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951 results for “Data release”
Harvey-Lab-UW/Harvey_etal_2023_GEB: Release of data for Harvey et al. 2023 GEB
<p>This is the latest release of data for reproducing the analyses in the manuscript 'Spatial interactions among short-interval fires reshape forest landscapes' by Harvey, Buonanduci, and Turner published in Global Ecology and Biogeography. See the main text of the manuscript for complete description of data processing.</p> <p>This information is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. Any user of these data ("User" hereafter) is required to cite it appropriately in any publication that results from its use. These data may be actively used by others for ongoing research, so coordination may be necessary to prevent duplicate publication. The User is urged to contact the authors of these data for questions about methodology or results. The User is encouraged to consider collaboration or co-authorship with authors where appropriate. Misinterpretation of data may occur if used out of context of the original study. Substantial efforts are made to ensure accuracy of the data and documentation, however complete accuracy of data sets cannot be guaranteed. All data are made available as is. Data may be updated periodically and it is the responsibility of the User to check for new versions of the data. The authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data.</p>
Phase transitions and light scalars in bottom-up holography—data release
<p>This dataset contains the data points in the plots of the preprint <a href="https://arxiv.org/abs/2212.07954\">Phase transitions and light scalars in bottom-up holography</a>.</p> <p>If you use this data release in the context of your research, please cite the aforementioned paper.</p> <p>Further details are given in the file ReadMe.md.</p>
CLAMATO DR2: IGM Lyman-Alpha Forest Tomography Survey Public Data Release of Spectra and Maps
<p> </p><p><strong>CLAMATO Data Release 2</strong></p><p>Updated 2023 Jan 11th by Khee-Gan Lee (<a href="mailto:kglee@ipmu.jp">kglee@ipmu.jp</a>)</p><p>Supporting paper has been published in <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..263...27H/abstract">ApJS</a> (arXiv:2109.09660)</p><p>These are data products associated with the second data release (DR2) of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey with the Keck-I telescope, which mapped 3D Lyman-alpha forest absorption at 2.05<z<2.55 within the COSMOS field. This is an updated version of the original DR2 repository (https://doi.org/10.5281/zenodo.5842842), which had accidentally left out several files. The other files that was in that repository are duplicated here.</p><p>The following is the summary of the main products:</p><ul><li>Source catalog (cl2020_valueadded_release_20200602.txt)</li><li>Reduced spectra, in spec_v0 .tar.gz tarball</li><li>Continuum-fitted 2.05<z<2.55 Lyman-alpha forest pixel data (pixel_data_v0.bin)</li><li>Wiener-reconstructed 3D absorption map (map_2020_v0.bin)</li><li>Reconstructed 3D matter density field using the TARDIS-II algorithm (TARDIS_CLAMATO_DR2_v0.4_smoothed.npy)</li><li>Eigenvalues of the pseudo-deformation tensor calculated from the density field (TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy)</li></ul><p><strong> Redshift Catalog and Spectra</strong></p><p>We provide our redshift catalog and reduced spectra obtained with Keck-I/LRIS-Blue.</p><p>The source catalog is provided in the ASCII file cl2020_valueadded_release_20200602.txt, with the following columns:</p><ul><li>SPECFIL: Filename of spectrum (within spec_v0.tar.gz)</li><li>TOMO_ID: CLAMATO ID number</li><li>GMAG: g-magnitude (AB) per Capak et al 2007 photometric catalog</li><li>CONF: Redshift confidence grade: see https://arxiv.org/abs/1710.02894</li><li>ZSPEC: Spectroscopic redshift as determined from CLAMATO spectrum</li><li>QSO: QSO flag (1 if QSO, 0 if non-QSO)</li><li>RA: R.A. in degrees (J2000)</li><li>DEC: Dec in degrees (J2000)</li><li>S/N_1: Estimated Lya-forest S/N at 2.05<z<2.15, -9.0 denotes no estimate</li><li>S/N_2: Estimated Lya-forest S/N at 2.15<z<2.35, -9.0 denotes no estimate</li><li>S/N_2: Estimated Lya-forest S/N at 2.35<z<2.55, -9.0 denotes no estimate</li><li>S/N_RED: Estimated S/N over restframe 1250 Å < \(\lambda\) < 1350 Å, -9.0 denotes no estimate</li><li>TOMOFLAG: Flag on whether sightline was used in tomographic map (0 for no, 1 for yes)</li><li>EXPTIME: Exposure time on the spectrum, in seconds (aggregate)</li></ul><p>The tarball spec_v0.tar.gz include all the reduced spectra from LRIS-Blue, with the respective filename indicated by the first column of the catalog. We decided not to make available the LRIS-Red spectra.</p><p>The individual LRIS spectra are provided in FITS format, with the following HDU Extensions:</p><ul><li>HDU0: Object spectral flux density, in units of \(10^{-17}\,\mathrm{erg\,s^{-1}\,cm^{-2}\,angstrom^{-1}}\)</li><li>HDU1: Noise standard deviation</li><li>HDU2: Pixel Wavelengths in angstroms</li></ul><p><strong>Pixel Data</strong></p><p>The binary file PIXEL_DATA_v0.BIN stores the concatenated Lyman-alpha forest pixels at 2.05<z<2.55 that have been extracted from the 1D spectra and continuum-fitted. </p><p>The first value in the binary is a 32-bit integer specifying the number of pixels (84608), followed by 5 double-precision floating point (64-bit) vectors storing the x, y, z, sigma_f, and delta_f of the pixels.</p><p>An example python script to read pixel_data is as follows:</p><p>import numpy as np with open('pixel_data_v0.bin','r') as f: npix = np.fromfile(f, dtype=np.int32, count=1) f.seek(4) pixel_data = np.fromfile(f,dtype=np.float64).reshape((npix,5))</p><p>LIST_TOMO_INPUT_2020.TXT is a summary file of corresponding to PIXEL_DATA.BIN, listing the [x,y,z] position of the sightlines that contributed to the file as well as, in the final two columns, the index range that can be used to grab the relevant pixels from the concatenated pixel list.</p><p>The origin of the map coordinates is at [RA=149.89150 deg, Dec=2.0915050 deg], where x is increasing in the R.A. dimension and y is increasing in the Dec.</p><p><strong>Tomographic Map</strong></p><p>The Wiener-reconstructed map of the 2.05<z<2.55 IGM within the CLAMATO field is the result of applying the "dachshund" algorithm (http://github.com/caseywstark/dachshund) to PIXEL_DATA.BIN, with the configuration file INPUT.CFG . (Caveat: the version of PIXEL_DATA.BIN here is not actually the right version to directly input into the dachshund code: the first integer in this file should not be present for input to dachshund). </p><p>The reconstructed map is MAP_2020_V0.BIN, which is a 68x56x876 = 3335808 pixel double-precision binary file. The dimension that changes fastest is the z-dimension along the line-of-sight (876 pixels per dimension), followed by the y-dimension in increasing Declination (56 pixels per dimension) and x-dimension along the direction of increasing R.A. (68 pixels per dimension).</p><p>Each map pixel represents a 0.5Mpc/h comoving voxel of the Ly-alpha forest absorption. See the Appendix of https://arxiv.org/abs/1710.02894 for the conversion factors to assume to switch between pixel/voxel and [RA, Dec, redshift].</p><p>Note that no additional smoothing has been applied in this binary, whereas most of the visualizations in the paper have had Gaussian smoothing applied.</p><p><strong>Reconstructed Density Field and Cosmic Web</strong></p><p>We also release the underlying matter density field in the CLAMATO volume, estimated using the TARDIS-II algorithm (https://arxiv.org/abs/2007.15994). This is in numpy format, and can be read directly into a 3D array using Python as follows:</p><p>>>> import numpy as np >>> den=np.load('TARDIS_CLAMATO_DR2_v0.4_smoothed.npy') >>> np.shape(den) (34, 28, 438) >>> eigen = np.load('TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy') >>> np.shape(eigen) (34, 28, 438, 3)</p><p>The 3 dimensions correspond to the R.A., Declination, and line-of-sight directions, respectively. </p><p>Note that unlike the Wiener-filtered absorption map<i>, the TARDIS-II reconstruction outputs are in 1Mpc/h comoving voxels. </i>The coordinate zero-point sand line-of-sight comoving distance-redshift relationship are otherwise the same as the Wiener map. </p><p>The eigenvalues of the pseudo-deformation tensor are in TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy, with the same array shape as the matter density, but with an additional array dimension storing the 3 sorted eigenvalues at each point in the volume. </p><p>For reference, we have included an iPython notebook (Tomographic_Maps.ipynb) that plots the Wiener-filtered absorption alongside the TARDIS-II densities and eigenvalues (e.g. Figure 8 of the main paper).</p>
Harvey-Lab-UW/Morris_etal_2023_Ecosystems: Release of data for Morris et al. 2023 Ecosystems
<p>This is the latest release of data for reproducing the analyses in the manuscript 'Fuel profiles and biomass carbon following bark beetle outbreaks: Insights for disturbance interactions from a historical silvicultural experiment' by Morris, Buonanduci, Agne, Battaglia, Donato, and Harvey published in Ecosystems. <em>See the main text of the manuscript for complete descriptions of data collection and processing.</em></p> <p>This information is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. Any user of these data ("User" hereafter) is required to cite it appropriately in any publication that results from its use. These data may be actively used by others for ongoing research, so coordination may be necessary to prevent duplicate publication. The User is urged to contact the authors of these data for questions about methodology or results. The User is encouraged to consider collaboration or co-authorship with authors where appropriate. Misinterpretation of data may occur if used out of context of the original study. Substantial efforts are made to ensure accuracy of the data and documentation, however complete accuracy of data sets cannot be guaranteed. All data are made available as is. Data may be updated periodically and it is the responsibility of the User to check for new versions of the data. The authors and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data.</p>
Precessing binary-black-hole numerical relativity catalogue (complete data release)
<p>This page contains the minimal data release associated with the catalogue presented in <a href="https://dcc.ligo.org/DocDB/0186/P2300054/001/catalogue.pdf">A catalogue of precessing black-hole-binary numerical-relativity simulations</a>. This catalogue contains 80 single-spin precessing black-hole-binary configurations. </p> <p>The content of the data release is described <a href="https://data.cardiffgravity.org/bam-catalogue/">here</a>, along with instructions on how to parse the data.</p>
Reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts"
<p>This is a basic reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts" by van Leeuwen et al. (2023).</p> <p>* arXiv:<a href="https://arxiv.org/abs/2205.12362">arXiv:2205.12362</a><br> * DOI: <a href="https://doi.org/10.1051/0004-6361/202244107">10.1051/0004-6361/202244107</a></p> <p> </p>
Radio Parallax of the Crab Pulsar Data Release
<p>This is the companion dataset to the publication <strong>"Radio Parallax of the Crab Pulsar: A First VLBI Measurement Calibrated with Giant Pulses" </strong>by <em>Lin, et al., (2023, submitted, preprint: <a href="https://arxiv.org/abs/2306.01617">https://arxiv.org/abs/2306.01617</a>)</em>.</p> <p>The FITS files contain images of the Crab Pulsar, SE_CAND3 and NE_CAND4 sources created from <a href="https://www.evlbi.org/">European VLBI Network</a> EK036A-D observations using the technique described in the paper. See the <strong>README.md</strong> and paper for more details.</p>
The second data release from the European Pulsar Timing Array III. Search for gravitational wave signals
<p>We present the results of the search for an isotropic stochastic gravitational wave background (GWB) at nanohertz frequencies using the second data release of the European Pulsar Timing Array (EPTA) for 25 millisecond pulsars and a combination with the first data release of the Indian Pulsar Timing Array (InPTA). A robust GWB detection is conditioned upon resolving the Hellings-Downs angular pattern in the pairwise cross-correlation of the pulsar timing residuals. Additionally, the GWB is expected to yield the same (common) spectrum of temporal correlations across pulsars, which is used as a null hypothesis in the GWB search. Such a common-spectrum process has already been observed in pulsar timing data. We analysed (i) the full 24.7-year EPTA data set, (ii) its 10.3-year subset based on modern observing systems, (iii) the combination of the full data set with the first data release of the InPTA for ten commonly timed millisecond pulsars, and (iv) the combination of the 10.3-year subset with the InPTA data. These combinations allowed us to probe the contributions of instrumental noise and interstellar propagation effects. With the full data set, we find marginal evidence for a GWB, with a Bayes factor of four and a false alarm probability of 4%. With the 10.3-year subset, we report evidence for a GWB, with a Bayes factor of 60 and a false alarm probability of about 0.1% (≳ 3σ significance). The addition of the InPTA data yields results that are broadly consistent with the EPTA-only data sets, with the benefit of better noise modelling. Analyses were performed with different data processing pipelines to test the consistency of the results from independent software packages. The latest EPTA data from new generation observing systems show non-negligible evidence for the GWB. At the same time, the inferred spectrum is rather uncertain and in mild tension with the common signal measured in the full data set. However, if the spectral index is fixed at 13/3, the two data sets give a similar amplitude of (2.5 ± 0.7) × 10−15 at a reference frequency of 1 yr−1 . Further investigation of these issues is required for reliable astrophysical interpretations of this signal. By continuing our detection efforts as part of the International Pulsar Timing Array (IPTA), we expect to be able to improve the measurement of spatial correlations and better characterise this signal in the coming years.</p>
Supplement data for : Intratumoral drug-releasing microdevices allow in situ high throughput pharmaco phenotyping in patients with gliomas
<p>Transcriptomic and metabolomic data associated with the manuscript: Intratumoral drug-releasing microdevices allow in situ high throughput pharmaco phenotyping in patients with gliomas.</p> <p> </p>
Fermi-GBM and Swift-BAT Data Release Related to Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run
<p>This material contains the data products associated with A Joint Fermi-GBM and Swift-BAT Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run [1]. It is based, in part, on data products associated with GWTC-2.1 [2][3] and GWTC-3 [4][5] from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration provided under a Creative Commons Attributional 4.0 International license. For more information, see the paper (<a href="https://arxiv.org/abs/2308.13666">https://arxiv.org/abs/2308.13666</a>) and the included README.txt.</p> <p>The data release includes the following directories:</p> <ul> <li><strong>3sigma_upper_limit_maps:</strong> 3 sigma flux upper limits reported by GBM as a function of sky position over a 10-1000 keV energy range.</li> <li><strong>5sigma_upper_limit_maps: </strong>5 sigma flux upper limits reported by GBM and BAT as a function of sky position over a 15-350 keV energy range.</li> <li><strong>gbm_targeted_results: </strong>GRB candidates from the GBM Targeted Search.</li> <li><strong>gbm_temporal_offset_analysis: </strong>input files to the time offset analysis applied to GBM on-board triggers and candidates from the GBM Untargeted Search.</li> <li><strong>bbh_model_fluxes: </strong>predicted gamma-ray fluxes over the 10-1000 keV energy range for likely BBH mergers.</li> <li><strong>gw_localizations:</strong> GW localization files used to overlay the 90% credible area onto GBM upper limits as a function of sky position.</li> <li><strong>m1_m2_contours: </strong>90% credible region paths for the GW component masses m1, m2.</li> </ul> <p>The data release also includes a set of example scripts to show how the contents of the data files are used. Refer to the included README.txt for more details.</p> <p><strong>References:</strong></p> <p><a href="https://arxiv.org/abs/2308.13666">[1] Fletcher, C. et al 2023, arXiv, 2308.13666</a><br> <a href="https://doi.org/10.5281/zenodo.6513631">[2] LIGO Scientific Collaboration and Virgo Collaboration. 2022, Zenodo, 6513631</a><br> <a href="https://doi.org/10.5281/zenodo.5759108">[3] LIGO Scientific Collaboration and Virgo Collaboration. 2021, Zenodo, 5759108</a><br> <a href="https://doi.org/10.5281/zenodo.5546663">[4] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration. 2021, Zenodo, 5546663</a><br> <a href="https://doi.org/10.5281/zenodo.5546665">[5] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration 2021, Zenodo, 5546665</a></p> <p><br> </p>
the supplemental data of 'It is the Frequency that Matters - Effects of Electromagnetic Fields on the Release and Content of Extracellular Vesicles.'
<p>This data set is the supplemental data of the manuscript ‘It is the Frequency that Matters - Effects of Electromagnetic Fields on the Release and Content of Extracellular Vesicles.’</p>
Large mass hierarchies from strongly-coupled dynamics---Recombined data release
<p>This release contains data associated with the publication <a href="https://arxiv.org/abs/1605.04258">Large mass hierarchies from strongly-coupled dynamics</a> (<a href="https://doi.org/10.1007/JHEP06(2016)114">JHEP 06 (2016) 114</a>).</p> <p>Compared to <a href="https://doi.org/10.5281/zenodo.13128485">the raw data release</a>, it makes the following changes:</p> <ul> <li>The two CSV files present in <a href="https://doi.org/10.5281/zenodo.13128485">the raw data release</a> are combined, with a common column schema. Since not all data were retained from all steps of the original computation, some of these data have been recomputed.</li> <li>The lattice data plotted in Figs. 2 and 3 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a> are also included, and plotted by the Mathematica notebook.</li> </ul> <p>It comprises five files:</p> <ul> <li><code>README.md</code>: containing this summary and further details of the contents of each file.</li> <li><code>su2_adjoint_lattice_data.csv</code>: the data for the points plotted in Figures 2 and 3 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. Masses of the scalar and tensor glueball, extracted from Monte Carlo ensembles of SU(2) with adjoint fermions.</li> <li><code>sigmamodel.csv</code>: the data shown in Figures 4 and 5 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. The mass of the composite spin-0 and spin-2 states, computed for , as a function of , normalised to the mass of the lightest scalar or tensor.</li> <li><code>DataRelease.nb</code>: A Mathematica notebook that will take the above three files and generate plots similar to thsoe shown in <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. This has been tested using Mathematica 14.0.</li> <li><code>unify_lmh_2016.zip</code>: A Snakemake and Python workflow to take the data from <a href="https://doi.org/10.5281/zenodo.13128485">the raw data release</a>, some data released as part of <a href="https://doi.org/10.5281/zenodo.12802810">the analysis workflow for arXiv:2408.00171</a>, and some previously unpublished data included in the archive, and generate the two CSV files above. Includes a <code>README.md</code> file containing more information about how to run the workflow.</li> </ul>
Data for Integrated Step Selection Analysis of translocated female greater sage-grouse in the 60 days post-release, North Dakota 2018-2020
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Correlative 3D SBFSEM data from: Intermittent bulk release of human cytomegalovirus
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Data from: Strength of enemy release from parasitoids is context-dependent in the invasive African Fig Fly, Zaprionus indianus
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Data from: Plant uptake offsets silica release from a large Arctic tundra wildfire
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Data from: Male lake char release taurocholic acid as part of a mating pheromone
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Intermittent bulk release of human cytomegalovirus: associated data
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Data from: Faster than expected: Release of nitrogen and phosphorus from decomposing woody litter
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SPECTRAL PROPERTIES OF QUASARS FROM SLOAN DIGITAL SKY SURVEY DATA RELEASE 14: THE CATALOG
<p>We present measurements of the spectral properties for a total of 526,265 quasars, out of which 63% have continuum S/N> 3 pixel<sup>−1</sup>, selected from the fourteenth data release of the Sloan Digital Sky Survey (SDSS-DR14) quasar catalog. We performed a careful and homogeneous analysis of the SDSS spectra of these sources, to estimate the continuum and line properties of several emission lines such as Hα, Hβ, Hγ, Mg ii, C iii], C iv and Lyα. From the derived emission line parameters, we estimated single-epoch virial black hole masses (M<sub>BH</sub>) for the sample using Hβ, Mg ii, and C iv emission lines. The sample covers a wide range in bolometric luminosity (log L<sub>bol;</sub> erg s<sup>−1</sup> ) between 44.4 and 47.3 and log M<sub>BH</sub> between 7.1 and 9.9 M. Using the ratio of L<sub>bol</sub> to the Eddington luminosity as a measure of the accretion rate, the logarithm of the accretion rate is found to be in the range between −2.06 and 0.43. We performed several correlation analyses between different emission line parameters and found them to match with that known earlier using smaller samples. We noticed that strong Fe ii sources with large Balmer line width, and highly accreting sources with large M<sub>BH</sub> are rare in our sample. We make available an extended and complete catalog that contains various spectral properties of 526,265 quasars derived in this work along with other properties culled from the SDSS-DR14 quasar catalog.</p> <p> </p> <p><strong>Here, you will find the entire spectral catalog in the FITS forma</strong>t.</p> <ul> <li>The main catalog (“ <strong>dr14q_spec_prop.fits</strong>”) is based on the spectral information from this study consisting of 274 columns, which are described in Table B4.</li> <li>An extended catalog (“ <strong>dr14q_spec_prop_ext.fits</strong>”) where all the columns of DR14Q (Paris et al. 2018) are appended after the main catalog (ie, after column # 274). The extended catalog has a total of 380 columns.</li> </ul> <p><strong>We also make available all the model components (in FITS format) for each quasar and the spectral decomposition plots (PDF format):</strong> <strong><a href="https://seafile.utu.fi/d/9866433c200743119e92/">HERE</a></strong></p> <ul> <li><em>Each model FITS file has the following 8 columns:</em></li> </ul> <blockquote> <ol> <li>wave : rest-frame wavelength</li> <li>flux : rest-frame spectral flux</li> <li>err : rest-frame spectral flux uncertainty</li> <li>model_host : decomposed host-Galaxy flux</li> <li>model_pl_bc : a sum of the model power law and Balmer continuum</li> <li>model_feii : a sum of the Fe II UV and Fe II optical</li> <li>model_line_bc : total broad line model</li> <li>model_line_nc : total narrow line model</li> </ol> </blockquote> <ul> <li><strong>A dedicated website has been created for Supplementary material:</strong> <strong><a href="http://www.utu.fi/sdssdr14">WEBSITE</a></strong></li> </ul>
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.