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275 results for “Data Storage”
Supplementary data as part of the article "The role of carbon capture, utilization and storage for economic pathways that limit global warming to below 1.5 °C" (https://doi.org/10.1016/j.isci.2022.104237).
<p>Data of the plots in Figure 1a and Figure 1b, showing, respectively, the incremental and the integrated global CO<sub>2</sub> emissions for the 1.5 °C-committed decarbonization pathways from IPCC (P1, P2, P3, and P4 pathways) and the one originally obtained in this work (Q pathway).</p>
Data from: Effects of fixatives and storage duration on avian sperm morphology
<p><span>Sperm cells are variable both within and among species. To be able to accurately measure sperm cells and understand their function, it is important that sperm cells are preserved in a manner that maintains their structural integrity. </span><span>Formalin is a widely used fixative and storage medium for sperm cells, but few studies have examined the effect of fixation and long-term storage on their morphological integrity. Ethanol is also a common fixation and storage agent for tissue samples, and here we examine if fixation and storage in formalin or ethanol alters sperm cell size and structural integrity. We found no significant effects of the fixation process on fresh sperm cells fixed in formalin or ethanol. Further, there were no </span><span>consistent length changes in sperm cells stored in formalin or ethanol over a period of 227 days, or in sperm cells stored in formalin for three years. A comparison across 13-14 years of storage time showed a small but significant reduction in sperm cell length of 0.93%. Furthermore, sperm cells initially fixed in formalin remained quite stable in dry storage on glass slides for a minimum of six months (we found a mean reduction in sperm cell length of 0.18% after six months). The proportion of sperm cells with head damage was, however, much higher for samples stored in ethanol than for those stored in formalin. Overall, 70% of sperm cells had acrosome damage in ethanol versus only 3% in formalin. Finding intact sperm cells for measuring length therefore required greater effort in ethanol samples than in formalin samples. Our findings indicate that use of sperm cells from long-term storage for the study of sperm morphometrics is justified for either fixative, although formalin clearly preserves the sperm cells better. </span></p>
Original data for publications: Direct solution–based synthesis of the Na4(B12H12)(B10H10) solid electrolyte, Experimental investigation of Mg(B3H8)2 dimensionality, materials for energy storage applications, Thermal Conversion of Unsolvated Mg(B3H8)2 to BH4– in the Presence of MgH2
<p>Original data for the following papers (Only part of University of Geneva, synthesis part):</p> <p>Direct solution–based synthesis of the Na4(B12H12)(B10H10) solid electrolyte<br> Gigante, A.; Duchêne, L.; Moury, R.; Pupier, M.; Remhof, A.; Hagemann, H.<br> ChemSusChem 2019, 12, 4832-4837</p> <p>Experimental investigation of Mg(B3H8)2 dimensionality, materials for energy storage applications<br> Romain Moury, Angelina Gigante, Arndt Remhof, Elsa Roedern and Hans Hagemann<br> Dalton Trans., 2020,49, 12168-12173, https://doi.org/10.1039/D0DT02170A</p> <p>Thermal Conversion of Unsolvated Mg(B3H8)2 to BH4– in the Presence of MgH2<br> Angelina Gigante, Noemi Leick, Andrew S. Lipton, Ba Tran, Nicholas A. Strange, Mark Bowden, Madison B. Martinez, Romain Moury, Thomas Gennett, Hans Hagemann, and Tom S. Autrey<br> ACS Appl. Energy Mater. 2021, 4,3737-3747. https://doi.org/10.1021/acsaem.1c00159</p> <p> </p>
Data for "Mineral soils are an important intermediate storage pool of black carbon in boreal forests"
<p>This data is used in the publication "Mineral soils are an important intermediate storage pool of black carbon in boreal forests".</p> <p>The column plot_id uniquely represents each sample plot in the study and matches the columns of the same name in the complementary dataset "<a href="https://doi.org/10.5281/zenodo.5078669">Dataset for 'Climatic Variation Drives Loss and Restructuring of Carbon and Nitrogen in Boreal Forest Wildfire'</a>". Odd numbers are burnt plots, while the burnt plot_id plus 1 is its paired control.</p> <p>Columns are labeled with the name of sampled soil layer and entries are their associated BC:W values. BC:W is unitless (formed by dividing grams black carbon by grams sample weight). Empty spots mean there was no material at the plot to collect.</p>
Supplementary Data for "Exploring the Chemical Space of Metal–Organic Frameworks with rht Topology for High Capacity Hydrogen Storage"
<p>This dataset includes optimization and simulation inputs, optimized structures, building blocks, computed hydrogen uptakes and textural properties of the metal–organic frameworks that correspond with work in "Exploring the Chemical Space of Metal–Organic Frameworks with rht Topology for High Capacity Hydrogen Storage" (DOI: 10.1021/acs.jpcc.4c00638).</p>
Data for: Environmental DNA storage and extraction method affects detectability for multiple aquatic invasive species
<p>Environmental DNA (eDNA) refers to genetic material released by organisms into their surrounding environment. Collecting and identifying eDNA has gained popularity for monitoring and surveillance of aquatic invasive species. Invasive species management is most successful when an invasion is identified early while population size is likely to be low, highlighting the importance of eDNA detection sensitivity. Various factors influence DNA yield recovered from environmental samples. Environmental DNA storage and extraction methods, for example, can be adjusted to maximize DNA yield, thereby improving detectability. In this study, we compared the performance of two eDNA storage and extraction methods in detecting three common aquatic invasive species (<em>Bythotrephes longimanus</em>, <em>Dreissena polymorpha</em>, and <em>Faxonius rusticus</em>) across five natural ecosystems of Minnesota, United States. One method involved storing filters in 95% ethanol (EtOH) and extracting DNA using a DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany), whereas the other method used cetyl trimethylammonium bromide (CTAB) for storage and a phenol–chloroform–isoamyl (PCI) procedure for DNA extraction. We also investigated the effect of DNA extract volume (1 μL relative to 3 μL) in qPCR reactions on eDNA detections for the commercial kit method. The CTAB‐PCI method yielded significantly more positive detections, across all three species, compared to the EtOH‐Qiagen method. Moreover, we found that using 1 μL of DNA extract in qPCR reactions was equally effective as using 3 μL. To improve detections of aquatic invasive species, we recommend that researchers store eDNA sample filters in CTAB or a similar lysis buffer such as Longmire's solution and extract with PCI when feasible, but note that lower extract volumes might be used without negative effect when either increasing technical replicates or repurposing samples for the detection of multiple species.</p>
Experimental data for "An End-to-End Coding Scheme for DNA-Based Data Storage With Nanopore Sequenced Reads"
<p>The experimental dataset used in "An End-to-End Coding Scheme for DNA-Based Data Storage With Nanopore Sequenced Reads."</p> <p>A set of 91,766 150-nt oligos were synthesised with GenScript (oligos.fasta). Each oligo consists of a pseudo-random 110-nt payload flanked by 20-nt primers at each end. The strands are split in three roughly equal groups (two groups of 30,589 and one group of 30,588). Each group has a dedicated primer pair for targeted PCR amplification (the primer pairs used for amplification are provided in primers_synthesis.fasta). The pseudo-random payload was designed to avoid primer-payload collisions.</p> <p>For each file, a sample from the synthesised pool was PCR amplified using the corresponding primer pair and sequenced using Oxford Nanopore Technologies MinION sequencing device following the standard library preparation protocol for amplicon DNA. The raw reads were basecalled using guppy, either in fast- ("acc-false") or high-accuracy ("acc-true") regime. The basecaller generated two groups of reads—"passQ-true" for the reads that passed the quality-score threshold of 8 and "passQ-false" for those that did not. For each group of reads, a BLAST-based fuzzy search for primer sequences was performed and, based on the resulting alignments, the segments containing the correct primer pairs and located at a distance of 150+-15nt were extracted (separately for forward and reverse-complemented reads). The segments are then assigned to the closest synthesized strand based on Levenshtein distance. The resulting clusters are used to estimate the parameters of the end-to-end DNA storage channel model and to test the proposed error-correction scheme.</p> <p>The archive clustered_read_segments.tar.gz contains 12 sub-archives, for each file (0,1,2), accuracy ("acc-true" or "acc-false"), and Q-score ("passQ-true" or "passQ-false"). Within each sub-archive, there are two folders (one for forward read segments and one for backward read segments), and each folder contains two files: one for the reference synthesised (or "transmitted") sequences that correspond to the file in question ("TX__" — e.g., "TX__file=0_accBaCa=true_passQ=true_filter=true_forward_.txt") and another file for the sequenced (or "received") segment clusters ("RX__" — e.g., "RX__file=0_accBaCa=true_passQ=true_filter=true_forward_.txt"). The received clusters in the "RX__" file are ordered in correspondence with the synthesised sequences in the "TX__" file, and a line "===============================" is used as a separator.</p>
Data: Green ammonia imports could supplement long-duration energy storage in the UK
<div> <p>Data used for the analysis presented in 'Green ammonia imports could supplement long-duration energy storage in the UK'. </p> <p> </p> </div>
Data, code and supplementary material - Gone but not forgotten: dynamics of sperm storage and potential ejaculate digestion in the black soldier fly Hermetia illucens
Open the record for dataset details and reuse information.
Data for the Article "Maturity Assessment of Grid-Scale Flexibility and Energy Storage Services Towards a Decarbonized Europe"
<p>This dataset includes the data collected form various stakeholders regarding grid-scale flexibility and energy services as part of the SINNOGENES Project.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Cost and performance data for electricity generation and storage technologies
<p>Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92% of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV.</p> <p>The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5510 datapoints from 56 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 in future.</p>
Storage and Transit Time Data and Code
<p><br>Author: Andrew J. Felton<br>Date: 5/5/2024</p> <p>This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis and figure production for the study entitled:</p> <p>"Global estimates of the storage and transit time of water through vegetation"</p> <p>Please note that 'turnover' and 'transit' are used interchangeably in this project.</p> <p>Data information:</p> <p>The data folder contains key data sets used for analysis. In particular:</p> <p>"data/turnover_from_python/updated/annual/multi_year_average/average_annual_turnover.nc" contains a global array summarizing five year (2016-2020) averages of annual transit, storage, canopy transpiration, and number of months of data. This is the core dataset for the analysis; however, each folder has much more data, including a dataset for each year of the analysis. Data are also available is separate .csv files for each land cover type. Oterh data can be found for the minimum, monthly, and seasonal transit time found in their respective folders. These data were produced using the python code found in the "supporting_code" folder given the ease of working with .nc and EASE grid in the xarray python module. R was used primarily for data visualization purposes. The remaining files in the "data" and "data/supporting_data"" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here. </p> <p>#Code information</p> <p>Python scripts can be found in the "supporting_code" folder.</p> <p>Each R script in this project has a particular function:</p> <p>01_start.R: This script loads the R packages used in the analysis, sets the<br>directory, and imports custom functions for the project. You can also load in the <br>main transit time (turnover) datasets here using the `source()` function.</p> <p>02_functions.R: This script contains the custom function for this analysis,<br> primarily to work with importing the seasonal transit data. Load this using the <br> `source()` function in the 01_start.R script. </p> <p>03_generate_data.R: This script is not necessary to run and is primarily<br>for documentation. The main role of this code was to import and wrangle<br>the data needed to calculate ground-based estimates of aboveground water storage.</p> <p>04_annual_turnover_storage_import.R: This script imports the annual turnover and<br>storage data for each landcover type. You load in these data from the 01_start.R script<br>using the `source()` function.</p> <p>05_minimum_turnover_storage_import.R: This script imports the minimum turnover and<br>storage data for each landcover type. Minimum is defined as the lowest monthly<br>estimate.You load in these data from the 01_start.R script<br>using the `source()` function.</p> <p>06_figures_tables.R: This is the main workhouse for figure/table production and <br>supporting analyses. This script generates the key figures and summary statistics <br>used in the study that then get saved in the manuscript_figures folder. Note that all<br>maps were produced using Python code found in the "supporting_code"" folder.</p>
FAIRmat Tutorial 14: Developing schemas and parsers for FAIR computational data storage using NOMAD-Simulations
<p><a href="https://nomad-lab.eu"><u>NOMAD</u></a> is an open-source, community-driven data infrastructure, focusing on materials science data. Originally built as a repository for data from DFT calculations, the NOMAD software can automatically extract data from the output of a large variety of simulation codes. Our previous computation-focused tutorials (<a href="https://fairmat-nfdi.github.io/AreaC-Tutorial-CECAM-2023/"><u>CECAM workshop</u></a>, <a href="https://fairmat-nfdi.github.io/AreaC-Tutorial10_2023/"><u>Tutorial 10</u></a>, and <a href="https://www.fairmat-nfdi.eu/events/fairmat-tutorial-7/tutorial-7-materials"><u>Tutorial 7</u></a>) have highlighted the extension of NOMAD’s functionalities to support advanced many-body calculations, classical molecular dynamics simulations, and complex simulation workflows. <br>But how can you utilize this infrastructure and associated suite of tools if your simulation code or method is not yet supported? <strong>This tutorial will provide foundational knowledge for customizing NOMAD to fit the specific needs of your computational research project</strong>. The following provides an outline of the major topics that will be covered:</p> <ul> <li>Introduction to the NOMAD software and repository</li> <li>Working with the NOMAD-Simulations schema plugin</li> <li>Extending NOMAD-Simulations to support custom methods and outputs</li> <li>Creating parser plugins from scratch</li> <li>Extra: Interfacing complex simulation and analysis workflows with NOMAD</li> </ul> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Data from "Evaluating the Risk of Data Loss Due to Particle Radiation Damage in a DNA Data Storage System"
<p>Raw sequencing data from irradiated DNA samples and controls.<br>Sequences for encoded files used in the irradiation studies.<br>Please see the publication's Source Data file for mapping to encoded files.</p>
Raw Data for Energy Consumption Comparison of DMA-Based and FatFs Storage Systems on Wearable Devices
<p>This dataset contains raw oscilloscope measurements comparing the energy consumption of a Direct Memory Access (DMA)-based storage system versus the FatFs file system for wearable devices. The data was collected as part of the study "Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner".</p> <p>The dataset includes voltage drop measurements across a 2-ohm shunt resistor, captured using an Analog Discovery 2 digital oscilloscope at a 500 kHz sampling rate. Measurements were taken under various conditions:</p> <ul> <li>Storage systems: DMA-based (proposed) and FatFs</li> <li>SD card capacities: 4 GB and 8 GB</li> <li>Write frequencies: 2 Hz and 5 Hz (referring to the frequency of writing a specific data block of 15,872 bytes)</li> <li>With and without a smoothing capacitor</li> </ul> <p>Each CSV file contains 20 million samples, equivalent to 40 seconds of data acquisition. File names encode the experimental conditions, including the storage system, write frequency, number of samples, acquisition rate, acquisition time, data format, SD card size, and absence of the smoothing capacitor.</p> <p>The data is organized into two main folders:</p> <ol> <li>"cap": Contains measurements with the smoothing capacitor</li> <li>"no_cap": Contains measurements without the smoothing capacitor</li> </ol> <p>This raw data can be used to reproduce the energy consumption and write speed analyses presented in the article, as well as for further investigation into the performance of embedded storage systems for wearable devices.</p>
Input Data for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary information accompanying "Energy Storage Profit Risk under Stochastic Fuel Prices" paper submitted to <a href="https://www.journals.elsevier.com/energy-economics/">Energy Economics</a>.</p>
Data supporting Molecular Simulation of Hydrogen Storage and Transport in Cellulose
<p>Data support article accepted for publication in Molecular Simulation. Data includes inputs for crystal relaxation, absorption and diffusion simulations. All processed data required to reproduce plots included and outputs where practicable. Where impractical simulations can be rerun from the inputs provided.</p>
Supplementary data for "An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system"
<p>The code for the Unit Commitment & Economic Dispatch model that was used in this work is available at: https://gist.github.com/vitali87/20688c161d7b5ad598b5d52b524f4585</p> <p>Sample output data can be found in the "Example Outputs.zip" file. This corresponds to the case outlined in the article that simulates a system with 5 Allam Cycle plants without Liquid Oxygen Storage, for the winter test week.</p> <p>To run the UCED model:</p> <ul> <li>Download "UC AIMMS Allam Cycle Model" code from the github and save as an AIMMS project file.</li> <li>Save the file in a folder that contains all the necessary input datasets, found in the "Universal Inputs for UCED Model.zip" file, and the example outputs, found in the "Example Outputs.zip" file, which are to be overwritten. Do not change the name of the input or output files.</li> <li>Open the project and execute the following procedures: <ul> <li>"Main Initialisation" - to initialise the problem</li> <li>"Read from Excell" - to read data from the input files</li> <li>"Main Execution" - to begin running the problem</li> </ul> </li> <li>Once the run is complete, execute "Run External Procedure" to overwrite the output files with the new data.</li> </ul> <p>To change the test week:</p> <ul> <li>Open "Demand Profiles" in 'sets' and change the set definition. Enter "C1" for the winter week and "C21" for the summer week. Another week can alternatively be selected. For example, entering "C45" would allow the model to run with the weather and demand data from the 45th week in the year 2010. </li> <li>Save and close the set.</li> </ul> <p>To change the number of plants in the system:</p> <ul> <li>Open "PCCSGenerators" in 'sets' and change the set definition. To run with 5 Post Combustion Capture plants, end the list of generators after plant number 5 by commenting the remaining plants. This is done by using "!" after the 5th plant name in the string. Then save and close the set.</li> <li>Repeat the above step for the "ACGenerators" and "AirSeparationUnits" sets, to change the number of Allam Cycle plants in the system.</li> </ul> <p>To add or remove oxygen storage capability from the Allam Cycle plants:</p> <ul> <li>Open the "Main Initialisation" procedure.</li> <li>To run the model without oxygen storage: <ul> <li>make sure the following command is stated: "AC_ASU_coupled := 0;"</li> <li>save and close the procedure</li> </ul> </li> <li>To run the model with oxygen storage: <ul> <li>make sure the following is command is stated: "AC_ASU_coupled := 1;"</li> <li>make sure that the number, 'X', of "map_AC_to_ASU('Gas_CCS_AC_X') := 'ASU_X';" commands that are active matches the number of active Allam Cycle plants in the model</li> <li>save and close the procedure.</li> </ul> </li> </ul>
Data set for "Stabilization of nesquehonite for application in carbon capture utilization and storage"
<p>The dataset contains all relavent raw data from the study titile, "Stabilization of nesquehonite for application in carbon capture utilization and storage". </p>
Minimum number of experimental data for the thermal characterization of a hot water storage tank. Videos of temperature evolution
<p>Videos that show the evolution of the instantaneous temperature profile measured and determined with the models and in the cases developed in the paper "Minimum number of experimental data for the thermal characterization of a hot water storage tank" by the same authors. Thermocline thickness evolution is also shown.</p> <p>Video 1. Evolution of the instantaneous temperature profile estimated with the 5 MEL models during the charging period. Evolution of thermocline thickness during the same period is displayed in a separate frame also for all MEL models.<br> Video 2. Evolution of the instantaneous temperature profile estimated with the 5 MDH models during the charging period. Evolution of thermocline thickness during the same period is displayed in a separate frame also for all MDH models.</p>
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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)
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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.