cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23802297
title: Sugarcane at varying TAW

description: The dataset is a compiled output of AquaCrop simulations on sugarcane at Black Bush Polder (BBP), Guyana.Crop: SugarcaneLocation: BBP (Black Bush Polder, Guyana)Season: S2 (Season 2: November - December planting)IRRat: 40, 50, 60, 70, 80, 90, 100 (scenarios of maintained Total Available Water [TAW])The column headers are standard AquaCrop outputs, details of which can be found at https://www.fao.org/3/br267e/br267e.pdfRunNr Number simulation run -Day1 Start day of simulation run -Month1 Start month of simulation run -Year1 Start year of simulation run -Rain Rainfall mmETo Reference evapotranspiration mmGD Growing degrees °C.dayCO2 Atmospheric CO2 concentration ppmIrri Water applied by irrigation mmInfilt Infiltrated water in soil profile mmRunoff Water lost by surface runoff mmDrain Water drained out of the soil profile mmUpflow Water moved upward by capillary rise mmE Soil evaporation mmE/Ex Relative soil evaporation (100 E/Ex) %Tr Total transpiration of crop and weeds mmTrw Crop transpiration in weed infested field mmTr/Trx Relative crop transpiration (100 Tr/Trx) %SaltIn Salt infiltrated in the soil profile ton/haSaltOut Salt drained out of the soil profile ton/haSaltUp Salt moved upward by capillary rise ton/haSaltProf Salt stored in the soil profile ton/haCycle Length of crop cycle daysSaltStr Average soil salinity stress %FertStr Average soil fertility stress %WeedStr Average relative cover of weeds %TempStr Average temperature stress %ExpStr Average leaf expansion stress %StoStr Average stomatal stress %Biomass Total above-ground dry biomass ton/haBrelative Relative biomass %HI Harvest Index adjusted %DryYield Dry crop yield (HI x Biomass) ton/haYield Fresh crop yield ton/haWPet ET Water Productivity for yield kg/m3DayN End day of simulation run -MonthN End month of simulation run -YearN End year of simulation run -
DOI: 10.5066/p9ifxycf
title: Water quality and contaminants in stream surface waters collected in the Shenandoah Valley, 2021

description: The data presented in this data release includes 11 field/water quality parameters, concentrations of 16 nutrients/anions, dissolved organic carbon, 14 organic contaminants, net estrogenicity concentrations, and 51 inorganic constituents in surface water collected twice from 28 stream sites and collected once from 2 stream sites in the Shenandoah Valley (Virginia and West Virginia, USA), in the summer of 2021 (Table_1_Sites and Table_2_Methods). The estrogenicity, inorganic, and organic chemical characteristics of river waters were analyzed using 19 separate analytical methods at 5 laboratories (Table_2_Methods). Surface water was analyzed for water quality and nutrients (Table_3_WQ_Nutrients_Anions_DOC), toxic contaminants that impact stream and organismal health including a variety of anthropogenic contaminants (i.e., chelating agents, pharmaceuticals, pesticides, and plasticizers) (Table_4_Detected_Toxics_PFAS), perfluoroalkyl and polyfluoroalkyl substances (Table_4_Detected_Toxics_PFAS), and additionally hormone activity (Table_5_Estogenicity), and major and trace elements (Table_6_Inorganics). Quality assurance (QA) field blanks (Table_7_Field_Blanks) and laboratory QA (Table_8_PFAS_Matrix_Spikes and Table_9_AQHAZ_Surrogates) also are presented.
DOI: 10.5281/zenodo.4290770
title: Potential impact of photoinhibition on microphytobenthic primary production on a large intertidal mudflat

description: SImulations data from the NoPhoto and Photo runs.
DOI: 10.5281/zenodo.4290769
title: Potential impact of photoinhibition on microphytobenthic primary production on a large intertidal mudflat

description: SImulations data from the NoPhoto and Photo runs.
DOI: 10.57745/5fqbea
title: FIG2F.tar.xz

description: Data for Figure 2F
DOI: 10.57745/xylrff
title: README.md

description: Full description of files/folders available here.
DOI: 10.57745/ld9p3u
title: FIG2E.tar.xz

description: Data for Figure 2E
DOI: 10.57745/vbobg5
title: FIG2C_D.tar.xz

description: Data for Figure 2C and 2D
DOI: 10.57745/2t6jre
title: FIG2B.tar.xz

description: Data for Figure 2B
DOI: 10.57745/ssjixa
title: FIG1.tar.xz

description: Data for Figure 1
DOI: 10.6084/m9.figshare.23802276
title: L2VS-CSRV-VE.sav

description: The data for L2 vocabulary selves, capacity for self-regulated vocabulary learning and L2 vocabulary learning enjoyment
DOI: 10.6084/m9.figshare.23802276.v1
title: L2VS-CSRV-VE.sav

description: The data for L2 vocabulary selves, capacity for self-regulated vocabulary learning and L2 vocabulary learning enjoyment
DOI: 10.5281/zenodo.8193531
title: Dataset underlying the paper Generating variability from motor primitives during infant locomotor development

description: EMG data after basic preprocessing Each file is a matrix of M colomns and S*T raws with : M = number of muscles (from m1 to m10: right Rectus Femoris, left Rectus Femoris, right Tibialis Anterior, left Tibialis Anterior, right Biceps Femoris, left Biceps Femoris, right Soleus, left Soleus, right Gluteus Medius, left Gluteus Medius). S = number of steps (specific to each file) T = number of time points per step (always equals to 200) File names are ordered as follows: inputMat_S{subjectnumber}_C{conditionnumber}_a{agenumber}_200bins_10muscles subject number car take values between 1 and 18 condition number can take the following values: 1 for stepping on a treadmill, 2 for stepping overground, 4 for kicking and 5 for walking age number can take the following values: 1 for around birth, 2 for around three months, 4 for around walking onset
DOI: 10.5281/zenodo.8193579
title: Raw GC/MS data used for MLOD (MetaboLiteLearner)

description: The zip file contains the raw Agilent GC/MS files used to generate the data tables in the <em>MetaboLiteLearner</em> study.
DOI: 10.5281/zenodo.8193555
title: panulirus_guttatus_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.8193554
title: panulirus_guttatus_decontaminated_gx.fasta.gz

description: .
DOI: 10.7910/dvn/kzltc4
title: Replication Data for: Unequivocal identification of spin-triplet, spin- singlet, and noncentrosymmetric superconductors with upper critical field and flux quantization

description: Replication Data for: Unequivocal identification of spin-triplet, spin- singlet, and noncentrosymmetric superconductors with upper critical field and flux quantization
DOI: 10.34740/kaggle/dsv/6211711
title: Costa Rica: Total deceases 2014 - 2021
DOI: 10.6084/m9.figshare.23792208.v4
title: Underlying data- total results

description: This is the underlying data for the article entitled "L2 Acquisition of <i>Wh-</i>interrogatives at the Syntax-discourse Interface: Interface Hypothesis again", by Mohammed Shormani
DOI: 10.6084/m9.figshare.21668156.v1
title: Abell and Winckler Supplementary Dataset S1

description: Abell and Winckler Supplementary Dataset S1 used in:<br>"Abell and Winckler. (2023). <b>Long-term variability in Pliocene North Pacific Ocean export production and its implications for ocean circulation in a warmer world</b>"<br>submitted to <i>AGU Advances.</i>
DOI: 10.5066/p9jhg4e1
title: Biofilm Percent Cover Maps in San Francisco Bay, 2020-2022

description: This data release includes 9 GeoTIFF rasters that represent percent cover of intertidal microbial biofilm on the mudflats of South San Francisco Bay, CA between June 2020, and June 2022. Rasters follow the naming scheme biofilmPC_YYYYMMDD_10m.tif, where ?biofilmPC? describes the dataset, YYYYMMDD is the date of the image, and 10m is the spatial resolution of the raster. Raster data products were derived from 10m Sentinel-2 Muti-Spectral Imager (MSI) imagery, collected and maintained by the European Space Agency (ESA) and published for public access on Copernicus Data Hub (https://scihub.copernicus.eu/dhus/#/home). The area of interest includes the Eden Landing Ecological Reserve and all tidally exposed mudflats within the bounds of San Francisco Bay proper, south of Hayward, CA. Maps report the percent cover of biofilm ranging from 0 to 1 and were generated via a series of processing steps in Python and VIPER tools in ENVI. The no data value is 0, reporting anywhere where there is no biofilm as a 0 value. The study area was constrained using a vector mask, then water and vegetated areas were recognized and removed using a series of indices, leaving only the mudflat areas. Data was imported to ENVI and run through Multiple Endmember Spectral Mixture Analysis (MESMA) in VIPER tools using a percent cover endmember library generated from an in-situ dataset. The accuracy of the raster data products was evaluated by comparing raster values to 43 reference data points taken around South San Francisco Bay, using linear regression. The R squared of the regression between raster percent cover values and in situ reference data was 53%. Accuracy was influenced by differences in scale between image data and in-situ reference data and possible error in image georectification.
DOI: 10.34740/kaggle/dsv/6211928
title: Balochi Language to English Translation Dataset
DOI: 10.57967/hf/0934
title: udacity-bangladesh
DOI: 10.5281/zenodo.8192916
title: TCGA Gene Expression Datasets

description: <strong>Abstract:</strong> The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. These datasets contain gene expression profiles of bladder urothelial carcinoma (BLCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), glioblastoma multiforme (GBM), head & neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and lower grade glioma (LGG). The gene expression profiles for BLCA, CESC, HNSC, KIRC, and LGG were measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The gene expression profile of the GBM dataset was measured experimentally using the Affymetrix HT Human Genome U133a microarray platform by the Broad Institute of MIT and Harvard University cancer genomic characterization center. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Instruction:</strong> The log<sub>2</sub>(x+1) normalization was removed, and z-normalization was performed on the BLCA, CESC, HNSC, KIRC, and LGG datasets. The log<sub>2</sub>(x) normalization was removed, and z-normalization was performed on the GBM dataset. <strong>Acknowledgments:</strong> Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8. The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764. <strong>U-BRITE last update: </strong>07/13/2023
description: U-BRITE location: /data/project/ubrite/gtkb/TCGA/GeneExp
description: {"references": ["Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8.", "The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113\u20131120 (2013). https://doi.org/10.1038/ng.2764."]}
DOI: 10.5281/zenodo.8192915
title: TCGA Gene Expression Datasets

description: <strong>Abstract:</strong> The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. These datasets contain gene expression profiles of bladder urothelial carcinoma (BLCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), glioblastoma multiforme (GBM), head & neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and lower grade glioma (LGG). The gene expression profiles for BLCA, CESC, HNSC, KIRC, and LGG were measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The gene expression profile of the GBM dataset was measured experimentally using the Affymetrix HT Human Genome U133a microarray platform by the Broad Institute of MIT and Harvard University cancer genomic characterization center. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Instruction:</strong> The log<sub>2</sub>(x+1) normalization was removed, and z-normalization was performed on the BLCA, CESC, HNSC, KIRC, and LGG datasets. The log<sub>2</sub>(x) normalization was removed, and z-normalization was performed on the GBM dataset. <strong>Acknowledgments:</strong> Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8. The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764. <strong>U-BRITE last update: </strong>07/13/2023
description: U-BRITE location: /data/project/ubrite/gtkb/TCGA/GeneExp
description: {"references": ["Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8.", "The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113\u20131120 (2013). https://doi.org/10.1038/ng.2764."]}
DOI: 10.5281/zenodo.8193639
title: Use Case Colorblind Accessibility Experiment Data and Replication Materials

description: Use Case Colorblind Accessibility Experiment Data and Replication Materials
DOI: 10.5281/zenodo.8193638
title: TCGA Clinical Datasets

description: <strong>Abstract:</strong> The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics". The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type. These datasets include phenotypic information about BLCA, CESC, GBM, HNSC, KIRC, and LGG. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Instruction:</strong> The survival and phenotype data were merged into one file. <strong>Acknowledgments:</strong> Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. https://doi.org/10.1016/j.cell.2018.02.052 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 <strong>U-BRITE last update: </strong>07/13/2023
description: UBRITE location: /data/project/ubrite/gtkb/TCGA/Clinical
description: {"references": ["Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8", "Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11.\u00a0https://doi.org/10.1016/j.cell.2018.02.052", "The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113\u20131120 (2013). https://doi.org/10.1038/ng.2764"]}
DOI: 10.5281/zenodo.8193637
title: TCGA Clinical Datasets

description: <strong>Abstract:</strong> The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics". The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type. These datasets include phenotypic information about BLCA, CESC, GBM, HNSC, KIRC, and LGG. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Instruction:</strong> The survival and phenotype data were merged into one file. <strong>Acknowledgments:</strong> Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. https://doi.org/10.1016/j.cell.2018.02.052 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 <strong>U-BRITE last update: </strong>07/13/2023
description: UBRITE location: /data/project/ubrite/gtkb/TCGA/Clinical
description: {"references": ["Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8", "Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11.\u00a0https://doi.org/10.1016/j.cell.2018.02.052", "The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113\u20131120 (2013). https://doi.org/10.1038/ng.2764"]}
DOI: 10.15156/bio/2938072
title: Full mothur UNITE+INSD dataset 2

description: This dataset comprises of all quality filtered unclustered fungal UNITE + INSD sequences in mothur format.
DOI: 10.15156/bio/2938073
title: Full mothur UNITE+INSD dataset 3

description: This dataset comprises of all quality filtered unclustered UNITE + INSD sequences included in the UNITE Species Hypotheses in mothur format.
DOI: 10.18739/a2v40k14q
title: Seismic and Electrical Resistivity Datasets for Characterizing Permafrost in Alaska (August 2023)

description: This dataset consists of Multichannel Analysis of Surface Waves (MASW) and Electrical Resistivity Tomography (ERT) data to examine permafrost variability across a range of disturbed and undisturbed locations and the impact of civil infrastructure on permafrost degradation in Utqiaġvik, Alaska. These geophysical methods allow us to image the sub-surface and understand permafrost properties variation. The results are discussed in the following paper: Tourei, A., Ji, X., Rocha dos Santos, J. F., Czarny, R., Rybakov, S., Wang, Z., Hallissey, M., Martin, E. R., Xiao, M., Zhu, T., Nicolsky, D., Jensen, A. (2023). Mapping Permafrost Variability and Degradation Using Seismic Surface Waves, Electrical Resistivity and Temperature Sensing: A Case Study in Arctic Alaska. Earth ArXiv. Preprint. MASW data collection dates: 2023-08-08 - 2023-08-12 ERT data collection date: 2023-09-13
DOI: 10.15156/bio/2938074
title: Full mothur UNITE+INSD dataset 4

description: This dataset comprises of all quality filtered unclustered UNITE + INSD sequences in mothur format.
DOI: 10.5281/zenodo.8193644
title: Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for five different lead times

description: Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for the lead times 24, 48, 72, 96 and 120 hours in the years 2015-2020. The data set is provided in .RData format supported by the statistical software R. The ensemble forecasts are retrieved from ECMWF and the observation data from the German Weather Service (DWD). <br> <br> For more information about the data set see: https://github.com/jobstdavid/paper_tsEMOS
DOI: 10.5281/zenodo.8193661
title: CGGA Clinical Datasets

description: <strong>Abstract:</strong> The Chinese Glioma Datasets (CGGA) are comprehensive and valuable collections of data related to glioma, a type of brain tumor, originating from Chinese patients. The CGGA is a data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples. This dataset contains phenotype information. The Sample IDs serve as unique identifiers for each sample. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Acknowledgments:</strong> Zhao, Z., Zhang, KN., Wang, QW., et al. Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients (2021). Genomics, Proteomics & Bioinformatics 19(1):1-12. Fang, S., Liang, J., Qian, T., et al. (2017). Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grade Gliomas Involving the Hand Knob Area. AMERICAN JOURNAL OF NEURORADIOLOGY. 38(10): 1990-1997. Wang, Y., Wang, Y., Fan, X., et al. (2017). Putamen involvement and survival outcomes in patients with insular low-grade gliomas. JOURNAL OF NEUROSURGERY. 126(6): 1788-1794. <strong>U-BRITE last update: </strong>07/28/2023
description: UBRITE Location: /data/project/ubrite/gtkb/CGGA/Clinical
description: {"references": ["Zhao, Z., Zhang, KN., Wang, QW., et al. Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients (2021). Genomics, Proteomics & Bioinformatics 19(1):1-12.", "Fang, S., Liang, J., Qian, T., et al. (2017). Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grade Gliomas Involving the Hand Knob Area. AMERICAN JOURNAL OF NEURORADIOLOGY. 38(10): 1990-1997.", "Wang, Y., Wang, Y., Fan, X., et al. (2017). Putamen involvement and survival outcomes in patients with insular low-grade gliomas. JOURNAL OF NEUROSURGERY. 126(6): 1788-1794."]}