• Energy Research
• Research software close

Data and code from Daudt et al. (2025) Estuarine, Coastal and Shelf Science [https://doi.org/10.1016/j.ecss.2025.109405]

We analysed the global geographical characteristics of how extreme surface air temperature and rainfall have evolved, based on the recurrence rate of record-breaking events, and found hot spots with anomalously high as well as regions with anomalously low numbers of record-breaking events. The recurrence rate was defined as the proportion of the actual count of record-breaking events over time to the number expected in a hypothetically stable climate. In a stable climate, the data is independent and identically distributed (iid) if the data is sampled at intervals that makes the autocorrelation between data points negligible. Anomalous recurrence rates indicate shifts in the tails of statistical distributions, and our analysis of record-high annual mean surface air temperatures revealed highest recurrence rates in the tropics, as opposed to the polar regions with the fastest warming. We present new evidence for extremely hot years becoming more common and widespread over the 1950-2023 period, based on recurrence rates as well as the global surface area fraction with daily mean surface air temperature exceeding 30°C and 40°C. A similar analysis for annual total precipitation highlights regions with increasingly more extreme annual precipitation as well as record-low annual precipitation typically associated with drought conditions. A multi-model ensemble of 306 runs with global climate models (CMIP6 SSP2-45) reproduced the statistics of record-breaking high annual mean surface air temperatures, but there were some differences with the reanalysis on annual total precipitation record-breaking recurrence rates. The global climate model simulations suggested a slightly altered geographical pattern for record-breaking annual precipitation recurrence rates, especially over parts of the Arctic. Analysis using R and R-markdown script. Data from the ERA5 and NCEP2 reanalyses as well as global glimate models (CMIP6 SSP2-45).

Description: The DGSD-VNE (DiGitization for resource-aware Subgraph Detection in Virtual Network Embedding) algorithm consists of three main phases: Graphize-VNE, GraphDetect-VNE, and ReverseGraphize-VNE. In the Graphize-VNE phase, the algorithm digitizes the edges of the Network Infrastructure and Virtual Network Requests through two steps: RequestPoint Conversion, which converts VNR edges into a digital format compatible with the infrastructure, and EdgePoint Conversion, which digitizes the infrastructure edges into a set of points. The GraphDetect-VNE phase is divided into two subphases: MidpointGraphing, which uses the K-Nearest Neighbors (KNN) algorithm to reduce the search space by treating infrastructure edges as data points and mapping them to a 2D plane through midpoint calculation, and Constraint Satisfaction and Structure Preservation, which ensures that the VNR is mapped onto the infrastructure while respecting structural and resource constraints. If structure preservation and constraint satisfaction are successful, the edges are added to the Selected list, allocated for a specific duration, and deallocated when the allocation period expires. Finally, the ReverseGraphize-VNE phase converts the digitized, embedded VNR back to its original graph form through ReversePointConversion, ensuring the VNR is restored to its graph-oriented structure for further analysis or processing. This simulation environment deploys a medium-sized network infrastructure consisting of 65 virtual nodes distributed across 15 substrate nodes. These virtual nodes interconnect with other virtual nodes with a link probability from0.5 to 0.9, forming the network infrastructure. Consequently, the total number of edges in this network is 1881. The processing capacity of each network infrastructure node, falls within the range of 60 to 80, following a uniform distribution. Additionally, the Link Capacity of network infrastructure edges falls in the range of 60 to 80. In the case of VNRs, their arrival rate follows an exponential distribution with λ= 0.25 to 0.75 as it is used traditionally. The processing capacity of requested VNR nodes, falls in the range of 40 to 60 following a uniform distribution, the Link Capacity of requested VNR links falls in the range of 40 to 60 and the entire system simulates for 400 VNRs with k = 10 as it gives better acceptance rate. Dependencies: 1. matplotlib 3.9.4 2. networkx 3.2.1 3. numpy 2.0.2 Usage: 1. Run the DGSD_VNE.py file to generate the dataset according to the simulation environment setup. 2. Measure the performance metrics by adjusting the values as needed. Note: The dataset used in this study is generated dynamically by running the Python code itself.

With this model, we investigate resource extraction and labor conditions in the Global South as well as implications for climate change originating from industry emissions in the North. The model serves as a testbed for simulation experiments with evolutionary political economic policies addressing these issues. In the model, heterogeneous agents interact in a self-organizing and endogenously developing economy. The economy contains two distinct regions – an abstract Global South and Global North. There are three interlinked sectors, the consumption good–, capital good–, and resource production sector. Each region contains an independent consumption good sector, with domestic demand for final goods. They produce a fictitious consumption good basket, and sell it to the households in the respective region. The other sectors are only present in one region. The capital good sector is only found in the Global North, meaning capital goods (i.e. machines) are exclusively produced there, but are traded to the foreign as well as the domestic market as an intermediary. For the production of machines, the capital good firms need labor, machines themselves and resources. The resource production sector, on the other hand, is only located in the Global South. Mines extract resources and export them to the capital firms in the North. For the extraction of resources, the mines need labor and machines. In all three sectors, prices, wages, number of workers and physical capital of the firms develop independently throughout the simulation. To test policies, an international institution is introduced sanctioning the polluting extractivist sector in the Global South as well as the emitting industrial capital good producers in the North with the aim of subsidizing innovation reducing environmental and social impacts. The DOI pointing to this resource is a `concept version` representing all versions of this computational model and will always redirect to the latest version of this computational model. See https://zenodo.org/help/versioning for more details on the rationale behind a concept version DOI that rolls up all versions of a given computational model or any other digital research object.

Executable files of building simulation models that will enable to simulate pre-defined building archetypes while tailoring the model by changing some input variables, such as envelope characteristics, technological solution sets for the HVAC system and weather data. Two building archetypes are available representing low rise and high rise multi-residential buildings with the project defined Plus Energy Buildings solution sets. The models allow for tailored input parameters options to account for 4 different climatic and cultural geoclusters characteristics. Detailed HVAC models and controls are available for two different solutions: 1) centralized HVAC system and 2) decentralized HVAC service performed by a compact heat pump unit for heating, cooling, dehumidification and Domestic Hot Water (DHW). The models have been developed with TRNSYS software (version 18.02), and have been exported into executable files and used by anyone, without the limitations of needing a TRNSYS license or having to be expert users of this simulation software.

PowProfiler is a tool to build computations energy models for power critical heterogeneous embedded devices. It models the overall energy, average power, and the resulting battery state of charge of the heterogeneous device in the function of software configuration, predicting the effect of various schedules on computations energy. It supports multiple embedded devices and enables energy and battery awareness in different optimization techniques.

{"references": ["Letay and Bett, Eur. Photovoltaic Sol. Energy Conf., Proc. Int. Conf., 17th, 2001, 178\u2013181. Source of the solar spectrum.", "Amillo et al, Remote Sens., 2014, 6, 8165. PVGIS database. Source of the irradiance data.", "Qiao et al, Chem. Soc. Rev., 2014, 43, 631. Source of some Gibb's Free energy data.", "Hong et al, Anal. Methods, 2013, 5, 1086. Source of some Gibb's Free energy data.", "Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source Scientific Tools for Python, 2001-, http://www.scipy.org/ [Online; accessed 2018-11-29].", "L. Kou, D. Labrie, and P. Chylek, Appl. Opt., 1993, 32, 3531-3540. Data on optical properties of water.", "R. M. Pope and E. S. Fry, Appl. Opt., 1997, 36, 8710\u20138723. Data on optical properties of water."]} YaSoFo was created in the search for a tool that extends detailed-balance calculations, which are common in photovoltaics to understand and improve solar cells, to solar fuel applications. The idea is that any parameter, from light absorption in the electrolyte over catalyst performance to electrochemical load can be varied in a scriptable loop. In doing so, one can determine the efficiency-limiting bottlenecks of a solar fuel device. The implementation in Python makes the tool platform-independent and easily extensible. The software is hosted at https://codeberg.org/photon/YaSoFo. v1.5.1 is an update that includes new functions with respect to external climatic parameters and charge-carrier recombination.

Pyra (name based on Python and Ra) is a software that automates the operation of EM27/SUN measurement setups. Operating EM27/SUN devices requires a lot of human interaction. Pyra makes it possible to autonomously operate these devices 24/7. Pyra has enabled the Technical University of Munich to collect continuous data from 5 stations around the city of Munich since 2019 using MUCCnet. Versions 1 to 3 of Pyra have been experimental tools, improved internally since 2016. The goal of Pyra version 4 is to make it even more stable, easy to understand and extend, and usable by the broad EM27/SUN community. The software is licensed under GPLv3 and is open-sourced here, on GitHub (https://github.com/tum-esm/pyra). Whenever using Pyra, please make sure to cite the codebase. This research has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (grant nos. CH 1792/2-1, INST 95/1544, PI: Jia Chen).

Codes to reproduce the manuscript 'Multi-decadal trends of low-clouds at the Tropical Montane Cloud Forests'

Finish Your Loops is a research prototype for automated analysis and structuring of Techno/House tracks. The pipeline splits the signal into harmonic and percussive layers (HPSS), computes operator-based dynamics features (e.g., escalation/slope, trend, residual), and marks musical events (energy shifts, harmonic changes, escalations). Events are snapped to the grid and exported as DAW-friendly cut plans (JSON/labels) — designed as an assistant mode, not an “autonomous DJ.”