cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24179076
title: Wolbachia alleles sexually reproducing Naupactini species

description: MLST for Wolbachia infecting Naupactini weevils
DOI: 10.6084/m9.figshare.24179076.v1
title: Wolbachia alleles sexually reproducing Naupactini species

description: MLST for Wolbachia infecting Naupactini weevils
DOI: 10.5067/met07/ceres/geo_ed4_fd_v02
title: SatCORPS CERES GEO Edition 4 Meteosat-7 Full Disk Version 2
DOI: 10.25345/c5dr2pk6c
title: MassIVE MSV000092930 - Mapping the Molecular Pheno-Conversion from Normal Human Coronary Artery to Advanced Atherosclerotic Plaque: Trizol
title: Background: More data are needed concerning human coronary molecular dynamics and its relationship to the initiation and progression of atherosclerosis. Methods: We assembled the largest-ever repository of proximal coronary arterial samples and associated untargeted mass-spec proteomic and RNA sequencing data from young adults who died of trauma with no ante-mortem suspicion of coronary disease (n=322, mean age (range): 34.1 yrs. (15-59); sex: M-239, F-83; race: W-218, B-88, other-16). Despite the absence of clinical disease, 56% of the samples had morphologic evidence of early atherosclerosis. State-of-the-art analytics of the proteomic data (n=1900 proteins) combining manifold learning and unsupervised latent feature detection methods were used to define the pseudo-temporal progression of disease across the samples and identify molecular drivers of its initiation and progression. Correlation and network mapping of the mRNA data (n=16,707 transcripts, including n=1707 annotated transcription factors) from the same arterial samples was used to define the transcriptional regulatory networks underlying the individual proteomic latent features. Results: The expression of four of the seven proteomic latent features, including n=100 signature proteins/latent feature, exhibited distinct patterns with respect to disease progression. The earliest changes in the arterial proteome reflect activation of the vascular unit (including pericytes) consistent with neuro-vascular and neuro-immune modulation, as well as dramatic declines in mitochondrial energy biosynthesis proteins (FDR p&lt;0.05). These early changes precede the characteristic immune cell recruitment and activation of innate immune response signaling characteristic of atherosclerotic plaque formation. The latent feature specific transcriptional regulatory networks within the RNASeq data highlight both known and novel transcription factors and master regulators that drive the initial and subsequent phases of disease progression. The molecular data also provide insights concerning the effect age and sex on development of coronary atherosclerosis that are generally not available from smaller tissue repositories. Conclusions: The composition of these proteomic latent features and their transcriptional regulatory partners illuminate biomarkers for early detection/disease monitoring and highlight targeted opportunities for novel interventions specifically relevant for human coronary atherosclerosis.
DOI: 10.5281/zenodo.8368187
title: Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"

description: The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather prediction data from the NOAA Rapid Refresh (RAP) model. These crowd-sourced mPING reports offer precipitation type labels (rain, snow, sleet, and freezing rain) across North America, while the RAP model provides atmospheric data, including temperature, humidity, and wind profiles, on pressure levels. The RAP data covers the contiguous United States (CONUS) from 2015 to 2022 on an hourly 13km grid. The mPING observations are matched to the nearest RAP grid cell and hour, allowing the two data sources to be merged into a labeled dataset suitable for classification tasks. The surface layer flux dataset (surface_layer.csv) contains high-frequency meteorological observations spanning from 2013 to 2015, collected at the Cabauw Experimental Site in the Netherlands. It includes measurements of various variables such as temperature, humidity, wind, radiation, and soil moisture, recorded every 10 minutes. The target output encompasses friction velocity, sensible heat, and latent heat. <br> The code used for processing the datasets and training neural network models is available in the Miles-Guess repository (https://github.com/ai2es/miles-guess).
DOI: 10.5281/zenodo.8368186
title: Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"

description: The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather prediction data from the NOAA Rapid Refresh (RAP) model. These crowd-sourced mPING reports offer precipitation type labels (rain, snow, sleet, and freezing rain) across North America, while the RAP model provides atmospheric data, including temperature, humidity, and wind profiles, on pressure levels. The RAP data covers the contiguous United States (CONUS) from 2015 to 2022 on an hourly 13km grid. The mPING observations are matched to the nearest RAP grid cell and hour, allowing the two data sources to be merged into a labeled dataset suitable for classification tasks. The surface layer flux dataset (surface_layer.csv) contains high-frequency meteorological observations spanning from 2013 to 2015, collected at the Cabauw Experimental Site in the Netherlands. It includes measurements of various variables such as temperature, humidity, wind, radiation, and soil moisture, recorded every 10 minutes. The target output encompasses friction velocity, sensible heat, and latent heat. <br> The code used for processing the datasets and training neural network models is available in the Miles-Guess repository (https://github.com/ai2es/miles-guess).
DOI: 10.5281/zenodo.8184818
title: Scientific Value Agent companion data

description: Companion data for the Scientific Value Agent manuscript. To use, unzip all tarballs into a "results" directory in the root of https://github.com/matthewcarbone/ScientificValueAgent. All notebooks should then work as-is. Results from 23-09-02 have also been uploaded. These contain some data used in the supplementary data of our manuscript in which we fix the regularization parameter of our procedure.
DOI: 10.5281/zenodo.8184819
title: Scientific Value Agent companion data

description: Companion data for the Scientific Value Agent manuscript. To use, unzip all tarballs into a "results" directory in the root of https://github.com/matthewcarbone/ScientificValueAgent. All notebooks should then work as-is.
DOI: 10.5281/zenodo.8368497
title: Scientific Value Agent companion data

description: Companion data for the Scientific Value Agent manuscript. To use, unzip all tarballs into a "results" directory in the root of https://github.com/matthewcarbone/ScientificValueAgent. All notebooks should then work as-is. Results from 23-09-02 have also been uploaded. These contain some data used in the supplementary data of our manuscript in which we fix the regularization parameter of our procedure.
DOI: 10.34943/5bb6096e-e0e9-4b8b-b0d0-738cdec704b7
title: Douglas Channel Junction Box Deployed 2022-07-14

description: The ONC Mini Junction Box 017 was deployed on 2022-07-14 at Douglas Channel. Douglas Channel is one of the principal inlets of the British Columbia coast. This device is a Junction Box. Junction Boxes supply power and communications to deployed instruments. Junction boxes have a number of serial and ethernet ports, including 400V ethernet ports that enable connections to other junction boxes and high-voltage instruments. Junction boxes can convert high voltages to lower voltages (15V, 24V or 48V) required by many instruments. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/dbdca136-bea3-459a-b9f4-09d1cd48812a
title: Douglas Channel Camera Lights Deployed 2022-07-14

description: The Ocean Networks Canada Light SN15 was deployed on 2022-07-14 at Douglas Channel. Douglas Channel is one of the principal inlets of the British Columbia coast. This device is a Camera Lights. Camera lights are used to improve visibility where cameras are deployed. Light controls and schedules can be remotely-controlled from on-shore using software. The length of time that lights are on is limited in order to minimize impact on the environment. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/3d35c4a0-3e9e-44ba-a66e-f9d80b54b896
title: Douglas Channel Camera Lights Deployed 2022-07-14

description: The Ocean Networks Canada Light SN14 was deployed on 2022-07-14 at Douglas Channel. Douglas Channel is one of the principal inlets of the British Columbia coast. This device is a Camera Lights. Camera lights are used to improve visibility where cameras are deployed. Light controls and schedules can be remotely-controlled from on-shore using software. The length of time that lights are on is limited in order to minimize impact on the environment. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/97b7be04-b2f9-4454-89a2-e48436455d49
title: Douglas Channel Fluorometer Deployed 2022-07-14

description: The WET Labs ECO FLRT 3905 was deployed on 2022-07-14 at Douglas Channel. Douglas Channel is one of the principal inlets of the British Columbia coast. This device is a Fluorometer. Fluorometers measure the amount of stimulated electromagnetic radiation (fluorescence) produced by pulses of electromagnetic radiation emitted into the water column. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.8368508
title: Hinode/EIS spectra and Gaussian fit parameters for cool loop footpoint datasets

description: Contains derived data for the article "Updated reference wavelengths for Si VII and Mg VII lines in the 272-281 Angstrom range" submitted to The Astrophysical Journal and authored by Dr. Peter Young (NASA Goddard Space Flight Center). The zip file contains 13 sub-directories corresponding to the 13 datasets studied in the article. In each directory there are two files: an IDL save file, and an ASCII text file. Upon restoring the IDL save file into IDL, you will see four quantities (the names may vary slightly between datasets): MAP: an IDL map containing the Si VII 275.37 image from which the pixel mask was selected.<br> MASK: a pixel mask generated with the routine pixel_mask_gui.pro using the input MAP. The pixels in the map are used to create the spectrum.<br> SWSPEC: A spectrum structure generated with the routine eis_mask_spectrum.pro containing the EIS short-wavelength channel spectrum.<br> LWSPEC: A spectrum structure generated with the routine eis_mask_spectrum.pro containing the EIS long-wavelength channel spectrum. LWSPEC was input to the Gaussian-fitting routine spec_gauss_eis.pro and fits were generated for the emission lines listed in the article. The routine writes the fit parameters to the ASCII text file. The Gaussian fit parameters can be read into IDL using the routine read_line_fits.pro. You can also use the routine line_fits_summary.pro to generate a printout of the fit parameters. The IDL software described here is available with the Solarsoft repository (https://sohoftp.nascom.nasa.gov/solarsoft/).
DOI: 10.5281/zenodo.8368507
title: Hinode/EIS spectra and Gaussian fit parameters for cool loop footpoint datasets

description: Contains derived data for the article "Updated reference wavelengths for Si VII and Mg VII lines in the 272-281 Angstrom range" submitted to The Astrophysical Journal and authored by Dr. Peter Young (NASA Goddard Space Flight Center). The zip file contains 13 sub-directories corresponding to the 13 datasets studied in the article. In each directory there are two files: an IDL save file, and an ASCII text file. Upon restoring the IDL save file into IDL, you will see four quantities (the names may vary slightly between datasets): MAP: an IDL map containing the Si VII 275.37 image from which the pixel mask was selected.<br> MASK: a pixel mask generated with the routine pixel_mask_gui.pro using the input MAP. The pixels in the map are used to create the spectrum.<br> SWSPEC: A spectrum structure generated with the routine eis_mask_spectrum.pro containing the EIS short-wavelength channel spectrum.<br> LWSPEC: A spectrum structure generated with the routine eis_mask_spectrum.pro containing the EIS long-wavelength channel spectrum. LWSPEC was input to the Gaussian-fitting routine spec_gauss_eis.pro and fits were generated for the emission lines listed in the article. The routine writes the fit parameters to the ASCII text file. The Gaussian fit parameters can be read into IDL using the routine read_line_fits.pro. You can also use the routine line_fits_summary.pro to generate a printout of the fit parameters. The IDL software described here is available with the Solarsoft repository (https://sohoftp.nascom.nasa.gov/solarsoft/).
DOI: 10.6084/m9.figshare.3761562.v2715
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.6976782
title: Modelling dataset of the São Sepé River watershed, RS, Brazil

description: This dataset provides maps (30m resolution) and time series suited for hydrological modelling of the São Sepé watershed, RS, Brazil. Reference period is from 01-01-2010 to 01-01-2020. This is a test. Details of the files can be found in the <strong><code>readme.csv</code> </strong>file <code>saosepe_dem.zip</code> -- full-extension DEM-based raster maps; <code>saosepe_lulc.zip</code> -- full-period and full-extension yearly-based land use and land cover raster maps; <code>saosepe_series_daily.txt</code> -- full period time series of daily precipitation, streamflow and temperature; <code>saosepe_series_weekly.txt</code> -- full period time series of weekly precipitation, streamflow and temperature; <code>saosepe_series_monthly.txt</code> -- full period time series of monthly precipitation, streamflow and temperature; saosepe_series_yearly.txt -- full period time series of yearly precipitation, streamflow and temperature; <code>saosepe_window_et24h.zip</code> -- full-period inspection window of Evapotranspiration raster maps; <code>saosepe_window_lstDem.zip</code> -- full-period inspection window of DEM-corrected Land Surface Temperature raster maps; <code>saosepe_window_ndvi.zip</code> -- full-period inspection window of Landsat 8 NDVI raster maps;
DOI: 10.5281/zenodo.6976781
title: Modelling dataset of the São Sepé River watershed, RS, Brazil

description: This dataset provides maps (30m resolution) and time series suited for hydrological modelling of the São Sepé watershed, RS, Brazil. Reference period is from 01-01-2010 to 01-01-2020. This is a test. Details of the files can be found in the <strong><code>readme.csv</code> </strong>file <code>saosepe_dem.zip</code> -- full-extension DEM-based raster maps; <code>saosepe_lulc.zip</code> -- full-period and full-extension yearly-based land use and land cover raster maps; <code>saosepe_series_daily.txt</code> -- full period time series of daily precipitation, streamflow and temperature; <code>saosepe_series_weekly.txt</code> -- full period time series of weekly precipitation, streamflow and temperature; <code>saosepe_series_monthly.txt</code> -- full period time series of monthly precipitation, streamflow and temperature; saosepe_series_yearly.txt -- full period time series of yearly precipitation, streamflow and temperature; <code>saosepe_window_et24h.zip</code> -- full-period inspection window of Evapotranspiration raster maps; <code>saosepe_window_lstDem.zip</code> -- full-period inspection window of DEM-corrected Land Surface Temperature raster maps; <code>saosepe_window_ndvi.zip</code> -- full-period inspection window of Landsat 8 NDVI raster maps;
DOI: 10.18120/005b-p742
title: Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) Project 2 - Preclinical Model for Antiepileptogenic Therapy Screening in Post-Traumatic Epilepsy
DOI: 10.25739/t1my-7s74
title: Carolyn_Lawrence_Dill_GOMAP_Rice_IRGSP_1.0_June_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Oryza sativa japonica (IRGSP-1.0) genome annotation with a median of 9 annotations per gene model with 100% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/4m2a-md74. It was created in June of 2023.
DOI: 10.25739/npk3-2273
title: Carolyn_Lawrence_Dill_GOMAP_Rapeseed_BnPIR_ZS11_June_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Brassica napus ZS11 genome annotation with a median of 10 annotations per gene model with 100% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/xgmr-hr31. It was created in June of 2023.
DOI: 10.25739/4ysp-s012
title: Carolyn_Lawrence_Dill_GOMAP_Pepper_PGP_cvCM334_June_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Capsicum annuum cv CM334 genome annotation with a median of 10 annotations per gene model with 100% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/cqn7-yt96. It was created in June of 2023.
DOI: 10.25739/58kg-eb48
title: Carolyn_Lawrence_Dill_GOMAP_Hop_HopBase_Cascade_July_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Humulus lupulus Cascade hop genome annotation with a median of 8 annotations per gene model with 99.99% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/ew58-8d76. It was created in July of 2023.
DOI: 10.25739/99tr-5r58
title: Carolyn_Lawrence_Dill_GOMAP_Grape_Genoscope_12x_May_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Vitis vinifera 12X genome annotation with a median of 10 annotations per gene model with 100% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/jtfk-q888. It was created in May of 2023.
DOI: 10.25739/keeg-7s93
title: Carolyn_Lawrence_Dill_GOMAP_Cotton_DOE-JGI_v2.1_June_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Gossypium raimondii v2.1 genome annotation with a median of 11 annotations per gene model with 100% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/a13t-zh47. It was created in June of 2023.
DOI: 10.25739/dtfc-br65
title: Carolyn_Lawrence_Dill_GOMAP_Coffee_CGH_v1.0_May_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Coffea canephora v1.0 genome annotation with a median of 10 annotations per gene model with 100% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/rm4j-3580. It was created in May of 2023.
DOI: 10.25739/n53n-zj72
title: Carolyn_Lawrence_Dill_GOMAP_Blueberry_GigaDB_Draper_v1.0_May_2023_v2.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Vaccinium corymbosum Draper v1.0 genome annotation with a median of 9 annotations per gene model with 100% coverage. This dataset is an updated version of the previously generated dataset https://doi.org/10.25739/q7rq-e992 . It was created in May of 2023.
DOI: 10.25739/he5y-h419
title: Carolyn_Lawrence_Dill_GOMAP_Barley_IPK_cv_Morex_V3_June_2023_v1.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in Hordeum vulgare cv. Morex V3 2020 genome annotation with a median of 10 annotations per gene model with 100% coverage. It was created in June of 2023.
DOI: 10.6084/m9.figshare.24139557
title: Supplemental File 12

description: <b>Genes used in concatenated tree. </b>This set of 171 genes selected from clade-grabbed trees (see methods section) was concatenated using IQ-Tree with partitioned models (given in PartitionFile.nex) to produce the phylogeny in Fig 2.
DOI: 10.6084/m9.figshare.24139557.v2
title: Supplemental File 12

description: <b>Genes used in concatenated tree. </b>This set of 171 genes selected from clade-grabbed trees (see methods section) was concatenated using IQ-Tree with partitioned models (given in PartitionFile.nex) to produce the phylogeny in Fig 2.
DOI: 10.6084/m9.figshare.24139557.v1
title: Supplemental File S12

description: <b>Genes used in concatenated tree. </b>This set of 171 genes selected from clade-grabbed trees (see methods section) was concatenated using IQ-Tree with partitioned models (given in PartitionFile.nex) to produce the phylogeny in Fig 2.
DOI: 10.6084/m9.figshare.24139527
title: Supplemental File 11

description: <b>3’ UTR sequences.</b>
DOI: 10.6084/m9.figshare.24139527.v2
title: Supplemental File 11

description: <b>3’ UTR sequences.</b>
DOI: 10.6084/m9.figshare.24139527.v1
title: Supplemental File S11

description: <b>3’ UTR sequences.</b>
DOI: 10.6084/m9.figshare.24175404
title: Comprehensive Single-Cell Immune Profiling Defines the Patient Multiple Myeloma Microenvironment Following Oncolytic Virus Therapy

description: Corresponding dataset for publication.All files ending with '.txt' are the raw files obtained by the Hyperion Imaging System used for data visualization as well as all other downstream applications.The 'HistoCAT_Exported' .zip contains the HistoCAT session files.
DOI: 10.6084/m9.figshare.24139518
title: Supplemental File 10

description: <b>5’ UTR sequences.</b>
DOI: 10.6084/m9.figshare.24139518.v2
title: Supplemental File 10

description: <b>5’ UTR sequences.</b>
DOI: 10.6084/m9.figshare.24139518.v1
title: Supplemental File S10

description: <b>5’ UTR sequences.</b>
DOI: 10.6084/m9.figshare.24139509
title: Supplemental File 9

description: <b>Pre-curation trees. </b>Newick-string tree files containing the initial trees from which were selected robust clades unique to foraminifera (see methods).