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1,020 results for “Storage”
Modelled root zone storage capacities Hubbard Brook
Modelled root zone storage capacities for the Hubbard Brook Forest - Watershed 2. Root zone storage capacities were derived based on a simple water balance based model (Nijzink et al. 2016). Long term equilibrium root zone storage capacities yearly root zone storage capacities were determined.
GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present
<p>Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional investigation of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940-2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent datasets such as the land-ocean mass budget, large-scale water balance in 341 large river basins, and streamflow measurements at 10,168 gauges. We find that the proposed approach performs overall as well as or is more reliable than previous TWS datasets. Moreover, our reconstructions successfully reproduce the impact of climate variability, such as strong El Niño events. GTWS-MLrec dataset consists of three reconstructions based on JPL, CSR and GSFC mascons, three detrended and de-seasonalized reconstructions, and six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a broad range of applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management.</p><p>Please cite the reference: <strong>Yin J, Slater L, Khouakhi A, et al. GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present. Earth System Science Data. 2023.</strong></p><p>For any inquiry about the dataset, welcome to contact Dr. Jiabo Yin (jboyn@whu.edu.cn).</p>
Carbon storage in old hedgerows: The importance of below-ground biomass
<p>Dataset to the manuscript: Drexler, S., Thiessen, E., & Don, A. (2023). Carbon storage in old hedgerows: The importance of below-ground biomass. GCB Bioenergy. https://doi.org/10.1111/gcbb.13112</p><ul><li>Drexler_et_al_2023-cn_biomass: contains the data on the biomass C/N measurements</li><li>Drexler_et_al_2023-overallstocks: contains the calculated carbon stocks per subplot for all carbon pools</li><li>Drexler_et_al_2023-soc_cropland: contains the calculated soil organic carbon stocks (0-100cm soil depth) of the reference cropland</li><li>Drexler_et_al_2023-soc_weight_fine_roots: contains the raw data on the dry weight of the fine roots and the raw data on the soil samples (C/N data, dry weight, stone/root fraction) per subplot and sampling depth</li><li>Drexler_et_al_2023-weight_above_ground_biomass: contains the raw data on the dry weight of the harvestable biomass and biomass of the mature trees per subplot</li><li>Drexler_et_al_2023-weight_coarse_roots_litter: contains the raw data on the dry weight of the coarse roots, litter and stumps per subplot</li></ul>
Optimizing Production and Storage: Carlsberg's Injection-Molding Operations
<p>The research paper investigates the optimization of production and storage for a custom molder, using a dataset that includes production times, weekly production hours, stockroom capacity, storage space per case, contribution per case, and customer limits for different types of glass produced using specific dies. The paper aims to determine the optimal production quantities for each type of glass to maximize the total contribution, taking into account production constraints and customer demand.</p>
Database for machine learning of hydrogen storage materials properties
<p><strong>Database for machine learning of hydrogen storage materials properties</strong></p> <p>Matthew Witman<sup>a</sup>, Mark Allendorf<sup>a</sup>, Vitalie Stavila<sup>a</sup></p> <p><sup>a</sup>Sandia National Laboratories, Livermore, CA</p> <p> </p> <p><strong>Description</strong></p> <p>This ML-HydPARK dataset provides a csv file of metal hydride compositions, capacities, and thermodynamic values that can be used as target properties for building, training, and testing machine learning models. It has been parsed and cleaned from the DOE’s original publicly available HydPARK database according to the procedure in [1] to make it more suitable for immediate use with data-driven models. Generally, this removed duplicate entries, removed entries missing critical data, and attempted to fix various entries with obvious errors in the data. It is continuously updated under version control as new metal alloy hydrides are published in the open literature. Most entries contain data on the enthalpy and entropy of the hydriding reaction, as well the maximum hydrogen capacity, for which compositional machine learning models can be trained [1,2].</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors gratefully acknowledge research support from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Fuel Cell Technologies Office through the Hydrogen Storage Materials Advanced Research Consortium (HyMARC). This work was supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy<br> or the United States Government.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Witman, M.; Ling, S.; Grant, D. M.; Walker, G. S.; Agarwal, S.; Stavila, V.; Allendorf, M. D. Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning. <em>J. Phys. Chem. Lett</em>. <strong>2020</strong>, 11, 40–47.</li> <li>Witman, M.; Ek, G.; Ling, S.; Chames, J.; Agarwal, S.; Wong, J.; Allendorf, M. D.; Sahlberg, M.; Stavila, V. Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability. <em>Chem. Mater</em>. <strong>2021</strong>, 33, 4067–4076.</li> </ol> <p> </p> <p><strong>Contact</strong></p> <p>Please email <a href="mailto:mwitman@sandia.gov">mwitman@sandia.gov</a> , <a href="mailto:mdallen@sandia.gov">mdallen@sandia.gov</a>, or <a href="mailto:vnstavi@sandia.gov">vnstavi@sandia.gov</a> for questions or to request addition of recent data from the literature to this dataset.</p>
Global rooting zone water storage capacity and rooting depth estimates
<p>Global rooting zone water storage capacity (<em>S</em><sub>CWDX80</sub>, mm) and rooting depth (<em>z</em><sub>CWDX80</sub>, mm) estimates from Stocker et al., (2023). </p> <p>Additional global maps for rooting zone water storage capacity and rooting depth are provided and may be used as vegetation model forcing. These are created using the code from <code>whc_forcing_map.Rmd</code> , available <a href="https://github.com/geco-bern/mct/blob/master/whc_forcing_map.Rmd">here</a> (Zenodo entry: https://doi.org/10.5281/zenodo.7429129). The following steps were taken for creating these maps:</p> <ol> <li>The relationship between vegetation height and rooting depth was fitted using quantile regression (lower 10%) and data from Tumber-Davila et al. (2023). This yields a lower-bound rooting depth.</li> <li>A global map of vegetation height (Simard et al., 2011) was used for predicting the lower-bound rooting depth distribution globally.</li> <li>The lower-bound rooting depth was converted into a lower-bound root zone water storage capacity following methods as described in Stocker et al. (2023).</li> <li>The maximum of the lower-bound rooting depth and the inferred rooting depth (<em>z</em><sub>CWDX80</sub>) from Stocker et al., (2023) was determined for each grid cell. This is what's in the file <code>zroot_cwdx80_forcing.nc</code>. Anaologusly for <code>cwdx80_forcing.nc</code>.</li> </ol> <p>Please cite published paper:</p> <div> <div>Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nat. Geosci., 1–7, <a href="https://doi.org/10.1038/s41561-023-01125-2">https://doi.org/10.1038/s41561-023-01125-2</a>, 2023.</div> </div> <p> </p>
Dataset for the article "Frequency regulation with storage: On losses and profits"
<p>This dataset complements the article <em>"Frequency regulation with storage: On losses and profits"</em> by Dirk Lauinger, François Vuille, and Daniel Kuhn, available at <a href="https://doi.org/10.1016/j.ejor.2024.03.022">https://doi.org/10.1016/j.ejor.2024.03.022</a> and at <a href="https://arxiv.org/pdf/2306.02987v2.pdf">https://arxiv.org/pdf/2306.02987v2.pdf</a>. </p> <p>The dataset contains the following files:</p> <p>1. <strong>Case_study.ipynb</strong>, which relies on the datafiles <strong>delta_10s.h5</strong>, <strong>pa.h5</strong>, <strong>pb.h5</strong>, <strong>pd.h5</strong>, and on the excel files <strong>conso_mix_RTE_2019.xls</strong> and <strong>ReserveAjustement_2019.xlsx</strong> to construct all figures in the article. The jupyter notebook is also available at <a href="https://github.com/lauinger/cost-of-frequency-regulation-through-electricity-storage">https://github.com/lauinger/cost-of-frequency-regulation-through-electricity-storage</a>.</p> <p>2. <strong>delta_10s.h5</strong>, which contains normalized frequency deviations with a 10s resolution from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw frequency measurement data in <strong>build_delta_10s.rar</strong>. The frequency measurements are taken from the website of the French transmission system operator RTE: <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=public_transmission_system&type=network_frequencies">https://www.services-rte.com/en/download-data-published-by-rte.html?category=public_transmission_system&type=network_frequencies</a> (link live as of 7 June 2023).</p> <p>3. <strong>pa.h5</strong>, which contains availability prices for delivering frequency regulation to RTE from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. The availability price data are taken from the website of the French transmission system operator RTE:<br><a href="https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=procured_reserves">https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=procured_reserves</a> (link live as of 7 June 2023).</p> <p>4. <strong>pd.h5</strong>, which contains delivery prices for delivering frequency regulation to RTE from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. The delivery price data are taken from the website of the French transmission system operator RTE:<br><a href="https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=actived_offers">https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=actived_offers</a> (link live as of 7 June 2023).</p> <p>5. <strong>pb.h5</strong>, which contains utility prices for a subscribed apparent power of 9kVA from the state regulated <em>"tarif bleu"</em> of EDF, the largest electricity provider in France. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. Current price data (including taxes and transportation fees) is available at <a href="https://particulier.edf.fr/content/dam/2-Actifs/Documents/Offres/Grille_prix_Tarif_Bleu.pdf">https://particulier.edf.fr/content/dam/2-Actifs/Documents/Offres/Grille_prix_Tarif_Bleu.pdf</a>. The French Energy Department publishes the electricity prices in the official French Government journal. The corresponding legal texts are accessible under the following links:<br>01/11/2014 - 31/07/2015: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000033172637">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000033172637</a><br>01/08/2015 - 31/07/2016: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000030954456">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000030954456</a><br>01/08/2016 - 31/07/2017: <a href="https://www.edf.fr/sites/default/files/contrib/collectivite/electricite-et-gaz/CGV%2018avril/jo_du_29_juillet_2016_trv.pdf">https://www.edf.fr/sites/default/files/contrib/collectivite/electricite-et-gaz/CGV%2018avril/jo_du_29_juillet_2016_trv.pdf</a><br>01/08/2017 - 31/01/2018: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000035297675">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000035297675</a><br>01/02/2018 - 31/07/2018: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000036559814">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000036559814</a><br>01/08/2018 - 31/05/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000037262170">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000037262170</a><br>01/06/2019 - 01/08/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038528381">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038528381</a><br>01/08/2019 - 31/12/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038850867">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038850867</a><br>These prices exclude taxes and transportation fees. The <em>"Option Base"</em> offers a flat price throughout the day. The <em>"Option Heures Creuses"</em> has a higher price for peak (6:00-22:00) than off-peak (22:00-6:00) hours. EDF refers to peak hours as <em>"Heures Pleines (HP)"</em> and to off-peak hours as <em>"Heures Creuses (HC)"</em>. The prices of these options only change, when EDF changes its electricity tariffs, which is up to three times per year. Conversely, the <em>"Option Tempo"</em> is a pricing scheme in which each day is either a high-price (<em>"Rouge"</em>), medium-price (<em>"Blanc"</em>) or low-price (<em>"Bleu"</em>) day. The price level of each day is announced by 10:30 am on the previous day. RTE, the French transmission system operator, keeps track of the daily price levels (<a href="https://www.services-rte.com/en/view-data-published-by-rte/schedule-of-Tempo-type-supply-offerings.html">https://www.services-rte.com/en/view-data-published-by-rte/schedule-of-Tempo-type-supply-offerings.html</a>). The price data can be downloaded from RTE's eco2mix platform (<a href="https://www.rte-france.com/eco2mix/telecharger-les-indicateurs">https://www.rte-france.com/eco2mix/telecharger-les-indicateurs</a>).</p> <p>6. <strong>conso_mix_RTE_2019.xls</strong>, which contains total French electricity demand throughout the year 2019 with 15 minute resolution. This data is provided by RTE, the French electricity system operator, at <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=consumption&type=short_term">https://www.services-rte.com/en/download-data-published-by-rte.html?category=consumption&type=short_term</a> (link live as of 27 March 2024).</p> <p>7. <strong>ReserveAjustement_2019.xlsx</strong>, which contains the quantities and availability price for four reserve products: "réserve primaire" (primary frequency regulation, which we use in this article), "réserve secondaire", "réserve rapide", and "réserve complémentaire", with 30 minute resolution throughout the year 2019. This data is provided RTE, the French electricity system operator, at <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=market&type=balancing_capacity&subType=procured_reserves">https://www.services-rte.com/en/download-data-published-by-rte.html?category=market&type=balancing_capacity&subType=procured_reserves</a> (link live as of 27 March 2024).</p>
Data and Results of eELib Simulations for the User-Based Multi-Use of Battery Storage Systems
<p>The dataset contains the configuration for the eElib models (model_data.json) and computed simulation results (.hdf5-files). The following scenarios were computed:</p> <ul> <li>ave_A_static-eq</li> <li>ave_A_static</li> <li>ave_A_dynamic_charging</li> <li>ave_A_fully_dynamic</li> <li>ave_B_static-eq_bss</li> <li>ave_B_static_bss</li> <li>ave_B_dynamic_charging_bss</li> <li>ave_B_fully_dynamic_bss</li> <li>MELANI_static</li> <li>MELANI_static_equal</li> <li>MELANI_dynamic_charging</li> <li>MELANI_fully_dynamic</li> </ul> <p><strong>Description of syntax of simulation results:</strong></p> <ul> <li>average (ave_B is half the size of the BSS of ave_A) and MELANI describe the considered multi-family house</li> <li>static/ static / dynamic_charging / fully_dynamic are the three developed operating strategies for the user-based multi-use</li> <li>static-equal: the allocation keys are equally, i.e., the PVS and BSS capabilities are equally distributed among the households of the multi-family house</li> </ul> <div> <div><strong>As part of the publication:</strong></div> <div>Henrik Wagner, Constantin von Lützow, Marcel Lüdecke, Michel Meinert, Bernd Engel "Empowering Collective Self-Consumption in Multi-Family Houses: User-Based Multi-Use of Residential Battery Storage Systems", 23rd Wind & Solar Integration Workshop 2024, Helsinki, Finland, doi: 10.1049/icp.2024.3901</div> <div> </div> <div><strong>Changelog:</strong></div> <div>v2: Added doi for WIW 2024 conference paper to improve citation possibilities</div> </div>
Floodplain Sediment Storage Times in a Simulated Meandering River
<p>This dataset contains four files which enable another researcher to bypass the most computationally intensive parts of the simulations (in MATLAB) associated with the publication of my dissertation thesis. These files result as the analysis of the Simulation of the Long-Term Evolution of a Meandering River (https://doi.org/10.5281/zenodo.5651840).</p> <p>The first of these files contain the storage time and age distributions of simulated sediments from the upstream reach of the simulated river, captured after analyzing the upstream 16 mini reaches of the floodplain.</p> <p>The second file contains the same distributions from two reaches after analyzing 36 mini reaches (the downstream reach values can be found by subtraction of the data in these two files)</p> <p>The third file contains other useful parameters tracked throughout the simulation</p> <p>The fourth file contains the X & Y coordinates which mark the boundaries of each mini reach</p> <p>See the accompanying dissertation document (to be referenced once the official reference is available) and the GitHub repository which includes the code required.</p>
Passive Perching with Energy Storage for Winged Aerial Robots Dataset
<p>This dataset corresponds to the publication:</p> <p>"Passive Perching with Energy Storage for Winged Aerial Robots" W. Stewart, L. Guarino, Y. Piskarev, and D. Floreano. Advanced Intelligent Systems, <a href="http://doi.org/10.1002/aisy.202100150">http://doi.org/10.1002/aisy.202100150</a></p>
An Effective Activation Method for Industrially Produced TiFeMn Powder for Hydrogen Storage [Dataset related to publication]
<p>Data type: XRD patterns; SEM micrographs and EDX maps; particle size distributions; atomic concentrations; hydrogen loading profiles; kinetic models; volume expansions. </p> <p>Data format: *.opj; *.tif.</p> <p>Origin of the data: laboratory equipment from Hereon (XRD, SEM, PSD Analyzer, BET, XPS, Sievert apparatus) and UniPV (SEM).</p> <p>Software needed to plot the data: folders need to be unzipped, Origin.</p>
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
TiFe0.85Mn0.05 alloy produced at industrial level for a hydrogen storage plant
<p>Data type: XRD patterns; SEM and EDX results, hydrogen sorption data (pcT-curves, absorption/desoprtion curves). </p> <p>Data format: *.opj; *.tif.; *docx; *jpg</p> <p>Software needed: Origin.</p>
Historical Annual Revenue of Energy Storage on European Electricity Markets
<p>This dataset provides the optimized annual revenue for 96 generic storage technologies (12 efficiency x 8 discharge duration). It covers 17 European electricity markets for up to 16 years (Austria, AT; Belgium, BE; Switzerland, CH; Czech Republic, CZ; German, DE; Spain, ES; France, FR; Italy, IT; Ireland, IR; Netherlands, NL; Nordpool which includes Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden, NP; Poland, PL; Portugal, PT; Romania, RO; Slovakia, SK; United Kingdom, UK). The optimization is an adaptation of the model presented in Gaudard et al. [2013]. It assumes perfect foresight assumption and a stochastic algorithm. Therefore, the results are an approximation of the maximum rather than the absolute optimum. Further information is provided in "Gaudard L. and Madani K., Energy storage race: Has the monopoly of pumped-storage in Europe come to an end?, forthcoming". </p> <p>The following information is provided:</p> <p>Country: Code of the specific market (also the filename)</p> <p>Currency: The currency in which the results are expressed</p> <p>Discharge duration [hours]: The time required to empty at full nominal power a device that is fully charged. </p> <p>Efficiency: Ratio between the amount of discharged and charged energy during a full cycle.</p> <p>Year: From January 1st to December 31st.</p> <p>The given numbers are in euros or GBP per year and normalized to 1kWh of energy storage. This means that for a specific device, the given figures must be multiplied by the volume of energy storage (in terms of kWh). As an example, for an energy device with the efficiency of 0.95, discharge duration of 6h and volume of energy storage of 2000kWh, the revenues in 2003 in Austria would be 9.26 x 2000=18520 euros. </p> <p>For any questions or further requirements, please feel free to get in touch with the authors.</p>
GRAiCE: Terrestrial water storage anomalies reconstructions
<p>Terrestrial Water Storage (TWS) is the total amount of freshwater stored on and below the Earth’s land surface, including surface water, groundwater, soil moisture, snow, and ice. As a result, TWS is a crucial variable of the global hydrologic cycle, representing an essential indicator of water availability.</p> <p>Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on (GRACE-FO) have been measuring temporal and spatial variations of TWS, namely the Terrrestrial Water Storage Anomalies (TWSA), enabling the monitoring of global hydrological changes over the last two decades. However, the lack of observations prior to 2002 along with the temporal gaps in GRACE/GRACE-FO time series limit our understanding of long-term variations of global freshwater availability.</p> <p>In this study, we use Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) neural networks and two sets of predictors to develop four global monthly reconstructions of TWSA from 1984 to 2021 at 0.5º spatial resolution (GR<em>Ai</em>CE). The first set of predictors is given by a combination of five fundamental meteorological forcings and data on vegetation dynamics, whereas the second set of predictors includes the five meteorological forcings only. Specifically, the meteorological predictors are monthly averaged data of total precipitation, snow depth water equivalent, surface net solar radiation, surface air temperature, and surface air relative humidity. We derive data on vegetation dynamics from a long-term reconstruction of solar-induced fluorescence (SIF), which represents a proxy for photosynthesis. Each model is trained with monthly TWSA data from the GRACE JPL mascon dataset. The GR<em>Ai</em>CE dataset accurately reproduces GRACE/GRACE-FO observations at the global scale and across different climatic regions. Moreover, we found that our models predict observed TWSA better than a previous reference reconstruction and produce reliable estimates of the water budget at the river basin scale. Beyond generating long-term continuous TWSA time series, our models allow us to detect and examine TWS changes due to climate variability/change.</p> <p>This repository contains the GR<em>Ai</em>CE dataset and includes four files in netCDF format. The dataset provides monthly TWSA estimates from 1984 to 2021 at a 0.5º spatial resolution. TWSA values are expressed in terms of cm of equivalent water thickness. GRAiCE_LSTM.nc and GRAiCE_BiLSTM.nc files contain TWSA reconstructions obtained from LSTM and BiLSTM models fed with all predictors (i.e., including SIF data), respectively. GRAiCE_LSTMnoSIF.nc and GRAiCE_BiLSTMnoSIF.nc files contain TWSA reconstructions obtained from LSTM and BiLSTM models fed with meteorological forcings only (i.e., without SIF data).</p>
The BEV*ARV Project; the Preservation Conditions of Museum Collection Storages in Denmark.
<p>A national survey on the preservation condition in Danish state subsidised museums’ storages was conducted in 2022-23. The collected data has been anonymized and is open for further study and research.</p> <p>The survey consisted of 25 questions (<em>BEV.ARV_Spørgeskema</em>) responded during physical inspections of the storages. 103 museums participated in the survey, 350 buildings and more than 850 storage rooms were physically inspected, and the results recorded. Upon inspection, the preservation/degradation risks addressed in each question were rated according to an A-B-C-D scale. Character A is the best (no degradation risk), D is the lowest (high degradation risk). A guideline (<em>BEV.ARV_Svarvejledning</em>) was used to assist uniform evaluations of the storage conditions.</p> <p>The survey covered museums with art collections, cultural history collections and natural history collections. The indoor climate over one calendar year was recorded in around five hundred of the storage rooms.</p> <p>The collected and uploaded data contain information on the condition of the buildings used for storages, the condition of the storage rooms and the objects stored therein, and how the museums manage and control their collection storage rooms. The survey method has been developed for future inspections and comparative reports of the storage conditions of museum collections.</p> <p>The files included in the datasets have been used in the report to the Ministry of Culture. Furthermore, the data has been applied for making individual museum storage reports with scores and characters for each storage facility. The data is available in Danish only.</p> <p>Content of the folder <strong>Klimadata</strong>:</p> <ul> <li>The file BEV.ARV_2024.04_Dataoversigt provides information regarding type of museum and whether climate data from the storages have been collected or not. </li> <li>The Excel files (<em>M00X-files</em>), one per museum, provides the climate data (Relative Humidity, RH % and Temperature, T °C) from the individual storages, naming corresponding to those applied in the file BEV.ARV_2024.04_Anonymiseret_Raadata.csv</li> </ul> <p>Content of the folder<strong> Rapporter_Supp.Info</strong>:</p> <ul> <li>The file BEV.ARV_2024.04_Anonymiseret_Raadata.csv holds the complete inspection results for all museum storages (the buildings and their rooms).</li> <li>The file BEV.ARV_2024.04_Karaktermodel+Analyse contains analyses carried out and used in the overall report to the Ministry of Culture.</li> <li>For completeness, the report to the Ministry of Culture<em> (BEV.ARV_Slutrapport)</em>, an example of an individual museum storage report ( BEV.ARV_<em>Magasinrapport_Museum_X</em>) the 25 survey questions (<em>BEV.ARV_Spoergeskema</em>) and the response guidelines (<em>BEV.ARV_Svarvejledning</em>), all in Danish, are included. </li> </ul>
Terrestrial water storage changes across the contiguous United States from GPS and GRACE, 2007–2017
<p>In this dataset, we provide terrestrial water storage anomalies (TWSA) from 2007-2017 at weekly time scales derived using Global Positioning System (GPS) displacements, further constrained by lower-resolution TWSA observations from the Gravity Recovery and Climate Experiment (GRACE).</p> <p>There are six fields in the HDF5 product provided here:</p> <ol> <li>'/cmwe', which provides terrestrial water storage in units of cm. of water equivalent.</li> <li>'/latitude', latitude at the center of each 0.5 degree grid cell</li> <li>/longitude', longitude at the center of each 0.5 degree grid cell</li> <li>'/time', time in days since January 1st, 2007. The resolution of our time series is weekly, and the first day in our record is January 3rd, 2007.</li> <li>'/signal_to_noise_ratio', the variance of the signal divided by variance of noise for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> <li>'/uncertainty', 95% confidence interval for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> </ol> <p>As a condition of using these data, we request that you acknowledge the authors of this data set by citing the following peer-reviewed publication. </p> <p>Adusumilli, S., Borsa, A. A., Fish, M. A., McMillan, H. K., & Silverii, F. (2019). A decade of water storage changes across the contiguous United States from GPS and Satellite Gravity. <em>Geophysical Research Letters</em>, 46, 13006-13015. <a href="https://doi.org/10.1029/2019GL085370">https://doi.org/10.1029/2019GL085370</a></p>
Ecohydrology of interannual changes in watershed storage
<p>Supplemental file Table S1 accompanying article DOI:10.1029/2019WR025164 published in Water Resources Research. Fields include mean absolute deviation in watershed storage (mean_ds) and other hydrologic fluxes for 2002-2011 period for each US Geological Survey station listed in first column. Header contains specific information about variables and units. Access the main article at <a href="https://doi.org/10.1029/2019WR025164">https://doi.org/10.1029/2019WR025164</a>.</p>
Ubuntu One multi-cloud object storage trace sublement to "SkyPIE: A Fast & Accurate Oracle for Object Placement"
<p>These are the workload traces of Ubuntu one used in the evaluation of "SkyPIE: A Fast & Accurate Oracle for Object Placement".</p> <p>These traces derive diverse multi-cloud access pattern on object stores from the trace published in "Dissecting UbuntuOne: Autopsy of a Global-Scale Personal Cloud Back-End." The derivation is described in the SkyPIE paper.</p> <p>The traces are stored in Parquet file format, hence can be read with a Parquet reader such as the one included in Pandas. The file names specify the number of regions issuing accesses and the percentage of accesses to object that originate from regions other than the home region, see the publication.</p>
Data for: Multi-year field measurements of home storage systems and their use in capacity estimation
<p>The dataset accompanies the Nature Energy publication by Figgener et al. (2024), Multi-year field measurements of home storage systems and their use in capacity estimation, <a href="https://doi.org/10.1038/s41560-024-01620-9">DOI 10.1038/s41560-024-01620-9</a>. <br><br>In addition, we use the dataset in Figgener et al. (2024), Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data, <a href="https://doi.org/10.48550/arXiv.2411.08025">DOI 10.48550/arXiv.2411.08025</a></p> <p>The ISEA / CARL of RWTH Aachen University measured 21 private home storage systems in Germany over up to eight years from 2015 to 2022. All these storage systems are combined with residential photovoltaic systems to increase self-consumption. The measured quantities published are system-level battery current, voltage, power, battery pack housing temperature, and room temperature. The sample rate is one second. The dataset consists of 106 system years, 14 billion data points, and 1,270 monthly files stored in 21 system folders. </p> <p>Use the data as follows:<br><br>1. Download the data (Data_ID_01.zip to Data_ID_21.zip) and the belonging repository (Metadata_and_Code.zip)</p> <p>2. Uncompress the files so that the uncompressed folders have the same name as the .zip files.</p> <p>3. Copy all data folders in folder "Metadata_and_Code/00_Data/01_Operational_Data". Read and execute the file "StartUp_Read_and_Execute.m" and stay in this folder for any script you execute. </p> <p>In addition, a detailed description of the dataset and how to use it can be found in the supplementary information of the publication.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.