cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14910546.v1
title: Spatial Autocorrelation Analysis using MIG-seq Data Revealed the Gamete and Larval Dispersal Range of the Blue Coral, Heliopora coerulea, within Reefs

description: Spatial autocorrelation analysis is a well-established technique for detecting spatial structures and patterns in ecology. However, compared to inter-population genetic structure, much less studies examined spatial genetic structure (SGS) within a<br>population by means of spatial autocorrelation analysis. More SGS analysis that compares the robustness of genome-wide single nucleotide polymorphisms (SNPs) and traditional population genetic markers in detecting SGS, and direct comparison between the estimated dispersal range based on SGS and the larval dispersal range of corals directly surveyed in the field would be important. In this study, we examined the<br>SGS of a reef-building coral species, Heliopora coerulea, in two different reefs (Shiraho and Akaishi) using genome-wide SNPs derived from multiplexed inter-simple sequence repeat (ISSR) genotyping (MIG)-seq analysis and nine microsatellite loci for comparison. Microsatellite data failed to reveal significant spatial patterns when using the same number of samples as MIG-seq, whereas MIG-seq analysis revealed significant spatial autocorrelation patterns up to 750 m in both Shiraho and Akaishi reefs based on the maximum significant distance method. However, detailed spatial genetic analysis using fine-scale distance classes (25–200 m) found an x-intercept of 255–392 m in Shiraho and that of 258–330 m in Akaishi reef. The latter results agreed well with a previously reported direct field observation of larval dispersal, indicating that the larvae<br>of H. coerulea settled within a 350 m range in Shiraho reef within one generation. Overall, our results empirically demonstrate that the x-intercept of the spatial correlogram agrees well with the larval dispersal distance that is most frequently found in field observations, and they would be useful for deciding effective conservation management units for maintenance and/or recovery within an ecological time scale. All supplementary files are deposited here. <br>
DOI: 10.5522/04/13308488
title: X-ray Tomography of N95 Face Mask

description: The N95 mask sample was scanned by lab-based X-ray micro-scale CT scanner (Zeiss Xradia 620 Versa, Carl Zeiss Inc.). 501 projections were obtained for the specimen. The standard reconstruction algorithm is used to reconstruct sample. The voxel resolution at 350 nm with the narrower (ca. 350 μm x 350 μm) field of view was achieved.
DOI: 10.5522/04/13308488.v1
title: X-ray Tomography of N95 Face Mask

description: The N95 mask sample was scanned by lab-based X-ray micro-scale CT scanner (Zeiss Xradia 620 Versa, Carl Zeiss Inc.). 501 projections were obtained for the specimen. The standard reconstruction algorithm is used to reconstruct sample. The voxel resolution at 350 nm with the narrower (ca. 350 μm x 350 μm) field of view was achieved.
DOI: 10.5281/zenodo.5061256
title: GVI: Sample data for computing VGVI. Vancouver, BC and Manchester.

description: This is a supplement for the GVI: Greenness Visibility Index R package. Description: This dataset contains raster (TIFF) data for computing the VGVI for the City of Vancouver and Manchester. <strong>Greater Manchester:</strong> Digital Terrain Model (DTM): Spatial Resolution: 5m Source: LIDAR Composite DTM 2017 - 50cm Licence: Open Government Licence (OGL) File name: GreaterManchester_DTM_5m.tif<br> Digital Surface Model (DSM): Spatial Resolution: 5m Source: LIDAR Composite DSM 2017 - 50cm Licence: Open Government Licence (OGL) File name: GreaterManchester_DSM_5m.tif<br> Greenspace Mask: Spatial resolution: 5m Source: Dennis et al. 2017 Licence: Open Government Licence (OGL) File name: GreaterManchester_GreenSpace_5m.tif <strong>Vancouver:</strong> Digital Terrain Model (DTM): Spatial Resolution: 1m Source: Canada’s Open Government Portal Licence: Open Government Licence - Canada File name: Vancouver_DTM_1m.tif<br> Digital Surface Model (DSM): Spatial Resolution: 1m Source: Canada’s Open Government Portal Licence: Open Government Licence - Canada File name: Vancouver_DSM_1m.tif<br> Greenspace Mask: Spatial Resolution: 2m Source: Land Cover Classification 2014 - 2m LiDAR Licence: Metro Vancouver File name: Vancouver_GreenSpace_2m.tif<br> Landuse Spatial Resolution: 2m Source: Land Cover Classification 2014 - 2m LiDAR Licence: Metro Vancouver File name: Vancouver_LULC_2m.tif
DOI: 10.5522/04/14781771
title: Data to support the paper "Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures"

description: This is the dataset to support the paper:<br>Fernando Pérez-García et al., 2021, <i>Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures</i>.<br>The paper has been accepted for publication at the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021).A preprint is available on arXiv: https://arxiv.org/abs/2106.12014<br>Contents:1) A CSV file "seizures.csv" with the following fields: - Subject: subject number - Seizure: seizure number - OnsetClonic: annotation marking the onset of the clonic phase - GTCS: whether the seizure generalises - Discard: whether one (Large, Small), none (No) or both (Yes) views were discarded for training.2) A folder "features_fpc_8_fps_15" containing two folders per seizure. The folders contain features extracted from all possible snippets from the small (S) and large (L) views. The snippets were 8 frames long and downsampled to 15 frames per second. The features are in ".pth" format and can be loaded using PyTorch: https://pytorch.org/docs/stable/generated/torch.load.html The last number of the file name indicates the frame index. For example, the file "006_01_L_000015.pth" corresponds to the features extracted from a snippet starting one second into the seizure video. Each file contains 512 numbers representing the deep features extracted from the corresponding snippet.3) A description file, "README.txt".
DOI: 10.6084/m9.figshare.13066136
title: Raw data and analysis scripts

description: Zipped file "cfa_wholesample.zip" containing raw data from the Mathematical Equivalence Assessment for whole sample and analysis scripts (Mplus) for the one-factor and two-factor models.Zipped file "cfa_eachcountry.zip" containing raw data from the Mathematical Equivalence Assessment for each country seperately and analysis scripts (Mplus) for the two-factor models for each country.Zipped file "measurement invariance.zip" containing raw data from the Mathematical Equivalence Assessment for whole sample and analysis scripts (Mplus) for measurement invariance models (configural model, scalar model, final partial scalar model).
DOI: 10.5281/zenodo.5036253
title: Experimental results dataset reported in the paper: "Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement."

description: <strong>Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement.</strong> Experimental results data-set (as reported in Figure 2 of the main paper). Caption of the Figure: Classical bound violation ∆S<em><sup>(l)</sup></em>(n) =S<em><sup>(l)</sup></em>(2n)−(2n−2) (<em>l=T,H,S</em>) for the multi-parameter <em>CHSH</em> inequalities in the temporal (red), spatial (grey) and “hybrid” (blue) domain. The dots represent the experimental results, with the uncertainty bars evaluated as statistical fluctuations among repeated measurement sets, while the solid curves show the theoretically-expected values (for the correlations belonging to the spatial domain, deviations from the ideal case due to the V<em><sub>s</sub></em>= 0.982 estimated visibility of the generated |<em>ψ<sub>−</sub></em>〉state were considered) Data-set description: Column 1 = X-coordinate: n (Number of Settings) Column 2 = Y-coordinate: ∆S<em><sup>(S)</sup></em>(n), Space-like (S) CHSH Violation [Grey color in the figure on the paper] Column 3 = Uncertainty on ∆S<em><sup>(S)</sup></em>(n) (Space-like, S, Grey in the figure on the paper), Column 4 = Y-coordinate: ∆S<em><sup>(H)</sup></em>(n), Hybrid (H) CHSH Violation [Blue color in the figure on the paper] Column 5 = Uncertainty on ∆S<em><sup>(H)</sup></em>(n) (Hybrid, H, Blue in the figure on the paper) Column 6 = Y-coordinate: ∆S<em><sup>(T)</sup></em>(n), Time-like (T) CHSH Violation [Red color in the figure on the paper] Column 7 = Uncertainty on ∆S<em><sup>(T)</sup></em>(n) (Time-like, T, Red in the figure on the paper)
DOI: 10.5281/zenodo.5036252
title: Experimental results dataset reported in the paper: "Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement."

description: <strong>Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement.</strong> Experimental results data-set (as reported in Figure 2 of the main paper). Caption of the Figure: Classical bound violation ∆S<em><sup>(l)</sup></em>(n) =S<em><sup>(l)</sup></em>(2n)−(2n−2) (<em>l=T,H,S</em>) for the multi-parameter <em>CHSH</em> inequalities in the temporal (red), spatial (grey) and “hybrid” (blue) domain. The dots represent the experimental results, with the uncertainty bars evaluated as statistical fluctuations among repeated measurement sets, while the solid curves show the theoretically-expected values (for the correlations belonging to the spatial domain, deviations from the ideal case due to the V<em><sub>s</sub></em>= 0.982 estimated visibility of the generated |<em>ψ<sub>−</sub></em>〉state were considered) Data-set description: Column 1 = X-coordinate: n (Number of Settings) Column 2 = Y-coordinate: ∆S<em><sup>(S)</sup></em>(n), Space-like (S) CHSH Violation [Grey color in the figure on the paper] Column 3 = Uncertainty on ∆S<em><sup>(S)</sup></em>(n) (Space-like, S, Grey in the figure on the paper), Column 4 = Y-coordinate: ∆S<em><sup>(H)</sup></em>(n), Hybrid (H) CHSH Violation [Blue color in the figure on the paper] Column 5 = Uncertainty on ∆S<em><sup>(H)</sup></em>(n) (Hybrid, H, Blue in the figure on the paper) Column 6 = Y-coordinate: ∆S<em><sup>(T)</sup></em>(n), Time-like (T) CHSH Violation [Red color in the figure on the paper] Column 7 = Uncertainty on ∆S<em><sup>(T)</sup></em>(n) (Time-like, T, Red in the figure on the paper)
DOI: 10.6084/m9.figshare.14910627
title: SDL-PAND model 2.5

description: Model developed in SDL that shows the trend of new cases in Catalonia. The execution can be done using SDLPS at https://sdlps.com or any software that understands SDL.This is part of the project http://pand.sdlps.com.
DOI: 10.25416/ntr.14866929
title: Academic oral presentations Pecha Kucha grading descriptors

description: Pecha Kucha formats are being increasingly used as forms of oral presentations for academic work. This grading descriptor was developed to assess paired Pecha Kucha style presentations for an academic module against 4 criteria : <b>Task Fulfilment and Knowledge</b><b>Communication Skills</b><b>Organisation, signposting and timing</b><b>Visual appeal and creativity.</b><br>it is developed on a 7 band scale from 'outstanding' to 'Fail ( severely limited)
DOI: 10.6084/m9.figshare.14912262
title: USAF_speckle_bin4.hdf5

description: example Data for ptyLab
DOI: 10.5281/zenodo.5070358
title: RRING Global Interviews Dataset (WP3)

description: The RRING Work Package 3 (WP3) objective was to clarify how Research Funding Organisations (RFOs) and Research Performing Organisations (RPOs) operated within region-specific research and innovation environments. It explored how they navigated the governance and regulatory frameworks for Responsible Research and Innovation (RRI), as well as offering their perspectives on the entities responsible for RRI-related policy and action in their locales. This dataset specifically covers the global interviews research part, which was designed to investigate bottom-up perspectives and experiences of researchers and innovators. The focus here is on collecting data through and from researchers and innovators themselves (i.e. ascertaining bottom-up views). We prioritised how and why research and innovation are supplied from those who are actually supplying it. In doing this, we also paid particular attention to: key RRI-related platforms, spaces and players operating in this region; interactions between different stakeholder types; domain-specific lessons related to Digital (ICT), Energy, Bioeconomy and Waste Management; as well as region-specific insights on what is shaping day-to-day research and innovation practice. Our sampling focused on the following geographical regions: African states; Arab states; Asian and Pacific states; European and North American States; and Latin American and Caribbean states. Of the 130+ interviews conducted, 29 participants consented to their transcripts being shared publicly
DOI: 10.6084/m9.figshare.14912229
title: Yapay Sinir Ağına Ait Her Katmandaki Ağırlık ve Bias Değerleri

description: Yapay sinir ağına ait her eğitimde her katmana ait ağırlık (weigth) ve bias değerleri tablosu.
DOI: 10.6084/m9.figshare.14912229.v2
title: Yapay Sinir Ağına Ait Her Katmandaki Ağırlık ve Bias Değerleri

description: Yapay sinir ağına ait her eğitimde her katmana ait ağırlık (weigth) ve bias değerleri tablosu.
DOI: 10.6084/m9.figshare.14912229.v1
title: Yapay Sinir Ağına Ait Her Katmandaki Ağırlık ve Bias Değerleri

description: Yapay sinir ağına ait her eğitimde her katmana ait ağırlık (weigth) ve bias değerleri tablosu.
DOI: 10.6084/m9.figshare.14912223
title: Yapay Sinir Ağına Ait Eğitim, Doğrulama ve Test Kümeleri

description: Yapay sinir ağına ait her iterasyonda seçtiği eğitim, doğrulama ve test veri kümleri tablosu.
DOI: 10.6084/m9.figshare.14912223.v2
title: Yapay Sinir Ağına Ait Eğitim, Doğrulama ve Test Kümeleri

description: Yapay sinir ağına ait her iterasyonda seçtiği eğitim, doğrulama ve test veri kümleri tablosu.
DOI: 10.6084/m9.figshare.14912223.v1
title: Yapay Sinir Ağına Ait Eğitim, Doğrulama ve Test Kümeleri

description: Yapay sinir ağına ait her iterasyonda seçtiği eğitim, doğrulama ve test veri kümleri tablosu.
DOI: 10.6084/m9.figshare.14910576.v1
title: Korelasyon Tablosu

description: 1995-2019 yılları arasında yıllık toplam SILSO ve AAVSO güneş leke sayıları ile hesaplanan şok parametreleri arasındaki korelasyon tablosu ve 1986-1994 yılları arasında yapay sinir ağı ile tahmin edilmiş şok parametreleri ile yıllık toplam SILSO ve AAVSO güneş leke sayıları arasındaki korelasyon tablosu.
DOI: 10.6084/m9.figshare.14910576
title: Korelasyon Tablosu

description: 1995-2019 yılları arasında yıllık toplam SILSO ve AAVSO güneş leke sayıları ile hesaplanan şok parametreleri arasındaki korelasyon tablosu ve 1986-1994 yılları arasında yapay sinir ağı ile tahmin edilmiş şok parametreleri ile yıllık toplam SILSO ve AAVSO güneş leke sayıları arasındaki korelasyon tablosu.
DOI: 10.6084/m9.figshare.14910576.v3
title: Korelasyon Tablosu

description: 1995-2019 yılları arasında yıllık toplam SILSO ve AAVSO güneş leke sayıları ile hesaplanan şok parametreleri arasındaki korelasyon tablosu ve 1986-1994 yılları arasında yapay sinir ağı ile tahmin edilmiş şok parametreleri ile yıllık toplam SILSO ve AAVSO güneş leke sayıları arasındaki korelasyon tablosu.
DOI: 10.6084/m9.figshare.14910576.v2
title: Korelasyon Tablosu

description: 1995-2019 yılları arasında yıllık toplam SILSO ve AAVSO güneş leke sayıları ile hesaplanan şok parametreleri arasındaki korelasyon tablosu ve 1986-1994 yılları arasında yapay sinir ağı ile tahmin edilmiş şok parametreleri ile yıllık toplam SILSO ve AAVSO güneş leke sayıları arasındaki korelasyon tablosu.
DOI: 10.6084/m9.figshare.14910561
title: Model ile Analiz Sonucu Şoklara Ait Parametreler

description: Cavus 2013 Model ile WIND uydu verilerinin analiz edildikten sonra elde edilen şoklara ait fiziksel parametreler (1995-2019)
DOI: 10.6084/m9.figshare.14910561.v2
title: Model ile Analiz Sonucu Şoklara Ait Parametreler

description: Cavus 2013 Model ile WIND uydu verilerinin analiz edildikten sonra elde edilen şoklara ait fiziksel parametreler (1995-2019)
DOI: 10.6084/m9.figshare.14910561.v1
title: Model ile Analiz Sonucu Şoklara Ait Parametreler

description: Cavus 2013 Model ile WIND uydu verilerinin analiz edildikten sonra elde edilen şoklara ait fiziksel parametreler (1995-2019)
DOI: 10.6084/m9.figshare.14910549
title: Model ile Udu Verilerinin Hazırlanması

description: WIND uydusundan alınan şok sonrası yoğunluk verileri ile CAVUS 2013 model ile analizi için hazırlanan tablo.
DOI: 10.6084/m9.figshare.14910549.v3
title: Model ile Udu Verilerinin Hazırlanması

description: WIND uydusundan alınan şok sonrası yoğunluk verileri ile CAVUS 2013 model ile analizi için hazırlanan tablo.
DOI: 10.6084/m9.figshare.14910549.v2
title: Model ile Udu Verilerinin Hazırlanması

description: WIND uydusundan alınan şok sonrası yoğunluk verileri ile CAVUS 2013 model ile analizi için hazırlanan tablo.
DOI: 10.6084/m9.figshare.14198927
title: Analysis of completeness for spontaneous reporting of disease-modifying therapies in multiple sclerosis

description: <b>Introduction:</b> Considering the need for effective postmarketing surveillance of disease-modifying therapies (DMTs) in multiple sclerosis (MS), we analyzed the potential of the spontaneous reports for safety signal detection, verifying the completeness of the reports in the FDA Adverse Event Reporting System (FAERS). <b>Methods:</b> All reports with DMTs for MS considered the primary suspect cause of ADRs and registered between January 2004 and June 2019 were selected. The vigiGrade completeness score was applied and reports with a score greater than 0.80 were considered well documented. Descriptive statistical analysis and comparisons of well-documented reports by DMTs were performed. <b>Results:</b> A total of 297,926 reports were analyzed. The lowest completeness rates were observed for type of report (13.5%), dose (62.7%), and time from treatment start to the ADR (79.0%). Overall, 80.8% of reports were classified as well documented and those related to natalizumab had the highest proportion (92.4%, p <b>Conclusions:</b> The high proportion of well-documented reports for DMTs indicates that they can be a valuable source for safety signal detection. A more careful analysis should be performed for data from the groups identified with low completeness to avoid the disclosure of spurious results.
DOI: 10.6084/m9.figshare.14198927.v1
title: Analysis of completeness for spontaneous reporting of disease-modifying therapies in multiple sclerosis

description: <b>Introduction:</b> Considering the need for effective postmarketing surveillance of disease-modifying therapies (DMTs) in multiple sclerosis (MS), we analyzed the potential of the spontaneous reports for safety signal detection, verifying the completeness of the reports in the FDA Adverse Event Reporting System (FAERS). <b>Methods:</b> All reports with DMTs for MS considered the primary suspect cause of ADRs and registered between January 2004 and June 2019 were selected. The vigiGrade completeness score was applied and reports with a score greater than 0.80 were considered well documented. Descriptive statistical analysis and comparisons of well-documented reports by DMTs were performed. <b>Results:</b> A total of 297,926 reports were analyzed. The lowest completeness rates were observed for type of report (13.5%), dose (62.7%), and time from treatment start to the ADR (79.0%). Overall, 80.8% of reports were classified as well documented and those related to natalizumab had the highest proportion (92.4%, p <b>Conclusions:</b> The high proportion of well-documented reports for DMTs indicates that they can be a valuable source for safety signal detection. A more careful analysis should be performed for data from the groups identified with low completeness to avoid the disclosure of spurious results.
DOI: 10.5517/ccdc.csd.ccvn2fz
title: CCDC 793861: Experimental Crystal Structure Determination

description: Related Article: Zhi-rong Luo, Bing Yang, Gui-fang Qin, Cui-ling Lan, Jin-qiao Long, Hai-juan Tong, Fa-yong Wang|2016|Huaxue shijie|57|88|
DOI: 10.5517/ccdc.csd.ccxd2t5
title: CCDC 845767: Experimental Crystal Structure Determination

description: Related Article: Yi Dong, Jiuliang Fan, Shuangsheng Zhou, Xuan Xue, Jiafeng Wang|2013|Zhongguo Yiyao Gongye Zazhi|44|337|
DOI: 10.17029/5e33ce44-0285-4a75-a0e8-fc28e0c2ee83
title: Dataset for 'Characteristics of those who fail on conditional release'.

description: Dataset for ‘Predictors of recidivism following release from custody: A meta-analysis’. Contains study data that underpinned the review.
DOI: 10.6084/m9.figshare.14910522.v1
title: WIND Uydu Verileri.xlsx

description: 1995-2019 yılları arasında WIND uydusunun ölçmüş olduğu şoklara ait yoğunluk, hız ve şok tiplerine ait tablo.
DOI: 10.6084/m9.figshare.14910522
title: WIND Uydu Verileri.xlsx

description: 1995-2019 yılları arasında WIND uydusunun ölçmüş olduğu şoklara ait yoğunluk, hız ve şok tiplerine ait tablo.
DOI: 10.6084/m9.figshare.14903103.v1
title: Aprendizado de Máquina para Descoberta de Conhecimento no RHC: Um estudo sobre câncer de boca

description: Tabela suplementar com regras de associação obtidas do estudo feito na base de dados dos Registros Hospitalares de Câncer (RHC) para os registros de câncer de boca.
DOI: 10.25345/c5tc2h
title: MassIVE MSV000087755 - Bone collagen from Australian marsupials for ZooMS peptide markers (1 of 2)
title: This dataset was used for the identification of ZooMS markers for peptide mass fingerprinting of bone collagen. The dataset includes: raw files, peak list files, result files, and the databases used for the searches. A metadata file is included with information about the sample numbers. This is part 1 of a two-part dataset. Part 1 has all samples up to DA829. Samples DA822-rep, and DA830-DA836 can be found in part 2.
DOI: 10.6084/m9.figshare.13637576
title: Row data manuscript Tommasi2021.xlsx

description: The file contains 5 spreadsheet:<br>1-ASV Table-pollen DNA metabarcoding: Matrix of quantitative data that represents the occurrence of all the plant taxa, identified through pollen ITS2 DNA metabarcoding, in pollen samples. Plant taxa are reported in rows and samples in columns. The first row indicates the sample ID and second row the sampling locality. 2-Pollen characterization: contains information about species richness and exotic or crop richness in pollen samples<br>3-Pantrap-morphospecies: Matrix of quantitative data describing the occurrence of the identified pollinator morphospecies in the investigated farms. Farms are reported in rows and morphospecies in columns.<br>4-Vegetation quadrats: Matrix of data that represents the number of flowers for each plant taxa, identified in vegetational quadrats. Farms are reported in rows and morphospecies in columns.<br>5-Sampling sites Data: Contains information about sampling sites localities, land use analysis results as well as local informations related to plants and pollinators