reapxdev/zenodo-scraper huggingface dataset:
afermg/JUMP_lite: JUMP-Lite v1.0
Authors: Alán F. Muñoz, Johan Fredin Haslum
Type: Software
Published: 2026-08-03

<p>First public code and release-documentation snapshot for JUMP-Lite v1.0.</p>
<p>This release includes the scripts and documentation used to construct, validate, and describe the JUMP-Lite dataset release. The public data layout is documented in <code>cpg_upload/JUMP_LITE_README.md</code>.</p>
JuliaDynamics/Attractors.jl: v2.0.1
Authors: George Datseris, Kalel Luiz Rossi, Alexandre Wagemakers, Andreas Morr, James Bluck, Orjan Ameye, Stefan Vayl, Reyk Börner, Guilherme Brondani Torri, Jay-sanjay, John Eismeier, Sebastian Persson, spaette
Type: Software
Published: 2026-08-03

<h2>Attractors v2.0.1</h2>
<p><a href="https://github.com/JuliaDynamics/Attractors.jl/compare/v2.0.0...v2.0.1">Diff since v2.0.0</a></p>
BVDOutbreakSize: joint forward-generative Turing model for the 2026 DRC Bundibugyo outbreak
Authors: Abbott, Sam, Sherratt, Katharine, Brand, Samuel, Funk, Sebastian
Type: Software
Published: 2026-08-03

<p>Model outputs and input data for commit d89929391a96a11801277ae1bfa0a458f49905b0.</p>
<p>Bundles the posterior summary tables, thinned posterior
draws, the latent symptom-onset ("symptomatic cases")
trajectory over time (<code>onsets_over_time.csv</code>), the one- to
four-week-ahead forecasts of the observed streams
(<code>forecast.csv</code>, with the one-week-back validation forecast in
<code>forecast_validation.csv</code>) for later scoring, a copy of the
input <code>observations.toml</code>, a
<code>site.zip</code> snapshot of the rendered report site, and
<code>analysis.html</code> — a self-contained single-file copy of the
report that opens offline.</p>
<p>Rendered analysis:
https://epiforecasts.io/BVDOutbreakSize/dev/analysis</p>
<p>Self-contained report (always latest build):
https://github.com/epiforecasts/BVDOutbreakSize/releases/latest/download/analysis.html</p>
EPFL GIS Robotics Cell
Authors: Eskandari, Farzaneh
Type: Software
Published: 2026-08-03
Keywords: ROS2, robotics, ABB IRB6700, force control, hand-eye calibration, perception, digital fabrication

ROS2 packages for the EPFL GIS multi-robot cell: end-effector descriptions, force sensing, digital I/O control, and eye-in-hand perception for an ABB IRB6700 175/3.05 on an IRBT6004 7m linear rail with a Schunk SWS-160 tool changer. Includes the full flange-to-tool kinematic chain, hand-eye calibrated perception, and RWS-based I/O control for the tool changer and vacuum gripper.
yieldcast: Heterogeneous Cross-State Generalization in In-Season County Corn Yield Forecasts
Authors: Marupilla, Gnaneswara, Bayina, Chandhini
Type: Software
Published: 2026-08-03
Keywords: crop yield forecasting, leave-one-state-out validation, cross-validation protocols, MODIS NDVI, ERA5, CatBoost, conformal prediction, US Corn Belt, reproducibility

Cross-validation protocol matters more than model choice for in-season county corn yield forecasting. Using MOD13Q1 NDVI, ERA5-Land climate aggregates, and CDL crop-rotation summaries for 587 counties across the US Corn Belt (IL, IN, IA, MN, MO, NE; 2010-2023), we contrast leave-one-year-out (LOYO) and leave-one-state-out (LOSO) cross-validation at three in-season horizons (Jun 30, Jul 31, Aug 15). Mean LOSO R^2 exceeds mean LOYO R^2 by 25-30 percentage points across all model classes -- inverting the standard intuition that spatial holdout is harder than temporal holdout -- and per-state LOSO R^2 spans IL=0.87 down to NE=0.23, exposing irrigation-driven heterogeneity that LOYO hides. Recommends dual LOYO+LOSO reporting + per-state disclosure as default protocol for county yield forecasting publications.
A global, event-based assessment of aquifer response to extreme precipitation in arid regions
Authors: Saleh, Hassan
Type: Software
Published: 2026-08-03
Keywords: GRACE, groundwater, arid regions, extreme precipitation, recharge efficiency

<h2>Summary</h2>
<p>First release of the analysis code, notebooks, and derived products for:</p>
<p><strong>A global, event-based assessment of aquifer response to extreme precipitation in arid regions</strong></p>
<h2>Contents</h2>
<ul>
<li>Notebooks <code>01</code>–<code>03</code> (download/preprocess → surface water → main analysis)</li>
<li><code>src/</code> Python modules</li>
<li>Shipped groundwater CSVs, arid-domain boundaries, and manuscript figures/tables/rasters under <code>outputs/</code></li>
<li><code>environment.yml</code>, README, MIT license, <code>CITATION.cff</code></li>
</ul>
<h2>Notes</h2>
<ul>
<li>Large raw remote-sensing granules and interim Zarr stores are not included; see the README for how to obtain or regenerate them.</li>
<li>Manuscript figures under <code>outputs/</code> are included so results can be inspected without a full re-run.</li>
</ul>
skylargale/arctic-monthly-internal: Version 2.0
Authors: Skylar Gale
Type: Software
Published: 2026-08-03

<p>Version 2.0 release accompanying Gale et al. (2026b) (in review), <em>Why April Stands Out: Monthly Impacts of Internal Variability on Arctic Amplification</em>. Includes all scripts required to reproduce figures in the manuscript.</p>
dfm/python-fsps: v0.5.0
Authors: Benjamin Johnson, Dan Foreman-Mackey, Jonathan Sick, Joel Leja, Mike Walmsley, Erik Tollerud, Henry Leung, Minjung Park
Type: Software
Published: 2026-08-03

<h2>What's Changed</h2>
<p>This release updates python-FSPS to work with v4.0 of FSPS, which had substantial source code and data file changes, largely to support a new alpha-enhancement dimension in the isochrone and spectral libraries.  See https://github.com/cconroy20/fsps/releases/tag/v4.0 for more details on the FSPS updates. Of particular note for python-FSPS users:</p>
<ul>
<li>The default spectral library is now <code>C3K_LR</code>, with R~100; the old MILES default can be reinstated using compiler flags.</li>
<li>The BASEL spectral library is no longer available.</li>
<li>New <code>afe</code> and <code>afeindx</code> parameters are exposed to python-FSPS; these will be 0 and 1 respectively unless python-FSPS is compiled with the <code>AFE_FLAG=1</code> pre-compiler directive</li>
<li>Values of <code>_zcontinuous</code> > 1 will now raise an error.</li>
<li>The <code>StellarPopulation.interp_ssp()</code> method is disabled.</li>
</ul>
<p>Smaller changes</p>
<ul>
<li>A new <code>use_lw_tpagb</code> (default 0) controls the library used for the TP-AGB stars</li>
<li><code>cloudy_dust</code> is now a setup variable (see #230)</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/dfm/python-fsps/compare/v0.4.8...v0.5.0</p>
Unsupervised Analysis Workflow
Authors: Reichl, Stephan, Bednarsky, Raphael, Hofer, Andreas, Bock, Christoph
Type: Software
Published: 2026-08-03
Keywords: Unsupervised Learning, Cluster Analysis, Dimensionality Reduction, Workflow, Snakemake

A general purpose Snakemake workflow to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.
potpourri: A Python-Package for Multi-period Optimal Power Flow in Active Distribution Grids
Authors: Kortmann, Steffen, Bong, Andreas, Braun, Simon, Och, Alexander, Nasr, Farah, Kvesic, Philip, Stumberger, Nina, Ulbig, Andreas
Type: Software
Published: 2026-08-03
Keywords: optimal power flow, distribution grid, power systems, multi-period, pyomo, pandapower, simbench, battery storage, hosting capacity

potpourri is a Python library for multi-period Optimal Power Flow (OPF) in distribution grids. It wraps Pyomo for optimisation modelling over pandapower network objects, supporting AC/DC power-flow formulations and flexible resources such as batteries, electric vehicles, heat pumps, photovoltaic systems, and wind generators.
Genomic Region Set & (Ranked) Gene Set Enrichment Analysis & Visualization Workflow for Human and Mouse Genomes.
Authors: Reichl, Stephan, Stoll, Martin, Bednarsky, Raphael, Zhao, Fangwen, Romanovskaia, Daria, ter Horst, Rob, Bock, Christoph
Type: Software
Published: 2026-08-03
Keywords: Bioinformatics, Workflow, Enrichment Analysis, Epigenetics, Visualization, Snakemake

Given human (hg19 or hg38) or mouse (mm9 or mm10) based genomic region sets (i.e., region sets) and/or (ranked) gene sets of interest and respective background region/gene sets, the enrichment of terms within the configured databases is determined using LOLA, GREAT, GSEApy (over-representation analysis (ORA) & preranked GSEA), pycisTarget, RcisTarget, before aggregation and visualization.