reapxdev/zenodo-scraper huggingface dataset:
Adverse Outcome Pathway Wiki RDF
Authors: Martens, Marvin, Willighagen, Egon, Evelo, Chris
Type: Dataset
Published: 2026-08-01

<p>This dataset is the RDF generated from the AOP-Wiki data release (<a href="https://aopwiki.org/downloads">aopwiki.org/downloads</a>). It was generated using a Python conversion pipeline that is available on GitHub (<a href="https://github.com/marvinm2/AOPWikiRDF">github.com/marvinm2/AOPWikiRDF</a>), and the process and additional description of the RDF have been published (<a href="https://doi.org/10.1089/aivt.2021.0010">doi.org/10.1089/aivt.2021.0010</a>).</p>
AgenticGenomics: pharmacogenomics benchmark dataset for "Trustworthy agentic genomics requires validated skills, not better models" (matched five-cell, real-genome and seven-model input-normalisation analyses)
Authors: Corpas, Manuel, Iacoangeli, Alfredo, Bourdenx, Mathieu, Skene, Nathan, Aldraimli, Mahmoud, Fatumo, Segun, Guio, Heinner
Type: Dataset
Published: 2026-08-01
Keywords: agentic genomics, pharmacogenomics, large language models, CPIC, retrieval-augmented generation, benchmark, reproducibility, clinical decision support, precision medicine, skill libraries

<p>Benchmark dataset for Corpas et al. (2026), "Trustworthy agentic genomics requires validated skills, not better models" (under revision at Cell Genomics; CELL-GENOMICS-D-26-00551).</p>
<p>Design. The synchronized package contains the matched five-cell comparison (13,200 attempted evaluations across eight models), the rule-extraction and bidirectional corrupted-contract experiments, GeT-RM external consensus comparisons, four-cohort real-genome analyses, and the input-normalisation experiment.</p>
<p>Version 1.4.1 (1 August 2026). This version supersedes v1.4.0. During Zenodo version import, nine same-named pre-C15 files were retained instead of replaced. A public-side checksum audit detected the mismatch. Version 1.4.1 explicitly replaces those files and verifies every staged release file byte-for-byte against the live record.</p>
<p>Parser corrections. C14 makes an optional gene prefix presentation-neutral. C15 accepts an explicit DIPLOTYPE marker inside Markdown, LaTeX or prose wrapping, applies the same policy to every model, and uses the last explicit marker as the final answer. Re-parsing 9,557 stored rows without new API calls recovered 87 marked calls and removed two provisional calls followed by an explicit final ABSTAIN. No raw response text was changed.</p>
<p>Seven-model freeze. The definition-supplied arm contains 3,689 attempts: 2,905 calls, 780 abstentions and four output-budget truncations. The frozen analysis records source-file hashes, exact paired units and 10,000-replicate sample-cluster bootstrap intervals. For Claude Opus 4.5, the difference from the deterministic caller against GeT-RM is +0.010 with a 95% interval from -0.004 to +0.024; neither equivalence nor superiority is claimed. Gemini is assigned no performance estimate because a complete comparable run was not collected; its boxed pilot response is accepted by the same model-neutral parser.</p>
<p>Reproducibility. Code is pinned to commit aeabc20 and immutable tag cg-revision-2026-08-01b. CHECKSUMS.sha256 covers the corrected data, release notes, provenance record and tagged source archive. PROVENANCE.md states which per-call fields survive and explicitly discloses that wall-clock timestamps and provider response IDs were not captured. Cohort data-access and redistributability limitations are documented in the repository; no new sequencing was performed.</p>
github.com/broadinstitute/warp/WholeGenomeReprocessing
Authors: broadinstitute
Type: Software
Published: 2026-07-31

<h3>WDL Analysis Research Pipelines</h3>
<p>The WDL Analysis Research Pipelines (WARP) repository is a collection of cloud-optimized pipelines for processing biological data from the Broad Institute Data Sciences Platform and collaborators.</p>
<p>WARP provides robust, standardized data analysis for the Broad Institute Genomics Platform and large consortia like the Human Cell Atlas and the BRAIN Initiative. WARP pipelines are rigorously scientifically validated, high scale, reproducible and open source, released under the <a href="https://github.com/broadinstitute/warp/blob/master/LICENSE">BSD 3-Clause license</a>.</p>
<h3>Pipeline releases</h3>
<p>All pipeline releases are listed on the WARP <a href="https://github.com/broadinstitute/warp/releases">releases page</a>. To discover and search releases, use the WARP command-line tool <a href="https://github.com/broadinstitute/warp/tree/develop/wreleaser">Wreleaser</a>.</p>
<h3>WARP Dockers and custom tools in warp-tools repository</h3>
<p>All Dockers and custom tools used for WARP's WDL Workflows are maintained in a separate repository, <a href="https://github.com/broadinstitute/warp-tools">warp-tools</a>.</p>
<h3>WARP documentation</h3>
<p>Read more about our pipelines and repository on the <a href="https://broadinstitute.github.io/warp/">WARP documentation site</a>.</p>
<p>To contribute to WARP, please read the <a href="https://broadinstitute.github.io/warp/docs/contribution/README">contribution guidelines</a>.</p>
<h3>Citing WARP</h3>
<p>When citing WARP, please use the following:</p>
<p>Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. https://doi.org/10.20944/preprints202401.2131.v1</p>
<p><a href="https://github.com/broadinstitute/warp/actions?query=workflow%3A%22Deploy+WARP+Website%22"></a></p>
github.com/broadinstitute/warp/WholeGenomeGermlineSingleSample
Authors: broadinstitute
Type: Software
Published: 2026-07-31

<h3>Warp Analysis Research Pipelines</h3>
<p>The Warp Analysis Research Pipelines (WARP) repository is a collection of cloud-optimized pipelines for processing biological data from the Broad Institute Data Sciences Platform and collaborators.</p>
<p>WARP provides robust, standardized data analysis for the Broad Institute Genomics Platform and large consortia like the Human Cell Atlas and the BRAIN Initiative. WARP pipelines are rigorously scientifically validated, high scale, reproducible and open source, released under the <a href="https://github.com/broadinstitute/warp/blob/master/LICENSE">BSD 3-Clause license</a>.</p>
<h3>Pipeline releases</h3>
<p>All pipeline releases are listed on the WARP <a href="https://github.com/broadinstitute/warp/releases">releases page</a>. To discover and search releases, use the WARP command-line tool <a href="https://github.com/broadinstitute/warp/tree/develop/wreleaser">Wreleaser</a>.</p>
<h3>WARP Dockers and custom tools in warp-tools repository</h3>
<p>All Dockers and custom tools used for WARP's WDL Workflows are maintained in a separate repository, <a href="https://github.com/broadinstitute/warp-tools">warp-tools</a>.</p>
<h3>WARP documentation</h3>
<p>Read more about our pipelines and repository on the <a href="https://broadinstitute.github.io/warp/">WARP documentation site</a>.</p>
<p>To contribute to WARP, please read the <a href="https://broadinstitute.github.io/warp/docs/contribution/README">contribution guidelines</a>.</p>
<h3>Citing WARP</h3>
<p>When citing WARP, please use the following:</p>
<p>Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. https://doi.org/10.20944/preprints202401.2131.v1</p>
<p><a href="https://github.com/broadinstitute/warp/actions?query=workflow%3A%22Deploy+WARP+Website%22"></a></p>
github.com/broadinstitute/warp/vds_to_vcf
Authors: broadinstitute
Type: Software
Published: 2026-07-31

<h3>Warp Analysis Research Pipelines</h3>
<p>The Warp Analysis Research Pipelines (WARP) repository is a collection of cloud-optimized pipelines for processing biological data from the Broad Institute Data Sciences Platform and collaborators.</p>
<p>WARP provides robust, standardized data analysis for the Broad Institute Genomics Platform and large consortia like the Human Cell Atlas and the BRAIN Initiative. WARP pipelines are rigorously scientifically validated, high scale, reproducible and open source, released under the <a href="https://github.com/broadinstitute/warp/blob/master/LICENSE">BSD 3-Clause license</a>.</p>
<h3>Pipeline releases</h3>
<p>All pipeline releases are listed on the WARP <a href="https://github.com/broadinstitute/warp/releases">releases page</a>. To discover and search releases, use the WARP command-line tool <a href="https://github.com/broadinstitute/warp/tree/develop/wreleaser">Wreleaser</a>.</p>
<h3>WARP Dockers and custom tools in warp-tools repository</h3>
<p>All Dockers and custom tools used for WARP's WDL Workflows are maintained in a separate repository, <a href="https://github.com/broadinstitute/warp-tools">warp-tools</a>.</p>
<h3>WARP documentation</h3>
<p>Read more about our pipelines and repository on the <a href="https://broadinstitute.github.io/warp/">WARP documentation site</a>.</p>
<p>To contribute to WARP, please read the <a href="https://broadinstitute.github.io/warp/docs/contribution/README">contribution guidelines</a>.</p>
<h3>Citing WARP</h3>
<p>When citing WARP, please use the following:</p>
<p>Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. https://doi.org/10.20944/preprints202401.2131.v1</p>
<p><a href="https://github.com/broadinstitute/warp/actions?query=workflow%3A%22Deploy+WARP+Website%22"></a></p>
github.com/broadinstitute/warp/VariantCalling
Authors: broadinstitute
Type: Software
Published: 2026-07-31

<h3>WDL Analysis Research Pipelines</h3>
<p>The WDL Analysis Research Pipelines (WARP) repository is a collection of cloud-optimized pipelines for processing biological data from the Broad Institute Data Sciences Platform and collaborators.</p>
<p>WARP provides robust, standardized data analysis for the Broad Institute Genomics Platform and large consortia like the Human Cell Atlas and the BRAIN Initiative. WARP pipelines are rigorously scientifically validated, high scale, reproducible and open source, released under the <a href="https://github.com/broadinstitute/warp/blob/master/LICENSE">BSD 3-Clause license</a>.</p>
<h3>Pipeline releases</h3>
<p>All pipeline releases are listed on the WARP <a href="https://github.com/broadinstitute/warp/releases">releases page</a>. To discover and search releases, use the WARP command-line tool <a href="https://github.com/broadinstitute/warp/tree/develop/wreleaser">Wreleaser</a>.</p>
<h3>WARP documentation</h3>
<p>Read more about our pipelines and repository on the <a href="https://broadinstitute.github.io/warp/">WARP documentation site</a>.</p>
<p>To contribute to WARP, please read the <a href="https://broadinstitute.github.io/warp/docs/contribution/README">contribution guidelines</a>.</p>
<p><a href="https://github.com/broadinstitute/warp/actions?query=workflow%3A%22Deploy+WARP+Website%22"></a></p>
github.com/broadinstitute/warp/UltimaGenomicsWholeGenomeCramOnly
Authors: broadinstitute
Type: Software
Published: 2026-07-31

<h3>Warp Analysis Research Pipelines</h3>
<p>The Warp Analysis Research Pipelines (WARP) repository is a collection of cloud-optimized pipelines for processing biological data from the Broad Institute Data Sciences Platform and collaborators.</p>
<p>WARP provides robust, standardized data analysis for the Broad Institute Genomics Platform and large consortia like the Human Cell Atlas and the BRAIN Initiative. WARP pipelines are rigorously scientifically validated, high scale, reproducible and open source, released under the <a href="https://github.com/broadinstitute/warp/blob/master/LICENSE">BSD 3-Clause license</a>.</p>
<h3>Pipeline releases</h3>
<p>All pipeline releases are listed on the WARP <a href="https://github.com/broadinstitute/warp/releases">releases page</a>. To discover and search releases, use the WARP command-line tool <a href="https://github.com/broadinstitute/warp/tree/develop/wreleaser">Wreleaser</a>.</p>
<h3>WARP Dockers and custom tools in warp-tools repository</h3>
<p>All Dockers and custom tools used for WARP's WDL Workflows are maintained in a separate repository, <a href="https://github.com/broadinstitute/warp-tools">warp-tools</a>.</p>
<h3>WARP documentation</h3>
<p>Read more about our pipelines and repository on the <a href="https://broadinstitute.github.io/warp/">WARP documentation site</a>.</p>
<p>To contribute to WARP, please read the <a href="https://broadinstitute.github.io/warp/docs/contribution/README">contribution guidelines</a>.</p>
<h3>Citing WARP</h3>
<p>When citing WARP, please use the following:</p>
<p>Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. https://doi.org/10.20944/preprints202401.2131.v1</p>
<p><a href="https://github.com/broadinstitute/warp/actions?query=workflow%3A%22Deploy+WARP+Website%22"></a></p>
github.com/broadinstitute/warp/UltimaGenomicsJointGenotyping
Authors: broadinstitute
Type: Software
Published: 2026-07-31

<h3>WDL Analysis Research Pipelines</h3>
<p>The WDL Analysis Research Pipelines (WARP) repository is a collection of cloud-optimized pipelines for processing biological data from the Broad Institute Data Sciences Platform and collaborators.</p>
<p>WARP provides robust, standardized data analysis for the Broad Institute Genomics Platform and large consortia like the Human Cell Atlas and the BRAIN Initiative. WARP pipelines are rigorously scientifically validated, high scale, reproducible and open source, released under the <a href="https://github.com/broadinstitute/warp/blob/master/LICENSE">BSD 3-Clause license</a>.</p>
<h3>Pipeline releases</h3>
<p>All pipeline releases are listed on the WARP <a href="https://github.com/broadinstitute/warp/releases">releases page</a>. To discover and search releases, use the WARP command-line tool <a href="https://github.com/broadinstitute/warp/tree/develop/wreleaser">Wreleaser</a>.</p>
<h3>WARP documentation</h3>
<p>Read more about our pipelines and repository on the <a href="https://broadinstitute.github.io/warp/">WARP documentation site</a>.</p>
<p>To contribute to WARP, please read the <a href="https://broadinstitute.github.io/warp/docs/contribution/README">contribution guidelines</a>.</p>
<p><a href="https://github.com/broadinstitute/warp/actions?query=workflow%3A%22Deploy+WARP+Website%22"></a></p>
github.com/broadinstitute/warp/UltimaGenomicsWholeGenomeGermline
Authors: broadinstitute
Type: Software
Published: 2026-07-31

<h3>WDL Analysis Research Pipelines</h3>
<p>The WDL Analysis Research Pipelines (WARP) repository is a collection of cloud-optimized pipelines for processing biological data from the Broad Institute Data Sciences Platform and collaborators.</p>
<p>WARP provides robust, standardized data analysis for the Broad Institute Genomics Platform and large consortia like the Human Cell Atlas and the BRAIN Initiative. WARP pipelines are rigorously scientifically validated, high scale, reproducible and open source, released under the <a href="https://github.com/broadinstitute/warp/blob/master/LICENSE">BSD 3-Clause license</a>.</p>
<h3>Pipeline releases</h3>
<p>All pipeline releases are listed on the WARP <a href="https://github.com/broadinstitute/warp/releases">releases page</a>. To discover and search releases, use the WARP command-line tool <a href="https://github.com/broadinstitute/warp/tree/develop/wreleaser">Wreleaser</a>.</p>
<h3>WARP documentation</h3>
<p>Read more about our pipelines and repository on the <a href="https://broadinstitute.github.io/warp/">WARP documentation site</a>.</p>
<p>To contribute to WARP, please read the <a href="https://broadinstitute.github.io/warp/docs/contribution/README">contribution guidelines</a>.</p>
<p><a href="https://github.com/broadinstitute/warp/actions?query=workflow%3A%22Deploy+WARP+Website%22"></a></p>
github.com/broadinstitute/warp/TestWholeGenomeReprocessing
Authors: broadinstitute
Type: Software
Published: 2026-07-31

<h3>WDL Analysis Research Pipelines</h3>
<p>The WDL Analysis Research Pipelines (WARP) repository is a collection of cloud-optimized pipelines for processing biological data from the Broad Institute Data Sciences Platform and collaborators.</p>
<p>WARP provides robust, standardized data analysis for the Broad Institute Genomics Platform and large consortia like the Human Cell Atlas and the BRAIN Initiative. WARP pipelines are rigorously scientifically validated, high scale, reproducible and open source, released under the <a href="https://github.com/broadinstitute/warp/blob/master/LICENSE">BSD 3-Clause license</a>.</p>
<h3>Pipeline releases</h3>
<p>All pipeline releases are listed on the WARP <a href="https://github.com/broadinstitute/warp/releases">releases page</a>. To discover and search releases, use the WARP command-line tool <a href="https://github.com/broadinstitute/warp/tree/develop/wreleaser">Wreleaser</a>.</p>
<h3>WARP Dockers and custom tools in warp-tools repository</h3>
<p>All Dockers and custom tools used for WARP's WDL Workflows are maintained in a separate repository, <a href="https://github.com/broadinstitute/warp-tools">warp-tools</a>.</p>
<h3>WARP documentation</h3>
<p>Read more about our pipelines and repository on the <a href="https://broadinstitute.github.io/warp/">WARP documentation site</a>.</p>
<p>To contribute to WARP, please read the <a href="https://broadinstitute.github.io/warp/docs/contribution/README">contribution guidelines</a>.</p>
<h3>Citing WARP</h3>
<p>When citing WARP, please use the following:</p>
<p>Degatano, K.; Awdeh, A.; Dingman, W.; Grant, G.; Khajouei, F.; Kiernan, E.; Konwar, K.; Mathews, K.; Palis, K.; Petrillo, N.; Van der Auwera, G.; Wang, C.; Way, J.; Pipelines, W. WDL Analysis Research Pipelines: Cloud-Optimized Workflows for Biological Data Processing and Reproducible Analysis. Preprints 2024, 2024012131. https://doi.org/10.20944/preprints202401.2131.v1</p>
<p><a href="https://github.com/broadinstitute/warp/actions?query=workflow%3A%22Deploy+WARP+Website%22"></a></p>
AI-Driven Smart Agriculture: An Integrated Approach for Soil Analysis, Irrigation and Crop-Fertilizer Recommendation
Authors: Pavithra S, Priyadharshini B, Sandhiya R, Vinotha R, Mrs.K.PRIYADEVI M.E.
Type: Publication
Published: 2026-08-03
Keywords: smart agriculture, soil moisture sensor, automated irrigation, crop-fertilizer recommendation, ESP32/Arduino

<p><span>The proposed system is an IoT-based smart agriculture monitoring and automation system designed to improve crop productivity, optimize water usage, and enable real-time decision-making. Traditional farming methods rely on manual monitoring and fixed irrigation schedules, leading to water wastage and inefficient resource utilization. To overcome these issues, the system integrates sensors, automation, and cloud-based monitoring into a single solution. Environmental sensors such as soil moisture, temperature, and humidity continuously monitor field conditions. The collected data is processed by a microcontroller, which compares values with predefined thresholds and automatically controls irrigation systems like pumps and valves. This ensures optimal water usage and reduces manual effort. For communication, Wi-Fi modules such as ESP8266/ESP32 transmit data to cloud platforms like ThingSpeak, where it is stored, analyzed, and visualized as graphs, allowing farmers to monitor fields remotely through mobile devices or computers. The system also provides alerts and remote control features, enabling quick action during abnormal conditions. Overall, the system is cost-effective, scalable, and energy-efficient, improving productivity while conserving water and supporting sustainable smart farming practices.</span></p>
Aspectos del impacto del COVID-19 en el desempeño académico en Colombia, a partir de la Teoría de la Expectancia Escalar y medición por estratos sociales
Authors: Leibniz Huxlay Flórez Guzmán, Sergio Carvajal Montiel, Estefanía Velásquez González, Juan Ricardo Andrés Cabrera Aguazaco
Type: Journal article
Published: 2026-06-25
Keywords: Teoría de la Expectancia Escalar, COVID 19, desempeño educativo, neurociencias, inclusión., Scalar Expectancy Theory, COVID-19, educational performance, neuroscience, inclusion.

<p class="MsoNormal"><span>Muchas de las apreciaciones acerca de los efectos de la pandemia del COVID 19 sostienen que se habría producido un deterioro de las prácticas pedagógicas, explicable por los hábitos de la virtualidad y por los mayores problemas de acceso a una tecnología, que ahora se aceptan como herramientas comunes del escenario didáctico.<span>   </span></span><span>Sin embargo, medido por el desempeño de la prueba Saber 11º <span> </span>(aplicada a los estudiantes de 11vo grado en Colombia), el desempeño educativo mostró una ligera respuesta “resorte”, </span><span>por lo que la idea de que el desempeño educativo se habría “rezagado”, no parece tener lugar. Sin embargo, esta prueba estadística tampoco reflejó un aumento considerable en el desempeño general desde el 2018 hasta el 2024, contraviniendo al efecto de las políticas educativas planificadas. Para explicar mejor este diagnóstico, este artículo reflexiona sobre la naturaleza neurocientífica del hábito en la medida de la relación entre aprendizaje y tiempo de aprendizaje, tal como revela la Teoría de la Expectancia Escalar (SET, en inglés), así como el efecto de la inequidad de la cobertura tecnológica escondida en las estadísticas nacionales. La pertinencia conceptual de este ángulo conlleva a introducir la consolidación de hábitos educativos desde los primeros años de la educación, y aumentar la cobertura en zonas no incluidas, para mejorar el desempeño educativo. </span></p>