Research, patent, capital, news and search-interest signals for applied engineering themes in Nepal and neighbouring Himalayan regions, 2017–2025 (Fliar BI Prism)

Aggregate tables behind the article "Hydropower, Landslide Resilience and Low-Cost Sensing: Research-to-Market Pathways in Nepalese and Himalayan Applied Engineering, 2017–2025" (Kumar, 2026). All counts were produced from Fliar BI Prism, an index maintained by Fliar BI that links scholarly works, patent publications, reported investment rounds, news and search-interest series, and were taken on 2026-10-27 with the full research, patent and investment index available; 13 of the 14 news servers answered. The deposit gives the yearly and provincial research counts for six applied engineering themes in Nepal (hydropower; landslides and slope stability; low-cost sensing and IoT; earthquake engineering and seismic risk; water resources and irrigation; roads, bridges and tunnels), the same counts for seven neighbouring Himalayan regions, the world baseline, every patent family with a Nepalese assignee and the world, Bhutan, India and China theme families, the reported investment rounds for Nepal, the news series by year, province and region, the national and geographic search-interest series, the precision check, the exact theme, CPC, exclusion and region rules, and the figures printed in the article. research_by_year.csv: Papers per year with at least one Nepalese affiliation, theme paper counts and theme share of national output (%) research_world_by_year.csv: World baseline: topic-tagged works per year and theme counts, same title rules research_by_province.csv: Papers and theme papers by Nepalese province of the affiliated institution (main campus), 2017–2025 research_neighbours_by_region.csv: Papers and theme papers by neighbouring Himalayan region (Indian Himalayan states and union territories, Bhutan, Tibet), 2017–2025 research_neighbours_by_region_year.csv: The same by region and year region_panel.csv: Theme papers per 1,000 papers for Nepal, its provinces, each neighbour region and the world patent_families_nepal.csv: Every patent family with an assignee located in Nepal: family id, publication number, year, title, CPC codes patent_families_by_theme.csv: Patent families per theme (CPC prefixes in theme_rules.json), 2017–2025: world and assignees in Nepal, Bhutan, India and China investment_rounds_nepal.csv: Investment and financing rounds for companies in Nepal reported in business news (de-duplicated), with round kind and theme news_by_year.csv: Nepal-focused news articles per year and theme counts (theme matched in the first 1,500 characters; 13 of 14 news servers available) news_by_region.csv: News articles and theme articles by Nepalese province and by neighbour region search_interest_monthly.csv: Monthly relative search interest (0–100) within Nepal for each theme term (each term fetched alone, on its own scale) search_interest_by_country.csv: Worldwide relative search interest by country for each term (Nepal, Bhutan, India, China, Pakistan, Bangladesh) and the country's rank search_interest_by_region.csv: Relative search interest by Nepalese development region and by Indian Himalayan state for each term theme_summary.csv: Direction and volume of each theme in each stream, as reported in the article precision_check.json: Hand check of 30 random 2023–2024 Nepalese titles per theme: relevant counts and the false matches analysis.json: Cross-stream comparisons used in the article (shares of world research and patents, intensities, rank agreement) funding.json: Investment rounds by year, kind and theme news.json: News shares and directions by theme and region neighbours.json: Neighbour-region directions and top institutions per region theme_rules.json: Affiliation, province, neighbour-region, exclusion and theme rules (title patterns, exclusions, CPC prefixes, search terms) and the direction rule figures/: Figures in the article Method: Cross-Domain Signal Triangulation (CDST). Each theme is defined once and measured separately in each stream; directions come from a log-linear fit over 2017–2025. See README.md.