Find research datasets worth reusing

opencc-by-4.0Sep 2023View details →

Data and code in support of "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower"

This dataset contains all the data and processing needed to produce results and figures reported in the manuscript "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower".   The README file guides through the material available to support replication of the results and figures.

Energy efficiency on Philips Lightings products for outdoor lighting

The dataset is a compilation of specifications and performance metrics for different lighting products from Philips Lighting catalogs. It spans various products across different technology types and years, which suggests a focus on the evolution and comparison of lighting efficiency over time. Here are some key points about the dataset: - **Product Information**: Each entry in the `Nombre` column provides specific details about a Philips Lighting product, likely including the model and technical specifications. - **Technology Classification**: The `Tecno` column classifies each product according to its lighting technology, such as LED, CDM, SOX, etc. This allows for analysis across different types of lighting technologies. - **Energy Consumption and Efficiency**: The dataset includes data on energy consumption (`Consumo`) and efficiency (`Efi(lm/w)` and `Efi2`). These metrics are crucial for understanding the energy cost of running the lights and for analyzing improvements in energy efficiency over time. Efi is the calculated energy efficiency from the catalogue data and the Efi2 is the reported energy eficiency. - **Light Output and Quality**: The `Lumens` and `CCT` columns provide information on the brightness and color temperature of the lighting products. This is valuable for assessing the quality and suitability of the light for various applications. - **Economic Considerations**: The `Precio` column, while not filled in for all entries, would give insights into the economic aspect of the lighting products, potentially allowing for cost-benefit analysis. - **Temporal Trends**: The `Año` column indicates the year associated with the product, which can be used to track changes and advancements in lighting technology over time. - **Product Longevity**: The `Vida` column, although unspecified in the dataset preview, would generally relate to the lifespan of the lighting product, an important factor in both consumer choice and sustainability considerations. In summary, this dataset serves as a resource for analyzing Philips Lighting products' performance over time, understanding trends in lighting technology efficiency, and potentially assisting in strategic decisions related to product development, marketing, and sustainability efforts.

Pre-built Swiss-Calliope sector-coupled energy model

<div> <h3>Swiss-Calliope prebuilt model</h3> <div>The model consists of Switzerland and its neighbours, as described in <a href="https://doi.org/10.1016/j.enconman.2024.118426" target="_blank" rel="noopener">Mellot et al., 2024</a>. Switzerland's heating and transport sectors are modelled on top of its electricity sector.</div> <div>The model is ready to be loaded into Calliope, for 2016--2018. This prebuilt was specifically designed for the study of Switzerland's winter deficit, but is easily modifiable for any other analysis. Refer to Calliope's <a href="https://calliope.readthedocs.io/en/stable/" target="_blank" rel="noopener">documentation</a> for information on how to do this.</div> <div>To run the same scenarios as for the Swiss winter deficit analysis, you need to do the following steps. Note that these scenarios were ran on ETH's Euler cluster which uses the slurm batch system. You can otherwise just adapt the following shell scripts to run the scenarios on other systems.</div> <div>1. Set up the conda environment with the correct version of calliope <code>conda env create -f environment.yaml</code>. On slurm systems you may also need to load gurobi <code>module load new gurobi/9.0.0</code>.</div> <div>2. Run the baseline scenarios, i.e. those corresponding to the EP2050+ configuration, by running <code>sh run_baselines 4</code>, where 4 corresponds to the time resolution.</div> <div>3. Once these runs are finished, run the python file <code>python read_baselines_and_fix_neighbours.py</code>. This will fix Switzerland's neighbouring countries' installed capacities for the next scenarios.</div> <div>4. Then you may run the study's scenarios by running <code>sh run_initial_scenarios.sh 4</code>, and the sensitivity analysis scenarios by running <code>sh run_sensitivies.sh 4</code>.</div> <div> </div> <div>The model's units are GW, GWh, Million euros, and Million kilometers.</div> </div>

Buoy-based detection of low-energy cosmic-ray neutrons (Seelhausener See, July 15 to Dec 02, 2014)

Contains two resources used in Schrön & Rasche et al. (2024): <ol> <li>Raw measurement data files from the buoy detector. Column names are provided in the header of the files. For detailed information about column names and descriptions, see the readme.</li> <li>Processed measurement data of the buoy detector. Data has been stored as CSV files, the column names are described in `Buoy.csv.readme`. Additional PDF files show the corresponding plots. Two versions of data are provided: <ol> <li>Buoy-1h contains data aggregated to 1 hour, and</li> <li>Buoy-1h-mavg25 contains the same data but the neutrons underwent a moving average filter with a window size of 25 (1 day).</li> </ol> </li> </ol> Processing has been performed using Corny v0.8.2 (<a title="Corny" href="https://git.ufz.de/CRNS/cornish_pasdy">git.ufz.de/CRNS/cornish_pasdy</a>) with the configuration file <code>Buoy-1h.cfg</code>.

Data for the publication "Radiative effects of precipitation on the global energy budget and Arctic amplification"

This dataset includes a set of 15yr simulations using the MIROC6 global aerosol-climate model with 1) diagnostic precipitation, 2) prognostic precipitation without radiative effect of precipitation, and 3) prognostic precipitation with radiative effect of precipitation. The data are used in the manuscript entitled "Radiative effects of precipitation on the global energy budget and Arctic amplification".

Results for the paper "The impact of temporal hydrogen regulation on hydrogen exporters and their domestic energy transition"

As global demand for green hydrogen rises, potential hydrogen exporters move into the spotlight. However, the large-scale installation of on-grid hydrogen electrolysis for export can have profound impacts on domestic energy prices and energy-related emissions. Our investigation explores the interplay of hydrogen exports, domestic energy transition and temporal hydrogen regulation, employing a sector-coupled energy model in Morocco. We find substantial co-benets of domestic climate change mitigation and hydrogen exports, whereby exports can reduce domestic electricity prices while mitigation reduces hydrogen export prices. However, increasing hydrogen exports quickly in a system that is still dominated by fossil fuels can substantially raise domestic electricity prices, if green hydrogen production is not regulated. Surprisingly, temporal matching of hydrogen production lowers domestic electricity cost by up to 31% while the effect on exporters is minimal. This policy instrument can steer the welfare (re-)distribution between hydrogen exporting firms, hydrogen importers, and domestic electricity consumers and hereby increases acceptance among actors.

Source data for "Halving the North Sea's offshore wind energy carbon footprint"

This dataset provides source data for the paper "Halving the North Sea’s offshore wind energy carbon footprint". It contains basic geographical factors, including wind speed, water depth, and distance from shore, and environmental impact intensities, including steel, Cu, and Al use, climate change, marine ecotoxicity, and marine eutrophication impacts. For more details, please refer to https://pubs.acs.org/doi/full/10.1021/acs.est.2c02183 and https://www.sciencedirect.com/science/article/pii/S1364032122004993.

Socio- and Techno-Economic Dataset for Energy Modelling in Sierra Leone

This repositary contains a Reference Energy Syatem (RES) and dataset containing the raw data used in the Sierra Leone energy models created by CCG and the Ministry of Energy in Sierra Leone including scenario-specific constraints used in the modelling. The models used were MAED and OSeMOSYS. Full information regarding data sources and assumptions used can be found in the corresponding Data in Brief. This work was supported by the Climate Compatible Growth Programme (#CCG) of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies.

Dataset related to article "Performance of dual-energy subtraction in contrast-enhanced mammography for three different manufacturers: a phantom study"

The dataset provided here contains the raw data used in the study discussed in this article. The primary objective of the study was to conduct a comparative analysis of the performance of dual energy subtraction (DES) images acquired using contrast-enhanced mammography (CEM) systems produced by three different manufacturers. The comparison, facilitated by a CEM-specific phantom, focused on the assessment of radiation dose and image quality. Composed of three separate CSV files, the dataset is structured as follows: 1) "Dose-related data": This file provides exposure parameters (including A/F combination, tube voltage and exposure) and the corresponding mean glandular dose (MGD for low-energy (LE) and high-energy (HE) images. Data are given for each CEM system and automatic exposure mode (AEC). 2) "CNR-related data": Encapsulated in this file are data extracted from the phantom DES images. These include measurements of the mean pixel value (MPV) for each iodinated contrast detail, as well as the MPV and standard deviation (SD) for the surrounding background. These data were used to calculate the contrast-to-noise ratio (CNR) for the iodinated contrast details. 3) “Residual CNR data”: This file includes MPV and SD data extracted from phantom DES images, which were useful for calculating the residual CNR after cancellation of the normal background tissue by the DES algorithm.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for hydro power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for hydroelectric generation from the open literature. Hydroelectric power is the largest source of renewable energy, supplying 15% of global electricity. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1011 datapoints from 11 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.  Technoeconomic data on utility-scale hydroelectric power was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for battery storage in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for battery energy storage systems from the open literature. Battery energy storage is the fastest growing form of power system flexibility, and will be critical to integrating large shares of variable renewable energy. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 671 datapoints from 18 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale batteries was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for wind power generation from the open literature. Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1506 datapoints from 28 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for gas power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for gas-fired power generation from the open literature. Natural gas supplies 23% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 620 datapoints from 14 sources.   The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.Technoeconomic data on new-build gas-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for coal power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for coal-fired power generation from the open literature. Coal supplies 35% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 345 datapoints from 12 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on new-build coal-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

Codes and Data for "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy"

This file contains the analysis code and a condensed version of data to reproduce figures in the paper "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy".

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for solar power generation from the open literature. Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 753 datapoints from 31 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.

opencc-by-4.0Nov 2024View details →

Papers and KPIs for the Evaluation of Renewable Energy Communities' Performance

This database contains the methodology used to explore and identify the papers that containes KPIs related to the evaluation of RECs performance. This methodology is divided into three phases:  1.1)    Papers Exploration – Comprehensive search of papers in the field of RECs using Scopus and Web Of Science databases; 1.2)    Papers Screening – Initial screening of collected literature based on research domain and accessibility; 1.3)    Papers Eligibility – Further filtering papers by extracting those that explicitly define KPIs through mathematical formulations in the context of the RECs.  In the Papers Exploration step, the authors conducted a systematic review of the state-of-the-art of literature on performance metrics in the context of the renewable energy community. The search was conducted in March 2024 using the search engines Scopus and Web Of Science (the used queries are detailed explain in thte database). The output of this phase is a large database of the most recent and relevant studies, cataloged by the following information: authors, article title, abstract, author keywords, index keywords, and year of publication. At this stage, only journal articles and research works published after 2010 were considered. In the Papers Screening phase, the articles are further filtered by the authors screening manually all papers based on keywords, titles, and abstracts, removing articles not relevant to the context of the RECs. In addition, articles for which it was not possible to access the full text are excluded. In the Papers Eligibility phase, the articles are entirely read to identify those articles that directly address the use of performance metrics. The eligibility criterion used by the reviewers’ team refers to the explicit definition of KPIs through mathematical formulas combined with their direct usage to evaluate RECs’ performances. The main objective of this phase is therefore to identify those articles that explicitly define and use KPIs, so that they can later be collected and labeled, based on their definition and usage. In additions, KPIs are extracted from the papers deemed elegible generating Tables A1, A2, A3 and A4. In these tables, similar KPIs are aggregated together in one single mathematical definition based on the methodology described in <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4991932">Key Performance Indicators for Renewable Energy Communities: A Comprehensive Review by Lorenzo Giannuzzo, Minuto Francesco Demetrio, Daniele Salvatore Schiera, Samuele Branchetti, Carlo Petrovich, Angelo Frascella, Nicola Gessa, Andrea Lanzini :: SSRN</a>

opencc-by-4.0Nov 2024View details →

Scripts and datas for "A unified energy-constrained mesoscale parameterisation for ocean climate models".

Scripts and datasets used for creating the results of a submitted work : R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak: A unified energy-constrained mesoscale parameterisation for ocean climate models. (submitted in JAMES). Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017"). The reference EKE of <a href="https://doi.org/10.1029/2023gl104688">Torres et al. (2023)</a> is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. <a href="https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18">World Ocean Atlas 2018</a>, <a href="https://gmd.copernicus.org/articles/13/3643/2020/">Tsujino et al. (2020)</a> and <a href="https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/04c79ece-3186-349a-e063-6c86abc0158c/">RAPID</a>) IPython notebooks for computing and plotting metrics are provided : <ul> <li>james-eke-heat_budget.ipynb : plots for heat transport and global heat storage (section 4.1)</li> <li>james-eke-southern_ocean.ipynb : plots for Southern Ocean (section 4.2) analysis</li> <li>james-eke-north_atlantic.ipynb : plots for North Atlantic and Labrador Sea (section 4.3) analysis</li> <li>james-eke-timeseries.ipynb : plot 0D metric timeseries for simulations (including spin-up)</li> </ul> Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.

opencc-by-4.0Aug 2024View details →

Exploring Gaussian processes for short-term forecasting in offshore energy systems: Supplementary material

Two supplementary videos are provided. The first video analyses the performance of wave excitation force forecasting across different horizons in a noise-free case. The second video examines the impact of noise on the forecast. Both videos include results from a Gaussian-based forecaster, an AR forecaster, and show the uncertainty bounds provided by the Gaussian forecaster. The variable analysed and forecasted in these videos is the wave excitation force.