cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7882263
title: Supplementary materials of the paper entitled: "Seeking Value in Extending a Requirements-Based Testing Method"

description: Supplementary materials of the paper entitled:<br> “Seeking Value in Extending a Requirements-Based Testing Method” <br> file01 - Feature test analysis results (FeatureTestsAnalysis.xlsx)<br> file02 - Webex March 2023 feature descriptions (Webex-March-2023-Features.pdf)<br> file03 - Webex feature testing results (Webex-March-2023-FeatureTestingResults.pdf)
DOI: 10.5281/zenodo.7882262
title: Supplementary materials of the paper entitled: "Seeking Value in Extending a Requirements-Based Testing Method"

description: Supplementary materials of the paper entitled:<br> “Seeking Value in Extending a Requirements-Based Testing Method” <br> file01 - Feature test analysis results (FeatureTestsAnalysis.xlsx)<br> file02 - Webex March 2023 feature descriptions (Webex-March-2023-Features.pdf)<br> file03 - Webex feature testing results (Webex-March-2023-FeatureTestingResults.pdf)
DOI: 10.5281/zenodo.7882268
title: Supplementary materials of the paper entitled: "Seeking Value in Extending a Requirements-Based Testing Method"

description: Supplementary materials of the paper entitled:<br> “Seeking Value in Extending a Requirements-Based Testing Method” file00 - Readme.txt<br> file01 - Feature test analysis results (FeatureTestsAnalysis.xlsx)<br> file02 - Webex March 2023 feature descriptions (Webex-March-2023-Features.pdf)<br> file03 - Webex feature testing results (Webex-March-2023-FeatureTestingResults.pdf)
DOI: 10.5281/zenodo.7882267
title: Supplementary materials of the paper entitled: "Seeking Value in Extending a Requirements-Based Testing Method"

description: Supplementary materials of the paper entitled:<br> “Seeking Value in Extending a Requirements-Based Testing Method” file00 - Readme.txt<br> file01 - Feature test analysis results (FeatureTestsAnalysis.xlsx)<br> file02 - Webex March 2023 feature descriptions (Webex-March-2023-Features.pdf)<br> file03 - Webex feature testing results (Webex-March-2023-FeatureTestingResults.pdf)
DOI: 10.25582/data.2022-05.2508088/1931473
title: Data for EMSL Project 51743 from May 2022
DOI: 10.25582/data.2022-05.2508007/1933818
title: Data for EMSL Project 51326 from May 2022
DOI: 10.6084/m9.figshare.22219720
title: Raw metabolomics data

description: Raw data files of metabolomics
DOI: 10.6084/m9.figshare.22219720.v1
title: Raw metabolomics data

description: Raw data files of metabolomics
DOI: 10.6084/m9.figshare.21821253.v1
title: Source data file

description: This source data includes dynamics of community composition, detected metabolites in metabolomics, differentially expressed genes in transcriptomic data, abundances of different genotypes in co-culture and concentrations of AAs.
DOI: 10.26179/96fg-8695
title: Airborne Topographic and Ice Thickness Survey of the Vanderford Glacier, 1983-85
DOI: 10.5281/zenodo.7652821
title: Data from: high-resolution bioclimatic surfaces for southern Peru: an approach to climate reality for biological conservation

description: Climatic and bioclimatic surfaces were elaborated for southern Peru (Arequipa, Moquegua and Tacna). For the interpolations, meteorological information from in-situ stations, as well as orographic and geographic covariates were used. Statistical evaluations gave good results, showing some differences with other models also performed for the area. These data will contribute to a better understanding of the ecoclimatic requirements of the species in terms of ENMs and SDMs.
description: The following files are the result of the following article: High resolution bioclimatic surfaces for southern Peru: an approximation to the climatic reality for biological conservation. Here are 12 surfaces for maximum temperature, 12 for minimum temperature, 12 for average temperature, 12 for precipitation and 19 bioclimatic surfaces for the study area. Details of some of the published figures are also included. Where: Bios asc = 19 bioclimatic surfaces in .asc format. Bios tif = 19 bioclimatic surfaces in .tif format. Prec = 12 monthly precipitation surface in .tif format. Tmax = 12 monthly maximum temperature surfaces in .tif format. Tmin = 12 monthly minimum temperature surfaces in .tif format. The names of the bioclimatic surfaces correspond to the following: bio1=average annual mean temperature (°C), bio2=average diurnal range (°C), bio3=isothermality, bio4=temperature seasonality (standard deviation), bio5=maximum temperature of the warmest month (°C), bio6=minimum temperature of the coldest month (°C), bio7=annual temperature range (°C), bio8=average temperature of the wettest quarter (°C), bio9=average temperature of the driest quarter (°C), bio10=average temperature of the warmest quarter (°C), bio11=average temperature of the coldest quarter (°C), bio12=annual precipitation (mm/year), bio13=precipitation of the rainiest month (mm/month), bio14=precipitation of the driest month (mm/month), bio15=precipitation seasonality (coefficient of variation), bio16=precipitation of the wettest quarter (mm/quarter), bio17=precipitation of the driest quarter (mm/quarter), bio18=precipitation of the warmest quarter (mm/quarter), bio19=precipitation of the coldest quarter (mm/quarter). The names of each climatic surface correspond to the following: jan = January, feb = February, mar = March, apr = April, may = May, jun = June, jul = July, aug = August, sep = September, oct = October, nov = November, dic = December.
DOI: 10.5281/zenodo.6975995
title: Data from: high-resolution bioclimatic surfaces for southern Peru: an approach to climate reality for biological conservation

description: Climatic and bioclimatic surfaces were elaborated for southern Peru (Arequipa, Moquegua and Tacna). For the interpolations, meteorological information from in-situ stations, as well as orographic and geographic covariates were used. Statistical evaluations gave good results, showing some differences with other models also performed for the area. These data will contribute to a better understanding of the ecoclimatic requirements of the species in terms of ENMs and SDMs.
description: The following files are the result of the following article: High resolution bioclimatic surfaces for southern Peru: an approximation to the climatic reality for biological conservation. Here are 12 surfaces for maximum temperature, 12 for minimum temperature, 12 for average temperature, 12 for precipitation and 19 bioclimatic surfaces for the study area. Details of some of the published figures are also included. Where: Bios asc = 19 bioclimatic surfaces in .asc format. Bios tif = 19 bioclimatic surfaces in .tif format. Prec = 12 monthly precipitation surface in .tif format. Tmax = 12 monthly maximum temperature surfaces in .tif format. Tmin = 12 monthly minimum temperature surfaces in .tif format. The names of the bioclimatic surfaces correspond to the following: bio1=average annual mean temperature (°C), bio2=average diurnal range (°C), bio3=isothermality, bio4=temperature seasonality (standard deviation), bio5=maximum temperature of the warmest month (°C), bio6=minimum temperature of the coldest month (°C), bio7=annual temperature range (°C), bio8=average temperature of the wettest quarter (°C), bio9=average temperature of the driest quarter (°C), bio10=average temperature of the warmest quarter (°C), bio11=average temperature of the coldest quarter (°C), bio12=annual precipitation (mm/year), bio13=precipitation of the rainiest month (mm/month), bio14=precipitation of the driest month (mm/month), bio15=precipitation seasonality (coefficient of variation), bio16=precipitation of the wettest quarter (mm/quarter), bio17=precipitation of the driest quarter (mm/quarter), bio18=precipitation of the warmest quarter (mm/quarter), bio19=precipitation of the coldest quarter (mm/quarter). The names of each climatic surface correspond to the following: jan = January, feb = February, mar = March, apr = April, may = May, jun = June, jul = July, aug = August, sep = September, oct = October, nov = November, dic = December.
DOI: 10.34740/kaggle/dsv/5463576
title: STCET-Teachers-Signature
DOI: 10.6084/m9.figshare.20501939
title: Natural controls on phosphorus concentrations in small Lakes in Central Alberta, Canada

description: Small lakes can act as environmental sensors for understanding regional hydrogeological conditions and elemental cycles. Because of their small size, they react quickly to changes in nutrient runoff, groundwater exchange and climate. In the post-glacial landscape of Alberta, the Carvel Pitted Delta harbours a variety of small lakes, which, despite being close together, can have markedly different chemistries and nutrient characteristics. Here, we focused on nine lakes with surface areas &lt;10 ha and maximal depths ranging from 5 to 18 m. We hypothesised that differences in their trophic states are caused by variations in Ca concentrations, controlled by groundwater inputs. All lakes tended toward permanent stratification due to small mixing depths (1.6–2.4 m). The deeper lakes were oligotrophic with high concentrations of Ca and a high fraction of phosphorus (Ca-bound P) in the sediments (HCl extractable fraction). A correlation was found between Ca-bound P and aqueous Ca concentrations at 0.5 m depth, suggesting that P stability could be estimated based on surface water chemistry. Calcium was shown to be concentrated in groundwater located above the bedrock, suggesting that groundwater inputs controlled Ca concentrations in the lakes. We conclude that the hydrogeological conditions and Ca inputs act as natural regulators for P availability and, by extension, water quality in these lakes.
DOI: 10.6084/m9.figshare.20501939.v1
title: Natural controls on phosphorus concentrations in small Lakes in Central Alberta, Canada

description: Small lakes can act as environmental sensors for understanding regional hydrogeological conditions and elemental cycles. Because of their small size, they react quickly to changes in nutrient runoff, groundwater exchange and climate. In the post-glacial landscape of Alberta, the Carvel Pitted Delta harbours a variety of small lakes, which, despite being close together, can have markedly different chemistries and nutrient characteristics. Here, we focused on nine lakes with surface areas &lt;10 ha and maximal depths ranging from 5 to 18 m. We hypothesised that differences in their trophic states are caused by variations in Ca concentrations, controlled by groundwater inputs. All lakes tended toward permanent stratification due to small mixing depths (1.6–2.4 m). The deeper lakes were oligotrophic with high concentrations of Ca and a high fraction of phosphorus (Ca-bound P) in the sediments (HCl extractable fraction). A correlation was found between Ca-bound P and aqueous Ca concentrations at 0.5 m depth, suggesting that P stability could be estimated based on surface water chemistry. Calcium was shown to be concentrated in groundwater located above the bedrock, suggesting that groundwater inputs controlled Ca concentrations in the lakes. We conclude that the hydrogeological conditions and Ca inputs act as natural regulators for P availability and, by extension, water quality in these lakes.
DOI: 10.5281/zenodo.7877313
title: FDBSCAN data

description: Datasets for the paper "Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUs" (Prokopenko, Lebrun-Grandie, Arndt)
DOI: 10.5281/zenodo.7877312
title: FDBSCAN data

description: Datasets for the paper "Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUs" (Prokopenko, Lebrun-Grandie, Arndt)
DOI: 10.34740/kaggle/dsv/5570307
title: NSE-Scripts-Data
DOI: 10.25345/c5bn9xc8j
title: MassIVE MSV000091827 - GuohaoWang_WeiLu_6LFQ_18bands_Pulldown
title: Use mass spectrometry-based method to analyze GST-Tmem132b pull down proteins from mice hippocampus
DOI: 10.25919/z7ja-jz17
title: Parkes observations for project P1183 semester 2023APRS_01

description: A new energetic repeating FRB 20220529 can be observed by both FAST and Parkes telescopes and the simultaneous observing time is up to one hour. We have observed this source using Parkes UWL green time and in total 21 bursts have been detected. Two bursts of them have the corresponding simultaneous FAST observations. Only one burst has 4-pol datasets and is bright enough to analyse the polarization properties. Therefore, we propose to continue the monitoring of the source of FRB 20220529 using the Parkes UWL with high-time resolution and 4-pol observation. The new proposed observation aims to: (1)detect more bursts by Parkes UWL with full polarization information; (2) detect more bursts by Parkes UWL with corresponding simultaneous FAST observations; (3) study the radiation spectrum by more bursts detected in simultaneous observations of Parkes and FAST; (4) study the burst microstructure using high time resolution datasets, and (5) study the frequency ranges changes of the repeating bursts.
DOI: 10.6084/m9.figshare.22724183
title: Data Viral Shedding COVID-19

description: Data Vira Shedding COVID 19
DOI: 10.25345/c56w96k58
title: MassIVE MSV000091828 - GuohaoWang_WeiLu_6ID_18bands_Pulldown
title: Use mass spectrometry-based method to analyze GST-Tmem132b pull down proteins from mice hippocampus
DOI: 10.6084/m9.figshare.22724229
title: Additional file 1 of Songbirds initiate migratory flights synchronously relative to civil dusk

description: Additional file 1. Table S1. Basic descriptive information for populations included in this study. Age classes include hatch year (HY), after hatch-year (AHY), second-year (SY), after second-year (ASY), and unknown (U).
DOI: 10.6084/m9.figshare.22724229.v1
title: Additional file 1 of Songbirds initiate migratory flights synchronously relative to civil dusk

description: Additional file 1. Table S1. Basic descriptive information for populations included in this study. Age classes include hatch year (HY), after hatch-year (AHY), second-year (SY), after second-year (ASY), and unknown (U).
DOI: 10.6084/m9.figshare.22724237
title: Additional file 2 of Inversion polymorphism in a complete human genome assembly

description: Additional file 2: Table S1. Nonredundant inversion callset reported in this study. Table S2. Putative novel inversions with respect to T2T-CHM13 reference. Table S3. Enrichment of inversions in pericentromeric regions. Table S4. List of minor alleles and resolved orientation errors in GRCh38. Table S5. Novel inversions in HPRC Strand-seq dataset.
DOI: 10.6084/m9.figshare.22724237.v1
title: Additional file 2 of Inversion polymorphism in a complete human genome assembly

description: Additional file 2: Table S1. Nonredundant inversion callset reported in this study. Table S2. Putative novel inversions with respect to T2T-CHM13 reference. Table S3. Enrichment of inversions in pericentromeric regions. Table S4. List of minor alleles and resolved orientation errors in GRCh38. Table S5. Novel inversions in HPRC Strand-seq dataset.
DOI: 10.26180/22313224
title: ASPREE Genomics Information System Collection

description: ASPREE has partnered with BC Platforms to develop an information system for clinical and genomics data processing. The share underpinned the ASPREE genomics program for integrating phenotypic and clinical outcome data with its novel techniques to understand the role of genetics in healthy ageing and diseases. The ASPREE genomics program references the international clinical trial being led by the School of Public Health and Preventive Medicine at Monash and the Berman Centre for Outcomes and Clinical Research in the USA. The trial investigated whether daily low-dose aspirin improves quality of life for older people. The randomised, double blind, placebo-controlled study for primary prevention of cardiovascular disease, dementia, depression and some cancers was undertaken in Australia and the USA and had over 19,000 participants. Participant follow-up was conducted longitudinally for an average of five years with extensive phenotypic and clinical data collection <br> To make data requests, you may undertake an approval process by contacting Paul Lacaze on Paul.Lacaze@monash.edu
DOI: 10.26180/21268050
title: Spatio-temporal development of the urban heat island in a socioeconomically diverse tropical city

description: Data and code required to complete urban heat island analyses. <br> <br> <strong>Code:</strong> data_extraction - gives examples of how to process and extract surface temperature from Landsat data (freely available from USGS). <br> gamms - code to run all urban heat island statistical analyses and corresponding figures. <br> RMA - ranged major axis regression analyses to compare satellite surface temperature and in situ ambient temperature <br> <strong>Data:</strong> Urban, change, and non-urban shapefiles show spatial regions defined for analysis. <br> Data csvs required for running code detailed above. <br> Makassar_urban_lc_change.tif is a raster showing the year a pixel was first detected as urban based on 11 supervised land cover classifications.
DOI: 10.5281/zenodo.7882299
title: Yak k-mer dumps for partition human chrX/Y in de novo assemblies

description: See yak for details.
DOI: 10.5281/zenodo.7882298
title: Yak k-mer dumps for partition human chrX/Y in de novo assemblies

description: See yak for details.
DOI: 10.5281/zenodo.7882206
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2016

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2016. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2017</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2016 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7882205
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2016

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2016. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2017</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2016 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.26180/22590316
title: The PCOS Phenotype in Unselected Populations Study (P-PUP)

description: This project consists of a data repository that will be used to define normal ranges and diagnostic cut-offs for all PCOS diagnostic features using individual participant data (IPD) from unselected populations around the world. It includes data of over 10,000 participants within the age range of 15 to 80 years from various PCOS research collaborating centres in China, USA, Italy, South Korea, Iran, Turkey, Russia and Nigeria. With evolving and new evidence, more collaborations are anticipated.
DOI: 10.6084/m9.figshare.21821253.v2
title: Source data file

description: This source data includes dynamics of community composition, detected metabolites in metabolomics, differentially expressed genes in transcriptomic data, abundances of different genotypes in co-culture and concentrations of AAs.