cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7115540
title: BioSR+: Dataset Extension of biological images for super-resolution microscopy

description: BioSR+ dataset is an extension of our pre-published BioSR dataset of biological images for super-resolution microscopy, currently including image pairs of low-and-high resolution images of five biology structures (CCPs, ER, MTs, F-actin, Myosin-IIA) and 8 signal levels for each ROI. The BioSR+ dataset is related to our Nature Methods paper "Evaluation and development of deep neural networks for image super-resolution in optical microscopy" (DOI: 10.1038/s41592-020-01048-5) and Nature Biotechnology paper "Rationalized deep learning super-resolution <br> microscopy for sustained live imaging of rapid subcellular processes" (DOI:10.1038/s41587-022-01471-3). Both BioSR and BioSR+ are freely available and can be used for non-commercial purposes with proper citations of above two papers.
description: {"references": ["Qiao, C. et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nature Methods 18, 194-202 (2021).", "Qiao, C. et al. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nature Biotechnology (2022)."]}
DOI: 10.5281/zenodo.7115539
title: BioSR+: Dataset Extension of biological images for super-resolution microscopy

description: BioSR+ dataset is an extension of our pre-published BioSR dataset of biological images for super-resolution microscopy, currently including image pairs of low-and-high resolution images of five biology structures (CCPs, ER, MTs, F-actin, Myosin-IIA) and 8 signal levels for each ROI. The BioSR+ dataset is related to our Nature Methods paper "Evaluation and development of deep neural networks for image super-resolution in optical microscopy" (DOI: 10.1038/s41592-020-01048-5) and Nature Biotechnology paper "Rationalized deep learning super-resolution <br> microscopy for sustained live imaging of rapid subcellular processes" (DOI:10.1038/s41587-022-01471-3). Both BioSR and BioSR+ are freely available and can be used for non-commercial purposes with proper citations of above two papers.
description: {"references": ["Qiao, C. et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nature Methods 18, 194-202 (2021).", "Qiao, C. et al. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nature Biotechnology (2022)."]}
DOI: 10.18112/openneuro.ds004144.v1.0.2
title: A behavioral, clinical and brain imaging dataset with focus on emotion regulation of females with fibromyalgia
DOI: 10.18739/a22j6857h
title: HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Campaign

description: The dataset is derived from HELiX Uncrewed Aircraft System flights that were conducted in the Central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The data include Universal Coordinated Time (UTC), downwelling and upwelling shortwave radiation measurements, and position and attitude from the Uncrewed Aircraft System (UAS). Temperature, relative humidity and pressure from two different sensors are also provided. A quality control flag is associated with each scientific measurement. A flight flag is also included to indicate the different phases of the flight - on the ground, take-off/landing phases, and in flight. All the data have been synchronized and interpolated at 10 hertz (Hz). The purpose of this dataset is to provide information on albedo over different features of the sea ice (snow, melt pond, ocean). Three flight patterns were implemented during the campaign with the HELiX, a grid pattern at constant altitude (15 meters or 7 meters above ground level), hovering flights ( 2-5 minutes hovering over identified sea ice features at low altitude ~ 3 meters above ground level), and profiles up to 400 meters above ground level. Displaying latitude, longitude and altitude will help users to identify the flight pattern. Albedo measurements have been validated with surface-based measurements and details can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
DOI: 10.18739/a2gh9bb0q
title: HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Campaign

description: The dataset is derived from HELiX Uncrewed Aircraft System flights that were conducted in the Central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The data include Universal Coordinated Time (UTC), downwelling and upwelling shortwave radiation measurements, and position and attitude from the Uncrewed Aircraft System (UAS). Temperature, relative humidity and pressure from two different sensors are also provided. A quality control flag is associated with each scientific measurement. A flight flag is also included to indicate the different phases of the flight - on the ground, take-off/landing phases, and in flight. All the data have been synchronized and interpolated at 10 hertz (Hz). The purpose of this dataset is to provide information on albedo over different features of the sea ice (snow, melt pond, ocean). Three flight patterns were implemented during the campaign with the HELiX, a grid pattern at constant altitude (15 meters or 7 meters above ground level), hovering flights ( 2-5 minutes hovering over identified sea ice features at low altitude ~ 3 meters above ground level), and profiles up to 400 meters above ground level. Displaying latitude, longitude and altitude will help users to identify the flight pattern. Albedo measurements have been validated with surface-based measurements and details can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
DOI: 10.26195/8ev3-q016
title: QDataSet: Quantum Datasets for Machine Learning
DOI: 10.5061/dryad.dc1q8.2
title: Data from: Population structure of two rabies hosts relative to the known distribution of rabies virus variants in Alaska
DOI: 10.5281/zenodo.6466538
title: Mental health, suicide attempt, and family function for adolescents' primary health care during the COVID-19 pandemic

description: The purpose of the study was to identify associations between mental health risk, suicide attempt, and family function. The instruments used were the Self-Report Questionnaire, the Suicide Risk Assessment Scale and the family APGAR.
DOI: 10.5281/zenodo.6554869
title: Emo-Fibro: dataset of woman with fibromyalgia

description: Demographic, clinical and behavioral data from 33 women with fibromyalgia and 33 healthy controls.
DOI: 10.7289/v56m34rt
title: NOAA Climate Data Record (CDR) of Zonal Mean Ozone Binary Database of Profiles (BDBP), version 1.0
title: NOAA Ozone ESRL CDR
DOI: 10.17632/c4pv8w8pmp.2
title: Twenty simulated pedigrees with different combinations of three parameters using R package pedSimulate

description: The data consists of twenty pedigrees simulated using R package pedSimulate with different combinations of females selection (random, positively or negatively based on own phenotype or parent (genetic) average), additive genetic variance (10 vs. 20), and proportion of males selected (10% vs. 20%). The code used to simulate and analyze the data is available at "JupyterNotebook.html", and the corresponding Jupyter notebook (JupyterNotebook.ipynb) and R Markdown (JupyterNotebook.Rmd) files.
DOI: 10.24381/cds.572bf382
title: Ocean surface wave time series for the European coast from 1976 to 2100 derived from climate projections
DOI: 10.25625/bipkp8
title: Public opinion towards global distribution of COVID-19 vaccines - Data from Germany and the United States

description: Responses to the survey asking a nationally representative sample of public in the US and Germany about fair distribution of COVID-19 Vaccines across the world including codebook, variable labels, raw data, and questionnaire
DOI: 10.5066/p9v2ufl3
title: Survival, and growth of Neocloeon triangulifer under different test conditions in effluent style evaluations

description: Dataset consists of 4, 7, 10, and 14-day responses of laboratory cultured mayflies (Neocloeon triangulifer) to sodium chloride exposure under different test condition scenarios. Responses were measured as mortality and growth (length). Water quality and analytical chemistry results associated with toxicity data are included. Experiments included 1) testing the influence of organism age on mortality and growth in 7-day tests, 2) testing the influence of test duration on mortality and growth, 3) testing the influence of temperature on mortality and growth, 4) testing the influence of nutrient amendments to mayfly test dilution water on mortality and growth
DOI: 10.5258/soton/d2177
title: Dataset for Low loss and broadband low back-reflection interconnection between a hollow-core and standard single-mode fiber

description: Dataset supporting the journal publication Low loss and broadband low back-reflection interconnection between a hollow-core and standard single-mode fiber. Submitted to Optics Express. The data consists of an uploaded archive (Zip) containing 7 data files in excel format.
DOI: 10.5281/zenodo.2525664
title: Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

description: Soil pH in H2O in × 10 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail <strong>here</strong>. Antarctica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, ph.h2o = variable: soil pH in H2O, usda.4c1a2a = determination method: laboratory method code, m = mean value, 250m = spatial resolution / block support: 250 m, b10..10cm = vertical reference: 10 cm depth below surface, 1950..2017 = time reference: period 1950-2017, v0.2 = version number: 0.2,
description: {"references": ["USDA-NRCS, (2014) Laboratory Methods Manual (SSIR 42). U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center.", "Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagoti\u0107 A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748.", "Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0."]}
DOI: 10.5281/zenodo.3255086
title: Dataset for "Method to retrieve cloud condensation nuclei number concentrations using lidar measurements"

description: This repository contains the source data for the manuscript "<strong>Method to retrieve cloud condensation nuclei number concentrations using lidar measurements</strong>" published in <em>Atmospheric Measurement Techniques</em>. In situ measured data from five filed campaigns and corresponding theoretical simulated CCN number concentrations, lidar extinction and backscatter are included.
description: This research has been supported by the National Key R&D Program of China (grant nos. 2016YFC0202004 and 2017YFC0209904) and the National Natural Science Foundation of China (grant nos. 41375008, 41590872, 9154400001, and 41527807).
DOI: 10.5281/zenodo.7099678
title: Data from: "IL-6 and cfDNA monitoring throughout COVID-19 hospitalization are accurate markers of its outcomes".

description: Data supporting the manuscript title: <strong>IL-6 and cfDNA monitoring throughout COVID-19 hospitalization are accurate markers of its outcomes</strong>
DOI: 10.5291/ill-data.7-02-165
title: Investigation of phonon softening close to a structural quantum critical point in Lu(Pt0.6Pd0.4)2In
DOI: 10.6092/unibo/amsacta/6226
title: PERCEIVE. WP2. Evaluation of EU citizens' perception of the EU project. Mapping Citizens’ Identification with EU

description: This data set contains data for estimating the “Ident_EU” probabilistic model used to classify EU citizens and regions according to their different patterns of identification with EU during PERCEIVE project. The model specification and its results are described in Deliverable D2.4 from the PERCEIVE project (http://doi.org/10.6092/unibo/amsacta/6113) and in Brasili, Calia and Monasterolo (2019, https://doi.org/10.1111/rsp3.12227). The file PERCEIVE_WP2_Ident_EU_DATA.csv includes as input variables a set of indicators and individual characteristics derived from the responses to the questions in the PERCEIVE Survey (http://doi.org/10.5281/zenodo.3346047), as well as regional characteristics derived from the PERCEIVE regional panel dataset (https://doi.org/10.5281/zenodo.3346052). It contains also the output variables produced by model estimation: the membership’s probabilities and the clusters’ classification for individuals and regions. The file PERCEIVE_WP2_Ident_EU_LG_syntax.txt contains the commands (syntax model) that can be used for the specification of the “Ident_EU” model with the software used for the analysis, Latent GOLD®.
DOI: 10.25318/1610004501-eng
title:  Lumber, production, shipments and stocks, monthly

description: Lumber monthly production, shipments and stocks; data are in thousands of cubic metres.
DOI: 10.26180/5db160fa6a50b
title: Police-involved deaths and homicides - data and analysis files

description: Analysis files and data for study of effect of police-involved civilian deaths on homicide rates. Includes csv of aggregated homicide and aggravated assault data for 44 US cities; an R project file; an R script for reproducible cleaning process; an interrupted time series analytical file, which also produces plots; a meta-analysis file, which also produces forest plots; records of police involved shootings with links to news reports. <br><b>To use:</b>Download all into one folder and open the R project file with R using RStudio. I have tried to make these fully functional on their own to maximise reproducibility. You will likely need to download packages (but RStudio should prompt you to the ones that are missing). If you want to re-run the cleaning file, you will have to download the UCR and city crime data. I have provided links to these sources. Otherwise, everything should run out of the box!<br><br><b>Disclaimer:</b>I do not own the original data files from cities and UCR. While I have not included these case-level data, they are all publicly available and I have provided links, aside from Tampa which I acquired through a data request. I am happy to assist any interested researchers with getting the source data.
DOI: 10.5281/zenodo.6230083
title: Segmentation Zoo UNet models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset.

description: <strong>Doodleverse/Segmentation Zoo UNet models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset.</strong> These UNet model data are based on the Coast Train v1 Landsat-8 labeled imagery subset. Models have been fitted to 4 different types of data 1. NDWI (1 band): (g-nir)/(g+nir) 2. MNDWI (1 band): (swir-g)/(swir+g) 3. RGB (3 band): red, green, blue 4. RGB-NIR-SWIR (5 band): red, green, blue, nir, swir Classes are: {0: water, 1: whitewater, 2:sediment, 3:other}. These classes have been remapped from the original 11 classes<br> These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images 3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym
DOI: 10.18739/a2d79596q
title: Elemental Quotas and Stoichiometry of Polar Diatoms, United States, Antarctica, Baffin Bay, and Norway, 2016-2018
DOI: 10.5066/p9f8qs08
title: Apple Creek Rapid Geomorphic Assessment, Outagamie County, Wisconsin

description: The Fox River network, which includes Apple Creek, contributes a significant amount of total phosphorus (P), including both sediment-bound P (sed-P) and dissolved reactive P (DRP, also known as orthophosphate) and suspended sediment to Green Bay/Lake Michigan. Most headwater streams of the Fox River are predominantly agricultural in land use, interspersed with rural communities and the industrialized Fox River Valley corridor. Implementation of best management practices on agricultural operations has worked to limit the amount of material that is carried to the stream from cropland. However, several studies have shown streambank erosion to be another source of suspended sediment in these streams. These files contain the descriptions of 30 rapid geomorphic assessments conducted during the summer of 2018 as part of the Apple Creek sediment-source study. Included in the file are reach lengths, drainage area at the downstream end of the reach, an extrapolation to all stream segments upstream of the USGS gage, and sediment/phosphorus budget estimates for the basin.
DOI: 10.5066/p9rrsmoj
title: Aerial Imagery of the North Carolina Coast: 2019-10-11

description: The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal vulnerability and response to disturbance events. A nadir (vertical) aerial imagery survey was conducted from Cape Lookout, North Carolina to Duck, North Carolina on October 11th, 2019, to document recovery conditions after the passage of Hurricane Dorian in September 2019. The observations along the coastline cover an approximately 250-kilometer-long by 300 to 700-meter-wide swath of coastline and encompass both highly developed towns as well as natural, undeveloped areas, including the federal lands of Cape Lookout National Seashore and Cape Hatteras National Seashore. Low altitude (300 meters above ground level) digital aerial imagery were acquired from a manned, fixed-wing aircraft using a Sony A7R 36 Megapixel digital camera, along with precise aircraft navigation Global Navigation Satellite System (GNSS) data. Data were collected in shore-parallel lines, flying at approximately 50 meters per second and capturing true color imagery at 1 Hertz, resulting in image footprints with approximately 75-80% endlap, 60-70% sidelap, and a ground sample distance (GSD) of 5.3 centimeters. The precise time of each image capture (flash event) was recorded, and the corresponding aircraft position was computed in post-processing from the aircraft navigation GNSS data; precise image positions can then be determined by accounting for the lever arm offsets between the aircraft GNSS antenna and the camera lens. Position data, provided as latitude/longitude/ellipsoid height, is referenced to the North American Datum of 1983 (NAD83(2011)).
DOI: 10.5066/p9tpkmbb
title: Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian

description: The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal vulnerability and response to disturbance events. A nadir (vertical) aerial imagery survey was conducted from Cape Lookout, North Carolina to the Virginia-North Carolina border between September 8th, 2019 and September 13th, 2019, to document post-storm conditions after the passage of Hurricane Dorian. The observations along the coastline cover an approximately 275-kilometer-long by 300 to 700-meter-wide swath of coastline and encompass both highly developed towns as well as natural, undeveloped areas, including the federal lands of Cape Lookout National Seashore and Cape Hatteras National Seashore. Low altitude (300 meters above ground level) digital aerial imagery were acquired from a manned, fixed-wing aircraft using a Sony A7R 36 Megapixel digital camera, along with precise aircraft navigation Global Navigation Satellite System (GNSS) data. Data were collected in shore-parallel lines, flying at approximately 50 meters per second and capturing true color imagery at 1 Hertz, resulting in image footprints with approximately 75&ndash;80% endlap, 60&ndash;70% sidelap, and a ground sample distance (GSD) of 5.3 centimeters. The precise time of each image capture (flash event) was recorded, and the corresponding aircraft position was computed in post-processing from the aircraft navigation GNSS data; precise image positions can then be determined by accounting for the lever arm offsets between the aircraft GNSS antenna and the camera lens. Position data, provided as latitude/longitude/ellipsoid height, is referenced to the North American Datum of 1983 (NAD83(2011)).
DOI: 10.5281/zenodo.5362362
title: A prospective, multicentre study of the investigation and management of hyponatraemia after subarachnoid haemorrhage: Supplementary materials

description: Supplementary materials to accompany main published manuscript.