reapxdev/zenodo-scraper huggingface dataset:
INDICATE - Building Whole Life Cycle Emissions Reporting Template
Authors: Röck, Martin
Type: Model
Published: 2026-07-03

<table>
<tbody>
<tr>
<td class="xl965">
<p><strong>Authors</strong></p>
</td>
<td class="xl966">
<p>Martin Röck</p>
</td>
</tr>
<tr>
<td>
<p><strong>DOI</strong></p>
</td>
<td class="xl964">
<p><a href="https://doi.org/10.5281/zenodo.21169904">https://doi.org/10.5281/zenodo.21169904</a></p>
</td>
</tr>
<tr>
<td class="xl965">
<p><strong>Version</strong></p>
</td>
<td class="xl968">
<p>v1.0 (260703)</p>
</td>
</tr>
<tr>
<td class="xl965">
<p><strong>Contact</strong></p>
</td>
<td class="xl967">
<p><a href="mailto:martin.roeck@riselabs.at">martin.roeck@riselabs.at</a></p>
</td>
</tr>
<tr>
<td class="xl965">
<p><strong>Project</strong></p>
</td>
<td class="xl966">
<p>INDICATE LIFE</p>
</td>
</tr>
<tr>
<td class="xl965">
<p><strong>Website</strong></p>
</td>
<td class="xl967">
<p><a href="http://www.indicatedata.com/">www.indicatedata.com</a></p>
</td>
</tr>
<tr>
<td class="xl965">
<p><strong>Purpose</strong></p>
</td>
<td class="xl964">
<p>This template file offers recommended Minimum Documentation Requirements (MDRs) for reporting project level whole life cycle  (WLC) emissions of buildings. All tabs and respective fields for 'user input' should be filled, with emphasis on ' high-priority' fields. In addition, a detailed bill-of-materials and related life cycle inventory should be reported as a separate file following national building element classification and WLC assessment methods, wherever available. While it is recommended, to use one instance of this template for reporting on one project, a multi-project overview tab is available to compile minimum information on multiple projects in one table. Advanced users can find further information and guidance on the recommended attributes and data structure for reporting bill of material and life cycle inventory via the INDICATE website (www.indicatedata.com) as well as the technical guidance of GBDI Global Building Data Initiative (GBDI) on the open building data format (openBDF) (www.gbdi.io).</p>
</td>
</tr>
<tr>
<td>
<p><strong>Cite as</strong></p>
</td>
<td>
<p>Röck M (2026). "INDICATE - Building Whole Life Cycle Emissions Reporting Template." DOI: <a href="https://doi.org/10.5281/zenodo.21169904" rel="noopener">https://doi.org/10.5281/zenodo.21169904</a>, Website: <a href="https://www.indicatedata.com">www.indicatedata.com</a></p>
</td>
</tr>
</tbody>
</table>
A ConvNeXt based model for tree detection
Authors: Kalita, Nehal, Yuktheswar, Alahari, Maurya, Rajesh Kumar
Type: Model
Published: 2026-07-31
Keywords: neural network, tree detection, GIS, ConvNeXt, U-Net, PyTorch, ONNX

<p>A ConvNeXt based U-net model trained to segment two classes of trees in 2D satellite images:<br><strong>Class 1</strong> segments individual trees and trees from sites of low vegetation density. <br><strong>Class 2</strong> segments trees from sites of high vegetation density.</p>
<p><strong>Blue</strong> pixels represent Class 1, whereas <strong>Orange</strong> pixels represent Class 2 (that can also include forested areas).</p>
<p><strong><em>How to test images?</em></strong></p>
<div>
<div>1) Install all the libraries used in the files `<em>convunext_to_onnx.py</em>`, `<em>convunext_test_final_1.py</em>` and `<em>remove_noise_1.py</em>` located inside the directories `<strong>training_pipeline</strong>` and `<strong>testing_pipeline</strong>` respectively.</div>
<div>2) Run the file `<em>convunext_test_final_1.py</em>`.</div>
<div>3) Specify the paths to the `.onnx` file, input image file and the output directory.</div>
<div>4) Specify perameter values for '<strong><em>minimum cluster area in terms of multiple of tree area</em></strong>' and '<strong><em>minimum cluster area (in %)</em></strong>'.</div>
</div>
<p>For training this model, images were collected from the following projects: - <br>1) <a title="NeonTreeEvaluation: RGB Dataset" href="https://datasetninja.com/neon-tree" rel="noopener">https://datasetninja.com/neon-tree</a><br>2) <a title="TOF_Detection" href="https://github.com/Taoorwell/TOF_Detection/" rel="noopener">https://github.com/Taoorwell/TOF_Detection/</a></p>
<p> </p>
<p><em><strong>Citation: </strong></em>If you find this tool helpful in your work, please cite the following</p>
<p><code>@misc{zenodo_project_2026,</code><br><code>  title={A ConvNeXt based model for tree detection},</code><br><code>  author={Kalita, Nehal and Alahari, Yukteshwar and Maurya, Rajesh Kumar},</code><br><code>  month={June},</code><br><code>  year={2026},</code><br><code>  publisher={Zenodo},</code><br><code>  url={https://doi.org/10.5281/zenodo.21714497}</code><br><code>}</code></p>
GiriAiPublication™: AI Use & Disclosure Advisory for Medical Publication (Edition 6, v6.0)
Authors: Gupta, Girish
Type: Model
Published: 2026-07-30
Keywords: artificial intelligence disclosure; AI authorship; medical publication ethics; generative AI in research; ICMJE guidelines; scholarly publishing; research integrity; clinical decision support; medical writing; AI reporting guidelines; CONSORT-AI; TRIPOD-AI; PRISMA; author guidance; publication ethics; GIRISH prompting architecture; NeonatAIlogy; neonatology; medical education technology; open educational resource

<p class="font-claude-response-body break-words whitespace-normal">Here's ready-to-paste Zenodo metadata for <strong>GiriAiPublication™ Edition 6, v6.0</strong>:</p>


<h4 class="text-text-100 mt-2 -mb-1 text-base font-bold">Title</h4>
<p class="font-claude-response-body break-words whitespace-normal">GiriAiPublication™: AI Use & Disclosure Advisory for Medical Publication (Edition 6, v6.0)</p>
<h4 class="text-text-100 mt-2 -mb-1 text-base font-bold">Description</h4>
<p class="font-claude-response-body break-words whitespace-normal">GiriAiPublication™ is an interactive, single-file HTML advisory tool that helps medical authors use generative AI responsibly and disclose it transparently in scholarly publication — turning disclosure from a source of reviewer anxiety into a mark of rigour.</p>
<p class="font-claude-response-body break-words whitespace-normal">The tool operationalises the "4D Rule" (Do · Document · Disclose · Defend) across eight interactive modules: a permitted/prohibited-use reference, a six-stage AI Use Mapper with live risk scoring, a 12-authority journal-and-publisher policy comparator (ICMJE, WAME, COPE, JAMA, BMJ, NEJM, Annals of Internal Medicine, Science, Nature/Springer Nature, Elsevier, PLOS, IEEE) plus an image-integrity distinction table, a SAFE-gated Disclosure Statement Builder that generates manuscript, Methods, and cover-letter declarations in ICMJE/BMJ/Elsevier/universal styles, a persistent multi-tool disclosure register, an Integrity Guard (risk matrix, reviewer-scrutiny patterns, editor-reassurance patterns), a weighted blocker-aware pre-submission audit, and an interactive study-type reporting-guideline recommender (CONSORT-AI, SPIRIT-AI, DECIDE-AI, TRIPOD+AI, PRISMA, CLAIM, STARD-AI) linking directly to the EQUATOR Network.</p>
<p class="font-claude-response-body break-words whitespace-normal">The tool is offline-capable, dependency-free, and stores all self-assessment data locally in the browser (no server, no account). It is built under the author's GIRISH™ Prompting Architecture and follows the disclosure practice it teaches: an explicit, dated, multi-tool AI-use disclosure is provided in the tool's own footer.</p>
<p class="font-claude-response-body break-words whitespace-normal">This is an educational and workflow advisory aid, not legal, regulatory, or research-ethics advice, and does not substitute for a target journal's current live instructions to authors.</p>
CDI Reference Models for CBED
Authors: Torbunov, Dmitrii
Type: Model
Published: 2026-07-30

<p># CDI Reference Models for CBED</p>
<p>This repository contains pretrained reference models for Conditional Diffusion Inference (CDI) experiments on the CBED dataset for magnesium diboride (MgB₂). The models are provided as tar archives containing complete CDI model directories with saved configurations, network weights, optimizer states, and training histories.</p>
<p>## Files</p>
<p>- `reference_diffusion_model.tar` — CDI conditional diffusion model for   uncertainty-aware CBED parameter inference.<br>- `reference_efficientnet_b2.tar` — deterministic EfficientNet-B2 regression baseline.<br>- `reference_resnet_50.tar` — deterministic ResNet-50 regression baseline.</p>
<p>## Archive Contents</p>
<p>Each archive unpacks to one model directory:</p>
<p>```text<br>reference_diffusion_model/<br>reference_efficientnet_b2/<br>reference_resnet_50/<br>```</p>
<p>The model directories include the saved training configuration (`config.json`), PyTorch model weights (`net_*.pth`), optimizer and scheduler states when available, and training/validation history files.</p>
<p>## Related Resources</p>
<p>- CDI code repository: https://github.com/realtime-intelligence/cdi-uq<br>- CBED-ML test split: https://zenodo.org/records/18340683<br>- CBED-ML training split, Part 1: https://zenodo.org/records/18344486<br>- CBED-ML training split, Part 2: https://zenodo.org/records/18356685<br>- CBED-ML training split, Part 3: https://zenodo.org/records/18476248</p>
MITgcm model and output for "Numerical Error Growth in the Maxwell Elasto-Brittle Rheology affects Physical Sea-Ice Fracturing and Spatial Heterogeneity"
Authors: Bourgett, Mirjam
Type: Model
Published: 2026-07-29

<p>This record contains the MITgcm model including the MEB implementation and the model output from simulations performed for the study "Numerical Error Growth in the Maxwell Elasto-Brittle Rheology affects Physical Sea-Ice Fracturing and Spatial Heterogeneity".</p>
<p>It contains:</p>
<ul>
<li>MITgcm model with MEB</li>
<li>model set-up for 36-km resolution</li>
<li>model set-up for 9-km resolution</li>
<li>model output data at 36-km resolution for MEB simulations using five different correction angles and the VP simulation with (_n) and without using a stochastic parameterisation</li>
<li>model output data at 9-km resolution for MEB simulations using five different correction angles and the VP simulation with (_n) and without using a stochastic parameterisation</li>
<li>the net-CDF files "2d" contain the snapshots of 2 dimensional sea ice area and sea ice shear deformation rate fields</li>
<li>the net-CDF files "1d" contain the spatially averaged time series of the roughness, pack ice area (only for 36km), heat flux (only 36km), the brittle damage</li>
</ul>
<p>Documentation for the MITgcm model can be found at https://mitgcm.readthedocs.io/en/latest/.</p>
<p> </p>
JaamSim discrete-event simulation model: small custom-made Roman blinds manufacturer
Authors: Jurczyk-Bunkowska, Magdalena
Type: Model
Published: 2026-07-30
Keywords: JaamSim, discrete-event simulation, open source, bottleneck, SME

<p class="font-claude-response-body break-words whitespace-normal">This repository contains a JaamSim (open-source discrete-event simulation, DES) model of the production process of a small, family-run firm producing custom-made Roman blinds to order. The model was developed as supplementary material for the article:</p>
<p class="font-claude-response-body break-words whitespace-normal">Jurczyk-Bunkowska, M. (2026). <em>Identification of production bottlenecks in a small manufacturing enterprise using open-source discrete-event simulation: A case study.</em> [conference / journal name and DOI to be added upon publication]</p>
<p>Software<br>Software: JaamSim (open-source DES software, Apache License 2.0)<br>File: Roman_blinds_JaamSim_model.cfg<br>Requires Java 7 or later and a graphics driver supporting OpenGL 3.0.<br>JaamSim can be downloaded free of charge from https://jaamsim.com/<br>Model description</p>
<p>The model represents the following production stages, modelled as a network of entity generators, queues, processors and an assembly point:</p>
<p>Cutting of fabric<br>Sewing of tunnels ("pockets")<br>Assembly of the blind (combining fabric, cords and rods)<br>Trimming<br>Addressing / packaging before shipment</p>
<p>All processing stages draw on a single shared Resource (employee), reflecting that workers in this firm are not permanently assigned to a single task but are flexibly allocated across stages. The base model uses a capacity of 3 employees (employee Capacity { 3 }); this value can be changed in the input editor to reproduce the staffing-level experiments described in the article (1–5 employees).</p>
<p>Processing times are represented as triangular distributions (minimum / mode / maximum), estimated jointly with the company owner in the absence of historical production data (no ERP/MES records were available). Each simulation run represents one 8-hour working day (Simulation RunDuration { 8 h }).</p>
<p>Assumptions and simplifications<br>Machine failures, unplanned stoppages and worker breaks are not modelled.<br>Processing times are expert-estimated (via an interview with the company owner) rather than measured from historical records.<br>The model was verified through face validity only: simulated daily output at the current staffing level (3 employees) was compared with the number of units the firm typically completes on a demanding day.</p>
<p>These simplifications are appropriate for the staffing/process-reorganisation decision the model was built to support, but should be kept in mind before reusing the model for other purposes.</p>
MedSegDiff thigh MRI model based on MyoSegmenTUM v0.0.0
Authors: Moore, Candace Makeda
Type: Model
Published: 2026-07-29

<p>These initial diffusion models are based on an adaption of MedSegDiff for MRI of the thigh ( the specific code that created them is available by the repository https://github.com/drcandacemakedamoore/dissector).  For each of the gracili and sartorius muscles there is a seperate model (they are therefore named  L_gracilis, R_gracilis, L_sartorius, R_sartorius with the capital letter denoting the side) — the diffusion architecture trains one binary segmentation model per muscle rather than a single multi-class model. </p>
<p class="font-claude-response-body break-words whitespace-normal">The original MedSegDiff (citation sources):</p>
<p class="font-claude-response-body break-words whitespace-normal"><strong>MedSegDiff (MIDL 2023):</strong></p>
<p class="font-claude-response-body break-words whitespace-normal">Wu, J., Fu, R., Fang, H., Zhang, Y., Yang, Y., Xiong, H., Liu, H., & Xu, Y. (2023). MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model. In <em>Medical Imaging with Deep Learning</em>.</p>
<p class="font-claude-response-body break-words whitespace-normal"><strong>MedSegDiff-V2 (arXiv 2023):</strong></p>
<p class="font-claude-response-body break-words whitespace-normal">Wu, J., Ji, W., Fu, H., Xu, M., Jin, Y., & Xu, Y. (2023). MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer. <em>arXiv preprint arXiv:2301.11798</em>.</p>
<p class="font-claude-response-body break-words whitespace-normal"> </p>
PGTL-AC model source code
Authors: Xu, Ming
Type: Model
Published: 2026-07-30

<p>PGTL-AC: A physics-guided transfer learning approach for atmospheric correction of tower-based water remote sensing at extreme zenith angles</p>
<p>This deposit accompanies the manuscript "A physics-guided transfer learning approach for atmospheric correction of low-altitude remote sensing at extreme zenith angles" (under review). Journal and citation information will be added upon publication.</p>
<p>Contents:<br>(i) 6S couple Hydrolight dataset generate.py — coupled 6S–HydroLight simulation script that generates the synthetic "physical prior" dataset by propagating water-leaving reflectance spectra through the atmosphere under a combinatorial matrix of aerosol optical depths (AOT550 = 0.1–0.8), aerosol models (maritime, urban, continental), solar zenith angles, and extreme sensor zenith angles (80–88°). This script requires a local 6S installation and Py6S, and reads a HydroLight-generated water-leaving Rrs library (output5_withCHL.txt, not included — an intermediate product of the commercial HydroLight software).<br>(ii) PGTL-AC.py — the complete CatBoost-based transfer learning pipeline: pre-training on the synthetic dataset, grid-search hyperparameter optimization with embedded five-fold cross-validation, fine-tuning on the in situ dataset, and independent validation, producing accuracy metrics (R2, MAE, RMSE, MAPD, Bias) and figures for all seven bands (412, 443, 490, 555, 670, 748, 865 nm).<br>(iii) in.xlsx / out.xlsx — a representative example dataset of the coupled 6S–HydroLight synthetic simulation library. The deposited example dataset is sufficient to execute and verify the complete PGTL-AC pipeline end to end; the full 1,036,800-sample library can be regenerated exactly with the deposited generation script.<br>(iv) in situ data in.xlsx / in situ data out.xlsx — the complete quality-controlled in situ dataset (167 matched tower-based spectral pairs: 151 for fine-tuning + 16 for convergence monitoring).<br>(v) valid data in.xlsx / valid data out.xlsx — the complete strictly independent test set (50 pairs: 47 from the Bahe River + 3 from the Dongtou Archipelago).</p>
<p>Model weights are not shipped: all pre-trained and fine-tuned weights are generated automatically when PGTL-AC.py is executed, and all random seeds are fixed, so the reported results are exactly reproducible from the deposited code and data alone.</p>
<p>Requirements: Python ≥3.9, catboost, numpy, pandas, scikit-learn, scipy, matplotlib, joblib, openpyxl. The optional dataset regeneration step additionally requires 6S and Py6S (https://py6s.readthedocs.io/).</p>
<p>License: source code under MIT License; datasets under CC BY 4.0. When using these resources, please cite the manuscript and this deposit.</p>
DevTwin AI: методология и функционал
Authors: Nagapetyan, Artur
Type: Model
Published: 2026-07-30

<p>Диагностика процессов разработки через модель внедрения искусственного интеллекта. Документ двухслойный: основной текст читается без формул, подробности — в раскрывающихся блоках. DevTwin AI — сервис, который ставит команде разработки диагноз: где она находится в работе с искусственным интеллектом, что её держит, куда ей можно двигаться, а куда нельзя и почему. Результат — не презентация с обещаниями, а расчёт, у которого каждое число имеет источник. Движок владеет числами. Языковая модель владеет языком. Ни одно число в отчёте не появляется иначе как расчётом, и у каждого видно, откуда оно взялось.</p>
DevTwin AI — Методология v5.3.1 (объединённая)  Цифровой двойник процесса разработки на оси «человек → ИИ»: что на входе, что внутри, что на выходе (+ DevTwin AI: методология и функционал)
Authors: Nagapetyan, Artur
Type: Model
Published: 2026-07-30

<p>*Единый источник контекста продукта DevTwin. Документ описывает: понятийный каркас, систему координат (пять осей), формальное вычислительное ядро из девяти слоёв + ко-симуляция (с формулами, числовыми примерами, эскизами и полным кодом), конвейер обработки из одиннадцати шагов, состав выходных артефактов, визуальный язык и **два технологических пути реализации — Python + React и R/Shiny**.*</p>
<p>*Методология разработана командой DevTwin. Её опоры — открытая научная литература (теория очередей, дискретно-событийная симуляция, системная динамика, агентное моделирование, process mining, причинный вывод, байесовская статистика, многокритериальная оптимизация под неопределённостью) и публичные отраслевые исследования по метрикам инженерной эффективности. Всё, что относится к системе координат A×C×P×V×B, исчислению переходов, guards, детекторам, множителям эффекта, политике контроля, закону верификационного потолка, оптимальной глубине декомпозиции, ABM-слою, DiD-калибровке и Pareto-анализу, — собственная разработка DevTwin.*</p>
A Coupled WRF-CLM5 Framework for Simulating Carbon-Nitrogen Dynamics and Biogeochemical Feedbacks
Authors: Li, Yarong
Type: Model
Published: 2026-07-29

<p><span>We develop and present a new online coupling framework</span> <span>WRF-CLM5</span><span>, </span><span>that embeds the state-of-the-art biogeochemical processes from CLM5 (including the FUN nitrogen fixation and uptake module, the LUNA leaf nitrogen utilization scheme, and the Medlyn stomatal conductance model) into the WRF model. </span></p>
Symphonie v3.8.9
Authors: Marsaleix, Patrick, Michaud, Heloise, Sous, Damien, Guélard Ancilotti, Emile
Type: Model
Published: 2026-06-03

<p>The archive contains a compressed tar file containing the source code for the Symphonie phase-resolving wave model v3.8.9, originally described in Marsaleix et al. (2019). This version was used for the Ricoré simulation described in Michaud et al. (2026), submitted to the journal GMD.</p>
FESOM2: non-conforming sea ice
Authors: Gärtner, Jan P., Danilov, Sergey
Type: Model
Published: 2026-07-29

<p>Source code of the model FESOM2 of the publication Gärtner, J. P., and Danilov, S. (2026) Sharper Resolution of Arctic Sea Ice Dynamics with Non-Conforming Finite Elements in FESOM2.</p>