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ABG-IMRHT and Ames-2000K IR line lists for N2O as reported in "Accurate N2O IR Line Lists with Consistent Empirical Line Positions: ABG-IMRHT and Ames-2000K"
<p><strong>[2025-06-17, v2.0</strong>, updated from v1.1 (<a href="https://doi.org/10.5281/zenodo.14834513">10.5281/zenodo.14834513</a>)]<br>(1). This upgraded version is recommended for analysis involving B1b or ABG(-IMRHT) related line lists, or E' > 10,000 cm<sup>-1</sup>. <br>(2). Energy level lists computed on the B1b PES and related 296K ABG-IMRHT line intensity and line lists, and B1b-based 1000-3000K line list files have been updated, along with n2olist.f90.v1.4. <br>(3). The full 296 K IR line lists of <strong>all 12 stable isotopologues</strong> are provided in "n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5corrected.100pct-abundance.xz", in which the line intensities assume 100% abundance for every isotopologue.<br>(4). Please note that the B1b PES-based energy levels in "empirical.corrections.for.ABG-IMRHT.zip" are still valid, but the B1b PES based energy levels are not updated in the files inside that .zip. </p> <p>[<strong>Link to <a href="https://www.sciencedirect.com/science/article/pii/S0022407325001645">the N2O paper</a> </strong>published at JQSRT (2025) 343, 109502, part of VSI:HITRAN2024, doi:<a href="https://doi.org/10.1016/j.jqsrt.2025.109502">10.1016/j.jqsrt.2025.109502</a>]</p> <p>[Updated from v1.0 (<a href="https://doi.org/10.5281/zenodo.14174307">10.5281/zenodo.14174307</a>). The J=151-210 transitions of <sup>14</sup>N<sub>2</sub><sup>16</sup>O were missing from v1.0 files ]</p> <p><strong>1. Second generation of Ames-296K IR line list for "natural" Nitrous Oxide (N<sub>2</sub>O), denoted ABG-IMRHT</strong>, which was computed from Ames-B1b PES refinement (using Benjamin Schröder's ab initio PES Comp I, doi:10.1515/zpch-2015-0622) and 2023 dmsG-10K accurately fitted from CCSD(T)/aug-cc-pV(T,Q,5)Z dipoles. In this major upgrade to Ames-296K N<sub>2</sub>O line list (10.1080/00268976.2023.2232892 and 10.5281/zenodo.7888194), rovibrational energy levels computed from NOSL-296 EH model were adopted to match and replace ~100,000 <sup>14</sup>N<sub>2</sub><sup>16</sup>O levels. More consistent empirical corrections are determined for multiple isotopologues from comparison with RITZ (IAO), MARVEL (ExoMol), HITRAN, and JPL datasets. The ABG-IMRHT line list provides the most reliable and consistent IR intensity predictions and accurate line positions in the range of 0 - 10,000 cm<sup>-1</sup>. All 12 stable isotopologues are included. </p> <p><strong>2. Ames-2000K</strong> IR line list provides complete, reliable and consistent IR predictions in the 0 - 15,000 cm<sup>-1</sup> range. It includes transitions of 12 isotopologues. Their intensities are scaled by corresponding terrestrial "natural" abundances. Ames-2000K is a composite list, including 4 component lists, to achieve better accuracy and reliability needs at both shorter and longer wavelengths: </p> <ul> <li>#1. a hot line list of <sup>14</sup>N<sub>2</sub><sup>16</sup>O, computed on Ames-1 PES and 2023 dmsG-wgt2d, J'<210, E'<25,000 cm<sup>-1</sup>, T=1000 / 1500 / 2000 / 3000 K. It occupies 24 GB in compressed .xz format. </li> <li>#2. 1000 K line lists of #2-#12 minor isotopologues, computed on Ames-1 PES and DMS, J'<150, E'<0.125 au - zpe (iso 2-6) or 0.08 -0.10 au - zpe (iso 7-12), S<sub>1000K </sub>> 10<sup>-34</sup> cm/molecule, size-reduction with 99.9% intensity conservation in cm<sup>-1</sup> bins. </li> <li>#3. a hot line list of <sup>14</sup>N<sub>2</sub><sup>16</sup>O, computed on Ames-B1b PES and 2023 dmsG-10Kcm<sup>-1</sup>, J'<150, E'<16,000 cm<sup>-1</sup>, T=1000 / 1500 / 2000 / 3000 K.</li> <li>#4. ABG-IMRHT IR line list at 296 K, J'<150, E'<16,000 cm<sup>-1</sup>, with best empirical line positions and highly consistent intensity predictions up to 10,000 cm<sup>-1</sup>. Coverage beyond 10,000 cm<sup>-1 </sup>is limited to strong lines.</li> </ul> <p><strong>3. List of files:</strong> (decompress .xz files first, "xz -dkf -T0 file.xz")</p> <ul> <li>IAO_N2O_levels.tar.xz: rovibrational energy levels of 6 N<sub>2</sub>O isotopologues, as computed using global Effective Hamiltonian (EH) models developed by Dr. Sergey Tashkun from IAO (Institute of Atmospheric Optics, Tomsk, Russia, <a href="https://www.iao.ru/">https://www.iao.ru/</a>). <br><br></li> <li>N2O.Ames-B1b.PES.and.Ames-2023.DMS.zip: Ames-B1b PES subroutine & coefficient file, see the note in n2opes2.f90; Ames 2023 dmsG subroutine and coefficients for fits up to 10K/12K/15K/17K/20K/25K cm<sup>-1</sup>, along with fitting residuals and compared to Ames-1 style (dmsC) coeffs and residuals. The geometry set has ~80 points in each 100 cm<sup>-1</sup>. <br><br></li> <li>n2olist.f90.v1.4: the main Fortran program for Ames-2000K generation, customizable, see the note at its beginning. <br>[ default Ames-2000K = Ames-1 (12 iso) + [B1b (446) + ABG-IMRHT (12 iso)] if (E'<15,000 cm-1 & J<=150) ]<br><br></li> <li>empirical.corrections.for.ABG-IMRHT.zip: subroutine and paired/corrected energy level lists used in the empirical correction of energy levels computed on B1b PES [<em>this file is not updated from v1.1 to v2.0</em>]<br><br></li> <li>n2o.partition.1-4000K.iso.1-12.scaled: partition sum for 12 isotopologues, input file required by n2olist.f90, excluding g_n<br>n2o.partition.1-4000K.iso.1-12.scaled.with.degeneracy.txt: same as above, g_n included, see the note inside<br><br></li> <li>list.of.n2o.xz.files : list of .xz files to read/regenerate from, under subdir "xz", input file required by n2olist.f90<br>ames.n2o.xx000-yy000.cm-1.xz: compressed data files for Ames-2000K (line list component #1+#2)<br><br></li> <li>n2o.iso1-12.levels.Ames-1.dat.xz: energy levels of 12 N<sub>2</sub>O isotopologues on Ames-1 PES, input file required by n2olist.f90<br><br></li> <li>n2o.446.B1b-PES.levels.xz: energy levels of <sup>14</sup>N<sub>2</sub><sup>16</sup>O computed on Ames-B1b PES, input file to n2olist.f90<br><br></li> <li>n2o.446.B1b-dmsG10K.1000K-3000K.Eup16K.0-10Kcm-1.compressed.xz: data file for <sup>14</sup>N<sub>2</sub><sup>16</sup>O hot list on Ames-B1b and dms 2023-G10K (component #3), input file required by n2olist.f90<br>n2o.446.B1b-dmsG10K.1000K-3000K.Eup16K.0-10Kcm-1.dat.xz: independent line list (component #3)<br><br></li> <li>n2o.iso1-12.levels.ABG-IMRHT.dat.xz: energy levels in ABG-IMRHT list, with empirical corrections included, input file required by n2olist.f90</li> <li>n2o.296K.ABG-IMRHT.20250602.dat.v5.corrected.xz: Latest Ames-296K line list with empirical energy level corrections (component #4), input file for n2olist.f90<br>n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5.corrected.xz: same as above, in HITRAN format, including line-broadening parameters<br><br></li> <li>n2o.296K.ABG-IMRHT.20250602.dat.v5corrected.100pct-abundance.xz: Latest Ames-296K line list with empirical energy level corrections, including the full set of IR transitions for <strong>12 stable isotopologues, assuming 100% abundannce </strong>for every isotopologue, and 1E-31 cm/molecule intensity cut-off at 296 K.</li> <li>n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5corrected.100pct-abundance.xz: same as above, in HITRAN format, including line-broadening parameters<br><br></li> <li>ames.n2o.intensity.xz: line count and intensity sum (original, selected, iso #1 and iso #2-12) in each 0.01 cm<sup>-1</sup> bins at 296 K, 1000 K, 1500 K, 2000 K, and 3000 K, for reference and statistics, optional input file to n2olist.f90<br><br></li> <li>ames.n2o.sint.reductions.xz: A check-point file during Ames-2000K file generation, for reference only. including # of lines (total & selected), intensity sum (total, iso #1, iso #2-12), and intensity retention ratio in each 0.01 cm<sup>-1</sup> bins for total, iso #1, and iso #2-12.<br><br></li> </ul> <p><strong>4. List of 12 isotopologues</strong>, abundances adopted, and number of lines in ABG-IMRHT and Ames-2000K line list, <br>[updated 2025-06-17 v2.0: the #lines in ABG-IMRHT are updated to S(296K) cut-off at 1E-31 cm/molecule, instead of 5E-31]</p> <table> <tbody> <tr> <td>#</td> <td>ISO</td> <td>abundance</td> <td># lines in ABG-IMRHT</td> <td># lines in Ames-2000K</td> </tr> <tr> <td>1</td> <td>446</td> <td>0.990333</td> <td>1388017</td> <td>3014643103</td> </tr> <tr> <td>2</td> <td>456</td> <td>3.64093E-3</td> <td>375541</td> <td>97932924</td> </tr> <tr> <td>3</td> <td>546</td> <td>3.64093E-3</td> <td>411353</td> <td>118432059</td> </tr> <tr> <td>4</td> <td>448</td> <td>1.98582E-3</td> <td>377552</td> <td>88169721</td> </tr> <tr> <td>5</td> <td>447</td> <td>3.69280E-4</td> <td>238921</td> <td>40212389</td> </tr> <tr> <td>6</td> <td>556</td> <td>1.33858E-5</td> <td>93933</td> <td>7534736</td> </tr> <tr> <td>7</td> <td>548</td> <td>7.30080E-6</td> <td>93674</td> <td>5583963</td> </tr> <tr> <td>8</td> <td>458</td> <td>7.30080E-6</td> <td>86446</td> <td>4995485</td> </tr> <tr> <td>9</td> <td>547</td> <td>1.35765E-6</td> <td>55360</td> <td>2588347</td> </tr> <tr> <td>10</td> <td>457</td> <td>1.35765E-6</td> <td>50754</td> <td>2275332</td> </tr> <tr> <td>11</td> <td>558</td> <td>2.68412E-8</td> <td>15814</td> <td>500190</td> </tr> <tr> <td>12</td> <td>557</td> <td>4.99134E-9</td> <td>8537</td> <td>274623</td> </tr> <tr> <td> </td> <td>Total</td> <td>1.00000069</td> <td>3195902</td> <td>3383142872</td> </tr> </tbody> </table> <p>5. This project is funded by NASA Grant 18-APRA18-0013 through NASA/SETI Institute Co-operative Agreement 80NSSC20K1358. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</p>
A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)
<p>The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5) Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange. (6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling (NEE_fsd) as a measure for spatial and temporal variation.</p> <p>References:</p> <ol> <li>Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, <a href="https://doi.org/10.1111/j.1365-2486.2008.01606.x">https://doi.org/10.1111/j.1365-2486.2008.01606.x</a>.</li> <li>Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731.</li> <li>Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020.</li> <li>Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, <a href="https://doi.org/10.5194/bg-11-1037-2014">https://doi.org/10.5194/bg-11-1037-2014</a>.</li> <li>Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, <a href="https://doi.org/10.1111/j.1365-2486.2005.001002.x">https://doi.org/10.1111/j.1365-2486.2005.001002.x</a>.</li> </ol>
A Consistent and Corrected Nighttime Light dataset (CCNL 1992-2013) from DMSP-OLS data
<p>DMSP-OLS provides the longest observations of NTL information, from 1992 to 2013, an unparalleled dataset for studying historical artificial lights. Version 4 of the DMSP-OLS Nighttime Lights Time Series is widely used ( Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency ). However, it suffers from three main problems: inter-annual inconsistency, saturation, and blooming effect.</p> <p>We used a series of methods to mitigate the impact and improve data quality. After processing, we get consistent and corrected nighttime light dataset (CCNL).</p> <p>The version 1 products span the globe from 75N latitude to 65S. The products are produced in 30 arc resolution and are made available in GeoTIFF format. Pixel Unit: 'DN'(Digital Number).</p> <p>Each GeoTIFF filename has 4 filename fields that are separated by an underscore "_". A filename extension follows these fields. The fields are described below using this example filename:</p> <p>CCNL_DMSP_1992_V1</p> <p>Field 1: CCNL(Consistent and Corrected Nighttime Light dataset)</p> <p>Field 2: Platform "DMSP"</p> <p>Field 3: Year “1992”</p> <p>Field 4: version “V1”</p>
Humans display a reduced set of consistent behavioral phenotypes in dyadic games
<p>Socially relevant situations that involve strategic interactions are widespread among animals and humans alike. To study these situations, theoretical and experimental research has adopted a game theoretical perspective, generating valuable insights about human behavior. However, most of the results reported so far have been obtained from a population perspective and considered one specific conflicting situation at a time. This makes it difficult to extract conclusions about the consistency of individuals’ behavior when facing different situations and to define a comprehensive classification of the strategies underlying the observed behaviors. We present the results of a lab-in-the-field experiment in which subjects face four different dyadic games, with the aim of establishing general behavioral rules dictating individuals’ actions. By analyzing our data with an unsupervised clustering algorithm, we find that all the subjects conform, with a large degree of consistency, to a limited number of behavioral phenotypes (envious, optimist, pessimist, and trustful), with only a small fraction of undefined subjects. We also discuss the possible connections to existing interpretations based on a priori theoretical approaches. Our findings provide a relevant contribution to the experimental and theoretical efforts toward the identification of basic behavioral phenotypes in a wider set of contexts without aprioristic assumptions regarding the rules or strategies behind actions. From this perspective, our work contributes to a fact-based approach to the study of human behavior in strategic situations, which could be applied to simulating societies, policy-making scenario building, and even a variety of business applications.</p> <p> </p> <p>The data from the "dr Brain" experiment is organized in two separated files: drbrain_users.csv<br> and drbrain_decisions.csv.</p> <p><br> 1.) drbrain_users.csv contains information about the participants of the experiment (or users).<br> There is one row per user, with the following information about each one of them:</p> <p>User_ID: unique ID number to identify the user.<br> Age: user's age<br> Gender: user's gender<br> Experiment_number: Number of the experiment the user participated in. For organizational reasons, our research actually was made 45 experiments (or replicas) run over a period of 2 days, each one run with differnt users. A user was only allowed to participate in one experiment. Each experiment included between 10-25 users typically, and they played around 13-18 game rounds, typically. Each round and each couple of users played in different games (that is, different values of S, Sucker's payoff, and T, Temptation to defect, while the values of P=5 , Punishment, and R=10, Reward, were always fixed).<br> Earnings: number of points the user obtained in total, over all rounds.</p> <p><br> 2.) drbrain_decisions.csv contains the information of the all game rounds for all experiments and all users.<br> User_ID: unique ID number to identify the user. <br> Experiment_number: Number of the experiment the user participated in.<br> Round_number: Number of the round within a given experiment.<br> S: Value for the "Sucker's payoff" in the game of that round.<br> T: Value for the "Temptation to defect" in the game of that round. <br> Game: Name of the game corresponding to those values of S and T for that round<br> Action: Action chosen by the user (C: cooperate, D: defect)<br> Opponent_ID: ID number of the user's opponent in that round. <br> Opponent_Action: Action (C or D) chosen by the user's opponent in that round.</p> <p>--------</p> <p>For more details, see our research article:</p> <p>Humans display a reduced set of consistent behavioral phenotypes in dyadic games.<br> Julia Poncela-Casasnovas, Mario Gutiérrez-Roig, Carlos Gracia-Lázaro, Julian Vicens, Jesús Gómez-Gardeñes, Josep Perelló, Yamir Moreno, Jordi Duch and Angel Sánchez.<br> Science Advances Vol. 2, no. 8, 2016.<br> DOI: 10.1126/sciadv.1600451<br> http://advances.sciencemag.org/content/2/8/e1600451</p>
Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories
<p><strong>Data Access Notice</strong></p> <p>Please note that, at present, the data for a sample of years are provided in this data record due to Zenodo's 50GB data limit. Data for all years 1959-2023 can be accessed via the following link:</p> <p><a href="http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html">http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html</a></p> <p><strong>Product Description</strong></p> <p>See Jones et al. (2021) for a detailed description of this dataset and the core methods used to produce it. Key details are provided below.</p> <p>GCP-GridFED (version 2024.0) is a gridded fossil emissions dataset that is consistent with the national CO<sub>2</sub> emissions reported by the Global Carbon Project (GCP; <a href="https://www.globalcarbonproject.org/">https://www.globalcarbonproject.org/</a>) in the annual editions of its Global Carbon Budget (Friedlingstein et al., 2023).</p> <p>GCP-GridFEDv2024.0 provides monthly fossil CO<sub>2 </sub>emissions for the period 1959-2023 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO<sub>2</sub> emitted by the oxidation of oil, coal and natural gas, international bunkers, and the calcination of limestone during cement production. The dataset also includes the cement carbonation sink of CO<sub>2</sub>. Note that positive values in GridFED signify a surface-to-atmosphere CO<sub>2 </sub>flux (emissions). Negative values signify an atmosphere-to-surface flux and apply only to the cement carbonation sink.</p> <p>GCP-GridFED also includes gridded uncertainties in CO<sub>2 </sub>emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O<sub>2</sub> uptake based on oxidative ratios for oil, coal and natural gas (see Jones et al., 2021).</p> <p><strong>Core Methodology in Brief</strong></p> <p>GCP-GridFEDv2024.0 was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2024 global carbon budget (GCP-NAE) for the years 1959-2023 (Friedlingstein et al., 2024). </p> <p>GCP-GridFEDv2024.0 uses a preliminary release of GCP-NAE covering the years 1959-2023 (timestamp 1st August 2024; an update from Andrew and Peters [2023]). The GCP-NAE estimates for year 2023 are based on data available at the timestamp and the estimates are thus expected to differ somewhat from those that will be presented by Friedlingstein et al. (2024), which will adopt updates to GCP-NAE since the timestamp.</p> <p>For full details of the core methodology, see Jones et al. (2021).</p> <p><strong>Changes to the Seasonality of Emissions in GCP-GridFEDv2022.2 onwards</strong></p> <p>The seasonality of emissions (monthly distribution of annual emissions) for the following countries/sources is now based on the seasonality observed in the Carbon Monitor dataset (Liu et al., 2020; Dou et al., 2022): </p> <ul> <li>Austria, Belgium, Brazil, Bulgaria, China, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, India, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Spain, Sweden, United Kingdom, United States.</li> <li>State or province-level data is used for Brazil, China, Russia, and the United States.</li> <li>This also applies for the Bunker Aviation and Bunker Shipping sectors.</li> </ul> <p>Seasonality is determined in the following ways for those countries/sources:</p> <ul> <li>The seasonality of emissions in 2019-2023 is taken from Carbon Monitor.</li> <li>The seasonality of emissions in all years prior to 2019 is assigned as the average of the seasonality from Carbon Monitor in all years excluding 2020 (due to the impact of COVID-19 on the seasonality of emissions in 2020).</li> </ul> <p>For all countries not listed above and all years 1959-2023, GCP-GridFED adopts the seasonality from EDGAR v4.3.2 (year 2010; Janssens-Maenhout et al., 2019) and applies a small correction based on heating/cooling degree days to account for inter-annual climate variability which effects emissions in some sectors (see Jones et al., 2021).</p> <p><strong>Other New Features of GCP-GridFEDv2024.0</strong></p> <ul> <li>There have been no changes to the functionality of the GridFED code in this update versus the previous update (v2023.1).</li> </ul> <p> </p>
Data for paper "Magnetohydrodynamic Equilibrium Reconstruction with Consistent Uncertainties"
<p>Data and scripts for the conference paper "Magnetohydrodynamic Equilibrium Reconstruction with Consistent Uncertainties" for the 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering.</p> <p><strong>Abstract</strong>: We report on progress towards a probabilistic framework for consistent uncertainty quantification and propagation in analysis and numerical modeling of physics in magnetically confined plasmas in the stellarator configuration. A frequent starting point in this process is the calculation of a magnetohydrodynamic equilibrium from plasma profiles. Profiles and therefore the equilibrium are typically reconstructed from experimental data. What sets equilibrium reconstruction apart from usual inverse problems is that profiles are given as functions over a magnetic flux derived from the magnetic field, rather than spatial coordinates. This makes it a fixed-point problem that is traditionally left inconsistent or solved iteratively in a least-squares sense[1–3]. The aim here is towards a straightforward and transparent process to quantify and propagate uncertainties and their correlations for function-valued fields and profiles in this setting. We propose a framework that utilizes a low dimensional prior distribution of equilibria, constructed with principal component analysis. A surrogate of the forward model[4] is trained to enable faster sampling.</p> <p><strong>Funding</strong>: The present contribution is supported by the Helmholtz Association of German Research Centers under the joint research school HIDSS-0006 'Munich School for Data Science - MUDS'. This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No 101052200 - EUROfusion). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.</p>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2023
<p><strong>Note: The authors recommend the use of the ESA CCI-SST version of this dataset.</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2023 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/r7xa-bt92">https://doi.org/10.25921/r7xa-bt92</a>) is a quality-controlled dataset containing 35.6 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore, the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models.</p> <p>Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values; Watson et al., 2020; Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system.</p> <p>Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in Goddijn-Murphy et al. (2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes.</p> <p>This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2023 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Holding et al., 2019; Shutler et al., 2016). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The reanalysis dataset was produced for two climate quality and depth consistent temperature datasets: (1) The ESA SST-CCI sea surface temperature product (Merchant et al., 2019) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007).</p> <p>For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2023 dataset.</p> <p>The original SOCAT version 2023 data are included in full, with four additional columns containing the reanalysed data:</p> <p>* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al., 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file.</p> <p><strong>Comments</strong></p> <p>1. Due to the temporal range of the OISST and CCI-SST datasets the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns.</p> <p>2. This submission contains four files contained within a single zip file: SOCATv2023with_header.tsv, SOCATv2023.nc, SOCATv2023with_header_ESACCI.tsv and SOCATv2023_ESACCI.nc. The first two files correspond to the OISST version, and the second two the ESA SST-CCI version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets.</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p><strong>How to cite these data</strong></p> <p>Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Holding et al., 2019; Shutler et al., 2016) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013).</p> <p><strong>Previous versions</strong></p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p>v2022: <a href="https://doi.org/10.5281/zenodo.8228585">https://doi.org/10.5281/zenodo.8228585</a></p> <p><strong>Acknowledgements</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p>These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>) and the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>). The UK part of the Horizon Europe OceanICU project is funded by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10063673].</p>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2022
<p><strong>Note: The authors recommend the use of the ESA CCI-SST version of this dataset.</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2022 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/1h9f-nb73">https://doi.org/10.25921/1h9f-nb73</a>) is a quality-controlled dataset containing 33.7 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore, the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models.</p> <p>Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values; Watson et al., 2020; Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system.</p> <p>Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in Goddijn-Murphy et al. (2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes.</p> <p>This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2022 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Holding et al., 2019; Shutler et al., 2016). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The reanalysis dataset was produced for two climate quality and depth consistent temperature datasets: (1) The ESA SST-CCI sea surface temperature product (Merchant et al., 2019) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007).</p> <p>For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2022 dataset.</p> <p>The original SOCAT version 2022 data are included in full, with four additional columns containing the reanalysed data:</p> <p>* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al., 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file.</p> <p><strong>Comments</strong></p> <p>1. Due to the temporal range of the OISST and CCI-SST datasets the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns.</p> <p>2. This submission contains four files contained within a single zip file: SOCATv2022with_header.tsv, SOCATv2022.nc, SOCATv2022with_header_ESACCI.tsv and SOCATv2022_ESACCI.nc. The first two files correspond to the OISST version, and the second two the ESA SST-CCI version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets.</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p><strong>How to cite these data</strong></p> <p>Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Holding et al., 2019; Shutler et al., 2016) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013).</p> <p>Previous versions:</p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p><strong>Acknowledgements</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p>These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>) and the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>). The UK part of the Horizon Europe OceanICU project is funded by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10063673].</p>
Interlaboratory testing of CuSO4 toxicity in the “Standardized Aquatic Microcosm” protocol consisting of multiple phytoplankton and animals in a chemically defined medium.
Four different laboratories conducted a total of ten experiments of the “Standardized Aquatic Microcosm” to test the reproducibility of results to control, low, medium, and high concentrations of CuSO4. In nine experiments, treatments consisted of six replicates of 0, 500, 1000, and 2000 ppb Cu++. One experiment, ME74, used 0, 127, 255, 509 ppb. The purpose was to test a chemically defined medium (thus negating differences due to local water supplies) and the same 10 species of phytoplankton and 5 animals, including Daphnia. Microbes were undefined. The protocol included the weekly re-introduction of small numbers of each species to allow potential recovery from toxicity. Control microcosms had a “spring algal bloom” terminated by zooplankton grazing and multiple competitive interactions. The copper inhibited some phytoplankton more than others and killed many grazers, especially Daphnia. The data set presented several interesting statistical properties that would yield new insights. (a) The results were very similar, but the timing varied— the higher the concentration of copper, the longer the inhibition and mortality of organisms, so those at 500 ppb recovered earlier, the 1000 ppb recovered later, and at 2000 ppb most never recovered. But if compared on each sampling day, e.g., 10, 14, … to 64, results appear highly variable. (b) In at least one experiment, the toxicity of copper was challenging to demonstrate statistically because high variability in the timing of recovery of the intermediate concentration increased pooled variances. (c) The elimination of highly-sensitive dominant organisms allowed less-sensitive organisms to increase in abundance. Within natural environments, the observation that some species increase in the presence of toxic substances has been used to discredit toxicity testing without considering the relative sensitivities of competing or predatory species. (d) The competitive interactions among organisms, e.g., cyanobacteria and green alga
Data from: Consistent trait-environment relationships within and across tundra plant communities
<p>A fundamental assumption in trait-based ecology is that relationships between traits and environmental conditions are globally consistent. We use field-quantified microclimate and soil data to explore if trait-environment relationships are generalisable across plant communities and spatial scales. We collected data from 6720 plots and 217 species across four distinct tundra regions from both hemispheres. We combine this data with over 76000 database trait records to relate local plant community trait composition to broad gradients of key environmental drivers: soil moisture, soil temperature, soil pH, and potential solar radiation. Results revealed strong, consistent trait-environment relationships across Arctic and Antarctic regions. This indicates that the detected relationships are transferable between tundra plant communities also when fine-scale environmental heterogeneity is accounted for, and that variation in local conditions heavily influences both structural and leaf economic traits. Our results strengthen the biological and mechanistic basis for climate change impact predictions of vulnerable high-latitude ecosystems.</p> <p>Kemppinen, Niittynen, le Roux, Momberg, Happonen, Aalto, Rautakoski, Enquist, Vandvik, Halbritter, Maitner & Luoto (2021). Consistent trait-environment relationships within and across tundra plant communities. Nature Ecology and Evolution</p> <p>These are the data and codes from Kemppinen et al. (2021).</p>
Stormwater runoff pollution of an existing catchment consisting essentially of apartment buildings in Braunschweig/Germany.
<p>This dataset includes stormwater runoff concentrations of pollutants (COD, TP, DP, NO<sub>3</sub>, NH<sub>4</sub>, TSS) taken from a sampling point in Braunschweig, Germany. The total catchment size is 5 ha with approximately 1.8 ha total imperviousness consisting essentially of apartment buildings from the 1970s and roads. This catchment was chosen for comparatively clear delimitation to different land uses considering location-independent results. Samples were taken as part of the research project TransMiT (https://www.transmit-zukunftsstadt.de/). More information including sampling and analytical methods are detailed in the corresponding journal paper "Dynamization of Urban Runoff Pollution and Quantity" and supplementary data, submitted to the MDPI-journal Water.</p> <p>Description of fields:</p> <p>- SamplingPoint:<br> - storm sewer: stormwater runoff sampled during individual rain events in a storm water sewer manhole receiving runoff from the entire catchment<br> - SamplingTime: time of sampling during individual rain event (CET)<br> - COD: measured value of chemical oxygen demand (COD)<br> - TP: measured value of total phosphorus (TP)<br> - DP: measured value of dissovled phosphorus (COD)<br> - NO<sub>3</sub>: measured value of nitrate (NO3)<br> - NH<sub>4</sub>: measured value of ammonium (NH4)<br> - TSS: measured value of total suspended solids (TSS)<br> - N/A: data not available<br> - UnitsAbbreviation: all data given in milligram per litre (mg/L)</p> <p>The data file is provided in comma separated format ("TransMiT.csv") and contains concentrations of all samples.</p> <p> </p>
A consistent discretization of the single-field two-phase momentum convection term for the unstructured finite volume Level Set / Front Tracking method - data
<p>Research data from the rhoLENT unstructured Level Set / Front Tracking method for simulating two-phase flows with large density ratios. </p>
Code for producing a consistent and corrected nighttime light dataset (CCNL 1992-2013) from DMSP-OLS data
<p>The DMSP-OLS NTL product suffers from three main problems, i.e.inter-annual inconsistency, saturation, and blooming effect which will affect the accuracy of urban extraction and the estimation of the social-economic indexes. To address these problems, we adopted three correction methods to rectify inter-annual inconsistency, saturation, and blooming effects.<br> The code is written based on the Javascript API provided by the Google Earth Engine platform(https://earthengine.google.com/)</p>
Supplemental Material to "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments"
<p>Supplemental material to manuscript "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments" published in IMMJ "Integrating Materials and Manufacturing Innovation" 2022</p>
wiki-category-consistency-eval
<p>Experiment results produced in the context of analyzing the consistency between Wikipedia and Wikidata categories using the Wikidata JSON dump of 2022-05-02 and the Wikipedia SQL dumps of 2022-05-01.</p> <p>Detailed information can be found on the <a href="https://github.com/fusion-jena/wiki-category-consistency">Github page</a>.</p>
wiki-category-consistency-dataset
<p>Candidate generation and cleaning results produced in the context of analyzing the consistency between Wikipedia and Wikidata categories using the Wikidata JSON dump of 2022-05-02 and the Wikipedia SQL dumps of 2022-05-01.</p> <p>Detailed information can be found on the <a href="https://github.com/fusion-jena/wiki-category-consistency">Github page</a>.</p>
Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.
<h2>Description (V2 - Latest):</h2> <p>To enable easy integration in the workflows, we have provided the main datasets in the following formats:</p> <p> </p> <ul> <li><strong><em>Vector dataset:</em><code> Folder - Vector</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>Geopackage (.gpkg)</em></code> file <strong>(</strong><strong><em>Results_Vis.gpkg</em></strong><strong>)</strong> with polygon geometries at 1/8-degree spatial resolution in an <strong>EPSG:4326 </strong>coordinate system. The <em>attribute table</em> of this file contains <em>FN_ID</em> column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5</em> for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em><strong><em> </em></strong>narratives with <em>Y</em><strong> </strong>representing the assessment year having values as <em>20, 30, 40, and 50</em> for years <em>2020, 2030, 2040, and 2050</em> and with <strong><em>km<sup>2</sup></em></strong> units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul> <p> </p> <ul> <li><strong><em>Raster datasets:</em></strong><strong> <code> Folder - Raster</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>geotiff (.tif)</em></code> files with <strong>LZW</strong> compression in an <strong>EPSG:4326</strong> coordinate system. The assessed gross rooftop area datasets are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5 </em>for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em><strong><em> </em></strong>narratives with<strong> </strong><em>Y</em> representing the assessment year having values as <em>20, 30, 40, and 50</em> for years <em>2020, 2030, 2040, and 2050</em><strong> </strong>and with <strong><em>km<sup>2</sup></em></strong> units.</li> </ul> <p> </p> <ul> <li><strong><em>Numerical dataset:</em></strong> <strong><code> Folder - Numerical</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>parquet (.parquet)</em></code> file <strong><em>(Results.parquet).</em></strong> This file contains <em>FN_ID</em> column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5</em> for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em> narratives with <em>Y </em>representing the assessment year having values as<strong> </strong><em>20, 30, 40, and 50</em><strong> </strong>for years <em>2020, 2030, 2040, and 2050</em><strong> </strong>and with <strong><em>km<sup>2</sup></em></strong> units. In addition, a <em>CF</em> column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul> <p> </p> <p>In addition to the main datasets, we have provided additional files to enable generating the vector and numerical datasets from this study: <strong><code> Folder - Models</code></strong></p> <ul> <li><strong><em>M2_Model.json:</em></strong><strong> </strong>This file contains the frozen parameters of the M2 model in <code><em>.json</em></code> format generated from <code>XGBoost version 2.0.3</code></li> <li><strong><em>SSP_drivers.parquet:</em><em> </em></strong>This file contains the driver data used for generating the main dataset in our study</li> <li><strong><em>FN_MAP.parquet:</em></strong><strong> </strong>This file contains the boundary information for each fishnet grid tile in a Well Known Text <em>(WKT)</em> format.</li> <li><strong><em>Prediction.ipynb:</em></strong><strong> </strong>This file provides a python notebook interface to generate inferencing from <em><code>M2_Model.json</code> </em>using <code><em>SSP_drivers.parquet</em></code> file. In addition, this file also generates the numerical dataset and converts it into vector dataset using <code><em>FN_MAP.parquet</em></code><code> </code>file.</li> <li><strong><em>environment.yaml:</em></strong><strong> </strong>This file contains the frozen configuration of python virtual environment used to generate the results presented in this study.</li> </ul> <p> </p> <h2><strong>Version history:</strong></h2> <p><strong>This version corresponds to the revised journal submission (Round 1). <em>The version will be updated upon the completion of the review of the main manuscript.</em></strong></p> <ul> <li><em>This version <strong>V2</strong> is supersedes <strong>V1</strong> to correspond with round 1 of review.</em></li> <li>The database(s) in this version is associated with a Data Descriptor paper manuscript entitled " <em>Global high-resolution growth projections for rooftop area consistent with the shared socioeconomic pathways, 2020-2050 </em>", submitted to <em>Scientific Reports</em> Journal (<a href="https://www.nature.com/srep/">https://www.nature.com/srep/</a>)</li> </ul> <p> </p> <h2>Changelog:</h2> <p>The following files from version <strong>V1</strong> of this dataset are now <strong><em>archived</em></strong> based on the reviews (Round 1).</p> <ol> <li> <blockquote><em><strong>1_Geospatial_Dataset_V1.gpkg</strong></em></blockquote> </li> <li> <blockquote><em><strong>2_Countrylevel_gross_rooftop_area_V1.parquet</strong></em></blockquote> </li> <li> <blockquote><em><strong>3_Analytics_Scripts_V1.ipynb</strong></em></blockquote> </li> </ol>
D-PLACE dataset derived from Wessel and Smith 2015 'Global Self-consistent, Hierarchical, High-resolution Geography Database'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Wessel, P., and W. H. F. Smith (1996), A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101(B4), 8741–8743, doi:10.1029/96JB00104. Wessel P, Smith, W. H. F. Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHS) v2.3.4 [Internet]. 2015. Available: https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html</p> </blockquote>
Consistency test scores for aftershock+mainshock RELM forecasts
<p><strong>Summary</strong></p> <p>Files are transcribed from Zechar et al. (2013) into comma separated values (csv) files. The consistency test scores are shown in the electronic supplement table S4 and the catalog is found in Table 1 of the main text.</p> <p><strong>Reference</strong></p> <p>Zechar, J. D., D. Schorlemmer, M. J. Werner, M. C. Gerstenberger, D. A. Rhoades, and T. H. Jordan (2013). Regional Earthquake Likelihood Models I: First-Order Results, Bulletin of the Seismological Society of America 103 787-798.</p> <p> </p>
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2)
<p><strong>Brief Introduction:</strong></p> <p>The PKU GIMMS Normalized Difference Vegetation Index product (PKU GIMMS NDVI, version 1.2) provides spatiotemporally consistent global NDVI data in half-month and 1/12° from 1982 to 2022. It is created to address the major uncertainties presented in current global long-term NDVI products, i.e., the effects of NOAA satellite orbital drift and AVHRR sensor degradation.</p> <p> </p> <p>The PKU GIMMS NDVI was generated based on biome-specific BPNN models that employed GIMMS NDVI3g product and 3.6 million high-quality global Landsat NDVI samples. It was then consolidated with the MODIS NDVI (MOD13C1) to extend the temporal coverage to 2022 via a pixel-wise Random Forests fusion method.</p> <p> </p> <p>The PKU GIMMS NDVI exhibits overall high accuracy evaluated by Landsat NDVI samples. Besides, it efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency with MODIS NDVI in terms of pixel value and global vegetation trend. It could potentially provide a more solid data basis for global change studies.</p> <p> </p> <p>Here we provide two versions of PKU GIMMS NDVI for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the MODIS NDVI (1982−2022). <strong>We strongly recommend an adequate use of the quality control (QC) layer in the product. </strong>Please refer to the Readme file for more details. <strong>We also recommend removing sparse vegetation by a threshold (e.g., 0.1) in trend analysis (Zhou et al., 2001; Liu et al., 2016)</strong></p> <p> </p> <p><strong>Major updates:</strong></p> <p>Version 1.0 (December 15, 2022):</p> <p>· The original version of the product.</p> <p> </p> <p>Version 1.1 (June 17, 2023):</p> <p>· A pixel-wise Random Forests consolidation method is used to replace the linear one.</p> <p>· The data files have been re-organized on a decade basis.</p> <p> </p> <p>Version 1.2 (August 17, 2023):</p> <p>· The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate NDVI values of EBF during the periods of 1982−1984 and all October to April, when the Landsat NDVI samples were relatively scarce.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN</p> <p>Projection: Geographic</p> <p>Spatial Resolution: 1/12 degree</p> <p>Temporal Resolution: Half month</p> <p>Temporal Coverage: January 1982 to December 2022</p> <p>Image Dimension: Rows-2160; Columns-4320</p> <p>Units: unitless</p> <p>Fill Value: 65535</p> <p>Data Type: uint16</p> <p>Valid Range: 0-1000</p> <p>Scale Factor: 0.001</p> <p>File Format: TIFF(.tif)</p> <p>File Size: ~8Mb each file</p> <p> </p> <p><strong>References:</strong></p> <p>Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, <a href="https://doi.org/10.5194/essd-15-4181-2023">https://doi.org/10.5194/essd-15-4181-2023</a>, 2023.</p> <p>Liu, Q., Fu, Y. H., Zhu, Z., Liu, Y., Liu, Z., Huang, M., Janssens, I. A., and Piao, S.: Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology, Global Change Biology, 22, 3702–3711, <a href="https://doi.org/10.1111/gcb.13311">https://doi.org/10.1111/gcb.13311</a>, 2016.</p> <p>Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., and Myneni, R. B.: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res., 106, 20069–20083, <a href="https://doi.org/10.1029/2000JD000115">https://doi.org/10.1029/2000JD000115</a>, 2001.</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.