cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6953735
title: Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)

description: This dataset gathers synthetic T2-weighted magnetic resonance (MR) images generated using FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation.<br> This dataset is associated with the following paper:<br> Lajous H. et al. (2022) A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports. https://doi.org/10.1038/s41598-022-10335-4 This dataset provides images simulated by FaBiAN based on the specific implementation of FSE sequences by two MR vendors (Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE), Siemens Healthcare, and Single-Shot Fast Spin Echo (SS-FSE), GE Healthcare) at 1.5 T or 3 T.<br> Automated brain tissue annotations of the low-resolution series and super-resolution (SR) reconstructions are also included. Works using any of these data should cite the following references:<br> - Lajous, H. et al. A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports (2022). https://doi.org/10.1038/s41598-022-10335-4<br> - Lajous, H., Roy, C. W., Yerly, J. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v1.2 (1.2). Zenodo (2022). https://doi.org/10.5281/zenodo.5471094<br> - Lajous, H. et al. Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Zenodo (2022). https://doi.org/10.5281/zenodo.6477946 <br> Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2022.
description: This work was supported by the Swiss National Science Foundation through grants 182602, 141283 and 173129. We acknowledge the expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE) and Geneva University Hospitals (HUG).
DOI: 10.5281/zenodo.6477946
title: Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)

description: This dataset gathers synthetic T2-weighted magnetic resonance (MR) images generated using FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation.<br> This dataset is associated with the following paper:<br> Lajous H. et al. (2022) A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports. https://doi.org/10.1038/s41598-022-10335-4 This dataset provides images simulated by FaBiAN based on the specific implementation of FSE sequences by two MR vendors (Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE), Siemens Healthcare, and Single-Shot Fast Spin Echo (SS-FSE), GE Healthcare) at 1.5 T or 3 T.<br> Automated brain tissue annotations of the low-resolution series and super-resolution (SR) reconstructions are also included. Works using any of these data should cite the following references:<br> - Lajous, H. et al. A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports (2022). https://doi.org/10.1038/s41598-022-10335-4<br> - Lajous, H., Roy, C. W., Yerly, J. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v1.2 (1.2). Zenodo (2022). https://doi.org/10.5281/zenodo.5471094<br> - Lajous, H. et al. Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Zenodo (2022). https://doi.org/10.5281/zenodo.6477946 <br> Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2022.
description: Some users reported issues when downloading the Simulated_HASTE_images.zip archive on Linux/Ubuntu systems (damaged zip archive). Please use the updated version 1.1 if you encounter the same difficulties. The data are exactly the same, but the folder was compressed using 7-Zip on a Windows machine.
DOI: 10.5281/zenodo.6982681
title: oxythentera

description: Plant growth
description: sun light is used for plant growth
DOI: 10.5281/zenodo.6982680
title: oxythentera

description: Plant growth
description: sun light is used for plant growth
DOI: 10.5281/zenodo.6982823
title: Evolution of ocean circulation in the North Atlantic Ocean during the Miocene: impact of the Greenland Ice Sheet and the Eastern Tethys Seaway

description: This dataset contains atmosphere and ocean outputs (NetCDF files) from modeling experiments with realistic early Miocene paleogeography as well as sensitivity to Greenland Ice Sheet and Eastern Tethys Seaway. The set of simulation targets the evolution of the North Atlantic Deep Water during the Miocene. The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed for multi-millennial climate simulations, GMD). It includes 3 ocean-atmosphere simulations. Data are monthly averages over the last 100 years of the simulations.
DOI: 10.5281/zenodo.6671071
title: Evolution of ocean circulation in the North Atlantic Ocean during the Miocene: impact of the Greenland Ice Sheet and the Eastern Tethys Seaway

description: This dataset contains atmosphere and ocean outputs (NetCDF files) from modeling experiments with realistic early Miocene paleogeography as well as sensitivity to Greenland Ice Sheet and Eastern Tethys Seaway. The set of simulation targets the evolution of the North Atlantic Deep Water during the Miocene. The simulations have been run using the IPSL-CM5A2 General Circulation Model (Sepulchre et al. 2020 - IPSL-CM5A2 – an Earth system model designed for multi-millennial climate simulations, GMD). It includes 3 ocean-atmosphere simulations. Data are monthly averages over the last 100 years of the simulations.
DOI: 10.5281/zenodo.3908563
title: metadata [IO Islamic 940] مرآت القدس

description: <strong>This manuscript is now IO Islamic 940 in the India Office Collections.</strong> <strong>[metadata: Hermann Ethé<em>, Catalogue of Persian Manuscripts in the Library of the India Office</em>, 2 vols. (Oxford: India Office, 1903): volume 1, no. 619, here with annotations and hyperlinks]</strong> 619 Mirât-alḳuds (مرآت القدس). The life of Christ according to the Gospels, a work, the materials of which were first collected and arranged by the Portuguese Jesuit, Padre Geronimo Xavier, and afterwards translated under his superintendence into Persian by Maulânâ ‘Abd-alsattâr bin Ḳâsim Lâhûrî at Âgra, A.D. 1602 (A.H. 1010, 1011), at the request and for the sake of the emperor Akbar. Xavier died as missionary in Goa, A.D. 1617; comp. on this work. Bodleian Cat.[Catalogue of the Persian, Turkish, Hindûstânî and Pushtû Manuscripts in the Bodleian Library]., No. 364; Rieu, i. p. 3 [Catalogue of Persian Manuscripts]; W. Pertsch, p. 57; Cat. des MSS. et Xyl., p. 243 sq., etc. This work was edited with a Latin translation by Louis de Dieu: ‘Historia Christi Persice,’ Lugd. Bat., 1639. It is from this printed edition, according to a note on fol. 1<sup>a</sup>, that the present copy was transcribed for Mr. Richard Johnson, A.H. 1185 (A.D. 1771, 1772), at Calcutta. The date given at the conclusion of the work, viz. A.H. 1027 (A.D. 1618), is apparently that of the original MS. on which Louis de Dieu based his edition; see Rieu, loc. cit. Beginning of the preface, on fol. 1<sup>b</sup>: راهنمونی و هدایت صاحب ترجمه برای خوانندگان، ای عزیزان و محبوبان من این داستان مسیح را پادری ژیرونیمو شویر بپاکیزگی الخ The preface ends on fol. 5<sup>a</sup>, first line, and the whole space from there to fol. 9<sup>a</sup> is left blank. Beginning of the life of Christ, on fol. 9<sup>b</sup>, with the <em>first bâb</em> (Christ’s childhood): در طفولیّت مسیح و ولادت مریم و پرورش یافتن، چون مسیح بواسطۀ مرد از مریم مقدّس زاده شد الخ <em>Second bâb</em> (Christ’s miracles and teaching): در معجزها و تعلیم, on fol. 43<sup>b</sup>. <em>Third bâb</em> (Christ’s sufferings and death): در جفاها و محنتها و مرگ مسیح, on fol. 151<sup>a</sup>. <em>Fourth bâb</em> (Christ’s resurrection and ascension to heaven): در برخاستن مسیح از قبر و رفتن او بر آسمان, on fol. 171<sup>b</sup>. Conclusion, on fol. 186<sup>a</sup>. As title is given on the fly-leaves, ترجمۀ قصّۀ حضرت عیسی; the proper title, مرآت القدس, only appears in the preface. No. 940, ff. 186, II. 13; Nasta’lîḳ; worm-eaten throughout; size, 8<sup>1/4</sup> in. by 5<sup>1/2</sup> in. [<strong>ed note</strong>: The prime appears to be the copy in the Cleveland Museum of Art, John L. Severance Fund 2005.145. See: Carvalho, Pedro de Moura, and W. M. Thackston. <em>Mirʼāt al-quds (Mirror of holiness): a life of Christ for Emperor Akbar: a commentary on Father Jerome Xavier's text and the miniatures of Cleveland Museum of Art, Acc. no. 2005.145</em>. Leiden: Brill, 2012.]
DOI: 10.5281/zenodo.3908564
title: metadata [IO Islamic 940] مرآت القدس

description: <strong>This manuscript is now IO Islamic 940 in the India Office Collections.</strong> <strong>[metadata: Hermann Ethé<em>, Catalogue of Persian Manuscripts in the Library of the India Office</em>, 2 vols. (Oxford: India Office, 1903): volume 1, no. 619, here with annotations and hyperlinks]</strong> 619 Mirât-alḳuds (مرآت القدس). The life of Christ according to the Gospels, a work, the materials of which were first collected and arranged by the Portuguese Jesuit, Padre Geronimo Xavier, and afterwards translated under his superintendence into Persian by Maulânâ ‘Abd-alsattâr bin Ḳâsim Lâhûrî at Âgra, A.D. 1602 (A.H. 1010, 1011), at the request and for the sake of the emperor Akbar. Xavier died as missionary in Goa, A.D. 1617; comp. on this work. Bodleian Cat.[Catalogue of the Persian, Turkish, Hindûstânî and Pushtû Manuscripts in the Bodleian Library]., No. 364; Rieu, i. p. 3 [Catalogue of Persian Manuscripts]; W. Pertsch, p. 57; Cat. des MSS. et Xyl., p. 243 sq., etc. This work was edited with a Latin translation by Louis de Dieu: ‘Historia Christi Persice,’ Lugd. Bat., 1639. It is from this printed edition, according to a note on fol. 1<sup>a</sup>, that the present copy was transcribed for Mr. Richard Johnson, A.H. 1185 (A.D. 1771, 1772), at Calcutta. The date given at the conclusion of the work, viz. A.H. 1027 (A.D. 1618), is apparently that of the original MS. on which Louis de Dieu based his edition; see Rieu, loc. cit. Beginning of the preface, on fol. 1<sup>b</sup>: راهنمونی و هدایت صاحب ترجمه برای خوانندگان، ای عزیزان و محبوبان من این داستان مسیح را پادری ژیرونیمو شویر بپاکیزگی الخ The preface ends on fol. 5<sup>a</sup>, first line, and the whole space from there to fol. 9<sup>a</sup> is left blank. Beginning of the life of Christ, on fol. 9<sup>b</sup>, with the <em>first bâb</em> (Christ’s childhood): در طفولیّت مسیح و ولادت مریم و پرورش یافتن، چون مسیح بواسطۀ مرد از مریم مقدّس زاده شد الخ <em>Second bâb</em> (Christ’s miracles and teaching): در معجزها و تعلیم, on fol. 43<sup>b</sup>. <em>Third bâb</em> (Christ’s sufferings and death): در جفاها و محنتها و مرگ مسیح, on fol. 151<sup>a</sup>. <em>Fourth bâb</em> (Christ’s resurrection and ascension to heaven): در برخاستن مسیح از قبر و رفتن او بر آسمان, on fol. 171<sup>b</sup>. Conclusion, on fol. 186<sup>a</sup>. As title is given on the fly-leaves, ترجمۀ قصّۀ حضرت عیسی; the proper title, مرآت القدس, only appears in the preface. No. 940, ff. 186, II. 13; Nasta’lîḳ; worm-eaten throughout; size, 8<sup>1/4</sup> in. by 5<sup>1/2</sup> in. [<strong>ed note</strong>: The prime appears to be the copy in the Cleveland Museum of Art, John L. Severance Fund 2005.145. See: Carvalho, Pedro de Moura, and W. M. Thackston. <em>Mirʼāt al-quds (Mirror of holiness): a life of Christ for Emperor Akbar: a commentary on Father Jerome Xavier's text and the miniatures of Cleveland Museum of Art, Acc. no. 2005.145</em>. Leiden: Brill, 2012.]
DOI: 10.5281/zenodo.6515085
title: JARVIS-MoCap monkey grasping recordings and annotations

description: 12 camera synchronized recording of a rhesus macaque grasping different objects and fruit with his left hand.
DOI: 10.5281/zenodo.6515022
title: JARVIS-MoCap monkey grasping recordings and annotations

description: 12 camera synchronized recording of a rhesus macaque grasping different objects and fruit with his left hand. Included in the dataset are checkerboard calibration recordings, one 12 camera recording and a trainingset containing roughly 3000 manual annotations of 23 hand keypoints.
DOI: 10.5281/zenodo.6515182
title: JARVIS-MoCap monkey grasping recordings and annotations

description: 12 camera synchronized recording of a rhesus macaque grasping different objects and fruit with his left hand. Included in the dataset are checkerboard calibration recordings, one 12 camera recording and a trainingset containing roughly 3000 manual annotations of 23 hand keypoints.
DOI: 10.5281/zenodo.6982878
title: data for "Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data"

description: data for "Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data"
DOI: 10.5281/zenodo.6980069
title: Survey on lattice data analysis, presentation, and curation practices

description: This repository contains the results of a survey on software workflows and open science in lattice field theory conducted in 2022 by Andreas Athenodorou, Ed Bennett, Julian Lenz, and Elli Papadopolou. These data were collected using LimeSurvey, and were first presented in a talk at Lattice 2022 by Andreas Athenodorou. The analysis is based on Julian Lenz's LimeSurvey CSV parser. The survey results are included in survey-results-redacted.csv. The survey structure is included in survey-structure.lss. Further details of the structure of the data, setup, see the included README.md file.
description: EB and JL are supported by the UKRI Science and Technology Facilities Council (STFC) Research Software Engineering Fellowship EP/V052489/1. AA and EP would like to thank Horizon 2020 European research infrastructures programme "NI4OS-Europe" with grant agreement no. 857645.
DOI: 10.5281/zenodo.6980070
title: Survey on lattice data analysis, presentation, and curation practices

description: This repository contains the results of a survey on software workflows and open science in lattice field theory conducted in 2022 by Andreas Athenodorou, Ed Bennett, Julian Lenz, and Elli Papadopolou. These data were collected using LimeSurvey, and were first presented in a talk at Lattice 2022 by Andreas Athenodorou. The analysis is based on Julian Lenz's LimeSurvey CSV parser. The survey results are included in survey-results-redacted.csv. The survey structure is included in survey-structure.lss. Further details of the structure of the data, setup, see the included README.md file.
description: EB and JL are supported by the UKRI Science and Technology Facilities Council (STFC) Research Software Engineering Fellowship EP/V052489/1. AA and EP would like to thank Horizon 2020 European research infrastructures programme "NI4OS-Europe" with grant agreement no. 857645.
DOI: 10.5281/zenodo.6981746
title: Data from Lehr, Luboeinski, & Tetzlaff, 2022

description: <strong>Processed and raw data</strong> <em>from the study</em> <strong>Neuromodulator-dependent synaptic tagging and capture retroactively controls neural coding in spiking neural networks</strong> <em>by Andrew B Lehr<sup>*</sup>, Jannik Luboeinski<sup>*</sup>, & Christian Tetzlaff (<sup>*</sup>these authors contributed equally)</em> The data provided here can be used with the Jupyter notebook located in the subfolder 'notebooks/lehr_luboeinski_tetzlaff_2022/' in this GitHub repository to reproduce most figures of the study.
DOI: 10.5281/zenodo.6981745
title: Data from Lehr, Luboeinski, & Tetzlaff, 2022

description: <strong>Processed and raw data</strong> <em>from the study</em> <strong>Neuromodulator-dependent synaptic tagging and capture retroactively controls neural coding in spiking neural networks</strong> <em>by Andrew B Lehr<sup>*</sup>, Jannik Luboeinski<sup>*</sup>, & Christian Tetzlaff (<sup>*</sup>these authors contributed equally)</em> The data provided here can be used with the Jupyter notebook located in the subfolder 'notebooks/lehr_luboeinski_tetzlaff_2022/' in this GitHub repository to reproduce most figures of the study.
DOI: 10.5281/zenodo.6977660
title: Modified Frequency Distribution Function in Wave Breaking Dissipation

description: Code and data supporting manuscript "Modified Frequency Distribution Function in Wave Breaking Dissipation" submitted to JGR Oceans
DOI: 10.5281/zenodo.6983008
title: Cone-Beam Computed Tomography Dataset of a Seashell

description: <strong>Summary</strong> This dataset is a collection of X-ray projection images of a seashell imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters, and photographs of the sample and the measurement setup. <strong>Description</strong> <em>Sample Information</em> The sample is an empty seashell of an unknown species, approximately 4.3 cm in length and 2.5 cm in diameter. The sample was placed in a plastic tube filled with cotton wool to prevent unwanted motion during the scan. <em>Scanner</em> The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22). <em>Scan Settings </em> 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 50 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms. <em>Data Post-Processing</em> Two correction images were acquired before scanning the sample. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3. <em>Data Format</em> The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable. <em>Notes</em> Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 4 pixels, using circular boundary conditions, before performing any other operations on the projections. <strong>Research Group</strong> This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: https://www2.helsinki.fi/en/researchgroups/inverse-problems. <strong>Additional Links</strong> To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at https://github.com/Diagonalizable/HelTomo <strong>Contact Details</strong> For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.
DOI: 10.5281/zenodo.6977659
title: Modified Frequency Distribution Function in Wave Breaking Dissipation

description: Code and data supporting manuscript "Modified Frequency Distribution Function in Wave Breaking Dissipation" submitted to JGR Oceans
DOI: 10.5281/zenodo.6971200
title: Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Training Data)

description: <strong>Overview </strong> This repository contains 742,688 samples of simulated Convergent Beam Electron Diffraction patterns (CBEDs); the training data for the paper "Phase Object Reconstruction for 4D-STEM using Deep Learning". The folder contains multiple hdf5 datasets. Each dataset has a corresponding Excel-sheet containing detailed information and simulation parameters for every datapoint, as well as a summary-report containing the parameter distributions, hdf5-infos and random number generator settings. This makes every dataset reproducible, using the simulation codes provided in https://github.com/ThFriedrich/ap_data_generation. <strong>Technical details</strong> Every Datapoint consists of a 3x3 set of adjacent Convergent Beam Electron Diffraction pattern (CBEDs), the coherent exit wave phase and amplitude in real and reciprocal space, and the probe functions phase and amplitude in real space. All patterns are 64x64 pixel in 16 bit unsigned integer data format. Every hdf5 file has the following structure: Attributes 'Seed': 6108236<br> 'State': 251786606 ...<br> 'Type': 'twister'<br> 'arch': 'glnxa64'<br> 'gpu': 'NVIDIA GeForce RTX 3080'<br> 'matlab_ver': '2021a' Dataset 'features' Size: 64x64x9x5000<br> Datatype: H5T_STD_U16LE (uint16) Dataset 'labels_k' Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16) Dataset 'labels_r' Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16) Dataset 'probe_r' Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16) Dataset 'meta' Size: 19x5000<br> Datatype: H5T_IEEE_F32LE (single) The data was written to hdf5 in matlab. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly. The integer arrays were scaled to use the full range of the uint16 datatype. The scaling values are stored under "meta". To restore the original values in floating point numbers, convert the arrays like this: Matlab: <pre><code>hdf_file = ['db_h5_b_5_Training.h5']; n = 128; % load `n` k-space exit waves x = single(h5read(hdf_file, '/labels_k', [1,1,1,1], [64,64,2,n])); % `meta` contains parameters and scaling factors for a given datapoint in following order: [E_0(keV), cond_lens_outer_aper_ang(mrad), collection angle(rA), step_size(A), scale_cbed_1 ... scale_cbed_9, scale_phase_k, scale_amp_k, scale_phase_r, scale_amp_r, scale_probe_phase_r, scale_probe_amp_r] s = h5read(hdf_file, '/meta', [14,1], [2,n]); amplitude = zeros(64,64,n); phase = zeros(64,64,n); for ix = 1:n phase(:,:,n) = (x(:,:,1,n)*s(1,ix) / 65536) - pi; amplitude(:,:,n) = (x(:,:,2,n)*s(2,ix)) / 65536; end % The 9 CBEDs correspond to a 3x3 kernel of patterns. The order in [x,y] is: %[[3, 6, 9]; % [2, 5, 8]; % [1, 4, 7]] </code></pre>