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Canopy-Atmosphere Exchange of Carbon, Water and Energy at Harvard Forest EMS Tower since 1991
The tower-based CO2 measurements and key meteorological drivers are intended to examine how regional and ecosystem level processes in a mid-latitude forest contribute to global carbon cycling. Specifically, we endeavor to understand quantitatively how and why forested ecosystems take up or release carbon, on time scales from hours to decades, and to elucidate responses to climate changes and management interventions. The tower was installed 1989 and the resulting eddy-flux measurements constitute the longest running record of the net-ecosystem carbon exchange in a North American Forest. The resulting long-term record of Net Ecosystem Exchange (NEE) has shown the effects of climate anomalies on carbon fluxes for seasonal and annual time scales. For example, reduced soil frost allows greater respiration in the winter leading to lower C sequestration. Cumulative gross photosynthesis depends on when the canopy emerges in the spring. Warmer springtime temperatures lead to greater uptake of C. As the NEE record is extended and augmented by supporting ecological measurements, we can further identify longer-term effects of climate perturbations on carbon fluxes and further define the relationship between stand history and carbon sequestration. Climatic anomalies in one season or year may have a longer-term effect on the sequestration of carbon in subsequent seasons or years. The flux and ecological measurements are coordinated with studies at other sites through the AmeriFlux network. By examining the relationships between carbon fluxes and the driving physical and biological variables across a range of sites we are enhancing understanding of the processes that control NEE.
Trout Lake USGS Water, Energy, and Biogeochemical Budgets (WEBB) Stream Data 1975-2013
This data was collected by the United States Geological Survey (USGS) for the Water, Energy, and Biogeochemical Budget Project. The data set is primarily composed of water chemistry variables, and was collected from four USGS stream gauge stations in the Northern Highland Lake District of Wisconsin, near Trout Lake. The four USGS stream gauge stations are Allequash Creek at County Highway M (USGS-05357215), Stevenson Creek at County Highway M (USGS-05357225), North Creek at Trout Lake (USGS-05357230), and the Trout River at Trout Lake (USGS-05357245), all near Boulder Junction, Wisconsin. The project has collected stream water chemistry data for a maximum of 36 different chemical parameters,. and three different physical stream parameters: temperature, discharge, and gauge height. All water chemistry samples are collected as grab samples and sent to the USGS National Water Quality Lab in Denver, Colorado. There is historic data for Stevenson Creek from 1975-1977, and then beginning again in 1991. The Trout Lake WEBB project began during the summer of 1991 and sampling of all four sites continues to date.
Dataset of "Electronic structure and defect states in bismuth and antimony sulphides identified by energy-resolved electrochemical impedance spectroscopy"
Understanding the nature of the defects in the absorber materials, namely point defects, their formation mechanism and the contribution to the properties is essential for the photovoltaic device performance improvement. They are one the reasons why chalcogenide-based solar cells do not yet meet expected high power conversion efficiencies. Here we identify and present energy distribution of defects in Bi2S3 and Sb2S3, and their (SbxBi(100-x))2S3 alloys (with x = 0, 10, 33, 50, 67, 90, 100 at% Sb content) chalcogenides, being explored for emerging photovoltaic applications as they are earth-abundant and highly absorbing in the visible light range. We show that their density of states (DOS) and related parameters can be obtained experimentally by energy-resolved electrochemical impedance spectroscopy (ER-EIS) in a technically simple and quick way, where ER-EIS data are well correlated with theoretical DFT calculations. ER-EIS reveals that in Bi2S3 there are only shallow defects at CBM. In Sb2S3, ER-EIS reveals also midgap states which can be the cause of low electrical conductivity of Sb2S3. We also explain the discrepancy in the reported values of ionisation potentials and the bandgaps of the Bi- and Sb-chalcogenides. Dominant sulphur vacancy defect was identified in Bi- and Sb-chalcogenides whereas in ternary (SbxBi(100-x))2S3 system, merely 10 at.% of Bi transforms the midgap sulphur defects to shallow ones. This provides novel strategy for healing the midgap defects in Sb2S3, which is crucial for boosting the PV performance and tuning the electrical conductivity in Sb2S3.
Dataset of "Tuning the morphology and energy levels in organic solar cells with metal- organic framework nanosheets"
<p>Metal-organic framework nanosheets (MONs) have proved themselves to be useful<br>additives for enhancing the performance of a variety of thin film solar cell devices. However,<br>to date only isolated examples have been reported. In this work we take advantage of the<br>modular structure of MONs in order to resolve the effect of their different structural and<br>optoelectronic features on the performance of organic photovoltaic (OPV) devices. Three<br>different MONs were synthesized using different combinations of two porphyrin-based ligands<br>meso-tetracarboxyphenyl porphyrin (TCPP) or tetrapyridyl-porphyrin (TPyP) with either zinc<br>and/or copper ions and the effect of their addition to polythiophene-fullerene (P3HT-PCBM)<br>OPV devices was investigated. The power conversion efficiency (PCE) of devices was found to<br>approximately double with the addition of MONs of Zn2(ZnTCPP), but was unchanged with<br>the addition of Cu2(ZnTPyP) and halved upon the addition of Cu2(CuTCPP) compared to<br>devices without nanosheets. Our analysis indicates that there are three different mechanisms<br>by which MONs can influence the photoactive layer – light absorption, energy level alignment,<br>and morphological changes. Analysis of external quantum efficiency, UV-vis photoelectron<br>spectroscopy data found that MONs have similar effects on light absorption and energy level<br>alignment. However, atomic force and Raman microscopy studies revealed that the nanosheet<br>thickness and lateral size are crucial parameters in enabling the MONs to act as beneficial<br>additives resulting in an improvement of the OPV device performance. We anticipate this<br>study will aid in the design of MONs and other 2D materials for future use in other light<br>harvesting and emitting devices.</p>
Dataset for "Methodology of Evaluating the Activation Energy of Oxygen Reduction Reaction on Pt-based Electrodes"
<p>High temperature proton-exchange membrane fuel cell (HT-PEMFC) technology is widely studied alternative to current energy conversion technologies based on fossil fuels. Compared to solid oxide fuel cells (SOFCs), HT-PEMFCs allow more flexibility and demand less operation control due to their lower temperature. On the other hand, HT-PEMFCs show an advantage over low-temperature PEMFCs in terms of less demand on the purity of the H2 used, the possibility to recover the generated heat, lower water management requirements, and easy heat management. One of the critical limitations of HT-PEMFC operation is a slow kinetics of the cathodic reduction of O2 (ORR) due to presence of H3PO4 which ensures proton conductivity in the system. Electrochemical dynamic methods such as cyclic voltammetry or linear sweep voltammetry (LSV) can be used to determine the kinetic parameters of ORR. These measurements can provide information on the Tafel slope and exchange current density (jex) of the ORR. However, performing these measurements under conditions relevant for HT-PEMFC operation is challenging due to presence highly concentrated H3PO4 and elevated temperature. First, determination of the kinetic parameters requires correct assessment of equilibrium potential of ORR (EORR). The value of the EORR is generally influenced by the activity (fugacity) of the reactants and products and the temperature, a discussion of the appropriate standard states of the components is also necessary. Second, the relationship between the jex and the reaction rate constant (k°), necessary for calculation of activation energy ( ), must be known. It includes consideration of the likely reaction mechanism. In this paper, the methodology for appropriate determination of was developed and used for estimation of of ORR from LSV curves measured on commercially available Pt/C catalyst under HT-PEMFC relevant conditions. In particular, the measurements were carried out using a rotating glassy carbon rod disk electrode (RRE) in purified 98 wt.% H3PO4 (as electrolyte) at temperatures of 120, 140, 160, 180 °C. Though the treatment was developed in context of ORR and HT-PEMFC, the approach is generally applicable to any electrochemical reaction.</p>
Energy Cycle Characteristics for 5G/6G Networks Supported by RES, UAVs, and RISs
<h2><strong>Overview</strong></h2> <p>The following dataset presents the energy cycle characteristics for 5G/6G mobile systems supported by Renewable Energy Sources (RES) and/or Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RISs). In addition, within the dataset, the energy gain related to the engagement of RES within the Radio Access Network (RAN) has also been distinguished.</p> <h2><strong>Scenario</strong></h2> <p>The considered network scenario includes 8 three- (<em>_results_gcas.csv</em>) or one-cell (<em>_results_scas.csv</em> & <em>_results_kras.csv</em>) base stations (BSs) placed within the Poznan city (surroundings of the old market) and supported by Renewable Energy Sources — photovoltaic panels (PVs) and/or wind turbines (WTs). The aforementioned base stations can be treated as stationary towers or mobile access points (e.g., drones/UAVs). Those latter have been additionally equipped with RIS devices, which are able to reflect and manipulate a radio signal to influence occurrences such as interferences, coverage, or human exposure. However, the use of RISs has been taken into account only to evaluate the impact of the engagement of such devices on the energy side of the mobile system, omitting the changes in radio characteristics. The network traffic has been assumed to be fixed (64 mobile users (UEs) with 100 Mbps downlink — DL, and 25 Mbps uplink — UL, per each), however, its density in specific parts of the city is modeled randomly for each simulation run. The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators. The weather conditions assumed within the simulation are typical for the climate in Poland. </p> <h2><strong>Methodology</strong></h2> <p>The energy-cycle calculations (system's power consumption, renewable energy production, and excessive energy storage) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using the Green Radio Access Network Design (GRAND) tool (developed by teams from the Ghent University & Poznan University of Technology). The UE-BS association process within the mobile system has been done by doing multi-objective optimization using the Gurobi software, which has taken into account parameters like path loss, predicted power consumption of BSs, and guaranteed DL & UL bit rates for UEs.</p> <h2><strong>Simulation setup</strong></h2> <p>The setup of the input parameters for used mathematical models (power consumption, energy generation, energy storage) has been done in accordance with the values attached within the delivered literature positions (cited within the publications included in the <em>Related works</em> section of the following dataset) and adjusted to the considered study. Furthermore, the data used to model the network environment (building distribution, coverage area, base stations' locations) as well as to predict weather conditions are the real data (for the year 2022) collected by the city hall of Poznan, one of the Polish mobile operators, and weather stations placed in Poznan, respectively. The number of simulation runs performed has been equal to 10 (each run has included energy-cycle calculations for 4 seasons of the year), with the time step of a single run set to 1 hour of the day.</p> <h2><strong>Results</strong></h2> <p>The results of the aforementioned investigations have been included in the attached files, which can be described as follows:</p> <h3><strong>File <em>_results_gcas.csv</em></strong></h3> <p>The first column denotes the date (season of the year), for which the values have been obtained. The columns from second to fifth present observed values of the State of Charge (SoC) of a battery system (in %) for a single network cell on average in a time step. Those columns are the obtained values for the RAN, in which no RES, only PVs, only WTs, and both types of RES generators have been enabled, respectively. </p> <h3><strong>Files <em>_results_scas.csv</em> & <em>_results_kras.csv</em></strong></h3> <p>The first column denotes the date (season of the year), for which the values have been obtained. The second and third columns denote the number of drone base station (DBS) exchanges within the wireless system on average in a particular time step, where no RES and only PVs are enabled, respectively. The fourth and fifth columns present the conventional (fossil-fuels-based) energy consumption (in kWh) for the whole system in a specific time step, in which no RES and only PVs are engaged for all the access nodes. The sixth column is the energy savings (in kWh) related to the use of RES generators within the mobile network. Furthermore, the seventh and eighth columns represent the amount of renewable energy harvested from the solar radiation in total and the peak value of this amount observed during the entire day, respectively.</p> <h2><strong>Acknowledgment</strong></h2> <p>More details about the conducted studies have been described within the attached papers (<em>Related works</em> section). The data has been collected within the COST CA10210 INTERACT. M. Deruyck is a Post-Doctoral Fellow of the FWO-V (Research Foundation – Flanders, ref: 12Z5621N). The work (including the following dataset preparation) by A. Samorzewski and A. Kliks was realized within project no. 2021/43/B/ST7/01365 funded by the National Science Center in Poland.</p>
On the Moreau–Jean scheme with the Frémond impact law: energy conservation and dissipation properties for elastodynamics with contact, impact and friction — data
<p>This deposit contains the data output of the systems described in <a href="https://hal.science/hal-04230941">On the Moreau–Jean scheme with the Frémond impact law. Energy conservation and dissipation properties for elastodynamics with contact impact and friction.</a> The codes that generated this data are available in another <a href="../records/10953181">deposit</a> archived on Zenodo, as well as in a GitHub repository archived on <a href="https://archive.softwareheritage.org/swh:1:dir:33ff6d960b70505c7939c0ce21c039cabbe1351c;origin=https://github.com/nickcollins-craft/On-the-Moreau-Jean-scheme-with-the-Fremond-impact-law;visit=swh:1:snp:72aede3d3a464732a36ef79c20ef07eebd1f9918;anchor=swh:1:rev:b63b68c25e72d23d7d9ee30225165fa0ebffb3c2">Software Heritage</a>, which is the preferred method of obtaining the codes. Two of the files in this deposit ("deformed_sliding_block_mesh.png" and "sliding_block_mesh.png") are required for one of the codes in the code deposit to run successfully ("block_mesh_plot.py", with the files assumed to be located in the folder specified in the data_folder variable of the file "path_file.py"), but the deposits are otherwise independent.</p>
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
Majadas de Tietar: Ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean tree-grass ecosystem
<p>This dataset contains a subset of measurements collected at the experimental site Majadas de Tietar. We collected ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean Savanna using the eddy covariance technique and a series of meteorological sensors for the time period December 2015 - February 2018. The dataset is used for the development of a series of R packages including 'bigleaf' (Knauer et al., 2018).</p> <p>The experimental site is collected in Majadas de Tietar (Casals et al., 2009) located in western Spain (39°56′25″N 5°46′29″W). The ecosystem is a typical “Iberic Dehesa”, which is characterized by an herbaceous stratum of native pasture and sparse trees, for the majority (~98%) Quercus ilex. The tree density is about 20–25 trees/ha, the fractional cover of trees is about 20%, mean DBH of 46 cm, and a canopy height of about 8 m. (El-Madany et al., 2018). The herbaceous layer is composed of native annual species of the three main functional plant forms (grasses, forbs and legumes), whose fractional cover varies seasonally and is characterized by important inter-annual variations in the seasonal dynamics related to the onset of the dry period.</p> <p>Fluxes were measured with the eddy covariance technique with two different systems, one at ecosystem scale to characterize the fluxes of the whole ecosystem (15.5 m above ground), and one at 1.65 m above ground in an open space to measure the fluxes of the well-established understory grass layer.</p> <p>The description of the set-up, equipment and processing used to calculate ecosystem scale fluxes are described in El-Madany et al., (2018), while for the understory tower can be found in Perez-Priego et al., (2017).</p> <p>The dataset is composed of two files: 'ESLMa_MainTower', which is the ecosystem eddy covariance system, and 'ESLMa_SubCanopy', which is the understory eddy covariance system. The dataset contains half-hourly, processed eddy covariance of the ecosystem and understory tower, as well as the main biometeorological data used in the big-leaf package (net radiation, soil heat fluxes, horizontal wind velocity, atmospheric pressure, precipitation, air temperature). All the processing was conducted with EddyPro software (version 5.2.0, LI-COR Biosciences Inc., Lincoln, NE, USA) and the ustar filtering, gap-filling and partitioning with the R package REddyProc (Wutzler et al., 2018). The variables and the units are described in the Readme.txt file released with the dataset.</p> <p><strong>References</strong></p> <p>Casals, P. et al., 2009. Soil CO2 efflux and extractable organic carbon fractions under simulated precipitation events in a Mediterranean Dehesa. Soil Biol. Biochem. 41, 1915–1922. <a href="https://doi.org/10.1016/j.soilbio.2009.06.015">https://doi.org/10.1016/j.soilbio.2009.06.015</a>.</p> <p>El-Madany, T.S.,et al., 2018. Drivers of spatio-temporal variability of carbon dioxide and energy fluxes in a Mediterranean savanna ecosystem 21. <a href="https://doi.org/10.1016/j.agrformet.2018.07.010">https://doi.org/10.1016/j.agrformet.2018.07.010</a></p> <p>Knauer, J., et al., 2018. bigleaf - An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLOS ONE, doi:10.1371/journal.pone.0201114</p> <p>Perez-Priego O, et al., 2017. Evaluation of eddy covariance latent heat fluxes with independent lysimeter and sapflow estimates in a Mediterranean savannah ecosystem. Agricultural and Forest Meteorology. 236: 87-99. doi: 10.1016/j.agrformet.2017.01.009.</p> <p>Wutzler, T., et al., 2018. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences Discuss., p. 1-39.</p> <p> </p>
Supplement to: Electron energy partition across interplanetary shocks
<p><strong>Quick Summary:</strong></p> <p>The three files herein comprise supplemental information and standalone datasets for a three-part study of <em>Electron energy partition across interplanetary shocks</em> that describe the modeling of solar wind electron velocity distribution functions (VDFs) near interplanetary shocks observed by the <em>Wind</em> spacecraft. Part I of the study (published in the <em>The Astrophysical Journal Supplement Series</em> on July 3, 2019 doi:10.3847/1538-4365/ab22bd) describes the methodology and how the two ASCII files (i.e., those stored here) were created and their contents. Part I also explains the nuances of the analysis, the limitations of the dataset, and how to use the data within the two ASCII files. Parts II and III (in preparation) present the statistical results and the detailed analysis of these results in the context of the dependence on relevant interplanetary shock parameters. Below are the descriptions of each data product starting with the PDF supplemental file to the three-part study and then the associated ASCII files. First we provide some background/definitions of jargon and terms used in each.</p> <p><strong>Solar Wind Electrons:</strong></p> <p>The solar wind electron VDF below ~1 keV is comprised of cold, dense core (subscript c or ec) population with thermal energies typically in the ~5-15 eV range, a hot, tenuous halo (subscript h or eh) population with thermal energies typically >20-30 eV, and an anti-sunward, field-aligned beam called the strahl or beam/strahl (subscript b or eb) population with thermal energies typically ~few 10s of eV. Most previous work modeled the core as a bi-Maxwellian and the halo and beam/strahl as bi-kappa VDFs. The work described in Part I (and the PDF supplement stored here) show that the core is more accurately described by a self-similar model VDF, which reduces to a bi-Maxwellian under appropriate conditions/limits and deviation from Maxwellian quantifies inelasticity in the plasma collisions. That is, if the plasma were controlled by elastic Coulomb particle-particle collisions (e.g., in the low corona or chromosphere or photosphere), the VDF would relax to a Maxwellian in the absence of other forces. When the plasma particles undergo inelastic collisions, the VDF profile changes from a Gaussian to something more like a "flattop" or box-like shape.</p> <p><strong>Wind Spacecraft:</strong></p> <p>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, <strong>B</strong><sub>o</sub>. The instruments used in this study and these datasets are the fluxgate magnetometer (MFI), the radio receivers (WAVES), ion Faraday cups (SWE), and the electron and ion electrostatic analyzers (3DP). The MFI measures 3-vector <strong>B</strong><sub>o</sub> 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.</p> <p><strong>PDF Supplement Description:</strong></p> <p>The PDF document contains descriptions and definitions of relevant interplanetary shock parameters and shock analysis techniques used by the Harvard Smithsonian Center for Astrophysics' Wind shock database at <a href="https://www.cfa.harvard.edu/shocks/wi_data/">https://www.cfa.harvard.edu/shocks/wi_data/</a>. It describes the details of the symbols/parameters used on the database website and their translation to plasma parameters or shock parameters. The PDF also defines the shock normal finding techniques listed as two-four character inputs on the database website. The PDF file lists the shocks analyzed and their relevant parameters in two tables, with the second listing the relevant critical Mach numbers. Next the PDF provides some extra statistics of the analysis performed in the three-part study on <em>Electron energy partition across interplanetary shocks</em> in the form of histograms comparing differences for different selection criteria (e.g., low versus high Mach number shocks). Finally, there are detailed descriptions and definitions of the model functions used to fit to the solar wind electron VDFs.</p> <p>Both ASCII files have detailed headers outlining and defining the parameters contained therein. They also provide column headings where the labels/names of each are defined and/or described in the header. The headers also provide links to the analysis software used to perform the model fits to the VDFs. We will first describe the contents of the file labeled Wind_ip_shock_3dp_fit_constraints_electrons.txt (FCONSTS for brevity) and then the file labeled Wind_ip_shock_3dp_fit_results_electrons.txt (FRESULTS for brevity). Below use the following definitions:</p> <ul> <li><span class="math-tex">\(N_{s}\)</span> = number density of species <em>s</em> [cm-3] (s = ec for core, eh for halo, eb for beam/strahl, p for proton, etc.)</li> <li><span class="math-tex">\(B_{o, j}\)</span>= j<sup>th</sup> component (GSE coordinate basis) of quasi-static magnetic field vector [nT]</li> <li><span class="math-tex">\(V_{Ts, j}\)</span> = j<sup>th</sup> component (relative to <strong>B</strong><sub>o</sub>) of thermal speed of species <em>s</em> [km/s] <ul> <li><span class="math-tex">\(V_{Ts,j} = \sqrt{{2 k_{B} T_{s,j} \over m_{s}}}\)</span>, where <span class="math-tex">\(T_{s, j}\)</span> is the j<sup>th</sup> component (relative to <strong>B</strong><sub>o</sub>) of the temperature of species <em>s</em> [eV]</li> </ul> </li> <li><span class="math-tex">\(V_{os, j}\)</span> = j<sup>th</sup> component (relative to <strong>B</strong><sub>o</sub>) of drift speed of species <em>s</em> [km/s] in ion rest frame</li> <li><span class="math-tex">\(V_{s, j}\)</span> = j<sup>th</sup> component (GSE coordinate basis) bulk velocity of species <em>s</em> [km/s] in spacecraft frame</li> <li><span class="math-tex">\(T_{s, tot} = {1 \over 3} (T_{s, \parallel} + 2 \ T_{s, \perp})\)</span>, where <span class="math-tex">\(\parallel(\perp)\)</span> is the parallel(perpendicular) component relative to <strong>B</strong><sub>o</sub></li> <li><span class="math-tex">\(s_{es}\)</span> = exponent for the symmetric self-similar model VDF of species <em>s</em></li> <li><span class="math-tex">\(\kappa_{es}\)</span> = kappa value for the bi-kappa VDF of species <em>s</em></li> <li><span class="math-tex">\(p_{es}(q_{es})\)</span> = parallel(perpendicular) exponent for the asymmetric self-similar model VDF of species <em>s</em></li> <li><span class="math-tex">\(\chi_{s}^{2}\)</span> = least chi-squared of fit to species <em>s</em></li> <li><span class="math-tex">\(\phi_{sc}\)</span> = spacecraft electric potential [eV]</li> <li><span class="math-tex">\(\delta R = \lvert 1 - Median(f^{data}/f^{model}) \rvert\)</span> = excess median deviation of fit [%]</li> </ul> <p><strong>FCONSTS File Description:</strong></p> <p>The FCONSTS file contains all the pertinent information used during the fit process for all VDFs that were analyzed including the fit results. The columns are organized by electron component from core to halo to beam/strahl, in that order, sorted by the time stamp (UTC) of the observed VDF (very first column). The first column in each set of electron component groups is a numerical indicator of the fit status for that component of the i<sup>th</sup> VDF. This is followed by 30 columns consisting of 5 sets of 6 numbers. Each model function has six fit parameters: <span class="math-tex">\(N_{s}\)</span> [0], <span class="math-tex">\(V_{Ts, \parallel}\)</span> [1], <span class="math-tex">\(V_{Ts, \perp}\)</span> [2], <span class="math-tex">\(V_{os, \parallel}\)</span> [3], <span class="math-tex">\(V_{os, \perp}\)</span> [4] (or <span class="math-tex">\(p_{es}\)</span> for asymmetric self-similar model VDF), and exponent of fit (i.e., <span class="math-tex">\(s_{es}\)</span>, <span class="math-tex">\(\kappa_{es}\)</span>, or <span class="math-tex">\(q_{es}\)</span>). Thus, there are six columns for each of the following for each of the three components (i.e., 18 columns for each of the following in total): initial guess values, returned fit values, lower limit constraints, upper limit constraints, and a logical value indicating whether the i<sup>th</sup> fit value sits on the lower (-1) or upper (+1) limit or neither (0). These columns are followed by four more containing the number of iterations necessary to find the fit values, the least chi-squared value of the fit, the degrees of freedom in the fit process, and a two-letter designator of the model fit function used (defined in the ASCII file header).</p> <p><strong>FRESULTS File Description:</strong></p> <p>The FRESULTS file contains the fit results used in the three-part study. Again, the first column starts each row with the time stamp (UTC) of the observed VDF. In the following, all parameters listed with subscript <em>j</em> will correspond to three columns (one for each component) except the drift velocities which only have two for <span class="math-tex">\(\parallel(\perp)\)</span>. That is followed by: <span class="math-tex">\(N_{p}\)</span> (SWE), <span class="math-tex">\(N_{\alpha}\)</span> (SWE), <span class="math-tex">\(N_{i}\)</span> (3DP), <span class="math-tex">\(T_{p, j}\)</span> (SWE), <span class="math-tex">\(T_{\alpha, j}\)</span> (SWE), <span class="math-tex">\(T_{i, j}\)</span> (3DP), <span class="math-tex">\(B_{o, j}\)</span> (MFI), <span class="math-tex">\(V_{p, j}\)</span> (SWE), <span class="math-tex">\(V_{\alpha, j}\)</span> (SWE), <span class="math-tex">\(V_{i, j}\)</span> (3DP), <span class="math-tex">\(\phi_{sc}\)</span> (multiple instruments), <span class="math-tex">\(\delta R\)</span> (3DP), <span class="math-tex">\(N_{ec}\)</span> (fit), <span class="math-tex">\(T_{ec, j}\)</span> (fit), <span class="math-tex">\(V_{oec, j}\)</span> (fit), <span class="math-tex">\(\kappa_{ec}\)</span> (fit), <span class="math-tex">\(s_{es}\)</span> (fit), <span class="math-tex">\(p_{es}\)</span> (fit), <span class="math-tex">\(q_{es}\)</span> (fit), reduced <span class="math-tex">\(\chi_{ec}^{2}\)</span> (fit), core fit status, and repeats for the halo and beam/strahl fits. The last four columns contain, in the following order, the total reduced chi-squared of the model fit of all components combined and fit flags (0 = worst, 10 = best) for each electron component. Note that all possible exponents are provided for each component but only the one that is not set as a fill value corresponds to the functional form used to model that electron component (e.g., if <span class="math-tex">\(s_{ec}\)</span> is the only non-fill exponent for the core, then the core was modeled as a symmetric self-similar VDF).</p>
ΔG-RDKit: Solvation Free Energy Database
<p>We present the full database of the article "Explainable Supervised Machine Learning Model to Predict Solvation Free Energy".</p> <p>This is the database used for a ML model, containing a variety of solvent-solute pairs with known experimental solvation free energy Δ<em>G</em><sub>solv</sub> values. Data entries were collected from two separate databases. The <a href="https://link.springer.com/article/10.1007/s10822-014-9747-x">FreeSolv</a> library, with 642 experimental aqueous Δ<em>G</em><sub>solv </sub>determinations and the <a href="https://mediatum.ub.tum.de/1452571?v=1">Solv@TUM</a> database with 5597 entries for non-aqueous solvents. Both databases were selected given their wide-scale of solute/solvents pairs, amassing 6239 experimental values across light and heavy-atom solutes with a diverse solvent structure and with small value uncertainties.</p> <p>Experimental Δ<em>G</em><sub>solv</sub> values range from -14 to 4 kcal mol<sup>-1</sup> and each solute/solvent pair is represented by their chemical family, SMILES string and InChlKey. We generated 213 chemical descriptors for every solvent and solute in each entry using <a href="http://http://www.rdkit.org/">RDKit</a> software, version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and Δ<em>G</em><sub>solv</sub>.</p> <p>Through Machine Learning regression algorithms, our models were able to make Δ<em>G</em><sub>solv</sub> predictions with high accuracy, based on the information encoded in each chemical feature.</p>
Pythia8 Quark and Gluon Jets for Energy Flow
<p>Two datasets of quark and gluon jets generated with Pythia 8, one with all kinematically realizable quark jets and one that excludes charm and bottom quark jets (at the level of the hard process). The one without c and b jets was originally used in <a href="https://arxiv.org/abs/1810.05165">Energy Flow Networks: Deep Sets for Particle Jets</a>. Generation parameters are listed below:</p> <ul> <li>Pythia 8.226 (without bc jets), Pythia 8.235 (with bc jets), <span class="math-tex">\(\sqrt{s}=14\,\text{TeV} \)</span></li> <li>Quarks from WeakBosonAndParton:qg2gmZq, gluons from WeakBosonAndParton:qqbar2gmZg with the Z decaying to neutrinos</li> <li>FastJet 3.3.0, anti-ki jets with R=0.4</li> <li><span class="math-tex">\(p_T^\text{jet}\in[500,550]\,\text{GeV},\,|y^\text{jet} |<1.7\)</span></li> </ul> <p>There are 20 files in each dataset, each in compressed NumPy format. Files including charm and bottom jets have 'withbc' in their filename. There are two arrays in each file</p> <ul> <li>X: (100000,M,4), exactly 50k quark and 50k gluon jets, randomly sorted, where M is the max multiplicity of the jets in that file (other jets have been padded with zero-particles), and the features of each particle are its pt, rapidity, azimuthal angle, and pdgid.</li> <li>y: (100000,), an array of labels for the jets where gluon is 0 and quark is 1.</li> </ul> <p>If you use this dataset, please cite this Zenodo record as well as the corresponding paper:</p> <ul> <li>P. T. Komiske, E. M. Metodiev, J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165.</li> </ul> <p>For the corresponding dataset of Herwig jets, see <a href="https://zenodo.org/record/2664330">this Zenodo record</a>. The datasets can be downloaded and read into python automatically using the <a href="https://energyflow.network/docs/datasets/#quark-and-gluon-jets">EnergyFlow Python package</a>.</p> <p>Changes:</p> <ul> <li>v1 - Added files with b and c quark jets.</li> </ul>
Flume Experiment Testing the Impact of Artificial Streambank Roots on Velocity, Reynold's Shear Stress, and Turbulent Kinetic Energy using an Acoustic Doppler Profiler
The data published here is expected to accompany one publicly available dissertation (Chapter 4 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Impact of Flexible and Rigid Artificial Roots on Stream Hydrodynamics
Impacts of invasive species on food web energy pathways and quality, St. Lawrence River, 2018-2021.
This dataset contains field measurements collected between 2018 and 2021 from three fluvial lakes in the Upper St. Lawrence River (Canada), including both invaded systems (with dreissenid mussels and round goby) and uninvaded reference sites. Data include georeferenced sampling information (site, lake, latitude, longitude, month, year), water chemistry (total phosphorus, µg/L; conductivity, µS/cm), and habitat descriptors (substrate). Biological records encompass seston, macroinvertebrates, and fish. Fish data comprise species identity, sex, total length (mm), weight (g), relative weight index (Wr), and detailed fatty acid composition expressed as relative proportions (%) and concentrations (µg/mg), including essential LC-PUFAs (EPA, DHA), n-3 and n-6 polyunsaturated fatty acids. Stable isotope data are provided, including carbon (δ13C) and nitrogen (δ15N) ratios, C:N ratios, and isotopic baselines from pelagic (δ13Cpel, δ15Npel) and benthic (δ13Cben, δ15Nben) sources. Derived variables, such as pelagic diet proportion and trophic position, were calculated using the two-source mixing model described by Post (2002) (DOI: https://doi.org/10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2). These data provide a comprehensive resource for examining food web structure, energy pathways, and the ecological impacts of invasive species in large river ecosystems.
Benchmark Data for AI Safety for High Energy Physics
<p><strong>Datasets for the paper "AI Safety for High Energy Physics" by Ben Nachman and Chase Shimmin (<a href="https://arxiv.org/abs/1910.08606">arXiv:1910.08606</a>)</strong></p> <p>This record contains two files: particles_jj.npz and particles_yz.npz, which contain simulated events of dijet and Z+photon production, respectively, from proton-proton collisions at sqrt(s)=13 TeV.</p> <p>The parton-level events are generated with MadGraph5 aMC@NLO, which are then passed to Pythia 8 for parton showering and hardonization, and then finally to Delphes3 for ATLAS-like detector simulation. Reconstructed calorimeter towers are clustered using the anti-kT algorithm with radius parameter R=1.0. The highest-pT jet from each event is selected, and only events with jet pT > 300 GeV are saved.</p> <p>The Npz files contain three dictionary keys:</p> <ul> <li><strong>jets</strong><strong>:</strong> (N, 4)-shape array containing the pT, eta, phi, and mass of the leading R=1.0 jet for each event</li> <li><strong>constituents:</strong> (N, 128, 3)-shape array containing the pT, eta, phi of up to 128 highest-pT constituent momenta from the leading jet cluster. Jets with fewer than 128 constituents are padded with zero values.</li> <li><strong>photons:</strong> (N, 3)-shape array containing the pT, eta, phi of the leading reconstructed photon (if any) of the event. Events with no photon are filled with zeros.</li> </ul> <p>pT and mass values are stored in units of TeV.</p>
Production line dataset for task scheduling and energy optimization - Demand Response Participation
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> it was simulated an announcement of a demand response program at period 757, describing a demand response event from period 937 (Friday at 21:00h) to 960 (Friday at 23:00h) , where each period represents five minutes. The demand response program imposed a limit consumption, during its event, of 2.5 kWh. The announcement of the demand response allowed the use of the proposed solution to limit the energy consumption. For that, the algorithm described in section 3.3 was executed at period 769 (Friday at 7:00h).</p> <p>The API can be found at <<a href="http://www.gecad.isep.ipp.pt/api/spear/%3E">http://www.gecad.isep.ipp.pt/api/spear/</a>></p> <p>File Description:</p> <ul> <li>Input_JSON_Demand_Response_Optimization - JSON input data for the demand response participation</li> <li>Output_JSON_Demand_Response_Optimization - JSON output data for the demand response participation</li> <li>Output_Statistics_Demand_Response_Optimization - Excel output demand response participation statistics</li> <li>Comparison_Output_Statistics_Demand_Response - Excel output statistics comparing the before and after the demand response participation</li> </ul>
Production line dataset for task scheduling and energy optimization - Schedule Optimization
<p>The case study of this dataset uses real production data, provided by a textile company that manufactures hang tags. Their working schedule is from 7h00 of Monday to 23h00 of Saturday. This dataset uses a period of 5 minutes for all task durations and energy data. The case study considers a six-day period from 7h00 of Monday to 23h00 of Saturday. The scheduling algorithm was used for three machines that share the same cell.<br> <br> The API can be found at <http://www.gecad.isep.ipp.pt/api/spear/><br> <br> File Description:</p> <ul> <li>Input_JSON_Schedule_Optimization - JSON input data for the schedule optimization</li> <li>Output_JSON_Schedule_Optimization - JSON output data for the schedule optimization</li> <li>Output_Statistics_Schedule_Optimization - Excel output schedule optimization statistics</li> </ul>
Density functional theory calculations of coherent bcc Fe-Cu interfacial energy densities
<p>File contains the data required to calculate interfacial energy densities of {100}, {110}, {111}, {210}, {211} and {221} orientated coherence bcc Fe-Cu interfaces.</p> <p>Data produced for the study detailed in: Cu nanoprecipitate morphologies and interfacial energy densities in bcc Fe from density functional theory (DFT) A.M. Garrett and C.P. Race.</p> <p>Submitted to Computational Materials Science.</p> <p>.txt files contain the total energies calculated for relaxed interface-containing and bulk simulation cells at a range of interfacial spacings. This data can be used to calculate the size independent interfacial energy densities for a range of Fe-Cu interface orientations using standard fitting approaches. Columns of the tables in the .txt files are no. atoms, interface-containing simulation cell length, interfacial area, total energy of the relaxed interface-containing simulation cell, total energy of the reference bulk Fe and total energy of the reference bulk Cu. Lengths are in Angstrom and energies are in eV.</p>
Working time, energy throughput and value added embodied in production, consumption and trade by subsectors for the US, the EU, China and rest of the world (2011)
<p>This repository contains the data needed to reproduce the results in:</p> <p>Pérez-Sánchez, L., Velasco-Fernández, R., Giampietro, M., The international division of labor and embodied working time in trade for the US, the EU and China, Ecological Economics. <a href="http://doi.org/10.1016/j.ecolecon.2020.106909">https://doi.org/10.1016/j.ecolecon.2020.1069097</a></p> <p>Sources of data are specified in the dataset (under tab "references")</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.