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1,034 results for “EMPIR”
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>
AMOC reconstruction between 1981 and 2016 from hydrographic data using an empirical linear regression model from Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, https://doi.org/10.5194/os-17-285-2021, 2021.
<p>Dataset used to create Figure 8 in Worthington et al., 2021 (https://doi.org/10.5194/os-17-285-2021). Details of the data and methods can be found in the journal article.<br> <br> Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, <a href="https://doi.org/10.5194/os-17-285-2021">https://doi.org/10.5194/os-17-285-2021</a>, 2021.</p>
An Empirical Evaluation of the Relationship between Technical Debt and Software Security
<p>This dataset contains the static analysis results of 50 open source software applications retrieved from Github. The results were produced by using SonarQube, PMD, CKJM Extended and Findbugs tools. Further details can be found in the relevant publication: </p> <ul> <li>Siavvas, M., Tsoukalas, D., Janković, M., Kehagias, D., Chatzigeorgiou, A., Tzovaras, D., Aničić, N., Gelenbe, E. <em>An Empirical Evaluation of the Relationship between Technical Debt and Software Security</em>. In: Konjović, Z., Zdravković, M., Trajanović, M. (Eds.) ICIST 2019 Proceedings Vol.1, pp.199-203, 2019</li> </ul>
Data for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>This dataset accompanies a replication package provided for a study on Semantic Versioning for Ansible Galaxy roles.</p> <p>The replication package is available at https://github.com/ROpdebee/ansible_semver_ext_replication</p>
Investigation of the properties of conductivity signals in BK channels by Empirical Mode Decomposition
<p>The idea of the project is the comprehensive time-frequency analysis of ion current data registered from BK channels of the different cell lines and measured under the different experimental conditions. Decomposition of signals into individual frequency modes and application of non-linear measures in the form of Information Entropy or Hurst exponent to individual signal components will allow for a more detailed analysis of the information hidden behind the complex ionic conduction sequences. The sample data contains patch-clamp sequences. </p>
Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study
<p>Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation—the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.</p>
Comparison of pandemic excess mortality in 2020-2021 across different empirical calculations
<p>Different modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic period. We compared 6 calculations of excess deaths (4 previously published and two new ones that we performed with and without age-adjustment) for 2020-2021. With each approach, we calculated excess deaths metrics and the ratio R of excess deaths over recorded COVID-19 deaths. The main analysis focused on 33 high-income countries with weekly deaths in the Human Mortality Database (HMD at mortality.org) and reliable death registration. Secondary analyses compared calculations for other countries, whenever available. Across the 33 high-income countries, excess deaths were 2.0-2.8 million without age-adjustment, and 1.6-2.1 million with age-adjustment with large differences across countries. In our analyses after age-adjustment, 8 of 33 countries had no overall excess deaths; there was a death deficit in children; and 0.478 million (29.7%) of the excess deaths were in people <65 years old. In countries like France, Germany, Italy, and Spain excess death estimates differed 2 to 4-fold between highest and lowest figures. The R values’ range exceeded 0.3 in all 33 countries. In 16 of 33 countries, the range of R exceeded 1. In 25 of 33 countries some calculations suggest R>1 (excess deaths exceeding COVID-19 deaths) while others suggest R<1 (excess deaths smaller than COVID-19 deaths). Inferred data from 4 evaluations for 42 countries and from 3 evaluations for another 98 countries are very tenuous Estimates of excess deaths are analysis-dependent and age-adjustment is important to consider. Excess deaths may be lower than previously calculated. </p>
Data supporting 'Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers'
<p><strong>Note: An updated dataset covering the majority of Greenland's marine-terminating glaciers is available as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project through the National Snow and Ice Data Center (NSIDC) at <a href="https://doi.org/10.5067/B28FM2QVVYWY">https://doi.org/10.5067/B28FM2QVVYWY</a>. </strong></p> <p>Data supporting the paper:</p> <blockquote> <p>Chudley, T. R., Howat, I. M., Yadav, B. N., & Noh, M. J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers. <em>The Cryosphere. </em>16, 2629–2642, https://doi.org/10.5194/tc-16-2629-2022</p> </blockquote> <p>Dataset consists of four netCDF files containing stacked Sentinel-2 velocity data of four Greenlandic outlet glaciers (Helheim Glacier, Jakobshavn Isbræ, Store Glacier, and Kangerlussuaq) between 2017 and 2021. Velocity data are derived and corrected following the methods outlined in Chudley <em>et al.</em> (2022). </p> <p>NetCDF files are created by, and tested to be readable by, Python's xarray package.</p> <p>The dimensions of the netCDF file are as follows:</p> <ul> <li><strong>X</strong> - <em>x </em>coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>Y</strong> - <em>y</em> coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>time</strong> - temporal midpoint of velocity field.</li> </ul> <p>The variables of the netCDF file are as follows:</p> <ul> <li><strong>dmag</strong> - the absolute magnitude of the velocity, in metres per day.</li> <li><strong>dx</strong> - the velocity in the <em>x</em> direction, in metres per day.</li> <li><strong>dy</strong> - the velocity in the <em>y</em> direction, in metres per day.</li> <li><strong>date1</strong> - the date and time of the first scene acquisition.</li> <li><strong>date2</strong> - the date and time of the second scene acquisition.</li> <li><strong>baseline</strong> - the temporal baseline, in days, between scene acquisitions.</li> <li><strong>orbit_pair</strong> - the combination of orbital pathways in the string format 'RXXX_RYYY', where XXX is relative orbit number of the first scene and YYY the relative orbit number of the second scene.</li> <li><strong>mag_rmse</strong> - the root mean square error of the absolute velocity of the off-ice area. </li> <li><strong>dx_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dx_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>y</em> direction.</li> </ul>
Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"
<p>This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.</p>
Empirical laws of plasmapause and plasmasphere outer edge location from the Van Allen Probes
<p>This webpage provides access to empirical laws of the plasmapause position and the dense outer edge of the plasasphere position (i.e. location of the 100 #/cc electron density) established from spacecraft charging of the Van Allen Probes (RBSP), using data from Probe B from 26 September 2012 to 16 July 2019. These empirical laws are published of the following article:</p> <p>Ripoll, J.-F., Thaller, S. A., Hartley, D. P., Cunningham, G. S., Pierrard, V., Kurth, W. S., et al. (2022). Statistics and empirical models of the plasmasphere boundaries from the Van Allen Probes for radiation belt physics. Geophysical Research Letters, 49, e2022GL101402. https://doi.org/10.1029/2022GL101402.</p> <p>Please refer to the article and this link if you make any use of the data.</p> <p>We first deliver 3 files which contain the plasmapause position versus a given geomagnetic index, either Kp, AE, or Dst. These data files are MLT-averaged. The filename is Lpp_v_XX_stats_1.txt with XX the index name. </p> <p>We then deliver 3 files which contains the plasmapause position versus a geomagnetic starred index (i.e. a max taken during the last 24 hours for Kp and AE and a min for Dst), either Kp*, AE*, or Dst* for 4 MLT sectors written successively in each file and then for all sectors averaged together. These data files are MLT-dependent for the 4 first blocks and then MLT-averaged in the fifth block. The filename is Lpp_v_XX_star_MLT_stats_2-1.txt with XX the starred index name. </p> <p>The MLT range is given in the last two columns. When the plasmapause location is undetermined (i.e. Lpp = 0 or no value), there is no MLT, so that in the file we just have the total number of points (all real) and not the three values of total, number of real, number of Nans.</p> <p>Similarly, we deliver 3 files which contains the plasmapause position versus a geomagnetic non starred index, either Kp, AE, or Dst for 4 MLT sectors written successively in each file. Filenames have the form "Lpp_v_XX_MLT_stats_1.txt" with XX the name of the index. Figures associated to this data were not given in the article and have been added here with a filename of the form Lpp_by_mlt_XX.pdf with XX the name of the index.</p> <p>Finally, the zip file "RBSP-A Figures and Laws" contains Figures (same format as each figure of Figure 4 in the article) and empirical laws (same format as above) for RBSP-A data (10/2012-04/2016). Being more limited in time, we rather recommend to use RBSP B data. They are provided to confirm both RBSP A and B data agree when statistics are converged.</p>
An empirical model of the Gaia DR3 selection function
<p>Precomputed maps of the M10 parameter used to predict the completeness of the Gaia DR3 source catalogue.</p> <p><strong>allsky_M10_hpx7.hdf5 </strong>is a tessellation of the whole sky in Galactic nested healpix scheme of order 7.</p> <p><strong>allsky_uniq_10.fits</strong> uses an adaptive resolution and is based on the 'uniq' numbering of healpix tiles. Regions of higher density use a finer resolution, up to order 10, chosen so that every tile contains a minimum of twenty sources to compute M10 from.</p> <p>Paper: https://ui.adsabs.harvard.edu/abs/2023A%26A...669A..55C/abstract</p> <p>Tutorial using the GaiaUnlimited python package to query these maps: https://github.com/gaia-unlimited/gaiaunlimited/blob/main/docs/notebooks/dr3-empirical-completeness.ipynb</p> <p>Documentation for the GaiaUnlimited package: https://gaiaunlimited.readthedocs.io/en/latest/</p>
Empirical relationship between calcium triplet equivalent widths and [Fe/H] using Gaia photometry
<p>I present a new empirical relationship for red giant branch stars between the overall metallicity of the star and the sum of equivalent widths of the near-infrared calcium triplet (CaT) spectral lines. This method takes advantage of the all-sky photometry and astrometry of the Gaia mission, and the archival spectra from 2050 red giant branch stars from 18 globular clusters (-0.69>[Fe/H]>-2.44) acquired with the Anglo-Australian Telescope's AAOmega spectrograph.</p> <ul> <li><cluster_name>.tar.gz <ul> <li>Raw and reduced spectra for 18 globular clusters (NGC104, NGC6752, NGC6809, NGC288, NGC7099, NGC362, NGC6218, NGC4590, IC4499, NGC1904, ESO452-SC06, ESO280-SC12, NGC1851, NGC6624, NGC2298, Pal 5, NGC5024, Terzan 8, NGC5053)</li> <li>Lists of likely members</li> <li>Measured equivalent widths and radial velocities</li> </ul> </li> <li>code.tar.gz <ul> <li>running.py – calculates the equivalent widths and radial velocities of the stars from the spectra</li> <li>cat_new_params.py – calculates the best fitting empirical relationship</li> </ul> </li> <li>paper.tar.gz <ul> <li>The LaTeX source for the submitted RNAAS paper.</li> </ul> </li> </ul> <p> </p>
An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization
<p>This is the data and source code used in the paper below:</p> <p>Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas Bäck, “An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization”, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi: 10.1109/SSCI44817.2019.9002805</p> <p>This research investigates the potential of using meta-modeling techniques in the context of robust optimization namely optimization under uncertainty/noise. A systematic empirical comparison is performed for evaluating and comparing different meta-modeling techniques for robust optimization. The experimental setup includes three noise levels, six meta-modeling algorithms, and six benchmark problems from the continuous optimization domain, each for three different dimensionalities. Two robustness definitions: robust regularization and robust composition, are used in the experiments. The meta-modeling techniques are evaluated and compared with respect to the modeling accuracy and the optimal function values. The results clearly show that Kriging, Support Vector Machine and Polynomial regression perform excellently as they achieve high accuracy and the optimal point on the model landscape is close to the true optimum of test functions in most cases.</p>
Designing Types for R, Empirically (Dataset)
<p>This dataset is intended to accompany the paper "Designing Types for R, Empirically" (@ OOPSLA'20, <a href="https://2020.splashcon.org/details/splash-2020-oopsla/57/Designing-Types-for-R-Empirically">link to paper</a>). This data was obtained by running the Typetracer (aka propagatr) dynamic analysis tool (<a href="https://github.com/PRL-PRG/propagatr">link to tool</a>) on the test, example, and vignette code of a corpus of >400 extensively used R packages.<br> <br> Specifically, this dataset contains:</p> <ol> <li>function type traces for >400 R packages (raw-traces.tar.gz);</li> <li>trace data processed into a more readable/usable form (processed-traces.tar.gz), which was used in obtaining results in the paper;</li> <li>inferred type declarations for the >400 R packages using various strategies to merge the processed traces (see type-declarations-* directories), and finally;</li> <li>contract assertion data from running the reverse dependencies of these packages and checking function usage against the declared types (contract-assertion-reverse-dependencies.tar.gz).</li> </ol> <p> A preprint of the paper is also included, which summarizes our findings.</p> <p><strong>Fair warning Re: data size:</strong> the raw traces, once uncompressed, take up nearly 600GB. The already processed traces are in the 10s of GB, which should be more manageable for a consumer-grade computer.</p>
Dataset: Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions
<p># Dataset for Paper "Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions" - Rev 1#</p> <p>This is the dataset for the paper titled "Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions". All mapping study data has the prefix *MAP*, while all survey data the prefix *SUR*. It has been updated for a major revision at Springer Empirical Software Engineering (Rev 1).</p> <p>In case of questions, feel free to contact the author, Grischa Liebel, ORCID: https://orcid.org/0000-0002-3884-815X, current affiliation and email: Reykjavik University, Iceland, grischal@ru.is</p> <p>## Systematic Mapping Study ##<br> The mapping study data is mainly contained in the *MAPmappingStudy.xlsx* file. Different tabs are used for the two phases: exclusion by title and abstract (tab *title_abs*), and exclusion by fulltext (tab *fulltextScreening*). The final set of papers is obtained by filtering the *fulltextScreening* tab by included papers (Column V).</p> <p>The tab *fulltextScreening* contains a number of columns named \*range (e.g., *noStudentsRange*). These columns contain the unified/categorised values for the verbatim values listed in the column with the same name without *range*. For instance, *noStudentsRange* contains the range of students in the primary study, while *noStudents* contains the actual number obtained from the studies.</p> <p>The file *MAPvenues.txt* contains the included venues in the mapping study.</p> <p>Finally, the raw search results are provided as BIB/RIS files with the prefix *MAPRAW*.</p> <p>## Survey ##<br> The survey folder contains the survey pages (named *surveyPageN.pdf*), as well as the raw data in *surveyDataAnon.xlsx*. Note that free-text answers have been aggregated, anonymised, and sorted alphabetically in individual tabs. Similarly, countries that only occur once have been changed to Do Not Disclose answers, and all answers have been sorted randomly. All questions are listed by their acronym. The corresponding questions, as well as possible answers, are described in the *QuestionKey* tab.</p>
An Empirical Evaluation of Video Conferencing Systems Used in Industry, Academia, and Entertainment
<p>Video Conferencing Systems~(VCS) are used daily--at work, in online education, and for get-togethers with friends and family. Many new VCSs have emerged in the past decade and a new market-leader has risen during the coronavirus period of 2020. Understanding how these systems work could help us improve them rapidly. However, no experimental comparison of such systems currently exists. </p> <p>We propose a method to compare VCSs in real-world operation and implement it as a tool. Our method considers 4 main real-world experiments. Each captures different aspects, such as communication channels~(audio, video, audio-video), types of network environments~(e.g., Ethernet, WiFi, 4G), and reports system and network utilization. We conduct real-world experiments with 3 VCSs, Zoom, Microsoft Team, and Discord.</p> <p>This dataset is the result of our experiments conducted with the Online Video Conference Benchmarking tool.</p>
An Empirical Characterization of Event Sourced Systems and Their Schema Evolution - Lessons from Industry - Accompanying Anonymized Transcripts
<p>Anonymized interviews with 25 engineers on their experience applying Event Sourcing, with accompanying classifications. These transcripts are used in our publication "An Empirical Characterization of Event Sourced Systems and Their Schema Evolution - Lessons from Industry".</p>
AusEFlux: Empirical upscaling of OzFlux eddy covariance flux tower data over Australia
<p>AusEFlux (<strong>Aus</strong>tralian <strong>E</strong>mpirical <strong>Flux</strong>es) is a high resolution (500 metre) gridded estimate of Gross Primary Productivity (GPP), Ecosystem Respiration (ER), Net Ecosystem Exchange (NEE), and Evapotranspiration over the Australian continent for the period January 2003 to Present. These datasets provide a benchmark for assessment against Land Surface Model simulations, and a means for monitoring of Australia’s terrestrial carbon cycle at an unprecedented high-resolution.</p> <p><strong>Version 2.1 </strong>of AusEFlux has just been released (as of May 2025) and was created to <strong>operationalise</strong> the research datasets published in this <a href="https://doi.org/10.5194/bg-20-4109-2023">EGU Biogeosciences publication.</a> In order to operationalise these datasets, changes to the input datasets were required to align the data sources with datasets that are regularly and reliably updated, along with general improvements. The datasets provided on Zenodo have been reprojected to 5 km resolution to facilitate easier uploading and sharing, but<strong> full resolution datasets (both v1.1 and v2.1) can be accessed freely through <a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html">NCI's THREDDS portal.</a></strong></p> <p><strong>Two Jupyter Notebooks</strong> have been created (one for GPP and one for NEE) that demonstrate the differences between the research datasets (v1.1) and the operational datasets (v2.1), including showing the differences in specifications and inputs. You can view/download these notebooks using the links below:</p> <p><a href="https://nbviewer.org/github/cbur24/AusEFlux/blob/master/notebooks/analysis/Compare_AusEFlux_versions_GPP.ipynb">GPP comparison between versions</a></p> <p><a href="https://nbviewer.org/github/cbur24/AusEFlux/blob/master/notebooks/analysis/Compare_AusEFlux_versions_NEE.ipynb">NEE comparisons between versions</a></p> <p>Each dataset contains three variables:</p> <ul> <li>"<flux>_median": represents the 'best-estimate' of a given flux, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> <li>"<flux>_25th_percentile": represents the lower uncertainty bound, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> <li>"<flux>_75th_percentile": represents the upper uncertainty bound, the units are gC/m<sup><sub>2</sub></sup>/mon<sup>-1</sup></li> </ul> <p><span><strong>Version Guide</strong>:</span></p> <p><em>v1.0:</em> DO NOT USE THIS VERSION. There was a mistake in the modelling of ecosystem respiration, so this version of the dataset should not be used. As of version 1.1, the error has been rectified.</p> <p><em>v1.1: </em>This version of the datasets are those used to inform the EGU Publication linked above. Its time range is 2003-July 2022, and its spatial resolution is 5 km on Zenodo, but the 1 km resolution datasets can be accessed through <a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html">NCI's THREDDS portal</a>.</p> <p><em>v2.0: <strong>IMPORTANT NOTE:</strong> a bug in the modelling of vegetation height resulted in data artefacts in the NEE and ER fluxes over very tall mesic forests in this version. This resulted in unnaturally high ER and lower than expected NEE (less negative than would be expected). This issue has been rectified in version 2.1.</em> <strong>Version 2 datasets represent the operational version of the datasets</strong>, it includes several improvements over version 1.1. Its time-range is 2003-2024 (and will be updated annually), and its spatial resolution is 500m. A 5 km reprojected version of the dataset is included here on Zenodo, but the 500 metre datasets can be accessed through<a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html"> NCI's THREDDS portal.</a></p> <p><strong>v2.1: </strong>This version is a patch to version 2.0 to remove a bug in the modelling of vegetation height. <strong>It is recommended to use this version </strong>over v2.0. 500 metre resolution datasets can be accessed through<a href="https://thredds.nci.org.au/thredds/catalog/ub8/au/AusEFlux/catalog.html"> NCI's THREDDS portal.</a></p>
Empirical data, qualitative codes, analysis: Schuur J.S. et al. Identifying levers of urban neighbourhood transformation. npj Urban Sustainability (2023)
<p>Please refer to the stand-alone "2023_SchuurJS_UrbanSustainabilityfinal.html" file where the analysis and results corresponding to the article titled: "Identifying levers of urban neighbourhood transformation using serious games" is presented. The underlying data sets and Rmarkdown script used for the analysis can be used to re-run the analysis. Ensure to read the "0_README.txt" file to build the appropriate folder structure to do so.</p>
ASVspoof2019LA-Sim: Augmented Dataset for An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems
<p>This is the dataset we augmented to study the channel effects for anti-spoofing. For more details, please refer to our Interspeech 2021 paper: "An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems".</p> <p>Proceeding: <a href="https://www.isca-speech.org/archive/interspeech_2021/zhang21ea_interspeech.html">https://www.isca-speech.org/archive/interspeech_2021/zhang21ea_interspeech.html</a></p> <p>Arxiv: <a href="https://arxiv.org/pdf/2104.01320.pdf">https://arxiv.org/pdf/2104.01320.pdf</a></p> <p>Code: <a href="https://github.com/yzyouzhang/Empirical-Channel-CM">https://github.com/yzyouzhang/Empirical-Channel-CM</a></p> <p>Contact: you.zhang@rochester.edu</p> <p><strong>Version 1.0</strong> contains the <strong>training</strong> and the <strong>development</strong> set. We have added the <strong>evaluation</strong> set in <strong>version 1.1 </strong>but deleted the training set due to the size limitation, but you can still access the training set in version 1.0.</p> <p>Please check it out.</p> <p>To extract the files, please use the following commands:</p> <pre><code class="language-bash">cat eval.tar.gz-part* > eval.tar.gz tar -xvzf *.tar.gz</code></pre> <p>After concatenation, to make sure the download is complete, you can check with the following:</p> <pre><code>md5sum *.tar.gz 15dea7d28b126994bb6b159778f706af dev.tar.gz 0615052b34ca6c7f58505eaa8647844f eval.tar.gz 3058dd9d407f3c9ae697acca8c34a6c3 train.tar.gz</code></pre> <p>Thanks.</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.