cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8219553
title: Tropical Andes Land Cover Dataset (TALANDCOVER)

description: The Tropical Andes Land Cover Dataset (TALANDCOVER) was built for the department of Antioquia Colombia and consists of three folders for the three types of sampling.

Random: 5000 images
Balanced of minimum 50% coverage per class: 1389 images
Balanced of minimum 70% coverage per class:731 images
The coordinate system of each dataset is EPSG:3857 - WGS 84 / Pseudo-Mercator with spatial resolution of 4.77 meters and 128*128px.
Example of image and corresponding label name:

image_PNICFI_D2019-05_T586-1068_C1_N100.tif
label_PNICFI_D2019-05_T586-1068_C1_N100.tif
Pixel values for label are:
0 Bare-degraded lands1 Grasslands2 Heterogeneous agricultural areas3 Dense forest4 Water bodies5 Built-up areas
There are multiple keywords intentionally inserted in each image name or label name that enable the split of the name in pieces of information about each image and label metadata.
Spliting the image or label name by the keywords, would get 6 items:

The item (image/label) describe if the file correspond to a patch image or a patch label.
The second item (Keyword "_P") gives the name of the product NICFI (https://assets.planet.com/docs/NICFI_User_Guide_v4_EN.pdf)
The third item (keyword "_D") gives a date for the composite
The fourth (keyword "_T") gives the tile number, as stated in the planet scope visual base map documentation: "The name of each basemap quad within the Basemaps API is designed to represent the x and y position of the quad within the two dimensional grid which makes up the basemap. It is generally {X}-{Y}, where X and Y are the x and y position of the quad in the grid". https://developers.planet.com/docs/data/visual-basemaps/
The fifth (keyword "_C") when available gives the cover class used by the slidding windows to extract the patch, resulting in at least a minimum of 50% or 70% of the pixels within the image correspond to that specific cover class, depending on the selected sample dataset.
Take into account that random samples dont have this keyword since the patches where collected at random from a 2d grid without regard of the cover classes present
 
Article : "Land Cover Classification in the Antioquia Region of the Tropical Andes Using NICFI Satellite Data Program Imagery and Semantic Segmentation Techniques".
DOI: 10.6084/m9.figshare.24495682
title: Mind Upload 3 Syntax and Data

description: Data and Syntax for Mind Upload 3 paper.This is aggregate anonymized data from a larger data-set which is still being used to produce further manuscripts. Therefore, it is not shared in full capacity.Variable names should be self-explanatory.
DOI: 10.6084/m9.figshare.24721608
title: Inclusion of Salicornia ramosissima biomass in diets for juvenile whiteleg shrimp (Penaeus vannamei) induces favourable but transient effects in the immune and oxidative status

description: Inclusion of Salicornia ramosissima biomass in diets for juvenile whiteleg shrimp (Penaeus vannamei) induces favourable but transient effects in the immune and oxidative status
DOI: 10.5281/zenodo.7188354
title: Ensemble of global landslide hazard from PHELS

description: Daily global landslide hazard from the Probabilistic Hydrological Estimation of LandSlides (PHELS) model on the 36-km EASE grid for different hydrological predictor variables alongside global landslide susceptibility estimates: A 7-day antecedent rainfall index (ARI7), daily rainfall, daily root-zone soil moisture (rzmc, 0-100cm depth) and the combination of rainfall&rzmc. PHELS is based on a quadratic exponential equation, fitted to 9367 landslide events. For all four hydrological predictor variable (combinations) deterministic hazard estimations are provided. Please note the different order of magnitude in the hazard values when using one or two hydrological predictor variables.  For rainfall&rzmc results of ensemble simulations (100 members) are additionally provided, more specifically the ensemble average and standard deviation. The latter is a measure for the uncertainty of the estimated hazard. Details can be found in  Felsberg, A., Heyvaert, Z., Poesen, J., Stanley, T., and De Lannoy, G. J. M. (2023): Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling (NHESS, https://doi.org/10.5194/egusphere-2023-869). The global landslide susceptibility estimation is described in Felsberg et al. (2022): Estimating global landslide susceptibility and its uncertainty through ensemble modelling (NHESS, https://doi.org/10.5194/nhess-22-3063-2022)
DOI: 10.5281/zenodo.7188355
title: Ensemble of global landslide hazard from PHELS

description: Daily global landslide hazard from the Probabilistic Hydrological Estimation of LandSlides (PHELS) model on the 36-km EASE grid for different hydrological predictor variables alongside global landslide susceptibility estimates: A 7-day antecedent rainfall index (ARI7), daily rainfall, daily root-zone soil moisture (rzmc, 0-100cm depth) and the combination of rainfall&rzmc. PHELS is based on a quadratic exponential equation, fitted to 9367 landslide events. For all four hydrological predictor variable (combinations) deterministic hazard estimations are provided. Please note the different order of magnitude in the hazard values when using one or two hydrological predictor variables.  For rainfall&rzmc results of ensemble simulations (100 members) are additionally provided, more specifically the ensemble average and standard deviation. The latter is a measure for the uncertainty of the estimated hazard. Details can be found in  Felsberg, A., Heyvaert, Z., Poesen, J., Stanley, T., and De Lannoy, G. J. M. (2023): Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling (NHESS, https://doi.org/10.5194/egusphere-2023-869). The global landslide susceptibility estimation is described in Felsberg et al. (2022): Estimating global landslide susceptibility and its uncertainty through ensemble modelling (NHESS, https://doi.org/10.5194/nhess-22-3063-2022)
DOI: 10.6084/m9.figshare.24723474
title: DATA.xlsx

description: <b>Research data</b>
DOI: 10.6084/m9.figshare.24723474.v1
title: DATA.xlsx

description: <b>Research data</b>
DOI: 10.5281/zenodo.10041704
title: Source data for the publication "Longitudinal coupling between a Si/SiGe double quantum dot and an off-chip TiN resonator"

description: This repository contains data reported in the publication "Longitudinal coupling between a Si/SiGe double quantum dot and an off-chip TiN resonator."
DOI: 10.5281/zenodo.10041705
title: Source data for the publication "Longitudinal coupling between a Si/SiGe double quantum dot and an off-chip TiN resonator"

description: This repository contains data reported in the publication "Longitudinal coupling between a Si/SiGe double quantum dot and an off-chip TiN resonator."
DOI: 10.5061/dryad.80d7p
title: Data from: Extracellular matrix-associated gene expression in adult sensory neurons cultured on laminin substrates
DOI: 10.5517/ccdc.csd.cc2b5yq6
title: CCDC 2150678: Experimental Crystal Structure Determination

description: Related Article: Yinli Zhang, Zhen Li, Fengqin Gao, Zhanying Ma, Wuwu Li, Xuan Gao, Guang Fan|2022|J.Solid State Chem.|316|123551|doi:10.1016/j.jssc.2022.123551
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fvmjg
title: CCDC 2259916: Experimental Crystal Structure Determination

description: Related Article: Nishithendu Bikash Nandi, Nishan Das, Susanta Ghanta, Krishti Rekha Puzari, Pranab Dutta, Julia Kłak, Lesław Sieroń, Waldemar Maniukiewicz, Tarun Kumar Misra|2023|New J.Chem.|47|21633|doi:10.1039/D3NJ04598F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fvmkh
title: CCDC 2259917: Experimental Crystal Structure Determination

description: Related Article: Nishithendu Bikash Nandi, Nishan Das, Susanta Ghanta, Krishti Rekha Puzari, Pranab Dutta, Julia Kłak, Lesław Sieroń, Waldemar Maniukiewicz, Tarun Kumar Misra|2023|New J.Chem.|47|21633|doi:10.1039/D3NJ04598F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fvmlj
title: CCDC 2259918: Experimental Crystal Structure Determination

description: Related Article: Nishithendu Bikash Nandi, Nishan Das, Susanta Ghanta, Krishti Rekha Puzari, Pranab Dutta, Julia Kłak, Lesław Sieroń, Waldemar Maniukiewicz, Tarun Kumar Misra|2023|New J.Chem.|47|21633|doi:10.1039/D3NJ04598F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.24723756
title: R Scripts and data used for peer-reviewed paper.

description: These analyses focus on assessing the comparative bias and precision of citizen-science derrived estimates of density and population size using structured surveys to account for imperfect detection. Three general frameworks were examined. See paper for more information.
DOI: 10.6084/m9.figshare.24718593
title: Supplementary Material 1. Use of camera traps in the research on terrestrial mammals in Brazil: History, methodological applications and perspective

description: Supplementary Material 1 for the manuscript: Use of camera traps in the research on terrestrial mammals in Brazil: History, methodological applications and perspective
DOI: 10.6084/m9.figshare.24718593.v1
title: Supplementary Material 1. Use of camera traps in the research on terrestrial mammals in Brazil: History, methodological applications and perspective

description: Supplementary Material 1 for the manuscript: Use of camera traps in the research on terrestrial mammals in Brazil: History, methodological applications and perspective
DOI: 10.15148/7d5e2f17-bad6-4ac3-87e2-84888d0d6699
title: DAS and pressure sensor datasets for the reconstruction of nearshore surface gravity wave spectrum from Distributed Acoustic Sensing time series

description: From december 23th 2020 to January 5th 2021, a DAS interrogator unit was connected on the land termination of the LSPM (Laboratoire Sous-marin Provence Méditerranée) seafloor cable. This 50 km-long cable extends cross-shore from the coast to the bottom of the NW Mediterranean basin. The DAS interrogator unit was a chirped-pulse phi-OTDR (phase-sensitive optical time-domain reflectometry) hDAS (High fidelity distributed acoustic sensor) which provides measurements in strain units. We set the acquisition parameters to sample every 10 m of the cable (same spatial sampling and gauge length) at 250 Hz. The dataset provided here is only one channel (#260) located in Baie des Sablettes, Saint-Mandrier-sur-Mer at 14.6 m depth. On the same period, a pressure sensor (Ruskin BRB Virtuoso 3) was set nearby the channel #260 along the fiber cable. The dataset includes the raw data from the bottom-mounted RBR virtuoso pressure sensor monitoring waves. The acquisition frequency was set to 8 Hz. It was deployed at 14.6 m depth. This data was collected as part of a project for reconstruction of nearshore surface gravity waves from Distributed Acoustic Sensing data. The Nanostrain data data provided here were low-pass filtered and resampled at 8 Hz. The processing involved the removal of some occasional outliers. The file is provided in hdf5. Pressure data were not processed. Original raw data are converted in hdf5.
DOI: 10.6084/m9.figshare.24723795
title: A Lightweight Comparative Approach to Enhance the Accuracy of Mobile App Privacy Labels

description: The data set for our IEEE Software paper "A Lightweight Comparative Approach to Enhance the Accuracy of Mobile App Privacy Labels"
DOI: 10.6084/m9.figshare.24723795.v1
title: A Lightweight Comparative Approach to Enhance the Accuracy of Mobile App Privacy Labels

description: The data set for our IEEE Software paper "A Lightweight Comparative Approach to Enhance the Accuracy of Mobile App Privacy Labels"
DOI: 10.17909/pc4f-f919
title: HAT-P-67 TESS lightcurves

description: TESS Lightcurves of HAT-P-67b  used in the paper "A Large and Variable Leading Tail of Helium in a Hot Saturn Undergoing Runaway Inflation"
DOI: 10.5281/zenodo.10257396
title: Designing Optimal Convolutional Neural Network Architecture Using Differential Evolution Algorithm

description: Convolutional Neural Networks (CNNs) are widely used deep learning models for solving various tasks such as computer vision, speech recognition, among others. However, CNNs are developed manually based on problem-specific domain knowledge and tricky settings, which are laborious, time-consuming and challenging. To address these issues, this study proposes an Improved Differential Evolution of Convolutional Neural Network algorithm, namely IDECNN, to design CNN layer architectures for image classification task.
DOI: 10.5281/zenodo.6567749
title: Designing Optimal Convolutional Neural Network Architecture Using Differential Evolution Algorithm

description: Convolutional Neural Networks (CNNs) are widely used deep learning models for solving various tasks such as computer vision, speech recognition, among others. However, CNNs are developed manually based on problem-specific domain knowledge and tricky settings, which are laborious, time-consuming and challenging. To address these issues, this study proposes an Improved Differential Evolution of Convolutional Neural Network algorithm, namely IDECNN, to design CNN layer architectures for image classification task.
DOI: 10.5281/zenodo.6567750
title: Designing Optimal Convolutional Neural Network Architecture Using Differential Evolution Algorithm

description: Convolutional Neural Networks (CNNs) are widely used deep learning models for solving various tasks such as computer vision, speech recognition, among others. However, CNNs are developed manually based on problem-specific domain knowledge and tricky settings, which are laborious, time-consuming and challenging. To address these issues, this study proposes an Improved Differential Evolution of Convolutional Neural Network algorithm, namely IDECNN, to design CNN layer architectures for image classification task.
DOI: 10.57703/ce-m2yy2
title: Surface Topography Challenge R58 (Dektak) Auburn University Submission

description: There are 10 line scan raw data files from the Veeco Dektak 150. Five line scans were taken from the South to North directions, and five were taken from the East to West directions. The directions are defined in the write-up in Figure 3. The scans were performed at a speed of 64 seconds per line, yielding a total of 9600 data points across a distance of 1.5 mm. This results in 6.5 data points per micron. The stylus tip has a 2 µm radius.
DOI: 10.57703/ce-84qgy
title: Surface Topography Challenge C85 (Dektak) Auburn University Submission

description: There are 10 line scan raw data files from the Veeco Dektak 150. Five line scans were taken from the South to North directions, and five were taken from the East to West directions. The directions are defined in the write-up in Figure 3. The scans were performed at a speed of 64 seconds per line, yielding a total of 9600 data points across a distance of 1.5 mm. This results in 6.5 data points per micron. The stylus tip has a 2 µm radius.
DOI: 10.6084/m9.figshare.23593641
title: Data from Molluscan genomes reveal extensive differences in photopigment evolution across the phylum

description: These data are from the analysis described in the manuscript "Molluscan genomes reveal extensive differences in photopigment evolution across the phylum", accepted at Molecular Biology and Evolution.https://doi.org/10.1093/molbev/msad263README.txt contains file details.Gene predictions for opsin and cryptochrome/photolyase proteins from 80 molluscan genome assemblies. BITACORA (version 1.3; Vizueta et al., 2022) was used for <i>de novo </i>gene prediction; using the “runBITACORA_genome_mode.sh” script to run in genome mode (i.e., no input genome annotation) using GeMoMa (parameter GEMOMA=T).Vizueta J, Sánchez-Gracia A, Rozas J. 2020. bitacora: A comprehensive tool for the identification and annotation of gene families in genome assemblies. <i>Mol. Ecol. Resour.</i> 20:1445–1452.
DOI: 10.57703/ce-2dc3v
title: Surface Topography Challenge C86 (Dektak) Auburn University Submission

description: There are 10 line scan raw data files from the Veeco Dektak 150. Five line scans were taken from the South to North directions, and five were taken from the East to West directions. The directions are defined in the write-up in Figure 3. The scans were performed at a speed of 64 seconds per line, yielding a total of 9600 data points across a distance of 1.5 mm. This results in 6.5 data points per micron. The stylus tip has a 2 µm radius.
DOI: 10.5281/zenodo.10046625
title: Data for: Ring Current Electron Precipitation During the 17 March 2013 Geomagnetic Storm: Underlying Mechanisms and Their Effect on the Atmosphere

description: All data are included as MATLAB figure files, png files and MATLAB MAT files.
File precipitated_flux.mat contains a 4-D array of values of precipitated electron flux in [1/(s cm^2 keV)] for 289 time points from 16 March 2013 to 19 March 2013, with a 15 min time step; 100 values of energy in a range from 10 keV to 1 MeV, with a 10 keV step; on a spatial grid of 28 by 49 (P, R).
netCDF data can be opened with a variety of software tools, including Matlab, Origin or Python.
DOI: 10.5281/zenodo.10046626
title: Data for: Ring Current Electron Precipitation During the 17 March 2013 Geomagnetic Storm: Underlying Mechanisms and Their Effect on the Atmosphere

description: All data are included as MATLAB figure files, png files and MATLAB MAT files.
File precipitated_flux.mat contains a 4-D array of values of precipitated electron flux in [1/(s cm^2 keV)] for 289 time points from 16 March 2013 to 19 March 2013, with a 15 min time step; 100 values of energy in a range from 10 keV to 1 MeV, with a 10 keV step; on a spatial grid of 28 by 49 (P, R).
netCDF data can be opened with a variety of software tools, including Matlab, Origin or Python.
DOI: 10.6084/m9.figshare.24723453
title: Tropical cyclone formation binary dataset

description: This binary dataset contains a specific customization of the National Center for Environmental Prediction (NCEP)/final analysis (FNL) data for ML applications, in which environmental conditions relevant for tropical cyclone (TC) formation during 1999-2023 are extracted for a range of forecast lead times (0-72 hours). The dataset is designed as multi-channel images centered on TC formation locations, with a positive and negative directory structure that can be readily input from any ML applications or common data interface. With its unique structure, this dataset provides users an opportunity to conduct ML application research on TC formation as well as related predictability at different forecast lead times.
DOI: 10.6084/m9.figshare.24723453.v1
title: Tropical cyclone formation binary dataset

description: Binary dataset for tropical cyclone genesis research
DOI: 10.6084/m9.figshare.24723453.v2
title: Tropical cyclone formation binary dataset

description: This binary dataset contains a specific customization of the National Center for Environmental Prediction (NCEP)/final analysis (FNL) data for ML applications, in which environmental conditions relevant for tropical cyclone (TC) formation during 1999-2023 are extracted for a range of forecast lead times (0-72 hours). The dataset is designed as multi-channel images centered on TC formation locations, with a positive and negative directory structure that can be readily input from any ML applications or common data interface. With its unique structure, this dataset provides users an opportunity to conduct ML application research on TC formation as well as related predictability at different forecast lead times.
DOI: 10.57703/ce-geef5
title: Surface Topography Challenge R71 (Dektak) Auburn University Submission

description: There are 10 line scan raw data files from the Veeco Dektak 150. Five line scans were taken from the South to North directions, and five were taken from the East to West directions. The directions are defined in the write-up in Figure 3. The scans were performed at a speed of 64 seconds per line, yielding a total of 9600 data points across a distance of 1.5 mm. This results in 6.5 data points per micron. The stylus tip has a 2 µm radius.
DOI: 10.5281/zenodo.10253423
title: SemMed VER43 Sentence Dataset

description: A version of the official SemMedDB data used for a project under the supervision of Dr. Fitzroy Nembhard, Dept. of Computer Engineering and Sciences, Florida Institute of Technology.
 
Due to size constraints, the complete file was split roughly every 250MB (+/- 15 MB). In order to ensure data integrity, the files should be concatenated in the order based on name. (partaa -> partab ->... -> partdr) .
DOI: 10.5281/zenodo.10253424
title: SemMed VER43 Sentence Dataset

description: A version of the official SemMedDB data used for a project under the supervision of Dr. Fitzroy Nembhard, Dept. of Computer Engineering and Sciences, Florida Institute of Technology.
 
Due to size constraints, the complete file was split roughly every 250MB (+/- 15 MB). In order to ensure data integrity, the files should be concatenated in the order based on name. (partaa -> partab ->... -> partdr) .
DOI: 10.5281/zenodo.7987265
title: Gecarcinus lateralis - Eyestalk Ganglia Transcriptome

description: Gecarcinus lateralis - Eyestalk Ganglia Transcriptome Assembly
Assembled de novo using the methods in Pérez-Moreno et al.(2023) with a multi-kmer multi-assembler approach (Trinity + rnaSpades) and processed via the EvidentialGene pipeline.
Nucleotide and amino acid sequence assemblies in FASTA format.
DOI: 10.5281/zenodo.7987266
title: Gecarcinus lateralis - Eyestalk Ganglia Transcriptome

description: Gecarcinus lateralis - Eyestalk Ganglia Transcriptome Assembly
Assembled de novo using the methods in Pérez-Moreno et al.(2023) with a multi-kmer multi-assembler approach (Trinity + rnaSpades) and processed via the EvidentialGene pipeline.
Nucleotide and amino acid sequence assemblies in FASTA format.