reapxdev/zenodo-scraper huggingface dataset:
«Breve introdução à...» História do Conhecimento
Authors: Simões, Catarina, Araújo Branco, Isabel
Type: Video/Audio
Published: 2026-07-31

<p>De carácter inclusivo e rejeitando uma perspectiva marcadamente eurocêntrica da história da ciência, a História do Conhecimento foca-se em ideias percepcionadas como conhecimento. Como explica a historiadora Catarina Simões, no centro estão, portanto, as sociedades, e não os indivíduos e as suas crenças individuais. Assim, a História do Conhecimento trata dos saberes e práticas informais, técnicas artesanais, crenças e percepções, tradições orais e cultura popular. É dado ênfase à grande pluralidade de sistemas de saber, destacando, por exemplo, as epistemologias indígenas e outras tradicionalmente arredadas da historiografia. «Em vez de se tratar apenas o cientista que ficou associado a uma obra científica, estuda-se cada vez mais os informadores, intérpretes, guias, preparadores e assistentes de trabalho técnico que, de diversas formas, participavam na produção de conhecimento», explica Catarina Simões. A entrevista é conduzida por Isabel Araújo Branco.</p>
<p>Catarina Simões, investigadora do CHAM e historiadora da época moderna. Na sua tese de doutoramento estudou a presença e a apropriação política de animais não europeus na corte portuguesa no Renascimento, e o papel cultural que esses animais desempenhavam na Europa no mesmo período. Entre 2017 e 2023, Catarina Simões trabalhou no Museu Nacional de História Natural e da Ciência da Universidade de Lisboa, onde estudou colecções de ciência colonial, refletindo sobre os legados do império português e as narrativas produzidas sobre o património cultural e material a ele associado. O seu projecto de pós-doutoramento foca-se nas interseções entre história imperial e história natural, com uma abordagem centrada nas relações entre humanos e animais não-humanos, bem como nos processos de produção de conhecimento e nos seus diferentes agentes.</p>
El Congreso Geológico Internacional de 1926. Cien años después. Capítulo III Paleontología de vertebrados
Authors: Romero Prados, Amalia, carcavilla, luis, Alfonso, Arribas Herrera, Díaz Losada, Elisabeth
Type: Video/Audio
Published: 2026-07-31
Keywords: Paleontología, Vertebrados, IGME, CSIC, Congreso Geológio

<p>En este capítulo III Paleontología de vertebrados, descubriremos cómo esta disciplina geológica ha evolucionado y contribuido, desde el Congreso Geológico Internacional de Madrid de 1926 hasta la actualidad, a reconstruir la historia de la vida en la Tierra y cómo se ha gestionado el patrimonio en dos puntos muy distintos del planeta.</p>
<p>En esta ocasión es Alfonso Arribas, investigador del Instituto Geológico y Minero de España (IGME, CSIC) y coordinador científico de la Estación Paleontológica Valle del río Fardes (Granada) quien nos lo cuenta.</p>
Asymmetric Flow Dynamics in a Pneumatically Actuated PDMS Micropump: Numerical Simulation and Experimental Validation
Authors: Hsu, Pei-Pei, Lin, Jr-Lung
Type: Video/Audio
Published: 2026-07-30

<p class="x_p">This study evaluates the long-term operational stability and continuous fluid delivery performance of the proposed micropump <span>with passive check valves</span>. Over a 10-minute continuous transportation test, the system successfully maintained stable flow rates and reliable membrane actuation without significant degradation.</p>
EP 3 DIALOGO CON EXPERTOS - LA IA LLEGA PARA QUEDARSE
Authors: PEDROZA, ANGELO, PEDROZA, BREINYS
Type: Video/Audio
Published: 2026-07-30

<p><span>En este episodio hablamos con experto en creación y desarrollo de software, nos hablan de los sesgos y de las oportunidades y criterios a la hora de la creación todo esto desde un enfoque o desde la vista de la migración. </span></p>
Adaptive Traffic Signal Control Model for a Single Isolated Intersection Using Deep Reinforcement Learning
Authors: Hana _, Hamzawy
Type: Model
Published: 2026-08-03

<p><strong><span>Traffic congestion has major social, environmental, and economic consequences, and adaptive traffic signal control is one approach for improving intersection performance under changing demand. This study presents a reinforcement-learning-based controller for a single isolated intersection and compares its performance with a firefly-based heuristic. The main contribution is a normalized composite reward that combines queue length and vehicle waiting time. These measures are correlated, but they describe different operational aspects of intersection performance: queue length reflects spatial occupancy and potential spillback, whereas waiting time reflects the temporal delay experienced by vehicles. A weighting parameter (β) is used to vary the relative emphasis assigned to the two measures. The proposed reward was compared with queue-only and waiting-time-only rewards under the tested simulation conditions. The results indicate that the composite reward can provide a tunable compromise between the two measures, although it does not consistently outperform the single-metric rewards for every performance indicator and traffic-demand level</span></strong></p>
Figure 2b (Angle-resolved RTA - Reflectance, Transmittance, Absorptance)
Authors: Quynh, Le-Van
Type: Model
Published: 2026-08-03

<p>This are the Comsol Multiphysics simulation files for a perovskite NCs on a dielectric 1D Photonic Crystal.</p>
<p>Other data sets, raw data, simulation files will be updated upon later right after the manuscript is published.</p>
An interpretable spatial-contextual graph transformer for groundwater level prediction with heterogeneous anthropogenic-activity information
Authors: Chen, Ning, Liang, Wei, Gou, Fen, Xu, Xinyao, Yao, Yingying, Ma, Enze, Yan, Jianwu, Lü, Yihe, Gao, Nan
Type: Model
Published: 2026-08-03
Keywords: Spatial-Contextual Graph Transformer (SCGT); Groundwater level prediction; Heterogeneous graph learning; Spatiotemporal modeling; Anthropogenic activities; Relation-aware attention; Explainable artificial intelligence; Multi-step forecasting

<p>The <strong>Spatial-Contextual Graph Transformer (SCGT)</strong> is an interpretable heterogeneous spatiotemporal graph-learning model for multi-step groundwater-level prediction. It represents monitoring wells, coal mines, and groundwater-abstraction grids as different node types and models well–well, mine–well, and abstraction–well relationships within a unified graph. SCGT integrates groundwater-level and hydro-meteorological sequences, spatial attributes, and annually updated anthropogenic-activity states through node-specific encoders, relation-aware attention, relation-gated message fusion, and multi-horizon decoding. It also supports multi-level interpretation of temporal contributions, node-source effects, relation-channel dependence, and source–receptor responses. Detailed information on the repository structure, input data formats, parameter settings, and execution procedures is provided in the README file.</p>
AdaSemSeg: Trained Model Checkpoints for Adaptive Few-Shot Semantic Segmentation of Seismic Facies
Authors: Saha, Surojit, Whitaker, Ross Tyler
Type: Model
Published: 2026-08-02
Keywords: few-shot learning, semantic segmentation, seismic facies, seismic interpretation, meta-learning, self-supervised learning, SimCLR, Gaussian process, deep learning

<p>Trained model checkpoints accompanying the paper "AdaSemSeg: An Adaptive Few-Shot Semantic Segmentation of Seismic Facies" (Saha & Whitaker, IEEE Transactions on<br>Geoscience and Remote Sensing, 2025, DOI: 10.1109/TGRS.2025.3595010).</p>
<p>Includes weights for all three seismic facies datasets studied in the paper (F3,Parihaka, Penobscot) under both 1-shot and 5-shot settings, following a leave-one-out meta-training protocol: each checkpoint is meta-trained on the two source datasets and evaluated on the held-out target dataset.</p>
<p>Contents:<br>- adasemseg_checkpoints.zip — AdaSemSeg best-model checkpoints, organized as <dataset>/<shots>-shot/bestmodel.pth.tar (6 files: F3, Parihaka, Penobscot x<br>  1-shot, 5-shot)<br>- protosemseg_checkpoints.zip — ProtoSemSeg (competing prototype-based few-shot baseline) checkpoints, same <dataset>/<shots>-shot layout (6 files)<br>- simclr_checkpoint.zip — SimCLR ResNet-50 checkpoint used to initialize the shared image encoder via self-supervised pretraining on unlabeled seismic data</p>
<p>Usage instructions and the corresponding evaluation/reproduction code are at the companion GitHub repository: https://github.com/Surojit-Utah/AdaSemSeg. See<br>checkpoints/scenarios.json in the repository for the exact mapping between each checkpoint and the paper's reported results.</p>
GiriGuru™ Mentorship, Values & Professional Growth Decision Support System
Authors: Gupta, Girish
Type: Model
Published: 2026-08-01
Keywords: mentorship; medical education; faculty development; reflective practice; professional development; character development; succession planning; guru-shishya parampara; sponsorship; entrustable professional activities; feedback literacy; professionalism; competency-based education; CanMEDS; ACGME; decision support system; clinical decision support system; health professions education; medical mentoring; legacy; generativity; self-assessment tool; GIRISH prompting architecture; GiriGuru; NeonatAIlogy; responsible AI in education; India; neonatology

<p class="font-claude-response-body break-words whitespace-normal">GiriGuru™ is a Character & Development Support System (CDSS) — an interactive, values-based mentorship, reflection, and professional development tool for health professionals. It is not a clinical decision support system and makes no diagnostic or treatment recommendations.</p>
<p class="font-claude-response-body break-words whitespace-normal">Built as a single-file, offline-capable HTML application, GiriGuru™ operationalizes the GiriGuru Operating Cycle™ — a nine-phase framework for mentorship and character development — through nine interlinked interactive modules: a Five-Pillar Model of the whole professional; a unified 30-minute Transformative Session (facilitator script); the Growth Compass™, a ten-domain self-assessment generating an individualized development plan; ten branching Mentor Decision Pathways covering common mentorship dilemmas (feedback, distress, ethical concerns, authorship disputes, and more); the Successor Development Pathway™, the first structured succession-readiness instrument in this series, grounded in the guru-shishya parampara alongside contemporary sponsorship and entrustment literature; a GIRISH-M™-aligned Five-Minute Reflection Engine with local reflection timeline; the Legacy Footprint™, a four-dimension (reach, depth, continuity, multiplication) contribution mapper; a Daily Guru Challenge; and a transparent Evidence-to-Feature Matrix stating, feature by feature, how validated each construct currently is.</p>
<p class="font-claude-response-body break-words whitespace-normal">The architecture is conceptually aligned — not presented as formally endorsed — with ACGME competency domains, the CanMEDS physician-roles framework, and WHO guidance on ethics and governance of AI in health. This first edition is a developmental educational prototype; formal validation, stakeholder co-design, usability testing, cultural adaptation, and outcome evaluation are required before institutional appraisal or research use.</p>
GiriGuru™ Mentorship, Values & Professional Growth Decision Support System
Authors: Gupta, Girish
Type: Model
Published: 2026-08-01
Keywords: mentorship; medical education; faculty development; succession planning; sponsorship; reflective practice; professional identity formation; entrustment; decision support system; Guru Purnima; guru-shishya parampara; leadership development; neonatology education; graduate medical education; values-based learning; coaching; feedback literacy; GiriGuru; NeonatAIlogy

<p class="font-claude-response-body break-words whitespace-normal">GiriGuru™ is an interactive, single-file, offline-capable Decision Support System for mentorship, values-based professional development, and structured succession planning in health professions education. Built around one named architecture — the GiriGuru Operating Cycle™ (Observe → Listen → Understand → Guide → Practise → Reflect → Empower → Release → Multiply) — it integrates nine linked modules: the Five-Pillar Model, a unified 30-minute Transformative Session, the Growth Compass (a self-reflection instrument across ten professional domains), ten branching Mentor Decision Pathways for common teaching and supervision dilemmas, the Successor Development Pathway™ (a nine-stage succession framework with explicit safeguards against favouritism, exclusivity and exploitation), a GIRISH-M™-aligned Reflection Engine with a local reflection timeline, the Legacy Footprint™ (a four-dimension model of professional contribution — Reach, Depth, Continuity, Multiplication), the Daily Guru Challenge, and a transparent Evidence-to-Feature Matrix distinguishing evidence-informed features from exploratory ones.</p>
<p class="font-claude-response-body break-words whitespace-normal">Developed for Guru Purnima teaching sessions and year-round faculty mentorship in academic medicine, the tool is explicitly a developmental and reflective aid, not a validated psychometric instrument, credentialing tool, or employment appraisal system. It requires no installation, server, or internet connection — a single HTML file runs offline in any modern browser.</p>
Appendix 4 - Tools for method replicability: WU spreadsheet templates
Authors: BOZZA, Sara, ISMAELLI, TOMMASO, Kosmopoulos, Lorenzo
Type: Model
Published: 2026-02-15

<p>This is the Appendix 4 of the article "The Workmanship Unit: an innovative methodology for analyzing and documenting production processes in stone architecture. Theory, tools, and case studies from Rome and Tripolis ad Maeandrum" - "L’Unità di Lavorazione: una metodologia innovativa di analisi e documentazione dei processi produttivi nell’architettura in pietra. Teoria, strumenti e casi studio da Roma e Tripolis ad Maeandrum", https://doi.org/10.19272/202615201002.</p>
<p>The provided tools are the Italian and English version of the Workmanship Unit spreadsheet. </p>
<p> </p>
An uncertainty-aware spatial overlay framework
Authors: Keshtkar, Hamidreza, Bagheri Bodaghabadi, Mohsen, Rimal, Bhagawat, Izadi, Parastoo
Type: Model
Published: 2026-08-01

<p>R code for an uncertainty-aware species distribution modeling framework. Four machine learning algorithms are calibrated and combined into a TSS-weighted ensemble; spatial uncertainty is then quantified via the coefficient of variation across models and used, together with predicted habitat suitability, to delineate confidence-stratified priority conservation zones. </p>
Archetypen für deutsche Bürogebäude der Baujahre 1970, 1998 und 2026 - Detaillierte Dokumentation der architektonischen und bauphysikalischen Gebäudemodelle im IFC 4x3 Schema
Authors: Dannapfel, Verena, Müller, Moritz, Streblow, Rita, Müller, Dirk, Karber, Lisa, Hickertz, Alexander
Type: Model
Published: 2026-07-31

<p>Im Forschungsprojekt Klassiqua wurden Büro-Archetypen für die Baualtersklassen 1970, 1998 und 2026 auf Grundlage statistischer Erhebungen entwickelt. Die Baujahre wurden mit dem Ziel gewählt, einen Altbau vor Inkrafttreten der ersten Wärmeschutzverordnung im Jahr 1977 zu zeigen, einen Zwischenbau und einen aktuellen Neubau.<br>Die vorliegende Veröffentlichung beschreibt diese Archetypen sowie einbezogene Vorgaben und Referenzen bei der Ausarbeitung. Die Archetypen wurden unter Nutzung von Building Information Modeling (BIM) mit der openBIM-Software Blender Bonsai im IFC 4x3-Schema erstellt und stehen auch über den Projektkontext hinaus für die Nutzung durch Dritte zur Verfügung. Der Detaillierungsgrad entspricht einer Entwurfsplanung nach HOAI-Leistungsphase 3 (LOD 300).</p>
<p>Die Bürogebäude-Archetypen dienen als Grundlage für energetische Jahressimulationen mit dem Ziel die Effekte von repräsentativen Smart Readiness Indicator (SRI)-Maßnahmenpaketen zu quantifizieren. Alle drei Archetypen basieren auf demselben architektonischen Grundmodell, welches mit baualterspezifischen Außenhüllen kombiniert wird. <br>Den Baualtersklassen wurden überdies typische technische Anlagen für Erzeugung und Übergabe zugeordnet, welche nachfolgend auch als Techniksets bezeichnet werden. Diese werden im weiteren Projektverlauf in der Simulation abgebildet und sind nicht in den IFC-Modellen enthalten. </p>
<p>Hinweis: Da Revit 2026 Kompatibilitätsprobleme mit dem IFC 4x3-Schema aufweist, sollten die IFC-Dateien dort als Verknüpfung geöffnet werden.</p>
Lifetime PBK model for PFOA and PFOS
Authors: Kalina, Jiří, Jiří, Komprda, Sapunova, Daria, Vogs, Carolina, Deepika, Kumar, Vikas, Westerhout, Joost, Ratier, Aude, Pachoulide, Chrysanthi, Kruisselbrink, Johannes, Engel, Jasper, Fletcher, Tony, Andersson, Axel, Piler, Pavel, Šenk, Petr, Komprdová, Klára
Type: Model
Published: 2026-07-31
Keywords: PBK, PFAS, uncertainty

<p class="MsoNormal"><span>The validated EFSA </span><span>lifetime PBK model modified by Westerhout </span><span>with 9 compartments (lung, skin, blood (arterial, venous), gut, liver, fat, kidney, filtrate and rest of the body) modified by incorporating excretion via menstrual blood and feces and extended to a lifetime framework, allowing simulations to be performed from birth while accounting for age-dependent changes in all physiological parameters.</span></p>
Analytical fits to Jupiter's auroral ovals derived from Juno/JIRAM L-band observations
Authors: Castagnoli, Chiara, Manni, Francesca, Mura, Alessandro, Moirano, Alessandro, Migliorini, Alessandra, Noschese, Raffaella, Sordini, Roberto, Tosi, Federico, Altieri, Francesca, Cicchetti, Andrea, Plainaki, Christina, Piccioni, Giuseppe, Sindoni, Giuseppe
Type: Model
Published: 2026-07-31

<p>The files</p>
<ul>
<li><code>coordinates_auroral_oval_fit_manni-castagnoli_2026_north.csv</code></li>
<li><code>coordinates_auroral_oval_fit_manni-castagnoli_2026_south.csv</code></li>
</ul>
<p>contain the coordinates of the analytical fits to Jupiter’s northern and southern main auroral ovals derived from Juno/JIRAM L-band (3.3–3.6 μm) images of H₃⁺ emission acquired between 2016 and 2022. Each file provides the fitted oval coordinates as longitude and planetocentric latitude in degrees (<code>longitude_deg</code>, <code>latitude_deg</code>), together with the corresponding Jupiter-centered System III Cartesian coordinates in kilometers (<code>x_km</code>, <code>y_km</code>). The coordinates represent the analytical models of the northern and southern main auroral ovals presented in Manni & Castagnoli (2026) and can be used as reference geometries for auroral mapping, comparison with observations, and studies of Jupiter’s magnetosphere–ionosphere coupling.</p>
<p>The files</p>
<ul>
<li><code>jiram_scan_points_manni-castagnoli_2026_north.csv</code> </li>
<li><code>jiram_scan_points_manni-castagnoli_2026_south.csv</code></li>
</ul>
<p>provide the original System III Cartesian coordinates (<code>x_km_jiram</code>, <code>y_km_jiram</code>) of the points along the main auroral ovals selected in Juno/JIRAM L-band images and used as input data for the analytical fitting procedure in Manni & Castagnoli (2026).</p>
Red Noctiluca Simulator
Authors: Mitra, Aditee
Type: Model
Published: 2026-07-31

<p>The red <em>Noctiluca</em> simulator describes ecophysiological processes of red <em>Noctiluca scintillans</em> within a 1-D water column. The total mixed layer depth (MLD) is divided into 1 m depths with vertical eddy diffusion between the blocks and across the ergocline; the latter describes movements into and out of the lowest block of the mixed layer depth. The mixed layer depth can be altered to represent nearshore (e.g., 5 m) to offshore (e.g., 30-50 m) coastal waters. </p>
<p>For further details about the simulator and the operation manual please see:</p>
<ul>
<li>Mitra, A. (2025). Developing a simple digital twin of red <em>Noctiluca scintillans</em>. In: <em>Defra Pelagic Monitoring Programme (mNCEA, ME4528) Report NC34 WP2 D28.1</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.16933559">https://doi.org/10.5281/zenodo.16933559</a></li>
<li>Mitra, A. (2025) Red <em>Noctiluca </em>Simulator: Operation Manual. In: <em>Defra Pelagic Monitoring Programme (mNCEA, ME4528) Report NC34 WP2 D28.2</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.16933521">https://doi.org/10.5281/zenodo.16933521</a></li>
</ul>