cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.20350/digitalcsic/16457
title: LandTrendr-based Vegetation Changes in Spain (1984-present)
title: Cambios de la Vegetación en España basados en LandTrendr (1984-presente)
title: LTR-VegChangeS
title: Long-Term Land Cover Changes

description: [Description of methods used for collection/generation of data] [EN] NDVI & EVI Seasonal composites of Landsat satellite images (5, 7 and 8) between 1984 and the present. [ES] Compuestos estacionales con NDVI y EVI de imágenes del satélite Landsat (5, 7 y 8) entre 1984 y el presente.
description: [Methods for processing the data] [EN] Processing with the Landtrendr algorithm (Kennedy et al, 2010) of seasonal compounds using a standard scenario. All processing tasks were carried out on the Google Earth Engine platform. [ES] Procesamiento con el algoritmo Landtrendr (Kennedy et al, 2010) de los compuestos estacionales mediante un escenario estándar. Todas las tareas de procesamiento se realizaron en la plataforma Google Earth Engine
description: [EN] LTR-VegChangeS is a vegetation change dataset based on the LandTrendr algorithm, from 1984 to the present and for the entire Spanish terrestrial territory.
description: [ES] LTR-VegChangeS es un conjunto de datos de cambio de vegetación basado en el algoritmo LandTrendr, desde 1984 hasta la actualidad y para todo el territorio terrestre español.
DOI: 10.21227/h2vc7j
title: TST Fall detection dataset v1
DOI: 10.26193/jgw1ld
title: ANU Poll 2020-2021 Longitudinal

description: This is the first longitudinal ANU poll data created with accumulate information over the surveys. The dataset covers waves 34, 35, 37, 38, 41, and 43-45 of the ongoing series of polls being undertaken by the Social Research Centre for the ANU. Their purpose is to assess Australians' opinions on important and topical issues, with an emphasis on international comparisons. This research is used to inform public debate and policy about issues affecting Australia. The waves of this data was collected in 2020-2021. Its aim is to provide a timely update on Australia's experiences during the COVID-19 pandemic. These polls include questions regarding experiences of COVID-19, opinions on a COVID-19 vaccine, COVID-19 response policies, financial impacts, support service use and mental health.
description: SAS, 9.4
description: SPSS, 25.0
description: Stata, 15.0
description: ANU Poll
description: Copyright: Copyright © 2020, Australian National University. All rights reserved.
DOI: 10.5066/p144ba54
title: U.S. Geological Survey Mineral Commodity Summaries 2024 Data Release (ver. 2.0, March 2024)

description: This database contains the United States salient statistics and world production statistics for over 88 nonfuel mineral commodities.  Salient statistics for the United States includes items such as production, imports, exports, price, stocks, apparent consumption, and/or net import reliance for nonfuel mineral commodities.  The database contains separate table files (entities) in CSV format.  There are metadata files associated with these CSV files that describe the data.
DOI: 10.5066/p9o75ydv
title: North American Bat Monitoring Program (NABat) Master Sample and Grid-Based Sampling Frame

description: The NABat sampling frame is a grid-based finite-area frame spanning Canada, the United States, and Mexico consisting of N total number of 10- by 10-km (100-km2) grid cell sample units for the continental United States, Canada, and Alaska and 5- by 5-km (25km2) for Hawaii and Puerto Rico. This grain size is biologically appropriate given the scale of movement of most bat species, which routinely travel many kilometers each night between roosts and foraging areas and along foraging routes. A Generalized Random-Tessellation Stratified (GRTS) Survey Design draw was added to the sample units from the raw sampling grids (https://doi.org/10.5066/P9M00P17). This sampling design produces an ordered list of units such that any ordered subset of that list is also randomized and spatially balanced.
DOI: 10.5258/soton/wp00675
title: Individual Countries 1km UN Adjusted Population Density (2000-2020)

description: Gridded population density datasets derived from 1km population distribution datasets adjusted to match UNPD totals (from the 2019 Revision of World Population Prospects)
DOI: 10.5281/zenodo.10223037
title: Orthophotos and 2D hydraulic modelling results used for habitat suitability modelling of the River Inn section (river km  35.3-48) in SE Germany

description: The aerial RGB picture acquisition was performed on October 7 (bypass channel) and 11 (side channel) 2022 using a DJI-Matrice 210 V2 RTK drone. For the image acquisition, the drone mounted the DJI Zenmuse X5S RGB camera. The flight was realized at an altitude of about 120 m, ensuring a lateral and longitudinal overlap of the images of about 80%. Gound Control Points (GCPs) have been disposed along the study site, and their position georeferenced using a Emlid Reach RS2 RTK GPS system.  After data collection, an RGB orthomosaic with a spatial resolution of 25 cm was generated, using PIX4Dmapper  v4.7.5 (www.pix4d.com).
The hydrodynamic simulations were performed with the freeware software BASEMENT v3.2 (https://basement.ethz.ch), which solves the 2D shallow-water equations using a finite volume approach over two-dimensional unstructured meshes. Computational meshes were created using the QGIS plugin BASEmesh 2, with spatially varying element sizes, which were set to be finer in areas expected to be suitable for spawning and as nursery grounds, or when needed to more accurately represent the local morphological complexity.
DOI: 10.5281/zenodo.10701389
title: Marine phosphate level during the Archean  constrained by the global redox budget

description: This dataset includes the data for the preprint submitted to Geophysical Research Letters: "Marine phosphate level during the Archean  constrained by the global redox budget"
DOI: 10.5281/zenodo.10854135
title: Dataset for "Permeability development during fault growth and slip in granite"

description: This is a dataset accompanying the publication entitled "Permeability development during fault growth and slip in granite" by F.M. Aben, A. Farsi, and N. Brantut (submitted). The dataset contains the data acquired during three rock deformation experiments (sample numbers WGMS2, WGMS3, WGMS4) on Westerly granite, and is comprised of:



Notes of the experiments

Mechanical data in .txt files, where the header indicates what the column's values are. The experiment name and phase are contained in the file names. 

Processed ultrasonic data in .jld2 format for experiment WGMS4
DOI: 10.5281/zenodo.10908553
title: Experimental Study on the Krauklis Wave Under Fluid Flow

description: This data set contains the code and data for the "Experimental Study on the Krauklis Wave Under Fluid Flow" and is reffered to the paper submitted in the Journal of Geophysical Research: Solid Earth named "Possibility of Fluid Flow Characterization via a Geophysical Signal: Experimental Study on the Krauklis Wave Under Fluid Flow".
DOI: 10.5281/zenodo.11099576
title: Dissolved nitrous oxide and methane in Taiwan strait: Distribution, seasonal variation and emission

description: The global ocean plays an important role in the overall budgets of nitrous oxide (N2O) and methane (CH4), especially in continental estuaries and shelf areas. To fill this gap in ocean area data, four cruises were conducted between 2021 and 2022, covering the spring, summer, and fall seasons, to study the spatial and seasonal characteristics of N2O and CH4 distributions and emissions in the Taiwan Strait (TWS). And we added readme file describing the quality control procedures to the Zenodo file to help people easily access the data.
DOI: 10.5281/zenodo.13129613
title: Resonant Scattering of Sub-keV Electrons by Beam-driven Electron Cyclotron Harmonic Waves

description: Data repository for ``Resonant Scattering of Sub-keV Electrons by Beam-driven Electron Cyclotron Harmonic Waves''.  This repository contains raytracing results of beam-driven ECH waves and loss-cone-driven ECH waves calculated from HOTRAY ray-tracing program.
DOI: 10.5281/zenodo.13626251
title: Hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity

description: Entitled “A novel hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity” for possible publication in Water Resources Research.

Data:

The Salinized Farmland Flux Station sites are located in the typical irrigated agricultural area of the arid continental monsoon region in northwest China.In this study, we used data from four flux tower located in saline farmland: two for maize (MZ1, MZ2) and two for sunflower (SF1, SF2).

For each site, we collected the following variables at half-hourly temporal resolution: (i) latent heat (LE, W m-2) fluxes, serving as a direct measure representing the energy component of ET (mm h-1), (ii) net radiation (Rn, W m-2), (iii) ground heat flux (G, W m-2), (iv) solar irradiance (Rs, W m-2), (v) air temperature (Ta, °C), (vi) vapor pressure deficit (VPD, KPa), (vii) wind speed (Us, m s-1), (viii) relative humidity (RH, %), and (ix) atmospheric carbon dioxide concentration (Ca, mg m-3). During the crop growth period, field in-situ measurements of soil and vegetation data from these farmlands are conducted approximately every 10 days.  

Code:

All the codes were executed in Python. The provided hybrid deep learning model code can be run on Jupyter Notebook.
DOI: 10.5281/zenodo.8304780
title: Microcontinent collision: input and dataset vtu files

description: Input and dataset vtu files
DOI: 10.6084/m9.figshare.25651131.v2
title: The Iron Spin Transitions in Hydrous Fe<sup>3+</sup>-bearing Bridgmanite and its Geophysical properties in the Lower Mantle

description: This is dateset repository for "The Iron Spin Transitions in Hydrous Fe<sup>3+</sup>-bearing Bridgmanite and its Geophysical properties in the Lower Mantle".
DOI: 10.15784/601739
title: Hercules Dome ApRES Data

description: Phase-sensitive radar data collected at Hercules Dome, Antarctica. These data are processed in two ways for two separate objectives. First, repeat acquisitions between years are interfered to interpret vertical velocity. Second, polarized acquisitions are interfered to interpret crystal orientation fabric. The full processing flow is included to recreate the given data products from the included raw data.
DOI: 10.1594/pangaea.957615
title: Precipitation amount of Pluvio rain gauge at AWIPEV, Ny-Ålesund (2019)

description: The OTT Pluvio2 Version 400 is an all-weather precipitation gauge that measures the amount of precipitation using the weighing principle. It measures the weight of the collecting bucket including its content. The difference between the current bucket content and the previous one recorded gives the precipitation amount. The data set includes the non-real-time output of the OTT software which is particularly suited for daily and monthly totals. The non-real-time output provides a more precise precipitation sum due to better filtering: fine precipitation is collected over a maximum of one hour and output after reaching the threshold of 0.05 mm within that hour. If the fine precipitation does not reach the threshold within an hour, there will be no output. The resolution of the precipitation values is 0.01 mm. The measurement uncertainty is the larger value of ± 0.1 mm or ±1%.
DOI: 10.17632/8h65ywd2jr.3
title: Extensive COVID-19 X-Ray and CT Chest Images Dataset

description: This COVID-19 dataset consists of Non-COVID and COVID cases of both X-ray and CT images. The associated dataset is augmented with different augmentation techniques to generate about 17099 X-ray and CT images. The dataset contains two main folders, one for the X-ray images, which includes two separate sub-folders of 5500 Non-COVID images and 4044 COVID images. The other folder contains the CT images. It includes two separate sub-folders of 2628 Non-COVID images and 5427 COVID images.
DOI: 10.17632/smzggbnkd2.2
title: Students Attention Detection Dataset

description: The purpose of the creation of this dataset for analyzing student's attention and behavior based on various visual cues. This dataset is generated using a combination of fundamental modules that enable the extraction of distinct high-level features from video frames. The key components involved in the dataset creation process are face detection, hand tracking, mobile phone detection, and pose estimation. These components collectively produce a set of features that characterize different aspects of student presence and orientation within the observed frames.
The face detection module yields the count of detected faces, along with their coordinates, width, and height. The hand tracking module provides the count of identified hands. The pose estimation component generates head orientation, x-axis rotation, and y-axis rotation. The mobile phone detection module outputs six features: mobile phone presence, phone coordinates, width, height, and detection confidence.
The dataset comprises a total of 17 columns, which consist of 16 feature columns and one label column. In total, there are 4,000 records within the dataset. The features are described as follows:
no_of_face: Indicates the number of faces detected in a frame.
face_x: Represents the x-coordinate of the upper-left corner of a detected face.
face_y: Denotes the y-coordinate of the upper-left corner of a detected face.
face_w: Reflects the width of a detected face in pixels.
face_h: Specifies the height of a detected face in pixels.
face_con: Provides the confidence score for face detection.
no_of_hands: Indicates the number of hands detected in a frame.
pose: Describes the orientation of a detected face (Forward, Left, Right, Down).
pose_x: Represents the x-axis rotation of the detected face's orientation.
pose_y: Represents the y-axis rotation of the detected face's orientation.
phone: Indicates the presence of a mobile phone (0: No phone detected, 1: Phone detected).
phone_x: Specifies the x-coordinate of the upper-left corner of a detected phone.
phone_y: Denotes the y-coordinate of the upper-left corner of a detected phone.
phone_w: Reflects the width of a detected phone in pixels.
phone_h: Specifies the height of a detected phone in pixels.
phone_con: Provides the confidence score for phone detection.
label: Serves as the target column, indicating whether the subject is attentive (0) or inattentive (1).
The features encompass a wide range of factors that contribute to assessing a student's level of attentiveness. The diverse set of features and corresponding label column of the dataset make it a valuable resource for training machine learning models aimed at recognizing and classifying attentive and inattentive behaviors of the student.
DOI: 10.17909/5d5a-es35
title: Data for J0446B paper
DOI: 10.17909/fe8t-na27
title: L 98-59 c transit 

description: HST observations of a transit of L 98-59 c on 2020 04 07.
DOI: 10.17909/za5q-ss90
title: MIRI Observations of the PN NGC 1514

description: MIRI imaging and MRS data of NGC 1514. The data in the paper are custom reprocessed from the uncal files, so the higher level products here will differ from the published version.
DOI: 10.20350/digitalcsic/15121
title: SPEIbase v.2.8 [Dataset]

description: The Global 0.5?? gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. The Global SPEI database, SPEIbase, offers long-time, robust information on the drought conditions at the global scale, with a 0.5 degrees spatial resolution and a monthly time resolution. It has a multi-scale character, providing SPEI time-scales between 1 and 48 months. The Standardized Precipitatin-Evapotranspiration Index (SPEI) expresses, as a standardized variate (mean zero and unit variance), the deviations of the current climatic balance (precipitation minus evapotranspiration potential) with respect to the long-term balance. The reference period for the calculation, in the SPEIbase, corresponds to the whole study period. Being a standardized variate means that the SPEI condition can be compared across space and time. Calculation of the evapotranspiration potential in SPEIbase is based on the FAO-56 Penman-Monteith method. Data type: float; units: z-values (standard deviations). No land pixels are assigned a value of 1.0x10^30. In some rare cases it was not possible to achieve a good fit to the log-logistic distribution, resulting in a NAN (not a number) value in the database. Dimensions of the dataset: lon = 720; lat = 360; time = 1356. Resolution of the dataset: lon = 0.5??; lat = 0.5??; time = 1 month. Created in R using the SPEI package (http://cran.r-project.org/web/packages/SPEI).
description: Global gridded dataset of the Standardized Precipitation-Evapotranspiration Index (SPEI) at time scales between 1 and 48 months.-- Spatial resolution of 0.5?? lat/lon.-- This is an update of the SPEIbase v2.6 (https://digital.csic.es/handle/10261/202305).-- What???s new in version 2.7: 1) Based on the CRU TS 4.05 dataset, spanning the period between January 1901 to December 2020. Using TLMoments::PWM instead of lmomco::pwm.ub for calculating distribution parameters. For more details on the SPEI visit http://sac.csic.es/spei
DOI: 10.21227/73zj-4303
title: 2024 IEEE GRSS Data Fusion Contest - Flood Rapid Mapping

description: The Challenge Task  As a result of climate change, extreme hydrometeorological events are becoming increasingly frequent. Flood rapid mapping products play an important role in informing flood emergency response and management. These maps are generated quickly from remote sensing data during or after an event to show the extent of the flooding. They provide important information for emergency response and damage assessment. The aim of this challenge is to develop data fusion algorithms that generate flood maps by processing spatial data from a variety of sources.   The goal of this challenge is to design and develop an algorithm that will combine multi-source data to classify flood surface water extent–that is, water and non-water areas. Provided data sources include optical and Synthetic Aperture Radar (SAR) remote sensing images as well as a digital terrain model, land-use and water occurrence. The output is a gridded flood map where each grid cell is labeled water or non-water.   How to extract water areas from a remote sensing image depends largely on the acquisition technology. This data fusion challenge has two tracks representing this variability.   Track-1: Flood rapid mapping with SAR data   Track-2: Flood rapid mapping with optical data   No guidance is given on the method to be used for data fusion and pixel-wise classification; it could be based on a statistical approach, machine learning, or a combination of different approaches.
DOI: 10.21338/ess9e03_1
title: ESS9 - integrated file, edition 3.1
DOI: 10.21949/1504474
title: San Diego Test Data Sets
DOI: 10.25318/1310039401-eng
title:  Leading causes of death, total population, by age group

description: Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.
DOI: 10.25810/qa19-t327
title: Data for "Heat conduction in magnetic insulators via hybridization of acoustic phonons and spin-flip excitations"
DOI: 10.3334/cdiac/atg.017
title: Atmospheric Carbon Dioxide Record from In Situ Measurements at Baring Head (1970 - 1993)

description: Determinations of atmospheric CO2 mixing ratios are made using a Siemens Ultramat-3 nondispersive infrared (NDIR) gas analyzer. The NDIR CO2 analyzer is connected via a gas manifold consisting of stainless steel tubing and computer-controlled solenoid switches to 12 gas cylinders and 2 sample air lines. The NDIR analyzer compares ambient air CO2 mixing ratios relative to known CO2 mixing ratios in tanks of compressed reference gases. The analyzer operates in a differential mode, with a "zero" reference gas of CO2 mixing ratio 20 to 30 parts per million (ppm) below ambient air CO2 levels flowing continuously through one cell of the analyzer at ~10 mL/min. When atmospheric CO2 is measured, a diaphragm pump pulls air through a sampling line at ~5 L/min. A small fraction of this (180 mL/min) is dried cryogenically to a temperature of approximately ¬70° Celsius and passed through the sample cell of the CO2 analyzer. Both the "zero" and sample gas are exhausted into the observatory building.
DOI: 10.48479/7epd-w356
title: The MeerKAT Galaxy Cluster Legacy Survey - Survey Overview and Highlights

description: The first data release of the MeerKAT Galaxy Cluster Legacy Survey (MGCLS) consists of the uncalibrated visibilities, a set of continuum imaging products, and several source catalogues. All clusters have Stokes-I products, and approximately 40% have Stokes-Q and U products as well.
DOI: 10.48670/moi-00016
title: Global Ocean 1/12° Physics Analysis and Forecast updated Daily
DOI: 10.5066/f7xw4gwx
title: Langelier Saturation Indices Computed for U.S. Groundwater, 1991-2015; Water Well Data and Characteristic Values for States

description: The occurrence of metals, such as lead and copper, in household drinking supplies can often be a result of the corrosion of pipes and joints in water distribution systems. One measure of the potential for water to cause corrosion is the Langelier Saturation Index (LSI) (Langelier, 1936). The LSI is a measure of the potential for water to deposit a mineral layer (scale) within a water distribution system that can inhibit the corrosion of pipes and joints. Negative values of LSI suggest mineral deposition is not likely to occur while positive values indicate conditions favorable to mineral deposition. Negative values of LSI might be indicative of conditions that lead to elevated concentrations of metals, such as lead and cooper, in household water.
DOI: 10.5066/p98bl0xf
title: Grain size, bulk density, and carbon content of sediment collected from Whale's Tail South marsh and adjacent bay floor, South San Francisco Bay, California, 2021-2022 (ver. 2.0, March 2025)

description: Sediment samples were collected on and adjacent to the Whale's Tail South marsh. Short push-cores of bed sediment were collected in South San Francisco Bay adjacent to Whale's Tail South marsh on five days from June through August 2021 and 3 days from November 2021 to January 2022. Additional samples were taken from ceramic tiles placed on the marsh to measure sediment deposition and from rip-up clasts deposited on the marsh edge. Samples were analyzed for sediment properties including bulk density, particle size distribution, and percent carbon. These data were collected as part of a collaborative study with the USGS Western Ecological Research Center to quantify sediment fluxes, deposition on the marsh, and changes in marsh morphology at Whale's Tail marsh in southern San Francisco Bay.