cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25984/2202668
title: Dynamically Downscaled Hourly Future Weather Data with 12-km Resolution Covering Most of North America

description: This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. The weather data is created for two emissions scenarios: RCP4.5 and RCP8.5 and spans two 10-year time slices in the future: 2045 - 2054 and 2085 - 2094. It offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide 20 years of future weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale. The data for RCP4.5 is still being processed and will be published soon.
DOI: 10.3886/e149861v1
title: The Pleasure Study 2020-2021

description: The pleasure study was collected in 2020 and 2021. It collect data on Millennials gender and sexual experiences. The data set collects data on gender, sexuality, race, sexualexperiences, sexual partners, orgasm, religiosity, sexual function, gender dysphoria andsexual empowerment.
description: A focus on collecting trans, non-binary, and intersex respondents as well as cis men and cis women.
description: web-based survey;
DOI: 10.5281/zenodo.13152005
title: Compilation of LCF and TMF Data for Single-Crystal and Directionally-Solidified Ni-base Superalloys (All Orientations)

description: This Excel file contains a compilation of low cycle fatigue (LCF) and thermomechanical fatigue (TMF) data for single-crystal (SX) and directionally-solidified (DS) Ni-base superalloys that are typically used for blades and vanes in gas turbines.  Most of the data is generated by strain-controlled uniaxial fatigue tests.  Both continuous cycling and cycles with dwells (hold times) at either the maximum or minimum of the cycle are included in the dataset.

Several different alloys with similar mechanical behaviors are included in the dataset.  The dataset only includes materials that have undergone a solution and age heat treatment typical of that applied to blade and vane components.  All the fatigue data in this dataset is limited to uniaxial cyclic loading.  The loading direction with respect to the crystal orientation or solidification direction varies.  The data in this dataset has been extracted from the public domain.  The sources of the data are included in the Excel file.

This data is used to train a Probabilistic Physics-guided Neural Network (PPgNN) that outputs a strain-life curve, including the mean response and 95% confidence intervals.  The INPUTS and OUTPUTS used in the PPgNN are highlighted in the Excel file.
DOI: 10.5281/zenodo.13974503
title: Comprehensive Dataset for Fault Detection and Diagnosis in Inverter-Driven PMSM Systems

description: This dataset contains multi-sensor measurements from an inverter-driven PMSM system under various fault conditions. It includes: 



10,892 samples across 9 operational conditions

8 raw sensor measurements

15 derived features

Data collected at 10 Hz sampling rate

 Fault scenarios including open-circuit, short-circuit, and overheating conditions


 Keywords:



PMSM

Fault Detection

Inverter Faults

Motor Drive Systems

Experimental Data

Machine Learning
DOI: 10.5281/zenodo.14517534
title: A CURE lab in an introductory biology course has minimal impact on student outcomes, self-confidence, and preferences compared to a traditional lab

description: This dataset includes data collected over three years from Biology students taking an introductory biology course for majors at Kutztown University of Pennsylvania. Data includes the version of the lab the student was enrolled in (CURE vs traditional), demographic information (gender, race, class level, transfer status, and prior research experience), academic performance (pre- and post-course test scores, final lab score, final lecture score, and final letter grade), retention in the Biology program (up to three years following course completion), and survey responses on a Likert-scale (including self-confidence in scientific abilities, interest in pursuing science, and preferences for lab courses, both pre- and post-course). All student identifying information has been removed.
DOI: 10.5281/zenodo.4552069
title: Refractive index data for 150 nm Ag measured with ellipsometry

description: This is the refractive index data for 150 nm Ag thin film measured using the Sopra GES 5E spectroscopic ellipsometer. The data is extracted using the bulk calculation model of the WinEliII software. The incidence angle was 75 degrees, the analyzer was kept at 45 degrees. The film was deposited using e-beam evaporation technique in Lab 600H. The substrate used was silicon wafer of 525 microns thick. 2 nm of titanium was deposited initially as an adhesion layer and 1 nm of AgOx was deposited as the seed layer. It was followed by deposition of Ag (50 nm each for 3 times). The deposition rate of all the metals was 4 angstorm/sec with a base pressure of 1.5*10^(-6) mbar.
DOI: 10.5284/1000327
title: Defence of Britain Archive

description: The Defence of Britain Project databases were created from field and documentary work carried out between April 1995 and December 2001. The purpose of the Project was to record the 20th century militarised landscape of the United Kingdom, and to inform the responsible heritage agencies at both local and national level with a view to the future preservation of surviving structures. The database contains nearly 20,000 records of military structures with nearly 800 photographs.
DOI: 10.5285/1dbe7a109c0244aaad713e078fd3059a
title: ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 5.0 Data

description: This collection contains version 5.0 datasets produced by the Ocean Colour project of the ESA Climate Change Inititative (CCI). The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies. Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm. Information on uncertainties is also provided. This dataset collection refers to the Version 5.0 data products held in the CEDA archive covering the period 1997-2020. Links to the individual datasets that make up this collection are given in the record below.
DOI: 10.5285/62866635ab074e07b93f17fbf87a2c1a
title: ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Grid product, version 1.1

description: The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Grid v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA. The dataset provides monthly information on global burned area on a 0.25 x 0.25 degree resolution grid from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in NetCDF files, and it includes 4 layers: sum of burned area, standard error, fraction of burnable area and fraction of observed area. For further information on the product and its format see the Product User Guide.
DOI: 10.60545/hihumm
title: Lista Roja Nacional de peces de agua dulce de Ecuador

description: En el marco del Proyecto Paisajes Vida Silvestre, ejecutado por el Ministerio del Ambiente, implementado y ejecutado por PNUD y en coordinación con el socio estratégico WCS-Ecuador, se realizó el proceso de evaluación y actualización de listas rojas de especies amenazadas, las cuales incluyen los grupos taxonómicos: orquídeas, aves, palmas, peces de agua dulce. Este proceso comenzó con la identificación de expertos o especialistas y la invitación respectiva para que formen parte del grupo que establecerá la evaluación de las especies y elaborará la lista roja, en base a los criterios de UICN.
description: Con todos los expertos se revisó las especies evaluadas, para aceptar la categoría propuesta por cada uno. En ciertos casos se reevaluó una vez más a las especies, tomando en cuenta la información disponible y el conocimiento de los investigadores. Con esto se completó la evaluación de la mayoría de las especies.
DOI: 10.6084/m9.figshare.28001798
title: Supplementary File 1 - Health Security Disparities in the Eastern Mediterranean Region

description: Summary: This supplementary document provides additional evidence for our research titled "Health Security Disparities in the Eastern Mediterranean Region: A Comparative Analysis Using an Integrated MCDM and Clustering Approach." It presents supplementary context and data tables to complement the primary analysis and results. This paper is structured into four sections. The first section delineates the primary health security criteria used to assess countries and their descriptions, which were applied to the decision matrices for 2019 and 2021. It also elucidates the information from the initial research phase, wherein decision matrices were constructed to establish health security criteria weights, ranks, and group countries. The second section presents the entropy calculation results employed to determine the significance of health security indicators for Eastern Mediterranean Region countries, including Gulf Cooperation Council (GCC) and non-GCC nations, for both 2019 and 2021. The third section examines the application of the Entropy-VIKOT model to rank these countries' health security performance during the aforementioned years. The final section presents the clustering analysis, which categorizes countries based on their health security performance scores derived from the Entropy-VIKOT model. This analysis identified distinct performance clusters and assigned grades, demonstrating the distribution of countries across regional groups (GCC and non-GCC countries) in both years.
DOI: 10.21335/nmdc-1859287013
title: Model output from the hindcast simulation and downscaled climate projections of regional ocean model (NEMO-NAA10km) and ecosystem model (NORWECOM.E2E)

description: The data set contains hydrographic data taken from the hindcast simulation of a regional ocean model (NEMO-NAA10km; Hordoir et al., 2022) and biological data taken from the hindcast simulation of an ecosystem model (NORWECOM.E2E; Aksnes et al., 1995; Skogen et al., 1995; Skogen and Søiland, 1998; Skogen et al., 2018; Sandø et al., 2021).
The data set also contains hydrographic and biological data obtained from downscaled climate projections for three different IPCC climate scenarios: low (SSP1-2.6), medium (SSP2-4.5), and high (SSP5-8.5) emission.
The hydrographic data consists of temperature, salinity, at surface (only for hindcast simulations) and 200 m depth and sea ice concentration in summer (September) and winter (March). The biological data consists of gross primary production (diatom and flagellate) and gross secondary production (micro and meso zooplankton).
The climate scenarios from the NorESM2 (the second version of the Norwegian Earth System Model; Seland et al., 2020; Tjiputra et al., 2020) are downscaled using the regional ocean model: NEMO-NAA10km to produce the physical forcing for the ecosystem model: NORWECOM.E2E.
DOI: 10.24432/c5688j
title: Tic-Tac-Toe Endgame
DOI: 10.34740/kaggle/dsv/9418767
title: Ethereum NFTs Flagged for Suspected Wash Trading
DOI: 10.5066/p9fzux6n
title: Landslide Inventories across the United States (ver. 2.0, June 2022)

description: Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information on landslide occurrence across the entire U.S. The data release includes digital inventories created [...]
DOI: 10.5281/zenodo.11352608
title: Code&Data_'Choosing fit-for-purpose biodiversity impact indicators for agriculture in the Brazilian Cerrado ecoregion'

description: File "R_Scripts_biodiversity_indicators.zip" includes codes for calculating the biodiversity impact on the terrestrial vertebrates of the Cerrado biome using the countryside Species Area Relationship (cSAR), the Species Threat Abatement and Restoration (STAR) metric and the Species Habitat Index (SHI).

File "results_biodiversity_indicators.zip" includes the result tables of the calculations done with the above mentioned codes.
DOI: 10.5281/zenodo.13774391
title: Dataset, analyses and figures for the paper "Alarming patterns of mature forest loss in the Brazilian Atlantic Forest"

description: The Repository.7z file in this data publication contains the GIS Folder (data, QGIS project), analysis, tables and figures used for the article "Alarming patterns of mature forest loss in the Brazilian Atlantic Forest", published in Nature Sustainability.

 

The GIS project was built under QGIS 3.38.1 and contains as layers :

1) pol_deforestation_EPSG5880_final - polygons of all deforested areas from 2010 to 2020

Attributes:







ID - polygon identifier

UF - Brazilian states

area_ha - deforestation area in hectare

ClassDF - include the period deforestation was observed. E.g. "Decremento ....2016-2017" = deforestation between 2016-2017.

phisiognomy - original Atlantic Forest Physiognomy according to Atlantic Forest Law map

LUC - LandUse and cover legend. 



Aa, An, and R = Water body (W)

CAp = Permanente crop (PC)

CAt and CAs = Temporary crop (TC)

I and MV = Infrastructure (I)

M = Mining (M)

P = Pasture (P)

S = Silviculture (S)

U  = Urbanization (U)

VSa = Medium secondary vegetation (SV2)

VSf = Advanced secondary vegetation (SV3)

VSi = Early secondary vegetation (SV1)



UC_ atributes related to Protected Areas intersected by deforestation polygons

QL_ atributes related to Quilombola lands intersected by deforestation polygons

TI_ atributes related to Indigenous lands intersected by deforestation polygons

CAR_Pequen indicates the fraction of deforested area (hectare) of that polygon within small properties

CAR_Medio indicates the fraction of deforested area (hectare) of that polygon within medium properties

CAR_Pequen indicates the fraction of deforested area (hectare) of that polygon within Large properties

ST_ atributes related to agrarian settlement projects intersected by deforestation polygons




 

2) pol_deforestation_EPSG5880_final_centroid

Centroids of deforested areas polygons

 

3) Remaining_atlanticforest_2010_EPSG5880

contain mask of remaining vegetation in the Atlantic Forest in 2010 with the Brazilian state, vegetation class (Mata = Forest type; Mangue and Restinga = vegetation typical of coastal zones), and total area they were associated with.

 

4) AtlanticForestLaw_Albers_sirgas 2000 - polygon of Atlantic Forest boundary according to Atlantic Forest Law 11.428/2006. The polygon is classified by Atlantic Forest physiognomies in "FITO" attribute.

 

5) Goup LandTenure - layers related to each land tenure







suffix '_EPSG5880_deforestation_centroid' the centroids of each land tenure intersected by deforestation polygon

prefix 'Remaining_atlanticforest_2010_EPSG5880_' the atlantic forest vegetation remaining in each land tenure in 2010




 

6) Group Hotspots - layers related to deforestation hotspot analyses.

 

7) Group Physiognomies - layers related to Atlantic forest biome and its physiognomies
DOI: 10.5281/zenodo.14500708
title: How extreme was the thunderstorm rain in Vienna on 17 August 2024? INCA Data and station data.

description: The data was used for the analyses in "Brief communication: How extreme was the thunderstorm rain in Vienna on 17 August 2024? A temporal and spatial analysis." 



INCA data: Aggregated gridded 2-hourly precipitation sums for the study domain around Vienna with 1x1 km spatial resolution (Temporal resolution: 1 hour)

Station data: Hourly precipitation sum (Temporal resolution: 1 hour). See list for metadata:





ID
Name  
Longitude
Latitude
 Elevation (m)
Start year (1-hour resolution)


5820
Brunn am Gebirge
16°16'12''
48°6'25''
291
2008


51
Langenlebarn
16°07'05''
48°19'26''
175
1992


31
Gross-Enzersdorf
16°33'33''
48°11'59''
154
1994


5990
Schwechat
16°34'53''
48°7'3''
183
2008


5935
Wien-Donaufeld
16°25'53''
48°15'26''
160
1996


105
Wien-Hohe Warte
16°21'23''
48°14'55''
198
1941


5925
Wien-Innere Stadt
16°22'1''
48°11'54''
177
1985


5802
Wien-Jubiläumswarte
16°15'55''
48°13'16''
450
2009


106
Wien-Mariabrunn
16°13'46''
48°12'25''
225
1997


4115
Wien-Stammersdorf
16°24'20''
48°18'21''
191
2008


107
Wien-Unterlaa
16°25'10''
48°7'30''
200
1996
DOI: 10.5281/zenodo.7674802
title: Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows

description: Title: <strong>Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows</strong> Author: Pamela A. Green (pg@pamelaagreen.com), Advanced Science Research Center, CUNY, New York, NY USA https://orcid.org/0009-0006-7803-8182 The python Jupyter Notebook <strong>SafeJustEarthSysBnd_EstressCUNY-Griffith2022-23.ipynb</strong> and accompanying data sets represent spatial modelling for development of the safe and just surface water target for Working Group 3 of the Earth Commission for the Earth Commission Long Report and the "Safe and Just Earth Systems Boundaries" publication. The surface water target includes spatial modelling of the extent of global-scale hydrological alteration of environmental flows. All input datasets required to run the model are located under the <strong>ModelInput</strong> folder with raster data in zipped format to minimize space requirements. The code extracts the zipped files and then deletes the uncompressed files upon completion. All model outputs are located under the <strong>ModelOutput</strong> folder. Please reference the <strong>README.xlsx</strong> file for a full listing of the model input and output data files.
description: If you use this software, please cite it as below.
DOI: 10.5676/dwd_gpcc/fd_m_v2020_100
title: GPCC Full Data Monthly Version 2020 at 1.0°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data
title: Gridded Monthly Totals

description: This is the centennial GPCC Full Data Monthly Product of monthly global land-surface precipitation based on ~ 85,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~20,000 to more than 50,000 stations. This product contains the monthly totals on a regular grid with a spatial resolution of 1.0° x 1.0° latitude by longitude. Precipitation anomalies at the stations are interpolated and then superimposed on the GPCC Climatology V2020 in the corresponding resolution (DOI: 10.5676/DWD_GPCC/CLIM_M_V2020_100). The temporal coverage of the dataset ranges from January 1891 until December 2019. The GPCC Full Data Monthly Product is the most accurate in situ precipitation reanalysis data set of GPCC. Moreover it supports regional climate monitoring, model validation, climate variability analysis and water resources assessment studies.
description: General user recommendations: Users are referred to following gridded Global Precipitation Climate Centre (GPCC) products: GPCC Climatology 1951-2000 (GPCC_CLIM) with high representativeness, for application as a reference, and for utilization of the anomaly interpolation method. GPCC Full Data Monthly Product(GPCC_FD) with high accuracy, for, e.g., verification of models, or for analysis of historic global precipitation, or for research concerning the global water cycle, e.g.. GPCC Monitoring Product (GPCC_MP) with high availability and reasonable timeliness, for applications like calibration of satellite data, or early annual reporting. GPCC First Guess Product (GPCC_FG) with high timeliness, to be applied for, e.g., drought monitoring.