cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13061720.v1
title: JVASP-13598 to JVASP-14456 (Negative bias)

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13061720
title: JVASP-13598 to JVASP-14456 (Negative bias)

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.8183063
title: S1 File. BOLD and GenBank output for samples used in statistical analyses (insects, n=17; macro-fungi, n=14; plants 2-loci, n=53; plants 4-loci, n=28).

description: BOLD and GenBank output for samples used in statistical analyses (insects, n=17; macro-fungi, n=14; plants 2-loci, n=53; plants 4-loci, n=28). <br>
DOI: 10.5281/zenodo.4071925
title: Accent Classification Dataset

description: This dataset contains audio recordings of 12 different accents across the UK: Northern Ireland (NI), Scotland, Wales (SW), North East England (NE), North West England (NW), Yorkshire and Humber (YAH), East Midlands (EM), West Midlands (WM), East of England (EE), Greater London (GL), South East England (SE), South West England (SW). We split the data into a Male: Female ratio of 1:1, this is labelled with either '_M' for male or '_F' for female within the dataset. The audio dataset was compiled using opensource YouTube videos and it a collation of different accents, the audio files were trimmed for uniformity. The Audio files are of length 30 seconds, with the first 5 seconds and last 5 seconds of the signal being blank. We also resample the audio signals at 8 kHz, again for uniformity and to remove any noise present in the audio signals whilst retaining the underlying characteristics. The intended application of this dataset was to be used in conjunction with a deep neural network for accent and gender classification tasks. The dataset also contains an unseen dataset of the Google opensource digit dataset, which contains audio files of the digits 1-9. This is included to test any models developed using the original dataset to confirm model performance to data variations.
DOI: 10.5281/zenodo.4071924
title: Accent Classification Dataset

description: This dataset contains audio recordings of 12 different accents across the UK: Northern Ireland (NI), Scotland, Wales (SW), North East England (NE), North West England (NW), Yorkshire and Humber (YAH), East Midlands (EM), West Midlands (WM), East of England (EE), Greater London (GL), South East England (SE), South West England (SW). We split the data into a Male: Female ratio of 1:1, this is labelled with either '_M' for male or '_F' for female within the dataset. The audio dataset was compiled using opensource YouTube videos and it a collation of different accents, the audio files were trimmed for uniformity. The Audio files are of length 30 seconds, with the first 5 seconds and last 5 seconds of the signal being blank. We also resample the audio signals at 8 kHz, again for uniformity and to remove any noise present in the audio signals whilst retaining the underlying characteristics. The intended application of this dataset was to be used in conjunction with a deep neural network for accent and gender classification tasks. The dataset also contains an unseen dataset of the Google opensource digit dataset, which contains audio files of the digits 1-9. This is included to test any models developed using the original dataset to confirm model performance to data variations.
DOI: 10.6084/m9.figshare.8182703
title: S4 Table: Specimen information and barcode results for plants.

description: Specimen information and barcode results for plant sequences when searched against GenBank and BOLD.<br>
DOI: 10.6084/m9.figshare.8182700.v1
title: S3 Table. Specimen information and barcode results for macro-fungi.

description: Specimen information and barcode results for macro-fungi ITS sequences when searched against GenBank and BOLD. <br>
DOI: 10.6084/m9.figshare.8182700
title: S3 Table. Specimen information and barcode results for macro-fungi.

description: Specimen information and barcode results for macro-fungi ITS sequences when searched against GenBank and BOLD. <br>
DOI: 10.6084/m9.figshare.8182631
title: S2 Table: Specimen information and barcode results for insects

description: Specimen information and barcode results for insect COI sequences when searched against GenBank and BOLD. <br>
DOI: 10.6084/m9.figshare.8171147
title: S1 Table. Primers and thermal cycling conditions used to amplify each barcoding region for insects, macro-fungi, and plants.

description: Primers and thermal cycling conditions used to amplify each barcoding region for insects, macro-fungi, and plants. <br>
DOI: 10.6084/m9.figshare.13061747
title: REVISED S1 File. BOLD and GenBank output for insect samples (n=17) used in statistical analyses.

description: BOLD and GenBank output for insect samples (n=17) used in statistical analyses.<br>
DOI: 10.6084/m9.figshare.13061747.v1
title: REVISED S1 File. BOLD and GenBank output for insect samples (n=17) used in statistical analyses.

description: BOLD and GenBank output for insect samples (n=17) used in statistical analyses.<br>
DOI: 10.34740/kaggle/dsv/1543666
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.4071985
title: Social comparison and envy in the workplace

description: This dataset contains a manipulation of upward social comparison and envy in the workplace.
DOI: 10.5281/zenodo.4071984
title: Social comparison and envy in the workplace

description: This dataset contains a manipulation of upward social comparison and envy in the workplace.
DOI: 10.5281/zenodo.4071991
title: Supplemental material for "The influence of temperature on the seasonality of historical plague outbreaks"

description: This repository provides the code and raw data for the project.
DOI: 10.5281/zenodo.4071993
title: Test for what

description: Whatever
DOI: 10.5281/zenodo.4071992
title: Test for what

description: Whatever
DOI: 10.6084/m9.figshare.13058045
title: Mice data and codes from Surface periarterial spaces of the mouse brain are open, not porous.zip

description: Mice datasets and Matlab codes for figures 2 and 4 from Surface periarterial spaces of the mouse brain are open, not porous.
DOI: 10.6084/m9.figshare.13058045.v1
title: Mice data and codes from Surface periarterial spaces of the mouse brain are open, not porous.zip

description: Mice datasets and Matlab codes for figures 2 and 4 from Surface periarterial spaces of the mouse brain are open, not porous.
DOI: 10.6084/m9.figshare.13062746.v1
title: KEGG Module Expression Network for 15 High-Mapping Genomes

description: Cytoscape Network of KEGG module expression for 15 high-mapping genomes from the EBPR ecosystem
DOI: 10.5281/zenodo.4072020
title: Expression-based polygenic score from the amygdala 5HTT gene network

description: This pipeline intends to facilitate the calculation of biologically informed polygenic scores from collected genomic data. This template can be adapted to create other expression-based polygenic risk scores. Data is 1) step by step description and 2) a list of genes that compose the gene network.
description: {"references": ["de Lima RMS, Barth B, Arcego DM, et al. Amygdala 5-HTT Gene Network Moderates the Effects of Postnatal Adversity on Attention Problems: Anatomo-Functional Correlation and Epigenetic Changes. Front Neurosci-Switz. 2020;14(198).", "Silveira PP, Pokhvisneva I, Parent C, et al. Cumulative prenatal exposure to adversity reveals associations with a broad range of neurodevelopmental outcomes that are moderated by a novel, biologically informed polygenetic score based on the serotonin transporter solute carrier family C6, member 4 (SLC6A4) gene expression. Dev Psychopathol. 2017;29(5):1601-1617.", "Miguel PM, Deniz BF, Deckmann I, et al. Prefrontal cortex dysfunction in hypoxic-ischaemic encephalopathy contributes to executive function impairments in rats: Potential contribution for attention-deficit/hyperactivity disorder. The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry. 2017:1-14.", "Mulligan MK, Mozhui K, Prins P, Williams RW. GeneNetwork: a toolbox for systems genetics. In: Systems Genetics. Springer; 2017:75-120.", "Miller JA, Ding S-L, Sunkin SM, et al. Transcriptional landscape of the prenatal human brain. Nature. 2014;508(7495):199-206.", "Hari Dass SA, McCracken K, Pokhvisneva I, et al. A biologically-informed polygenic score identifies endophenotypes and clinical conditions associated with the insulin receptor function on specific brain regions. EBioMedicine. 2019;42:188-202."]}
DOI: 10.15468/odznll
title: Guide to the Vascular Flora of the Savannas and Flatwoods of Shaken Creek Preserve and Vicinity (Pender & Onslow Counties, North Carolina, U.S.A.)

description: Shaken Creek Preserve (“SCP”) is a 2,448 ha (6,050 ac) natural area in Pender and Onslow Counties, North Carolina (U.S.A). Best known for its high-quality longleaf pine savanna habitat, the site contains seven savanna or savanna-like plant community types (i.e., flatwoods or sandhills), three of which are globally critically imperiled (G1): Sandy Pine Savanna (Rush Featherling subtype), Wet Loamy Pine Savanna, and Very Wet Loamy Pine Savanna. SCP hosts three Federally Endangered plant species and six Federal Species of Concern. Formerly a private hunting club, the site was virtually unknown to scientists until the 1990s; consequently, few biological inventories of SCP have been conducted. In particular, no systematic floristic inventories of the species-rich savannas have been undertaken, despite the fact that floristic data is critical to the effective management of any natural area. The goals of this study were to (1) inventory the vascular flora of the savannas, flatwoods, and sandhill community types on site through the collection of voucher specimens; (2) provide a comprehensive checklist of the flora based on collections and reports made from the site and from the same or similar habitats in the vicinity (i.e., within 2 miles of SCP); and (3) create an illustrated guide based on the checklist. In order to increase the usefulness of the guide, taxa not currently known from SCP but collected or reported from the same or similar habitats within two miles of SCP, are included in the guide. Eighty-three families containing 450 taxa, including thirty-two Significantly Rare and thirty-eight Watch List taxa, were collected or reported from SCP; an additional seven families containing a total of 102 taxa, including eighteen Significantly Rare and seven Watch List taxa, were collected or reported from the vicinity. In total, ninety families containing 552 taxa, including fifty Significantly Rare and forty-five Watch List taxa, are treated in the guide. Dichotomous keys are provided to all vouchered or reported families, genera, and species. The following features are provided for all species and infraspecific taxa: flowering and fruiting phenology; synonymy with Manual of the Vascular Flora of the Carolinas, the Flora of North America, and Flora of the Southern and Mid-Atlantic States; relevant voucher information; and, for most taxa, line drawings and/or photographs. For taxa collected from SCP, community types in which the taxa occur and estimates of abundance on site are also provided.
DOI: 10.5517/ccdc.csd.cc240md6
title: CCDC 1966807: Experimental Crystal Structure Determination

description: Related Article: Jacob A. Smith, Anna Schouten, Justin H. Wilde, Karl S. Westendorff, Diane A. Dickie, Daniel H. Ess, W. Dean Harman|2020|J.Am.Chem.Soc.|142|16437|doi:10.1021/jacs.0c08032
DOI: 10.5517/ccdc.csd.cc240mf7
title: CCDC 1966808: Experimental Crystal Structure Determination

description: Related Article: Jacob A. Smith, Anna Schouten, Justin H. Wilde, Karl S. Westendorff, Diane A. Dickie, Daniel H. Ess, W. Dean Harman|2020|J.Am.Chem.Soc.|142|16437|doi:10.1021/jacs.0c08032
DOI: 10.5517/ccdc.csd.cc240mg8
title: CCDC 1966809: Experimental Crystal Structure Determination

description: Related Article: Jacob A. Smith, Anna Schouten, Justin H. Wilde, Karl S. Westendorff, Diane A. Dickie, Daniel H. Ess, W. Dean Harman|2020|J.Am.Chem.Soc.|142|16437|doi:10.1021/jacs.0c08032
DOI: 10.5517/ccdc.csd.cc240mh9
title: CCDC 1966810: Experimental Crystal Structure Determination

description: Related Article: Jacob A. Smith, Anna Schouten, Justin H. Wilde, Karl S. Westendorff, Diane A. Dickie, Daniel H. Ess, W. Dean Harman|2020|J.Am.Chem.Soc.|142|16437|doi:10.1021/jacs.0c08032
DOI: 10.5517/ccdc.csd.cc240mjb
title: CCDC 1966811: Experimental Crystal Structure Determination

description: Related Article: Jacob A. Smith, Anna Schouten, Justin H. Wilde, Karl S. Westendorff, Diane A. Dickie, Daniel H. Ess, W. Dean Harman|2020|J.Am.Chem.Soc.|142|16437|doi:10.1021/jacs.0c08032
DOI: 10.5517/ccdc.csd.cc240mld
title: CCDC 1966813: Experimental Crystal Structure Determination

description: Related Article: Jacob A. Smith, Anna Schouten, Justin H. Wilde, Karl S. Westendorff, Diane A. Dickie, Daniel H. Ess, W. Dean Harman|2020|J.Am.Chem.Soc.|142|16437|doi:10.1021/jacs.0c08032
DOI: 10.25387/g3.13023269
title: Supplemental Material for Lee et al., 2020

description: Here we provided the supplementary figure and tables of Genome sequence of the oyster mushroom Pleurotus ostreatus strain PC9.<b></b>
DOI: 10.25387/g3.13023269.v1
title: Supplemental Material for Lee et al., 2020

description: Here we provided the supplementary figure and tables of Genome sequence of the oyster mushroom Pleurotus ostreatus strain PC9.<b></b>
DOI: 10.5066/f78w3bbt
title: National Fish Habitat Partnership Projects

description: These map service layers provide information for projects funded and supported by the 20 regional fish habitat partnerships. Projects funded and supported since 2006 are represented in the service layer and the data are expected to be updated annually. Projects in the database range from on-the-ground restoration, partnership coordination, outreach and education events, and species distribution and habitat assessments.
DOI: 10.25345/c5r78s
title: MassIVE MSV000086245 - 10072020_George_lab_rat_drug_samples
title: A longitudinal behavioral characterization of addiction liability in the heterogenous outbred stock of rats that are dependent on cocaine and oxycodon.
DOI: 10.21227/ce7m-8265
title: Gaussian Blobs of Varying numbers of samples, centers and features for Testing Clustering Algorithms

description: The dataset has Gaussian Blobs of varying samples, centers and features. The datasets can be used for testing clustering algorithms for their effectiveness. This dataset is related to a paper which is under review.
DOI: 10.6084/m9.figshare.13063592
title: Transcriptomic and Quantitative Proteomic Profiling Reveals Signaling Pathways Critical for Pancreatic Islet Maturation

description: Supplemental Tables.xlsx
DOI: 10.6084/m9.figshare.13063592.v1
title: Transcriptomic and Quantitative Proteomic Profiling Reveals Signaling Pathways Critical for Pancreatic Islet Maturation

description: Supplemental Tables.xlsx
DOI: 10.6084/m9.figshare.13063622
title: GEOID12A-NAD83 datum offset for CONUS

description: This TIF file is a raster that describes the vertical difference between the GEOID12A and NAD83 vertical datums, covering the Contiguous United States. This raster was developed as a part of the NEON-GEDI tutorials found on neonscience.org
DOI: 10.6084/m9.figshare.13063622.v1
title: GEOID12A-NAD83 datum offset for CONUS

description: This TIF file is a raster that describes the vertical difference between the GEOID12A and NAD83 vertical datums, covering the Contiguous United States. This raster was developed as a part of the NEON-GEDI tutorials found on neonscience.org
DOI: 10.21227/1f95-xb88
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Abstract: Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of powered lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and Artificial Intelligence. Under Review.
DOI: 10.6084/m9.figshare.13063634
title: WGS84-NAD83 datum offset for CONUS

description: This TIF raster dataset describes the vertical difference between the WGS84 and NAD83 vertical datums across the Contiguous United States (CONUS). This dataset was developed by NEON scientists for their research, and further refined to be a part of the NEON-GEDI tutorials found on neonscience.org
DOI: 10.6084/m9.figshare.13063634.v1
title: WGS84-NAD83 datum offset for CONUS

description: This TIF raster dataset describes the vertical difference between the WGS84 and NAD83 vertical datums across the Contiguous United States (CONUS). This dataset was developed by NEON scientists for their research, and further refined to be a part of the NEON-GEDI tutorials found on neonscience.org
DOI: 10.21227/28za-mp71
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Abstract: Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of powered lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and Artificial Intelligence. Under Review.
DOI: 10.21227/2x1c-pt67
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Abstract: Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of powered lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and Artificial Intelligence. Under Review.
DOI: 10.21227/akgf-2y05
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Abstract: Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of powered lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and Artificial Intelligence. Under Review.
DOI: 10.6084/m9.figshare.13058456
title: R0170_20170707a_videos

description: Rat 0170, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13058456.v1
title: R0170_20170707a_videos

description: Rat 0170, skilled reaching data collected in Leventhal laboratory by Alexandra Bova