

May 2025 monthly summary for OpenITI/FASDH25: Delivered two major features focused on interactive analytics and visualization, enabling data-driven insights for article performance and trends.
May 2025 monthly summary for OpenITI/FASDH25: Delivered two major features focused on interactive analytics and visualization, enabling data-driven insights for article performance and trends.
Month: 2025-04 — OpenITI/FASDH25 delivered targeted text-analysis enhancements to enable more precise analytics on conflict-related content. Key features include dictionary-based pattern counting and gazetteer-driven place-name tagging. The work adds two Python scripts: safwan_diarali_5.py for counting predefined patterns using a dictionary, and safwan_diarali_6.py for reading a gazetteer, counting place-name occurrences in Al Jazeera articles, and tagging found patterns in text. This enables reproducible experiments and scalable processing of article corpora. Commit 57fc4308f9ee766095dba775f8d237d09a023e34 ("python exercises"). No major bugs reported this month; stability maintained.
Month: 2025-04 — OpenITI/FASDH25 delivered targeted text-analysis enhancements to enable more precise analytics on conflict-related content. Key features include dictionary-based pattern counting and gazetteer-driven place-name tagging. The work adds two Python scripts: safwan_diarali_5.py for counting predefined patterns using a dictionary, and safwan_diarali_6.py for reading a gazetteer, counting place-name occurrences in Al Jazeera articles, and tagging found patterns in text. This enables reproducible experiments and scalable processing of article corpora. Commit 57fc4308f9ee766095dba775f8d237d09a023e34 ("python exercises"). No major bugs reported this month; stability maintained.
Month: 2025-03 — Closed on two core capabilities that advance scholarly research: (1) literary content corpus with page-level annotations and structured organization for easier navigation, and (2) modular Python-based text analysis toolkit to process articles, extract headings, count named entities, and split documents into title/body across multiple sources. No critical bugs reported; stable builds and clear integration points. Overall impact: richer data for analysis, faster discovery, and cross-source analytics. Technologies demonstrated: Python, modular design, data annotation workflows, and text analytics.
Month: 2025-03 — Closed on two core capabilities that advance scholarly research: (1) literary content corpus with page-level annotations and structured organization for easier navigation, and (2) modular Python-based text analysis toolkit to process articles, extract headings, count named entities, and split documents into title/body across multiple sources. No critical bugs reported; stable builds and clear integration points. Overall impact: richer data for analysis, faster discovery, and cross-source analytics. Technologies demonstrated: Python, modular design, data annotation workflows, and text analytics.
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