cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15454/cueho6/kafpy7
title: Functions-phyllochron-gaussian.R

description: R functions that implement the phyllochron model and the statistical analyses
DOI: 10.15454/cueho6/qhywqv
title: Res-lasso-clim-model-28climVar.Rdata

description: Predictions of the climate model on years 2014, 2015 and 2016.
DOI: 10.15454/cueho6/rsbsxh
title: Readme-Data-files.txt

description: Description of the experimental data sets
DOI: 10.15454/cueho6/28cw2x
title: Script-figures-tables.R

description: Script for figures and tables in Plancade et al (2021)
DOI: 10.15454/cueho6/qmyiky
title: pred-permYear-lasso-28climVar.Rdata

description: Predictions of the climate model implemented on 252 false climates.
DOI: 10.15454/cueho6/rcjan5
title: Script-statistical-analysis-Plancade2021.R

description: Script to implement all analyses in Plancade et al (2021)
DOI: 10.57745/rrmdrs
title: Functions-phyllochron-gaussian.R

description: R functions that implement the phyllochron model and the statistical analyses
DOI: 10.57745/sp6u6p
title: Metadata.tab

description: Metadata associated with genotypes (cf description in Readme-Data-files.txt)
DOI: 10.57745/7p2pwg
title: Readme.txt

description: Description of data files and scripts
DOI: 10.57745/bruzlo
title: mu_bestParamMod_2014.tab

description: Estimate of the average phyllochron for the best parametric model in 2014
DOI: 10.57745/edli7n
title: Script-pretreatment.R

description: Script of data pretreatment (see details in Readme-Data-files.txt)
DOI: 10.57745/j1by7q
title: mu_bestParamMod_2016.tab

description: Estimate of the average phyllochron for the best parametric model in 2016
DOI: 10.57745/mkr4la
title: Script-figures-tables.R

description: Script for figures and tables in Plancade et al
DOI: 10.57745/p2la3m
title: Res-lasso-clim-model-28climVar.Rdata

description: Predictions of the climate model on years 2014, 2015 and 2016.
DOI: 10.57745/wx2cer
title: Script-statistical-analysis.R

description: Script to implement all analyses in Plancade et al
DOI: 10.5281/zenodo.7347842
title: Dataset for manuscript "Sleep does not influence schema-facilitated motor memory consolidation"

description: Dataset containing the raw as well as the subject-level data for the two experiments reported in the manuscript "Sleep does not influence schema-facilitated motor memory consolidation".
DOI: 10.5281/zenodo.7347841
title: Dataset for manuscript "Sleep does not influence schema-facilitated motor memory consolidation"

description: Dataset containing the raw as well as the subject-level data for the two experiments reported in the manuscript "Sleep does not influence schema-facilitated motor memory consolidation".
DOI: 10.6084/m9.figshare.21608754
title: The role of online education in educational development

description: This paper aims to describe the role online education can play in the development of educational sector, due to the continual rise of internet and computer technology, the increase in access and the use of computers around the globe, plus the effect the advancement of internet is having on the way people live and function daily including that of learning, and the fact that online learning contains tremendous advantages over traditional method of learning especially that of financial management and time management.
DOI: 10.6084/m9.figshare.21609456
title: Plant Diversity along an Urbanization Gradient of a Tropical City

description: The data describes the plant species that were collected from four urbanization levels (wildland, rural, suburban, and urban area). For each species, information on its nativity status (native to Malaya vs. non-native to Malaya), and its trait (plant height and specific leaf area) were provided. At each urbanization levels, environmental data (air temperature, moisture, and light intensity) were included.
DOI: 10.6084/m9.figshare.21609456.v1
title: Plant Diversity along an Urbanization Gradient of a Tropical City

description: The data describes the plant species that were collected from four urbanization levels (wildland, rural, suburban, and urban area). For each species, information on its nativity status (native to Malaya vs. non-native to Malaya), and its trait (plant height and specific leaf area) were provided. At each urbanization levels, environmental data (air temperature, moisture, and light intensity) were included.
DOI: 10.5281/zenodo.5036650
title: Enriched CONLLU Ancora for ML training

description: This is an enriched version for Machine Learning purposes of the CONLLU adaptation of AnCora corpus . This version of the corpus was developed by BSC TeMU as part of the AINA project, and has been used to do multi-task learning for the Catalan language Spacy 3.4 models. <strong>Versió enriquida de l'adaptació del corpus AnCora al format CONLLU orientada a l'aprenentatge automàtic.</strong> <strong>Aquesta versió del corpus ha estat desenvolupada per BSC TeMU com a part del projecte Aina, i s'ha fet servir per a l'entrenament multitasca dels models Spacy 3.0 per al català.</strong>
description: supercedes previous version
DOI: 10.5281/zenodo.5036651
title: Enriched CONLLU Ancora for ML training

description: This is an enriched version for Machine Learning purposes of the CONLLU adaptation of AnCora corpus . This version of the corpus was developed by BSC TeMU as part of the AINA project, and has been used to do multi-task learning for the Catalan language Spacy 3.0 models. <strong>Versió enriquida de l'adaptació del corpus AnCora al format CONLLU orientada a l'aprenentatge automàtic.</strong> <strong>Aquesta versió del corpus ha estat desenvolupada per BSC TeMU com a part del projecte Aina, i s'ha fet servir per a l'entrenament multitasca dels models Spacy 3.0 per al català.</strong>
DOI: 10.5281/zenodo.5509997
title: Enriched CONLLU Ancora for ML training

description: This is an enriched version for Machine Learning purposes of the CONLLU adaptation of AnCora corpus . This version of the corpus was developed by BSC TeMU as part of the AINA project, and has been used to do multi-task learning for the Catalan language Spacy 3.4 models. <strong>Versió enriquida de l'adaptació del corpus AnCora al format CONLLU orientada a l'aprenentatge automàtic.</strong> <strong>Aquesta versió del corpus ha estat desenvolupada per BSC TeMU com a part del projecte Aina, i s'ha fet servir per a l'entrenament multitasca dels models Spacy 3.0 per al català.</strong>
description: supercedes previous version
DOI: 10.5281/zenodo.7350954
title: Recombinant Expression and Chemical Amidation of Isotopically Labeled Native Melittin

description: <strong>Table S1</strong>. Melittin backbone chemical shifts (ppm). Deuterium isotope shift corrections were applied to all chemical shifts and pressure corrections were applied to the pressure denatured monomer chemical shifts. All shifts were measured on a 1.0-mM <sup>2</sup>H,<sup>13</sup>C,<sup>15</sup>N-labeled melittin sample in 25-mM potassium phosphate buffer, pH 7.0, 50-mM NaCl, 3% D2O. The folded tetramer peaks were measured at atmospheric pressure, while the pressure denatured monomer resonances were measured at 2.25 kbar. <strong>Table S2.</strong> Melittin experimental <sup>15</sup>N-<sup>1</sup>H isotropic J-couplings, (J+<sup><em>1</em></sup><em>D</em><sub><em>NH</em></sub>) anisotropic couplings and <sup><em>1</em></sup><em>D</em><sub><em>NH</em></sub> residual dipolar couplings. All couplings and errors are reported in Hz. Isotropic J-couplings were not measured for G3 and A4. In these cases, an average J-coupling of -93.3 Hz was used with an uncertainty of 1.1 Hz. <strong>Table S3</strong>. 2MLT atomic coordinates. The X-ray crystal structure was retrieved from the protein data bank (PDB). Then it was symmetry expanded into a tetramer. <strong>Table S4.</strong> AlphaFold-Multimer structural model atomic coordinates.
DOI: 10.5281/zenodo.7350918
title: Table S1 - Economic Costs of Protecting Islands from Invasive Alien Species

description: Dataset analysed in relation to the paper 'Economic Costs of Protecting Islands from Invasive Alien Species' published in Conservation Biology in 2022
DOI: 10.5281/zenodo.7350917
title: Table S1 - Economic Costs of Protecting Islands from Invasive Alien Species

description: Dataset analysed in relation to the paper 'Economic Costs of Protecting Islands from Invasive Alien Species' published in Conservation Biology in 2022
DOI: 10.34740/kaggle/dsv/4570515
title: YouTube Daily
DOI: 10.34740/kaggle/dsv/4570554
title: Elon Musk Tweets
DOI: 10.5281/zenodo.7092905
title: Dataset for a JGR paper

description: Dataset for a manuscript, submitted to JGR-Solid Earth, entitled Three-Dimensional Thermoporoelastic Modeling of Hydrofracturing and Fluid Circulation in Hot Dry Rock: EGS Collab Experiment 1.
DOI: 10.5281/zenodo.7092904
title: Dataset for a JGR paper

description: Dataset for a manuscript, submitted to JGR-Solid Earth, entitled Three-Dimensional Thermoporoelastic Modeling of Hydrofracturing and Fluid Circulation in Hot Dry Rock: EGS Collab Experiment 1.
DOI: 10.5281/zenodo.7350988
title: Dataset for a JGR paper

description: Dataset for a manuscript, submitted to JGR-Solid Earth, entitled Three-Dimensional Thermoporoelastic Modeling of Hydrofracturing and Fluid Circulation in Hot Dry Rock: EGS Collab Experiment 1.
DOI: 10.5523/bris.7ob8ukji8iwp2n0l94n4ykc08
title: WiFi localisation of festival visitors

description: Deposit contains anonymous WiFi localisation data of festival visitors indicating if WiFi enabled devices are within the range of a detector (from: doi:10.1109/WoWMoM.2013.6583443) and derived from this sequences of locations visited by devices alongside properties of the locations. This dataset is published under the Creative Commons Attribution-ShareAlike CC BY SA license.
DOI: 10.5281/zenodo.7057219
title: kac_drumset: A Dataset Generator for Arbitrarily Shaped Drums

description: This publication documents the various datasets generated using the kac_drumset codebase. The aims of kac_drumset is to provide a robust framework for the generation and analysis of arbitrarily shaped drums. The source code for this project is available here: https://github.com/lewiswolf/kac_drumset. <strong>Background</strong> Arbitrarily shaped drums are a strange family of percussion instruments and a wholly meta-physical construction in this contemporary setting. These percussive instruments possess a number of interesting musical characteristics resulting from their particular geometric designs. As it currently stands, these instruments remain largely unexplored throughout musical practice, as they were originally devised as a collection of hypothetical mathematical objects. These datasets serve to sonify these objects so as to explore these conceptual constructions in the audio domain. <strong>Usage</strong> To use these datasets, first install kac_drumset: <pre><code class="language-bash">pip install "git+https://github.com/lewiswolf/kac_drumset.git#egg=kac_drumset"</code></pre> And then in python: <pre><code class="language-python">from kac_drumset import ( # methods loadDataset, transformDataset, # classes TorchDataset, ) dataset: TorchDataset = transformDataset( # load a dataset (any folder containing a metadata.json) loadDataset('absolute/path/to/data'), # alter the dataset representation, either as an end2end, fft or mel. {'output_type': 'end2end'}, ) # use the dataset for i in range(dataset.__len__()): x, y = dataset.__getitem__(i) ...</code></pre> For more details on using kac_drumset, see the project's documentation. <strong>2000 Convex Polygon Drums of Varying Size</strong> Each sample in this dataset corresponds to a randomly generated convex polygon. The audio for each sample was generated using a two-dimensional physical model of a drum. Each sample is one second long and decays linearly. Contained in this dataset are ten different sizes of drums - 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5, 0.6 - each of which is a measure of the longest vertex of each drum in meters. There are 40 different drums sampled for each size. Each drum is sampled five times, first by being struck in the geometric centroid, and then by being struck four more times in random locations. This dataset is labelled with the vertices of each polygon, normalised to the unit interval, and the strike location of each sample. The audio is sampled at 48khz, and the default representation is raw audio. Each sample is stored in the metadata.json, alongside being made available audibly as a 24-bit .wav and graphically as a .png.
DOI: 10.6084/m9.figshare.21590718
title: High-Demand Spinal Deformity with Multi-Rod Constructs and Porous Fusion/Fixation Implants: A Finite Element Study

description: raw data in Matlab format
DOI: 10.6084/m9.figshare.21590718.v1
title: High-Demand Spinal Deformity with Multi-Rod Constructs and Porous Fusion/Fixation Implants: A Finite Element Study

description: raw data in Matlab format
DOI: 10.6084/m9.figshare.13720546.v1
title: Tracking momentary fluctuations in human attention with a cognitive brain-machine interface

description: Data and scripts necessary for reproducing all figures in the paper titled, "Tracking momentary fluctuations in human attention with a cognitive brain-machine interface".
DOI: 10.5281/zenodo.6123848
title: Large Andreev Bound State Zero Bias Peaks in a Weakly Dissipative Environment

description: This repository contains the raw data and processing Python scripts corresponding to the paper "Large Andreev bound state zero bias peaks in a weakly dissipative environment"
DOI: 10.5281/zenodo.6123849
title: Large Andreev Bound State Zero Bias Peaks in a Weakly Dissipative Environment

description: This repository contains the raw data and processing Python scripts corresponding to the paper "Large Andreev bound state zero bias peaks in a weakly dissipative environment"
DOI: 10.5281/zenodo.7351074
title: Large Andreev Bound State Zero Bias Peaks in a Weakly Dissipative Environment

description: This repository contains the raw data and processing Python scripts corresponding to the paper "Large Andreev bound state zero bias peaks in a weakly dissipative environment"
DOI: 10.5281/zenodo.7350920
title: MEMEX_9c_Videos_v1.0

description: Videos collected and used for stories from Paris Pilot participants of the MEMEX project, resulting from group and individual dynamics. Video-stories produced by pilot participants. Collected during the facilitated workshop sessions with the pilot group participants, and stored in an anonymized format.
DOI: 10.5281/zenodo.7350919
title: MEMEX_9c_Videos_v1.0

description: Videos collected and used for stories from Paris Pilot participants of the MEMEX project, resulting from group and individual dynamics. Video-stories produced by pilot participants. Collected during the facilitated workshop sessions with the pilot group participants, and stored in an anonymized format.
DOI: 10.23728/b2share.9b6816153b974c9ead36334db043a069
title: GesauseNationalPark - Snow Cover Duration - 2019/2020

description: Snow Cover Duration (SCD), First Snow Day (FSD), Last Snow Day (LSD) extracted from Sentinel-2 time series of years 2019 and 2020. The snow year is considered from the 1th September to 31st August of the following year. The snow season is considered continuous if at least 14 consecutive days of snow are detected. FSD: first day of a continuous snow season LSD: last day of a continuous snow season SCD: days between FSD and LSD of the same snow year (days)
description: Detection methodology used will be soon accessible
description: The final output consists of 20 m resolution raster layers in GeoTIFF format with the following values per pixel: FSD = first day of a continuous snow season, expressed as Day Of the Snow Year (DOYS) LSD = last day of a continuous snow season, expressed as Day Of the Snow Year (DOYS) SCD = number of days of continuous snow cover The SCD maps were validated against in situ data collected by weather stations equipped with snow gauges. The following metrics were computed: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE). RMSE = 16.5 days MAE = 14 days MBE = 6.5 days
DOI: 10.5525/gla.researchdata.1370
title: Superinfection exclusion creates spatially distinct influenza virus populations

description: Dataset supports publication in PLOS Biology named 'Superinfection exclusion creates spatially distinct influenza virus populations.' The dataset is c65GB in size. Please see the readme file for details and use the 'Request Data' button to be sent a link to download the files.
DOI: 10.6084/m9.figshare.19473818
title: Expression files of healthy human nucleus pulposus and annulus fibrosus data

description: Five human nucleus pulposus were subjected to 10x Genomics platform for scRNA-seq, related to GSE160756. Expression data were recorded as UMI matrix, of which rownames are genes and colnames are cells (nucleus pulposus), or cellranger output matrics (annulus fibrosus). Notably, Matrics_hAF_1 is the updated version of which in GEO website (human_AF_1: GSM4878543).
DOI: 10.6084/m9.figshare.19473818.v1
title: Expression files of healthy human nucleus pulposus data

description: Five human nucleus pulposus were subjected to 10x Genomics platform for scRNA-seq. Expression data were recorded as UMI matrix, of which rownames are genes and colnames are cells. All files have been converted to loom files.
DOI: 10.5281/zenodo.7319243
title: Dataset for paper: Multi-Fidelity Surrogate Modelling of Wall Mounted Cubes

description: Dataset including samples of LES and RANS data of the flow around tandem wall mounted cubes. Samples are taken as lines along the centre of the domain and a number of slices. This data is used in the paper "Multi-Fidelity Surrogate Modelling of Wall Mounted Cubes". The data is used by code found in the repository https://github.com/admole/Multi-Fidelity-Surrogate/tree/FTaC
DOI: 10.25921/yhns-5553
title: ES60 Water Column Sonar Data Collected During EBS16VA
DOI: 10.34740/kaggle/dsv/4570684
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.34740/kaggle/dsv/4570701
title: UNET Lung Segmentation Weights for Chest X Rays
DOI: 10.5281/zenodo.7350724
title: Data files for: The Urban Lightning Effect Revealed with Geostationary Lightning Mapper Observations

description: Warm season (June, July, August; JJA) Geostationary Lightning Mapper (GLM) observations from 2018-2021. Original processing of 20-second Level 2 GLM packets into 5-min files and quality control performed by CPTEC/INPE. Complete description provided by Oda et al. (2022). Further processing conducted locally to isolate GLM flash data for the Southeast U.S., accumulate the 5-minute files into yearly and 4-year bins, and to derive total flash count ("flash"), flash days ("fday"), and average flashes per flash day ("fpfd"). Included files: GOES-16 Full Disk Yearly files containing all GLM data classes (flash, group, and event) with 5-minute timesteps Yearly files containing only GLM flash data with 5-minute timesteps Southeast (lat-lon bounds: -96.00, -74.00, 41.00, 24.00) Final 4-year aggregate file containing derived total lightning metrics ready for analysis in GIS
description: {"references": ["CPTEC/INPE. (2022). Accumulated GOES-16 GLM Data [Dataset]. Center for Weather Forecasting and Climate Studies (CPTEC)/National Institute for Space Research (INPE). http://ftp.cptec.inpe.br/goes/goes16/goes16_web/glm_acumulado_nc/", "Oda, P. S. S., Enor\u00e9, D. P., Mattos, E. V., Gon\u00e7alves, W. A., & Albrecht, R. I. (2022). An initial assessment of the distribution of total Flash Rate Density (FRD) in Brazil from GOES-16 Geostationary Lightning Mapper (GLM) observations. Atmospheric Research, 270, 106081. https://doi.org/10.1016/j.atmosres.2022.106081"]}
DOI: 10.5281/zenodo.7350723
title: Data files for: The Urban Lightning Effect Revealed with Geostationary Lightning Mapper Observations

description: Warm season (June, July, August; JJA) Geostationary Lightning Mapper (GLM) observations from 2018-2021. Original processing of 20-second Level 2 GLM packets into 5-min files and quality control performed by CPTEC/INPE. Complete description provided by Oda et al. (2022). Further processing conducted locally to isolate GLM flash data for the Southeast U.S., accumulate the 5-minute files into yearly and 4-year bins, and to derive total flash count ("flash"), flash days ("fday"), and average flashes per flash day ("fpfd"). Included files: GOES-16 Full Disk Yearly files containing all GLM data classes (flash, group, and event) with 5-minute timesteps Yearly files containing only GLM flash data with 5-minute timesteps Southeast (lat-lon bounds: -96.00, -74.00, 41.00, 24.00) Final 4-year aggregate file containing derived total lightning metrics ready for analysis in GIS
description: {"references": ["CPTEC/INPE. (2022). Accumulated GOES-16 GLM Data [Dataset]. Center for Weather Forecasting and Climate Studies (CPTEC)/National Institute for Space Research (INPE). http://ftp.cptec.inpe.br/goes/goes16/goes16_web/glm_acumulado_nc/", "Oda, P. S. S., Enor\u00e9, D. P., Mattos, E. V., Gon\u00e7alves, W. A., & Albrecht, R. I. (2022). An initial assessment of the distribution of total Flash Rate Density (FRD) in Brazil from GOES-16 Geostationary Lightning Mapper (GLM) observations. Atmospheric Research, 270, 106081. https://doi.org/10.1016/j.atmosres.2022.106081"]}
DOI: 10.23728/b2share.bd747e84066c43b18a96eae8aebdd11b
title: KalkalpenNationalPark - Snow Cover Duration - 2019/2020

description: Snow Cover Duration (SCD), First Snow Day (FSD), Last Snow Day (LSD) extracted from Sentinel-2 time series of years 2019 and 2020. The snow year is considered from the 1st September to 31st August of the following year. The snow season is considered continuous if at least 14 consecutive days of snow are detected. FSD: first day of a continuous snow season LSD: last day of a continuous snow season SCD: days between FSD and LSD of the same snow year (days)
description: Detection methodology used will be soon accessible
description: The final output consists of 20 m resolution raster layers in GeoTIFF format with the following values per pixel: FSD = first day of a continuous snow season, expressed as Day Of the Snow Year (DOYS) LSD = last day of a continuous snow season, expressed as Day Of the Snow Year (DOYS) SCD = number of days of continuous snow cover The SCD maps were validated against in situ data collected by weather stations equipped with snow gauges. The following metrics were computed: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE). RMSE = 16.5 days MAE = 14 days MBE = 6.5 days