d-fine/DatalandQALab
Dec 2025 – Feb 2026
3 Months active
Languages Used
JSON
Technical Skills
data extractionenvironmental data analysisprompt engineeringenergy metricsenvironmental regulationsJSON manipulation
Muriel Schindler
PROFILE
Muriel Schindler
Worked on DatalandQALab to deliver a series of data extraction and compliance features focused on ESG, energy, and regulatory metrics. Developed robust JSON-based prompts for extracting Scope 4 GHG emissions, enterprise value, and renewable energy data, standardizing outputs with explicit unit conversions and currency handling. Enhanced prompt engineering practices by introducing structured input sections and consistent naming, improving reliability and downstream data analysis. Extended the platform’s capabilities to automate corporate responsibility compliance checks, supporting SFDR and environmental regulations. Collaborated across teams, maintained clear documentation, and ensured traceability through well-scoped commits, establishing a foundation for repeatable, auditable data workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
141
Activity Months3
Your Network
13 peopleSame Organization
@stud.tu-darmstadt.de5
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary focusing on delivering the Corporate Responsibility Compliance Prompts in DatalandQALab, with SFDR-related prompts extended and tracked via commit 3a39f610bda50f4355bfb9bc8757e015115f56b0. No major bugs reported; feature enhances regulatory risk screening and governance for clients, enabling automated evaluation of corporate responsibility across human rights and environmental regulations.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary focusing on delivering the Corporate Responsibility Compliance Prompts in DatalandQALab, with SFDR-related prompts extended and tracked via commit 3a39f610bda50f4355bfb9bc8757e015115f56b0. No major bugs reported; feature enhances regulatory risk screening and governance for clients, enabling automated evaluation of corporate responsibility across human rights and environmental regulations.
February 2026
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 — DatalandQALab: Delivered energy data extraction prompts for GHG emissions and renewable energy, delivering standardized unit handling, conversion rules, and rounding for reliable climate metrics. Implemented explicit conversions (PJ ⇄ GWh, with 1 PJ ≈ 277.78 GWh) and rounded numeric outputs to two decimals. Cleaned up prompts with consistent naming and return units; removed non-applicable climate sector prompts. This work advances accurate, auditable energy data extraction for SFDR disclosures and downstream analytics.
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 — DatalandQALab: Delivered energy data extraction prompts for GHG emissions and renewable energy, delivering standardized unit handling, conversion rules, and rounding for reliable climate metrics. Implemented explicit conversions (PJ ⇄ GWh, with 1 PJ ≈ 277.78 GWh) and rounded numeric outputs to two decimals. Cleaned up prompts with consistent naming and return units; removed non-applicable climate sector prompts. This work advances accurate, auditable energy data extraction for SFDR disclosures and downstream analytics.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Key feature delivery and SFDR improvements in DatalandQALab. Implemented new data extraction prompts for ESG and financial metrics with standardized EUR outputs, improved prompt reliability and formatting, and updated SFDR-related prompts and documentation. Strong cross-team collaboration and established a foundation for repeatable data extraction workflows.
1 Commits • 1 Features
Dec 1, 2025December 2025: Key feature delivery and SFDR improvements in DatalandQALab. Implemented new data extraction prompts for ESG and financial metrics with standardized EUR outputs, improved prompt reliability and formatting, and updated SFDR-related prompts and documentation. Strong cross-team collaboration and established a foundation for repeatable data extraction workflows.
December 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
JSON
Technical Skills
JSON manipulationdata analysisdata extractionenergy metricsenvironmental data analysisenvironmental regulationsprompt engineeringregulatory compliance
Repositories Contributed To
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