cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5945728
title: Additional file 4: of Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

description: Table S3. Significantly differentially methylated sites in transcriptional cluster 2 placentas versus transcriptional cluster 1 placentas. (XLSX 9040Â kb)
DOI: 10.6084/m9.figshare.5945728.v1
title: Additional file 4: of Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

description: Table S3. Significantly differentially methylated sites in transcriptional cluster 2 placentas versus transcriptional cluster 1 placentas. (XLSX 9040Â kb)
DOI: 10.6084/m9.figshare.5945725
title: Additional file 16: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S13. Newick file of D1/D2 sequences generated for 578 yeast species in RAxML-HPC BlackBox. (XLSX 23 kb)
DOI: 10.6084/m9.figshare.5945725.v1
title: Additional file 16: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S13. Newick file of D1/D2 sequences generated for 578 yeast species in RAxML-HPC BlackBox. (XLSX 23 kb)
DOI: 10.6084/m9.figshare.5945704
title: Additional file 15: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S12. Proportion of correctly predicted traits across each method implemented to handle missing data. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.5945704.v1
title: Additional file 15: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S12. Proportion of correctly predicted traits across each method implemented to handle missing data. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.5945686
title: Additional file 14: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S11. Imputed binary species trait matrix. (XLSX 137 kb)
DOI: 10.6084/m9.figshare.5945686.v1
title: Additional file 14: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S11. Imputed binary species trait matrix. (XLSX 137 kb)
DOI: 10.6084/m9.figshare.5945668
title: Additional file 13: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S10. Species trait matrix, scored with growth (+), no growth (â ), variable (v), or data unavailable (n), curated from The Yeasts: A Taxonomic Study [26]. (XLSX 245 kb)
DOI: 10.6084/m9.figshare.5945668.v1
title: Additional file 13: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S10. Species trait matrix, scored with growth (+), no growth (â ), variable (v), or data unavailable (n), curated from The Yeasts: A Taxonomic Study [26]. (XLSX 245 kb)
DOI: 10.6084/m9.figshare.5945653
title: Additional file 13: of Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

description: Table S9. Genes with significant integrated gene expression and methylation scores by SMITE analysis in transcriptional clusters 2 and 3. (XLSX 86Â kb)
DOI: 10.6084/m9.figshare.5945653.v1
title: Additional file 13: of Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

description: Table S9. Genes with significant integrated gene expression and methylation scores by SMITE analysis in transcriptional clusters 2 and 3. (XLSX 86Â kb)
DOI: 10.6084/m9.figshare.5945647
title: Additional file 12: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S9. Χ2 associations between gene presence and carbon traits. The “Same” column represents the number of times a gene was present and there was growth on the carbon source. The “Different” column represents the count of gene absence and growth plus gene presence and no growth. We corrected for multiple tests across associations with the Benjamini–Hochberg correction (q &lt; 0.05 considered significant), which is represented by the column labeled “p Adj ”. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.5945647.v1
title: Additional file 12: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S9. Χ2 associations between gene presence and carbon traits. The “Same” column represents the number of times a gene was present and there was growth on the carbon source. The “Different” column represents the count of gene absence and growth plus gene presence and no growth. We corrected for multiple tests across associations with the Benjamini–Hochberg correction (q &lt; 0.05 considered significant), which is represented by the column labeled “p Adj ”. (XLSX 11 kb)
DOI: 10.21227/7pkd-7126
title: OCTA-500

description: Optical coherence tomography angiography (OCTA) is a novel imaging modality that allows a micron-level resolution to present the three-dimensional structure of the retinal vascular.We propose a new multi-modality dataset, dubbed OCTA-500. It contains 500 subjects with two field of view (FOV) types, including OCT and OCTA volumes, six types of projections, four types of text labels and two types of pixel-level labels. The dataset contains more than 360K images with a size of about 80GB. We will upload the encrypted dataset first, and the password will be released later in this article.
DOI: 10.6084/m9.figshare.5945632
title: Additional file 12: of Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

description: Table S8. Significant KEGG pathways associated with the significant SMITE modules in transcriptional clusters 2 and 3 (XLSX 58Â kb)
DOI: 10.6084/m9.figshare.5945632.v1
title: Additional file 12: of Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

description: Table S8. Significant KEGG pathways associated with the significant SMITE modules in transcriptional clusters 2 and 3 (XLSX 58Â kb)
DOI: 10.6084/m9.figshare.5945605
title: Additional file 10: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S8. Gene and trait presence for 79 species with curated genome sequences [55]. Gene presence was detected using TBLASTX and BLASTN searches using query sequences from the characterized pathways in model organisms (e.g., S. cerevisiae) versus the curated genome assemblies [55], using an e value cutoff of 10â 10. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.5945605.v1
title: Additional file 10: of Factors driving metabolic diversity in the budding yeast subphylum

description: Table S8. Gene and trait presence for 79 species with curated genome sequences [55]. Gene presence was detected using TBLASTX and BLASTN searches using query sequences from the characterized pathways in model organisms (e.g., S. cerevisiae) versus the curated genome assemblies [55], using an e value cutoff of 10â 10. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.5945590
title: Additional file 10: of Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

description: Table S7. Significant gene expression correlations associated with the significantly differentially methylated sites in transcriptional cluster 3 placentas versus transcriptional cluster 1 placentas. (XLSX 63Â kb)
DOI: 10.6084/m9.figshare.5945590.v1
title: Additional file 10: of Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia

description: Table S7. Significant gene expression correlations associated with the significantly differentially methylated sites in transcriptional cluster 3 placentas versus transcriptional cluster 1 placentas. (XLSX 63Â kb)
DOI: 10.6084/m9.figshare.5945578
title: Thumbs up: Movements made by the thumb are smoother and larger than fingers in finger-thumb opposition tasks.

description: The File consists of Raw Data set collected in the experiment.<br>This data set is segregated based on subjects participated in the experiment. Inside each subject folder, the files are named based on performed task, performed trial and the subject ID. Task 1 to 12 are number based on phalanges of the fingers (There are 12 finger phalanges other than thumb). So index finger distal phalanx is named as the task 1, then task 2 is the middle phalanx of the index finger, task 3 is the proximal of the index finger, task 4 is the distal phalanx of the middle finger and so on until little finger proximal phalanx. Each Task is repeated 15 times. Error trials are also included in the data set.<br>
DOI: 10.6084/m9.figshare.5945578.v1
title: Thumbs up: Movements made by the thumb are smoother and larger than fingers in finger-thumb opposition tasks.

description: The File consists of Raw Data set collected in the experiment.<br>This data set is segregated based on subjects participated in the experiment. Inside each subject folder, the files are named based on performed task, performed trial and the subject ID. Task 1 to 12 are number based on phalanges of the fingers (There are 12 finger phalanges other than thumb). So index finger distal phalanx is named as the task 1, then task 2 is the middle phalanx of the index finger, task 3 is the proximal of the index finger, task 4 is the distal phalanx of the middle finger and so on until little finger proximal phalanx. Each Task is repeated 15 times. Error trials are also included in the data set.<br>
DOI: 10.6084/m9.figshare.5945575
title: Subgroup identification by recursive segmentation

description: A new modeling approach called ‘recursive segmentation’ is proposed to support the supervised exploration and identification of subgroups or clusters. It is based on the frameworks of recursive partitioning and the Patient Rule Induction Method (PRIM). Through combining these methods, recursive segmentation aims to exploit their respective strengths while reducing their weaknesses. Consequently, recursive segmentation can be applied in a very general way, that is in any (multivariate) regression, classification or survival (time-to-event) problem, using conditional inference, evolutionary learning or the CART algorithm, with predictor variables of any scale and with missing values. Furthermore, results of a synthetic example and a benchmark application study that comprises 26 data sets suggest that recursive segmentation achieves a competitive prediction accuracy and provides more accurate definitions of subgroups by models of less complexity as compared to recursive partitioning and PRIM. An application to the German Breast Cancer Study Group data demonstrates the improved interpretability and reliability of results produced by the new approach. The method is made publicly available through the R-package <b>rseg</b> (http://rseg.r-forge.r-project.org/).
DOI: 10.6084/m9.figshare.5945575.v1
title: Subgroup identification by recursive segmentation

description: A new modeling approach called ‘recursive segmentation’ is proposed to support the supervised exploration and identification of subgroups or clusters. It is based on the frameworks of recursive partitioning and the Patient Rule Induction Method (PRIM). Through combining these methods, recursive segmentation aims to exploit their respective strengths while reducing their weaknesses. Consequently, recursive segmentation can be applied in a very general way, that is in any (multivariate) regression, classification or survival (time-to-event) problem, using conditional inference, evolutionary learning or the CART algorithm, with predictor variables of any scale and with missing values. Furthermore, results of a synthetic example and a benchmark application study that comprises 26 data sets suggest that recursive segmentation achieves a competitive prediction accuracy and provides more accurate definitions of subgroups by models of less complexity as compared to recursive partitioning and PRIM. An application to the German Breast Cancer Study Group data demonstrates the improved interpretability and reliability of results produced by the new approach. The method is made publicly available through the R-package <b>rseg</b> (http://rseg.r-forge.r-project.org/).
DOI: 10.6084/m9.figshare.5945569
title: Reproducible results for: LimoRhyde: a flexible approach for differential analysis of rhythmic transcriptome data

description: The zip file contains all code, data, results, and figures.<br>The analysis can be reproduced by using snakemake and the provided Snakefile, after running scripts/install_packages.R.
DOI: 10.6084/m9.figshare.5945569.v1
title: Reproducible results for: LimoRhyde: a flexible approach for differential analysis of rhythmic transcriptome data

description: The zip file contains all code, data, results, and figures.<br>The analysis can be reproduced by using snakemake and the provided Snakefile, after running scripts/install_packages.R.
DOI: 10.6084/m9.figshare.5945560
title: Raw data-PeerJ.xlsx

description: The raw data contains all the diabetic patients information selected from the larger population for our study.
DOI: 10.6084/m9.figshare.5945560.v1
title: Raw data-PeerJ.xlsx

description: The raw data contains all the diabetic patients information selected from the larger population for our study.
DOI: 10.6084/m9.figshare.5945554
title: Dataset of CIRS

description: The data are part of large-scale survey organized by the China Institute for Rural Studies (CIRS) at Tsinghua University from 2012 to 2016. The survey questionnaire covered seven major topics: basic information of family and family members, family economic conditions, irrigation and farmland water conservancy, rural governance and organization in rural areas, education, rural hygiene and social guarantees, and rural living environment. The survey contains information at both the household and village level. The survey data cove a wide range of topics and could be used for various research purposes. The data here reflects one of the questionnaire parts “irrigation and farmland water conservancy”, but also draws some information from the other parts of the questionnaire.
DOI: 10.6084/m9.figshare.5945554.v1
title: Dataset of CIRS

description: The data are part of large-scale survey organized by the China Institute for Rural Studies (CIRS) at Tsinghua University from 2012 to 2016. The survey questionnaire covered seven major topics: basic information of family and family members, family economic conditions, irrigation and farmland water conservancy, rural governance and organization in rural areas, education, rural hygiene and social guarantees, and rural living environment. The survey contains information at both the household and village level. The survey data cove a wide range of topics and could be used for various research purposes. The data here reflects one of the questionnaire parts “irrigation and farmland water conservancy”, but also draws some information from the other parts of the questionnaire.
DOI: 10.6084/m9.figshare.5945494
title: Supplementary tracks produced by individual operators before being merged.

description: Supplementary file: tracks produced by individual operators before being merged. This should not be used to validate algorithms.
DOI: 10.6084/m9.figshare.5945494.v1
title: Supplementary tracks produced by individual operators before being merged.

description: Supplementary file: tracks produced by individual operators before being merged. This should not be used to validate algorithms.
DOI: 10.6084/m9.figshare.5945482
title: Raw Data

description: This repository is part of the COMBImage project.<br>It contains all the raw data that are described and analyzed in our manuscript. <br>Download and read the text file "COMBImage_readme.txt" for general descriptions of the raw data as well as how to set up and run COMBImage on a Windows machine.
DOI: 10.6084/m9.figshare.5945482.v1
title: Raw Data

description: This repository is part of the COMBImage project.<br>It contains all the raw data that are described and analyzed in our manuscript. <br>Download and read the text file "COMBImage_readme.txt" for general descriptions of the raw data as well as how to set up and run COMBImage on a Windows machine.
DOI: 10.25919/5f4b9246cd5f3
title: Parkes observations for project P958 semester 2020APRS_34

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until last year, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last three years searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 35 bursts, eight of which have been localised to host galaxies at redshifts ranging from 0.1 to 0.5. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. However one of the arcminute-localised ASKAP FRBs has recently been found to repeat. Here we propose to search and study repetitions in this FRB population. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections to characterise the fraction of bursts that repeat. For bursts that repeat the unique wide bandwidth observations will be used to test leading models of burst emission.
DOI: 10.6084/m9.figshare.5945317
title: Flood Damage Estimation

description: Estimated damage for both deterministic and probabilistic approaches using FDE model developed by Karamouz et al. (2016).<br><br><br><br>Karamouz, M., Fereshtehpour, M., Ahmadvand, F., & Zahmatkesh, Z. (2016). Coastal flood damage estimator: An alternative to FEMA’s HAZUS platform. <i>Journal of Irrigation and Drainage Engineering</i>, <i>142</i>(6), 04016016.<br>
DOI: 10.6084/m9.figshare.5945317.v2
title: Flood Damage Estimation

description: Estimated damage for both deterministic and probabilistic approaches using FDE model developed by Karamouz et al. (2016).<br><br><br><br>Karamouz, M., Fereshtehpour, M., Ahmadvand, F., & Zahmatkesh, Z. (2016). Coastal flood damage estimator: An alternative to FEMA’s HAZUS platform. <i>Journal of Irrigation and Drainage Engineering</i>, <i>142</i>(6), 04016016.<br>
DOI: 10.6084/m9.figshare.5945317.v1
title: Damage Estimation.rar

description: Estimated damage for both deterministic and probabilistic approaches using FDE model developed by Karamouz et al. (2016).<br><br><br><br>Karamouz, M., Fereshtehpour, M., Ahmadvand, F., & Zahmatkesh, Z. (2016). Coastal flood damage estimator: An alternative to FEMA’s HAZUS platform. <i>Journal of Irrigation and Drainage Engineering</i>, <i>142</i>(6), 04016016.<br>
DOI: 10.6084/m9.figshare.5945296
title: Deterministic and Probabilistic Flood risk assessment

description: <br>Flood modeling, as an important part of coastal hazard assessment, is highly influenced by topography dataset and specifically ground elevation. Lower resolution Digital Elevation Models (DEMs) are usually used because of their availability and less computational burden. However, inherent errors in these DEMs propagate into flood risk analysis through spatial modeling. This study aims to explore the DEM resolution effects on coastal flood risk assessments. For this purpose, deterministic and probabilistic approaches are employed. Flood inundation modeling is carried out using hydrologically connected bathtub method. Given the high resolution Light Detection And Ranging (LiDAR) DEM, different resolution maps are obtained used resampling techniques and incorporated into an error analysis framework along with USGS national elevation dataset (NED) DEMs. The probabilistic framework is developed by simulating the spatial variability of elevation errors compared to LiDAR DEM through a Monte Carlo based method called sequential Gaussian simulation. The proposed methodology is applied to the lower Manhattan in New York City. By integrating the flood model into the developed framework, this approach results in flood inundation probability at each grid cells. In this study, using the concept of accuracy-efficiency tradeoffs, a framework for selecting a suitable spatial resolution for probabilistic flood risk assessment has been suggested. The results show that by exercising a range of options presented in this paper, a broader insight into mapping resolution can be made for making better flood assessment, evacuation zones, and mitigation plans depending upon the data availability in a region for flood preparedness.<br>
DOI: 10.6084/m9.figshare.5945296.v1
title: Uncertainty Analysis.rar

description: Input and output to probabilistic flood risk assessment.<br>Input: DEM, Control Points, Boundary line<br>Output: Flood inundation map, Inundation probability map<br>Note that Floodsim000.asc shows the deterministic inundation map. The other realizations from 001 to 1000 is used for calculating the probability of inundation. <br><br>
DOI: 10.6084/m9.figshare.5945296.v3
title: Deterministic and Probabilistic Flood risk assessment

description: <br>Flood modeling, as an important part of coastal hazard assessment, is highly influenced by topography dataset and specifically ground elevation. Lower resolution Digital Elevation Models (DEMs) are usually used because of their availability and less computational burden. However, inherent errors in these DEMs propagate into flood risk analysis through spatial modeling. This study aims to explore the DEM resolution effects on coastal flood risk assessments. For this purpose, deterministic and probabilistic approaches are employed. Flood inundation modeling is carried out using hydrologically connected bathtub method. Given the high resolution Light Detection And Ranging (LiDAR) DEM, different resolution maps are obtained used resampling techniques and incorporated into an error analysis framework along with USGS national elevation dataset (NED) DEMs. The probabilistic framework is developed by simulating the spatial variability of elevation errors compared to LiDAR DEM through a Monte Carlo based method called sequential Gaussian simulation. The proposed methodology is applied to the lower Manhattan in New York City. By integrating the flood model into the developed framework, this approach results in flood inundation probability at each grid cells. In this study, using the concept of accuracy-efficiency tradeoffs, a framework for selecting a suitable spatial resolution for probabilistic flood risk assessment has been suggested. The results show that by exercising a range of options presented in this paper, a broader insight into mapping resolution can be made for making better flood assessment, evacuation zones, and mitigation plans depending upon the data availability in a region for flood preparedness.<br>
DOI: 10.6084/m9.figshare.5945296.v2
title: Deterministic and Probabilistic inundation mapping

description: Input and output to probabilistic flood risk assessment.<br>Input: DEM, Control Points, Boundary line<br>Output: Flood inundation map, Inundation probability map<br>Note that Floodsim000.asc shows the deterministic inundation map. The other realizations from 001 to 1000 is used for calculating the probability of inundation. <br><br>
DOI: 10.6084/m9.figshare.5945236
title: TIFFS - LTDB001 to LTDB020

description: Contains the tiffs of the videos with ID LTDB001 to LTDB020
DOI: 10.6084/m9.figshare.5945236.v1
title: TIFFS - LTDB001 to LTDB020

description: Contains the tiffs of the videos with ID LTDB001 to LTDB020
DOI: 10.6084/m9.figshare.5945233
title: TIFFS - CS001 to CS018

description: Contains the TIFF files of the videos with ID CS001 to CS018
DOI: 10.6084/m9.figshare.5945233.v1
title: TIFFS - CS001 to CS018

description: Contains the TIFF files of the videos with ID CS001 to CS018
DOI: 10.6084/m9.figshare.5945227
title: Values obtained for each replicate of matrix effects and recovery samples (lung tissue, plasma and culture medium) (xlsx)

description: Demonstrated all values obtained for each replicate of matrix effects and recovery samples (lung tissue, plasma and culture medium). In these experiments the analytes corresponded to internal standards (deutered compounds) for eliminate the interference of matrix endogenous metabolites. The individual values were used to determine the average (n = 6) of matrix effect and recovery of the extraction method.