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IAM_COMPACT_Study_4_EU_Industry

This dataset contains the underling raw data of IAM COMPACT "Study 4 - EU Industry". The study started from stakeholders’ questions around relocation of European Industries and associated impacts on costs, sustainability and labour and condensed these into two sub-studies: <ol> <li>The first one analysed the European steel industry and a potential relocation thereof to other world regions because of high energy and CO2 prices in the EU.</li> <li>The second one addressed – in the context of potential re-shoring of critical net-zero technologies to the EU – the raw material demand for upscaling solar and wind technologies in four global regions, including the EU (section 3 of D4.7 (Holtz et al., 2023)).</li> </ol> The first sub-study on the EU steel industry involved the soft-linking of several models to assess the potential impact of different degrees of trade restrictions for steel. Three scenarios are analysed in each of which all countries implement their current climate policies and achieve the GHG emissions reduction targets set in their respective NDCs and in their long-term targets, but different steel-related trade constraints are assumed in the three scenarios: <ul> <li>“NDC_LTT” scenario: no trade restriction</li> <li>“CBAM” scenario: steel imports to the EU are penalized based on their CO2 footprint</li> <li>“INDEPENDENCE” scenario: steel production levels in the EU may not drop below the level of 2019 (which is guaranteed by subsidies provided for steel production)</li> </ul> The second sub-study on raw material demand for upscaling solar and wind technologies builds on the three scenarios of the steel-related study (see above). Results from GCAM on renewables upscaling were used as an input to some modules of WILIAM which allowed to calculate the raw materials demands implied, which were then compared to current annual extraction and the known global reserves. The GCAM model was used to develop scenarios for the steel production in different global regions considering trade and trade restrictions and all other model applications in this study built on these results. The three steel-related scenarios analysed with GCAM all build on the NDC_LTT scenario of study 1 used for the comparison of EU Fit-for-55 policy to a cost-optimal scenario (section 3 of D4.5 (Mittal et al., 2023)). Therefore, all policies included in the NDC_LTT scenario of Study 1 are implicitly included in Study 4 scenarios as well. However, these were not analysed specifically in Study 4. The policy measures explicitly addressed in Study 4 are related to steel trade. Furthermore, the CO2 price resulting from GCAM was used as an input by the WISEE EDM-I bottom-up steel model. <div>Results of the study have been documented in D4.7 - Sectoral and cross-sectoral analysis (DOI <a href="../doi/10.5281/zenodo.13839232">10.5281/zenodo.13839232</a>)</div>

opencc-by-4.0Sep 2024View details →

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NSNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers

The data for all NSNS simulations shown in "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers".  Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)   Contents:  <ul> <li>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E)  and "unstable case BB + optimistic CE" (model F) files. These zip files are:  <ul> <li>fiducial.zip,  the Fiducial model (A) and Optimistic CE model (K)</li> <li>massTransferEfficiencyFixed_0_25.zip, the $\beta$ = 0.25 model (B) </li> <li>massTransferEfficiencyFixed_0_5.zip, the $\beta$ = 0.5 model (C) </li> <li>massTransferEfficiencyFixed_0_75.zip, the $\beta$ = 0.75 model (D)</li> <li>unstableCaseBB.zip, the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li>alpha0_1 zip, the $\alpha = 0.1$ model (G) </li> <li>alpha0_5.zip, the $\alpha = 0.5$ model (H) </li> <li>alpha2_0.zip, the $\alpha = 2.0$ model (I) </li> <li>alpha10_0.zip, the $\alpha = 10.0$ model (J) </li> <li>rapid.zip, the rapid SN model (L) </li> <li>maxNSmass2_0.zip, the max $m_{\rm{NS}} = 2\, \rm{M}_{\odot}$ model (M) </li> <li>maxNSmass3_0.zip, the max $m_{\rm{NS}} = 3\, \rm{M}_{\odot}$ model (N)</li> <li>noPISN.zip, the no PISN model (O) </li> <li>ccSNkick_100km_s.zip, the $\sigma_{\rm{cc}}$= 100 km/s model (P) </li> <li>ccSNkick_30km_s.zip, the $\sigma_{\rm{cc}}$= 30 km/s model (Q)</li> <li> noBHkick.zip, the no BH SN kick model (R)</li> <li>wolf_rayet_multiplier_0_1.zip, the model with Wolf-Rayet wind factor $f_{\rm{WR}} = 0.1$ (S)</li> <li>wolf_rayet_multiplier_5.zip, the model with Wolf-Rayet wind factor $f_{\rm{WR}} = 5$ (T)  </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates:  <ul> <li>csvFilesForFigure1_DCOpaper.zip # contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li>csvFilesForFigure2_and_3_DCOpaper.zip # contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names:  <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul>   Details of how to use the data (a readme),  as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a>  If you use this data, please cite  Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a>

opencc-by-4.0Aug 2021View details →

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BHNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers

The data for all BHNS simulations shown in "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers".  Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)   Contents:  <ul> <li>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E)  and "unstable case BB + optimistic CE" (model F) files. These zip files are:  <ul> <li>fiducial.zip,  the Fiducial model (A) and Optimistic CE model (K)</li> <li>massTransferEfficiencyFixed_0_25.zip, the $\beta$ = 0.25 model (B) </li> <li>massTransferEfficiencyFixed_0_5.zip, the $\beta$ = 0.5 model (C) </li> <li>massTransferEfficiencyFixed_0_75.zip, the $\beta$ = 0.75 model (D)</li> <li>unstableCaseBB.zip, the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li>alpha0_1 zip, the $\alpha = 0.1$ model (G) </li> <li>alpha0_5.zip, the $\alpha = 0.5$ model (H) </li> <li>alpha2_0.zip, the $\alpha = 2.0$ model (I) </li> <li>alpha10_0.zip, the $\alpha = 10.0$ model (J) </li> <li>rapid.zip, the rapid SN model (L) </li> <li>maxNSmass2_0.zip, the max $m_{\rm{NS}} = 2\, \rm{M}_{\odot}$ model (M) </li> <li>maxNSmass3_0.zip, the max $m_{\rm{NS}} = 3\, \rm{M}_{\odot}$ model (N)</li> <li>noPISN.zip, the no PISN model (O) </li> <li>ccSNkick_100km_s.zip, the $\sigma_{\rm{cc}}$= 100 km/s model (P) </li> <li>ccSNkick_30km_s.zip, the $\sigma_{\rm{cc}}$= 30 km/s model (Q)</li> <li> noBHkick.zip, the no BH SN kick model (R)</li> <li>wolf_rayet_multiplier_0_1.zip, the model with Wolf-Rayet wind factor $f_{\rm{WR}} = 0.1$ (S)</li> <li>wolf_rayet_multiplier_5.zip, the model with Wolf-Rayet wind factor $f_{\rm{WR}} = 5$ (T)  </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates:  <ul> <li>csvFilesForFigure1_DCOpaper.zip # contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li>csvFilesForFigure2_and_3_DCOpaper.zip # contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names:  <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul>   Details of how to use the data (a readme),  as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a>  If you use this data, please cite  Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a>

opencc-by-4.0Aug 2021View details →

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Text-fig. 3. Textures of main volcaniclastic deposits exposed in abandoned Ludvíkovice quarry. a: radial cracks surrounding some boulders (see arrows) in hot lahar deposit. b: jig-saw fit of fractures (see arrows) within a mega-block of debris-avalanche deposit. c: pseudo-fiamme texture of compacted argillized pumice-fall deposit. d: trachybasaltic lapilli-stone of phreato-magmatic eruption. e: palaeo-relief developed and buried within the pyroclastic unit. f: diagonal bedding in fluvial volcanigenic sandstones. g: diluted and fine-grained lahars embedded in volcanigenic sandstones. in A New Oligocene Flora From Ludvíkovice Near Děčín (České Středohoří Mts., The Czech Republic)

Text-fig. 3. Textures of main volcaniclastic deposits exposed in abandoned Ludvíkovice quarry. a: radial cracks surrounding some boulders (see arrows) in hot lahar deposit. b: jig-saw fit of fractures (see arrows) within a mega-block of debris-avalanche deposit. c: pseudo-fiamme texture of compacted argillized pumice-fall deposit. d: trachybasaltic lapilli-stone of phreato-magmatic eruption. e: palaeo-relief developed and buried within the pyroclastic unit. f: diagonal bedding in fluvial volcanigenic sandstones. g: diluted and fine-grained lahars embedded in volcanigenic sandstones.

opencc-by-4.0Dec 2020View details →

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Text-fig. 4. Scanning electron microscope (SEM) images of megaspores with possible affinities to Selaginellales; Torres Vedras locality, Portugal. a, b) Hughesisporites galericulatus, lateral view of megaspore (a) with almost smooth surface and spore wall of thin elements forming a dense reticulum (b); c) Trileites sp., proximal view of megaspore with almost smooth surface and raised trilete mark; d–f) Rugotriletes sp., proximal (e) and lateral (f) views of megaspores showing coarsely reticulate-rugulate surface ornamentation and prominent gula around the trilete mark and compact perforate spore wall (d); g, h) Erlansonisporites sp., distal (g) and lateral (h) views of megaspores showing coarsely reticulate-rugulate surface and fibrous spore wall; i, j) Striatriletes sp. 1, megaspore in oblique proximal view (i) showing raised laesurae and irregular striate-rugulate surface, and detail of spore wall (j) showing dense packing of sculptural elements; k, l) Striatriletes sp. 2, megaspore in proximal view (k) showing trilete mark, striate-rugulate surface, and detail of spore wall (l) composed of loosely packed fibers; m) Striatriletes sp. 3, megaspore in proximal view showing raised trilete mark and striate-rugulate surface; n, o) Verrutriletes sp., megaspore in oblique proximal view (n) showing short laesurae of the trilete mark, and the dense verrucate surface (o); p) Megaspore sp. 1, oblique proximal view showing in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community

Text-fig. 4. Scanning electron microscope (SEM) images of megaspores with possible affinities to Selaginellales; Torres Vedras locality, Portugal. a, b) Hughesisporites galericulatus, lateral view of megaspore (a) with almost smooth surface and spore wall of thin elements forming a dense reticulum (b); c) Trileites sp., proximal view of megaspore with almost smooth surface and raised trilete mark; d–f) Rugotriletes sp., proximal (e) and lateral (f) views of megaspores showing coarsely reticulate-rugulate surface ornamentation and prominent gula around the trilete mark and compact perforate spore wall (d); g, h) Erlansonisporites sp., distal (g) and lateral (h) views of megaspores showing coarsely reticulate-rugulate surface and fibrous spore wall; i, j) Striatriletes sp. 1, megaspore in oblique proximal view (i) showing raised laesurae and irregular striate-rugulate surface, and detail of spore wall (j) showing dense packing of sculptural elements; k, l) Striatriletes sp. 2, megaspore in proximal view (k) showing trilete mark, striate-rugulate surface, and detail of spore wall (l) composed of loosely packed fibers; m) Striatriletes sp. 3, megaspore in proximal view showing raised trilete mark and striate-rugulate surface; n, o) Verrutriletes sp., megaspore in oblique proximal view (n) showing short laesurae of the trilete mark, and the dense verrucate surface (o); p) Megaspore sp. 1, oblique proximal view showing

opencc-by-4.0Nov 2019View details →

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Stable and compact RF-to-optical link using lithium niobate on insulator waveguides

Stable and compact RF-to-optical link using lithium niobate on insulator waveguides Optical frequency combs have become a very powerful tool in metrology and beyond thanks to their ability to link radio frequencies with optical frequencies via a process known as self-referencing. Typical self-referencing is accomplished in two steps: the generation of an octave-spanning supercontinuum spectrum and the frequency-doubling of one part of that spectrum. Traditionally, these two steps have been performed by two separate optical components. With the advent of photonic integrated circuits, the combination of these two steps has become possible in a single small and monolithic chip. One photonic integrated circuit platform very well suited for on-chip self-referencing is lithium niobate on insulator - a platform characterised by high second and third order nonlinearities. Here we show that combining a lithium niobate on insulator waveguide with a silicon photodiode results in a very compact and direct low-noise path towards self-referencing of mode-locked lasers. Using digital servo electronics, the resulting frequency comb is fully stabilized. Its high degree of stability is verifed with an independent out-of-loop measurement and is quantifed to be 6.8 mHz. Furthermore, we show that the spectrum generated inside the lithium niobate waveguide remains stable over many hours.

opencc-by-4.0Oct 2021View details →

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An Overview of Compact Star Populations and Some of Its Open Problems

This repository contains the orbital parameters (where applicable) and calculated masses of the 35 Galactic black holes collected in Tables 2 and 3 of the paper: 'An Overview of Compact Star Populations and Some of Its Open Problems' by L. M. de Sá, A. Bernardo, R. R. A. Bachega, L. S. Rocha, P. H. R. S. Moraes  and J. E. Horvath, Galaxies (2023) 11(1), 9

opencc-by-4.0Jan 2023View details →

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Compact metasurface-based optical pulse-shaping device

Dataset of the publication “Compact metasurface-based optical pulse-shaping device“ by René Geromel, Philip Georgi, Maximilian Protte, Shiwei Lei, Tim Bartley, Lingling Huang, and Thomas Zentgraf.  The provided data includes all the measured SHG-FROG traces at all compressor positions (SCMP) that are provided in the publication. The columns correspond to the time delay and the rows to the wavelengths. More information regarding the specific measurements can be found in the beginning of each file. A reference file containing the detected/resolved wavelength points from the used spectrometer is included in “wavelength_reference”. Besides this, the phase information of the different MS2 designs along the x-axis in steps of 300 nm (periodicity of the unit cell) is documented. The plotted data from Fig. 4 and 5 of the paper can be found in the “dispersion_data” and “double_pulse_temporal_data” file.

opencc-by-4.0Jan 2023View details →

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Daset from the paper "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"

This repository contains the dataset produced by the population-synthesis code SEVN   for the paper: "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN" In this paper, we exploit the SEVN code (publicly available at <a href="https://gitlab.com/sevncodes/sevn">https://gitlab.com/sevncodes/sevn</a>) to analyse the formation and properties of binary compact objects. The repository also includes the initial condistions used as input and the SEVN version used to run the simulations. #Content The repository contains the following folders: - data_from_simulations: the folder contains all the data produced by the simulations and used in the Iorio+22 paper - InitialConditions: the folder contains all the intial conditions (and the code to generate them) that have been used for the Iorio+22 paper - SEVN_iorio22: this folder contains the version of the SEVN code that has been used to run the simulations in the Iorio+22 paper Each folder contains a specific README with additional information   #Contatcts giuliano.iorio.astro@gmail.com

opencc-by-4.0Oct 2022View details →

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Supplemental Material for IEEE VIS 2023 Short Paper 'Compact Phase Histograms for Guided Exploration of Periodicity'

Supplemental video explaining the approach described in our 2023 IEEE VIS short paper "Compact Phase Histograms for Guided Exploration of Periodicity," which is going to be presented in Melbourne, Australia October 22–27, 2023.

opencc-by-4.0Aug 2023View details →

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GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data behind the figures

This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog 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. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>). Data behind the figures This page contains the data behind various paper figures. The material for each figure is contained in a tar file. A short description can be found below. Figures not included here are associated with one of the other GWTC-3 data releases.   Figure 1 <ul> <li>Figure01.tar.gz</li> </ul> Data and script to produce GWTC-3: Figure 1. This shows the number of candidates with probability of astrophysical origin > 50% as a function of surveyed time–volume. The dates for the first observing run (<a href="https://doi.org/10.1103/PhysRevX.6.041015">O1</a>) and second observing run (<a href="https://doi.org/10.1103/PhysRevX.9.031040">O2</a>) candidates are hard-coded into the script, and the dates for third observing run (<a href="https://arxiv.org/abs/2108.01045">O3a</a> and O3b) candidates are included in two text files. The effective binary neutron star time–volume (BNS-VT) for each observing run is stored in separate .csv files. Each .csv file contains two columns, the first is the GPS time and the second is the cumulative effective BNS VT in Mpc3 kyr (this is converted to Gpc3 yr in the included script). The included script reproduces Figure 1 from GWTC-3 using the supplied data.   Figure 2 <ul> <li>Figure02.tar.gz</li> </ul> Data and plotting scripts for GWTC-3: Figure 2. The figure shows sensitivity curves for LIGO Hanford, LIGO Livingston, and Virgo. The Python script reads the .txt files containing strain data for Hanford, Livingston, and Virgo and saves figures as PDF files. The sensitivity curves are representative of performance during O3b. Further examples of sensitivity curves across observing runs can be found from the <a href="https://www.gw-openscience.org/detector_status/">Gravitational Wave Open Science Center</a>.   Figure 3 <ul> <li>Figure03.tar.gz</li> </ul> Data and script to produce GWTC-3: Figure 3. The left panel shows the <a href="https://doi.org/10.1088/1361-6382/abd594">binary neutron star inspiral range</a> of LIGO Hanford, LIGO Livingston, and Virgo versus time. The right panel shows histograms of the binary neutron star ranges for LIGO Hanford, LIGO Livingston, and Virgo. The Python script (figure_3.py) reads the range.txt files and the histogram.txt files to produce each panel and saves them as PDF files. Further summary information about the O3b run can be obtained from the <a href="https://www.gw-openscience.org/detector_status/O3b/">Gravitational Wave open Science Center</a>.   Figure 4 <ul> <li>Figure04.tar.gz</li> </ul> Data and script to produce GWTC-3: Figure 4. This plot shows the rate of <a href="https://doi.org/10.1088/1361-6382/abfd85">non-Gaussian noise transients (glitches)</a> in the LIGO Hanford, LIGO Livingston and Virgo data across O3b. The recorded glitches are identified by the <a href="https://virgo.docs.ligo.org/virgoapp/Omicron/">Omicron</a> pipeline with signal-to-noise ratio of > 6.5. There is a reduction in the LIGO glitch rate after the introduction of <a href="https://doi.org/10.1088/1361-6382/abc906">reaction chain (RC) tracking</a>, which reduced the incidence of scattered light (slow scattering) glitches. The script glitch_rates_GWTC-3_Fig_4.py produces Figure 4 of GWTC-3 making use of the glitch rates stored in the glitch_rates_GWTC-3_Fig_4.h5 file. Run the script within an <a href="https://computing.docs.ligo.org/conda/environments/igwn-py37/">igwn-py37</a> or <a href="https://computing.docs.ligo.org/conda/environments/igwn-py38/">igwn-py38</a> Conda environment, paying attention to having the hdf5 file glitch_rates_GWTC-3_Fig_4.h5 in the same directory of the script. Pass the argument -v or --verbose for additional info about the rates.   Figure 5 <ul> <li>Figure05.tar.gz</li> </ul> Script to produce GWTC-3: Figure 5. This figure illustrates the time–frequency structure of two common types of glitch seen in O3: <a href="https://doi.org/10.1088/1361-6382/abc906">slow scattering</a> and <a href="https://doi.org/10.1088/1361-6382/ac1ccb">fast scattering</a>. Both are caused by light scattering within the LIGO detectors. The Python script scattering_GWTC-3_Fig_5.py produces Figure 5 in the GWTC-3 Catalog paper using <a href="https://www.gw-openscience.org/O3/">open data</a>. The script saves the plot as a PDF file namely, scattering_GWTC-3_Fig_5.pdf and the data used to generate the plot in the files data_fast_scattering.txt and data_slow_scattering.txt. For further examples of the time–frequency structure of glitches, the community-science project <a href="https://gravityspy.org/">Gravity Spy</a> catalogs visualizations of glitches in gravitational-wave data.   Figure 12 <ul> <li>Figure12.tar.gz</li> </ul> Data and script to produce GWTC-3: Figure 12. This plots results of the waveform consistency test (as does Figure 13), plotting the match between waveform templates and minimally modeled reconstructions. The on-source results are for the candidate signals, while the off-source results are for simulated signals with compatible properties. The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze. The Python script (figure_12.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB to produce the corresponding match–match plots (PDF files).   Figure 13 <ul> <li>Figure13.tar.gz</li> </ul> Data and script to produce GWTC-3: Figure 13. This plots results of the waveform consistency test (as does Figure 12), plotting the p-values for the minimally modeled waveform reconstructions. The p-values are plotted in increasing order. The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze. The script (figure_13.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB (the same files used to produce Figure 12) to produce the corresponding p-value plots (PDF files).   Figure 14 <ul> <li>Figure14.tar.gz</li> </ul> Data and script to produce GWTC-3 Figure 14. This figure shows representative noise amplitude spectral densities for LIGO Hanford, LIGO Livingston, and Virgo during Observing Run 2 and Observing Run 3b. The script (figure_14.py) reads data from the .txt files included in the release to reproduce Figure 14 from GWTC-3 in PDF format.   Figure 15 <ul> <li>Figure15.tar.gz</li> </ul> Script to produce GWTC-3: Figure 15. This shows differences in the data used to analyze <a href="https://doi.org/10.7935/b024-1886">GW200115_042309</a>, with and without glitch subtraction. A low frequency cut (illustrated by the dotted white line) was used to remove the glitch in the <a href="https://doi.org/10.3847/2041-8213/ac082e">first analysis</a> of this candidate, whereas glitch subtraction is now used when performing parameter estimation. The curving orange line shows the approximate signal track. The script reads in the deglitched frame L-L1_HOFT_CLEAN_SUB60HZ_C01_T1700406_v4-1263095808-4096.gwf, downloaded from <a href="http://doi.org/10.5281/zenodo.5546679">an associated data release</a>, query raw <a href="https://www.gw-openscience.org/O3/">public LIGO Livingston data</a>, and reproduce Figure 15 from GWTC-3 in PDF format.   Figure 16 <ul> <li>Figure16.tar.gz</li> </ul> Script to produce GWTC-3: Figure 16. This figure illustrates the <a href="https://gwpy.github.io/docs/latest/examples/timeseries/qscan/">time–frequency structure</a> of data containing three O3 candidates identified only by <a href="https://gwburst.gitlab.io/">cWB</a> (the same as shown in Figure 17). Each shows evidence of instrumental origin.  The script queries <a href="http://www.gw-openscience.org/O3/">public LIGO data</a> and reproduce Figure 16 from GWTC-3 in PDF format.   Figure 17 <ul> <li>Figure17.tar.gz</li> </ul> Data and script for GWTC-3: Figure 17. This figure illustrates the <a href="https://doi.org/10.1088/1742-6596/363/1/012032">time–frequency structure</a> of candidate signals as reconstructed by <a href="https://gwburst.gitlab.io/">cWB</a> for three O3 candidates identified only by cWB (the same as shown in Figure 16). Each shows evidence of instrumental origin. For a compact binary coalescence signal, we would expect the signal to have a chirp structure, sweeping up from low to high frequencies. The script figs.py reads data (the reconstruction from cWB) from the .txt files to produce the corresponding time–frequency plots (PDF files). The script must be run three times to produce the panels of Figure 17: the event names are hardcoded into the script, which must be edited to produce the desired panel.   How to download all files from this page If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>: <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> where the record ID for the most recent version of this page is 5571766 and IDs for other versions can be found in the Versions section at the side of this page.   For more general background on gravitational-wave data analysis, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.

opencc-by-4.0Nov 2021View details →

zenodo40/100

Multi-scale effects on the hydraulic behaviour of a root-permeated and compacted soil

Dataset obtained from multi-scale observations on the hydraulic behaviour of a root-permeated and compacted soil.

opencc-by-4.0May 2019View details →

dryad40/100

Monitoring the compaction of single DNA molecules in Xenopus egg extract in real time

Open the record for dataset details and reuse information.

publicMar 2023View details →

zenodo36/100

Data for "Compact SQUID realized in a double layer graphene heterostructure"

This dataset contains the measurement data and its metadata of the publication "Compact SQUID realized in a double layer graphene heterostructure".

opencc-by-4.0Jun 2020View details →

zenodo36/100

Temperature response of water-saturated compact Longmenshan limestone and porous Rajasthan sandstone to changes in confining pressure

Three data sets are stored in this data submission. The first data set includes the temperature response of water-saturated compact Longmenshan limestone (L27) and porous Rajasthan sandstone (RJS) to changes in confining pressure under drained/undrained conditions. The second data set includes the thermal properties of rock-forming minerals and estimations of thermal characteristic time/distance. The third data set includes the internal temperature evolution of the water-saturated sample within 1 s after instantaneous loading in models M-01, M-02 and M03, in which the thermal properties of the solid grains are set to be that of gypsum, average values of the main rock-minerals and α-quartz, respectively.

opencc-by-4.0Nov 2020View details →

zenodo36/100

IAM_COMPACT_Open_access_models_for_case_study_countries_datasets

This dataset contains the excel files with all required input data to run OSeMOSYS model for Ukraine, CLEWs model for Kenya, Sri Lanka, and Ethiopia, and OnSSET model model for Ethiopia. Model factsheets, their scope and structure as well as preliminary results are documented in D6.7 - Open-access models for case-study countries (<a href="../doi/10.5281/zenodo.13643021">link</a>). The dataset also features zip files that can be run following the instructions on the report for each model.  The preliminary versions of each model were created as part of the capacity development activities of <a href="https://iam-compact.eu/">IAM COMPACT</a> Horizon Europe project.  The update of models is documented in D6.8 - Open-access models for case-study countries - Update and is supplemented by Version 2 of this dataset.

opencc-by-4.0Aug 2024View details →

zenodo36/100

Reproduction package for the paper "A LOFAR sample of luminous compact sources coincident with nearby dwarf galaxies"

Scripts to reproduce analyses from Vohl et al. 2023, A&A.

opencc-by-4.0Oct 2023View details →

zenodo36/100

Data from "Compact Disks in a High-resolution ALMA Survey of Dust Structures in the Taurus Molecular Cloud"

Continuum fits images for all disks in our ALMA Cycle 4 Taurus disk survey - see details in Long et al., 2018, ApJ, 869, 17 and Long et al., 2019, ApJ, 882, 49

opencc-by-4.0Nov 2023View details →

zenodo36/100

Makeup Compact (early-mid 20th century)

This makeup compact was found during CAP investigations at the Brody Complex/Emmons Amphitheater. This part of campus was once the East Lansing Dump until the college expanded into this area. The compact is made of a silver metal that is rusting and shows patina. Geometric lines decorate the outside of the compact. On the inside, is a faded mirror on one side and the original applicator and makeup powders on the other. Metal compacts such as these were the norm until the 1960s when plastic disposible compacts became available. Once the makeup was used up in these older metal compacts, replacements could be purchased and stored in the small compartment in the device. For more information on this artifact and others related to cosmetics and beauty at MSU, check oput [Mari Isa's CAP blog post linked here!](http://campusarch.msu.edu/?p=5504) Photogrammetric model made by Jack A. Biggs. Source: Objaverse 1.0 / Sketchfab

opencc-byMay 2019View details →

zenodo36/100

Observation of Gravitational Waves from the Coalescence of a 2.5-4.5 Msun Compact Object and a Neutron Star --- Data Release

This data release contains the analysis results and data behind the figures of the GW230529 discovery paper (<a href="https://urldefense.com/v3/__https://dcc.ligo.org/LIGO-P2300352/public/__;!!Dq0X2DkFhyF93HkjWTBQKhk!W4i4x3JfGgemcFsnnEYP5qxiknddvrG1LWpTLjs_JGK907kTrEBkS8o6i5T6RUFMX0v04jCPhtTq9K2SLcv_4g$" target="_blank" rel="nofollow noreferrer noopener">https://dcc.ligo.org/LIGO-P2300352/public/</a>). Strain data for this event (the L1:GDS-CALIB_STRAIN_CLEAN_AR channel) can be downloaded on GWOSC (<a href="https://doi.org/10.7935/6k89-7q62" target="_blank" rel="noopener">https://doi.org/10.7935/6k89-7q62</a>). The PESummary metafile containing the parameter estimation posterior samples for all analyses performed in the paper (posterior_samples.h5) and skymap fits file (skymap_combined_PHM_high_spin.fits) for the preferred parameter estimation analysis (high-spin, combined samples using binary black hole waveforms) can be downloaded directly as individual files. The other analysis results are grouped by type: rates, populations, searches, and tidal. The figure_scripts.tar.gz file contains all the paper figures in jpeg format along with a Jupyter notebook to reproduce them and additional required helper scripts. Example code for working with the individual result files is given in the PaperPlots.ipynb notebook included in this tar file. In brief, the rates.tar.gz file contains two files that each include a subset of the rates probability distributions shown in Fig. 3 of the paper. The populations.tar.gz file contains all the data behind Figs. 4-8, with subdirectories for each of the three population analyses considered in the paper: Binned Gaussian Process, NSBH-pop, and Power-Law + Dip + Break. In addition to the data behind the figures, the Power-Law + Dip + Break subdirectory additionally includes two *result.json files for the hyper-parameter posterior samples. These files have the same format as the corresponding NSBH-pop *result.json files and can be manipulated in the same way, as shown in the figures notebook. The searches.tar.gz file contains the data behind Figs. 9-11 for each of the three search pipelines whose results are included in the paper. Finally, the tidal.tar.gz file contains the four probability distributions plotted in Fig. 14. All other figures are produced only using the posterior_samples.h5 file.

opencc-by-4.0Apr 2024View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record