Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

Data set: UAS-based optical- and thermal infrared remote sensing of the fumarole field of La Fossa cone, Vulcano Island (Italy), reveals the degassing and hydrothermal alteration structure

This is the data set supporting the paper "Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy" (DOI: <a href="https://doi.org/10.5194/egusphere-2023-1692" target="_blank" rel="noopener noreferrer">10.5194/egusphere-2023-1692</a>).   Short description of the study: Hydrothermal alteration is common on actively degassing volcanoes and can lead to significant changes in the physical and chemical properties of the volcanic rocks, such as changes in permeability or rock strength. Despite the potentially far-reaching consequences of hydrothermal alteration for volcano stability, less is known about the detailed structures and dynamics of degassing and alteration systems. In this study, we use UAS-derived high-resolution data to analyze the fumarole field at La Fossa cone, Vulcano Island (Italy), aiming to better understand the structures and dynamics of volcanic degassing and alteration systems. By combining Principal Component Analysis, image analysis, and classification applied to high-resolution optical data and analysis of thermal infrared data, we resolve the detailed structure of the surficial degassing and alteration system based on optical and thermal anomalies. We identified characteristic anomaly patterns that indicate local degassing and alteration variability, and larger units of diffuse activity that, next to high-temperature fumaroles, contribute significantly to the total activity. We compared the observed anomaly patterns with the mineralogical and geochemical composition of representative rock samples, and with the surface degassing activity, and are able to provide the anatomy of the La Fossa fumarole field at great resolution. We show local alteration gradients, the presence of larger diffuse active complexes, and evidence for dynamic processes associated with the hydrothermal alteration. For more details, please read on: "Müller, D., Walter, T. R., Troll, V. R., Stammeier, J., Karlsson, A., De Paolo, E., ... & De Jarnatt, B. (2023). Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy. EGUsphere, 2023, 1-45.  https://doi.org/10.5194/egusphere-2023-1692".     Data set: We provide a UAS-based high-resolution dataset covering the whole La Fossa cone, including aerial Orthomosaic, Digital Elevation Model, and a Temperature Map derived from an airborne optical- and thermal infrared sensor (acquired in 2018 and 2019).  The dataset is organized in 1) photogrammetric data, and 2) relevant processing results and related data. Filenames are written in bold letters and are a composite of the file type and the date (YYYYMMDD).      1)  Photogrammetric data:  <ul> <li>Orthomosaic_20191114.tif is the in Agisoft Metashape processed orthomosaic of a 150 m (above fumarole field) optical overflight (DJI Phantom 4 Pro camera). </li> <li>DigitalElevationModel_20191114.tif is the in Agisoft Metashape processed Digital Elevation Model (DEM) from the above-mentioned 150 m overflight. </li> <li>Hillshade_20191114.tif is the 2.5-D representation of the DigitalElevationModel_20191114. Note, for viewing use a stretched (black to white) color scale.</li> <li>TemperatureMap_20181115.tif is showing the apparent surface temperature for the La Fossa cone, acquired by a Flir Tau 2 thermal infrared camera at ~150 m (above fumarole field) flight altitude in the early morning hours (before sunrise) of 15 November 2018. Note that apparent temperatures shown may underestimate real in situ fumarole temperatures due to pixel-to-vent size ratios and atmospheric- or gas-plume distortion effects. Note further that the data has some processing artifacts, due to blind pixels of our IR camera system. For more detailed information or an updated data set please contact dmueller@gfz-potsdam.de.</li> <li>T_20to40C.tif shows the diffuse thermally active surface at the fumarole field of the La Fossa cone (units a-g, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692). This raster shows the extracted pixels from TemperatureMap_20181115 in the range of 22 - 40 °C.</li> <li>T_higher40C.tif outlines the high-temperature fumarole locations of the La Fossa fumarole field (HTF, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692), based on the extracted pixels with temperatures > 40 °C from TemperatureMap_20181115.</li> </ul> Shapefiles for temperatures > 40 °C representing the high-temperature fumarole locations (HTF) and for temperatures of 20 - 40 °C representing diffuse active units, are attached at the end of the upload list and named T_higher40C_polygon and T_20_40C_polygon and consist of multiple files per shapefile with the file extensions .CPG, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, .shx.  The coordinate system of the data sets is WGS84 EPSG:4326. For nadir projection use WGS 84 / UTM zone 33N - EPSG:32633. Note that the data might have horizontal and vertical offsets in the typical range of SfM-derived products with single-band GPS accuracy.     2) Relevant processing steps and related data: <ul> <li>Step 1) Principal Component Analysis applied to Orthomosaic_20191114 results in the following 3 Principal Components (decorrelated variance representations of the initial RGB bands):  <ul> <li>1_PCA_PC1.tif 1st principal component </li> <li>1_PCA_PC2.tif 2nd principal component</li> <li>1_PCA_PC3.tif 3rd principal component - highlights well the effects of concentrated and diffuse degassing, resulting in different alteration effects from a simple shift from reddish oxidized surface to gray, up to strong silicic alteration effects. This can be used to extract the data of interest, the hydrothermally altered surface, and to create a new alteration sub-dataset. </li> </ul> </li> <li>Step 2) Extraction of hydrothermally altered surface / alteration sub-dataset <ul> <li>2_alteration_subdata_RGB.tif The alteration sub-data set was extracted from the original Orthomosaic_20191114 based on a mask obtained from Principal Component 3 (1_PCA_PC3) for values > 85. The resulting raster data set is an extract of the original RGB data.</li> </ul> </li> <li>Step 3)  PCA applied to 2_alteration_subdata_RGB will adjust to the reduced spectral range of the alteration sub-data set, provide a more sensitive variance representation, and highlight variability within the hydrothermally altered surface. <ul> <li>3_PCA_PC1.tif 1st principal component of 2_alteration_subdata_RGB</li> <li>3_PCA_PC2.tif 2nd principal component of 2_alteration_subdata_RGB</li> <li>3_PCA_PC3.tif 3rd principal component of 2_alteration_subdata_RGB</li> </ul> </li> <li>Step 4) Unsupervised classification  <ul> <li>4_classification.tif is the unsupervised classification result of 3_PCA (all Principal Components), classified into 32 classes to achieve a high class resolution. When combining different classes, they form larger spatial units / surface types with similar spectral characteristics. This way, we divide the alteration surface into 3 surface types (see Fig. 4B in "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) representing different alteration gradients and important structural units. To achieve the same results, combine classes 1 -19 (surface type 3), 20 - 25 (surface type 2), 26 - 30 (surface type 1), and 31 - 32 for sulfur/fumarole plume. See Image optical_structure.jpg for comparison. </li> </ul> </li> </ul> Note that Principal Components and Classification of Principal Components highlight data variability along the axes of highest data variance. Results have to be evaluated carefully and may be valid only locally. They are efficient for identifying variability in degassing and alteration areas, but at the same time may also highlight certain fractions of vegetation or settlements for instance. We evaluated the structure defined by our classification results by analyzing the thermal structure (thermal_structure.jpg) of the fumarole field and additional geochemical- and mineralogical investigations (XRD and XRF) of rock samples and by measuring the diffuse degassing from surface (see "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) to prove that the observed degassing/alteration units are true. To highlight alteration effects throughout the entire La Fossa cone, including the southern inner and outer crater rim, the alteration zones of La Forgia, or alteration on the outer flanks of La Fossa e.g. the 1988 Landslide, we provide the raster La_Fossa_alteration.tif and image La_Fossa_alteration.jpg (Note that the color scale for strong alteration (classes 31 - 32) was changed from white to purple for highlighting purpose).   In case of further questions about the dataset, please contact dmueller@gfz-potsdam.de.

opencc-by-4.0Jul 2024View details →

Data from: Multifaceted density dependence: Social structure and seasonality effects on Serengeti lion demography

This dataset contains the data and R scripts to estimate the survival, transition, and detection probabilities (Lions_Survival_Transition_MultistateCMRModel.zip) as well as the probability of reproduction and recruitment to 1 year old (Lions_Reproduction_Recruitment_GLMM.zip) in a population of African lions (Panthera leo) monitored between 1984 and 2014 in the Serengeti National Park, Tanzania. We assessed the season-specific effects of density measures at the intra- (number of females in a pride and male coalition size) and extra-group levels (number of nomadic coalitions in the home range of a group) using a Bayesian multistate capture-mark-recapture model for the survival and transition rates and Bayesian generalized linear mixed models for reproduction probability and recruitment.  The README file further describes each uploaded file.

opencc-by-4.0Nov 2017View details →

Glucosinolate and Desulfoglucosinolate NMR Data and Structures

1H, 13C, 15N NMR spectra, NMR parameters, and structures of a set of 31 glucosinolates or desulfoglucosinolates

opencc-by-4.0Jun 2014View details →

An Automatic Neuroimaging Infrastructure For Synthesis and Analysis of Structural MRI Data

We    have    designed,    implemented    and    distributed    a    fully  automatic    neuroimaging    infrastructure  for  the  synthesis  and analysis of structural  magnetic resonance imaging (MRI) data.   The  framework  provides  a  concrete  environment  for  the quantitative  validation  of  various methods  for  the analysis  of  brain  asymmetries,  for  comparisons of methods and measures of brain shape asymmetry,  and possibly for clarifying contradicting neuroimaging findings of brain lateralizations. See <a href="https://sites.google.com/site/brainmorphorg/home">https://sites.google.com/site/brainmorphorg/home  </a> and  A. Pepe, I. Dinov, and J. Tohka . An Automatic Framework for Quantitative Validation of Voxel Based Morphometry Measures of Anatomical Brain Asymmetry. <a href="http://dx.doi.org/10.1016/j.neuroimage.2014.06.029">NeuroImage , 100: 444 - 459, 2014</a><a href="https://doi.org/10.1016/j.neuroimage.2014.06.029"> </a> for more information.

opencc-by-4.0May 2018View details →

Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry

Original videos and reference bulk velocity and water depth data sets used to develop the study: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry. The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.

opencc-by-4.0Jun 2018View details →

Supplementary data for "Heterometallic perovskite-type metal-organic framework with an ammonium cation: structure, phonons, and optical response"

Optimised structures of [NH4][Na0.5M0.5(COOH)3] (M = Al, Cr) Phonon output for [NH4][Na0.5Cr0.5(COOH)3] Gif of the T’(NH4+) mode (no. 23). The c-axis is the vertical direction. For further information please see the associated publication.

opencc-by-4.0Feb 2019View details →

Data Structure of Clinical Research

Bronchial asthma is one of the most common respiratory pathologies in children, characterized by rising incidence around the world. Early disease onset, severe clinical signs of bronchial asthma, the ineffectiveness of high doses of hormone therapy reduce the quality of life in patients and lead to disability. Analysis of bronchial asthma heterogeneity is now possible in virtue of computer technology and big data processing onrush. The information on 70 children suffering from bronchial asthma and 20 children from the control group was analyzed in this study. Gender, age, duration of disease, associated diseases, family history of allergic diseases, clinical blood and urine test, spirography and blood immunoassay results, total IgE, thymic stromal lymphopoietin and results of skin allergy tests were taken into account.

opencc-by-4.0Aug 2024View details →

From Gas to Solution: The Changing Neutral Structure of Proline Upon Solvation - data

Data set pertaining to the manuscript "From Gas to Solution: The Changing Neutral Structure of Proline Upon Solvation", submitted for peer review. In this work, Liquid-jet photoelectron spectroscopy (LJ-PES) and electronic-structure theory were employed to investigate the chemical and structural properties of the amino acid L-proline in aqueous solution for its three ionized states (protonated, zwitterionic, deprotonated). Experimental data were recorded by photoemission spectroscopy from a liquid jet source using synchrotron radiation. The data set documents the experimentally recorded spectra, including the proline photoelectron spectra and spectra of the zero energy cut-off, that were used to calibrate the binding energy scale. Files with extension .h5 are hdf5-files structured according to the NeXus standard, see the NeXus Data Format definition (v2024.02), https://manual.nexusformat.org/index.html NXmpes expansion for FAIRmat data (v.2024.07), https://fairmat-nfdi.github.io/nexus_definitions/classes/contributed_definitions/NXmpes.html NXmpes_liquid expansion to NXmpes (v.2024.07), https://fairmat-nfdi.github.io/nexus_definitions/mpes-liquid/classes/contributed_definitions/NXmpes_liquid.html NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format: * nexpy (distributed with python) * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium) In each NeXus file-entry, two types of spectra are shown: 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').  2. As-measured data ('raw'). If you use these data for your scientific work we kindly ask you to send us an electronic version or the citation of your work. Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .

opencc-by-4.0Apr 2022View details →

Country-wide data products for the ecosystem structure metrics derived from ALS data across the Netherlands (AHN3)

This data repository contains country-wide data products for the ecosystem structure metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands (AHN3). Twenty-five ecosystem structure metrics (at 10-meter resolution, GeoTIFF format) were derived from AHN3 dataset (<a href="https://downloads.pdok.nl/ahn3-downloadpage/">https://downloads.pdok.nl/ahn3-downloadpage/</a>) using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow (<a href="../record/5636773">https://zenodo.org/record/5636773</a>). Laserfarm is a free and open-source workflow that enables efficient, scalable, and distributed processing of multi-terabyte LiDAR point clouds from national and regional ALS surveys into LiDAR metrics of ecosystem structure. All code of Laserfarm is hosted and freely available on GitHub (<a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>). The Jupyter Notebooks for the processing of the AHN3 dataset are available on GitHub (<a href="https://github.com/eEcoLiDAR/AHN/tree/main/AHN3">https://github.com/eEcoLiDAR/AHN/tree/main/AHN3</a>). The twenty-five LiDAR metrics are related to three key dimensions of ecosystem structure (ecosystem height, ecosystem cover, and ecosystem structural complexity), and a layer of point density and a layer of building/road/water mask are also provided. Each GeoTIFF layer represents one LiDAR metric at 10 m resolution covering the whole Netherlands (file name as "ahn3_10m_feature_name.tiff"). An overview of all the listed metrics (maps) is also provided in the PDF version (AHN3.pdf). A detailed description of the dataset is available from the following data publication: Kissling, W. D., Y. Shi, Z. Koma, C. Meijer, O. Ku, F. Nattino, A. C. Seijmonsbergen, and M. W. Grootes. 2022. Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey. Data in Brief: 108798. <a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1016%2Fj.dib.2022.108798&data=05%7C01%7Cy.shi%40uva.nl%7C177a19a4359a422b0ef808dad9d30ef8%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C638061797757145956%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=2R7NSGli4Mw6Pp5FAIyOzBu4USPZXigng46EFVT4X68%3D&reserved=0">https://doi.org/10.1016/j.dib.2022.108798</a> A detailed description of all the metrics can be found in the README file (README.docx).  A .zip file is also provided containing all the data for the validation of the AHN3 data products (AHN3_validation.zip).

Data of the publication Rare Earth‐Diamond Hybrid Structures for Optical Quantum Technologies

Data of the publication published under the reference: I.G. Balașa et al., Advanced Optical Materials, 2401487 (2024).

P+H: Structures Data

This dataset was employed in the study of the reaction dynamics of H atoms reacting with P atoms in amorphous solid water. The protocol for the generation of the dataset is  highlighted in the publication "Reaction dynamics on amorphous solid water surfaces using interatomic machine-learned potentials" The data is stored in python compressed array format (.npz) with the atomization energies in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains five numpy arrays import numpy as np data = np.load('P+H_ThirdF.npz') data['R']   # Cartesian coordinates of nuclei (Ang.) data['E']   # Total energy (kcal/mol) data['F']   # Atomic forces (kcal/mol/Ang.) data['N']   # Number of atoms in each structure data['Z']   # Nuclear charges

R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on derived metrics

This repository contains R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on spectral and LiDAR-derived metrics. The scripts cover LiDAR data processing, canopy height model (CHM) generation, calculation of forest canopy metrics, and PCA analysis.

Data for "SeaMoon: from protein language models to continuous structural heterogeneity"

Datasets used for development of SeaMoon:  <a href="https://github.com/PhyloSofS-Team/seamoon">https://github.com/PhyloSofS-Team/seamoon</a>. This upload contains the following data: <ul> <li>precomputed_emb.tar.gz is a compressed archive containing the precomputed data used for training and testing the models of the SeaMoon method, in Torch .pt format.  The file prefixes consist of two IDs, "ID1_ID2_", identifying the <a href="https://github.com/PhyloSofS-Team/DANCE">DANCE</a> [1] protein conformational collection used for its generation. "ID1" represents the first member of the collection in alphabetical order, while "ID2" is the reference conformation for the structural alignment. The "ESM_data" or "ProstT5_data" suffixes designate the type of embeddings, generated by either ESM2 [2] or ProstT5 [3]. The dictionnary contains the following keys: <ul> <li>emb: The per-residue embedding.</li> <li>data: A tuple containing "ID2" (the reference), the amino acid sequence, and the coverage of the positions in the original DANCE collection.</li> <li>eigvect: The eigenvectors of the covariance matrix of the "ID1_ID2" collection, centered on reference conformaton "D2".</li> <li>eigval: The associated eigenvalues.</li> <li>ref: The coordinates of the C-alpha atoms of the reference conformaton "ID2".</li> </ul> </li> <li>train_list.txt, train_list_5ref.txt, val_list.txt and test_list.txt contain the identifiers of the samples used for training and evaluating the SeaMoon models. In the "5ref" setting, we used up to 5 reference conformations per collection. </li> </ul> For details on SeaMoon see: <div> <div>SeaMoon: Prediction of molecular motions based on language models</div> </div> <div>Valentin Lombard, Dan Timsit, Sergei Grudinin, Elodie Laine</div> <div>bioRxiv 2024.09.23.614585; doi: https://doi.org/10.1101/2024.09.23.614585</div> <div> </div> <div>For more information on data usage and generation please see <a href="https://github.com/PhyloSofS-Team/seamoon">https://github.com/PhyloSofS-Team/seamoon</a>.</div> <div> </div> <div>Abstract:</div> How protein move and deform determines their interactions with the environment and is thus of utmost importance for cellular functioning. Following the revolution in single protein 3D structure prediction, researchers have focused on repurposing or developing deep learning models for sampling alternative protein conformations. In this work, we explored whether continuous compact representations of protein motions could be predicted directly from protein sequences, without exploiting nor sampling protein structures. Our approach, called SeaMoon, leverages protein Language Model (pLM) embeddings as input to a lightweight (~1M trainable parameters) convolutional neural network. SeaMoon achieves a success rate of up to 40% when assessed against ~1,000 collections of experimental conformations exhibiting a wide range of motions. SeaMoon capture motions not accessible to the normal mode analysis, an unsupervised physics-based method relying solely on a protein structure's 3D geometry, and generalises to proteins that do not have any detectable sequence similarity to the training set. SeaMoon is easily retrainable with novel or updated pLMs.    [1] Lombard, V.; Grudinin, S.; Laine, E. Explaining Conformational Diversity in Protein Families through Molecular Motions. Scientific Data 2024, 11, 752. [2] Lin, Z.; Akin, H.; Rao, R.; Hie, B.; Zhu, Z.; Lu, W.; Smetanin, N.; Verkuil, R.; Kabeli, O.; Shmueli, Y.; Dos Santos Costa, A.; Fazel-Zarandi, M.; Sercu, T.; Candido, S.; Rives, A. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 2023, 379, 1123–1130. [3] Heinzinger, M.; Weissenow, K.; Sanchez, J. G.; Henkel, A.; Steinegger, M.; Rost, B. ProstT5: Bilingual language model for protein sequence and structure. bioRxiv 2023, 2023–07.

opencc-by-4.0Dec 2021View details →

Data for: Assets in Periphery, Agents in the Core: Mapping the Micro Structures of International Tax Planning

Data for: Assets in Periphery, Agents in the Core: Mapping the Micro Structures of International Tax Planning See paper at: https://osf.io/preprints/socarxiv/hyc4p In the last two decades, tax avoidance has risen to the top of the agenda of policy makers and international organizations. The majority of political action and academic research has focused on the macro-level of states, pointing towards the responsibility of ‘tax havens’ or ‘offshore financial centers’. Research on the micro-level has demonstrated the importance of non-state actors who facilitate tax planning, but tax advisors have never been studied systematically with global data. In this paper, we connect the micro and macro levels. We map tax advisors geographically using a novel empirical approach based on LinkedIn. We show that tax advisors generally locate in large cities in the EU and OECD, rather than in places targeted as ‘tax havens’. We further consider what determines the locations of tax advisors. Using multiple regression analysis, we find that locations of tax advisors does not correlate with the location of corporate profits, financial secrecy, or economic activity. Rather, it correlates with the managerial and financial activity. Our results underscore the core-periphery structure in offshore finance. Effective regulation of tax avoidance should focus on tax advisors, not only on the destination of money flows, since the active facilitation does not occur in those places.

opencc-by-4.0May 2022View details →

Research data supporting "Tin phosphide anodes for potassium-ion batteries: insights from crystal structure prediction"

This dataset contains the output files of crystal structure prediction calculations (density-functional theory relaxations, bandstructures, phonon calculations, GIPAW-NMR calculations) on the ternary K-Sn-P phase diagram. All calculations were performed with the CASTEP DFT package (https://www.castep.org/) and the "matador" Python library (https://github.com/ml-evs/matador). Contents: <ul> <li>"convergence_tests.zip": contains the results of convergence tests on the K-P system at two levels of accuracy "polish" and "searches" on the corresponding edge of the K-Sn-P ternary system</li> <li>"phonons.zip": contains CASTEP output files for phonon calculations on the predicted low-lying phases on the corresponding edge of the K-Sn-P phase diagram</li> <li>"polish.zip": contains CASTEP output files of relaxations on the corresponding edge of the K-Sn-P system at the "polish" level of accuracy using various different xc-functionals or external pressures.</li> <li>"searches.zip": contains ".res" files that provide the relaxed structure from each different crystal structure prediction method on the corresponding edge of the K-Sn-P system.</li> <li>"bulk_modulus.zip" contains CASTEP output files for calculation of E(V) curves for low-lying KP phases with different xc-functionals.</li> <li>"nmr.zip" contains CASTEP output files for GIPAW-NMR calculations of chemical shifts for low-lying K-Sn-P phases.</li> <li>"spectral.zip" contains CASTEP and OptaDOS output files for projected bandstructure and DOS calculations of low-lying K-Sn-P phases.</li> <li>"digests.zip" contains JSON representations of all the structures from polish and searches, broken down into K-P and K-Sn-P specific digests.</li> </ul>

Data for: The structure of evolutionary model space for proteins across the tree of life

Supporting data for "The structure of evolutionary model space for proteins across the tree of life," submitted by GE Scolaro and EL Braun. The data files correspond to three gzipped tarballs including protein multiple sequence alignments, PAML format models of protein evolution, and model fit data; see included README for details.

Result data related to "Bersalli et al. (2023) -- Most industrialised countries have peaked carbon dioxide emissions during economic crises through strengthened structural change"

This repository contains the result data of our study investigating the relationship between emission peaks and economic crises. The repository contains mainly two datasets: <ul> <li>multiplicative-contributions.csv / .nc</li> <li>prepost-growth-rates.csv / .nc</li> </ul> Both datasets exist in CSV and NetCDF file format for convenience. The dataset multiplicative-contributions contains year-to-year change factors of GDP, population, energy-intensity, and carbon-intensity for every country in our study. The dataset prepost-growth-rates contains growth over a multi-year period pre- and post- crisis for each country and each crises in our study.

Deposited data for 'Structural and functional map for forelimb movement phases between cortex and medulla'; Yang, Kanodia and Arber; 2023

Primary source data for figures in  'Structural and functional map for forelimb movement phases between cortex and medulla'; <a href="https://doi.org/10.1016/j.cell.2022.12.009">Yang et al. 2023</a>

opencc-by-4.0Jan 2023View details →

Simulation Data for the article 'The Influence of Cloud Condensation Nucleus (CCN) Coagulation on the Venus Cloud Structure'

This dataset contains the NetCDF output files from simulations using PlanetCARMA in support of the work published in the manuscript, "The Influence of Cloud Condensation Nucleus (CCN) Coagulation on the Venus Cloud Structure."  A summary of the included NetCDF data is found in the README file that is part of the data object.  The submission version of this dataset contains only those simulations that provided data that were discussed in the accepted final manuscript.  However, additional simulations were carried out in the course of the work, and are described in the manuscript.  Upon request, the authors will revise this data repository by adding such data products from among that list as may be requested by others.

Data and results in "Relationship between crustal structure and plate convergence around the Izu collision zone in central Japan"

“allrfstationlist.dat” contains the list of the used seismic stations. The four columns indicate the name, latitude, longitude, and altitude (m) of each station, respectively. “allrfevent.dat” contains the list of the used teleseismic events. From left to right, the 10 columns indicate the year, month (in number), day, hour, minute, and second of the origin time (Japan Standard Time), and the latitude (from –90 to 90), longitude (from –180 to 180), depth of the hypocenter, and magnitude of each event, respectively. “RFmoho.dat” contains the depth distribution of the Moho determined by our RF analysis. The third column indicates the depth (km) of the Moho at the given latitude (the second column) and longitude (the first column) “Tomo_depth_limited.txt” contains the depth distribution of the lower boundary of a layer with a P-wave velocity of 7.5–7.7 km/s in the model by Ishise et al. (2021), which was assumed as the Moho. The third column indicates its depth (km) at the given latitude (the first column) and longitude (the second column) “crustthickness_tomorf.dat” contains the thickness distribution of the crust of the Philippine Sea Plate determined from the geometry of its upper surface estimated by Hirose et al. (2008a, b) and Nakajima et al. (2009) and the Moho depth distribution shown in RFmoho.dat and Tomo_depth_limited.txt. The third column indicates the thickness (km) of the crust at the given latitude (the second column) and longitude (the first column). “RF” and “tomo” in the fourth column indicate the corresponding thickness determined based on the RF analysis and the model by Ishise et al. (2021), respectively.