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818 results for “Neutrality”
Dataset of "From Gas to Solution: The Changing Neutral Structure of Proline Upon Solvation"
Liquid-jet photoelectron spectroscopy (LJ-PES) and electronic-structure theory were employed to investigate the chemical and structural properties of the amino acid L-proline in aqueous solution for its three ionized states (protonated, zwitterionic, deprotonated). This is the first PES study of this amino acid in its most biologically relevant environment. Proline's structure in the aqueous phase under neutral conditions is zwitterionic, distinctly different from the non-ionic neutral form in the gas phase. By analyzing the carbon 1s and nitrogen 1s core-levels as well as the valence spectra of aqueous-phase proline, we found that the electronic structure is dominated by the protonation state of each constituent molecular site (the carboxyl and amine) with small yet noticeable interference across the molecule. The site-specific nature of the core-level spectra enables probing of individual molecular constituents. The valence photoelectron spectra are more difficult to interpret because of overlapping signals of proline with the solvent and pH-adjusting agents (HCl and NaOH). Yet we are able to reveal subtle effects of specific (hydrogen-bonding) interaction with the solvent on the electronic structure. We also demonstrate that the relevant conformational space is much smaller for aqueous-phase proline than it is for its gas phase analogue. This study suggests that caution must be taken when comparing photoelectron spectra for gaseous and aqueous-phase molecules, particularly if those molecules are readily protonated / deprotonated in solution.
Arctic grayling neutral genomic microsatellite loci from the Kuparuk, the Sagavanirktok (primarily Oksrukuyik Creek) and the Itkillik (primarily the I-Minus outlet stream) watersheds, 2010-2014
Since 2009, The FISHSCAPE Project (National Science Foundation grants: 1719267, 1417754, and 0902153), based at Toolik Field Station, has monitored physical, chemical, and biological parameters within three watersheds: The Kuparuk (including Toolik Lake and Toolik outlet stream), The Sagavanirktok (primarily Oksrukuyik Creek, but also including sections of the Atigun River and Tea and Galbraith Lakes), and Itkillik (primarily the I-Minus outlet stream a tributary that that feeds into the Itkilik River). Goals of the FISHSCAPE project are to understand and predict the adaptability and persistence of a key Arctic species, the Arctic grayling (Thymallus arcticus), to changing climate and hydrology. Research questions include: (1) Does landscape structure determine movement within and among watersheds; (2) do populations adapt to stream characteristics at local and regional scales; and (3) will the relative adaptability of populations determine their persistence under future climate change. We used genetics to investigate population structure and landscape genetics for Arctic grayling. Adult and young-of-the-year fish were captured at sampling locations and coordinates and/or specific station locations were noted. Fin clip samples (adults) or whole fish (young-of-the-year) were collected and preserved in 95% ethanol until Deoxyribonucleic acid (DNA) was extracted. Polymerase chain reaction (PCR) products from neutral genomic microsatellite loci were scored and used to assess population genetic structure and other population parameters. Adult capture and movement data, including length, weight and Passive Integrated Transponder (PIT) tag information, can be found in a separate data package.
Size distribution of neutral and charged particles smaller than 42 nm measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition (ACE).
<p>The size distribution of neutral and charged particles was measured using a neutral cluster and air ion spectrometer (NAIS) instrument. The concentration was corrected for diffusional losses in the inlet.</p> <p>The concentration and temporal dynamics of small particles is fundamental to characterize the first step of new particle formation (NPF) and growth. Moreover, naturally charged particles and ions can provide information about the role of ion induced nucleation. Newly formed particles can grow to larger sizes where they act as cloud condensation nuclei, directly affecting the Earth radiative budget and cloud properties.</p> <p>Measurements were performed on the upper deck of icebreaker Akademik Tryoshnikov along the track of the Antarctic Circumnavigation expedition. Temporal coverage is from January 22, 2017 to April 11, 2017. The concentration is reported as dN/dlog(Dp) per cubic centimetre, where Dp indicates the corresponding diameter size bin. Data were collected with one-second time resolution and averaged automatically by the acquisition software to 120 seconds before January 31 2017 and to 90 seconds after that date. The instrument was calibrated before the campaign by the manufacturer and periodically cleaned during the campaign (one time per leg).</p> <p>Pollution from the ship exhaust and other human activities (e.g. helicopter flights) was identified as described in Schmale et al., 2019 (<a href="https://doi.org/10.1175/BAMS-D-18-0187.1">https://doi.org/10.1175/BAMS-D-18-0187.1</a>) and a corresponding flag was associated to the data (with 1 meaning clean data and 0 polluted data).</p> <p> </p> <p>***** Dataset contents *****</p> <p>- 01_neutral_particles_size_distribution.csv, data file, comma-separated values</p> <p>- 02_negative_ions_size_distribution.csv, data file, comma-separated values</p> <p>- 03_positive_ions_size_distribution.csv, data file, comma-separated values</p> <p>- 04_neutral_particles_size_distribution_header.txt, metadata, text</p> <p>- 05_negative_ions_size_distribution_header.txt, metadata, text</p> <p>- 06_positive_ions_size_distribution_header.txt, metadata, text</p> <p>- README.txt, metadata, text</p> <p>Data that were missing or bad because of instrumental problems were simply removed from the file (no entry).</p>
Results of "Storm Time Data Assimilation in the Thermosphere Ionosphere with TIDA" CHAMP, GRACE-A, and GRACE-B neutral density data assimilation into CTIPe for 2003 Halloween Storms
<p># README</p> <p>Results for the article "Storm Time Neutral Density Assimilation in the Thermosphere Ionosphere with<br> TIDA".</p> <p>There are three storms presented here:</p> <p>1. 2003 storm: October 26-30, 2003<br> 2. 2004 storm: July 26-30, 2004<br> 3. 2002 storm: September 27 - October 2, 2002.</p> <p>For each of these three storms, there are four runs. For each storm, we've done a run<br> assimilating all satellites, and then three more assimilating each satellite individually and<br> comparing against the others.</p> <p>Each directory name before underscore identifies the date the run was<br> started. After the underscore identifies the date assimilated.</p> <p>This readme uses the notation that in curly brackets the satellites assimilated are given.</p> <p>- a stands for GRACE-A<br> - b stands for GRACE-B<br> - c stands for CHAMP</p> <p>The runs are summarized below:</p> <p>1. 2003 storm: {a, b, c}: 2021-12-29T1259_...<br> 3. 2003 storm: {a}: 2022-01-01T1654_...<br> 4. 2003 storm: {b}: 2022-01-02T1423_...<br> 2. 2003 storm: {c}: 2021-12-29T2342_...</p> <p>5. 2004 storm: {a, b, c}: 2022-01-03T1614_...<br> 6. 2004 storm: {a}: 2022-01-04T1027_...<br> 7. 2004 storm: {b}: 2022-01-04T2144_...<br> 8. 2004 storm: {c}: 2022-01-05T0502_...</p> <p>9. 2002 storm: {a, b, c}: 2022-01-02T2025_...<br> 10. 2002 storm: {a}: 2022-01-05T1056_...<br> 11. 2002 storm: {b}: 2022-01-05T1854_...<br> 12. 2002 storm: {c}: 2022-01-06T1017_...</p> <p>## Example result directory</p> <p>2021-12-29T1259_d2003-10-27<br> ├── density_champ_density.csv<br> ├── density_grace-a_density.csv<br> ├── density_grace-b_density.csv<br> └── inputs<br> ├── reference_2003-10-27_input.txt<br> └── special_2003-10-27_input.txt<br> <br> 1 directory, 5 files</p> <p>## Zenodo doesn't support directories</p> <p>So, the file structure has been flattened in the following way:</p> <p>Before: ./aaa/bbb/ccc.png</p> <p>After: ./aaa-bbb-ccc.png</p> <p>https://unix.stackexchange.com/~/45659</p> <p> </p>
Results of "Ensemble Kalman Filter for the Thermosphere Ionosphere", CHAMP neutral density assimilation into CTIPe for March 20, 2007
<p>These data are the result of assimilating neutral density measurements from the CHAMP satellite on March 20, 2007 into the CTIPe model and and comparison of results with observations made by the GRACE satellite. Data assimilation is performed in three configurations: Configuration (i) is ds, state correction. Configuration (ii) is dfds, both input estimatation and state correction. Configuration (iii) is df, estimation of model inputs only.</p> <p>This data is associated with the following publication:</p> <blockquote> <p>Codrescu S., M.V. Codrescu, and M. Fedrizzi (2018), An Ensemble Kalman Filter for the Thermosphere-Ionosphere, Space Weather, 16, doi:<a href="http://dx.doi.org/10.1002/2017SW001752" title="Link to external resource: 10.1002/2017SW001752">10.1002/2017SW001752</a>.</p> </blockquote> <p> </p>
Data from Neutral genetic structuring of pathogen populations during rapid adaptation
<p><strong>Datasets and temporary dataframes relating to the article "Neutral genetic structuring of pathogen populations during rapid adaptation".</strong></p> <p>These datasets and temporary dataframes are necessary to run the scripts from the public GitLab repository: <a href="https://gitlab.com/saubin.meline/neutral-genetic-structuring-adaptation">https://gitlab.com/saubin.meline/neutral-genetic-structuring-adaptation</a>. Please refer to this public GitLab repository for the latest version of the codes and to perform all analyses presented in the article.</p> <p>Original datasets from the demogenetic model:</p> <ul> <li>Output_RandomDesign.txt</li> <li>Output_RegularDesign_With_host_alternation.txt</li> <li>Output_RegularDesign_Without_host_alternation.txt</li> <li>Output_RandomDesign_Mnull_Medoid_With_host_alternation.txt</li> <li>Output_RandomDesign_Mnull_Medoid_Without_host_alternation.txt</li> </ul> <p>All remaining files correspond to temporary dataframes generated by the scripts in the GitLab repository, provided here for reproducibility of the results and to save time at certain time-consuming scripts.</p>
Dataset of "Black Titanium Oxide/Activated TaS2 Flakes Photoelectrode for Plasmon Assisted Hydrogen Evolution at Neutral pH at High Current Density"
<p>Nanotubular structure of black titania with sputtered gold and incorporation of 3R-TaS2 self-activated flakes for high current density and neutral pH usage for hydrogen evolution reaction. Dataset consists of electrochemical data (LSV, EIS, CA), x-ray difractograms, Raman spectra, SEM images with EDX mapping, UV-vis spectra, DEMS records, ICP-MS records, XPS spectra and compositional analysis and BET records.</p>
Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster
<h2>Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster</h2> <ul> <li>Authors: Matteo Guaita, Alberto Marín-Cebrián, Eduardo Ahedo, Mario Merino, Fabrice Cipriani, Käthe Dannenmayer</li> <li>Contact email: mguaita@pa.uc3m.es</li> <li>Date: 15/11/2024</li> <li>Keywords: Plasma Physics, Plasma Plumes, Gridded Ion Thruster, Cathode, Facility Effects, Particel in Cell</li> <li>Version: 1.0.0</li> <li>Digital Object Identifier (DOI): 10.5281/zenodo.14165272</li> <li>License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/" target="_blank" rel="noopener">Open Data Commons Attribution License</a></li> </ul> <h2>Abstract</h2> <p>This dataset contains the data from the simulations presented in the article submitted for pubblicaiton in the Journal: Plasma Sources Science and Technology (PSST):</p> <p>"Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster"</p> <p>The data in this repository is the result of several hybrid PIC simulations as described in the reference. For further information on the setup, numerical parameters and physical meaning of the simulations please refer to the article</p> <h2>Dataset description</h2> <p>The simulations that produced the datasets in this repository were run with the full PIC code Picaso. The majority of the data is at steady-state, and has been averaged over the last 7000 simulation time-steps to reduce numerical noise. This averaging has been performed as a first step directly by the code through time-step accumulation techniques, and at a later stage in post-processing by averaging over the last 20 print-outs of the code. The data inside the "time_dependent" folder is instead time-varying.</p> <h2>Data files</h2> <p>Each HDF5 data-group contains the mesh and time coordinates and plasma properties of a specific simulation. In particular, the naming convention is the following:</p> <ul> <li><strong>Ref_planar.hdf5: </strong>Contains the results of the "reference planar simulation" presented in Sections III and IV of the article.</li> <li><strong>2Te_planar.hdf5: </strong>Contains the results of the simulation with a doubled electron temperature at the cathode presented in Section V of the article.</li> <li><strong>2Ie_planar.hdf5: </strong>Contains the results of the simulation with a doubled electron current at the cathode presented in Section V of the article.</li> <li><strong>No_coll_planar.hdf5: </strong>Contains the results of the simulation without inelastic electron collisions presented in Section V of the article.</li> <li><strong>Ref_axisym.hdf5: </strong>Contains the results of the non-accelerated axis-symmetric simulation presented in Section VI of the article</li> <li><strong>fcol_2.5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 2.5, presented in Section VI of the article</li> <li><strong>fcol_5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 5, presented in Section VI of the article</li> <li><strong>fcol_7.5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 7.5, presented in Section VI of the article</li> <li><strong>fcol_10_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 10, presented in Section VI of the article</li> </ul> <p>In each of these files the data is organized in a series of subfolders:</p> <ul> <li><strong>Electrons_prim: </strong>Contains the steady-state properties of primary electrons</li> <li><strong>Electrons_trap: </strong>Contains the steady-state properties of trapped electrons</li> <li><strong>Ions_fast: </strong>Contains the steady-state properties of fast ions (ions injected through the thruster grids)</li> <li><strong>Ions_slow: </strong>Contains the steady-state properties of slow ions (ions produced by collisions in the plume)</li> <li><strong>Time_dependent: </strong>Contains the vector of time-stamps and spatially global data saved at the corresponding time</li> </ul> <p>The data files found in the outer simulation folder are:</p> <ul> <li><strong>xs:</strong> Physical x coordinates [cm]</li> <li><strong>zs:</strong> Physical z coordinates [cm]</li> <li><strong>phi: </strong>electric potential [V]</li> <li><strong>rho_el: </strong>space charge density [C/m³]</li> <li><strong>nn: </strong>Total neutral density [1/m³]</li> </ul> <p>The data files for each particle population are:</p> <ul> <li><strong>n: </strong>Plasma (ion) density [1/m³]</li> <li><strong>f_x: </strong>Particle flux along x [1/(m² s)]</li> <li><strong>f_y: </strong>Particle flux along y [1/(m² s)]</li> <li><strong>f_z: </strong>Particle flux along z [1/(m² s)]</li> <li><strong>p_xx: </strong>xx component of the pressure tensor [J/m³]</li> <li><strong>p_yy: </strong>yy component of the pressure tensor [J/m³]</li> <li><strong>p_zz: </strong>zz component of the pressure tensor [J/m³]</li> </ul> <p>The data files in the time dependent folder are:</p> <ul> <li><strong>t: </strong>Time coordinates [s]</li> <li><strong>phi_W: </strong>Potential of the vacuum chamber walls [V]</li> <li><strong>phi_max:</strong> Maximum value of the potential in the plume [V]</li> <li><strong>nte_frac: </strong>Fraction between the number of trapped electrons and ions in the plume bulk [%]</li> <li><strong>nu_te_ela: </strong>globally averaged trapped electron-neutral elastic collision frequency [Hz]</li> <li><strong>nu_te_ion: </strong>globally averaged trapped electron-neutral ionization collision frequency [Hz]</li> <li><strong>nu_te_exc: </strong>globally averaged trapped electron-neutral excitation collision frequency [Hz]</li> <li><strong>nu_te_cou: </strong>globally averaged trapped electron-neutral Coulomb collision frequency [Hz]</li> <li><strong>nu_pe_ela: </strong>globally averaged primary electron-neutral elastic collision frequency [Hz]</li> <li><strong>nu_pe_ion: </strong>globally averaged primary electron-neutral ionization collision frequency [Hz]</li> <li><strong>nu_pe_exc: </strong>globally averaged primary electron-neutral excitation collision frequency [Hz]</li> <li><strong>nu_pe_cou: </strong>globally averaged primary electron-neutral Coulomb collision frequency [Hz]</li> </ul> <p> </p> <p>Note that all the other quantities shown in the article may be obtained from the ones saved here. We remind here that the gas employed is Xenon and that all ions are considered to be singly charged.</p> <h2>Citation</h2> <p>Any works using this dataset or any part of it in any form shall cite it as follows. The BibTeX entry s provided for convenience:</p> <p>@dataset{sim_data_guai25b,<br> author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},<br> title = {Data from: Electron populations and neutralization process in the plume of a gridded ion thruster},<br> month = November,<br> year = 2024,<br> publisher = {Zenodo},<br> version = {1.0.1},<br> doi = {10.5281/zenodo.14165272},<br> url = {https://doi.org/10.5281/zenodo.12751281}<br>}</p> <p>The journal article associated with this data-set shall also be cited as follows:</p> <p>@article{guai25b,<br> doi = {10.1088/1361-6595/adc482},<br> year = {2025},<br> month = {mar},<br> publisher = {IOP Publishing},<br> author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},<br> title = {Electron populations and neutralization process in the plume of a gridded ion thruster},<br> journal = {Plasma Sources Science and Technology },<br>}</p> <p> </p> <p><br><br></p> <h2>Acknowledgments</h2> <p>This work, and the corresponding dataset, has been supported by the ECOMODIS project, funded by the European Space Agency, under contract 4000137869/22/NL/RA</p>
Poker Flat Incoherent Scatter Radar (PFISR) Observations of E-region Neutral Winds
<p>Updated: 12-15-2021</p> <p><strong>RULES OF THE ROAD:</strong></p> <p>You are welcome to use the data 'as is', however, please inform me via email if you plan to use the dataset. There are a number of small issues with the dataset that are best discussed. We are interested in publications that use the data and derived values that are presented within the dataset. <strong>If you plan to publish these results, please circulate a draft by me (SRK) and we would appreciate an offer of co-authorship or at minimum an acknowledgement. You should include the NSF funding numbers NSF AGS - 1853408</strong></p> <p> </p> <p>As a general warning, the data from PFISR are quite noisy and you may need to perform significant averaging to produce usable results. Again, please contact me and we can discuss this in more detail.</p> <p>Version v0.6.4.2021.07.12 - This was the final processed version at the time that the final report was submitted to the NSF.</p> <p> </p> <p><strong>--------------- Previous from before ------------------</strong></p> <p>This file contains Poker Flat Incoherent Scatter Radar (PFISR) E-region Neutral Winds Data. These data correspond to monthly data files that include the E-region neutral winds and other parameters for the from March 2013-June 2019.</p> <p><strong>Publications of the Joule Heating Results:</strong></p> <p>https://doi.org/10.1029/2021JA029371</p> <p>https://doi.org/10.1029/2021JA029719</p> <p> </p> <p><strong>Publication of Neutral Wind Results:</strong></p> <p>Hopefully we will have something in 2021. </p> <p> </p> <p><strong>RAW ISR Data:</strong> These data were processed from the following files found in: https://data.amisr.com/database/tmp/Kaeppler/winds/ and https://data.amisr.com/database/tmp/Kaeppler/missing_IPY.tar.gz Please note that the error on the line of sight velocities may have been overestimated in these data and we scaled them by a eVLOS/sqrt(10). Interested persons should contact Ashton Reimer or Roger Varney at SRI International for more information about these data, please see amisr.com</p> <p>Truthfully, the ISR data should eventually be reprocessed and then the winds algorithm run over it again. This is a step for future work.</p> <p> </p> <p><strong>Processing Code is available upon request via email.</strong></p> <p> </p> <p><strong>File Documentation:</strong></p> <p> </p> <p><strong>Please see the change log:</strong></p> <p>Purpose: This is the overarching program and functions which process the<br> E region neutral winds from the fitted AC and LP data from PFISR.<br> This is a conversion fo process_eregwinds_srk.py which was originally written by<br> Nicolls into a more formal python class structure.</p> <p>2017-10-05 - v0.2</p> <p>The ProcessEregionNeutralWinds.py file has been validated against process_eregwinds_srk.py<br> using 20161121.001_ac_3min-fitcal.h5, 20170301.013_ac_3min-fitcal.h5, 20170302.001_ac_3min-fitcal.h5.<br> The program to run these is ComparePrograms.py. At this point these program match.<br> I am going to start diverging the code base, first subtly in the Joule Heating<br> since I found that Mike just looped over Nbeams, which isn't quite right, you need to loop<br> over the beams that were selected.</p> <p>Changes from this point forward will produce different results.</p> <p>2017-10-10 - v0.3.2017.10.10</p> <p>Version v0.3, I made some IO changes but I may start processing some data with this version.</p> <p>Version v0.4 - lots of small edits made to the IO and the plotting software. It all seems to work<br> I have also included the SNR and Ne into the monthly plots and other information.<br> Made processing smoother.</p> <p>03 13 2018 - added solar local time converion</p> <p>v0.4.1 - 09 08 2018 added some ability to extract out the raw electron and SNR densities for each altitude bin<br> v0.4.2 - 10 15 2018 added in obtaining the F-region flows - want to check against the electric field.<br> v0.4.3 - 10 29 2018 added in some more altitude into the Joule Heating so I can make better figures<br> v0.4.4 - 11 20 2018 made some pretty major changes to IO to include consistent calculation of<br> Pedersen conductivity from FastConductivity.py. Made some changes to the Joule heating calculation and checked<br> formulas. It is worth checking again.</p> <p>v0.4.5 - 11 20 2018: added in Hall and Pedersen conductivities from fitted electron density data.<br> v0.4.6 - 12 03 2018: Tried to fix some of the double counting and time problems in testMakeMonthlyh5</p> <p>05 22 2019: added some statements to bypass the geophysical parameters. Also need in config file now.<br> Additionally wrote in IOEregionwinds a try except statement</p> <p>07 29 2019: Running the code for the 06 data reprocessed by Ashton</p> <p>v0.5.0 - 10-15-2019: put in some filtering on the LOS velocity discharging bad Chi square and bad error codes on the fit.</p> <p>v0.5.1 - 10-23-2019: changed chi square to 0.01 for lower boundary</p> <p>v0.5.3 - 12-02-2019: Added in that now passing in the Chi2 and Fitcode filtering by Config file<br> Bigger change that I am scaling the AC dVlos by some sort of factor while Ashton figures this out.<br> We decided that a conversative scaling would be to reduce the dVLOS by 1/sqrt(10).<br> The chi square produced in the data Ashton sent me typically was around 0.01, so the uncertaintiies on the LOS velocities<br> may be over estimated. So we are just changing this as a temporary fix while Ashton fixes the uncertainty estimation.</p> <p>v0.5.5 02 01 2020 - Added in calculation of Coriolis, Centrifugal, and Lorentz forcing<br> v0.5.5 02 10 2020 - Added a correction to qvert so that way I can calculate the lorentz term.<br> Found an error where qvert = 0 in the if statement goes to false.</p> <p>v0.5.5 02 15 2020 - Put in nuInscaler into the main program, scaling ALL kappas by the scaler number</p> <p>v0.5.6 02 28 2020 -- Added some more vlos diagostics and the calculation of the scale height. Added Altitude offset</p> <p>v0.5.6.2020.03.12_nuin_fracoff - testing putting in the Brekke formula for ion neutral collision frequency and took out frac</p> <p>v0.5.7.2020.04.10 - Put in Ashton's revised ion neutral collision frequency formulas into IO.<br> Also wrote a testscript and at least for the file I used was only different by 2.5%.<br> Revised where the mag data is being pulled from since the URL is deprecated<br> Added in Kappa which is now being interpolate - plan to see where kappa =1 is located for the paper.<br> commented out nuin scaler just so I am not chasing my tail</p> <p> test v0.5.7.2020.04.13_org commented back in original ion neutral collision frequency method<br> possible mistake that not summing up properly.</p> <p> test v0.5.7.2020.04.13_newnuin_orgsum_noTr800 - new formula for nuin except took off Tr>800.<br> I expect this should be almost the same as before since the formulas are basically the same.<br> did the original sum using frac[0] and frac[1] want to see if I am underestimating</p> <p> v0.5.7.2020.04.13_newnuin_orgsum_yesTr800 - same as above except now including Tr>800.</p> <p> 'v0.5.7.2020.04.13_newnuin_newsum_noTr800' - using the new sum now and new col freq</p> <p> v0.5.7.2020.04.13_updatedorg - updated original uses original method but including the NO term</p> <p>v0.6.0.2020.04.15 -- Now think I have the new ion neutral collision frequency working and validated.<br> Found a mistake in how I was calculating the ion neutral collision frequency that<br> the fraction weight I was using only included the O+ and O2+ terms and not NO+<br> Turns out I was basically weighting by about 0.5, so I was effectively reducing the<br> ion neutral collision frequency by about a factor of 0.5 or less...<br> From this point forward need to start using any results from > v0.6<br> This revision has changed previous results signi</p> <p>v0.6.0.2020.04.21 -- updated to now include the temperature correction for the O2+</p> <p>v0.6.1.2020.04.23 -- made a number of changes to the geomagnetic files and reprocessed from CDAweb.<br> Wrote new code to be able to process the files from CDAweb in the new format.<br> Also changed the geomagnetic data files</p> <p>v0.6.1.2020.06.07 -- changed the generation of Monthly files to hopefully be in order now<br> Added in missingIPY files given to me by ashton, maybe improve data covarege<br> Some work going to need to be done to make sure that all of the 10, 15, and 20 minute data are there.</p> <p>v0.6.1.2020.06.15_Weijia -- Updated the data for Weijia's study in particular since we are missing a lot of IPY data for 02-04 2013 and 2014.</p> <p>'v0.6.2.2020.07.01' -- Updated the data with new IPY27 mode for 2013 and 2014 Ashton processed. Also now put in mechanical Joule heating term.<br> Put in the conductance and conductivity now too.</p> <p>v0.6.2.2020.07.30 -- Made some changes to IO since Weijia noticed the mechanical heating terms were missing from the monthly files.</p> <p>v0.6.3.2020.10.19 -- Tried to elimated all extra instance of nuinscaler, and also output that variable. Added in variables<br> To get the Ti, Tn, ion neutral collision frequency along the vertical beam for diagnostic purposes<br> included dVest for F-region plasma drifts for Rafael</p> <p>v0.6.4.2020.11.20 -- Extracted some more parameters including F107 and the Hall and Pedersen Drags</p> <p>v0.6.4.2021.07.21 -- Final Run of data for NSF project</p> <p> </p>
Diversity of options to eliminate fossil fuels and reach carbon-neutrality across the entire European energy system
<p><strong>Sector-coupled Euro-Calliope model outputs</strong></p> <p>The subdirectories found here cover cost-optimal and cost relaxation (SPORES) carbon-neutrality runs for a sector-coupled, sub-national resolution European energy system model.</p> <p>The underlying model to produce these results, <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-coupled Euro-Calliope</a>, is an extension of the power-sector only <a href="https://github.com/calliope-project/euro-calliope">Euro-Calliope model</a>. It incorporates all energy consuming sectors and includes a more detailed representation of transmission capacities between 98 model regions in Europe.</p> <p>The model runs here are based on specific Sector-Coupled Euro-Calliope minor releases:</p> <ul> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/74f6a9b2e157b6147e155b556f521c03ef23246a">cost-opt</a></li> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/519a4fb26920114e451b8247b38ed86b93b6af89">slack-*</a></li> </ul> <p>The models were optimised using the <a href="https://github.com/calliope-project/calliope">Calliope open energy system modelling framework</a>, again based on different minor releases:</p> <ul> <li><a href="https://github.com/calliope-project/calliope/commit/1faed85eeddbe41c29d52982a6bfb147ef9001a3">cost-opt</a></li> <li><a href="https://github.com/calliope-project/calliope/commit/19460da2e23e752995a9a02ae6dca49379565d43">slack-*</a></li> </ul> <p><code>slack-*</code> results are for cost relaxation runs, where <code>*</code> refers to the percentage relaxation from the optimal cost of the 2018 energy system. All results use the <a href="https://github.com/sentinel-energy/friendly_data">friendly data</a> format. Data files are structured according to standardised sector-coupled Euro-Calliope output processing provided by the <a href="https://github.com/brynpickering/friendly-calliope">friendly-calliope</a> package + additional processing to produce data relevant to nine high-level metrics (see script <a href="https://github.com/calliope-project/sector-coupled-euro-calliope/blob/main/src/analyse/result_to_friendly.py">here</a>).</p> <p>Both cost optimal and SPORES results related to a projected demand scenario are given in the directories ending in "demand-update".</p> <p>To explore the data, please refer to the <a href="https://sentinel-energy.github.io/friendly_data/">friendly data documentation</a>.</p>
From Gas to Solution: The Changing Neutral Structure of Proline Upon Solvation - data
<p>Data set pertaining to the manuscript "From Gas to Solution: The Changing Neutral Structure of Proline Upon Solvation", submitted for peer review.</p> <p>In this work, Liquid-jet photoelectron spectroscopy (LJ-PES) and electronic-structure theory were employed to investigate the chemical and structural properties of the amino acid L-proline in aqueous solution for its three ionized states (protonated, zwitterionic, deprotonated). Experimental data were recorded by photoemission spectroscopy from a liquid jet source using synchrotron radiation. The data set documents the experimentally recorded spectra, including the proline photoelectron spectra and spectra of the zero energy cut-off, that were used to calibrate the binding energy scale.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard, see the<br>NeXus Data Format definition (v2024.02), https://manual.nexusformat.org/index.html<br>NXmpes expansion for FAIRmat data (v.2024.07), https://fairmat-nfdi.github.io/nexus_definitions/classes/contributed_definitions/NXmpes.html<br>NXmpes_liquid expansion to NXmpes (v.2024.07), https://fairmat-nfdi.github.io/nexus_definitions/mpes-liquid/classes/contributed_definitions/NXmpes_liquid.html<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br>1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data'). <br>2. As-measured data ('raw').</p> <p>If you use these data for your scientific work we kindly ask you to send us an electronic version or the citation of your work.</p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Data for "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa" by Haynes et al.
<p>Accompanying data products for publication entitled "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa". The manuscript was submitted to JGR Space Physics shortly after upload.</p> <p>Data includes all simulation outputs that are depicted in this work, both for the AIKEF hybrid model (i.e., Figure 4) and the model used to produce synthetic ENA images (Figures 3, 6, 8, 9, 11, A1, and B1). All other figures in the work are used for illustrative purposes and were not generated with simulation output. </p> <p>Information regarding the organization and file structure can be found in H24_data_readme.txt , as well as which dataset corresponds to which figure. Any inquiries, questions, or comments may be addressed through the email associated with this data publication.</p>
GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"
<p>GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"</p>
Data and code for "Carbon neutrality should not be the end goal: Lessons for institutional climate action from U.S. higher education"
<p>Code and data for the paper "Carbon neutrality should not be the end goal: Lessons for institutional climate action from U.S. higher education"</p> <p>File descriptions:</p> <p>'HEI_analysis_OneEarth.Rmd' is the code with improved annotation and colorblind-friendly figures.</p> <p>All other data files are provided as excel and csv for convenience.</p> <p>'working_master_data' contains data from the Second Nature reporting platform on emissions by category for each institution analyzed in the paper (measured in metric tons). All adjustments necessary to fill in the data gaps in this file are documented at the beginning of 'HEI_analysis'.</p> <p>'offsets' contains data on the type(s) of offsets purchased by each school in their carbon neutral year (measured in metric tons). This data was assembled from a variety of sources which are documented at the beginning of 'HEI_analysis'.</p> <p>'carbon_neutral_years' contains yearly counts of higher education neutrality goals that were reported to Second Nature as of November 2020.</p>
Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"
<p>The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration.</p> <p>Unit: Euro/Megawatthour</p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>
Ancillary files for "Reinterpreting the ATLAS bounds on heavy neutral leptons in a realistic neutrino oscillation model [arXiv: 2107.12980]"
<p><em>(Description copied from Appendix A "Ancillary files" of the companion paper)</em></p> <p>In order to simplify the interpretation of experimental results within realistic HNL models, we are including a number of data files along with the present publication. They can be used to generate the relevant signal samples, or to implement the extrapolation method presented in section 3.2.</p> <p><strong>Card files for the Monte-Carlo event generation</strong></p> <p>The /attachments/card_files folder contains the MadGraph card files (ending in .dat) and scripts (ending in .txt) for generating the signal samples used in this analysis, as well as for computing the total HNL width. Due to the OSSF veto, only processes with no opposite-charge same-flavor lepton pairs have been included. Additional relevant processes can easily be added by modifying the <em>generate</em> and <em>add process</em> lines in the *.txt files. All samples (except the ones used to compute the HNL width, which are generated at parton level) are generated at leading order, include up to two hard jets, and are showered and hadronized using Pythia 8. This is essential for obtaining a realistic W spectrum. The shower parameters could probably benefit from further tuning, and further improvements in the W spectrum accuracy are expected at NLO (using a suitable model). To allow computing the signal efficiencies, all cuts have been disabled in the run card (with the exception of the maximum <span class="math-tex">\(|\eta_{\mathrm{jet}}|\)</span> which needs to be set to 5 for correct matching).</p> <p><strong>Signal cross sections</strong></p> <p>The cross sections for the various processes considered in this analysis, as well as the total HNL width (both computed using MadGraph as described in section 3.2), are provided as JSON files in the /attachments/cross_sections folder.</p> <p>The file total_hnl_width.json contains the total HNL width <span class="math-tex">\(\hat{\Gamma}_{\alpha}(M_N)\)</span> (expressed in GeV), computed for the 5 mass points used in this analysis, and under the assumption of unit mixing with a single flavor <span class="math-tex">\(\alpha\)</span>, for each flavor. The total HNL width can then be computed for any combinations of mixing angles using eq. (3.2). The file is organized as two nested dictionaries, with the first key denoting the HNL mass <span class="math-tex">\(M_N\)</span>, and the second one the flavor <span class="math-tex">\(\alpha\)</span> for which the total width <span class="math-tex">\(\hat{\Gamma}_{\alpha}(M_N)\)</span> has been computed for a unit mixing angle <span class="math-tex">\(|\Theta_{\alpha}|^2 = 1\)</span> (with <em>Wtot_e</em> for <span class="math-tex">\(\alpha=e\)</span>, <em>Wtot_mu</em> for <span class="math-tex">\(\mu\)</span> and <em>Wtot_tau</em> for <span class="math-tex">\(\tau\)</span>).</p> <p>The file cross_sections.json contains the reference cross sections <span class="math-tex">\(\sigma_P^{\mathrm{ref}}\)</span> (in pb) for all the processes <em>P</em> considered in this analysis, expressed for <span class="math-tex">\(|\Theta|_{\mathrm{ref}}^2 = 1\)</span> and <span class="math-tex">\(\Gamma_{\mathrm{ref}} = 10^{-5}\,\mathrm{GeV}\)</span>. The file is organized as two nested dictionaries, with the first key denoting the HNL mass <span class="math-tex">\(M_N \)</span> and the second the process <em>P</em>. The correspondence between the key and the physical process can be found in table 7.</p> <p><strong>Signal efficiencies</strong></p> <p>The efficiencies resulting from the event selection described in section 3.1, as well as their parametrization according to eq. (3.6) (as discussed in section 3.3) can respectively be found in the files efficiencies.json and fitted_efficiencies.json in the /attachments/efficiencies folder.</p> <p>The file efficiencies.json is organized as follows. The data is located in a triply nested dictionary under the data key: the first level corresponds to the HNL mass hypothesis <span class="math-tex">\(M_N\)</span>, the second to the process key (cf. table 7) and the third to the <span class="math-tex">\(M(l_{\mathrm{sublead}},l')\)</span> bin for which the efficiency is computed. The values of the bottom-most dictionary are lists containing the efficiencies for a number of HNL lifetimes, as listed in meters in levels/lifetime.</p> <p>Finally, the file fitted_efficiencies.json is also organized as a triply nested dictionary, with the first level corresponding to the HNL mass <span class="math-tex">\(M_N\)</span>, the second to the process key, and where the third level denotes the fit parameter from eq. (3.6). tau0 is for <span class="math-tex">\(\tau_0\)</span>, epsilon0_total for <span class="math-tex">\(\epsilon_0\)</span> (the unbinned prompt efficiency), and epsilon0_binned is a list containing the prompt efficiencies <span class="math-tex">\(\epsilon_{0,b}\)</span> for the five <span class="math-tex">\(M(l_{\mathrm{sublead}},l')\)</span> bins <em>b</em> (in the same order as in efficiencies.json). The layout described here (or a similar one) can be used by experiments to report their signal efficiencies in a way that allows theorists to compute the expected signal for arbitrary choices of mixing angles.</p>
Electronic Supplement / Data Archive for "Global Variations in the Time Delays Between Polar Ionospheric Heating and the Neutral Density Response"
<p>These files provide supplemental data to accompany the paper "Global Variations in the Time Delays Between Polar Ionospheric Heating and the Neutral Density Response" submitted to AGU journal <em>Space Weather</em>, with manuscript number 2022SW003410. Details are provided in the file <strong>ReadMe_DataArchive.pdf</strong>.<br> </p>
Netcdf of LCN-PyCCS negative emission potentials as described in Werner et al., 2022 "Potential of land-neutral negative emissions through biochar sequestration"
<p><br> - This data includes three netcdfs providing the negative emission potentials<br> of land- and calorie-neutral Pyrogenic Carbon Capture and Storage (PyCCS) as<br> described in WERNER, C., LUCHT, W., GERTEN, D. & KAMMANN, C. 2022.<br> Potential of Land-Neutral Negative Emissions Through Biochar Sequestration.<br> Earth's Future, 10, e2021EF002583.</p> <p>- the negative emission potential is given in tonnes per grid cell<br> - the data ranges from 2020 to 2099 with a linear ramp up of the approach until 2025<br> - the negative emission potentials are given for each yield increase scenario,<br> 15%, 20% and 30% yield increase<br> - the LPJmL model source code and analysis script can be found in:<br> https://doi.org/10.5281/zenodo.6595002</p>
PsPM-trSP4: SCR measurement in response to face photographs withangry, neutral, and fearful expression while subjected to auditory distractors
<p>This dataset includes skin conductance response (SCR) measurements for each of 42 healthy unmedicated participants (21 males and 21 females aged 25.2 +/- 4.0 years) in response to 38 face photographs (modified from the Karolinska Directed Emotional Faces set, KDEF), each presented once with angry, neutral, and fearful expression for 1 s each. Meanwhile, participants were listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was selected randomly on each trial from 7.5 s, 9.0 s, or 10.5 s (misprinted in the publications), plus a variable delay of around 0.1 s for image loading. The experiment was preceded by a 2-minute resting period and divided into 3 blocks, separated by resting periods. Each resting period begins and ends with an event marker in the SCR recordings.</p>
Katrin Mueller - Solar Energy for a Carbon-Neutral Society
<p>Are we on the right track towards reaching negative net-zero CO2 emissions by 2050? Find out more about how solar power could help achieve a climate-neutral Europe & don't miss our interview with Katrin Mueller, sustainability engineer at SIEMENS AG and a SUNRISE consortium member.</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.