cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/dxwr-q211
title: Magnetic Induction Spectroscopy Data of Buried Objects

description: This dataset accompanies a paper titled "Detection of Metallic Objects in Mineralised Soil Using Magnetic Induction Spectroscopy". The detection of small metallic objects buried in mineralised soil poses a challenge for metal detectors, especially when the response from the metallic objects is orders magnitude below the response from the soil. This dataset contains data from a new detector based on Magnetic Induction Spectroscopy (MIS). Experimental results consisting of 1,669 passes across either buried objects or empty soil are presented. In total 14 different objects were buried at 3 different depths, in three soil types including non-mineralised and mineralised soils.
DOI: 10.5281/zenodo.3771710
title: Companion for "Measuring Phenology Uncertainty with Large Scale Image Processing"

description: This is the software and dataset companion for the paper entitled "Measuring Phenology Uncertainty with Large Scale Image Processing". Further instructions can be found in the README.org file.
DOI: 10.5281/zenodo.3771709
title: Companion for "Measuring Phenology Uncertainty with Large Scale Image Processing"

description: This is the software and dataset companion for the paper entitled "Measuring Phenology Uncertainty with Large Scale Image Processing". Further instructions can be found in the README.org file.
DOI: 10.5281/zenodo.3876351
title: A Deep Learning Based Cardiac Cine Segmentation Framework for Clinicians - Transfer Learning Application to 7T - Additional Data

description: <strong>Automatically Generated Segmentation Masks for Data Science Bowl Cardiac Challenge Data</strong> These segmentation masks have been automatically generated with the ukbb_cardiac network by Bai et al. (2018, doi:10.1186/s12968-018-0471-x). In order to reproduce the data cleaning and conversion: download the data from Kaggle and follow the data curation and conversion steps outlined in: cmr-seg-tl and the associated publication (Link to be added). As this is a derived dataset please abide by the data use rules of the original dataset at kaggle and provide proper citation to the original data: The data for the Data Science Bowl is available for research and academic pursuits. Please cite as ‘Data Science Bowl Cardiac Challenge Data’. Please also cite the Bai et al. article for the algorithm and our publication for the data curation.
description: Please be aware that the masks were automatically generated, not quality controlled. Validation has been performed against ground truth volume information from kaggle. This can be used as a measure of confidence but does not replace proper quality control.
DOI: 10.5281/zenodo.3876350
title: A Deep Learning Based Cardiac Cine Segmentation Framework for Clinicians - Transfer Learning Application to 7T - Additional Data

description: <strong>Automatically Generated Segmentation Masks for Data Science Bowl Cardiac Challenge Data</strong> These segmentation masks have been automatically generated with the ukbb_cardiac network by Bai et al. (2018, doi:10.1186/s12968-018-0471-x). In order to reproduce the data cleaning and conversion: download the data from Kaggle and follow the data curation and conversion steps outlined in: cmr-seg-tl and the associated publication (Link to be added). As this is a derived dataset please abide by the data use rules of the original dataset at kaggle and provide proper citation to the original data: The data for the Data Science Bowl is available for research and academic pursuits. Please cite as ‘Data Science Bowl Cardiac Challenge Data’. Please also cite the Bai et al. article for the algorithm and our publication for the data curation.
description: Please be aware that the masks were automatically generated, not quality controlled. Validation has been performed against ground truth volume information from kaggle. This can be used as a measure of confidence but does not replace proper quality control.
DOI: 10.25387/g3.12045375
title: Supplemental Material for Aklilu et al., 2020

description: Supplementary Data
DOI: 10.25387/g3.12045375.v1
title: Supplemental Material for Aklilu et al., 2020

description: Supplementary Data
DOI: 10.17638/datacat.liverpool.ac.uk/1083
title: Differential guest location by host dynamics enhances propylene-propane separation in a metal-organic framework
DOI: 10.5281/zenodo.3891474
title: M3 Quarterly Dataset

description: This dataset contains 756 quarterly time series used in the M3 forecasting competition. The series are belonging to 5 different domains: demographic, micro, macro, industry and finance.
description: {"references": ["Makridakis, S., Hibon, M., 2000. The m3-competition: results, conclusions and implications. International Journal of Forecasting 16 (4), 451\u2013476."]}
DOI: 10.17632/bt662ptnnt.1
title: Data for: A new Quaternary Stratigraphy of the Kallang River Basin, Singapore

description: The borehole database here comprises of 161 quality boreholes selected from ~4000 boreholes obtained from BCA in the Kallang River Basin, Singapore. Nomenclature (GEOCODES) used here is based on old names from PWD (1976) and DSTA (2009). This data is utilized to produce the cross-sections and 3D geological model in this paper.
DOI: 10.5281/zenodo.3891584
title: M3 Other Dataset

description: This dataset contains 174 time series used in the M3 forecasting competition other than the yearly, monthly and quarterly time series. The series are belonging to 3 different domains: micro, finance and other.
description: {"references": ["Makridakis, S., Hibon, M., 2000. The m3-competition: results, conclusions and implications. International Journal of Forecasting 16 (4), 451\u2013476."]}
DOI: 10.5281/zenodo.3891572
title: M3 Monthly Dataset

description: This dataset contains 1428 monthly time series used in the M3 forecasting competition. The series are belonging to 6 different domains: demographic, micro, macro, industry, finance and other.
description: {"references": ["Makridakis, S., Hibon, M., 2000. The m3-competition: results, conclusions and implications. International Journal of Forecasting 16 (4), 451\u2013476."]}
DOI: 10.5281/zenodo.3891601
title: Experimental simulation of Titan's stratospheric photochemistry: benzene (C6H6) ices

description: Titan, often compared to the early Earth, is the only moon in the solar system to have a dense atmosphere, mainly composed of nitrogen and methane. In the upper part of the atmosphere (&gt;1000 km), UV photons, photoelectrons, energetic ions and magnetospheric electrons induce the dissociation and the ionization of N<sub>2</sub> and methane. These reactions lead to the formation of complex organic molecules - including hydrocarbons such as benzene - and aerosols in the high atmosphere (an organic haze responsible for Titan’s brownish color), which are subject to different UV radiation classes depending on the altitude. Therefore, during their sedimentation toward the surface, these organic photoproducts are known to be modified. Once the tropopause is reached, molecules like benzene (C<sub>6</sub>H<sub>6</sub>) condense and evolve under long UV radiations to contribute to aerosol formation.
DOI: 10.5281/zenodo.3891437
title: M3 Yearly Dataset

description: This dataset contains 645 yearly time series used in the M3 forecasting competition. The series are belonging to 6 different domains: demographic, micro, macro, industry, finance and other.
description: {"references": ["Makridakis, S., Hibon, M., 2000. The m3-competition: results, conclusions and implications. International Journal of Forecasting 16 (4), 451\u2013476."]}
DOI: 10.5281/zenodo.3891576
title: smer_acm_bcb_20

description: Structural representations of DNA regulatory substrates can enhance sequence-based algorithms by associating functional sequence variants
DOI: 10.5281/zenodo.3891575
title: smer_acm_bcb_20

description: Structural representations of DNA regulatory substrates can enhance sequence-based algorithms by associating functional sequence variants
DOI: 10.5281/zenodo.3891605
title: MAJA look-up tables for Sentinel-2 A&B, Venus and Landsat-8 sensors, for default continental model

description: The archive contains the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B, Venus and Landsat-8 sensors. These look-up tables correspond to the aerosol types for the default continental model. Current version history (from 06/2019 onwards): * Version 1.0 Reformatted all files * Version 1.1 Added compatibility to Maja 4.2 Former version history (until 06/2019): * Version 1.1 has new LUT for water vapour estimates, which corrects for a bias observed for large water vapour contents (above 2.5 g/cm2) * Version 1.2 just changed the Folder name for a better integration with Start_maja. * Version 1.3 added the Header files
DOI: 10.5281/zenodo.3891613
title: M4 Yearly Dataset

description: This dataset contains 23000 yearly time series used in the M4 forecasting competition.
description: {"references": ["Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2020. The m4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 36 (1), 54 \u2013 74.", "Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2018. The m4 competition: results, findings, conclusion and way forward, International Journal of Forecasting 34 (4), 802 \u2013 808."]}
DOI: 10.5281/zenodo.3694083
title: DataHawk2 files from LAPSE-RATE

description: This dataset includes measurements obtained using the University of Colorado DataHawk2 UAS during the 2018 LAPSE-RATE (Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment) field campaign. This campaign took place in the San Luis Valley of Colorado between 14-21 July, 2018. <strong>All parties downloading the data are asked to cite the original research team and funding body when appropriate, and when using images and data in publications, presentations or other public displays. The DOI for the current dataset, and associated articles should be referenced whenever the dataset is used for scientific analysis.</strong>
description: Funding supported by the US National Science Foundation (AGS 1807199) and the US Department of Energy (DE-SC0018985).
DOI: 10.25850/nioz/7b.b.l/ps5vi5
title: EMIR2 POREWATER NO3.tab
DOI: 10.25850/nioz/7b.b.l/vahnsy
title: Shipboard report 64PE196 North Sea Emir leg 2.pdf
DOI: 10.25850/nioz/7b.b.l/jewlap
title: EMIR2 POREWATER Si.tab
DOI: 10.25850/nioz/7b.b.l/efecns
title: readme.txt
DOI: 10.25850/nioz/7b.b.l/8elaai
title: EMIR2 POREWATER NH4.tab
DOI: 10.25850/nioz/7b.b.l/117yw1
title: EMIR2 POREWATER DIC.tab
DOI: 10.25850/nioz/7b.b.l/z3j9wu
title: Station info.tab
DOI: 10.5281/zenodo.3679328
title: Data to "Near-optimal combination of disparity across a log-polar scaled visual field"

description: This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in: Maiello, G., Chessa, M., Bex, P. J., & Solari, F. (2020) Near-optimal combination of disparity across a log-polar scaled visual field. <em>PLOS Computational Biology, 16</em>(4), e1007699. https://doi.org/10.1371/journal.pcbi.1007699
DOI: 10.5281/zenodo.3679327
title: Data to "Near-optimal combination of disparity across a log-polar scaled visual field"

description: This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in: Maiello, G., Chessa, M., Bex, P. J., & Solari, F. (2020) Near-optimal combination of disparity across a log-polar scaled visual field. <em>PLOS Computational Biology, 16</em>(4), e1007699. https://doi.org/10.1371/journal.pcbi.1007699
DOI: 10.7910/dvn/mf5huy/aspl77
title: Deliverable D1.2.pdf

description: Reference Architecture for IoT Enabled Agriculture Development.
DOI: 10.7910/dvn/mf5huy/uwzr4j
title: Deliverable D3.2.pdf

description: Estimated costs of off-the-shelf hardware components and software services recommended for the IoT sensor network pilot.
DOI: 10.7910/dvn/mf5huy/bfnjmj
title: Deliverable D2.1.pdf

description: An updated comparison of the technological alternatives for doing data processing from field sensors either on premises or in the cloud using both open-source and commercial platforms, and a recommendation of the corresponding software services.
DOI: 10.7910/dvn/mf5huy/kr6zue
title: Deliverable D3.1.pdf

description: A list of suggested hardware elements for the IoT network in terms of communications, processing, and sensing modules.
DOI: 10.7910/dvn/mf5huy/oq2k46
title: Deliverable D1.1.pdf

description: Review of Reference Architectures for IoT Enabled Agriculture Development.
DOI: 10.7910/dvn/mf5huy/xglmqx
title: Deliverable D2.1_Tables.xls

description: This report included comprehensive tables with open-source and commercial alternatives for implementing the required IoT services.
DOI: 10.25919/5ee3b0e1b8778
title: Parkes observations for project PX600 semester 2020APRS_07

description: The Galactic Center (GC) is a key target for the radio component of Breakthrough Listen (BL) program. Offering the largest amount of stars in any given direction in the sky, the GC is widely cited as a location believed to harbour advanced civilizations, and it is also the most energetic region in the Milky Way. We aim to conduct BL-GC Survey with Parkes radio telescope for around 350 hours. We will cover the frequency range from 700 MHz to 4 GHz, utilizing the newly installed ultra-wideband receiver. Our team has already leveraged both standard and bespoke tools to construct a flexible software stack to search data for signals of interest. We will look for signals from extraterrestrial intelligence (ETI) by searching for both simple narrow-band signals and complex modulated signals. Along with that, as an ancillary science, we will also search for accelerated pulsars — likely orbiting a supermassive black hole at the centre of the Milky Way Galaxy. Data products produced during this program will also be publically available to engage larger pulsar community
DOI: 10.18430/m37bwh
title: X-ray diffraction data for the soluble cytochrome b5 from Ramazzottius varieornatus (7BWH)
DOI: 10.7303/syn22153884
title: Spatial transcriptomics and in situ sequencing uncover coordinated cell
title: responses to amyloid-β plaques in Alzheimer’s disease
DOI: 10.5281/zenodo.3891748
title: Propagating mechanisms of the 2016 Summer BSISO Event: air-sea coupling, vorticity, and moisture

description: <pre>This repository contains the data from the WRF+HYCOM coupled simulations and WRF simulations for the BSISO event in July and August, 2016. </pre>
DOI: 10.5281/zenodo.3891747
title: Propagating mechanisms of the 2016 Summer BSISO Event: air-sea coupling, vorticity, and moisture

description: <pre>This repository contains the data from the WRF+HYCOM coupled simulations and WRF simulations for the BSISO event in July and August, 2016. </pre>
DOI: 10.5281/zenodo.3891767
title: Sample Research Object for Zenodo

description: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque sodales, dolor imperdiet viverra gravida, turpis est aliquet nisi, vel fermentum augue nisi ut mi. Curabitur placerat egestas scelerisque. Nunc sit amet justo non eros rhoncus egestas a semper dui. Sed at ipsum nec elit sodales rhoncus. Fusce vel ante id mi consectetur tincidunt. Proin lobortis dui vel nunc lacinia pellentesque. Sed pretium justo quis dolor ornare, in egestas turpis eleifend. Sed tellus tellus, fermentum cursus ligula vitae, tincidunt consectetur velit. Vivamus a magna varius, pulvinar purus quis, molestie odio. Nam auctor semper nisi at vehicula. Vestibulum et dui quis metus fringilla tempus. Integer vitae purus nisi. Curabitur a elit at eros mattis lobortis et at lectus.
DOI: 10.5281/zenodo.3891766
title: Sample Research Object for Zenodo

description: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque sodales, dolor imperdiet viverra gravida, turpis est aliquet nisi, vel fermentum augue nisi ut mi. Curabitur placerat egestas scelerisque. Nunc sit amet justo non eros rhoncus egestas a semper dui. Sed at ipsum nec elit sodales rhoncus. Fusce vel ante id mi consectetur tincidunt. Proin lobortis dui vel nunc lacinia pellentesque. Sed pretium justo quis dolor ornare, in egestas turpis eleifend. Sed tellus tellus, fermentum cursus ligula vitae, tincidunt consectetur velit. Vivamus a magna varius, pulvinar purus quis, molestie odio. Nam auctor semper nisi at vehicula. Vestibulum et dui quis metus fringilla tempus. Integer vitae purus nisi. Curabitur a elit at eros mattis lobortis et at lectus.
DOI: 10.5281/zenodo.3886551
title: mHM_UFZ gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie

description: This dataset provides gridded model simulations in netcdf format over the Lake Erie using the mHM model (Samaniego, et al., 2010, Kumar et al, 2013), done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). Model code is available under: https://git.ufz.de/mhm/mhm, revision number: 8271b54 Domain boundaries (WGS84 system): lon_min = -86.0, lon_max = -78.0, lat_min = 40.0, lat_max = 45.0 Model variables are simulated at spatial grid of 0.125deg x 0.125deg, daily time step. Simulation period: 01 Jan 2011 - 31 Dec 2014 (with 1year -2010- warm-up) Meteorological input data, see Mai et al. 2020 (in prep) Following model variables are provided for two mHM_parameter.nml realizations (*obj1 and *obj2, as defined in Mai et al. 2020 in prep). double <strong>snowpack</strong>(time, northing, easting) ;<br> snowpack:long_name = "depth of snowpack" ;<br> snowpack:unit = "mm" ;<br> double <strong>SM_Lall</strong>(time, northing, easting) ;<br> SM_Lall:long_name = "average soil moisture over all layers" ;<br> SM_Lall:unit = "mm mm-1" ;<br> double <strong>unsatSTW</strong>(time, northing, easting) ;<br> unsatSTW:long_name = "reservoir of unsaturated zone" ;<br> unsatSTW:unit = "mm" ;<br> double <strong>satSTW</strong>(time, northing, easting) ;<br> satSTW:long_name = "water level in groundwater reservoir" ;<br> satSTW:unit = "mm" ;<br> double <strong>aET</strong>(time, northing, easting) ;<br> aET:long_name = "actual Evapotranspiration" ;<br> aET:unit = "mm d-1" ;<br> double <strong>Q</strong>(time, northing, easting) ;<br> Q:long_name = "total runoff generated by every cell" ;<br> Q:unit = "mm d-1" ;<br> double <strong>QD</strong>(time, northing, easting) ;<br> QD:long_name = "direct runoff generated by every cell (runoffSeal)" ;<br> QD:unit = "mm d-1" ;<br> double <strong>QIf</strong>(time, northing, easting) ;<br> QIf:long_name = "fast interflow generated by every cell (fastRunoff)" ;<br> QIf:unit = "mm d-1" ;<br> double <strong>QIs</strong>(time, northing, easting) ;<br> QIs:long_name = "slow interflow generated by every cell (slowRunoff)" ;<br> QIs:unit = "mm d-1" ;<br> double <strong>QB</strong>(time, northing, easting) ;<br> QB:long_name = "baseflow generated by every cell" ;<br> QB:unit = "mm d-1" ;<br> double <strong>recharge</strong>(time, northing, easting) ;<br> recharge:long_name = "groundwater recharge" ;<br> recharge:unit = "mm d-1" ; =============================================================== These data and model runs have been performed under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. This work has received funding from the Initiative and Networking Fund of the Helmholtz Association through the project Advanced Earth System Modelling Capacity (ESM) (www.esm-project.net).
DOI: 10.5281/zenodo.3886550
title: mHM_UFZ gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie

description: This dataset provides gridded model simulations in netcdf format over the Lake Erie using the mHM model (Samaniego, et al., 2010, Kumar et al, 2013), done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). Model code is available under: https://git.ufz.de/mhm/mhm, revision number: 8271b54 Domain boundaries (WGS84 system): lon_min = -86.0, lon_max = -78.0, lat_min = 40.0, lat_max = 45.0 Model variables are simulated at spatial grid of 0.125deg x 0.125deg, daily time step. Simulation period: 01 Jan 2011 - 31 Dec 2014 (with 1year -2010- warm-up) Meteorological input data, see Mai et al. 2020 (in prep) Following model variables are provided for two mHM_parameter.nml realizations (*obj1 and *obj2, as defined in Mai et al. 2020 in prep). double <strong>snowpack</strong>(time, northing, easting) ;<br> snowpack:long_name = "depth of snowpack" ;<br> snowpack:unit = "mm" ;<br> double <strong>SM_Lall</strong>(time, northing, easting) ;<br> SM_Lall:long_name = "average soil moisture over all layers" ;<br> SM_Lall:unit = "mm mm-1" ;<br> double <strong>unsatSTW</strong>(time, northing, easting) ;<br> unsatSTW:long_name = "reservoir of unsaturated zone" ;<br> unsatSTW:unit = "mm" ;<br> double <strong>satSTW</strong>(time, northing, easting) ;<br> satSTW:long_name = "water level in groundwater reservoir" ;<br> satSTW:unit = "mm" ;<br> double <strong>aET</strong>(time, northing, easting) ;<br> aET:long_name = "actual Evapotranspiration" ;<br> aET:unit = "mm d-1" ;<br> double <strong>Q</strong>(time, northing, easting) ;<br> Q:long_name = "total runoff generated by every cell" ;<br> Q:unit = "mm d-1" ;<br> double <strong>QD</strong>(time, northing, easting) ;<br> QD:long_name = "direct runoff generated by every cell (runoffSeal)" ;<br> QD:unit = "mm d-1" ;<br> double <strong>QIf</strong>(time, northing, easting) ;<br> QIf:long_name = "fast interflow generated by every cell (fastRunoff)" ;<br> QIf:unit = "mm d-1" ;<br> double <strong>QIs</strong>(time, northing, easting) ;<br> QIs:long_name = "slow interflow generated by every cell (slowRunoff)" ;<br> QIs:unit = "mm d-1" ;<br> double <strong>QB</strong>(time, northing, easting) ;<br> QB:long_name = "baseflow generated by every cell" ;<br> QB:unit = "mm d-1" ;<br> double <strong>recharge</strong>(time, northing, easting) ;<br> recharge:long_name = "groundwater recharge" ;<br> recharge:unit = "mm d-1" ; =============================================================== These data and model runs have been performed under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. This work has received funding from the Initiative and Networking Fund of the Helmholtz Association through the project Advanced Earth System Modelling Capacity (ESM) (www.esm-project.net).
DOI: 10.5519/qd.fhh1q9zf
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (95 records)
DOI: 10.5281/zenodo.3891809
title: Supplementary Code and Data: Classifying rodent diets for comparative research

description: Data and code for reproducing results and figures. "data" folder contains the MammalDIET and MammalDIET2 datasets as downloaded from their respective repositories. 01_explore-MammalDIET2.R is an R script for reproducing the results and figures in the manuscript. The published version of Figure 1 may differ from the output in appearance, but the underyling visual representation of the data is unchanged. This dataset ("Supplementary Code and Data: Classifying rodent diets for comparative research") is made available under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0.