Find research datasets worth reusing

openCC (other)Oct 2024View details →

The dataset for the research "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"

In recent years, the topic of digitalisation and sustainability of supply chains has become increasingly important. In addition, as the environmental dynamism becomes more complex, it is essential to explore how technologies impacts on sustainability under the supply chain dynamism. Hence, there is a study to explore the relationship between technologies and sustainability under the supply chain dynamism in the energy supply chain. In this study, the author collects quantitative data from two Chinese companies, including China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd. This is a questionnaire survey and it has 24 questions, including 3 general questions, 5 technologies dimension questions, 12 sustainability dimension questions and 4 supply chain dynamism questions. The author collected data from 30 May 2024 to 6 June May 2024, and there are totally 316 answers.

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-I

This data file contains data for changes in atmospheric heating due to changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1910-1940. See Euskirchen et al. (2007) for full study details.

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-II

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-III

This data file contains data for the pan-arctic vegetation map depicted in Figure 1 of Euskirchen et al (2007). See Euskirchen et al. (2007) for further details on the construction of this map.

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-IV

This data file contains data for changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1910-1940. See Euskirchen et al. (2007) for full study details.

Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-V

This data file contains data for changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1970-2000. See Euskirchen et al. (2007) for full study details.

openOpenJan 2019View details →

Surface carbon, water and energy fluxes measured by eddy covariance at 3 sites within the Alaska Peatlands Experiment and Bonanza Creek Experimental Forest 2013-2016

These data are simultaneous and continuous measurements of carbon, water and energy fluxes of the terrestrial landscape. These fluxes are major regulatory drivers of the boreal climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. At the APEX project site, within Bonanza Creek Experimental Forest, this monitoring is repeated across a chronosequence of permafrost degradation; the Black Spruce site is an area of stable permafrost with intact black spruce forest (APEX gamma site), the Thermokarst site is an active thermokarst zone with considerable tree mortality (APEX betaSW site), the Fen site is within a stable treeless fen with deep active layer depth (APEX apexcon,low, and ele sites). The main variables being monitored are the instananeous fluxes of CO2, water vapor and surface energy (shortwave, longwave and net radiation), secondary variables included photosynthetically active radiation (PAR), air and soil temperatures, rainfall, snow depth, soil moisture content, wind direction and speed, and average atmospheric concentrations of CO2 and H2O through the year. Our site naming scheme is as follows: 1) gamma = Black Spruce site = YF_2472, 2) betaSW = Thermokarst site= BC_5166, 3) (apexcon+apexele+apexlow) = Fen site = BC_FEN

openCC (other)Jan 2019View details →

Community Land Model version 4.5 (CLM4.5) simulations of water, energy, and carbon fluxes for Saddle vegetation communities, 2008 - 2013

Single point simulations of CLM4.5 that include (1) forcing data that were input to the model and subsequent (2) model output for simulations that approximate conditions in fellfield, dry meadow, moist meadow, wet meadow, and snowbed vegetation communities. Forcing data were generated with observed atmospheric conditions from Tvan, Saddle precipitation, and incoming shortwave radiation measured from the AmeriFlux tower site (US-NR1) from 2008-2013. Wintertime precipitation inputs were modified to approximate average snow depth for each vegetation community observed across the Saddle grid. Land models, like CLM, provide a cohesive framework to investigate biogeophysical and biogeochemical effects of environmental change on ecosystem processes. We used CLM4.5 to investigate if a global-scale model can represent local-scale patterns of water, energy, and carbon fluxes in a heterogeneous mountain environment. Specifically, we were interested in generating testable projections of potential ecosystem responses to climate change. Model output includes half-hourly data on fluxes of energy, water, and carbon, as well as vegetation carbon stocks and edaphic conditions. We also conducted sensitivity analyses to look at ecosystem responses to modifications intended to extend growing season length by decreasing snow albedo and warming air temperatures (black sand and M-A warm, respectively). Information on the variables, units, and data are included as attributed in the network Common Data Form (NetCDF) files for this dataset. For users unfamiliar with using NetCDF files, we have included R scripts that write (forcing data) and read (model output) .nc files include in this data archive. More information about NetCDF files is available at http://www.unidata.ucar.edu/software/netcdf/docs/index.html.

LCI data for materials and processes comparing energy and water use of aqueous and gas-based metalworking fluids

Datasets containing life cycle inventories for materials and processes, and results of the analysis in the article titled, "Comparing energy and water use of aqueous and gas-based metalworking fluids" published in the Journal of Industrial Ecology.

Post-processed output from WACCM: historical simulation of 1957–2005 with medium energy electrons (compset B55TRWCN) and sensitivity studies (compset FW)

This dataset contains post-processed monthly output from WACCM (version 4) simulations: <ol> <li>historical simulation of 1957–2005 with medium energy electrons (compset B55TRWCN, B55TRWCN.zip)</li> <li>sensitivity study: 50-years experiments designed with different geomagnetic and solar conditions (compset FW, FW.zip)</li> </ol> The postprocessing involved (1) zonal averaging, (2) Transformed Eulerian mean (TEM) budget calculation in log-pressure coordindates from model output (h0 files). Contact Monika Szelag (monika.szelag@fmi.fi) Raw data The raw, ungridded monthly data (2TB, h0 files) are currently available on FMI storage system, under path /ibrix/arch/waccm, and can be requested through the contact person.  Dataset contents <pre><code>B55TRWCN.zip files: vdk.zonal.002.1957-2005.nc vdk.zonal.003.1957-2005.nc vdk.zonal.004.1957-2005.nc tem.vdk.zonal.002.1957-2005.nc tem.vdk.zonal.003.1957-2005.nc tem.vdk.zonal.004.1957-2005.nc</code></pre> WACCM output: historical simulations, zonal averages, 3 ensemble members of 49 years each (002,003,004). Variables are NOY, O3, OH, QJOULE, QRL_TOT, QRLNLTE, QRS_TOT, T, U (vdk.zonal.00[234].1957-2005.nc) and delf, fphi, fz, vres, wres, z3 (tem.vdk.zonal.00[234].1957-2005.nc <pre><code>FW.zip files: zonal_F2000_max.nc zonal_F2000_avg.nc zonal_F2000_min.nc tem_zonal_F2000_max.nc tem_zonal_F2000_avg.nc tem_zonal_F2000_min.nc</code></pre> WACCM output: perpetual year 2000, zonal averages, 50 years experiments with 3 different solar and geomagnetic conditions (min, max, avg). Variables are NOY, NOX, O3, OH, QRL_TOT, QRS_TOT, T, U, V, CO, CO2, PS, TS (zonal_F2000_*.nc) and delf, fphi, fz, vres, wres, z3 (tem_zonal_F2000_*.nc)

Data supplement for Wind Energy Science Paper 'Implementation of the blade element momentum model on a polar grid and its aeroelastic load impact'

Contains the data for most figures in the article, as well as a plotting file written in python that generates the figures.

Energy Balance Flanders quarterly and monthly data and related auxiliary data

This data set is used in the VITO pilot study of the UNECE Machine Learning project 2019-2020.

Data for the publication "Reconciling compensating errors between precipitation constraints and the energy budget in a climate model"

These data are a set of 6yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments (diagnostic and prognostic) of precipitation under the present-day (PD, aerosol emission at the year 2000) and preindustrial (PI, aerosol emission at the year 1850) conditions. The data are used in the manuscript entitled "Reconciling compensating errors between precipitation constraints and the energy budget in a climate model".

PROSEU Collective Renewable Energy Prosumers Database (Template)

As part of work package nº2 of the H2020 PROSEU project, which aimed to establish a baseline review and characterisation of renewable energy sources (RES) prosumer (self-consumption) initiatives across Europe, databases identifying the diversity of collective forms of RES prosumers and related stakeholders were built by the project partners using the templates and respective variables presented here (English language). The databases served to create a stratified sample of RES prosumer initiatives for purposes of a survey, as well as distinguish them from other stakeholders in the field.

Data files of 'Large spatial extension of the zero-energy Yu-Shiba-Rusinov state in magnetic field'

Data files of 'Large spatial extension of the zero-energy Yu-Shiba-Rusinov state in magnetic field'

Data of the paper: Atmospheric energy budget response to idealized aerosol perturbation in tropical cloud systems

Here you can find the data presented in the paper: Atmospheric energy budget response to idealized aerosol perturbation in tropical cloud systems The data include all variables included in the paper for the shallow-cloud and the deep-cloud dominated cases. The variable names are as in the paper (beside T_tot which is the 2m temperature). The numbers in the names of the variables represent the CDNC case. The time series variables are as a function of t. The vertical profiles are as a function of the pressure p. The maps are as a function of latitude and longitude.

Supplement to: Electron energy partition across interplanetary shocks: III. Analysis

Quick Summary: The PDF file herein provides additional example superposed epoch analysis (SEA) plots in addition to reference tables of the upstream values used to normalize the SEA data in this file and those in the paper this supplement supports.  This is a supplement to Part 3 of a three-part study of the electron velocity distribution functions (VDFs) observed near interplanetary (IP) shocks by the Wind spacecraft.  Paper I [<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab22bd">Wilson et al., 2019a</a>] introduced the methodology and data products [<a href="https://doi.org/10.5281/zenodo.2875806">Wilson et al., 2019c</a>] for fitting the electron VDFs to the sum of three model functions.  Paper II [<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab5445">Wilson et al., 2019b</a>] presents the statistics of the fit parameters produced and provided in the data products from Paper I.  Paper III presents and summarizes the analysis of the fit parameters.  The papers share the title Electron energy partition across interplanetary shocks. Wind Spacecraft: The Wind spacecraft (<a href="http://wind.nasa.gov/">https://wind.nasa.gov</a>) was launched on November 1, 1994 and currently orbits the first Lagrange point between the Earth and sun.  It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, Bo.  The instruments used in this study and these datasets are the fluxgate magnetometer (<a href="https://doi.org/10.1007/BF00751330">MFI</a>), the radio receivers (<a href="https://doi.org/10.1007/BF00751331">WAVES</a>), ion Faraday cups (<a href="https://doi.org/10.1007/BF00751326">SWE</a>), and the electron and ion electrostatic analyzers (<a href="https://doi.org/10.1007/BF00751328">3DP</a>).  The MFI measures 3-vector Bo at ~11 samples per second (sps); the SWE measures reduced VDFs of the thermal proton and alpha-particle populations from which velocity moments are derived and used herein; WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density; and 3DP observes full 4π steradian VDFs of electrons and ions from a few eV to ~30 keV which provide both ion velocity moments and the electron VDFs modeled herein. PDF Supplement Description: Definitions: <ul> <li>VDF = velocity distribution function</li> <li>Electron Components/Populations [taken from <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab22bd">Wilson et al., 2019a</a>,<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab5445">b</a>] <ul> <li>Core (s = ec):  cold, dense population with energies \(E_{ec} \lesssim \text{15 eV}\)</li> <li>Halo (s = eh):  hot, tenuous population with energies \(E_{eh} \gtrsim \text{20 eV}\)</li> <li>Beam/Strahl (s = eb):  anti-sunward propagating, magnetic field-aligned beam (or strahl) with \(E_{eb} \sim \text{a few tens of eV}\)</li> <li>Effective (s = eff):  effective total electron population, i.e., used for approximate moments rather than integrating entire VDF</li> </ul> </li> <li>Ion Components/Populations [taken from <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab22bd">Wilson et al., 2019a</a>,<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ab5445">b</a>] <ul> <li>Proton (s = p):  core solar wind proton beam, i.e., main proton population streaming away from sun</li> <li>Alpha-particles (s = \(\alpha\)):  alpha-particle magnetic field-aligned beam</li> </ul> </li> <li>\(k_{B}\) = the Boltzmann constant [J K-1]</li> <li>\(\mu_{o}\) = permeability of free space [T m A-1]</li> <li>\(n_{s}\)= number density of species s [cm-3] (s = ec for core, eh for halo, eb for beam/strahl, p for proton, etc.)</li> <li>\(B_{o, j}\)= jth component (GSE coordinate basis) of quasi-static magnetic field vector [nT]</li> <li>\(V_{Ts, j}\) = jth component (relative to Bo) of thermal speed of species s [km/s] <ul> <li>\(V_{Ts, j} = \sqrt{ \tfrac{ 2 \ k_{B} \ T_{s, j} }{ m_{s} }}\), where \(T_{s, j}\) is the jth component (relative to Bo) of the temperature of species s [eV]</li> </ul> </li> <li>\(V_{os, j}\) = jth component (relative to Bo) of drift speed of species s [km/s] in ion rest frame</li> <li>\(V_{s, j}\) = jth component (GSE coordinate basis) bulk velocity of species s [km/s] in spacecraft frame</li> <li>\(T_{s, tot} = {1 \over 3} (T_{s, \parallel} + 2 \ T_{s, \perp})\), where \(\parallel(\perp)\) is the parallel(perpendicular) component relative to Bo</li> <li>\(P_{s, j} = n_{s} \ k_{B} \ T_{s, j}\) = partial thermal pressure [eV cm-3] of the jth component of species s</li> <li>\(P_{t, j} = \sum_{s} \ P_{s, j}\) = total thermal pressure [eV cm-3] of the jth component summed over all species including ions</li> <li>\(\mathcal{A}_{s} = \left(\tfrac{ T_{\perp} }{ T_{\parallel} } \right)_{s}\) = temperature anisotropy [N/A] of species s</li> <li>\(\xi_{s, j} = \tfrac{1}{2} m_{s} \ n_{s} \ V_{os, j}^{2}\) = ram energy density [eV cm-3] jth component of species s</li> <li>\(\epsilon_{j} = \tfrac{ B_{o}^{2} }{ 2 \ \mu_{o} } + \sum_{s} \left[ P_{s, j} + \xi_{s, j} \right]\) = total energy density [eV cm-3] of the jth component of the system in the plasma bulk flow rest frame</li> <li>\(\zeta_{s, j} = \tfrac{ \xi_{s, j} }{ \epsilon_{j} }\) = ratio of the ram energy density of the jth component of species s to the total energy density [N/A]</li> <li>\(\psi_{s, j} = \tfrac{ P_{s, j} }{ \epsilon_{j} }\) = ratio of the thermal energy density of the jth component of species s to the total energy density [N/A]</li> <li>\(\Pi_{s, j} = \tfrac{ P_{s, j} }{ P_{t, j} }\) = ratio of the partial thermal pressure of the jth component of species s to the total thermal pressure [N/A]</li> <li>\(s_{es}\) = exponent for the symmetric self-similar model VDF of species s</li> <li>\(\kappa_{es}\) = kappa value for the bi-kappa VDF of species s</li> <li>\(p_{es}(q_{es})\) = parallel(perpendicular) exponent for the asymmetric self-similar model VDF of species s</li> <li>\(n_{eff} = \sum_{s} \ n_{s}\) = effective number density of all electron populations</li> <li>\(T_{eff, j} = \tfrac{ \sum_{s} \ n_{s} \ T_{s, j} }{ n_{eff} }\) = effective temperature of the jth component of all electrons populations</li> <li>\(\beta_{s, j} = \tfrac{ 2 \ \mu_{o} \ n_{s} \ k_{B} \ T_{s, j} }{ B_{o}^{2} }\) = plasma beta [N/A] of the jth component of species s</li> </ul>   This PDF supplement contains the following SEA plots: <ul> <li>\(T_{s, j}\) vs \(\Delta\)t (for s = ec, eh, and eb and j = \(\parallel \text{ or } \perp \text{ or tot}\))</li> <li>\(\mathcal{A}_{s}\) vs  \(\Delta\)t (for s = ec, eh, and eb)</li> <li>\(\left( \tfrac{ T_{s} }{ T_{eff} } \right)_{j}\) vs  \(\Delta\)t (for s = ec, eh, and eb and j = \(\parallel \text{ or } \perp \text{ or tot}\))</li> <li> \(\psi_{s, j}\) vs  \(\Delta\)t (for s = ec, eh, eb, p, and \(\alpha\) and j = \(\parallel \text{ or } \perp \text{ or tot}\)) </li> <li> \(\Pi_{s, j}\) vs  \(\Delta\)t (for s = ec, eh, eb, p, and \(\alpha\) and j = \(\parallel \text{ or } \perp \text{ or tot}\)) </li> </ul> The PDF supplement contains tables of upstream median values for each shock used for normalizing the SEA plots, where the parameters listed include: <ul> <li>\(T_{s, j}\) (for s = ec, eh, and eb and j = \(\parallel \text{ or } \perp \text{ or tot}\))</li> <li>\(n_{s}\) (for s = ec, eh, eb, and eff and j = \(\parallel \text{ or } \perp \text{ or tot}\))</li> <li>\(\tfrac{ n_{s} }{ n_{eff} }\) (for s = ec, eh, and eb and j = \(\parallel \text{ or } \perp \text{ or tot}\))</li> <li>\(\beta_{s, j}\) (for s = ec, eh, and eb and j = \(\parallel \text{ or } \perp \text{ or tot}\))</li> <li>\(s_{ec}\text{, }\kappa_{eh}\text{, and }\kappa_{eb}\)</li> <li>\(\mathcal{A}_{s}\) (for s = ec, eh, eb, and eff)</li> <li>\(\left( \tfrac{ T_{s} }{ T_{eff} } \right)_{j}\) (for s = ec, eh, and eb and j = \(\parallel \text{ or } \perp \text{ or tot}\))</li> </ul>