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823 results for “hydrogen”

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zenodo44/100

Hydrogen storage properties of Mn and Cu for Fe substitution in TiFe0.9 intermetallic compound - Dataset related to publication.

Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.

opencc-by-4.0Dec 2019View details →

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Fundamental hydrogen storage properties of TiFe-alloy with partial substitution of Fe by Ti and Mn - Dataset related to publication

Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.

opencc-by-4.0Dec 2019View details →

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TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>HCl Profile Processing and Bias Correction</h3> Collocated HCl profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Separate XGBoost regression models are then trained for these HCl differences at each height level within a given latitude bin. These trained models are subsequently used to estimate HCl bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved HCl profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean HCl profiles: <ul> <li> <code>zmhcl_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmhcl_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023

opencc-by-4.0Feb 2023View details →

zenodo44/100

From Lab to Technical CO2 Hydrogenation Catalysts: Understanding PdZn Decomposition

Supplementary material:  Additional experimental results, including catalytic performances of the different scaled-up samples, SEM images, and additional XAS data related to the linear combination fit

opencc-by-4.0Jan 2023View details →

zenodo44/100

Dataset of publication "Investigation of the discharge coefficient in the laminar boundary layer regime of critical flow Venturi nozzles calibrated with different gases including hydrogen"

The attached files contain experimental raw data and fluid properties for the nozzles 1 and 2 mentioned in the paper. The data in the attached files can be used to calculate the Cd values published in the paper.

opencc-by-4.0Jun 2023View details →

zenodo44/100

Dataset of publication "Derivation and validation of a reference data-based real gas model for hydrogen"

In this repository, a new real gas model for hydrogen based on the Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) v10.0 is provided for the use in the simulation software OpenFOAM v2012. The model is valid in a temperature and pressure range of 150-400 K and 0.1-1000 bar, respectively. Usage beyond this range is not recommended as it may lead to unrealistic results.

opencc-by-4.0Mar 2023View details →

zenodo44/100

Artificial Neural Networks-generated Dataset: pH, Total Alkalinity, and Hydrogen Ion Concentration in Ría de Vigo (NW Spain), 1995–2020

This dataset comprises input data from INTECMAR and the predicted outcomes. The variables and their units are as follows: station: 'Station ID [1-6]' year: 'Year [1995-2020]' month: 'Month [1-12]' day: 'Day' latitude: 'Latitude (decimal degrees)' longitude: 'Longitude (decimal degrees)' depth: 'Depth (meters)' temperature: 'Temperature (degrees Celsius)' salinity: 'Salinity (psu)' phosphate: 'Phosphate (umol/kg)' nitrate: 'Nitrate (umol/kg)' silicate: 'Silicate (umol/kg)' cweek: 'Cosine week' sweek: 'Sine week' TA: 'Total Alkalinity predicted (umol/kg)' NTA: 'Normalized Total Alkalinity (umol/kg)' NAT_st: 'Normalized per station Total Alkalinity (umol/kg)' NTA_gl: 'Normalized globally Total Alkalinity (umol/kg)' pHTS_insitu: 'pH insitu (pH units)' HT: 'Hydrogen ion concentration predicted (nmol/kg)'   The authors gratefully acknowledge the financial support by the Programa de axudas á etapa predoutoral da Xunta de Galicia (Axencia Galega de Innovación) (Grant nº IN606A-2022/025). F.F.P. and A.V. were supported by REDEIRA (TED2021-132188B-I00) project, funded by MCIN/AEI/10.13039/501100011033. The authors also express their gratitude to the Instituto Tecnolóxico para o Control do Medio Mariño de Galicia (INTECMAR), for the analyses and production of the database used to make predictions.

opencc-by-4.0Dec 2023View details →

zenodo44/100

Discovering indium as hydrogen production booster for a Cu/SiO2 catalyst in steam reforming of methanol

Indium is  as an effective H2 production promoter in a Cu/SiO2 catalyst for the steam reforming of methanol. We prepared silica-supported Cu-In catalyst via a urea-assisted co-precipitation method that showed a higher H2 productivity compared to the monometallic catalyst and a H2/CO2 molar ratio of almost 3 at 493 K. By means of XPS, XRPD and HRTEM-EDX along with H2 and CO-TPR, H2O-TPD, and N2O titrations, supported by computational modeling, the superior performances were attributed to an easier H2O activation due to lower oxidation state of the Cu, resulting from the electron density transfer from the InOx phase. Here are available: Hydrogen Temperature Programmed Reduction data; Carbon Monoxide Temperature Programmed Reduction data; Water Temperature Programmed Desorption data; Nitrogen Physisorption data.

opencc-by-4.0Nov 2021View details →

zenodo44/100

Database for machine learning of hydrogen storage materials properties

Database for machine learning of hydrogen storage materials properties Matthew Witmana, Mark Allendorfa, Vitalie Stavilaa aSandia National Laboratories, Livermore, CA   Description 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].   Acknowledgements 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 or the United States Government.   References <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. J. Phys. Chem. Lett. 2020, 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. Chem. Mater. 2021, 33, 4067–4076.</li> </ol>   Contact 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.

opencc-by-4.0Nov 2022View details →

zenodo44/100

NMR Data for "Unveiling a Key Catalytic Pocket for the Ruthenium NHC-Catalysed Asymmetric Heteroarene Hydrogenation" (DOI: 10.1039/D1SC06409F)

# NMR Data for "Unveiling a Key Catalytic Pocket for the Ruthenium NHC-Catalysed Asymmetric Heteroarene Hydrogenation" (DOI: 10.1039/D1SC06409F) In the following, the original NMR Data for the publication "Unveiling a Key Catalytic Pocket for the Ruthenium NHC-Catalysed Asymmetric Heteroarene Hydrogenation" (DOI: 10.1039/D1SC06409F) is provided.  ## Experimental methodology and associated data ### Dataset 200113.40a Inside an argon filled glovebox, 4.6 mg of **1-A** (5.4 µmol) was dissolved in 0.65 mL THF-d8 (distilled over sodium/benzophenone and stored over 3 Å molecular sieves), yielding a clear, dark yellow solution. The solution was then transferred into a medium pressure J Young NMR tube and sealed. Subsequently, initial NMR spectra under argon atmosphere were recorded (t = 0 h). Afterwards, 2 bar of H2 pressure were applied to the J Young NMR tube, resulting in a partial H2 pressure of 1 bar (due to the presence of 1 bar argon). After shaking to dissolve the added H2, a slow color change to orange could be observed. The reaction was monitored using 1H NMR (Bruker AV 400, 400 MHz), showing consumption of dissolved H2 as evidenced by a decrease in intensity for the H2 signal at δ(1H) = 4.55 ppm. Six hours after the first addition of H2, the J Young NMR tube was re-pressurized with 2 bar H2, and again after 36 h, shaking the NMR tube regularly to dissolve H2. After the third pressurization, no further decrease of dissolved H2 could be observed. #### NMR experiments and timestamps File name | NMR Experiment | Time stamp / h (relative to H2 addition) --- | --- | --- 200113.40a.1 | 1H | 0 200113.40a.2 | 1H | 0 200113.40a.3 | 1H | 0.5 200113.40a.4 | 1H | 1 200113.40a.5 | 1H | 2.5 200113.40a.6 | 1H | 2.5 200113.40a.7 | 1H | 5  200113.40a.8 | 1H COSY-45 | 5 200113.40a.9 | 1H | 6.5 200113.40a.10 | 1H | 22.5 200113.40a.11 | 1H | 30 200113.40a.12 | 1H | 31 200113.40a.13 | 29Si-inept | 31 200113.40a.14 | 1H-29Si HMBC | 31 200113.40a.15 | 1H | 45.5 200113.40a.16 | 1H-29Si HMBC | 45.5 200113.40a.17 | 1H | 55 200113.40a.18 | 1H | 69.5 200113.40a.110 | 1H (larger measurement window) | 22.5 200113.40a.111 | 1H (larger measurement window) | 30 200113.40a.115 | 1H (larger measurement window) | 45.5 200113.40a.117 | 1H (larger measurement window) | 55 200113.40a.118 | 1H (larger measurement window) | 69.5 ### Dataset 200117.40a 69.5 h after the first addition of H2 no significant changes could be observed in the 1H NMR spectra anymore. At this point, 0.15 mL of a 0.052 M solution of benzofuran in THF-d8 (7.8 µmol benzofuran, ca. 1.5 equivalents relative to **1-A**) were added to the NMR tube while applying 2 bar of H2 pressure. No significant color change was observed upon addition of the substrate. Subsequently, the NMR tube was sealed and the reaction was monitored using 1H NMR for an additional 119 h, especially following the hydride signals at δ(1H) = −3.7 ppm and δ(1H) = −3.8 ppm as well as the signals of 2,3-dihydrobenzofuran at δ(1H) = 4.48 ppm and δ(1H) = 3.15 ppm. After 119 h of reaction time, the color of the reaction solution had changed to light orange. #### NMR experiments and timestamps File name | NMR Experiment | Time stamp / h (relative to substrate addition) --- | --- | --- 200117.40a.1 | 1H | 0.5 200117.40a.2 | 1H | 1.5 200117.40a.3 | 1H | 7 200117.40a.4 | 1H | 24 200117.40a.5 | 1H | 24.5  200117.40a.6 | 1H | 72  200117.40a.7 | 1H COSY-45 | 72 200117.40a.101 | 1H (larger measurement window) | 0.5 200117.40a.103 | 1H (larger measurement window) | 7 200117.40a.104 | 1H (larger measurement window) | 24

opencc-by-4.0Dec 2021View details →

zenodo44/100

Automatic learning of hydrogen-bond fixes in an AMBER RNA force field - dataset

Supporting data related to manuscript "Automatic learning of hydrogen-bond fixes in an AMBER RNA force field"

opencc-by-4.0Jan 2022View details →

zenodo44/100

A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation

Thermocatalytic CO2 hydrogenation to methanol is an attractive decarbonization technology to combat climate change while producing a valuable platform chemical and energy carrier. However, predicting the performance of catalytic systems for this process remains a challenge. Herein, we present a machine learning framework to predict catalyst performance from experimental descriptors. A database of Cu-, Pd-, In2O3-, and ZnO-ZrO2-based catalysts with 1425 datapoints is compiled from literature and subjected to data mining. Accurate ensemble-tree models (R2 > 0.85) are developed to predict the methanol space-time yield (STY) from 12 descriptors, where the significance of space velocity, pressure, and metal content is revealed. The model prediction and its insights are experimentally validated, with a root mean squared error of 0.11 gMeOH h−1 gcat−1 between the actual and predicted methanol STY. The framework is purely data-driven, interpretable, cross-deployable to other catalytic processes, and serves as an invaluable tool for guided experiments and optimization.

opencc-by-4.0Mar 2022View details →

zenodo44/100

An Effective Activation Method for Industrially Produced TiFeMn Powder for Hydrogen Storage [Dataset related to publication]

Data type: XRD patterns; SEM micrographs and EDX maps; particle size distributions; atomic concentrations; hydrogen loading profiles; kinetic models; volume expansions.   Data format: *.opj; *.tif. Origin of the data: laboratory equipment from Hereon (XRD, SEM, PSD Analyzer, BET, XPS, Sievert apparatus) and UniPV (SEM). Software needed to plot the data: folders need to be unzipped, Origin.

opencc-by-4.0Jun 2022View details →

zenodo44/100

Raw Data - High resolution electrochemical additive manufacturing of microstructured active materials: case study of MoSx as a catalyst for the hydrogen evolution reaction

The dataset contains raw data that complements the article: High resolution electrochemical additive manufacturing of microstructured active materials: Case study of MoSx as a catalyst for the hydrogen evolution reaction, J. Mater. Chem. A, 2021, 9, 22072-22081. C. Iffelsberger and M. Pumera* https://doi.org/10.1039/D1TA05581J Related to the MSCA Project: 888797 LoCatSpot

opencc-by-4.0Aug 2022View details →

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TiFe0.85Mn0.05 alloy produced at industrial level for a hydrogen storage plant

Data type: XRD patterns; SEM and EDX results, hydrogen sorption data (pcT-curves, absorption/desoprtion curves).  Data format: *.opj; *.tif.; *docx; *jpg Software needed: Origin.

opencc-by-4.0Aug 2022View details →

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Data for: Hydrogen production via methane pyrolysis

This dataset contains information regarding patented methods for the production of hydrogen via methane pyrolysis. The processes described in scientific literature can be divided into three categories: thermal, plasma and catalytic decomposition. [1] The same categories are found in the patent literature. The most popular and currently used methods for making hydrogen are coal gasification, steam methane reforming (SMR) and water electrolysis.  Methane pyrolysis can be seen as a suitable alternative and environmentally friendly technology; It consists in the thermal decomposition of methane, with the formation of solid carbon (instead of CO/CO2) as reaction by-product. The catalytic methane pyrolysis has been widely investigated. Iron and carbon-based (carbon black, graphite, carbon nanotubes) catalysts are considered the best candidates for industrial implementation.[2] The following patent databases used have been used for data mining:  -          Espacenet (a free of charge database provided by the European Patent Office), accessed on Sept. 10, 2022 -          Orbit Intelligence (FamPat database) (a fee-based platform provided by Questel), accessed on Sept. 10, 2022 The file titled “Methane pyrolysis _ Espacenet” contains information related to Title, Inventors, Applicants, Publication number, Earliest priority, IPC, CPC, Publication date, Earliest publication, and Family number. The file titled “Methane pyrolysis _ Orbit” contains information related to Priority numbers, Application numbers, Publication numbers, Priority dates, Application dates, Publication dates, Title, Abstract, and Current assignees. Patent searches were carried out by means of keywords and classification/indexing codes. [3, 4] Both IPC (International Patent Classification) and CPC (Cooperative Patent Classification) systems were used.

opencc-by-4.0Sep 2022View details →

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Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"

This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld,  M. Finkbeiner,  J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”, Sustainable Energy Fuels, 2022, 6, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>. It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see  <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and  “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.

opencc-by-4.0Sep 2022View details →

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Dataset for the comparison of performance of two leak detectors using hydrogen reference leaks (supplement to paper "Advancing Hydrogen Leak Detection: Design and Calibration of Reference Leaks")

Excel file containing some measurements made in December 2023, using three hydrogen reference leaks, to assess the performance of two distinct leak detectors, one portable and made specifically for hydrogen and one MSLD in hydrogen-mode.

opencc-by-4.0Jun 2024View details →

zenodo44/100

Methodological and practical lessons learned from exploring the material criticality of two hydrogen-related products

As the European Union embarks on the energy transition, several challenges need to be faced to ensure that this shift is conducted from a holistic perspective that avoids burden-shifting across sustainability dimensions. One of the main concerns refers to the future availability of materials that clean technologies require. Critical raw material assessment serves to guide the management of such mineral resources in a new paradigm of increasing demand. This work delves into the methodological fundamentals of several product-level criticality indicators in order to discuss their implications within the context of the ecodesign of two hydrogen-related products. Overall, criticality is advised to be assessed making use of several indicators. In the case study of a proton exchange membrane fuel cell stack, the combined interpretation of criticality indicators leads to identifying platinum as the main hotspot, while yttrium and lanthanum account for the most relevant criticality contributions in the case study of a solid oxide electrolysis cell stack.

opencc-by-4.0Jun 2024View details →

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Catalog of laboratory parameters for the Lyman and Werner bands of molecular hydrogen

Catalog of laboratory parameters for the Lyman and Werner bands of molecular hydrogen based on the compilation of Malec et al. (2010). The wavelength values are partly updated to the measurements from Table 11 and 12 in Bailly et al. (2010). The values for the oscillator strength f and the damping constant gamma are re-calculated following (Morton 2003).

opencc-by-sa-4.0Oct 2018View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record