cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7827147
title: Single-Shot Optical Neural Network

description: Data from classification of MNIST [1], Fashion-MNIST [2] and QuickDraw [3] images reported in "Single-Shot Optical Neural Network" by L. Bernstein et al. The networks of 784 -&gt; N (-&gt; N) -&gt; 10 activations performed inference on the test sets through consecutive matrix products implemented on the optical hardware, with ReLU applied electronically between each layer (see main text for more details). Folders for each tested network contain the following text files: Inputs: 2D matrices of size B x 784 containing training (B = 50,000 or 100,000), validation (B = 10,000) and test (B = 10,000) sets. Each row is an input vector that can be reshaped into an input image of size 28 x 28. True labels: B-length vectors containing the true label of each input in the training, validation and test sets. Neural network weights: 2D matrices of size K x N used in inference experiments to classify the test sets. Weights were pre-trained on the training set using a digital electronic computer as described in Materials and Methods. Weight values were normalized such that all values fall between -255 and 255. In the optical neural network, the weighting SLM displays the absolute values of the weights (rounded to the nearest integer), and the negative weight signs are applied in post-processing. The 32-bit weight values were used for inference performed on the digital electronic computer for the ground truth comparison. Outputs (normalized): 2D output matrices of size 10,000 x 10 from the networks processed on a digital electronic computer (ground truth) and the optical neural network. Each row is an output vector where the position of the maximum value indicates the predicted label of the input in the same row of the test set. Predicted labels: 10,000-element vectors that represent the labels predicted by the networks processed on a digital electronic computer (ground truth) and the optical neural network. These predicted labels were used to generate the confusion matrices and calculate the classification accuracies (versus the true labels). The classes for the Fashion-MNIST dataset are the following: 0: T-shirt 1: Trouser 2: Pullover 3: Dress 4: Coat 5: Sandal 6: Shirt 7: Sneaker 8: Bag 9: Ankle boot And the randomly selected classes for QuickDraw are: 0: Hourglass 1: Saw 2: Golf club 3: See saw 4: Spoon 5: Horse 6: Onion 7: Light bulb 8: Harp 9: Flip flops [1] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. <em>Proceedings of the IEEE</em> <strong>86</strong>, 2278–2324 (1998). [2] H. Xiao, K. Rasul, R. Vollgraf, Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. Preprint at https://arxiv.org/abs/1708.07747 (2017). [3] J. Jongejan, H. Rowley, T. Kawashima, J. Kim, N. Fox-Gieg, The Quick, Draw! AI experiment, https://quickdraw.withgoogle.com/ (2016).
DOI: 10.5281/zenodo.7827146
title: Single-Shot Optical Neural Network

description: Data from classification of MNIST [1], Fashion-MNIST [2] and QuickDraw [3] images reported in "Single-Shot Optical Neural Network" by L. Bernstein et al. The networks of 784 -&gt; N (-&gt; N) -&gt; 10 activations performed inference on the test sets through consecutive matrix products implemented on the optical hardware, with ReLU applied electronically between each layer (see main text for more details). Folders for each tested network contain the following text files: Inputs: 2D matrices of size B x 784 containing training (B = 50,000 or 100,000), validation (B = 10,000) and test (B = 10,000) sets. Each row is an input vector that can be reshaped into an input image of size 28 x 28. True labels: B-length vectors containing the true label of each input in the training, validation and test sets. Neural network weights: 2D matrices of size K x N used in inference experiments to classify the test sets. Weights were pre-trained on the training set using a digital electronic computer as described in Materials and Methods. Weight values were normalized such that all values fall between -255 and 255. In the optical neural network, the weighting SLM displays the absolute values of the weights (rounded to the nearest integer), and the negative weight signs are applied in post-processing. The 32-bit weight values were used for inference performed on the digital electronic computer for the ground truth comparison. Outputs (normalized): 2D output matrices of size 10,000 x 10 from the networks processed on a digital electronic computer (ground truth) and the optical neural network. Each row is an output vector where the position of the maximum value indicates the predicted label of the input in the same row of the test set. Predicted labels: 10,000-element vectors that represent the labels predicted by the networks processed on a digital electronic computer (ground truth) and the optical neural network. These predicted labels were used to generate the confusion matrices and calculate the classification accuracies (versus the true labels). The classes for the Fashion-MNIST dataset are the following: 0: T-shirt 1: Trouser 2: Pullover 3: Dress 4: Coat 5: Sandal 6: Shirt 7: Sneaker 8: Bag 9: Ankle boot And the randomly selected classes for QuickDraw are: 0: Hourglass 1: Saw 2: Golf club 3: See saw 4: Spoon 5: Horse 6: Onion 7: Light bulb 8: Harp 9: Flip flops [1] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. <em>Proceedings of the IEEE</em> <strong>86</strong>, 2278–2324 (1998). [2] H. Xiao, K. Rasul, R. Vollgraf, Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. Preprint at https://arxiv.org/abs/1708.07747 (2017). [3] J. Jongejan, H. Rowley, T. Kawashima, J. Kim, N. Fox-Gieg, The Quick, Draw! AI experiment, https://quickdraw.withgoogle.com/ (2016).
DOI: 10.5281/zenodo.6972721
title: The dataset of "A Closer Look at the Security Risks in the Rust Ecosystem"

description: A dataset of the metadata of 84,105 Rust packages on crates.io, 433 reported vulnerabilities, 300 vulnerable code repositories, and 218 vulnerability fix commits, spanning over 7 years in history.
DOI: 10.5281/zenodo.6972722
title: The dataset of "A Closer Look at the Rust Ecosystem: Evolution and Security Risks"

description: A dataset of the metadata of 84,105 Rust packages on crates.io, 776 reported vulnerabilities, 300 vulnerable code repositories, and 298 vulnerability fix commits, spanning over 7 years in history.
DOI: 10.5281/zenodo.7828059
title: The dataset of "A Closer Look at the Security Risks in the Rust Ecosystem"

description: A dataset of the metadata of 84,105 Rust packages on crates.io, 433 reported vulnerabilities, 300 vulnerable code repositories, and 218 vulnerability fix commits, spanning over 7 years in history.
DOI: 10.5281/zenodo.7830459
title: Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure

description: These datasets accompany the Zemuhe fault surface coordinates that were used for construction of the fault geometry model, the observational fault surface slip of the 1850 Xichang M 7.5 earthquake and the intensity distribution in Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure submitted to Earth and Space Science by Yu et al. (2023). The data is structured as follows: Once unzipped the data within the archive are six txt files: fault_surface_slip_Feng.txt, which is from Feng et al. (2000); fault_surface_slip_Ren.txt, which is from Ren and Lin (2010), fault_surface_slip_Yu.txt, which is from Yu et al., (2001); intensity.txt, which is from Feng et al. (2000); landslides.txt, which is from Guo et al. (2014) and Cao (2015); zemuhe_fault_surface_coordinates.txt, which is from Working group for geologic mapping on Zemuhe fault (2000). The file folder stress_configuration contains the initial stress setup for the model in Figure 5c.
DOI: 10.5281/zenodo.7830458
title: Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure

description: These datasets accompany the Zemuhe fault surface coordinates that were used for construction of the fault geometry model, the observational fault surface slip of the 1850 Xichang M 7.5 earthquake and the intensity distribution in Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure submitted to Earth and Space Science by Yu et al. (2023). The data is structured as follows: Once unzipped the data within the archive are six txt files: fault_surface_slip_Feng.txt, which is from Feng et al. (2000); fault_surface_slip_Ren.txt, which is from Ren and Lin (2010), fault_surface_slip_Yu.txt, which is from Yu et al., (2001); intensity.txt, which is from Feng et al. (2000); landslides.txt, which is from Guo et al. (2014) and Cao (2015); zemuhe_fault_surface_coordinates.txt, which is from Working group for geologic mapping on Zemuhe fault (2000). The file folder stress_configuration contains the initial stress setup for the model in Figure 5c.
DOI: 10.34740/kaggle/dsv/5411095
title: UrbanEye: Urban Landscape Dataset
DOI: 10.5517/ccdc.csd.cc2f1ygx
title: CCDC 2236199: Experimental Crystal Structure Determination

description: Related Article: Kunal Kumar Jha, Anil Kumar, Parthapratim Munshi|2023|Cryst.Growth Des.|23|2922|doi:10.1021/acs.cgd.3c00052
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1ym2
title: CCDC 2236204: Experimental Crystal Structure Determination

description: Related Article: Kunal Kumar Jha, Anil Kumar, Parthapratim Munshi|2023|Cryst.Growth Des.|23|2922|doi:10.1021/acs.cgd.3c00052
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1yn3
title: CCDC 2236205: Experimental Crystal Structure Determination

description: Related Article: Kunal Kumar Jha, Anil Kumar, Parthapratim Munshi|2023|Cryst.Growth Des.|23|2922|doi:10.1021/acs.cgd.3c00052
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1yp4
title: CCDC 2236206: Experimental Crystal Structure Determination

description: Related Article: Kunal Kumar Jha, Anil Kumar, Parthapratim Munshi|2023|Cryst.Growth Des.|23|2922|doi:10.1021/acs.cgd.3c00052
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1yq5
title: CCDC 2236207: Experimental Crystal Structure Determination

description: Related Article: Kunal Kumar Jha, Anil Kumar, Parthapratim Munshi|2023|Cryst.Growth Des.|23|2922|doi:10.1021/acs.cgd.3c00052
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1yr6
title: CCDC 2236208: Experimental Crystal Structure Determination

description: Related Article: Kunal Kumar Jha, Anil Kumar, Parthapratim Munshi|2023|Cryst.Growth Des.|23|2922|doi:10.1021/acs.cgd.3c00052
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1ys7
title: CCDC 2236209: Experimental Crystal Structure Determination

description: Related Article: Kunal Kumar Jha, Anil Kumar, Parthapratim Munshi|2023|Cryst.Growth Des.|23|2922|doi:10.1021/acs.cgd.3c00052
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1yt8
title: CCDC 2236210: Experimental Crystal Structure Determination

description: Related Article: Kunal Kumar Jha, Anil Kumar, Parthapratim Munshi|2023|Cryst.Growth Des.|23|2922|doi:10.1021/acs.cgd.3c00052
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1yfw
title: CCDC 2236198: Experimental Crystal Structure Determination

description: Related Article: Kunal Kumar Jha, Anil Kumar, Parthapratim Munshi|2023|Cryst.Growth Des.|23|2922|doi:10.1021/acs.cgd.3c00052
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7830625
title: Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure

description: These datasets accompany the Zemuhe fault surface coordinates that were used for construction of the fault geometry model, the observational fault surface slip of the 1850 Xichang M 7.5 earthquake and the intensity distribution in Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure submitted to Earth and Space Science by Yu et al. (2023). The data is structured as follows: Once unzipped the data within the archive are six txt files: fault_surface_slip_Feng.txt, which is from Feng et al. (2000); fault_surface_slip_Ren.txt, which is from Ren and Lin (2010), fault_surface_slip_Yu.txt, which is from Yu et al., (2001); intensity.txt, which is from Feng et al. (2000); landslides.txt, which is from Guo et al. (2014) and Cao (2015); zemuhe_fault_surface_coordinates.txt, which is from Working group for geologic mapping on Zemuhe fault (2000). The file folder stress_configuration contains the initial stress setup for the model in Figure 5c.
DOI: 10.5281/zenodo.7830624
title: Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure

description: These datasets accompany the Zemuhe fault surface coordinates that were used for construction of the fault geometry model, the observational fault surface slip of the 1850 Xichang M 7.5 earthquake and the intensity distribution in Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure submitted to Earth and Space Science by Yu et al. (2023). The data is structured as follows: Once unzipped the data within the archive are six txt files: fault_surface_slip_Feng.txt, which is from Feng et al. (2000); fault_surface_slip_Ren.txt, which is from Ren and Lin (2010), fault_surface_slip_Yu.txt, which is from Yu et al., (2001); intensity.txt, which is from Feng et al. (2000); landslides.txt, which is from Guo et al. (2014) and Cao (2015); zemuhe_fault_surface_coordinates.txt, which is from Working group for geologic mapping on Zemuhe fault (2000). The file folder stress_configuration contains the initial stress setup for the model in Figure 5c.
DOI: 10.15151/esrf-es-1058388825
title: BAG Barcelona

description: BAG Barcelona has a long tradition of accessing the ESRF. It is constituted by 10 groups that belong to the Institute of Molecular Biology Barcelona from the Spanish Research Council (IBMB-CSIC), the Institute for Research in Biomedicine (IRB), and the Research Institute from Hospital Santa Creu i Sant Pau (HSCSP-IR). The main subjects of research are protein/DBA complexes, molecular machines, compounds of viruses, molecular mechanisms of proteases, and other enzymes. Recently, a new group (E. Marcos) interested in de-novo designed protein scaffolds has joined the BAG. Our Scientific results strongly rely on the high-quality data and excellent performance and support of ESRF macromolecular crystallography beamlines.
DOI: 10.15151/esrf-es-1059609084
title: Munich Crystallography BAG

description: The diverse groups of the Munich Crystallography BAG study a far range of subjects, reaching from structural aspects of fundamental cellular processes - such as RNA processing, protein folding and degradation, DNA repair, chromatin remodelling, enzymes - to the design of foldamers. The research groups are located at the LMU and TUM universities in Munich, at the Max-Planck-Institute of Biochemistry, Martinsried, and the Helmholtz Center, Neuherberg. Out-groups are located at the universities in Darmstadt and Ulm, Germany. The research groups use X-ray crystallography, single-particle cryo-EM, SAXS, NMR, Alphafold and HDX and XL-MS mass-spectrometry for structural studies.
DOI: 10.15151/esrf-es-1160700517
title: Pore volume evolution during hot rolling of 2050 aluminium alloys : interrupted in situ multi-resolution microtomography during plain compre

description: The aim of this proposal is to track pore evolution during thermo-mechanical tests representative of hot rolling using ex situ multi-resolution micro-CT of plain compression tests of large (40x60x80mm) aluminum 2050 samples at BM05. This study is critical to link the macroscopic pore evolution, previously studied in small specimens, with the industrial hot rolling process [1,2]. Using the ability to image both the full specimen (40x60x80mm) at low-resolution (20µm/pixel) and some region of interests at high-resolution (2µm/pixel), we can track pore closure or nucleation and growth (shape, size, volume, pore density) in selected volumes of interest as a function of the strain field (triaxiality and Lode parameter). Interrupted in situ plain strain condition tests will be conducted to reproduce different hot rolling schemes (temperature, thickness reduction, strain rate) and help us refine and extend the previously proposed pore evolution laws for all the mechanical conditions.
DOI: 10.15151/esrf-es-1069353183
title: The fundamental interaction between localised X-ray pulses and electronic devices at the nanoscale: development of a tailored facility for a
DOI: 10.15151/esrf-es-1092782502
title: Compound formation in the CH4–N2 system at high-pressure high-temperature conditions

description: CH4–N2 mixtures have been predicted to react to yield high-energy-density materials under pressure that could be quenched to ambient. Such reactions could also hold importance in our understanding of the structural evolution of planetary satellites, such as Titan. Our preliminary studies indicate CH4-N2 van der Waals compound formation at pressures as low as 5 GPa, whilst upon further compression above 100 GPa covalent bonded compounds emerge. Here we propose to initially determine the structure and equation of state of the van der Waals compounds. We will then explore both the high-pressure and high-pressure high-temperature synthesis pathways of the undetermined C-N-H compounds, determine their structures, and the possibility of recovering the materials to ambient conditions.
DOI: 10.15151/esrf-es-1092782777
title: Direct observation of structural changes in chemically homogeneous and self-doped metallic glasses upon in-situ ultrafast heating and cooli

description: Recently, our group has shown the existence of multiple critical cooling rates which generate different types of monolithic metallic glasses. Via fast differential scanning calorimetry (FDSC), using a Au-based MG as an example, we proved that MGs should be classified into two types of amorphous/monolithic glass. The first type, termed self-doped glass (SDG), forms quenched-in nuclei or nucleation precursors upon cooling, whereas in the so-called chemically homogeneous glass (CHG) no quenched-in structures are found. Although the kinetics of these processes and the stability of the crystalline structures were established, there is no direct observation of the structural changes. These changes will be investigated by analyzing in-situ X-ray diffraction data (reciprocal space or direct space, i.e. pair distribution functions) as recorded with a fast detector upon rapid heating and cooling with the help of a FDSC chip sensor.
DOI: 10.15151/esrf-es-1092785089
title: Selective gas separation using combined diffraction techniques and IR spectroscopy

description: The objective is to follow the adsorption efficiency of a series of zeolites toward CO2 capture in situ, combining high-resolution powder X-ray diffraction for high-quality structure refinement, and fast powder diffraction combined with diffuse-reflectance infrared spectroscopy to follow the process dynamically.
DOI: 10.5281/zenodo.7813606
title: A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (2006–2021)

description: Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021. Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K. More details of this dataset can be seen in <em>readme.pdf.</em> This dataset provides GT-LST product from 2006 to 2021.