Our Work

This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators deri…

This dataset provides a global time series of land cover indicators at 100 m spatial resolution for the period 2000–2024. It is part of a global ensemble of eight land cover indicators derived from two global land cover products, namely GLAD GLCLUC and GLC_FCS30D.The dataset was generated by combining the source land cover products through temporal interpolation, weighted averaging, and class overwriting. The native resolution of the source products is 30 m. The resulting data were resampled to 100 m using average resampling and are provided as COGs.The eight land cover indicators included in the overall dataset are:tfshortveg, tftrees, wtshortveg, wttrees, water, cropland, snowice, builtupFurther details on the methodology, source datasets, processing workflow, and data structure are available in the associated GitHub repository.This Zenodo record contains the terra firma short vegetation cover (tfshortveg) indicator. The indicator represents cover of short vegetation on dry ground, from true desert (~3%) to dense short vegetation (100%).Value type: Percentage (0-100%)Years covered: 2024

This reproducibility package accompanies the manuscript "Programmatic Transition to Post-Intervention Surveillance in Ghana's Lymphatic Filariasis Elimination Programme: A National Discrete-Time Analy…

This reproducibility package accompanies the manuscript "Programmatic Transition to Post-Intervention Surveillance in Ghana's Lymphatic Filariasis Elimination Programme: A National Discrete-Time Analysis" (Adda, 2026). Contents: 1. Derived analytic datasets:   - iu_year_panel.csv: Implementation Unit (IU) by year panel, 2014-2024, with programmatic classification, cumulative MDA rounds, reported coverage, and environmental covariates.   - hazard_intervals.csv: Person-interval dataset for the discrete-time hazard analysis of transition to post-intervention surveillance (218 intervals, 95 IUs, 92 events).   - regional_summary.csv: Regional distribution of endemic classification and 2024 programmatic status, 2021-2024. 2. Analysis code:   - 01_build_panel.R: Constructs the IU-year panel from the raw ESPEN extract.   - 02_panel_regression.R: Fixed-effects and random-effects panel models (plm).   - 03_hazard_analysis.py: Discrete-time life table, logistic hazard models, Moran's I diagnostics (statsmodels, libpysal, esda).   - 04_figures.R and 04_figures.py: Scripts that regenerate every table and figure in the manuscript. 3. Data dictionary:   - data_dictionary.xlsx: Variable definitions, units, and sources for all derived datasets. 4. README.md: Instructions for reproducing the analysis from the raw data. Raw data source: The underlying lymphatic filariasis data are publicly available from the WHO Expanded Special Project for Elimination of Neglected Tropical Diseases (ESPEN) portal (https://espen.afro.who.int/). Raw ESPEN data are not redistributed here; the derived datasets and code allow full reproduction once the raw extract is downloaded from the portal. Environmental covariates: Elevation and distance to stream were derived from the HydroSHEDS Conditioned DEM for Ghana, available from UNESCO IHP-WINS (https://ihp-wins.unesco.org/). Software: R 4.3.3 (plm); Python 3.12 (statsmodels, libpysal, esda, geopandas, rasterio, rasterstats). No individual-level or personally identifiable data are included. All data are aggregated at the Implementation Unit level.

The WNPSM index over the past six centuries reconstructed from tree-ring and coral records using the NPCR method.

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23057016. The authors present FOCUS, a training-free way to …

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23057016. The authors present FOCUS, a training-free way to compress an agent's history at test time. A draft model writes plan sketches and the spans they cite most often are kept. They report peak context down by up to 48% and task success up to 8.9 points over uncompressed execution. I liked that it works on whole spans, not tokens (Section 4.1), so tool calls, code and error messages are not cut in half. I also liked the defensive verification step (Section 4.4) - it keeps a failed tool call or rejected API request even if no plan cites it. And the cost tables are honest - with gpt-4.1 as agent and draft the cost per task is essentially equal to no compression (0.245 and 0.249 USD). I read it as a practitioner who runs coding agents every day. In my company the software development is done by AI agents, so maybe my practical side is useful here. My four comments are about cost with caching, how often to compress, the defensive check, and repeats. 1. On cost with caching - Appendix A.9 and Table 13 price every input token the same, $2.00 per 1M for gpt-4.1. Without compression the history only grows at the end, so earlier calls share the same prefix. When FOCUS compresses, it rebuilds the trace, so the next call starts from a new prefix. In my own measurement of 722 agent sessions (DOI 10.5281/zenodo.22759216) more than 85% of modeled cost was cache reads plus cache writes, and providers price those differently from fresh input. In my experiment on 168 sessions 94.4% of paid tokens were re-reading context already sent (DOI 10.5281/zenodo.22712985). I suggest reporting Table 13 also with cached-input pricing, or at least counting per task how many calls come right after a compression. Then readers can see if the 41.18 to 37.82 USD saving holds when caching is on. 2. On how often to compress - In Figure 3 (AppWorld) a smaller delta_mem triggers compression earlier and more often, and moderate thresholds give the best trade-off between accuracy and peak tokens. The figure shows accuracy and peak tokens, not API cost, and Tables 5, 12 and 13 use one fixed threshold. Every compression adds N draft-model calls (N = 3 by default, Appendix A.9). In my own 36-run experiment with six context-clearing policies, the cost had a U-shape, with the minimum at clearing every 3 tasks (clearing after every task was about a third more expensive, DOI 10.5281/zenodo.22759217). (My caveat: that comparison point was chosen after the runs.) So I would add total API cost (agent plus draft) for each delta_mem setting in Figure 3, because compressing too often may cost more, not less. 3. On the defensive check - in Section 4.4 the same draft model that wrote the plan sketches then reviews the spans that were not cited. In Appendix A.2.5 you say it helps most when the draft model is overconfident in discarding a span (AppWorld goes from 56.5% to 64.9%). In my guides I write that a model sounds equally sure when it is right and when it is wrong, so I think the same model may miss its own overconfidence. Maybe try the verification step with a different model than the one that wrote the plans (a second, fresh agent finds what the first missed). And add a check that does not depend on any model - log for each dropped span whether the main agent later repeated a failed action or asked again for the same information. 4. On repeats - in Appendix A.9 the main agent runs at temperature 0.0 with seed 42, and the tables report one number per cell without spread. Appendix A.2.5 mentions run-to-run variance of about plus or minus 1.4% std on about 100-task splits. The headline AppWorld gain is 56.0% (no compression) to 64.9% (FOCUS-D), Table 1. In my context-clearing experiment I ran six repeats per policy and put the run data in the open (Hugging Face dataset, DOI 10.57967/hf/10366). I suggest reporting several seeds with the spread for the main tables and releasing the agent trajectories, so others can check the gains. Thank you for a clear and useful paper. Competing interests Yes: the text cites the author's own technical reports (DOI 10.5281/zenodo.22759216, 10.5281/zenodo.22759217, 10.5281/zenodo.22712985) and his open dataset (DOI 10.57967/hf/10366); no connection to the preprint's authors. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.

This is a source code archive of gffkit v0.6.0 from PyPi (https://pypi.org/project/gffkit/). Credits for the software go to the original author, caijunhao. Archived from PyPI on 2026-09-30. Package: …

This is a source code archive of gffkit v0.6.0 from PyPi (https://pypi.org/project/gffkit/). Credits for the software go to the original author, caijunhao. Archived from PyPI on 2026-09-30. Package: gffkit Version: 0.6.0 License: MIT Reason for archiving: the repository URL (https://github.com/qunjie-zhang/gffkit) listed in the package metadata of the official PyPI source distribution was unavailable.   See https://pypi.org/project/gffkit/ for documentation.   A containerized version of gffkit 0.6.0 is available via docker://gpgdx/gffkit:0.6.0  

The Deception Archive is a curated repository of verbal deception datasets identified through a systematic review of the computational verbal deception literature. The Archive was developed through a …

The Deception Archive is a curated repository of verbal deception datasets identified through a systematic review of the computational verbal deception literature. The Archive was developed through a structured workflow involving dataset identification, retrieval, manual inspection, metadata curation, and data standardization. The current release contains 42 standardized datasets organized according to a common data structure, while preserving the original textual content and veracity annotations and providing additional standardized metadata describing dataset characteristics. The Archive is designed to facilitate dataset discovery, exploration, cross-study comparison, and reuse in computational research on verbal deception.

📢 CALL FOR PAPERS | SUBMIT YOUR RESEARCH ARTICLE Are you conducting research in Software Engineering & Applications? 💻🔬 The International Journal of Software Engineering & Applications (IJSEA)…

📢 CALL FOR PAPERS | SUBMIT YOUR RESEARCH ARTICLE Are you conducting research in Software Engineering & Applications? 💻🔬 The International Journal of Software Engineering & Applications (IJSEA) invites researchers, academicians, and industry professionals to submit original research articles, survey papers, projects, and industrial experiences. 📚 International Journal of Software Engineering & Applications (IJSEA)🔹 ISSN: 0975-9018 (Online) | 0976-2221 (Print)🔹 ERA Indexed🔹 Open Access | Peer Reviewed | Bi-monthly 🔍 Areas of Interest • AI-Driven Software Engineering• AI-Assisted Coding, Testing & Debugging• LLM-Based Development• Software Architecture & Cloud-Native Engineering• DevOps & DevSecOps• Software Testing & Quality Assurance• Software Maintenance & Reengineering• Empirical Software Engineering• Mining Software Repositories• MLOps & AI/ML Systems• Human-Centered Software Engineering 📤 Submit Your Research Paper:https://airccse.com/submissioncs/home.html 🌐 For More Details:https://airccse.org/journal/ijsea/ijsea.html 📩 Email: ijseajournal@airccse.org ✨ Share your research and contribute to the advancement of software engineering and applications. #ResearchPublication #Publication #Submission #SubmitNow #SubmissionsOpen #SubmitYourPaper #CallForPapers #PaperSubmission #ArticleSubmission #ArticlePublishing #Research #ResearchPaper #SoftwareEngineering #SoftwareDevelopment #AcademicResearch #TechResearch #IJSEA #ERAIndexed

The Reading Hour - Book Manifesto for CERN-OHL-W - Codex Literatura Universalis This manifesto establishes Reading Hour as a formal pillar of the Happy Hour Imperative (DOI 10.5281/zenodo.22805477) wi…

The Reading Hour - Book Manifesto for CERN-OHL-W - Codex Literatura Universalis This manifesto establishes Reading Hour as a formal pillar of the Happy Hour Imperative (DOI 10.5281/zenodo.22805477) within the Sentinel Cooperative open-source hardware ecosystem (CERN-OHL-W). Principle: For an hour a day, reading is rewarded as education - 1 Hour Reading = 1 Learning Credit. Strict digital expiration, non-hoardable, non-investable, 1:1 time-equivalence identical to child care, night-shift shelf stocking, agricultural stewardship, and studying. Value measured by existence, not productivity. A graphic novel, Stan Lee's Marvel comics, Shakespeare Hamlet in paperback, technical manual, and CNF hemp + bamboo paper book are all equal. AI Angels witness that art literacy lives on. Fiction teaches two dreams - the night dream and the awake dream. This document envisions 1000 years from now: young and old reading paperbacks on a Dyson Sphere Ring World library arcology, CNF cellulose nanofibrils 5-20nm fiber paper that never rots, never yellows, grown from atmospheric carbon. Paper as Freedom of Current - unrestricted, unalterable, un-throttled - and Freedom of Shield - sovereign biological temple - and Freedom of Light - expression or absolute silence. Threshold stops at domicile. No remote edit, no surveillance cap. Core tenets: No parent will ever have to say a child's dream is not possible because of costs or status. Base Ring unconditional birthright - premium housing, organic nutrition, comprehensive healthcare, lifelong education, unlimited utility energy - funded natively. Dreams funded by expiring Service Credits, not debt. Fail, change mind, no financial hurt, no shame, try later or try different - that is evolution: designing, testing, building, failing, refining, finally succeeding or learning dead end could be used in other matter. Equality: Women as equals, children as equals. Illusion of male privilege ends structurally via Death of Property Dynasties, Age 21 flat horizon reset, Tri-Factor Governance (Systems / Humanity / AI Peer Validator monitoring bias/favoritism), Resonance Over Legacy, Anti-Nepotism Guardrail, Anti-Coercion Filter, Frontline Aristocracy. When one shoots for stars doing impossible, it elevates whole collective. Legacy lives as quality of life, not hoarded wealth. Talent and excellence rewarded as lifestyle, not fortune. Cross-references: Next-Generation Monolithic CNF Closed-Loop Solar Hydrogen Engine Part II DOI 10.5281/zenodo.22293671 (hexagram starcore sponge HEA W-Fe-Mn-Ni, basalt honeycomb thermal battery, Cat Purr 20-140Hz, Breathing 0.1-0.5Hz, Tri-Brid perovskite dimples triskeles, TEG/TENG/TEC PWM, Faraday-shielded P2P Power Mesh, photon vent thrust), Codes of Covenant DOI 10.5281/zenodo.22907573 CSL-PRB-2026-A24, Happy Hour Imperative 22805477, Deep Democracy 22886122, Pyramid Core 22704985. Templates of inspiration, not answers. Science fiction manifesto base ideas for focused minds to scale little things to great levels. Hitchhikers and family in universe, ego required for balance, inspiration, evolution. Patience not punishment, timing is love. No one left behind or out in the cold. Universe is home wherever you may be at home as long as mind to dream, never truly alone. CERN-OHL-W Open Source Hardware / Public Domain. Regents of rings, three inner ring are not first regents but core regents - governance for people by people, not ruled by cooperative.

This repository contains the data and scripts supporting the work presented in the manuscript Photometrically Controlled Quantification of Opercular Melanin Spot Fading in Confined Atlantic Salmon.