Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Datasets from "Auroral Energy Deposition and Conductance During the 2013 St. Patrick's Day Storm: Meso-Scale Contributions" by Gabrielse et al.

1. README File for netCDF Data to be published alongside “Mesoscale Contributions to Auroral Energy Deposition and Conductance During the 2013 St. Patrick’s Day Storm” by Christine Gabrielse et al. in the Journal of Geophysical Research. The data to be stored on Zenodo (<a href="https://zenodo.org/">https://zenodo.org/</a>) are in a netCDF format. This README applies to the following files:    thg_ej_all-sky-imager_20130317050000_v01.nc    thg_ej_all-sky-imager_20130317060000_v01.nc    thg_ej_all-sky-imager_20130317070000_v01.nc    thg_ej_all-sky-imager_20130317080000_v01.nc    thg_ej_all-sky-imager_20130317090000_v01.nc    thg_ej_all-sky-imager_20130317100000_v01.nc    thg_ej_all-sky-imager_20130317110000_v01.nc    thg_ej_all-sky-imager_20130317120000_v01.nc    thg_ej_all-sky-imager_20130317130000_v01.nc   The following are attributes published within the netCDF’s metadata: SUMMARY: FILENAME:    thg_ej_all-sky-imager_20130317050000_v01.nc   PROJECT:    STP>Solar-Terrestrial Physics   SOURCE_NAME:    THG>THEMIS (Time History of Events and Macroscale Interactions during    Substorms) Ground-Based   DISCIPLINE:    Space Physics>Ionospheric Science    Space Physics>Magnetospheric Science   DATA_TYPE:    EJ>Earth Camera Images, processed   DESCRIPTOR:    All-Sky-Imager   FILE_NAMING_CONVENTION:    source_datatype_descriptor_yyyyMMddHHmmss   DATA_VERSION:    01   PI_NAME:    Christine Gabrielse   PI_AFFILIATION:    The Aerospace Corporation   TEXT:    Parameters (energy flux, mean energy, Hall conductance) derived from the THEMIS    white light all-sky-imagers. See Gabrielse et al. (2021, Frontiers) and    Gabrielse et al. (2024, JGR) for derivation methodology.   INSTRUMENT_TYPE:    Ground-Based Imagers   MISSION_GROUP:    Ground-Based Investigations   LOGICAL_SOURCE:    thg_ej_all-sky-imager   LOGICAL_FILE_ID:    thg_ej_all-sky-imager_00000000000000_v01   LOGICAL_SOURCE_DESCRIPTION:    Parameters (energy flux, mean energy, Hall conductance) derived from the THEMIS    white light all-sky-imagers.   TIME_RESOLUTION:    3s   RULES_OF_USE:    Invitation to co-authorship for use of this data is required. It is best to    reach out to Dr. Gabrielse early in the project for guidance.   GENERATED_BY:    Christine Gabrielse   ACKNOWLEDGEMENT:    The work effort for this project was gratefully funded by NASA grants    80NSSC20K0725, 80GSFC22CA011, NAS5-02099, 80NSSC21K1552, AFOSR Grant    FA9559-16-1-0364   GLOBAL ATTRIBUTES: PROJECT:    STP>Solar-Terrestrial Physics SOURCE_NAME:    THG>THEMIS (Time History of Events and Macroscale Interactions during    Substorms) Ground-Based DISCIPLINE:    Space Physics>Ionospheric Science    Space Physics>Magnetospheric Science DATA_TYPE:    EJ>Earth Camera Images, processed DESCRIPTOR:    All-Sky-Imager FILE_NAMING_CONVENTION:    source_datatype_descriptor_yyyyMMddHHmmss DATA_VERSION:    01 PI_NAME:    Christine Gabrielse PI_AFFILIATION:    The Aerospace Corporation TEXT:    Parameters (energy flux, mean energy, Hall conductance) derived from the THEMIS    white light all-sky-imagers. See Gabrielse et al. (2021, Frontiers) and    Gabrielse et al. (2024, JGR) for derivation methodology. INSTRUMENT_TYPE:    Ground-Based Imagers MISSION_GROUP:    Ground-Based Investigations LOGICAL_SOURCE:    thg_ej_all-sky-imager LOGICAL_FILE_ID:    thg_ej_all-sky-imager_00000000000000_v01 LOGICAL_SOURCE_DESCRIPTION:    Parameters (energy flux, mean energy, Hall conductance) derived from the THEMIS    white light all-sky-imagers. TIME_RESOLUTION:    3s RULES_OF_USE:    Invitation to co-authorship for use of this data is required. It is best to    reach out to Dr. Gabrielse early in the project for guidance. GENERATED_BY:    Christine Gabrielse ACKNOWLEDGEMENT:    The work effort for this project was gratefully funded by NASA grants    80NSSC20K0725, 80GSFC22CA011, NAS5-02099, 80NSSC21K1552, AFOSR Grant    FA9559-16-1-0364   DIMENSIONS: EPOCH:    1200 LATITUDE:    400 LONGITUDE:    400   VARIABLES: EPOCH: CAT_DESC:    Time in seconds since 1970-01-01 00:00:00 TIME_SCALE:    UTC TIME_BASE:    1970 (POSIX) FIELDNAM:    Time in seconds since 1970-01-01 00:00:00 FILLVAL:    LONG = -2147483648 FORMAT:    I10 UNITS:    S VALIDMIN:    LONG = 1363496400 VALIDMAX:    LONG = 1363528800 VAR_TYPE:    support_data LATITUDE: _FILLVALUE:    FLOAT = NaN CAT_DESC:    Geodetic latitude FIELDNAM:    Latitude FORMAT:    F4.1 FILLVAL:    DOUBLE = -1.0000000e+31 UNITS:    deg VALIDMIN:    DOUBLE = 45.000000 VALIDMAX:    DOUBLE = 84.900000 VAR_TYPE:    support_data LONGITUDE: _FILLVALUE:    FLOAT = NaN CAT_DESC:    Geodetic longitude FIELDNAM:    Longitude FORMAT:    F5.1 FILLVAL:    DOUBLE = -1.0000000e+31 UNITS:    Deg VALIDMIN:    DOUBLE = 180.00000 VALIDMAX:    DOUBLE = 339.60000 VAR_TYPE:    support_data CONDUCTANCE: _FILLVALUE:    DOUBLE = NaN CAT_DESC:    Hall conductance in a 2D grid organized by geographic latitude and longitude,    units of mho DEPEND_0:    Epoch DEPEND_1:    Latitude DEPEND_2:    Longitude DISPLAY_TYPE`:    Image FIELDNAM:    Hall conductance FILLVAL:    DOUBLE = -1.0000000e+31 FORMAT:    F8.6 LABLAXIS:    Hall conductance UNITS:    Mho VALIDMIN:    DOUBLE = 0.00043800000 VALIDMAX:    DOUBLE = 88.000000 VAR_TYPE:    Data Energy flux: _FILLVALUE:    FLOAT = NaN CAT_DESC:    Precipitated energy flux in a 2D grid organized by geographic latitude and    longitude, units of ergs/cm^2/s DEPEND_0:    Epoch DEPEND_1:    Latitude DEPEND_2:    Longitude DISPLAY_TYPE`:    Image FIELDNAM:    Energy Flux (ergs/cm^2/s) FILLVAL:    DOUBLE = -1.0000000e+31 FORMAT:    F9.4 LABLAXIS:    energy flux UNITS:    ergs/cm^2/s VALIDMIN:    DOUBLE = 0.010000000 VALIDMAX:    DOUBLE = 1100.0000 VAR_TYPE:    Data ENERGY: _FILLVALUE:    FLOAT = NaN CAT_DESC:    Mean energy of the precipitated population in a 2D grid organized by geographic    latitude and longitude, units of keV DEPEND_0:    Epoch DEPEND_1:    Latitude DEPEND_2:    Longitude DISPLAY_TYPE`:    Image FIELDNAM:    Energy (keV) FILLVAL:    DOUBLE = -1.0000000e+31 FORMAT:    F6.3 LABLAXIS:    Energy UNITS:    keV VALIDMIN:    DOUBLE = 0.010000000 VALIDMAX:    DOUBLE = 22.000000 VAR_TYPE:    Data   Examples of the data are as follows: EPOCH: data.epoch.data[0] =   1363496400   This data is presented as number of seconds since January 1, 1970 in UT. The example above converts to 2013-03-17/05:00 UT. There are 1200 time values stored in each file.   LATITUDE: data.latitude.data[0] = 45.0000   This data is the geographic latitude in degrees of the 400x400 grid of data points. There are 400 latitude values stored in each file.   LONGITUDE: data.LONGITUDE.data[0] =  180.000   This data is the geographic longitude in degrees of the 400x400 grid of data points. There are 400 longitude values stored in each file.   CONDUCTANCE: data.conductance.data[0] = -1.0000000e+31   This data is the Hall conductance in mho measured at 45 deg latitude, 180 deg longitude. The value in this example indicates that no conductance was measured here. A valid value would be something in the range of 0.00043800000 to 88 mho. There are 1200x400x400 conductance values stored in each file.   ENERGY FLUX: data.eflux.data[0] = -1.00000e+31   This data is the energy flux in ergs/cm^2/s measured at 45 deg latitude, 180 deg longitude. The value in this example indicates that no energy flux was measured here. A valid value would be something in the range of 0.01 to 1100.0000 ergs/cm^2/s. There are 1200x400x400 energy flux values stored in each file.   ENERGY: data.energy.data[0] = -1.00000e+31   This data is the energy in keV measured at 45 deg latitude, 180 deg longitude. The value in this example indicates that no energy was measured here. A valid value would be something in the range of 0.01 to 22 keV. There are 1200x400x400 energy values stored in each file.   2. README for text file Data to be published alongside “Mesoscale Contributions to Auroral Energy Deposition and Conductance During the 2013 St. Patrick’s Day Storm” by Christine Gabrielse et al. in the Journal of Geophysical Research. This README applies to the following files:    march172013_fortyukon_photometer_products.dat    march172013_pokerflat_photometer_products.dat Header information in the files describe the contents.  These are the photometer derived data taken on March 17, 2023. They include the time [UT], energy flux (Q) [ergs/cm^2/s], average energy (Eavg) [keV], and oxygen scale factor (fo).

opencc-by-4.0Oct 2024View details →

Full electronic tables for 'Spectrum and energy levels of the high-lying singly excited configurations of Nd III'

Full electronic versions of table extracts of the accepted version of the preprint at <a href="https://doi.org/10.48550/arXiv.2408.07830">https://doi.org/10.48550/arXiv.2408.07830</a>

opencc-by-4.0Oct 2024View details →

THE ROLE OF PROPERTY MANAGEMENT IN PROMOTING ENERGY-EFFICIENT SOLUTIONS FOR RENTALS

Real estate management plays a key role in promoting energy efficient solutions when renting out properties. The purpose of the study is to analyze the impact of management companies on the introduction of energy-efficient technologies to increase the competitiveness of facilities and reduce operating costs. The methodology is based on the analysis of data on the application of modern energy-efficient solutions, including lighting, heating and automation systems in buildings in the Czech Republic. The results showed that the use of such technologies helps to reduce utility costs by 20-40% and increases the attractiveness of facilities for tenants. In conclusion, property management aimed at energy efficiency ensures the achievement of sustainable development and economic benefits for owners and tenants. These measures increase the market value of the properties and extend the lease terms, which strengthens the position in the real estate rental market.

opencc-by-4.0Jun 2021View details →

Survey questionnaire and results on user needs for energy models for the European energy transition, related to Süsser et al. (2021)

The online survey was designed and conducted in the framework of the EU H2020 project SENTINEL in collaboration with the project openENTRANCE. The aim of the survey was to identify needs by modellers and model result users across Europe for energy modelling. We developed it as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “LimeSurvey”. We performed the online survey among different stakeholders from academia, policy, NGO’s and energy industry.  The study by Süsser et al. (2021) investigates the differences between energy model improvements and adjustments as perceived by modellers, and the actual needs of users of model results. If you use this questionnaire in an academic publication, please cite the corresponding article: Süsser, D., Gaschnig, H., Ceglarz, A., Stavrakas, V., Flamos, A. & Lilliestam, J. (under review). Better suited or just more complex? On the fit between user needs and modeller-driven improvements of energy system models. Energy.

opencc-by-4.0Jul 2021View details →

In-stream tidal energy resources in macrotidal non-cohesive sediment environments: effect of morphodynamic changes at two bays in the upper Gulf of California

Project_info: This dataset was obtained during the project CeMIE-Oceano (2017-2021), and was party financed by SENER-CONACyT (contract no. 249795). License: The authors appreciate that users of these data: 1) Contact Vanesa Magar (vmagar@cicese.edu.mx) to follow the uses of the data, and 2) Include the requested acknowledgment (cite using the DOI of this dataset) in any presentations or publications. This dataset includes data used for producing Figures 4,5 and Tables 1,2 of paper "IN-STREAM TIDAL ENERGY RESOURCES IN MACROTIDAL NON-COHESIVE SEDIMENT ENVIRONMENTS: EFFECT OF MORPHODYNAMIC CHANGES AT TWO BAYS IN THE UPPER GULF OF CALIFORNIA" published in Journal of Marine Science and Engineering. Bermúdez-Romero, Anahí; Vanesa Magar; Markus S. Gross; Victor M. Godínez; Manuel López-Mariscal; Julio Candela. In-Stream tidal energy resources in macrotidal non-cohesive sediment environment: Effect ofmorphodynamic changes at two bays in the upper Gulf of California. Journal of Marine Science and Enginnering, 9:411. https://doi.org/10.3390/jmse9040411

opencc-by-4.0Mar 2021View details →

Safeguarding the energy transition against political backlash to carbon markets - figure raw data

Figure raw data for the article "Safeguarding the energy transition against political backlash to carbon markets" (published in Nature Energy).

opencc-by-4.0Jul 2021View details →

Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data2

The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles. File Description: <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and houses' data</li> <li>Houses_Input_Output_JSONs_and_Statistics - Zip containing the input and output files from the proposed system, as well as their corresponding schedule statistics</li> </ul>

opencc-by-4.0Jul 2021View details →

Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data

The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles. File Description: <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and other house data</li> <li>Houses_Input_JSONs - Zip containing the input files, from each house, for the proposed system</li> </ul>

First Adoption for National Renewable Energy Targets in 187 Countries (1975-2017)

This dataset was used in the publication of "All Roads Lead to Paris: The Eight Pathways to Renewable Energy Target Adoption" in the journal of Energy Research & Social Science. The objective was to compile data on the first national adoption of a renewable energy target in each country to analyze its mechanisms of diffusion (learning, economic competition, emulation, and coercion). The data were compiled for 187 countries for the period ranging from 1975 to 2017. The list of countries was gathered from the Annex I of IRENA's "Renewable Energy Target Setting" report. We used primarily the IEA policies database (<a href="https://www.iea.org/policies">https://www.iea.org/policies</a>) to identify the first adoption of a renewable energy target in each country. Other sources were used when data was unavailable in such repository for specific countries. Additionally, we include the data gathered from various sources, as they were used in our paper for measuring variables. The variables in this dataset include: target adoption (or “Target”, from various sources listed in the dataset); year of adoption (or “Year”, from various sources listed in the dataset); cumulative membership to energy-related international environmental agreements (or “IEA”, with data from Mitchell’s International Environmental Agreements Database Project); net energy imports as a percentage of energy use (or “Energy”, with data from the World Bank); a similarity index (or “Similarity”, created with data from the Polity Index, population and GDP per capita from the World Bank, and revenue from the World Bank); official development assistance as a percentage of gross national income (or “ODAGNI”, with data from the World Bank and OECD); income level (“Income”, with data from the World bank); and the international price for oil (“Oil”, with data from the Federal Reserve Bank of St. Louis). For more details, refer to the manuscript. Note that in 2018 the “IEA’s policy database” was actually the “IEA/IRENA RE Policies and Measures database”. The links for the sources for renewable energy target adoption for Norway and Albania were lost in the transition from one to the other; all other sources could be retrieved by the authors.

Original Data for Manuscript "Energy and Momentum Distribution of Surface Plasmon-induced Hot Carriers Isolated via Spatiotemporal Separation"

Raw data of the time-dependent energy density calculation and time-resolved photoemission electron microscopy (TR-PEEM) measurements used in the manuscript. A preprint of the manuscript is available on arXiv: <a href="https://arxiv.org/abs/2107.14277">2107.14277</a> The manuscript was published in ACS Nano 2021, 15, 12, 19559-19569 <a href="https://doi.org/10.1021/acsnano.1c06586">10.1021/acsnano.1c06586</a> The data is provided in hdf5 files, which were produced using the snomtools python package (<a href="https://github.com/hartelt/snomtools">availale on github</a>) and can be read with any <a href="https://support.hdfgroup.org/HDF5/tools5desc.html">hdf5 compatible software</a>. The calculated data was produced as described in Ref.1 with the parameters given in the manuscript. For the experimental data, each zip file contains the raw data (hdf5 Files) as well as the PEEM settings used (sav Files as output from the experiment control software) for the respective measurement. Parts of the manuscript that are generated from the calculated data [calculation_energy_density.zip]: <ul> <li>Figure 2 A</li> <li>Figure S2</li> </ul> Parts of the manuscript that are evaluated from the PEEM real space dataset [PEEM_realspace.zip]: <ul> <li>Figure 2 B</li> <li>Figure 3</li> <li>Figure S1</li> <li>Figure S3</li> <li>Figure S4</li> <li>Movie S2 [timeseries_binned_fermi.avi] in the supplementary material</li> </ul> Parts of the manuscript that are evaluated from the PEEM momentum space (momentum microscopy) SPP dataset [PEEM_k-space_timesteps_SPP.zip]: <ul> <li>Figure 4 (in combination with the pump pulse reference dataset)</li> <li>Figure S5 B</li> <li>Figure S6 B</li> <li>Figure S7 B</li> </ul> Parts of the manuscript that are evaluated from the PEEM momentum space (momentum microscopy) pump pulse reference dataset [PEEM_k-space_timesteps_pump.zip]: <ul> <li>Figure 4 (in combination with the SPP dataset)</li> <li>Figure S5 A</li> <li>Figure S6 A</li> <li>Figure S7 A</li> </ul> Parts of the manuscript that are evaluated from the PEEM momentum space (momentum microscopy) data of the full timetrace, combining SPP dataset [PEEM_k-space_full-timetrace_SPP.zip] and pump pulse reference dataset [PEEM_k-space_full-timetrace_pump.zip]: <ul> <li>Figure S8</li> </ul>

Dataset for article "Energy flux paths in lakes and reservoirs" by Guseva et al., 2021

The dataset includes the measured parameters that have been analyzed in the manuscript “Energy flux paths in lakes and reservoirs” by Guseva S., Casper P., Sachs T., Spank U. and Lorke A. The manuscript has been submitted to Water.

Simulation systems of: "Free energies of membrane stalk formation from a lipidomics perspective"

Simulation systems of: Free energies of membrane stalk formation from a lipidomics perspective Chetan S. Poojari, Katharina C. Scherer, Jochen S. Hub Nature Communications, 12,  6594 (2021), <a href="https://doi.org/10.1038/s41467-021-26924-2">https://doi.org/10.1038/s41467-021-26924-2</a>   First published as  a preprint manuscript in BioRxiv as: Free energies of stalk formation in the lipidomics era Chetan S. Poojari, Katharina C. Scherer, Jochen S. Hub, BioRxiv, https://www.biorxiv.org/content/10.1101/2021.06.02.446700v1, 2021 The archive contains <ul> <li>starting conformations of double-membrane systems</li> <li>topologies</li> <li>MD parameter files</li> </ul> Running the simulations requires a modified version of GROMACS, which implements the chain coordinate available at GitLab: <a href="https://gitlab.com/cbjh/gromacs-chain-coordinate">https://gitlab.com/cbjh/gromacs-chain-coordinate</a>

Figure 2 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches

Figure 2. Mean slope (°, ¡SE) of the five studied beaches calculated using three replicate random samples in each site. Identical letters indicate non-significant differences in the post hoc Scheffé's test for multiple pair-wise comparisons.

Figure 5 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches

Figure 5. Comparison of the mean number of burrows per m2 (¡SE) of Ocypode quadrata among beaches and zones. Numbers in brackets represent the numbers of squares sampled. Numbers at the left side of the bars indicate the results of Scheffé's test for multiple comparisons of zones among beaches. Letters at the right side of the bars indicate the results of Scheffé's test for multiple comparisons of zones within beaches. Identical labels (numbers or letters) indicate non-significant differences in the post hoc Scheffé's test. See Table III for the results of the ANOVA.

Figure 4 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches

Figure 4. Schemes of zonation of Ocypode quadrata in the study areas. Horizontal dotted lines indicate heights of mean number of individuals per m2 for each 1 m interval estimated using five randomized replicated samples. This the same site were equivalent in length (x-axis): Segredo, 21 m; Cabelo Gordo, 21 m; Pitangueiras, 24 m; Zimbro

Figure 1 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches

Figure 1. Map of the São Sebastião Channel, south-eastern Brazil, illustrating the five study beaches.

Figure 6 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches

Figure 6. Comparison of the mean burrow diameter (¡SE) of Ocypode quadrata among beaches and zones. Numbers in brackets represent the numbers of burrows sampled. Numbers at the left side of the bars indicate the results of the non-parametric Tukey-type test for multiple comparisons of zones among beaches. Letters at the right side of the bars indicate the results of the non-parametric Tukey-type test for multiple comparisons of zones within beaches. Identical labels (numbers or letters) indicate non-significant differences in the post hoc Scheffé's test. See Table IV for the results of the non-parametric Kruskal–Wallis tests.

Figure 3 in Spatial distribution of the ghost crab Ocypode quadrata in low-energy tide-dominated sandy beaches

Figure 3. Mean sand grain size (phi, ¡SE) and mean sorting coefficient (phi, ¡SE) for each zone and beach calculated using five replicate measures in each zone. Mi, medium intertidal; Ui, upper intertidal; Sf, subterrestrial fringe.

opencc-by-4.0Feb 2007View details →

Fig. 1 in The Role Of Birds In Matter And Energy Flowin The Ecosystem

Fig. 1. The ratio of matter flow in the adults of the various avian species with similar matter flow, using millet as diet (Gere,1983). NB: Only data for Passer domesticus is incorporated from the present study.