cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2frn84
title: CCDC 2257056: Experimental Crystal Structure Determination

description: Related Article: Qiusi Shi, Yingyong Ni, Longmei Yang, Lin Kong, Peiyang Gu, Chengyuan Wang, Qichun Zhang, Hongping Zhou, Jiaxiang Yang|2023|J.Mater.Chem.B|11|6859|doi:10.1039/D3TB01034A
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: 10.6084/m9.figshare.23626644.v1
title: Supplementary Material for: Dilemmas in the management of an infant with neuroblastoma metastasized to the muscles

description: The risk stratification of infants with metastatic neuroblastoma (NB) has evolved over time from stage 4/M or IVs/4S/MS/Ms according to various staging systems. Despite these developments for some genetic aberrations the prognostic value and the impact of soft tissue metastases in infants are not fully understood, nor well described in the different classification systems, hampering the definitions to uniformly treat patients and prediction of prognosis. A literature review on staging of infants with M/MS disease was performed at the occasion of the diagnosis of NB in an 8-month-old boy who presented with atypical metastatic sites in soft tissue and aberrant tumor biology. The definitions of stage 4/4S/4s/M/MS/Ms were evaluated and compared to enable tumor risk stratification and inform management. International NB groups use different criteria for defining stage of infants with metastasized NB, resulting in differences in management. Limited literature is available on soft tissue metastases, especially muscular metastases, and are poorly incorporated into management guidelines mainly due to the lack of data. The uncertain prognosis of rare genetic aberrancies may add to the difficulties in treatment decisions. In some rare cases of NB in infants the international treatment classification is not sufficient for staging and treatment decisions. Based on tumor progression, biology of unknown significance and a lack of evidence to classify a child under 12 months with NB and multiple muscular metastases, the patient was treated as stage 4/M and intermediate risk protocols with a favorable outcome.
DOI: 10.13140/rg.2.2.28319.94884
title: zingiberene 3D
DOI: 10.5281/zenodo.8186548
title: Simulated ChannelSpin Dataset

description: This example dataset corresponds to the paper https://arxiv.org/abs/1907.01898. Moscovich, A., Halevi, A., Andén, J., & Singer, A. (2020). Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes. <em>Inverse Problems</em>, <em>36</em>(2), 024003.<br> <br> Expresses the ChannelSpin feature without ChannelStretch. Includes 54 generated volumes sized (54,54,54) and stack of 10000 projection images (54,54).<br> <br> The dataset was originally created by the paper authors in using several legacy MATLAB functions denoted by "gen_fakekv*". That dataset was converted into a compressed Numpy (.npz) dataset compatible with ASPIRE-Python. The dataset includes parameters (rotations, amplitudes, states, etc) required to simulate an equivalent image stack in Python starting from the volumes, or used directly to provide a ground truth reference. Alternatively, these parameters can be modified to generate different image stacks.
DOI: 10.5281/zenodo.8186547
title: Simulated ChannelSpin Dataset

description: This example dataset corresponds to the paper https://arxiv.org/abs/1907.01898. Moscovich, A., Halevi, A., Andén, J., & Singer, A. (2020). Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes. <em>Inverse Problems</em>, <em>36</em>(2), 024003.<br> <br> Expresses the ChannelSpin feature without ChannelStretch. Includes 54 generated volumes sized (54,54,54) and stack of 10000 projection images (54,54).<br> <br> The dataset was originally created by the paper authors in using several legacy MATLAB functions denoted by "gen_fakekv*". That dataset was converted into a compressed Numpy (.npz) dataset compatible with ASPIRE-Python. The dataset includes parameters (rotations, amplitudes, states, etc) required to simulate an equivalent image stack in Python starting from the volumes, or used directly to provide a ground truth reference. Alternatively, these parameters can be modified to generate different image stacks.
DOI: 10.14469/hpc/12675
title: Carbocation 3, no rotation and Cl atom, as a control group

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12677
title: Carbocation 3 with 90 rotation

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12678
title: Carbocation 3 with Cl atom away from the H atom attached to +ve carbon

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12679
title: Carbocation 3 with Cl atom away from the H atom attached to +ve carbon

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12691
title: Carbocation 3 with Cl atom close to the H atom attached to +ve carbon

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12692
title: Carbocation 3 with 90 degree rotation and Cl atom close to the H atom attached to +ve carbon

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12718
title: Re-run of Cheryl's phenyl cyclopropylidene input file with chloroform as solvent

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12719
title: Re-run of Cheryl's cyclopropyl phenyl cation input file but with chloroform as solvent

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12725
title: Re-run of Cheryl's MCP cation and Cl ion pair input file with chloroform as solvent

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12732
title: HCl as ion pair

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12733
title: Parent phenyl MCP

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12738
title: MCP with its cation and Cl close to cation

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12760
title: MCP with its cation and Cl away from cation

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12761
title: MCP with its cation and Cl close to MCP

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12762
title: MCP with its cation and Cl away from MCP

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12763
title: MCP with its cation and Cl in between phenyl rings

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12769
title: MCP with its cation and Cl paired up as energy minimum

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12775
title: MCP with its cation and Cl paired up as energy minimum

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12776
title: 2 MCPs with 1 HCl away from them

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12777
title: 2 MCPs with 1 HCl in between H attached to allylic carbon

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12778
title: 2 MCPs with 1 HCl close to the 'allylic' carbon

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12779
title: Parent phenyl MCP

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12780
title: HCl as ion pair

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12781
title: HCl as ion pair (E(RB3LYP)=-460.83866Hartree)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12784
title: Parent phenyl MCP (E(RB3LYP)=-387.20643Hartree)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12815
title: Carbocation 3 with Cl atom close to the H atom attached to +ve carbon in triplet z

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12816
title: Carbocation 3 with Cl atom close to the H atom attached to +ve carbon (Def2-TZVPP)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12817
title: HCl (def2tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12818
title: Parent phenyl MCP (def2tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12820
title: 2 MCPs with 1 HCl close to the 'allylic' carbon (def2tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12821
title: 2 MCPs with 1 HCl close to the 'allylic' carbon (triplet z)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12822
title: Parent phenyl MCP (triplet z)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12823
title: Parent phenyl MCP (def2svp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12826
title: MCP with its cation and Cl paired up as energy minimum (def2tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12827
title: MCP with its cation and Cl paired up as energy minimum (triplet z)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12864
title: p-Bromo-MCP(Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12865
title: p-Bromo-MCP, Cl ion pair (Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12866
title: p-CF3-MCP(Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12867
title: p-CF3-MCP, Cl ion pair (Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12868
title: p-CH3-MCP(Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12869
title: p-CH3-MCP, Cl ion pair (Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12870
title: p-chloro-MCP(Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12871
title: p-chloro-MCP, Cl ion pair (Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12872
title: p-fluoro-MCP(Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12873
title: p-fluoro-MCP, Cl ion pair (Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12874
title: p-nitro-MCP(Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12875
title: p-nitro-MCP, Cl ion pair (Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12876
title: p-OMe-MCP(Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12877
title: p-OMe-MCP, Cl ion pair(Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12878
title: p-NMe2-MCP(Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12879
title: p-NMe2-MCP, Cl ion pair (Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12880
title: p-isopropyl-MCP(Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12881
title: p-isopropyl-MCP, Cl ion pair (Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12884
title: 3-CH3-MCP-1

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12885
title: 3-CH3-MCP-Cl-1

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12886
title: 3-CH3-MCP-2

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12887
title: 3-CH3-MCP-Cl-2

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12888
title: p-NMe2-MCP, Cl ion pair (Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12889
title: 2-CH3-MCP-1

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12890
title: 2-CH3-MCP-Cl-1

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12891
title: 2-CH3-MCP-2

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12892
title: 2-CH3-MCP-Cl-2

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12893
title: 3-Br-MCP-1

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12894
title: 3-Br-MCP-Cl-1

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12895
title: 3-Br-MCP-2

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12896
title: 3-Br-MCP-Cl-2

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12897
title: 3-iPr-MCP-1

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12898
title: 3-iPr-MCP-Cl-1

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12899
title: 3-iPr-MCP-2

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12900
title: 3-iPr-MCP-Cl-2

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12904
title: 4-NMe2-MCP-Cl

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12905
title: 4-NHMe2-MCP-cation-Cl

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12906
title: p-NHMe2-MCP-cation (Def2-tzvpp)

description: Gaussian 16 (C01) calculation
DOI: 10.13140/rg.2.2.15212.74888
title: IoT-BASED SYSTEM FOR MONITORING WATER QUALITY
DOI: 10.13140/rg.2.2.21923.63523
title: IoT-BASED SYSTEM FOR MONITORING WATER QUALITY
DOI: 10.13140/rg.2.2.12696.16645
title: AN ENERGY EFFICIENT SYSTEM FOR WIRELESS SENSOR NETWORKS
DOI: 10.13140/rg.2.2.19407.05280
title: AN ENERGY EFFICIENT SYSTEM FOR WIRELESS SENSOR NETWORKS
DOI: 10.13140/rg.2.2.32828.82562
title: Cognitive Effects of Retained Primitive Reflexes Through the Lifespan in Autism Spectrum Disorder
DOI: 10.5281/zenodo.8186666
title: agave_fourcroydes_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8186665
title: agave_fourcroydes_spades_01.fasta.gz

description: .
DOI: 10.34876/q34b-q773
title: Raw data meta-analysis and literature review of Pneumocystis in pet, farm, zoo, and wild mammal species

description: The file contains raw data on zoological taxonomy of 276 published mammal species, information on the assignment to pet, farm, zoo, and wild animals, social and environmental factors, signalment, origin, Pneumocystis prevalence, investigated sample number, methods, level of infection, pathological description, disease-related data, genetic information, and the respective references.
DOI: 10.5281/zenodo.8186731
title: pteraster_tesselatus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8186730
title: pteraster_tesselatus_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23773592
title: Local Gaussian process extrapolation for BART models with applications to causal inference

description: Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard implementations of BART typically suffer from inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data. This paper proposes a novel extrapolation strategy that grafts Gaussian processes to the leaf nodes in BART for predicting points outside the range of the observed data. The new method is compared to standard BART implementations and recent frequentist resampling-based methods for predictive inference. We apply the new approach to a challenging problem from causal inference, wherein for some regions of predictor space, only treated or untreated units are observed (but not both). In simulation studies, the new approach boasts superior performance compared to popular alternatives, such as Jackknife+.
DOI: 10.6084/m9.figshare.23773592.v1
title: Local Gaussian process extrapolation for BART models with applications to causal inference

description: Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard implementations of BART typically suffer from inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data. This paper proposes a novel extrapolation strategy that grafts Gaussian processes to the leaf nodes in BART for predicting points outside the range of the observed data. The new method is compared to standard BART implementations and recent frequentist resampling-based methods for predictive inference. We apply the new approach to a challenging problem from causal inference, wherein for some regions of predictor space, only treated or untreated units are observed (but not both). In simulation studies, the new approach boasts superior performance compared to popular alternatives, such as Jackknife+.
DOI: 10.34740/kaggle/dsv/6198909
title: COVID Vaccination in World (updated daily)
DOI: 10.5258/soton/d2628
title: Dataset supporting the publication "Bi-frequency operation in a membrane external-cavity surface-emitting laser"

description: This dataset supporting the article "Bi-frequency operation in a membrane external-cavity surface-emitting laser", by J Daykin, J Woods, R Bek, M Jetter, P Michler, B Mills, P Horak, J Wilkinson and V Apostolopoulos. PLOS One The dataset contains the raw data and MATLAB scripts required for recreating figures in the publication. The figures are as follows: Figures 3B - Simulated mode losses for a range of mask widths Figure 4 - MECSEL output power and spectrum while off-mask Figure 5 - Spectral emission from MECSEL while operating on each of the spatial loss masks and while on an unablated area. Requires the running of individual .m files from subfolders Figure 6 - Calculated etalon transmission plotted with spectral emission from MECSEL while centred on the 10µm mask Figure 7C - Spectral profile from MECSEL while operating on the 50µm mask at a pump power of 3.96W Figure 8 - RF spectral data for MECSEL operating on mask with detector both blocked and unblocked, as well as the residual of the two plots.
DOI: 10.5517/ccdc.csd.cc2gjlyk
title: CCDC 2280079: Experimental Crystal Structure Determination

description: Related Article: Andreas Fischer, Helmut Buschmann, Michael Gruss, Dagmar Lischke|2023|U.S.Patents|||
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: 10.48432/fvlr30
title: Produtividade, fisiologia de bovinos e microclima de Sistema de Integração Lavoura-Pecuária-Floresta da Embrapa Gado de Corte.

description: Os dados aqui apresentados se referem a informações produtividade (peso diário) e fisiológicas (temperatura da cabeça, frequências cardíaca e respiratória e incidência de radiação ultravioleta) de bovinos, bem como de microambiente (temperatura do ar, do ponto de orvalho, de bulbo seco, de bulbo úmido, da água, umidade relativa do ar, velocidade do vento e radiação solar) coletados por sistema de Internet das Coisas (IoT) em área de Integração Lavoura-Pecuária-Floresta (ILPF) da Embrapa Gado de Corte. Os dados de produtividade (planilha "pesagensIndividuais-2021-12-17_2022_07_13.tab") foram coletados por três balanças de passagem instaladas em microambientes dos três sistemas da área de estudo (ILPF-28, ILPF-22 e ILP) e obtidos a partir de API (Application Programming Interface) disponibilizada pelo fornecedor das balanças, contendo, para cada animal, pesos diários consolidados no período entre 17/12/2021 e 13/07/2022. Os dados fisiológicos foram coletados por dispositivos (cabrestos) inteligentes chamados de BEP (Bovine Electonic Platform) instalados em um conjunto de animais, no período entre 20/05/2022 e 08/07/2022, com coletas realizadas a cada 15 minutos (planilha "dadosFisiologicos.tab") e disponiblizados em API pelo fornecedor dos equipamentos. Os dados de microclima (planilhaa "dadosMicroclima.tab") foram coletados por cinco estações instaladas nos microambientes dos três sistemas da área de estudo (2 estações no sistema ILPF-28, duas estações no sistema ILPF-22 e uma estação no sistema ILP), disponibilizados em plataforma do fornecedor das estações, considerando todos os dados, com intervalos de 15 minutos; e dados diários consolidados, no período de 31/03/2022 à 13/07/2022 . A planilha "qrCode_BEPxID_animal_RFID_Lote2021.tab" informa a relação estabelecida entre os códigos de identificação dos animais, dispositivo BEP utilizado, período e sistema de localização, permitindo o relacionamento entre todos os dispositivos instalados no sistema IoT. Os dados contidos nessas planilhas passaram por procedimentos padrão de limpeza considerando dados espúrios fora dos intervalos de medida especificados pelos dispositivos.
DOI: 10.5281/zenodo.8186611
title: Real-space correlation functions of nearest-neighbour spin ice

description: Real-space correlation functions \(\langle \sigma_i\sigma_j\rangle\) of the nearest-neighbour spin ice Hamiltonian \(H = J \sum_{\langle ij\rangle} \sigma_i\sigma_j\) on the pyrochlore lattice, from Monte Carlo simulations on 128×128×128 cubic unit cells. <strong>File formats</strong> The file name indicates the temperature (in units of J) where the measurement was taken. Files with extension .dat contain the correlation functions as double-precision real floats.<br> Files with extension .err contain the standard errors as single-precision real floats. Both file types contain an array of shape (4, 4, 4, 128, 128, 128). The meaning of the indices (from major to minor): sublattice index μ sublattice index ν index λ of FCC lattice point in the cubic unit cell offset of cubic lattice cells Entry [μ, ν, λ, x, y, z] corresponds to the correlator \(\langle \sigma(\vec R + \vec r^\mathrm{FCC}_\lambda + \vec r_\mu)\sigma(\vec r_\nu)\rangle\), where \(\vec R = [xyz] \), the FCC index λ corresponds to the lattice points \(\vec r^\mathrm{FCC}_0 = [000], \vec r^\mathrm{FCC}_1=[011]/2, \vec r^\mathrm{FCC}_2=[101]/2,\vec r^\mathrm{FCC}_3 = [110]/2\), and the sublattice indices correspond to the site offsets \(r_0 = [111]/8, r_1=[1\bar1\bar1]/8, r_2=[\bar11\bar1]/8,r_3=[\bar1\bar1 1]/8\) from the nearest FCC lattice point. Coordinates x,y,z run between 0 and 127 in periodic boundary conditions. The utility loader.py loads the files, shapes them in the correct array format, and extracts individual correlators. <strong>Details of data generation</strong> We performed Monte Carlo simulations of nearest-neighbour spin ice using the efficient loop-string algorithm introduced in Phys. Rev. B 90, 220406(R). For nonzero temperatures, correlations between different strings were excluded, which effectively averages all spin configurations compatible with a given loop graph. We ran 32 independent Markov chains at each temperature point and obtained 4096 Monte Carlo samples in each. The reported standard errors are the error on the mean of the 32 Markov chains.
description: Computing resources were provided by STFC Scientific Computing Department's SCARF cluster. A. Sz. gratefully acknowledges the ISIS Neutron and Muon Source and the Oxford–ShanghaiTech collaboration for support of the Keeley–Rutherford fellowship at Wadham College, Oxford.