cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7356498
title: PROCRAFT_Form C_Spitfire_B2-2C1

description: Results of scientific analyses on a part collected from the Spifire aircraft wreck (related to PROCRAFT_Form B_Spitfire_Plérin_B2): analysis of alloy
DOI: 10.5281/zenodo.7753275
title: PROCRAFT_Form C_Spitfire_B2-2C1

description: Results of scientific analyses on a part collected from the Spifire aircraft wreck (related to PROCRAFT_Form B_Spitfire_Plérin_B2): analysis of alloy
DOI: 10.5281/zenodo.7057580
title: PROCRAFT_Form C_Dornier 217_B1C1

description: Results of scientific analyses on a part collected from a Dornier 217 (See PROCRAFT_Form A_Dornier 217_Sacoué)
description: Germany Super Duralumin (3125) Paint
DOI: 10.5281/zenodo.7753225
title: Oceanic Prokaryotes Metagenome-Assembled Genomes reconstructed using metagenomic distances

description: Sets of reconstructed Metagenome-Assembled Genomes (MAGs) from Tara Oceans dataset. The reconstructed MAGs belong to Magneto paper: https://doi.org/10.1128/msystems.00432-22 The dataset is composed of 93 oceanic metagenomes sampled from non-polar oceanic regions. The Metagenomic Distance MAGs were reconstructed following a co-assembly protocol driven by nucleotidic composition similarity, as detailed in the publication. The Oceanic Region MAGs were reconstructed by co-assembly of samples belonging to the same Oceanic Regions. The file clusters.tsv sum up the metagenomic distance cluster and the oceanic region each sample belongs to.
DOI: 10.5281/zenodo.7753224
title: Oceanic Prokaryotes Metagenome-Assembled Genomes reconstructed using metagenomic distances

description: Sets of reconstructed Metagenome-Assembled Genomes (MAGs) from Tara Oceans dataset. The reconstructed MAGs belong to Magneto paper: https://doi.org/10.1128/msystems.00432-22 The dataset is composed of 93 oceanic metagenomes sampled from non-polar oceanic regions. The Metagenomic Distance MAGs were reconstructed following a co-assembly protocol driven by nucleotidic composition similarity, as detailed in the publication. The Oceanic Region MAGs were reconstructed by co-assembly of samples belonging to the same Oceanic Regions. The file clusters.tsv sum up the metagenomic distance cluster and the oceanic region each sample belongs to.
DOI: 10.5281/zenodo.7356469
title: PROCRAFT_Form C_Spitfire_B2-1C1

description: Results of scientific analyses on a part collected from the Spifire aircraft wreck (related to PROCRAFT_Form B_Spitfire_Plérin_B2): analysis of alloy
DOI: 10.5281/zenodo.7753323
title: PROCRAFT_Form C_Spitfire_B2-1C1

description: Results of scientific analyses on a part collected from the Spifire aircraft wreck (related to PROCRAFT_Form B_Spitfire_Plérin_B2): analysis of alloy
DOI: 10.5281/zenodo.7356468
title: PROCRAFT_Form C_Spitfire_B2-1C1

description: Results of scientific analyses on a part collected from the Spifire aircraft wreck (related to PROCRAFT_Form B_Spitfire_Plérin_B2): analysis of alloy
DOI: 10.48366/r576876
title: Comparison of the Logical Tensor Network model to the state-of-the-art model for the Top-N recommendation problem

description: The table below presents the comparison of the Logical Tensor Network model by considering additional information in the model (adding genre) or not to the state-of-the-art models.
DOI: 10.5281/zenodo.7753331
title: PROCRAFT_Form C_Dornier 217_B2-12C1

description: Results of scientific analyses on parts (stringer + plate + rivet) collected from a Dornier 217 (See PROCRAFT_Form A_Dornier 217_Sacoué)
description: Germany Plate, stringer, Rivet
DOI: 10.5281/zenodo.7753330
title: PROCRAFT_Form C_Dornier 217_B2-12C1

description: Results of scientific analyses on parts (stringer + plate + rivet) collected from a Dornier 217 (See PROCRAFT_Form A_Dornier 217_Sacoué)
description: Germany Plate, stringer, Rivet
DOI: 10.5281/zenodo.7753344
title: Distribution of functionally distinct native and non-indigenous species within marine urban habitats

description: This data file (.xls) is composed of 5 sheets: The “Taxon labels”: Taxon code, full name, authority and status/type (Abiotic, Unassigned, Native, Cryptogenic, Non-Indigenous Species) The “Trait labels”: Trait modality and labels and correspondences. The “Taxon-by-Trait matrix”: Fuzzy coded scores for each trait modality and taxon The “Taxon-by-sample matrix”: Abundance data of retained taxa in samples The “Sample labels and description”: Site and experimental factors (Habitat, Age, Experimental Unit, Replicate, nested within site) corresponding to each sample. Sheets 4 and 5 are extracted from a published dataset, which cannot be shared at this stage of revision without revealing the name of several of the manuscript authors. This is done in respect with the journal guidelines about data storage.
DOI: 10.5281/zenodo.7753343
title: Distribution of functionally distinct native and non-indigenous species within marine urban habitats

description: This data file (.xls) is composed of 5 sheets: The “Taxon labels”: Taxon code, full name, authority and status/type (Abiotic, Unassigned, Native, Cryptogenic, Non-Indigenous Species) The “Trait labels”: Trait modality and labels and correspondences. The “Taxon-by-Trait matrix”: Fuzzy coded scores for each trait modality and taxon The “Taxon-by-sample matrix”: Abundance data of retained taxa in samples The “Sample labels and description”: Site and experimental factors (Habitat, Age, Experimental Unit, Replicate, nested within site) corresponding to each sample. Sheets 4 and 5 are extracted from a published dataset, which cannot be shared at this stage of revision without revealing the name of several of the manuscript authors. This is done in respect with the journal guidelines about data storage.
DOI: 10.6084/m9.figshare.19686453.v4
title: RNA-seq Titration Results used in plotting for "Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously"

description: This data accompanies the manuscript "Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously" by Foltz, Taroni, and Greene. Please refer to our github page. <br> The file contains all data necessary to recreate the main and supplementary figures in our manuscript. <br> Abstract: Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, the majority of available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. The data structure and distributions between the platforms differ, making it challenging to combine them directly. Here we perform supervised and unsupervised machine learning evaluations to assess which existing normalization methods are best suited for combining microarray and RNA-seq data. We find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously. Nonparanormal normalization and z-scores are also appropriate for some applications, including pathway analysis with Pathway-Level Information Extractor (PLIER). We demonstrate that it is possible to perform effective cross-platform normalization using existing methods to combine microarray and RNA-seq data for machine learning applications.
DOI: 10.6084/m9.figshare.22302553.v2
title: Data and analysis code for "Rapid Assessment of Landslide and Rockslide Scale using an Enhanced Probabilistic Neural Network”

description: The file includes all codes, landslide and rockslide seismic signal datasets for the establishment of ENPP method and case analysis in paper "Rapid Assessment of Landslide and Rockslide Scale using an Enhanced Probabilistic Neural Network"
DOI: 10.34740/kaggle/dsv/5202023
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.7742302
title: Investigación Formación del Editor Formulario_Líderes_ Nov 2020

description: Este documento presenta las respuestas de la encuesta a Líderes Editoriales de Iberoamérica consultados sobre la formación posgraduada de editores.
DOI: 10.5281/zenodo.7742301
title: Investigación Formación del Editor Formulario_Líderes_ Nov 2020

description: Este documento presenta las respuestas de la encuesta a Líderes Editoriales de Iberoamérica consultados sobre la formación posgraduada de editores.
DOI: 10.17894/ucph.ed9a1a1f-d6e6-41d5-894c-7e526f75fdd7
title: NEGIS anisotropy
DOI: 10.6084/m9.figshare.22009431
title: Spatial Linear Regression with Covariate Measurement Errors: Inference and Scalable Computation in a Functional Modeling Approach

description: For spatial linear models, the classical maximum-likelihood estimators of both regression coefficients and variance components can be biased when the covariates are measured with errors. This work introduces a theoretically backed-up estimation framework for the spatial linear errors-in-variables model in a functional approach. Compared with the structural models, the functional approach treats the unobserved true covariates as fixed unknown parameters without imposing additional structures, thus leading to more robust parameter inference. Our model parameters are estimated simultaneously based on a set of unbiased estimating equations. Under some regularity conditions, we prove the consistency of the proposed estimating-equation estimators and derive their asymptotic distribution. In addition, a consistent variance estimator is developed for the estimating-equation estimators. To handle large spatial datasets, we provide two approaches to obtain scalable estimations based on our proposed estimating equations, where the required computational time and storage are reduced to be linear with sample size for each estimating-function evaluation. Simulation studies under different settings show that our estimators are consistent and the scalable algorithms work well. Finally, the proposed method is applied to studying the relationship between Arctic sea ice and related geophysical variables. Supplementary materials for this article are available online.
DOI: 10.18739/a24m91c1v
title: The Influence of Arctic-Lower-Latitude Interactions on Weather and Climate Variability: Mechanisms, Predictability, and Prediction

description: This file contains (metadata) information on the datasets created as well as those existing datasets and models used by the project indicated in the title. The overall goal of this collaborative project was to investigate the tropical and mid-latitude (collectively referred to as lower latitudes) oceanic and atmospheric drivers of regional Arctic changes as well as the Arctic impacts on the Northern Hemisphere climate and weather variability, using a combination of available observations and state-of-the-art climate models, to improve our understanding of the fundamental dynamics, modeling capabilities, and capacity for prediction. The primary foci were on how the Arctic influences the lower latitude climate changes through atmospheric connections, and on evaluation of and enhancing capacity for seasonal-to-decadal predictions in the Arctic and over the Northern Hemisphere. To address the first focus area, we performed several sets of ensemble simulations using a state-of-the-art atmospheric model forced by different sea-ice and sea surface temperature conditions. We also performed additional sets of ensemble simulations with the Community Earth System Model (CESM) with the sea-ice conditions set to preindustrial, present day, and future conditions, respectively. For the second focus area, we used existing datasets from the Community Earth System Model version 1 (CESM1): CESM1 Large Ensemble and CESM1 Decadal Prediction Large Ensemble. The primary methods used include a maximum covariance analysis (MCA) to examine the lagged atmospheric circulation response to Arctic sea ice concentration variability at interannual time scales and a lead-time dependent drift removal method to extract anomalies to determine prediction skill.
DOI: 10.6084/m9.figshare.21960398
title: Data for Fig 5: Trajectories of beads in hydrogel

description: Data are 2D trajectories of individual beads seeded into 3D cell culture samples and tracked over three days. Each text file is organised as three columns: time (in seconds), x and y (in um). <br> Bead radius: 3 um Temperature: 37C
DOI: 10.6084/m9.figshare.21960398.v1
title: Data for Fig 5: Trajectories of beads in hydrogel

description: Data are 2D trajectories of individual beads seeded into 3D cell culture samples and tracked over three days. Each text file is organised as three columns: time (in seconds), x and y (in um). <br> Bead radius: 3 um Temperature: 37C
DOI: 10.6084/m9.figshare.21960581
title: Data for Fig 5: Probe Measurements Relative to Cluster

description: Table of G'<sub>0</sub> (low frequency elastic plateau) measurements in Pa, relative distances between the centre of beads and the edge of the cell cluster in microns and volume of cell clusters in micron<sup>3</sup>.
DOI: 10.6084/m9.figshare.21960581.v1
title: Data for Fig 5: Probe Measurements Relative to Cluster

description: Table of G'<sub>0</sub> (low frequency elastic plateau) measurements in Pa, relative distances between the centre of beads and the edge of the cell cluster in microns and volume of cell clusters in micron<sup>3</sup>.
DOI: 10.6084/m9.figshare.19626837
title: Data for Fig 4: Trajectories of optically trapped beads

description: Data are 3D trajectories of individual optically trapped beads at varying depths from the coverslip saved in two files per bead; oragnised as time (in seconds), x and y or z (in um). <br> bead radius: 3 um temperature: 30C (50-300 um deep), 25C (400 um deep)
DOI: 10.6084/m9.figshare.19626837.v1
title: Data for Fig 4: Trajectories of optically trapped beads

description: Data are 3D trajectories of individual optically trapped beads at varying depths from the coverslip saved in two files per bead; oragnised as time (in seconds), x and y or z (in um). <br> bead radius: 3 um temperature: 30C (50-300 um deep), 25C (400 um deep)
DOI: 10.6084/m9.figshare.21391035.v1
title: Demographics of Data Science Students at a Two-Year College (deidentified)

description: Demographics of students taking data science classes at a two year college during spring 2022 semester. All subjects are deidentified
DOI: 10.6084/m9.figshare.21391035
title: Demographics of Data Science Students at a Two-Year College (deidentified)

description: Demographics of students taking data science classes at a two year college during spring 2022 semester. All subjects are deidentified
DOI: 10.6084/m9.figshare.19626969
title: Data for Fig6: Eta in 3D

description: Data are relative viscosity measurements at individual beads optically trapped near live cell spheroids. Each text file is organised as four columns: directional vector (nx3) and relative viscosity
DOI: 10.6084/m9.figshare.19626969.v1
title: Data for Fig6: Eta in 3D

description: Data are relative viscosity measurements at individual beads optically trapped near live cell spheroids. Each text file is organised as four columns: directional vector (nx3) and relative viscosity
DOI: 10.6084/m9.figshare.19626954
title: Data for Fig6: Trajectories of optically trapped beads near spheroids

description: Data are 3D trajectories of individual beads optically trapped and used to measure viscosity near the surface of live cell spheroids. Each text file represents either trajectory in xy or xz and is organised as three columns: time (in seconds), x and y (or z, in um). <br> Bead radius: 3 um Temperature: 37C <br>
DOI: 10.6084/m9.figshare.19626954.v1
title: Data for Fig6: Trajectories of optically trapped beads near spheroids

description: Data are 3D trajectories of individual beads optically trapped and used to measure viscosity near the surface of live cell spheroids. Each text file represents either trajectory in xy or xz and is organised as three columns: time (in seconds), x and y (or z, in um). <br> Bead radius: 3 um Temperature: 37C <br>
DOI: 10.25345/c59c6s98s
title: MassIVE MSV000091511 - Discovery of a Highly Potent and Selective HDAC3 and HDAC8 PROTAC Dual Degrader
title: HDAC3 and HDAC8 are members of class I deacetylases involved in several biological mechanisms and represent a highly sought-after therapeutic target for drug development. It is historically challenging to develop selective deacetylase inhibitors due to their conserved catalytic domains. HDAC3 also has deacetylase-independent activity, which cannot be blocked by conventional enzymatic inhibitors. Recent advances in proteolysis-targeting chimeras (PROTACs) provide an opportunity to eliminate the whole protein selectively, abolishing both enzymatic and scaffolding functions. Here, we report a novel HDAC3/8 dual degrader YX968 that induces highly potent, rapid and selective degradation of both HDAC3 and HDAC8 without trigging pan-HDAC inhibitory effects. Unbiased quantitative proteomics experiments further confirmed its high selectivity. This dual-specific degrader specifically ablates cellular pathways attributed to HDAC3 and HDAC8 and exhibits high potency in killing cancer cells. YX968 represents a new probe for dissecting the complex biological functions of HDAC3 and HDAC8.
DOI: 10.5281/zenodo.6541919
title: SIG4_Benchmarking

description: SIG4 Benchmarking Datasets from the Core Unit for Bioinformatics Bonn using the Nvidia Clara Parabricks pipeline for germline variant calling on hg38: 1. WES CCG 75M Reads hg38 - "118711_75M.vcf.gz" 2. WES CCG 200M Reads hg38 - "118711_200M.vcf.gz" 3. WES Twist Bonn 200M Reads hg38 - "NA12878_HG001_Twist.vcf.gz" 4. WGS CCG hg38 - "A006200148.vcf.gz" 5. WGS Synthetic Genome hg38 - "PRJEB13208_hg38.vcf.gz" 6. SV Benchmarking Delly hg38 - "NA24385_Delly_SVs_hg38.vcf.gz"
DOI: 10.5281/zenodo.6541918
title: SIG4_Benchmarking

description: SIG4 Benchmarking Datasets from the Core Unit for Bioinformatics Bonn using the Nvidia Clara Parabricks pipeline for germline variant calling on hg38: 1. WES CCG 75M Reads hg38 - "118711_75M.vcf.gz" 2. WES CCG 200M Reads hg38 - "118711_200M.vcf.gz" 3. WES Twist Bonn 200M Reads hg38 - "NA12878_HG001_Twist.vcf.gz" 4. WGS CCG hg38 - "A006200148.vcf.gz" 5. WGS Synthetic Genome hg38 - "PRJEB13208_hg38.vcf.gz" 6. SV Benchmarking Delly hg38 - "NA24385_Delly_SVs_hg38.vcf.gz"
DOI: 10.5281/zenodo.7753351
title: GRAND-SLAM analysis of increasing 4sU concentration SLAM-seq datasets for grandRescue

description: These are processed datasets of 3 cell lines (U20S, HFF-TerT, HCT116) labeled for 1 hour with 4sU concentrations of 100µM, 200µM, 400µM and 800µM for the purpose of investigating 4sU-induced toxicity, mapping impairment and the potential of grandRescue to alleviate these effects. The zip files contain the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The *.tsv.gz files are the GRAND-SLAM output tables. <br> To generate the GRAND-SLAM output yourself, first prepare the mouse & human genomes. Then run the following command with the respective cit-files, prefixes (*.cit) and genomes (h.ens90): gedi -e Slam -trim5p 15 -reads *.cit -genomic h.ens90 -prefix grandslam_t15/* -plot -D -modelall To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it. You can also start from scratch (i.e., the json file): Prepare the human genome and their rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j *.json rnaseq_mapping.sh report.sh grandslam.sh Software versions: gedi toolkit 1.0.5<br> GRAND-SLAM 2.0.7<br> STAR version 2.7.10b
DOI: 10.5281/zenodo.7753352
title: GRAND-SLAM analysis of increasing 4sU concentration SLAM-seq datasets for grandRescue

description: These are processed datasets of 3 cell lines (U20S, HFF-TerT, HCT116) labeled for 1 hour with 4sU concentrations of 100µM, 200µM, 400µM and 800µM for the purpose of investigating 4sU-induced toxicity, mapping impairment and the potential of grandRescue to alleviate these effects. The zip files contain the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The *.tsv.gz files are the GRAND-SLAM output tables. <br> To generate the GRAND-SLAM output yourself, first prepare the mouse & human genomes. Then run the following command with the respective cit-files, prefixes (*.cit) and genomes (h.ens90): gedi -e Slam -trim5p 15 -reads *.cit -genomic h.ens90 -prefix grandslam_t15/* -plot -D -modelall To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it. You can also start from scratch (i.e., the json file): Prepare the human genome and their rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j *.json rnaseq_mapping.sh report.sh grandslam.sh Software versions: gedi toolkit 1.0.5<br> GRAND-SLAM 2.0.7<br> STAR version 2.7.10b
DOI: 10.5281/zenodo.7753460
title: GRAND-SLAM analysis of increasing 4sU concentration SLAM-seq datasets for grandRescue

description: These are processed datasets of 3 cell lines (U20S, HFF-TerT, HCT116) labeled for 1 hour with 4sU concentrations of 100µM, 200µM, 400µM and 800µM for the purpose of investigating 4sU-induced toxicity, mapping impairment and the potential of grandRescue to alleviate these effects. The zip files contain the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The *.tsv.gz files are the GRAND-SLAM output tables. <br> To generate the GRAND-SLAM output yourself, first prepare the mouse & human genomes. Then run the following command with the respective cit-files, prefixes (*.cit) and genomes (h.ens90): gedi -e Slam -trim5p 15 -reads *.cit -genomic h.ens90 -prefix grandslam_t15/* -plot -D -modelall To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it. You can also start from scratch (i.e., the json file): Prepare the human genome and their rRNA sequence<br> Run: gedi -e Pipeline -r parallel -j *.json rnaseq_mapping.sh report.sh grandslam.sh Software versions: gedi toolkit 1.0.5<br> GRAND-SLAM 2.0.7<br> STAR version 2.7.10b
DOI: 10.34740/kaggle/dsv/5202173
title: AdultoMayor_11_fc
DOI: 10.6084/m9.figshare.13095380.v6
title: Data for "Genetic erosion reduces biomass temporal stability in wild fish populations."

description: "Prunier et al._Biomass_Genetic_Environmental data.txt": Data used in path analyses. For each STATION, the dataset provides : - sample WEIGHTs used in causal models, - total fish biomass BIOM, - biomass stability BSTA, - per capita mean biomass pcBIOM, - per capita mean biomass stability pcBSTA, - bottleneck probability BOT, - intraspecific genetic diversity IGD, - upstream-downstream gradient UGD - eutrophic gradient EG. Interactions are obtained as the cross-products of corresponding variables. <br> "Prunier et al._R-code.R" R-code used to perform path analyses and produce figures 1 to 3. <br> "Prunier_et_al._GeneticData.rda" Raw genetic data used to compute IGD, in .rda format. Loading this file in R opens "GeneticData", a list-object composed of three sublists (one for each species), each containing raw microsatellite individual genotypes in .genind format (R-library adegenet) and Poolseq population-based SNP allelic frequencies.
DOI: 10.13145/bacdive166205.20230509.8
title: Weissella cryptocerci

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive166206.20230509.8
title: Pelagibacterium sediminicola

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive166207.20230509.8
title: Empedobacter tilapiae

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.