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Replication Data for figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s
<p>Supporting data to reproduce figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s</p>
Data: Constraining Ice Slab Thickness at the Onset of Visible Surface Runoff from the Greenland Ice Sheet
<h1>Data repository associated with the manuscript 'Constraining Ice Slab Thickness at the Onset of Visible Surface Runoff from the Greenland Ice Sheet', Nicolas Jullien, Andrew J. Tedstone, Horst Machguth, (under review in the Journal of Glaciology)</h1> <h2> </h2> <h2>Introduction:</h2> <p>We provide a short description of each file present in this data repository, and flag to the corresponding reference when applicable. Please cite the appropriate references when using these data.</p> <h2> </h2> <h2>Data:</h2> <h3>In this repository:</h3> <ul> <li>'Ice_Layer_Output_Thicknesses_Likelihood_2010_2018_jullienetal2021_modified.csv'. Modified 2010-2018 ice slabs thickness retrievals from Jullien et al., (2023) where ice slabs thickness > 16 m thick and < 1 m thick are retained, and flight-lines not holding ice slab were set to hold an ice content of 0 m thick.</li> <li>'master_maps.zip'. Raster files. Surface hydrology connectivity map over the Greeland Ice sheet, first presented in Tedstone and Machguth (2022). The easiest way to handle this dataset is to use the 'master_map_GrIS_mean.vrt' file.</li> <li>'MARv.3.14_MoA_2000_2012.nc'. Melt over accumulation from 2000 to 2012 extracted from MARv3.14. See file '<a title="melt_over_accumulation_calculations.py" href="https://github.com/jullienn/IceSlabs_SurfaceRunoff/blob/main/melt_over_accumulation_calculations.py">melt_over_accumulation_calculations.py</a>' in the code repository for post processing analysis.</li> <li>'RunoffLimits.zip'. '.csv' files. Maximum visible runoff limits in 2012 and 2019, sorted for each boxes generated by Tedstone and Machguth (2022). Each '.csv' file stores the data points coordinates (Geographical Reference System: WGS 84 / NSIDC Sea Ice Polar Stereographic North (EPSG:3413)) of the maximum visible runoff limit retrievals after filtering out the outliers. The maximum visible runoff limits where first presented in Tedstone and Machguth (2022).</li> </ul> <h3>Used in this study but from other datasets:</h3> <ul> <li>The ice slabs extent and ice slabs thickness were first presented in Jullien et al., (2023), and are accessible at: https://zenodo.org/records/7505426</li> <li>The radargrams displayed in Fig. 5c-f were first presented in Jullien et al., (2023), and are accessible at: https://zenodo.org/records/7505426. The following files were used: <ul> <li>'L1_may12_03_1_aggregated.pickle'</li> <li>'L1_may12_03_2_aggregated.pickle'</li> <li>'20100508_01_114_115_Depth_CORRECTED.pickle'</li> <li>'20140424_01_002_004_Depth_CORRECTED.pickle'</li> <li>'20180427_01_170_172_Depth_CORRECTED.pickle'</li> </ul> </li> <li>The surface topography present in Fig. 5g are 10 m resolution mosaics from the ArcticDEMv3 (Porter et al., 2018), and accessible at: https://data.pgc.umn.edu/elev/dem/setsm/ArcticDEM/mosaic/v3.0/</li> <li>The winter time strain rates map displayed in Fig. 5h were first presented in Poinar and Andrews (2021), and are accessible at: https://ubir.buffalo.edu/xmlui/handle/10477/82127</li> </ul> <p> </p> <h2>References:</h2> <p>Jullien, N., Tedstone, A. J., Machguth, H., Karlsson, N. B., & Helm, V. (2023). Greenland Ice Sheet Ice Slab Expansion and Thickening. <em>Geophysical Research Letters</em>, <em>50</em>(10), e2022GL100911. https://doi.org/10.1029/2022GL100911</p> <p>Poinar, K., & Andrews, L. C. (2021). Challenges in predicting Greenland supraglacial lake drainages at the regional scale. <em>The Cryosphere</em>, <em>15</em>(3), 1455–1483. https://doi.org/10.5194/tc-15-1455-2021</p> <p>Porter, C., Morin, P., Howat, I., Noh, M.-J., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C., Cloutier, M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington, M., Jr., Williamson, C., … Bojesen, M. (2018). <em>ArcticDEM, Version 3</em> (Version V1) [dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/OHHUKH</p> <p>Tedstone, A. J., & Machguth, H. (2022). Increasing surface runoff from Greenland’s firn areas. <em>Nature Climate Change</em>. https://doi.org/10.1038/s41558-022-01371-z</p>
Constraining the Properties of the Thermonuclear Burst Oscillation Source XTE J1814-338 Through Pulse Profile Modelling
<p>Constraining the Properties of the Thermonuclear Burst Oscillation Source XTE J1814-338 Through Pulse Profile Modelling</p>
Synthetic data (Part 2) for HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the rendered images and the segmentation masks that we use to train our model on HO3Dv2 dataset. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the rendered images for HO3Dv2.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Synthetic data (Part 1) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed SDF samples. Meanwhile, we also include rendered data for HO3Dv2 here. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 rendered images.</div> <div>├── <a href="../api/records/13228003/draft/files/train_ho3d.zip/content" target="_blank" rel="noopener noreferrer">train_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 training set.</div> <div>├── <a href="../api/records/13228003/draft/files/full_test_dexycb.zip/content" target="_blank" rel="noopener noreferrer">full_test_dexycb.zip</a> - Contains the processed SDF files for DexYCB full test set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Processed data and trained models for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf">https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</a></p> <p>Link to the Arxiv article: <a href="https://arxiv.org/abs/2402.17062">https://arxiv.org/abs/2402.17062</a></p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.</div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/11668766/draft/files/ckpts.zip/content" target="_blank" rel="noopener noreferrer">ckpts.zip</a> - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)</div> <div>├── <a href="../api/records/11668766/draft/files/annotations.zip/content" target="_blank" rel="noopener noreferrer">annotations.zip</a> - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.</div> <div>├── <a href="../api/records/11668766/draft/files/simple_ycb_models.zip/content" target="_blank" rel="noopener noreferrer">simple_ycb_models.zip</a> - Contains the preprocessed YCB objects for batched evaluation.</div> <div>├── <a href="../api/records/11668766/draft/files/test.zip/content" target="_blank" rel="noopener noreferrer">test.zip</a> - Contains the processed SDF files for DexYCB test set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_render_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_render_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: <a href="https://github.com/amathislab/HOISDF">https://github.com/amathislab/HOISDF</a></div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Thermal adaptation in Aedes aegypti does not constrain temperature-sensitive growth of bacteria or dengue virus
<p>Data set and R script used for the following manuscript : </p> <p><strong>Thermal adaptation in <em>Aedes aegypti</em> does not constrain temperature-sensitive growth of bacteria or dengue virus</strong></p> <p><span lang="EN-US">Alida Kropf<sup>1*#</sup>, Stéphanie Dabo<sup>2</sup>, Marine Amann<sup>3</sup>, Louis Lambrechts<sup>2</sup>, Jacob C Koella<sup>1</sup></span></p> <p><span lang="EN-US">PROCEEDINGS OF THE ROYAL SOCIETY B THE ROYAL SOCIETY B BIOLOGICAL SCIENCES</span></p> <p><strong><em><span lang="IT-CH">DOI: 10.1098/rspb.2025-0832.R1 </span></em></strong></p>
Reproduction package for the paper "Constraining a neutron star merger origin for localized fast radio bursts"
<p>This is a reproduction package for the paper <a href="https://academic.oup.com/mnras/article/497/3/3131/5875920">"Constraining a neutron star merger origin for localized fast radio bursts"</a> by Gourdji et al. (2020) and published in MNRAS. This package provides a Jupyter notebook and the necessary information to reproduce the figures and main results of this paper.</p>
Progress Toward SHAPE Constrained Computational Prediction of Tertiary Interactions in RNA Structure
<p>Supplementary repository for the "Progress Toward SHAPE Constrained Computational Prediction of Tertiary Interactions in RNA Structure" article. Contains the simulation on the <em>Didymium iridis</em> lariat-capping ribozyme (DiLCrz, PDB ID: 4P8Z).</p>
Prey diversity constrains the adaptive potential of predator foraging traits
<p>Predators are generally under selective pressure to get better at foraging, leading to steeper functional responses and stronger predator-prey interactions. Yet strong interactions can de-stabilize food webs, and most interactions across ecological communities are thought to be weak. This conflict between evolutionary and community expectations for the strength of predator-prey interactions represents a fundamental gap in our understanding of how the evolution of foraging plays out in food webs. Here we help to resolve the conflict by showing analytically that the expectation for the evolution of steeper functional responses is relaxed in communities with diverse prey types. We simulate communities with varying prey richness and show that increasing prey richness can indeed constrain the adaptive potential of predator foraging traits, but that at low prey richness predators can evolve to have a stronger interaction with prey that have high net energy yields. Our results also indicate that handling time plays a role in determining whether predators may evolve to have a stronger interaction with abundant prey, suggesting that the evolution of keystone predator modules in food webs is most likely when handling times are negligible. Our results also provide a new mechanism predicting more diffuse interactions in diverse tropical communities relative to more species-poor communities at higher latitudes.</p>
Floral preferences of mountain bumble bees are constrained by functional traits but flexible through elevation and season
Patterns of resource use by animals can clarify how ecological communities have assembled in the past, how they currently function, and how they are likely to respond to future perturbations. Bumble bees (Hymentoptera: Bombus spp.) and their floral hosts provide a diverse yet tractable system in which to explore resource selection in the context of plant-pollinator networks. Under conditions of resource limitation, the ability of bumble bees species to coexist should depend on dietary niche overlap. In this study, we report patterns and dynamics of floral morphotype preferences in a mountain bumble bee community based on ~13,000 observations of bumble bee floral visits recorded along a 1400 m elevation gradient. We found that bumble bees are highly selective generalists, rarely visiting floral morphotypes at the rates predicted by their relative abundances. Preferences also differed markedly across bumble bee species, and these differences were well-explained by variation in bumble bee tongue length, generating patterns of preference similarity that should be expected to predict competition under conditions of resource limitation. Within species, though, morphotype preferences varied by elevation and season, possibly representing adaptive flexibility in response to the high elevational and seasonal turnover of mountain floral communities. Patterns of resource partitioning among bumble bee communities may determine which species can coexist under the altered distributions of bumble bees and their floral hosts caused by climate and land use change.
Novel polarimetric technique to constrain the magnetic field structure and strength of Gamma-ray burst jets
<p>Gamma-ray bursts (GRBs) are extremely energetic events of cosmological origin. Observed GRBs have high luminosity and rapid variability that requires ultra-relativistic motion in the production mechanism which drive the synchrotron radiation associated with the relativistic jets and their shocked interactions with the local ambient medium. They are broadly divided into two types based on the gamma-ray duration; long GRBs (>2 seconds), and short GRBs (<2 seconds). Long GRBs are thought to be originated from explosions of very massive stars and short GRBs are thought to be produced by the merger of compact binaries. Several key open questions about our understanding of GRB physics remain: What is the driving mechanism of GRB jets? What is the origin and role of magnetic fields in driving the explosion? Since these events happen at cosmological distances, they can not be resolved using traditional astronomical techniques. However, polarimetric observations of GRBs have allowed us to start the exploration of the structure and magnetic field configurations of their relativistic jets. Generally, polarization is measured via the ratio of fluxes by taking consecutive exposures, however for rapidly varying objects such as GRBs, it is not an effective way to observe polarization. Liverpool Telescope (LT) has utilized rapidly rotating polaroids to overcome this problem and created a series of polarimeters that have successfully detected early-time optical polarimetry of various GRBs. I will present photometric and polarimetric results of various GRBs observed by RINGO3. 10 GRBs were bright enough to perform analysis and we were able to perform polarimetric analysis for 7 GRBs. I will discuss how polarimetric detection for a long GRB 191016A along with photometric data constraint the energy injection mechanism for the central engine. In addition, I will present how polarization depends on various properties of GRBs such as photometric decay index, isotropic energy of GRBs, redshift etc.</p>
Rupture Process of the 2017 Mw 6.3 Earthquake in Jinghe, Northwest China Constrained by GNSS, InSAR and teleseismic waveforms
<p>This dataset include:</p> <p>1. Slip model of 2017 Mw 6.3 Jinghe earthquake invert with GNSS, InSAR and teleseismic waveforms.</p> <p>2. InSAR LOS offsets caused by the mainshock (The file named by sar.static )</p> <p>3. Aftershocks locations relocated with hypoDD</p> <p> </p>
Supplementary Dataset for Deep learning based kcat prediction enables improved enzyme constrained model reconstruction
<p>This dataset is the supplementary dataset for the paper "<strong>Deep learning based <em>k</em><sub>cat</sub> prediction enables improved enzyme constrained model reconstruction</strong>". Protein sequence fasta files, deep learning predicted <em>k</em><sub>cat</sub> values, classcial-ecGEMs, DL-ecGEMs and <em>Posterior</em>-mean-ecGEMs for 343 yeast/fungi species are available in this dataset.This repository also contains the computed results for reproducing the figures as model_build_files . The scripts can be found in Github (https://github.com/SysBioChalmers/DLKcat)</p>
COCO BBOB-constrained Benchmark Results of Two Evolution Strategy Variants
<p>The three zip-files of this data set provide the performance data of the algorithms presented in the paper<strong> "Benchmarking 𝜖MAg-ES and BP-𝜖MAg-ES on the bbob-constrained Testbed"</strong> submitted to the GECCO 2022 Workshop "<a href="https://gecco-2022.sigevo.org/Workshops#BBOB 2022">BBOB 2022 — Black Box Optimization Benchmarking 2022</a>".</p> <ul> <li><em>coco2.6.2_bbob-constrained_epsMAg.zip</em> <-- results of the 𝜖MAg-ES on COCO version 2.6.2</li> <li><em>coco2.6.2_bbob-constrained_BPepsMAg.zip</em> <-- results of the BP-𝜖MAg-ES on COCO version 2.6.2</li> <li><em>coco2.6.2_bbob-constrained_fmincon</em><em>.zip</em> <-- results of FMINCON (Matlab 2021b) on COCO version 2.6.2</li> </ul> <p> </p>
FaIR v1.6.2 calibrated and constrained parameter set
<p>These .json files provides the 2237 ensemble members that are used to run the FaIR simple climate model in the IPCC's Sixth Assessment Report contributions to Working Group 1 and Working Group 3.</p> <p>v1.1 of this dataset includes two versions of the files:</p> <ul> <li>the original full parameter set `fair-1.6.2-wg3-params.json` as in v1.0</li> <li>reduced file size versions `fair-1.6.2-wg3-params-slim.json` and `fair-1.6.2-wg3-params-common.json` where only the parameters that vary between ensemble members are included in the former and everything else is in the latter. This is optimised for running the Working Group 3 climate assessment.</li> </ul> <p>Drawn from an initial prior ensemble of 1 million, the following constraints are placed upon the results:</p> <ul> <li>representation of observed warming from 1850-2019, within observational uncertainty</li> <li>representation of observed ocean heat content change 1971-2018, within observational uncertainty</li> <li>reproduction of near-present day atmospheric concentrations of CO2 from the carbon cycle</li> <li>airborne fraction of CO2 with a distribution similar to the assessed range of Chapter 5, IPCC Working Group 1 AR6</li> <li>equilibrium climate sensitivity with a similar distribution to Chapter 7, IPCC Working Group 1, AR6</li> <li>transient climate response with a similar distribution to Chapter 7, IPCC Working Group 1, AR6</li> <li>projected future warming that is of a similar distribution to the SSP scenarios of Chapter 4, IPCC Working Group 1, AR6, when run with prescribed concentrations</li> </ul> <p><strong>Changelog</strong></p> <ul> <li>v1.1: inclusion of lightweight parameter sets.</li> <li>v1.0: original upload full parameter sets.</li> </ul>
Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion
<p>The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and nonlinear analysis tools to extract information from electromyographic recordings during walking and running overground and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a nonlinear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives resulted overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion. A data-driven indication that treadmills induce perturbations to the neural control of locomotion.</p> <p> </p> <p>In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG; e) the muscle synergies extracted via NMF; f) the code to process the data. In total, 120 trials from 30 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII and RData format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (W=walking, R=running)</li> <li>Environment: to distinguish between overground (O) and treadmill (T)</li> <li>Speed: the speed at which the recordings were conducted in [m/s] (1.4 m/s for walking, 2.8 m/s for running)</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 consist of 21, 29, 29 and 26 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0003_OR_01”, where the characters “P0003” indicate the participant number (in this example the 3<sup>rd</sup>), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data are named, following the same rules, like “FILT_EMG_P0003_OR_01”.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P0020” indicate the participant number (in this example the 20<sup>th</sup>), the characters “TW” indicate the locomotion type and environment (O=overground, T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 only contain 21, 29, 29 and 26 cycles, respectively.</p> <p>The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with 30000 rows (one for each recorded data point) and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P0003_OR_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0003” indicate the participant number (in this example the 3<sup>rd</sup>), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P0003_OR_01”.</p> <p>The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the file named “SYNS.RData”. Each element of this R list represents one trial and contains the factorization rank (list element named “synsR2”), the motor modules (list element named “M”), the motor primitives (list element named “P”), the reconstructed EMG (list element named “Vr”), the number of iterations needed by the NMF algorithm to converge (list element named “iterations”), and the reconstruction quality measured as the coefficient of determination (list element named “R2”). The motor modules and motor primitives are presented as direct output of the factorization and not in any functional order. Motor modules are data frames with 13 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules M), one for each synergy and for each muscle. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives P), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Trials are named like “SYNS_ P0012_OW_01”, where the characters “SYNS” indicate that the trial contains muscle synergy data, the characters “P0012” indicate the participant number (in this example the 12<sup>th</sup>), the characters “OW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper.</p> <p>All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.</p>
Constraining Andean Propagation of Exhumation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context - Supporting Information
<p>Supporting information accompanying the publication "Constraining Andean Propagation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context" published in Tectonics. The dataset contains apatite and zircon (U-Th-Sm)/He and apatite fission track data from the Tilcara Range and San Lucas block, Jujuy, Argentina, as well as additional QTQt thermal models that are discussed in the paper.</p> <p>Table S1 contains full single-grain results from apatite fission track, apatite (AHe) (U-Th-Sm)/He and zircon (ZHe) (U-Th-Sm)/He analyses. Outliers are marked in grey and are not included in the weighted mean age. Figure S1 supports (U-Th-Sm)/He data graphically. Apatite fission track (AFT) data is supported by radial plots in Figure S2. Figure S3 shows QTQt thermal models using either AHe, AFT or ZHe single-grain ages. All of the models results are explained in the main text.</p>
Supplementary Data for "The history of Cenozoic carbonate flux in the Atlantic Ocean constrained by multiple regional carbonate compensation depth reconstructions"
<p>The files on this site accompany the paper:</p> <p>Dutkiewicz, A. And Müller, R.D., in review, The history of Cenozoic carbonate flux in the Atlantic Ocean constrained by multiple regional carbonate compensation depth reconstructions, Geochemistry, Geophysics, Geosystems.</p> <p>There are two zipped file archives:</p> <p>1) backtracked_sites.zip</p> <p>This archive contains two directories of backtrack site files, one for the North Atlantic and one for the South Atlantic.</p> <p>Each directory contains a set of files listing, by site:</p> <p>age(Ma), compacted_depth (observed)(mbsf), compacted_thickness (observed)(m), decompacted_thickness(m), decompacted_density(g/cm3), water_depth(m), tectonic_subsidence (since formation of crust)(m), decompacted_depth(mbsf) dynamic_topography(m) lithology</p> <p>The lithology classification follows the lithology classes defined in Muller et al. (2018).</p> <p>A second set of files contains:</p> <p>age(Ma), depth(mbsf), paleowaterdepth(m), dry_bulk_density(g/cm3), DLSR(m/my), carbonate(weight_%) CAR(mg/cm2/kyr)</p> <p>DLSR=decompacted linear sedimentation rate<br> CAR=carbonate accumulation rate</p> <p>2) regional_Cenozoic_carbonate_thickness_grids.zip</p> <p>This archive contains 3 folders with grids for modelled Cenozoic carbonate thicknesses for the South Atlantic, central North Atlantic and northern North Atlantic. They can be viewed with netcdf viewers like panoply, or plotted using the Generic Mapping Tools. The workflow for creating these grids can be found on GitHub:</p> <p>https://github.com/EarthByte/CarbonateSedimentThickness</p> <p><br> This site also contains a spreadsheet entitled "Dutkiewicz_Muller_G3_2022_model_data_summary.xlsx"</p> <p>It contains our model outputs including regional decompacted carbonate sediment volumes and thicknesses, depositional areas, carbonate carbon fluxes and carbonate compensation depths for the northern and central North Atlantic and South Atlantic.</p> <p>A video entitled "compacted_carb_thick_atlantic_66-0Ma.mp4" shows the Cenozoic evolution of carbonate sediment thickness in the Atlantic Ocean.</p> <p><br> References:</p> <p>Spasojevic, S., & Gurnis, M. (2012). Sea level and vertical motion of continents from dynamic earth models since the Late Cretaceous. AAPG bulletin, 96(11), 2037-2064. https://doi.org/10.1306/03261211121</p> <p>Müller, R. D., Cannon, J., Williams, S. and Dutkiewicz, A., 2018, PyBacktrack 1.0: A Tool for Reconstructing Paleobathymetry on Oceanic and Continental Crust, Geochemistry, Geophysics, Geosystems, 19, 1898-1909, https://doi.org/10.1029/2017GC007313.</p> <p><br> </p>
Structure and stability constrained substitution models outperform traditional substitution models used for evolutionary inference
<p>The current knowledge about how protein structures influence sequence evolution is rarely incorporated into substitution models adopted for phylogenetic inference, which are commonly based on independent with the same substitution process and ignore the known variation of the evolutionary rates across sites with different structural properties. In previous works, we presented site-specific substitution models of protein evolution based on selection on the folding stability of the native state (Stab-CPE), which predict more realistically the evolutionary variability across protein sites. However, those Stab-CPE present qualitative differences from observed data, probably because they ignore changes in the native structure, despite empirical studies suggesting that conservation of the native structure is a strong selective force. Here we present novel structurally constrained substitution models (Str-CPE) based on Julián Echave's model of the structural change due to a mutation as the linear response of the protein to a perturbation and on the explicit model of the perturbation generated by a specific amino-acid mutation. Compared to our previous Stab-CPE models, the novel Str-CPE models are more stringent (they predict lower sequence entropy and substitution rate), provide higher likelihood to multiple sequence alignments (MSA) of the wild-type protein, and better predict the observed substitution rates. Next, we combine Str-CPE and Stab-CPE models to obtain structure and stability constrained substitution models (SSCPE) that fit the empirical MSAs even better. Importantly, these SSCPE models present a relevant improvement of the phylogenetic likelihood for all ten protein families that we analyzed with the program RAxML-NG. We implemented the SSCPE models in the program Prot evol, freely available at <a href="https://github.com/ugobas/Prot_evol">https://github.com/ugobas/Prot_evol</a>.</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.