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Genotype data and related phenotype data for Ovarian Cancer Association Consortium project investigating common variantion in GTPAse genes and ovarian cancer risk

Underlying UV-Vis, PL, EL and OLED current-voltage-luminance data for the samples discussed in the main article.

Multimodal biological and cognitive data used as predictors and outcomes for machine learning models can be found in 'master data sheet.xls'. With the exception of the derived PLS Derived GM all data were downloaded from the ADNI repository http://adni.loni.usc.edu/. For description on derivation of the PLS Dervived GM see ���Methods: Partial Least Squares Regression with Recursive Feature Elimination (PLSr-RFE).��� in the final publication DATA SETS: 1.) ���Methods: Partial Least Squares Regression with Recursive Feature Elimination (PLSr-RFE).��� Data available: RIDS: The ADNI identifier, DIAG(1CN, 2MCI): Baseline diagnosis (1:cognitively normal, 2: MCI) ADNI Mem: ADNI Memory composite measure used as outcome variable for the PLSr-RFE, PLS Derived GM: Variable derived from the PLSr-RFE procedure. These data are presented in ���Results: Composite grey matter score for predicting cross-modality associations��� 2.) ���Statistical Validation: Out-of-Sample validation[cross-modality associations ]��� Data available: RIDS: The ADNI identifier, DIAG(1CN, 2DEM, 3MCI): Baseline diagnosis (1:cognitively normal, 2:demented, 3: MCI), PLS Derived GM: Variable derived out-of-sample. FTP Braak(12): tau PET SUVR for Braak stage (1,2), FTP Braak(34): tau PET SUVR for Braak stage (3,4), FTP Braak(56): tau PET SUVR for Braak stage (5,6). These data are presented in ���Results: Composite grey matter score for predicting cross-modality associations��� 3.)���Statistical Validation: Out-of-Sample validation [Cross-modal associations -adni mem]��� Data available: RIDS: The ADNI identifier ADNI Mem: ADNI Memory composite measure used as outcome variable. These data are presented in ���Results: Composite grey matter score for predicting cross-modality associations��� 4.) ��� Methods:GMLVQ Cognitive model��� Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor. 1pMCI, 2sMCI: Outcome classes, 1:progressive Mild Cognitive Impairment, 2: stable Mild Cognitive Impairment. ���Results: Cognitive Classification Models for predicting sMCI vs pMCI��� 5.) ��� Methods:GMLVQ Biological model��� Data available: RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor. 1pMCI, 2sMCI: Outcome classes, 1:progressive Mild Cognitive Impairment, 2: stable Mild Cognitive Impairment. ���Results: Biological Classification Models for predicting sMCI vs pMCI��� 6.) ��� Methods: GMLVQ-Scalar Projection *Cognitive model*��� Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. 7.) ��� Methods: GMLVQ-Scalar Projection *Biological model*��� Data available: RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. ���Results: Trajectory modelling: Predicting Individual Variability in the Rate of Future Cognitive Decline. 8.) ���Methods: Statistical Validation: Out-of-Sample-[Cognitive model]��� Data available: RIDS: The ADNI identifier, ADNI Mem: ADNI memory composite used as predictor, ADNI EF: ADNI executive function composite used as predictor, GDS: Geriatric Depression Score used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. 9.) ���Methods: Statistical Validation: Out-of-Sample-[Biological model]��� : RIDS: The ADNI identifier, PLS Derived GM: grey matter score used as predictor, FBP: florbetapir SUVR used as a predictor, APOE4: APOE 4 genotype used as predictor, �� ADNI-Mem: Change in ADNI mem from baseline. ���Results: Trajectory modelling: Predicting Individual Variability in the Rate of Future Cognitive Decline.��� For a more detailed description of the populations these data were extracted for see 'description of uploaded files.doc'

the Summary of the data is detailed in the provided document "OpenDataSummary.pdf" and ordered by the figures they contributed to, including for the Figures in Supporting Information. M.J.M. thanks NSERC for a Discovery Grant.

The data set contains IR spectra, PXRD patterns, TGA curves and theoretical optimized crystal structures of ZIF materials.

Summary results for association of 11 million single nucleotide polymorphisms with epithelial ovarian cancer in Asian women. OBJECTIVE: Genome-wide association studies (GWASs) for epithelial ovarian cancer (EOC) have focused largely on populations of European ancestry. We aimed to identify common germline variants associated with EOC risk in Asian women. METHODS: Genotyping was performed as part of the OncoArray project. Samples with >60% Asian ancestry were included in the analysis. Genotyping was performed on 533,631 SNPs in 3238 Asian subjects diagnosed with invasive or borderline EOC and 4083 unaffected controls. After imputation, genotypes were available for 11,595,112 SNPs to identify associations. Please refer to the README file in 'AsianResultsChr9_X' for more information.

This research data supports 'Connective auxin transport in the shoot facilitates communication between shoot apices', which will be published in the 'PLoS Biology' journal. This record will be updated with publication details. This work was supported by the ERC [grant number N° 294514 – EnCoDe] and the Swedish Governmental Agency for Innovation Systems (VINNOVA) and the Swedish Research Council (VR) [grant number GAT3272C. Full datasets underlying graphs presented in the above named paper. 10.1371/journal.pbio.1002446

This dataset provides the data that underlies the paper "Efficient Energy Transport in an Organic Semiconductor Mediated by Transient Exciton Delocalization". Details on the organization of the dataset are provided in a read-me file in the dataset.

The figures and modelling data described in the manuscript are enclosed in this dataset. Data are provided for the experimental alloy (Alloy X) and characterised with Wagner’s oxidation criteria in metals (.xlsx file). Wagnerian analyses for commercial alloys are also included. In addition, scanning electron micrographs (.tif files) obtained for Alloy X are provided. Energy dispersive X-ray spectroscopy data are also included (.oip file) with the relevant data files (.dat) and can be opened using the proprietary Oxford Instruments AZtec software. Also included are the raw data for the thermo-gravimetric analysis and the surface areas of Alloy X used to calculate normalised mass gain data (.xlsx file).

R.R.S also acknowledges funding support from the Mexican National Council for Science and Technology (CONACYT, Grant No. 472427) and the Cambridge Trust. C.B. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie (RNA-Rep, MSCA grant no. 839899) and from the Royal Commission for the Exhibition of 1851. L.D.M. also acknowledges funding from the European Research Council (ERC) under the Horizon 2020 Research and Innovation Programme (ERC-STG No 851667 NANOCELL). D.K.O. acknowledges funding from the Natural Sciences and Engineering Research Council of Canada (NSERC, Early Career Researcher Grant no. 401667), Representative data set including: turbidity (UV-vis spectrophotometry) traces, fluorescene measurements, and data for statistical analyses, all in csv files, confocal and cryo-EM micrographs (pngs), and epifluorescence time and temperature-resolved movies (mp4). Epifluorescence microscopy movies are compressed for reasons of space, and the raw data are available through the authors.