A. Lyoubi-Idrissi | I-Field Laboratory -

SISAL_monv1: a global database of cave monitoring observations

Abstract. Cave monitoring is the process of collecting observational data such as microclimate conditions, hydrogeochemistry and water movement in cave systems. The most common motivation is for speleothem science – the reconstruction of past climate and environmental variability from cave formations such as stalagmites. Applications also include the monitoring of potential recharge to aquifers, as well as cave conservation. PAGES-SISAL (Past Global Changes – Speleothem Isotope Synthesis and AnaLysis), an international working group focused on speleothem science, has created a new global database of cave monitoring data consisting of drip rates and drip water isotopes, and the modern carbonate precipitates that form from these drip waters (so called farmed carbonates, grown on artificial substrates). Moreover, we report meteoric precipitation amounts and water isotopes at or near to cave sites. The draft version of the database can be found at https://repo.researchdata.hu/privateurl.xhtml?token=43a43257-f06e-4dc5-9e96-6603eabe775f, which will be available under doi: 10.5158/ARP/29W5J3 upon publication. The database contains datasets from 75 caves, with summary information including meta-data on location, elevation, cave depth, lithology, measurement methods and citations for original publications. Speleothem records previously curated by SISAL in SISAL speleothem database versions and corresponding to monitored drip sites are also identified in the new SISAL monitoring database. To supplement observational data gaps and provide accessible and consistent climate data for users, surface climate (precipitation, evaporation, temperature) and meteoric water isotope data extracted from global climate model products are also included in the database.

Neurodegeneration as Thermodynamic Failure: A Unified Framework for Alzheimer's, Parkinson's, ALS, and Huntington's Disease

We introduce a unified field-theoretic framework that identifies neurodegeneration as a fundamental failure of thermodynamic stability. The core of this theory is the I-field, a scalar field representing the local density of accumulated entropy. This field accumulates wherever neural tissue dissipates energy, propagates along axonal pathways, and modulates ionic conductances through a conformal suppression mechanism. Its evolution is governed by a single master equation:$$\gamma\,\partial_t\mathcal{I} - D\,\nabla^2\mathcal{I} + m^2\,\mathcal{I} + \lambda\,\mathcal{I}^3 = \kappa\,P_\text{diss}$$       We demonstrate that the four primary neurodegenerative pathologies represent specific and predictable failure modes of this dynamics. Alzheimer’s disease emerges as a failure of metabolic clearance. Parkinson’s disease is characterized by a blockade of spatial transport. Amyotrophic lateral sclerosis results from the hyper-production of entropy, and Huntington’s disease is driven by the collapse of the field’s structural self-regulation.       By analyzing these field dynamics, we derive a dimensionless collapse index, $\Phi$, which quantifies the thermodynamic distance between a healthy state and the point of no return. When this index exceeds unity, the functional attractor of the neural substrate vanishes, making clinical collapse a thermodynamic necessity.      Unlike traditional models, this framework avoids reliance on empirical rate functions or fitted parameters. It provides a first-principles bridge between non-equilibrium thermodynamics and neural electrophysiology. This approach yields falsifiable predictions regarding representational drift rates and spatial field signatures, offering a new physical foundation for the early diagnosis and thermodynamic classification of neurodegenerative disease.

Rechtliche Herausforderungen und Lösungsvorschläge zu Open Data in der klinisch-psychologischen Forschung | PsychArchives

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