cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2022-11.2636224/2207780
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2625594/2207781
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2625004/2207782
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2625589/2207783
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2625203/2207784
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2632815/2207785
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2631737/2207786
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2631330/2207787
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2632642/2207788
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2622420/2207789
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2640117/2207790
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2622785/2207791
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2636707/2207792
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2637171/2207793
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2622076/2207794
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2625009/2207795
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2630038/2207796
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2623751/2207797
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2622507/2207798
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2625066/2207799
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2636171/2207800
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2623855/2207801
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2626727/2207802
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2623766/2207803
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2624421/2207804
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2622345/2207805
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2634795/2207806
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2623650/2207807
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2623344/2207808
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2636149/2207809
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2623823/2207810
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2636694/2207811
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2631891/2207812
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2625116/2207813
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2624010/2207814
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2624225/2207815
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2622407/2207816
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2624065/2207817
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2631943/2207818
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2630025/2207819
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2632334/2207820
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2623699/2207821
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2637958/2207822
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2622435/2207823
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2622271/2207824
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2633378/2207825
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2627128/2207826
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2629994/2207827
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2625176/2207828
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2622174/2207829
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-10.2625889/2207830
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2629639/2207831
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-10.2623901/2207832
title: Data for EMSL Project 51241 from October 2022
DOI: 10.25582/data.2022-11.2637948/2207833
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2636495/2207834
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2633778/2207835
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-10.2626362/2207836
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-11.2636829/2207837
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2630604/2207838
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2637208/2207839
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2020-09.2052063/2207840
title: Data for EMSL Project 51127 from September 2020
DOI: 10.25582/data.2022-10.2627653/2207841
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-10.2627168/2207842
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-11.2632777/2207843
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2637163/2207844
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2632279/2207845
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-10.2626140/2207846
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-11.2637083/2207847
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2632858/2207848
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-10.2626055/2207849
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-11.2633787/2207850
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2633788/2207851
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2637039/2207852
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2638745/2207853
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-10.2626047/2207854
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-10.2628382/2207855
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-10.2627021/2207856
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-10.2627655/2207857
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-10.2626687/2207858
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-11.2631463/2207859
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-10.2627581/2207860
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-10.2627834/2207861
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-11.2633922/2207862
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-10.2625999/2207863
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-10.2628136/2207864
title: Data for EMSL Project 51127 from October 2022
DOI: 10.25582/data.2022-11.2639366/2207865
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2636491/2207866
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2631662/2207867
title: Data for EMSL Project 51127 from November 2022
DOI: 10.6084/m9.figshare.19640886.v6
title: Replication Package - Taxonomy of Inline Code Comments Smells

description: Artifacts of our study on the taxonomy of inline code comment smells
DOI: 10.25446/oxford.21564528
title: M111 [The Reader], Set 2 of 3. Typescript fragment, unsigned and undated. 4p. Transcription.

description: TEI-XML transcription of M111-1 [The Reader]. Set 2 of 3. Typescript fragment, with the author's ms. corrections, unsigned and undated. 4p. <br> Text encoded as part of 'The Digital "Anon": A Digital Genetic Edition of Virginia Woolf's Final Essays'. This project, funded by Leverhulme Grant ECF-2022-602, seeks to construct a TEI-XML based digital edition of the drafts of 'Anon' and 'The Reader', among the final documents Woolf wrote before her death. <br> <strong>Author</strong> Woolf, Virginia <br> <strong>Editor</strong> Phillips, Joshua <br> <strong>Record Type</strong> Transcription <br> <strong>Location</strong> New York Public Library
DOI: 10.5281/zenodo.8183581
title: HotBENT microbiol. analysis: DNA quantificaton

description: The file contains a detailed list of samples processed for sequencing together with their individual primers pairs and final DNA concentrations.
DOI: 10.5281/zenodo.8183580
title: HotBENT microbiol. analysis: DNA quantificaton

description: The file contains a detailed list of samples processed for sequencing together with their individual primers pairs and final DNA concentrations.
DOI: 10.6084/m9.figshare.23744652
title: Data for "From desiccation to re-integration of the Yellow River since the last glaciation"

description: Dataset S1: Detrital zircon U-Pb age data. Dataset S2: Geochemical elements data (Major and trace elements, including rare-earth elements). Dataset S3: Sr isotope data shown in Main Text Fig. 3a. Code S1: A MATLAB-based method for calculating the crustal flexural deformation by a water body.
DOI: 10.34740/kaggle/dsv/6192937
title: Bitcoin Blockchain Historical Data
DOI: 10.6084/m9.figshare.21816306
title: Data from: Assessing the genetic diversity of Nymphoides peltata in the native and adventive range using microsatellite markers

description: Set of population data for <em>Nymphoides peltata</em>, which is an invasive plant species in the USA. Microsatellite data for eight markers, from plants in the native and adventive ranges.
DOI: 10.6084/m9.figshare.21816306.v1
title: Data from: Assessing the genetic diversity of Nymphoides peltata in the native and adventive range using microsatellite markers

description: Set of population data for <em>Nymphoides peltata</em>, which is an invasive plant species in the USA. Microsatellite data for eight markers, from plants in the native and adventive ranges.
DOI: 10.34740/kaggle/dsv/6081484
title: Bitcoin Hist. KLines | All Intervals (2017-2023)
DOI: 10.14473/csda/rgptg0
title: Our Society 2018 - September

description: Research was conducted by the Public Opinion Research Center (CVVM) of the Institute of Sociology of the Czech Academy of Sciences as a part of the research "Our Society 2018". Data collection was secured by the interviewer network of CVVM. Please note that this research is an English version / translation of materials from research "Naše společnost září 2018"
DOI: 10.21227/3ej9-e459
title:  Synthetic Whole-Head MRI Brain Tumor Segmentation Dataset

description: The demand for artificial intelligence (AI) in healthcare is rapidly increasing. However, significant challenges arise from data scarcity and privacy concerns, particularly in medical imaging. While existing generative models have achieved success in image synthesis and image-to-image translation tasks, there remains a gap in the generation of 3D semantic medical images. To address this gap, we introduce Med-DDPM, a diffusion model specifically designed for semantic 3D medical image synthesis, effectively tackling data scarcity and privacy issues.The novelty of Med-DDPM lies in its incorporation of semantic conditioning, enabling precise control during the image generation process. Our model outperforms Generative Adversarial Networks (GANs) in terms of stability and performance, generating diverse and anatomically coherent images with high visual fidelity. Comparative analysis against state-of-the-art augmentation techniques demonstrates that Med-DDPM produces comparable results, highlighting its potential as a data augmentation tool for enhancing model accuracy.In conclusion, Med-DDPM pioneers 3D semantic medical image synthesis by delivering high-quality and anatomically coherent images. Furthermore, the integration of semantic conditioning with Med-DDPM holds promise for image anonymization in the field of biomedical imaging, showcasing the capabilities of the model in addressing challenges related to data scarcity and privacy concerns. Our code and model weights are publicly accessible on our GitHub repository at https://github.com/mobaidoctor/med-ddpm/, facilitating reproducibility.
DOI: 10.14473/csda/xhn6kh
title: Our Society 2018 - February

description: Research was conducted by the Public Opinion Research Center (CVVM) of the Institute of Sociology of the Czech Academy of Sciences as a part of the research "Our Society 2018". Data collection was secured by the interviewer network of CVVM. Please note that this research is an English version / translation of materials from research "Naše společnost Únor 2018"
DOI: 10.5281/zenodo.7576785
title: Properties of binary systems in a one-dimensional approximation

description: We present numerically obtained tables of properties of stars in a binary system as a function of the effective potential: volume-equivalent radii of the equipotential surfaces, effective accelerations averaged over the same equipotential surfaces and the properties of the L<sub>1</sub> plane cross-sections. The tables are obtained for binaries where the ratios of the primary star mass to the companion star mass are from 10<sup>-6</sup> to 10<sup>5</sup> and include equipotential surfaces up to the star's outer Lagrangian point. We supply a sample code showing how to use our tables to get the average effective accelerations in one-dimensional stellar codes. Updates will be available at https://github.com/AliPourmand/1D_binary_star_properties. Paper describing the tables can be found on ApJ, <strong>DOI</strong> 10.3847/1538-4357/acd4c1, https://iopscience.iop.org/article/10.3847/1538-4357/acd4c1 If you make use of the provided tables or the code, we ask you to cite the paper (and zenodo deposit if such reference is allowed).
description: Manuscript describing the numerical method to obtain tables is published in Astrophysical Journal. If any updates to the tables-reading code will be issued, they can be found on https://github.com/AliPourmand/1D_binary_star_properties
DOI: 10.5281/zenodo.7576784
title: Properties of binary systems in a one-dimensional approximation

description: We present numerically obtained tables of properties of stars in a binary system as a function of the effective potential: volume-equivalent radii of the equipotential surfaces, effective accelerations averaged over the same equipotential surfaces and the properties of the L<sub>1</sub> plane cross-sections. The tables are obtained for binaries where the ratios of the primary star mass to the companion star mass are from 10<sup>-6</sup> to 10<sup>5</sup> and include equipotential surfaces up to the star's outer Lagrangian point. We supply a sample code showing how to use our tables to get the average effective accelerations in one-dimensional stellar codes. Updates will be available at https://github.com/AliPourmand/1D_binary_star_properties. Paper describing the tables can be found on ApJ, <strong>DOI</strong> 10.3847/1538-4357/acd4c1, https://iopscience.iop.org/article/10.3847/1538-4357/acd4c1 If you make use of the provided tables or the code, we ask you to cite the paper (and zenodo deposit if such reference is allowed).
description: Manuscript describing the numerical method to obtain tables is published in Astrophysical Journal. If any updates to the tables-reading code will be issued, they can be found on https://github.com/AliPourmand/1D_binary_star_properties
DOI: 10.6084/m9.figshare.22221004
title: Output files of AUGUSTUS.

description: Output files of AUGUSTUS. “new_Acj_gene.gtf” shows structural information of the predicted genes in the assembled genome. “new_Acj_gene_cds.fa” and “new_Acj_gene_aa.fa” are fasta files containing the coding and translated amino acid sequences, respectively.
DOI: 10.6084/m9.figshare.22221004.v1
title: Output files of AUGUSTUS.

description: Output files of AUGUSTUS. “new_Acj_gene.gtf” shows structural information of the predicted genes in the assembled genome. “new_Acj_gene_cds.fa” and “new_Acj_gene_aa.fa” are fasta files containing the coding and translated amino acid sequences, respectively.
DOI: 10.6084/m9.figshare.22221106
title: Functional annotation results of the predicted genes.

description: Functional annotation results of the predicted genes by fanflow4Insects.
DOI: 10.6084/m9.figshare.22221106.v1
title: Functional annotation results of the predicted genes.

description: Functional annotation results of the predicted genes by fanflow4Insects.
DOI: 10.14473/csda/vidbho
title: Our Society 2018 - January

description: Research was conducted by the Public Opinion Research Center (CVVM) of the Institute of Sociology of the Czech Academy of Sciences as a part of the research "Our Society 2018". Data collection was secured by the interviewer network of CVVM. Please note that this research is an English version / translation of materials from research "Naše společnost leden 2018"
DOI: 10.6084/m9.figshare.22220404
title: Accession IDs of the assemble genome sequences (IDs in gff file, description raw of fasta file and DDBJ accession IDs).

description: Accession IDs of the assemble genome sequences (IDs in gff file, description raw of fasta file and DDBJ accession IDs).
DOI: 10.6084/m9.figshare.22220404.v1
title: Accession IDs of the assemble genome sequences (IDs in gff file, description raw of fasta file and DDBJ accession IDs).

description: Accession IDs of the assemble genome sequences (IDs in gff file, description raw of fasta file and DDBJ accession IDs).
DOI: 10.6084/m9.figshare.22220488
title: Output files of RepeatModeler2 and RepeatMasker.

description: Output files of RepeatModeler2 and RepeatMasker. The fasta file (new_Acj.fa) and stk (new_Acj.stk) file contains consensus sequences of transposable elements in <em>Apis cerana japinica </em>from RepeatModeler2. The two files, new_Acj.out and new_Acj.tbl, are the result files from RepeatMasker. new_Acj.masked.gz is the genome sequence file of <em>Apis cerana japonica </em>in which repetitive regions and TE sequences are shown in “N” (used input file for gene prediction).
DOI: 10.6084/m9.figshare.22210963
title: Accession numbers of raw RNA-Seq data for Apis cerana japonica gene predictions.

description: Accession numbers of RNA-Seq for Apis cerana japonica gene predictions.
DOI: 10.6084/m9.figshare.22210963.v1
title: Accession numbers of raw RNA-Seq data for Apis cerana japonica gene predictions.

description: Accession numbers of RNA-Seq for Apis cerana japonica gene predictions.
DOI: 10.5281/zenodo.8183358
title: panulirus_versicolor_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8183357
title: panulirus_versicolor_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23741475
title: Model for NCA game

description: Model for NCA game.
DOI: 10.6084/m9.figshare.23741475.v1
title: Model for NCA game

description: Model for NCA game.
DOI: 10.15129/f313b62f-c982-4fe4-a797-1e2d1de12dba
title: Data for: "Low phase noise operation of a cavity-stabilized 698 nm AlGaInP-based VECSEL"

description: The dataset presented here corresponds to the experimental data reported in the manuscript "Low phase noise operation of a cavity-stabilized 698 nm AlGaInP-based VECSEL" to be published in Optics Express (DOI to be attributed). This paper describes low phase noise operation of a novel AlGaInP-based vertical-external-cavity surface-emitting-lasers (VECSEL) with emission at 698 nm. It includes the data for: &gt; Fig. 2a: Emission wavelength tuning curves via BRF rotation for VECSEL sample mount temperature of 18 and 24 °C; &gt; Fig. 2a(inset): Free-running linewidth measured from the reference cavity normalized transmitted spectrum.; &gt; Fig. 2b: Frequency noise PSD of the cavity locked VECSEL and the β-separation line (dash line); &gt; Fig. 2c: PSD of the optical field of the locked VECSEL; &gt; Fig. 2d: RIN of the cavity locked VECSEL; &gt; Fig. 3a: Fast-phase noise measurements obtained from the beat note between the unmodulated laser light and reference cavity transmission. Measured detector noise is shown in grey; &gt; Fig. 4b: Beat note peak measured using an electrical spectrum analyser and Voigt fit with linewidth of 110 Hz; &gt; Fig. 4b(inset): beat note peak over a 48 MHz frequency range with no peak pedestal observed.
DOI: 10.6084/m9.figshare.23617209
title: Supplementary Material for: Association between Rapid Ventricular Response and Stroke Outcomes in Atrial Fibrillation-Related Cardiac Embolic Stroke

description: <b><i>Introduction:</i></b> Patients with atrial fibrillation-related stroke (AF-stroke) are prone to developing rapid ventricular response (RVR). We investigated whether RVR is associated with initial stroke severity, early neurological deterioration (END) and poor outcome at 3 months. <b><i>Methods:</i></b> We reviewed patients who had AF-stroke between January 2017 and March 2022. RVR was defined as having heart rate &gt;100 beats per minute on initial electrocardiogram. Neurological deficit was evaluated with National Institutes of Health Stroke Scale (NIHSS) score at admission. END was defined as increase of ≥2 in total NIHSS score or ≥1 in motor NIHSS score within first 72 h. Functional outcome was score on modified Rankin Scale at 3 months. Mediation analysis was performed to examine potential causal chain in which initial stroke severity may mediate relationship between RVR and functional outcome. <b><i>Results:</i></b> We studied 568 AF-stroke patients, among whom 86 (15.1%) had RVR. Patients with RVR had higher initial NIHSS score (<i>p</i> &lt; 0.001) and poor outcome at 3 months (<i>p</i> = 0.004) than those without RVR. The presence of RVR [adjusted odds ratio (aOR) = 2.13; <i>p</i> = 0.013] was associated with initial stroke severity, but not with END and functional outcome. Otherwise, initial stroke severity [aOR = 1.27; <i>p</i> = &lt;0.001] was significantly associated with functional outcome. Initial stroke severity as a mediator explained 58% of relationship between RVR and poor outcome at 3 months. <b><i>Conclusion:</i></b> In patients with AF-stroke, RVR was independently associated with initial stroke severity but not with END and functional outcome. Initial stroke severity mediated considerable proportion of association between RVR and functional outcome.