cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10067274
title: Twenty-two thousand Common Era benthic foraminifera from the Santa Barbara Basin

description: Here we provide an image and 2D shape dataset of recent benthic foraminifera from two core records sampled from the center of the Santa Barbara Basin that span an ~800-year-long interval during the Common Era (1249-2008 CE). Information on more than 36,000 objects is included, of which more than 22,000 are complete or partially damaged benthic foraminifera. Skeletonized objects classified also include planktonic foraminifera, ostracods, pteropods, diatoms, radiolarians, fish teeth and skeletal structures, shark dermal denticles, and benthic foraminifer test fragments. The image dataset was produced using a high-throughput imaging method (AutoMorph) designed to extract 2D data from photographic images of fossils. This repository contains 8 distinct data types uploaded as distinct files, and includes the following:



	
bulk_images.zip: Bulk images with objects identified by segment boxed in red
	
individual_images.zip: EDF images of individual objects within the dataset
	
identification_files.zip: Classifications for individual objects, including both general categories and species-specific classifications (when possible) for benthic foraminifera
	
cleaning_scripts.zip: Directory containing R scripts used to clean object category misspellings or inconsistencies
	
outline_images.zip: EDF images of objects successfully extracted for 2D outlines and measurements; included for quality control. This includes one text file (unextracted_objects_2D.txt) listing objects with failed extractions
	
2d_coordinates.zip: CSV files containing all extracted outline coordinates for each of the samples imaged, a text file of failed 2D extractions (unextracted_objects_2D.txt), and a summary CSV file including coordinates for all extracted objects (all_coordinates.csv)
	
2d_properties.zip: 2D measurements for all objects
	
metadata_tables.zip: Tables 1, 2, and 3 and Supplementary Table 1 from a forthcoming data descriptor publication, describing sample metadata, including site coordinates, sample names, object information, and summary statistics



The dataset we provide here comprises the most extensive publicly available archive of benthic foraminiferal morphology and 2D morphological variation to date.
DOI: 10.26093/cds/vizier.22350006
title: Updated Multiple Star Catalog (MSC): Dec 2023

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'The updated Multiple Star Catalog.' (bibcode: 2018ApJS..235....6T)
DOI: 10.34740/kaggle/dsv/7365644
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.25739/m69q-8k22
title: FloodPlanet

description: This hand-labeled inundation dataset generated by Social[Pixel] lab is a high-resolution multi-sensor co-registered dataset labeled based on 3m PlanetScope data. It contains 366 manual labels of size 1024*1024 based on PlanetScope data, a modified 8-bit version of PlanetScope data, and spatially overlapping, temporally near Sentinel-1, Sentinel-2, and Landsat-8 data. It includes 19 flood events that occurred between 2017 and 2020. It was created in December of 2023.
DOI: 10.5287/ora-2zq2ywbjg
title: Direct Linearly Polarised Electroluminescence from Perovskite Nanoplatelet Superlattices

description: All raw data and data collection details can be found in readme file included in this deposition
DOI: 10.34740/kaggle/dsv/7365661
title: Public Opinion on Climate Change (Updated Daily)
DOI: 10.18112/openneuro.ds003020.v2.2.0
title: An fMRI dataset during a passive natural language listening task
DOI: 10.26183/yg76-j869
title: Developing holistic financial risk management to optimize financial performance of Public-Private Partnership (PPP) projects in Ghana

description: This survey aims to validate the comprehensiveness and practicability of the results from the factor analysis and partial least squares- structural equation modelling (PLS-SEM) analysis and the best practice framework developed from it to mitigate financial risk management and increase the financial success of public-private partnership projects in Ghana. 
<p>Dataset includes qualitative questionnaire survey doc., validation, Expert forum survey, Exploratory factor analysis dataset. PLS_SEM dataset.
DOI: 10.13020/xpma-vn76
title: Supporting data for Tough Polycyclooctene Nanoporous Membranes from Etchable Block Copolymers

description: These files contain primary data along with associated output from instrumentation supporting all results reported in Hoehn et al. "Tough Polycyclooctene Nanoporous Membranes from Etchable Block Copolymers". In Hoehn et al. we found porous materials with pore dimensions of the nanometer length scale are useful as nanoporous membranes. ABA triblock copolymers are convenient precursors to such nanoporous materials if the end blocks are easily degradable (e.g., polylactide or PLA) in thin films leaving nanoporous polymeric membranes (NPMs). The membrane properties are dependent on midblock monomer structure, triblock copolymer composition, overall molar mass, and polymer processing conditions. Polycyclooctene (PCOE) NPMs were prepared using this method, with tunable pore sizes on the order of tens of nanometers. Solvent casting was shown to eliminate film defects and allowed achievement of superior mechanical properties over melt processing techniques, and PCOE NPMs were found to be very tough, a major advance over previously reported NPMs. Oxygen plasma etching was used to remove the surface skin layer to obtain membranes with higher surface porosity, membrane hydrophilicity, and flux of both air and water. This is a straightforward method to reliably produce highly tough NPMs with high levels of porosity and hydrophilic surface properties.
DOI: 10.6084/m9.figshare.24964338
title: Ramirez et al., Supplementary files (Data and Scripts)

description: All data and scripts used in Ramirez et al., 2024
DOI: 10.6084/m9.figshare.24964338.v1
title: Ramirez et al., Supplementary files (Data and Scripts)

description: All data and scripts used in Ramirez et al., 2024
DOI: 10.5281/zenodo.8021854
title: Transcriptome data from silica-preserved leaf tissue reveals gene flow patterns in a Caribbean bromeliad

description: Transcriptome sequencing is a cost-effective approach that allows researchers to study a broad range of questions. However, to preserve RNA for transcriptome sequencing, tissue is often kept under special conditions, such as immediate ultracold freezing. Here, we demonstrate that RNA can be obtained from six-month-old, field-collected samples stored in silica gel at room temperature. Using these transcriptomes, we explore the evolutionary relationships of the genus Pitcairnia (Bromeliaceae) in the Dominican Republic and infer barriers to gene flow.
DOI: 10.5281/zenodo.8021855
title: Transcriptome data from silica-preserved leaf tissue reveals gene flow patterns in a Caribbean bromeliad

description: Transcriptome sequencing is a cost-effective approach that allows researchers to study a broad range of questions. However, to preserve RNA for transcriptome sequencing, tissue is often kept under special conditions, such as immediate ultracold freezing. Here, we demonstrate that RNA can be obtained from six-month-old, field-collected samples stored in silica gel at room temperature. Using these transcriptomes, we explore the evolutionary relationships of the genus Pitcairnia (Bromeliaceae) in the Dominican Republic and infer barriers to gene flow.
DOI: 10.18739/a2h70824c
title: High-Resolution Topography of Landfast ice in Elson Lagoon, Alaska from Terrestrial Laser Scanning from the Snow ALbedo eVOlution (SALVO) campaign April 2022

description: Many processes involving the fluxes of mass, energy, and momentum in Arctic sea ice impact and are impacted by the snow and ice topography. This project measured the snow and ice topography on approximately a 200 x 50 meter (m) area of landfast, level sea ice in Elson Lagoon, Alaska on 17 April 2022 via Terrestrial Laser Scanning (TLS) as part of the Snow ALbedo eVOlution (SALVO experiment). We have aligned the TLS data into an ice-fixed, reference frame using reflector posts frozen into the ice and co-located the measurements with one another to an uncertainty of 3 millimeters (mm) using a plane-matching algorithm in RiSCAN (Riegl's software for TLS acquisition and processing).
 These data are stored within a directory tree (see Methods section below). To access the entire dataset with the correct directory structure, click "Download All". A processing library ('pydar') for working with these data is available at: https://zenodo.org/records/10152870.
DOI: 10.34740/kaggle/dsv/7314210
title: TMOD DataSet
DOI: 10.57745/qknz4i
title: 01_Phenotypic_data_per_individual_leaf.tab

description: This file contains the raw phenotypic data, with one value per individual leaf, for leaf area and LMA measured on the grapevine diversity panel. “CodeVar” is the cultivar identifier, and “Cultivar.name” the full name of the genotype. “snp.imp.f.d.assign.noNA” is the genetic group (TE, table east; WE, Wine East; WW, Wine West) retrieved from Flutre et al. 2022. “LeafArea.cm2” is the leaf area in cm² and “LMA.g_per_cm2” the leaf mass per area in g per cm². “Date” is the date of measurement and “day” the corresponding day of year. “Orientation_plant” is the orientation of the plant on which the leaf was sampled and “Orientation_leaf” the orientation of the leaf itself (either SW, South West, or NE, North East). “Row” and “Position_plant” the spatial location of the plant in the experimental vineyard (row and position within the row). “"Plant_state" indicates whether the plant was assessed as holding virus or not. “Year_planted” indicates the year of obtention of the potted plant. This dataset was analysed in the R scripts “Leaf_area_analysis.html” and “Leaf_LMA_analysis.html” in order to explore the variability of LeafArea.cm2 and LMA.g_per_cm2 and to extract genotypic values (BLUPs).
DOI: 10.57745/dq3885
title: 02_Leaf_area_analysis.Rmd

description: Rmarkdown document used to generate Leaf_area_analysis.html
DOI: 10.57745/wowgre
title: 02_Leaf_area_analysis.html

description: This html document contains the analysis that was led on the leaf area data contained in the file “Phenotypic_data_per_individual_leaf.csv”. It was generated using R markdown. Distribution of the data and their variability depending on different factors were visually examined and tested by ANOVA. Then, mixed-models were tested (R/lme4) with random effect of the genotype and two optional fixed effects, sampling date and orientation West or North-East. Analysing the Akaike Information Criterion showed that models accounting for orientation only were similarly performant as models accounting for both orientation and sampling date. Moreover, genotypic ranking was strictly maintained whatever sampling date was included or not. Thus, we selected the model including genotype and orientation effects for extracting BLUPs. Variance components were used to estimate broad-sense heritability as: H² = σ2G/(σ2G+σ2Res/n) with σ2G the genotypic variance, σ2Res the residual variance, and n the mean number of replicates (leaves) per cultivar.
DOI: 10.57745/fwevrx
title: 03_Leaf_LMA_analysis.html

description: This html document contains the analysis that was led on the LMA data contained in the file “Phenotypic_data_per_individual_leaf.csv”. It was generated using R markdown. Distribution of the data and their variability depending on different factors were visually examined and tested by ANOVA. Then, mixed-models were tested (R/lme4) with random effect of the genotype and two optional fixed effects, sampling date and orientation West or North-East. Analysing the Akaike Information Criterion showed that models accounting for orientation only were similarly performant as models accounting for both orientation and sampling date. Moreover, genotypic ranking was strictly maintained whatever sampling date was included or not. Thus, we selected the model including genotype and orientation effects for extracting BLUPs. Variance components were used to estimate broad-sense heritability as: H² = σ2G/(σ2G+σ2Res/n) with σ2G the genotypic variance, σ2Res the residual variance, and n the mean number of replicates (leaves) per cultivar.
DOI: 10.57745/zany66
title: 03_Leaf_LMA_analysis.Rmd

description: Rmarkdown document used to generate Leaf_LMA_analysis.html
DOI: 10.57745/3gyfnu
title: 04_Phenotypic_data_per_genotype.tab

description: This file contains the phenotypic data, with one value per cultivar, for leaf area, LMA, as well as firing symptoms observed following the heatwave, collected on the grapevine diversity panel. “CodeVar” is the cultivar identifier, and “Cultivar.name” the full name of the genotype. “LeafArea.cm2” is the leaf area in cm² and “LMA.g_per_cm2” the leaf mass per area in g per cm². “Proportion_SW” is the proportion of leaf firing on the South-West orientation. “Intensity_SW” is the intensity of leaf firing on the South-West orientation. “Proportion_NE” and “Intensity_NE” are the proportion and intensity on the North-East orientation. Proportion of leaf firing was scored from 0 to 100% as the proportion of leaves at least partly burnt within the canopy. Intensity of leaf firing was scored as 0, 1, 2, 3, 4 or 5 to describe the extent of the firing within one representative fired leaf (0 when no symptoms, 1 when only leaf margins were affected, up to 5 when the whole leaf was affected). LeafArea.cm2 and LMA.g_per_cm2 are BLUPs of genotypic values (see Leaf_LMA_analysis.html and Leaf_area_analysis.html). For leaf firing proportion and intensity, each score already consisted in a single average observation for each cultivar and orientation.
DOI: 10.57745/fxruzs
title: 05_IndepSNPs_GBSReseq_full.html

description: This html document contains the analysis that was performed on the initial genotyping dataset from Flutre et al. 2022 that contained 197,885 SNPs issued from microarray and genotyping-by-sequencin. Here, it was reduced to 82,868 SNPs after excluding i) SNPs that did not vary across the panel, ii) SNPs with a minor allelic frequency below 5% and iii) duplicated positions. Here we further estimated a number of nearly independant SNPs for multiple testing correction of GWAS using several approaches as those implemented in the poolr package or in plink.
DOI: 10.57745/mkmu2e
title: 05_snp-chip-gbs-genotypes_concat-fill_beagle_rm-dup_filter-maf5.tab

description: This Rdata file contains the genotyping dataset obtained via IndepSNPs_GBSReseq_full.html
and further used for GWAS analyses.
DOI: 10.57745/togjgx
title: 06_GWAS_Intensity_SW.html

description: GWAS analysis. Three types of GWAS were performed: 1) An SNP-by-SNP model implemented in R/MM4LMM version 2.1.0 (Laporte et al., 2022) based on a linear mixed model was used to test the significance of the effect of each SNP; 2) the multi-locus mixed model MLMM (Segura et al., 2012), which jointly analyses all SNPs to handle LD while selecting a subset of SNPs with a stepwise regression procedure ; 3) SNP selection in a Bayesian setting (Flutre et al., 2022) fitted with the variational algorithm implemented in R/varbvs version 2.5.7 (Carbonetto & Stephens, 2012).
DOI: 10.57745/dnfyze
title: 06_GWAS_Intensity_SW.Rmd

description: R markdown document used to generate the corresponding html document.
DOI: 10.5281/zenodo.10460674
title: Model dataset for the journal publication titled "Implementation and evaluation of SNICAR snow albedo scheme in Noah-MP (version 5.0) land surface model"

description: This is the Noah-MP/SNICAR model simulation dataset for the journal publication titled "Implementation and evaluation of SNICAR snow albedo scheme in Noah-MP (version 5.0) land surface model".
DOI: 10.5281/zenodo.10460675
title: Model dataset for the journal publication titled "Implementation and evaluation of SNICAR snow albedo scheme in Noah-MP (version 5.0) land surface model"

description: This is the Noah-MP/SNICAR model simulation dataset for the journal publication titled "Implementation and evaluation of SNICAR snow albedo scheme in Noah-MP (version 5.0) land surface model".
DOI: 10.57745/gzhbpc
title: 06_GWAS_log.proportion.html

description: GWAS analysis. Three types of GWAS were performed: 1) An SNP-by-SNP model implemented in R/MM4LMM version 2.1.0 (Laporte et al., 2022) based on a linear mixed model was used to test the significance of the effect of each SNP; 2) the multi-locus mixed model MLMM (Segura et al., 2012), which jointly analyses all SNPs to handle LD while selecting a subset of SNPs with a stepwise regression procedure ; 3) SNP selection in a Bayesian setting (Flutre et al., 2022) fitted with the variational algorithm implemented in R/varbvs version 2.5.7 (Carbonetto & Stephens, 2012).
DOI: 10.57745/l5fppy
title: 06_GWAS_LeafArea.cm2.Rmd

description: R markdown document used to generate the corresponding html document.
DOI: 10.57745/feo9rw
title: 06_GWAS_LMA.g_per_cm2.html

description: GWAS analysis. Three types of GWAS were performed: 1) An SNP-by-SNP model implemented in R/MM4LMM version 2.1.0 (Laporte et al., 2022) based on a linear mixed model was used to test the significance of the effect of each SNP; 2) the multi-locus mixed model MLMM (Segura et al., 2012), which jointly analyses all SNPs to handle LD while selecting a subset of SNPs with a stepwise regression procedure ; 3) SNP selection in a Bayesian setting (Flutre et al., 2022) fitted with the variational algorithm implemented in R/varbvs version 2.5.7 (Carbonetto & Stephens, 2012).
DOI: 10.57745/wrma1j
title: 06_GWAS_LeafArea.cm2.html

description: GWAS analysis. Three types of GWAS were performed: 1) An SNP-by-SNP model implemented in R/MM4LMM version 2.1.0 (Laporte et al., 2022) based on a linear mixed model was used to test the significance of the effect of each SNP; 2) the multi-locus mixed model MLMM (Segura et al., 2012), which jointly analyses all SNPs to handle LD while selecting a subset of SNPs with a stepwise regression procedure ; 3) SNP selection in a Bayesian setting (Flutre et al., 2022) fitted with the variational algorithm implemented in R/varbvs version 2.5.7 (Carbonetto & Stephens, 2012).
DOI: 10.57745/eqijzn
title: 06_GWAS_log.proportion.Rmd

description: R markdown document used to generate the corresponding html document.
DOI: 10.57745/zq5qsi
title: 06_GWAS_Proportion_SW.html

description: GWAS analysis. Three types of GWAS were performed: 1) An SNP-by-SNP model implemented in R/MM4LMM version 2.1.0 (Laporte et al., 2022) based on a linear mixed model was used to test the significance of the effect of each SNP; 2) the multi-locus mixed model MLMM (Segura et al., 2012), which jointly analyses all SNPs to handle LD while selecting a subset of SNPs with a stepwise regression procedure ; 3) SNP selection in a Bayesian setting (Flutre et al., 2022) fitted with the variational algorithm implemented in R/varbvs version 2.5.7 (Carbonetto & Stephens, 2012).
DOI: 10.57745/pccpjf
title: 06_GWAS_Magnitude_SW.Rmd

description: R markdown document used to generate the corresponding html document.
DOI: 10.57745/siwadq
title: 06_GWAS_Magnitude_SW.html

description: GWAS analysis. Three types of GWAS were performed: 1) An SNP-by-SNP model implemented in R/MM4LMM version 2.1.0 (Laporte et al., 2022) based on a linear mixed model was used to test the significance of the effect of each SNP; 2) the multi-locus mixed model MLMM (Segura et al., 2012), which jointly analyses all SNPs to handle LD while selecting a subset of SNPs with a stepwise regression procedure ; 3) SNP selection in a Bayesian setting (Flutre et al., 2022) fitted with the variational algorithm implemented in R/varbvs version 2.5.7 (Carbonetto & Stephens, 2012).
DOI: 10.57745/xgjikw
title: 06_GWAS_log.magnitude.Rmd

description: R markdown document used to generate the corresponding html document.
DOI: 10.57745/vdss7v
title: GWAS_LMA.g_per_cm2.Rmd

description: R markdown document used to generate the corresponding html document.
DOI: 10.57745/ypcgrw
title: 06_GWAS_log.magnitude.html

description: GWAS analysis. Three types of GWAS were performed: 1) An SNP-by-SNP model implemented in R/MM4LMM version 2.1.0 (Laporte et al., 2022) based on a linear mixed model was used to test the significance of the effect of each SNP; 2) the multi-locus mixed model MLMM (Segura et al., 2012), which jointly analyses all SNPs to handle LD while selecting a subset of SNPs with a stepwise regression procedure ; 3) SNP selection in a Bayesian setting (Flutre et al., 2022) fitted with the variational algorithm implemented in R/varbvs version 2.5.7 (Carbonetto & Stephens, 2012).
DOI: 10.57745/cqojyj
title: 06_GWAS_Proportion_SW.Rmd

description: R markdown document used to generate the corresponding html document.
DOI: 10.25345/c5w951000
title: MassIVE MSV000093794 - Trichomonas vaginalis 20S proteasome subunit specific MSP-MS
title: MSP-MS data for Tv20S. Data files are named PF_Treatment_concentration_time_replicate. Possibilities include Treatment: 1-45 = CP-17 CFZ = carfilzomib AND CP-17 both at 10uM. 616 = KZR-616 DMSO = DMSO Concentration: 10uM or 1uM Time: NTC = 0h 3 = 3h 20 = 20h 38 = 38h Replicate: 1-4
DOI: 10.5281/zenodo.10470841
title: The STRINGS queries to identify documents related to the SDGs (+ country-SDG data)

description: This page contains three documents related to the work the STRINGS team has developed on mapping research related to the Sustainable Development Goals (SDGs): 1. A document explaining the methodology used to create search queries for identifying documents related to SDGs 1-16. 2. An Excel file containing the search queries themselves. 3. An additional Excel file containing country-SDG level data used in the paper "Countries’ research priorities in relation to the Sustainable Development Goals".The procedure for creating SDG queries was developed for the STRINGS project. For both the project and the paper, the procedure to identify SDG-related publications does not rely exclusively on search queries. We apply each SDG query to research areas obtained from a publication-level clustering algorithm, based on direct backward and forward citations. This enables us to select research areas related to SDGs and include all the publications contributing to that area. This approach has several advantages, including the ability to include publications that do not use SDG-related language in their abstract or title but still contribute to SDG-related research. For further insights, the platform, data, and thresholds used to understand which research areas are associated with an SDG are openly available here.In the STRINGS report, you can explore applications to map and characterize publications and patents related to the SDGs. Additionally, the paper "Countries’ research priorities in relation to the Sustainable Development Goals" provides a country-level analysis of the alignment between research priorities and SDG challenges.