cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10001504
title: Alaska Landfast Ice Extent and Breakup Timing over 2000-2022

description: This dataset contains a landfast ice climatology calculated from MODIS imagery. For a complete description of the methods, please see Cooley and Ryan (forthcoming). 
This dataset contains a shapefile "landfast_ice_edge_2000_2022.shp" of maximum ice edge positions derived from MODIS imagery. The 'time period' column refers to the 30-day period over which the edge position was calculated, specifically 1: March 1st to April 1st, 2: March 15th to April 15th and 3: April 1st to May 1st. For years with multiple lines and/or multiple time periods present in the dataset, we retain the maximum position in our analysis; in other words, we calculate the ice edge position based on the furthest line from the coast.
This dataset also contains time series of ice edge position (distance to the ice edge from the coastline, in km) and breakup timing. The ice edge position is calculated as the maximum ice edge distance from coastline using the ice edge shapefile described above. The breakup date is defined as the day when 75% of the area contained within the maximum ice edge position for that year reaches open water. This is corrected for cloud cover by assigning the actual breakup date as the midpoint between the first day when the ice was open water and the previous cloud-free observation. This uncertainty (in days) is recorded as "breakup_uncertainty". 
Any usage of this data should cite Cooley and Ryan (forthcoming). For any questions about the dataset, please reach out to Sarah Cooley at scooley2@uoregon.edu
DOI: 10.5281/zenodo.10413148
title: Alaska Landfast Ice Extent and Breakup Timing over 2000-2022

description: This dataset contains a landfast ice climatology calculated from MODIS imagery. For a complete description of the methods, please see Cooley and Ryan (2024), Community-scale changes to landfast ice along the coast of Alaska over 2000-2022, Environmental Research Letters, doi.org/10.1088/1748-9326/ad1c7b

This dataset contains a shapefile “landfast_ice_edge_2000_2022.shp” of maximum ice edge positions derived from MODIS imagery. The ‘time period’ column refers to the 30-day period over which the edge position was calculated, specifically 1: March 1st to April 1st, 2: March 15th to April 15th and 3: April 1st to May 1st. For years with multiple lines and/or multiple time periods present in the dataset, we retain the maximum position in our analysis; in other words, we calculate the ice edge position based on the furthest line from the coast.

This dataset also contains time series of seaward landfast ice edge (SLIE) position (distance to the ice edge from the coastline, in km) and breakup timing (in day of year). The ice edge position is calculated as the maximum ice edge distance from coastline using the ice edge shapefile described above. The breakup date is defined as the day when 75% of the area contained within the maximum ice edge position for that year reaches open water. This is corrected for cloud cover by assigning the actual breakup date as the midpoint between the first day when the ice was open water and the previous cloud-free observation. This uncertainty (in days) is recorded as “Breakup Uncertainty”. 

Any usage of this data should also cite Cooley and Ryan (2024). For any questions about the dataset, please reach out to Sarah Cooley at scooley2@uoregon.edu
DOI: 10.5281/zenodo.12633797
title: Alaska Landfast Ice Extent and Breakup Timing over 2000-2022

description: This dataset contains a landfast ice climatology calculated from MODIS imagery. For a complete description of the methods, please see Cooley and Ryan (2024), Community-scale changes to landfast ice along the coast of Alaska over 2000-2022, Environmental Research Letters, doi.org/10.1088/1748-9326/ad1c7b

This dataset contains a shapefile “landfast_ice_edge_2000_2022.shp” of maximum ice edge positions derived from MODIS imagery. The ‘time period’ column refers to the 30-day period over which the edge position was calculated, specifically 1: March 1st to April 1st, 2: March 15th to April 15th and 3: April 1st to May 1st. For years with multiple lines and/or multiple time periods present in the dataset, we retain the maximum position in our analysis; in other words, we calculate the ice edge position based on the furthest line from the coast.

This dataset also contains time series of seaward landfast ice edge (SLIE) position (distance to the ice edge from the coastline, in km) and breakup timing (in day of year). The ice edge position is calculated as the maximum ice edge distance from coastline using the ice edge shapefile described above. The breakup date is defined as the day when 75% of the area contained within the maximum ice edge position for that year reaches open water. This is corrected for cloud cover by assigning the actual breakup date as the midpoint between the first day when the ice was open water and the previous cloud-free observation. This uncertainty (in days) is recorded as “Breakup Uncertainty”. 

Any usage of this data should also cite Cooley and Ryan (2024). For any questions about the dataset, please reach out to Sarah Cooley at scooley2@uoregon.edu
DOI: 10.5281/zenodo.12547836
title: iSALE Datasets of "Evidence for magnetized ejecta deposits on the Moon based on observations of demagnetized craters"

description: The input data (*.inp) is used for iSALE impact simulation for the four craters (Chaplygin, Keeler, Gauss, and Fermi) and the output data (*.dat) are its result.
DOI: 10.15468/dl.lycuc9
title: Occurrence Download

description: A dataset containing 186 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Glossodoris cincta (Bergh, 1888)"
 ]
}

 The dataset includes 186 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002988-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.108oy5
title: Occurrence Download

description: A dataset containing 1277 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phyllidiella pustulosa (Cuvier, 1804)"
 ]
}

 The dataset includes 1277 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003004-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2jdtpp
title: Occurrence Download

description: A dataset containing 301 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chromodoris annae Bergh, 1877"
 ]
}

 The dataset includes 301 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002982-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sx5yat
title: Occurrence Download

description: A dataset containing 176 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "Geometry POLYGON((101.39666 2.80661,147.97137 2.80661,147.97137 21.41941,101.39666 21.41941,101.39666 2.80661))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pterocarpus indicus Willd."
 ]
}

 The dataset includes 176 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009700-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/yc7cdfgjm5.1
title: Multimedia effects on AIL

description: As part of Master's dissertation, a survey of 300 undergraduate and graduate students from universities in three countries, UK, India and Kenya, studying arts and humanities and social sciences, was conducted. The aim was to survey 100 students from each country to obtain varied responses, which could attempt to represent the student body to an extent. Students from various universities in each country were invited to participate in the survey to ensure a wide range of responses, including academic and artistic students in each region. The findings from the survey informs students multimedia preferences in academic information literacy instruction and identifies the key barriers affecting students’ capabilities for multimedia learning. Finally, it discusses the current & future trends of multimedia influence on pedagogical instruction.
The purpose of the survey was to evaluate the effectiveness of multimedia in teaching academic information literacy.
DOI: 10.25318/3310000701-eng
title: Quarterly balance sheet and income statement, by industry

description: Selected quarterly aggregate balance sheet and income statement items representing incorporated enterprises operating in Canada, by the North American Industry Classification System (NAICS), presented in millions of dollars.
DOI: 10.5439/1052449
title: Solar Resource & Meteorological Assessment Project (SOLRMAP): Rotating Shadowband Radiometer (RSR); Milford, Utah (Data)

description: The U.S. Department of Energy's National Renewable Energy Laboratory collaborates with the solar industry to establish high quality solar and meteorological measurements. This Solar Resource and Meteorological Assessment Project (SOLRMAP) provides high quality measurements to support deployment of power projects in the United States. The no-funds-exchanged collaboration brings NREL solar resource assessment expertise together with industry needs for measurements. The end result is high quality data sets to support the financing, design, and monitoring of large scale solar power projects for industry in addition to research-quality data for NREL model development. NREL provides consultation for instrumentation and station deployment, along with instrument calibrations, data acquisition, quality assessment, data distribution, and summary reports. Industry participants provide equipment, infrastructure, and station maintenance.
DOI: 10.7927/h4f47m2c
title: Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates
title: Administrative Unit Center Points with Population Estimates, v4 (2000, 2005, 2010, 2015, 2020)

description: The Gridded Population of the World, Version 4 (GPWv4) Administrative Unit Center Points with Population Estimates consists of UN-adjusted estimates of human population counts and densities for the years 2000, 2005, 2010, 2015 and 2020 by administrative unit center point (centroid) location. Specifically, the population data were adjusted to the 2015 Revision of UN World Population Prospects. The center points are based on approximately 12.5 million input administrative units used in GPWv4. Additionally, the administrative unit names, unit areas, and data context of the units are included.
DOI: 10.15468/dl.u1dqtv
title: Occurrence Download

description: A dataset containing 91 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chromodoris dianae Gosliner & Behrens, 1998"
 ]
}

 The dataset includes 91 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002981-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/wdcc/chelsa_v1_1
title: CHELSA climatologies at high resolution for the earth's land surface areas (Version 1.1)

description: Project: Climatologies at high resolution for the earth's land surface areas - CHELSA (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas currently under development in coorporation with the Department of Geography of the University of Hamburg (Prof. Dr. Jürgen Böhner, Dr. Olaf Conrad, Tobias Kawohl), the Swiss Federal Institute for Forest, Snow and Landscape Research WSL (Prof. Dr. Niklaus Zimmermann), the University of Zurich (Dr. Dirk N. Karger, Dr. Michael Kessler), and the University of Göttingen (Prof. Dr. Holger Kreft). Version 1.0 is a first release. CHELSA is an ongoing research project and we are updating it continuously. Swiss National Funds (SNF 147630, SNF 146906) Summary: CHELSA_v1.1 (http://chelsa-climate.org/) is a high resolution (30 arc sec, ~1 km) climate data set for the earth land surface areas. It includes monthly and annual mean temperature and precipitation patterns as well as derived bioclimatic and interannual parameters for the time period 1979-2013. CHELSA_v1.1 is based on a quasi-mechanistical statistical downscaling of the ERA interim global circulation model (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim) with a GPCC (https://www.dwd.de/EN/ourservices/gpcc/gpcc.html) and GHCN (https://www.ncdc.noaa.gov/ghcnm/) bias correction.
DOI: 10.17632/5d78g6mmng.1
title: Mexico´s forest diversity: wood densities per specimen

description: The data are associated with the following article (please look there for all information): Ricker, M., M.Á. Castillo-Santiago, G. Gutiérrez-García, E.M. Martínez, and E. Mondragón. Mexico’s forest diversity: wood densities, collection sites of identified tree species, and shapefiles of forest-vegetation provinces. Data in Brief. In preparation (July 2023).
DOI: 10.17632/cr65yxbmvx.2
title: Water Quality Data - Lake Allatoona, Georgia, U.S., 2017

description: These data include water quality and surface reflectance data at three sites in Lake Allatoona, Georgia, U.S. in the summer of 2017. Water quality parameters were measured including chlorophyll-a, temperature, pH, DO, and EC. The data were gathered using a YSI sonde and GER 1500 spectroradiometer in the field, and samples taken and analyzed and in a lab by the HPLC method. These data are in CSV and spreadsheet format. The spectroradiometer data were used for calibration of historical Landsat data by methods outlined in the included spreadsheet. These data and methods are a part of our study integrating Landsat data and in-situ data from public datasets in which we hypothesized that some of the major drivers of phytoplankton blooms in Lake Allatoona were drought severity and rainfall intensity. We found that the drivers may differ depending on location in a lake or reservoir, and that drought indices showed the highest correlations in most of the linear models.
DOI: 10.25919/18h2-vf51
title: CMRSET (CSIRO MODIS ReScaled EvapoTranspiration) monthly actual evapotranspiration (ETa) estimates at 500 m resolution for the canal commands in the Indus Basin Irrigated System (IBIS) in Pakistan from March 2000 to December 2018

description: Monthly actual evapotranspiration (ETa) computed with the CMRSET model at 500 m resolution. The monthly data spans from March 2000 to December 2018. The data is geographically limited to the area subtended by the 48 main canal commands in the IBIS. The data was produced to support water resources assessments and the IBIS water balance (see e.g. Ahmad et al., 2020). The format is in unsigned 16 bit integer GeoTIFF in the WGS 1984 World Mercator projected coordinate system and uses a scaling factor of 100 (i.e. divide by 100 to obtain ETa in mm per month).
DOI: 10.5281/zenodo.291798
title: Aeolian Saltation Fieldwork 30-Minute Wind And Saltation Values

description: Excel spreadsheet (.XLSX) containing 30-minute values for shear velocity, shear stress, wind direction, stability parameter, saltation layer height, total saltation flux, saltation detection frequency, and associated uncertainties for most of these values for field deployments at Jericoacoara (Brazil), Rancho Guadalupe (California), and Oceano (California).
DOI: 10.5439/1052222
title: NREL National Wind Technology Center (NWTC): M2 Tower; Boulder, Colorado (Data)

description: The National Wind Technology Center (NWTC), located at the foot of the Rocky Mountains near Boulder, Colorado, is a world-class research facility managed by NREL for the U.S. Department of Energy. NWTC researchers work with members of the wind energy industry to advance wind power technologies that lower the cost of wind energy through research and development of state-of-the-art wind turbine designs.  NREL's Measurement and Instrument Data Center provides data from NWTC's M2 tower which are derived from instruments mounted on or near an 82 meter (270 foot) meteorological tower located at the western edge of the NWTC site and about 11 km (7 miles) west of Broomfield, and approximately 8 km (5 miles) south of Boulder, Colorado.  The data represent the mean value of readings taken every two seconds and averaged over one minute. The wind speed and direction are measured at six heights on the tower and air temperature is measured at three heights.  The dew point temperature, relative humidity, barometric pressure, totalized liquid precipitation, and global solar radiation are also available.
DOI: 10.6084/m9.figshare.23699505.v1
title: Pork_database_risk_factors.xlsx

description: Questionnaire responses for the study on prevalence, serovars, and risk factors associated with the presence of <em>Salmonella</em> in pork sold in public markets in Quito, Ecuador
DOI: 10.6084/m9.figshare.25399030
title: <b>Challenges of recruitment processes to a randomized dietary trial in irritable bowel syndrome</b>

description: We have performed a dietary intervention in IBS with either SSRD or low FODMAP for 4 weeks. This manuscript describes the recruitment process
DOI: 10.6084/m9.figshare.25562292
title: <b>Challenges of recruitment processes to a randomized dietary trial in irritable bowel syndrome</b>

description: We have performed a dietary intervention in IBS with either SSRD or low FODMAP for 4 weeks. This manuscript describes the recruitment process
DOI: 10.25380/iastate.11356430.v1
title: GIS data and juptyer Notebook for Random Forest models for soybean Sudden Death Syndrome (SDS)

description: GIS_data_and_jupyter_python_notebook.zip: Data for Modeling SDS via Random Forest Models. Contains a ArcGIS Pro project with example data collected at Marston Farm (Boone, IA) and cropped Planet scope 4-band imagery of the area for 2016, 2017 and 2018.Preview for jupyter notebook: preview of a jupyter (Python 3) notebook that demonstrates the use of Random forest classifier using the GIS data.<br>
DOI: 10.5281/zenodo.3591396
title: TAASRAD19 Radar Scans 2017-2019

description: TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps.<br> The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration.<br> Software methods for data pre-processing, model training and inference, and a pre-trained model are<br> publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility.
description: This dataset contains the radar scans for the years 2017 - 2019. The radar scans for years 2010 - 2016 are available here: https://doi.org/10.5281/zenodo.3577451 The precipitation sequences in HDF5 format, extracted from the full scan dataset are available here: https://doi.org/10.5281/zenodo.3591404
DOI: 10.5517/ccdc.csd.cc104q84
title: CCDC 928055: Experimental Crystal Structure Determination

description: Related Article: C. Henao Castañeda, J.F. Cadavid Vargas, J.L. Jíos, C.O. Della Védova, G.A. Echeverría and O.E. Piro|2019|CSD Communication|||
DOI: 10.5281/zenodo.3577451
title: TAASRAD19 Radar Scans 2010-2016

description: TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps.<br> The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration.<br> Software methods for data pre-processing, model training and inference, and a pre-trained model are<br> publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility.
description: This dataset contains the radar scans for the years 2010 - 2016. The radar scans for years 2017 - 2019 are available here: https://doi.org/10.5281/zenodo.3591396 The precipitation sequences in HDF5 format, extracted from the full scan dataset are available here: https://doi.org/10.5281/zenodo.3591404
DOI: 10.34740/kaggle/dsv/8847870
title: AutoXGB
DOI: 10.34740/kaggle/dsv/8847871
title: OnlineFree
DOI: 10.15468/dl.erva96
title: Occurrence Download

description: A dataset containing 117 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Thailand",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Chelonia mydas (Linnaeus, 1758)"
 ]
}

 The dataset includes 117 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009701-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h5f9dv
title: Occurrence Download

description: A dataset containing 176 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "Geometry POLYGON((101.39666 2.80661,147.97137 2.80661,147.97137 21.41941,101.39666 21.41941,101.39666 2.80661))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pterocarpus indicus Willd."
 ]
}

 The dataset includes 176 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009703-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/f9zw-za75
title: Marine CSEM Data Acquisition Using WHAM

description: Computer experiment is crucial for marine controlled-source electromagnetic (CSEM) forward modeling in hydrocarbon exploration. Marine CSEM computer experiment is used to discretize the governing equations of the electromagnetic (EM) fields. One dimensional (1D) simulation usually is fully utilized to determine the reliability of the higher dimensional marine CSEM computations. All the data were generated using Web Hosted Active-Source Modeling (WHAM) online resource. The simulation models were developed based on unique electrical resistivity values parameterized in each layer. Three different transmission frequencies (0.125 Hz, 0.25 Hz, and 0.5 Hz) were tested. Here, two main input parameters were studied; hydrocarbon depth and hydrocarbon electrical resistivity.