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Fig. 2 in Magneto-biostratigraphic age constraints on the palaeoenvironmental evolution of the South Caspian basin during the Early-Middle Pleistocene (Kura basin, Azerbaijan)
Fig. 2. Lithostratigraphic subdivision of the Goychay section (A) and the Hajigabul section (B). Logs, general view and characteristic photos of each sedimentary unit.
Fig. 1 in Magneto-biostratigraphic age constraints on the palaeoenvironmental evolution of the South Caspian basin during the Early-Middle Pleistocene (Kura basin, Azerbaijan)
Fig. 1. Location map of the Goychay and Hajigabul sections. Columns on the left: Global polarity time scale (Hilgen et al., 2012) Epoch/Age; Regional Stages: a* classical definition (Shantser, 1982; Arslanov et al., 1988; Nevesskaya et al., 2003, 2004), b* this study. Position of the studied sections in relation to the Caspian Sea (A) and to the Kura Basin (B) (The map base is taken from www.maps-for-free.com); Geological maps for the Goychay section (C) and the Hajigabul section (D) modified after Bairamov et al. (2008).
Fig. 13 in Magneto-biostratigraphic age constraints on the palaeoenvironmental evolution of the South Caspian basin during the Early-Middle Pleistocene (Kura basin, Azerbaijan)
Fig. 13. Correlation of polarity patterns to the Global Polarity Time Scale (GPTS), the main paleoenvironmental events and characteristic mollusc fauna in the Goychay and Hajigabul sections. Sedimentation rate curves: Hajigabul section (A), the Goychay section (B).
Fig. 11 in Magneto-biostratigraphic age constraints on the palaeoenvironmental evolution of the South Caspian basin during the Early-Middle Pleistocene (Kura basin, Azerbaijan)
Fig. 11. Equal area plots, Zijderveld diagrams and thermomagnetic curves for samples of the Hajigabul section. Equal are plots for: B. The low temperature component (20 oC-300 o C, LT_N): in situ and in tectonic coordinates (tc); E. All normal ChRM directions - in situ and in tectonic coordinates (tc); F. All reversed ChRM directions - in situ and in tectonic coordinates (tc); G. All mean directions for all reversed (MT_R and HT_R), all normal (HT_N and MT_N) and LT_N groups; L. Remagnetized samples marked as "Full overprint" (FO) - in situ and in tectonic coordinates (tc); A, C, H- Characteristic Zijderveld diagrams for various samples. D, M - thermomagnetic runs for various samples; Zijderveld diagrams with separate (H) and overlapped (I) demagnetization of two components. J - Zijderveld diagram given for one sample measured with different techniques: th - thermally demagnetized, af - demagnetized in alternating field.
Fig. 6 in Magneto-biostratigraphic age constraints on the palaeoenvironmental evolution of the South Caspian basin during the Early-Middle Pleistocene (Kura basin, Azerbaijan)
Fig. 6. Equal area plots, Zijderveld diagrams and thermomagnetic curves for samples from the Goychay section. Equal area plots for: B. The low temperature component (20 o C-300 o C, LT_N): all LT_N direction in situ and in tectonic coordinates; C. Isolated group of LT_N directions; E. The medium temperature component with reversed directions (330 o C-400 oC, MT_R), in situ and in tectonic coordinates (tc); I. High temperature component (440 o C-580 o C (670 oC), HT_R) with reversed directions, in situ and in tectonic coordinates (tc); J. High temperaturecomponent (440 o C-580 o C (670 oC), HT_N) with normal directions, in situ and in tectonic coordinates (tc); N. All reversed direction (MT_R and HT_R) in tectonic coordinates; O. All mean directions for all reversed (MT_R and HT_R), LT_N and HT_N groups; A, D, G and H - characteristic Zijderveld diagrams; F, K, L and M - characteristic thermomagnetic runs for various samples.
Fig. 5. Selected gastropods from the Goychay section. A in Magneto-biostratigraphic age constraints on the palaeoenvironmental evolution of the South Caspian basin during the Early-Middle Pleistocene (Kura basin, Azerbaijan)
Fig. 5. Selected gastropods from the Goychay section. A. Theodoxus pallasi; B. Theodoxus pallasi; C. Laevicaspia sp. D. Laevicaspia subcaspia; E. Caspia apsheronica; F. Caspia sp.; G. Clessiniola cf. subvariabilis; H. Ecrobia cf. grimmi; I. Laevicaspia subcaspia; J. Melanopsis bergeroni; K. Lymnaea sp.; L. Turricaspia sp.; M. Laevicaspia sp.; N. Streptocerella sp.; O. Gyraulus sp.; P. Valvata sp. (Scale bars 1 mm).
Fig. 4 in Magneto-biostratigraphic age constraints on the palaeoenvironmental evolution of the South Caspian basin during the Early-Middle Pleistocene (Kura basin, Azerbaijan)
Fig. 4. Selected bivalve species and charophyta from the Goychay section: A. Dreissena carinatocurva; B. Dreissena rostriformis; C. Dreissena polymorpha; D. Pseudocatillus sp.; E. Didacnomya sp.; F. Apscheronia propinqua; G. Corbicula fluminea (paired bivalve); H. Corbicula fluminea; I. Monodacna sp. 1; J. Monodacna sp. 1; K. Adacna sp; L. Oogonium of charophyta. (Scale bars 1 mm).
Fig. 10. Middle Pleistocene Didacna species from the Hajigabul section. Scale bar 5 in Magneto-biostratigraphic age constraints on the palaeoenvironmental evolution of the South Caspian basin during the Early-Middle Pleistocene (Kura basin, Azerbaijan)
Fig. 10. Middle Pleistocene Didacna species from the Hajigabul section. Scale bar 5 mm. A-B. Didacna bergi (1954 m, early Khazarian); C-G. D. parvula (1795 m, Late Bakunian); H-I. D. cf carditoides (1795 m, Late Bakunian); J-K. Didacna sp (1795 m, Late Bakunian).
Fig. 9 in Magneto-biostratigraphic age constraints on the palaeoenvironmental evolution of the South Caspian basin during the Early-Middle Pleistocene (Kura basin, Azerbaijan)
Fig. 9. Mollusc fauna from the Akchagylian clay interval in the Hajigabul section. Scale bar 5 mm. (a). Cardiidae sp. A. (428 m); (b-d). Cardiidae sp. B. (395 m); (e-g). Cardiidae sp. (395 m); (h, i). Avicardium nikitini (395 m); ((i) is a reconstruction); (j) Pirenella caspia (288 m).
Supporting Data from Ecological Opportunities and Constraints in the Evolution of Diatoms
<p>2016-02-01<br /> Teofil Nakov</p> <p>Data and code for Nakov, Ruck, Alverson: Ecological opportunities and constraints in the evolution of diatom</p> <p>This packet contains the following files:</p> <p>1. Sequence alignment for 540 diatoms and 2 Bolidomonas: Nakov-etal-542-taxon-alignment-clean.fasta<br /> 2. The constraint topology used in tree searches: Nakov-etal-constraint-small-clades.phylip<br /> 3. Alignment partition file: Nakov-etal-tiger-partitions-rax<br /> 4. Character data for 540 diatoms and 2 Bolidomonas: Nakov-etal-542-taxon-discrete-characters.csv<br /> 5. The best tree from 420 RAxML optimizations: Nakov-etal-RAxML_bestTree.opt_89<br /> 6. 99 additional trees used in the analyses: Nakov-etal-99-randomly-sampled-unique-trees.newick<br /> 7. All 100 trees converted into chronograms: Nakov-etal-100-chronograms-smooth-1000.newick<br /> 8. Script with random seeds for RAxML optimizations: Nakov-etal-RAxML-calls-for-420-optimizations<br /> 9. R scripts with random seeds: Nakov-etal-R-scripts.zip (three scripts: equal-rates, independent, and coordinated + paint regimes models)<br /> 10. TreePL control file: Nakov-etal-TreePL-control-file.txt</p>
On the Understandability of Semantic Constraints for Behavioral Software Architecture Compliance: A Controlled Experiment
<p>Software architecture compliance is concerned with the alignment of implementation with its desired architecture and detecting potential inconsistencies. The study is specifically concerned with behavioral architecture compliance. That is, the focus is on semantic alignment of implementation and architecture. In particular, the study evaluates three representative approaches for describing semantic constraints in terms of their understandability, namely natural language descriptions as used in many architecture documentations today, a structured language based on specification patterns that abstract underlying temporal logic formulas, and a structured cause-effect language that is based on Complex Event Processing. We conducted a controlled experiment with 190 participants using a simple randomized design with one alternative per experimental unit.</p>
Demonstration of semantic and inter-input constraints on software in OWL 2 and SPARQL for fulfilling the M1 Machine FAIR Use Case
<p>This video demonstrates using hypothetical examples how to (1) find a valid dataset for input into a software using OWL 2 classification inference, (2) validly combine two software using OWL 2 subsumption inference to infer that the output of software 1 is valid input to software 2, and (3) combine OWL 2 inference with a SPARQL query to find two datasets that satisfy a software's inter-input constraints.</p>
Supplemental files for "Lateral and Temporal Constraints on The Depositional History of The Bonneville Salt Flats, Utah, USA"
<p>Radiocarbon, strontium isotope, optically stimulated luminescence, X-ray diffraction, X-ray fluorescence, tephra microprobe, grain-size, ostracode, and diatom occurrence data, from the Bonneville Salt Flats as well as regional strontium isotope data.</p>
Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data
<p>This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023).</p> <p>First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized.</p> <ul> <li><strong>Cl Concentrations - Wang et al. (2021).zip:</strong> Original Cl concentrations field from Wang et al. (2021). </li> <li><strong>CH4 Fluxes - Saunois et al. (2020).zip: </strong>Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).</li> </ul> <p>Second, we store the processed input data generated for the purpose of our study.</p> <ul> <li><strong>CH4 Fluxes - LMDz9696.zip:</strong> Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>d13C Signatures - LMDz9696.zip:</strong> δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>dD Signatures - LMDz9696.zip:</strong> δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>OH O1D Concentrations - LMDz9696-INCA.zip:</strong> OH and O1D monthly concentrations simulated with LMDz-INCA.</li> <li><strong>Masks regions.zip</strong>: Masks for the regions used for the input data and the analysis.</li> </ul> <p> </p>
Homologous Missense Constraint scores
<p>For all missense variants with HMC scores:</p> <p>1. If one missense variant could be mapped to the multiple Pfam domains, we report the worst HMC scores (smallest, more likely to be deleterious) since it's the worst scenario. </p> <p>2. The genomic positions are provided with genome build in both hg19 and hg38. </p>
Dataset "Comprehensive laboratory constraints on thermal desorption of interstellar ice analogues"
<p>### Dataset Overview ###</p> <p>This dataset contains Temperature-Programmed Desorption data corresponding to the series of experiments described in Table 2 of our accompanying paper "Comprehensive laboratory constraints on thermal desorption of interstellar ice analogues". The focus of these experiments is to explore the thermal desorption of various molecular ice mixtures under specific conditions.</p> <p> </p>
Data for "Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols"
<p>This repository contains calculated/simulated data on the imaginary part of the complex refractive index, single scattering albedo, and/or optical depth for dust aerosols in the visible band or at the wavelength of 550 nm.</p> <p>For detailed information on (1) the acquisition and utilization of this data, (2) comprehensive configurations for model simulations, (3) the principal findings, and (4) the methodology employed to achieve these findings, please refer to the article authored by Li, Mahowald et al. (2024; Commun. Earth Environ).</p> <p>Other datasets, including the code and laboratory observations presented in the paper, can be found elsewhere (refer to the Data and Code Availability sections of the paper).</p> <p>For any clarification regarding the data and code, inquiries related to the publication, or potential collaboration, please contact Longlei Li (<a href="mailto:ll859@cornell.edu">ll859@cornell.edu</a>) or Natalie M. Mahowald (<a href="mailto:mahowald@cornell.edu">mahowald@cornell.edu</a>).</p>
Multiwavelength Constraints on the Origin of a Nearby Repeating Fast Radio Burst Source in a Globular Cluster (Public Data Release)
<p>This Zenodo dataset contains the data for radio bursts B1-B9 from FRB 20200120E, as described in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) (see: https://doi.org/10.1038/s41550-024-02386-6).</p> <p>The following data products are included:</p> <ul> <li>Channelized total intensity (Stokes I) data containing radio bursts B1-B5 from FRB 20200120E, recorded using the Effelsberg radio telescope during Pinpointing Repeating CHIME Sources with the EVN (PRECISE) VLBI observations. These data have a time resolution of 8 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b1_8us_burst_data.npy</li> <li>frb20200120e_b2_8us_burst_data.npy</li> <li>frb20200120e_b3_8us_burst_data.npy</li> <li>frb20200120e_b4_8us_burst_data.npy</li> <li>frb20200120e_b5_8us_burst_data.npy</li> </ul> </li> <li>Channelized total intensity (Stokes I) data containing radio bursts B6-B9 from FRB 20200120E, recorded using the Effelsberg radio telescope. These data have a time resolution of 64 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b6_64us_burst_data.npz</li> <li>frb20200120e_b7_64us_burst_data.npz</li> <li>frb20200120e_b8_64us_burst_data.npz</li> <li>frb20200120e_b9_64us_burst_data.npz</li> </ul> </li> <li>Frequency-summed total intensity (Stokes I) burst profiles of radio burst B4. The frequency range and time resolution of the data are listed below. These data were used in Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).<br> <ul> <li>frb20200120e_b4_8us_1254-1510mhz_burst_profile.npz; (frequency range, time resolution) = (1254-1510 MHz, 8 μs)</li> <li>frb20200120e_b4_1us_1302-1478mhz_burst_profile.npy; (frequency range, time resolution) = (1302-1478 MHz, 1 μs)</li> <li>frb20200120e_b4_31.25ns_1398-1414mhz_burst_profile.npy; (frequency range, time resolution) = (1398-1414 MHz, 31.25 ns)</li> </ul> </li> </ul> <p>We also provide the following Python code containing functions that can be used to load and plot the radio data. The plots generated by this code are similar to those shown in Figure 1 and Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).</p> <ul> <li>plot_frb20200120e_radio_data_pearlman+2024_nature_astronomy.py</li> </ul> <p>The X-ray data (from <em>NICER</em>, <em>XMM-Newton</em>, <em>Chandra</em>, and <em>NuSTAR</em>) used in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) are publicly available and can be accessed through NASA's High Energy Astrophysics Science Archive Research Center (HEASARC) archive.</p> <p>If the data or Python code included in this Zenodo repository are used, please include the following two citations in your work:</p> <ol> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster. <em>Nature Astronomy</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.1038/s41550-024-02386-6</a></li> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster (public data release). <em>Zenodo</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.5281/zenodo.13359005</a></li> </ol> <p>If you have questions about the contents of this Zenodo repository, please contact the lead author: Dr. Aaron B. Pearlman (<a href="mailto:aaron.b.pearlman@physics.mcgill.ca">aaron.b.pearlman@physics.mcgill.ca</a>)</p>
Binary black hole merger rate constraints using GWTC-3 and full-O3 stochastic background constraints
<h1>README</h1> <p>This dataset contains posterior measurements of the redshift-dependent merger rate, mass distribution, and spin distribution of binary black holes following the O3b observing run of the LIGO-Virgo-KAGRA network, including both direct compact binary detections and constraints on the astrophysical gravitational-wave background.</p> <p>In particular, the goal of this work is to constrain a more complex model for the black hole merger rate, with the comoving rate density evolving as</p> <p>$$<br>R(z) \propto \frac{(1+z)^\alpha}{1 + \left(\frac{1+z}{1+z_p}\right)^{\alpha + \beta}}.<br>$$</p> <p>At redshifts \(z < z_p\), the merger rate grows approximately as \(R(z) \propto (1+z)^\alpha\), whereas at \(z>z_p\) it falls as \(R(z) \propto (1+z)^{-\beta}\).</p> <p>The analysis was performed as described in <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ab9743">Callister <em>et al</em> (2020)</a> and <a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> (2021)</a>, now using binary black hole detections from the GWTC-3 catalog (<a href="https://link.aps.org/doi/10.1103/PhysRevX.13.041039">Abbott <em>et al</em> 2023a</a>, <a href="https://link.aps.org/doi/10.1103/PhysRevX.13.011048">2023b</a>).</p> <ul> <li>The parameter estimation samples used are those provided by the LIGO-Virgo-KAGRA collaboration at https://zenodo.org/records/8177023</li> <li>Selection effects are calculated and mitigated using the suite of pipeline injections available at https://zenodo.org/records/7890398</li> <li>Cross-correlation measurements of the stochastic gravitational-wave background are available at https://dcc.ligo.org/LIGO-G2001287, and correspond to results presented in <a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> (2021).</a></li> </ul> <p>As discussed in <a href="https://link.aps.org/doi/10.1103/PhysRevX.13.011048">Abbott <em>et al</em> (2023b)</a>, the results of this combined BBH + stochastic analysis are categorically unchanged relative to results previously obtained using GWTC-2 events (<a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> 2021</a>); sensitivities are not yet sufficient to resolve the redshift at which the black hole merger rate peaks and turns over.</p> <h1>Contents</h1> <ul> <li><code><strong>processed_emcee_samples_together_r00r01.npy</strong></code>: File containing posterior samples when jointly analyzing BBH detections and stochastic background upper limits.</li> <li><code><strong>processed_emcee_samples_noStochastic_r00r01.npy</strong></code>: File containing posterior samples analyzing only direct BBH detections.</li> <li><code><strong>run_emcee_plPeak.py</strong></code>: Script performing joint hierarchical inference using BBH detections and stochastic background limits; used to generate posterior samples in <code>processed_emcee_samples_together_r00r01.npy</code></li> <li><code><strong>run_emcee_plPeak_noStochastic.py</strong></code>: Script performing joint hierarchical inference using BBH detections and stochastic background limits; used to generate posterior samples in <code>processed_emcee_samples_noStochastic_r00r01.npy</code></li> </ul> <h1>Accessing posterior samples</h1> <p>Posterior samples are contained in the files <code>processed_emcee_samples_together_r00r01.npy</code> and <code>processed_emcee_samples_noStochastic_r00r01.npy</code>. This is loaded via python as, e.g.</p> <blockquote> <p>>>> import numpy as np</p> <p>>>> dataset = np.load('processed_emcee_samples_together_r00r01.npy')</p> </blockquote> <p>Contained in this file is a single <code>numpy</code> array of size <code>(# of posterior samples, # of hyperparameters)</code>:</p> <blockquote> <p>>>> dataset.shape</p> <p>(1152, 13)</p> </blockquote> <p> </p> <p>The 13 hyperparameters are defined as follows:</p> <table> <tbody> <tr> <td>Column</td> <td>Name</td> <td>Definition</td> </tr> <tr> <td><code>dataset[:, 0]</code></td> <td><code>xeff_mu</code></td> <td>Mean effective inspiral spin</td> </tr> <tr> <td><code>dataset[:, 1]</code></td> <td><code>xeff_sig</code></td> <td>Standard deviation of effective inspiral spin</td> </tr> <tr> <td><code>dataset[:, 2]</code></td> <td><code>R0</code></td> <td>Total BBH merger rate at redshift \(z=0\)</td> </tr> <tr> <td><code>dataset[:, 3]</code></td> <td><code>mMin</code></td> <td>Minimum black hole mass</td> </tr> <tr> <td><code>dataset[:, 4]</code></td> <td><code>mMax</code></td> <td>Maximum black hole mass</td> </tr> <tr> <td><code>dataset[:, 5]</code></td> <td><code>lmbda</code></td> <td>Power-law index on primary mass distribution</td> </tr> <tr> <td><code>dataset[:, 6]</code></td> <td><code>mu_peak</code></td> <td>Mean of Gaussian peak in primary mass distribution</td> </tr> <tr> <td><code>dataset[:, 7]</code></td> <td><code>sig_peak</code></td> <td>Standard deviation of Gaussian peak</td> </tr> <tr> <td><code>dataset[:, 8]</code></td> <td><code>frac_peak</code></td> <td>Fraction of events occupying Gaussian peak</td> </tr> <tr> <td><code>dataset[:, 9]</code></td> <td><code>bq</code></td> <td>Power-law index on mass ratio distribution \(p(q\|m_1)\)</td> </tr> <tr> <td><code>dataset[:, 10]</code></td> <td><code>alpha</code></td> <td>Slope of \(R(z) \propto (1+z)^\alpha \) at low redshifts</td> </tr> <tr> <td><code>dataset[:, 11]</code></td> <td><code>beta</code></td> <td>Slope of \(R(z) \propto (1+z)^{-\beta}\) at high redshifts</td> </tr> <tr> <td><code>dataset[:, 12]</code></td> <td><code>zpeak</code></td> <td>Redshift at which \(R(z)\) peaks and turns over</td> </tr> </tbody> </table> <p> </p> <p>The exact usage of the above parameters can be seen in the included scripts <code>run_emcee_plPeak.py</code> and <code>run_emcee_plPeak_noStochastic.py</code>, with which the inference was performed. </p>
Data distribution of Constraints on the cosmic expansion history from the GWTC-3
<p>This is the data distribution associated with the publication "Constraints on the cosmic expansion history from the GWTC--3". Please, refer to the README_icarogw.md and README_gwcosmo.md files for a description of the data distribution files.</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.