC4230, and NREC4410 coursebook sites.
  • LaTeX and Beamer examples for academic writing and presentations.
  • Checklist-based AI-supported workflows for drafting, coding support, source review, formatting, and document preparation.

No private datasets, confidential student records, restricted institutional files, copyrighted PDFs, personal identifiers, or generated binary artifacts should be committed to this repository. AI outputs should be checked carefully for factual accuracy, citation integrity, and appropriate tone before use.