cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6466009
title: Observations of the breakthrough phase of attachment process in laboratory long sparks

description: The data supports the manuscript entitled "Observations of the breakthrough phase of attachment process in laboratory long sparks". The *.xlsx file contains the applied voltage waveform and discharge current waveform, and can be opened by Excel. The *.rar file contains the high-speed video frames, and can be decompressed and opened. The data can be used freely for scientific purposes with appropriate citation.
DOI: 10.34740/kaggle/dsv/3479297
title: WallStreetBets 2022
DOI: 10.5281/zenodo.6451900
title: Graph Theoretical Measures of Fast Ripple Networks Support the Epileptic Network Hypothesis

description: MongoDB JSON files of the (high-frequency oscillation) HFO and electrode databases used for this study and others.
DOI: 10.5281/zenodo.6451899
title: Graph Theoretical Measures of Fast Ripple Networks Support the Epileptic Network Hypothesis

description: MongoDB JSON files of the (high-frequency oscillation) HFO and electrode databases used for this study and others.
DOI: 10.5281/zenodo.6466446
title: Fast raster-scan optoacoustic mesoscopy enables assessment of human melanoma microvasculature in vivo

description: The folder contains raw optoacoustic imaging data and the reconstruction code. 1. raw data to compare the motion effects; 2. waw data to compare the two ultrasound transducers. 3. the main function of the image reconstruction algorithm.
DOI: 10.5281/zenodo.6466445
title: Fast raster-scan optoacoustic mesoscopy enables assessment of human melanoma microvasculature in vivo

description: The folder contains raw optoacoustic imaging data and the reconstruction code. 1. raw data to compare the motion effects; 2. waw data to compare the two ultrasound transducers. 3. the main function of the image reconstruction algorithm.
DOI: 10.21227/rgd8-2r66
title: Datatset of human gaze, environmental point clounds and RGB images during indoor locomotion

description: This is the data for the paper "Fusion of Human Gaze and Machine Vision for Predicting Intended Locomotion Mode" published on IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022.
DOI: 10.5281/zenodo.6463435
title: An interactive meta-analysis of MRI biomarkers of myelin

description: Dataset provided for NeuroLibre preprint. Author repo: https://github.com/Notebook-Factory/myelin-meta-analysis NeuroLibre fork:https://github.com/roboneurolibre/myelin-meta-analysisFor details, please visit the corresponding NeuroLibre technical screening. <strong>https://neurolibre.org</strong>
DOI: 10.5281/zenodo.6463434
title: An interactive meta-analysis of MRI biomarkers of myelin

description: Dataset provided for NeuroLibre preprint. Author repo: https://github.com/Notebook-Factory/myelin-meta-analysis NeuroLibre fork:https://github.com/roboneurolibre/myelin-meta-analysisFor details, please visit the corresponding NeuroLibre technical screening. <strong>https://neurolibre.org</strong>
DOI: 10.6084/m9.figshare.19579588
title: Oyster_Uptake_Depuration.xlsx

description: Uptake and depuration of microplastic particles (fibers, fragments, and crumb rubber) in eastern oysters (<em>Crassostrea virginica</em>)
DOI: 10.6084/m9.figshare.19579588.v1
title: Oyster_Uptake_Depuration.xlsx

description: Uptake and depuration of microplastic particles (fibers, fragments, and crumb rubber) in eastern oysters (<em>Crassostrea virginica</em>)
DOI: 10.6084/m9.figshare.3761562.v2191
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.6466387
title: English fastText Wikipedia embeddings

description: This repository contains the fastText embeddings, pre-trained on English Wikipedia.
DOI: 10.5281/zenodo.6466386
title: English fastText Wikipedia embeddings

description: This repository contains the fastText embeddings, pre-trained on English Wikipedia.
DOI: 10.25919/zeyg-6p67
title: Parkes observations for project P1153 semester 2022APRS_01

description: The ThunderKAT and MeerTRAP teams at the MeerKAT radio telescope have discovered a slow, pulsating radio source in the image and time domain data. We estimate a best fit period P = 75.88 s and period derivative Pdot = 2.9 x 10^-13 s/s. The source is seen to lie in the P-Pdot pulsar parameter phase space "death valley" where radio emission from neutron stars is not expected according to models in the literature. Single pulse analysis of MeerKAT data in the L- and UHF-bands reveals remarkable and unusual spectro-temporal properties, quite unlike anything seen in known radio neutron stars, with some pulses showing quasi-periodic sub-pulse components. We request 9 hours in the April 2022 semester to utilise the excellent ultra wide-band sensitivity of Parkes to study the pulse shape evolution and spectro-tempo-polarimetric properties of the source, probe the broadband nature and evolution of the sub-pulse components, and investigate any radius-to-frequency mapping, allowing us to characterise and/or distinguish it from other types of known neutron stars.
DOI: 10.48366/r178201
title: Ontologies in internet of Things

description: Study the capabilities of IoT ontologies in representing IoT dependencies between devices.
DOI: 10.25919/yk9z-qk22
title: Parkes observations for project P1154 semester 2022APRS_01

description: Pulsars with high surface magnetic field-strengths (high-B pulsars) provide a rich seam of study for both observers and theorists alike. Studies of their population properties have the potential to answer several fundamental questions about the nature of these objects and how they fit in with the overall pulsar population. These questions include: How do their magnetic fields arise? How is pulsed radio emission produced above the quantum critical field? How do the various populations of high-B pulsars evolve with time? In this proposal we will use the Ultra-Wideband Low (UWL) receiver system on Parkes to complete a Southern-Sky radio census of these high-B pulsars in order to determine their geometric and emission properties with the aim of providing the answers to the questions posed above.
DOI: 10.18112/openneuro.ds004101.v1.0.0
title: Resting-state fMRI data acquired before and after a course of infra-low frequency neurofeedback
DOI: 10.5281/zenodo.6456604
title: Multi-site environmental field recordings for audio event recognition in Upstate NY

description: The first soundscape is located in a lightly wooded suburban area north of Albany, New York, at about 100 meters above sea level. The surrounding vegetation consists of white pines, spruces, oaks, and maples. The acoustic data was originally collected using a Zoom H2n Handy Recorder in 4-channel mode, yet only data from a single microphone capsule is presented.. Recordings were originally made at 16 bits in Waveform Audio File Format (WAV) at a frequency of 44,100 Hz. This dataset was collected from August 2019 to August 2020. <br> The second soundscape is located in a forested area in Lake George, New York, also at about 100 meters above sea level. The surrounding vegetation consists largely of white pines. The microphone array consisted of two H2n Zoom recorders mounted perpendicularly to simulate four-channel recording conditions, yet only data from a single microphone capsule is presented. Recordings were originally made at 256 kbps in MPEG-2 Audio Layer III (MP3) format. This dataset was collected from February 2020 until February 2021. Once all of the audio data was collected, it was segmented into 8 second-long clips, to ensure that both biological and anthropogenic sound sources with longer call lengths were provided with sufficient temporal context, while still achieving reasonable computational efficiency. Each segment was then downsampled to 16 kHz for processing efficiency, allowing each to be represented as a 128,000-sample vector. Sounds ECMK- Eastern chipmunk "chuck" DGBR- dog bark DOWO- Downy woodpecker drum RNFL- rainfall ENGE- engine RCCR- remote control car BUBP- back-up beeper (truck) SREN- siren FFCR- fall field cricket DDCC- dog-day cicada ECMC- Eastern chipmunk "chirp" EGSK- Eastern gray squirrel "kuk" AMRO- American robin AMCR- American crow BLJA- Bluejay NOCA- Northern cardinal BCCH- Black-capped chickadee EAPH- Eastern phoebe BAWW- Black-and-white warbler WBNU- White-breasted nuthatch RBNU- Red-breasted nuthatch **Sounds from site 1 have no specific label tag while sounds from site 2 are specifically labeled with "-site2"
DOI: 10.5281/zenodo.6456603
title: Multi-site environmental field recordings for audio event recognition in Upstate NY

description: The first soundscape is located in a lightly wooded suburban area north of Albany, New York, at about 100 meters above sea level. The surrounding vegetation consists of white pines, spruces, oaks, and maples. The acoustic data was originally collected using a Zoom H2n Handy Recorder in 4-channel mode, yet only data from a single microphone capsule is presented.. Recordings were originally made at 16 bits in Waveform Audio File Format (WAV) at a frequency of 44,100 Hz. This dataset was collected from August 2019 to August 2020. <br> The second soundscape is located in a forested area in Lake George, New York, also at about 100 meters above sea level. The surrounding vegetation consists largely of white pines. The microphone array consisted of two H2n Zoom recorders mounted perpendicularly to simulate four-channel recording conditions, yet only data from a single microphone capsule is presented. Recordings were originally made at 256 kbps in MPEG-2 Audio Layer III (MP3) format. This dataset was collected from February 2020 until February 2021. Once all of the audio data was collected, it was segmented into 8 second-long clips, to ensure that both biological and anthropogenic sound sources with longer call lengths were provided with sufficient temporal context, while still achieving reasonable computational efficiency. Each segment was then downsampled to 16 kHz for processing efficiency, allowing each to be represented as a 128,000-sample vector. Sounds ECMK- Eastern chipmunk "chuck" DGBR- dog bark DOWO- Downy woodpecker drum RNFL- rainfall ENGE- engine RCCR- remote control car BUBP- back-up beeper (truck) SREN- siren FFCR- fall field cricket DDCC- dog-day cicada ECMC- Eastern chipmunk "chirp" EGSK- Eastern gray squirrel "kuk" AMRO- American robin AMCR- American crow BLJA- Bluejay NOCA- Northern cardinal BCCH- Black-capped chickadee EAPH- Eastern phoebe BAWW- Black-and-white warbler WBNU- White-breasted nuthatch RBNU- Red-breasted nuthatch **Sounds from site 1 have no specific label tag while sounds from site 2 are specifically labeled with "-site2"
DOI: 10.22008/fk2/kule1q/i7nehl
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_18.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_18.nc, date: 2023-01-18
DOI: 10.22008/fk2/qkvxc9/tkyuxa
title: SICEv2.3.2_Svalbard_1000m_2023_05_21.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_21.nc, date: 2023-05-21
DOI: 10.58160/46
title: DynaBench: Advection Grid High

description: This package contains the Advection equation simulations for a high number of structured (grid) observation locations. It is part of the DynaBench dataset.
DOI: 10.6084/m9.figshare.24030925
title: Additional file 2 of The circadian clock circuitry modulates leukemia initiating cell activity in T-cell acute lymphoblastic leukemia

description: Additional file 2.
DOI: 10.22008/fk2/kule1q/gezweb
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_19.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_19.nc, date: 2023-01-19
DOI: 10.22008/fk2/qkvxc9/dxermg
title: SICEv2.3.2_Svalbard_1000m_2023_05_22.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_22.nc, date: 2023-05-22
DOI: 10.58160/45
title: DynaBench: Advection Grid Medium

description: This package contains the Advection equation simulations for a medium number of structured (grid) observation locations. It is part of the DynaBench dataset.
DOI: 10.58160/48
title: DynaBench: Burgers Cloud Low

description: This package contains the Burgers' equation simulations for a low number of scattered (non-grid) observation locations. It is part of the DynaBench dataset.
DOI: 10.22008/fk2/kule1q/5jteq0
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_21.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_21.nc, date: 2023-01-21
DOI: 10.22008/fk2/kule1q/etrlif
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_20.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_20.nc, date: 2023-01-20
DOI: 10.22008/fk2/qkvxc9/7qlv1w
title: SICEv2.3.2_Svalbard_1000m_2023_05_23.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_23.nc, date: 2023-05-23
DOI: 10.58160/53
title: DynaBench: Burgers Grid High

description: This package contains the Burgers' equation simulations for a high number of structured (grid) observation locations. It is part of the DynaBench dataset.
DOI: 10.58160/55
title: DynaBench: Gas-Dynamics Cloud Low

description: This package contains the Gas-Dynamics equation simulations for a low number of scattered (non-grid) observation locations. It is part of the DynaBench dataset.
DOI: 10.22008/fk2/kule1q/hihmcq
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_22.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_22.nc, date: 2023-01-22
DOI: 10.22008/fk2/qkvxc9/hgzzgf
title: SICEv2.3.2_Svalbard_1000m_2023_05_25.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_25.nc, date: 2023-05-25
DOI: 10.22008/fk2/qkvxc9/zfq25l
title: SICEv2.3.2_Svalbard_1000m_2023_05_24.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_24.nc, date: 2023-05-24
DOI: 10.22008/fk2/kule1q/wh0gey
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_23.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_23.nc, date: 2023-01-23
DOI: 10.22008/fk2/qkvxc9/2k2vna
title: SICEv2.3.2_Svalbard_1000m_2023_05_26.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_26.nc, date: 2023-05-26
DOI: 10.58160/63
title: DynaBench: Kuramoto-Sivashinsky Cloud Medium

description: This package contains the Kuramoto-Sivashinsky equation simulations for a medium number of scattered (non-grid) observation locations. It is part of the DynaBench dataset.
DOI: 10.22008/fk2/kule1q/nqo35i
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_24.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_24.nc, date: 2023-01-24
DOI: 10.22008/fk2/qkvxc9/ovjazm
title: SICEv2.3.2_Svalbard_1000m_2023_05_27.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_27.nc, date: 2023-05-27
DOI: 10.22008/fk2/kule1q/t8t5pv
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_25.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_25.nc, date: 2023-01-25
DOI: 10.22008/fk2/qkvxc9/tjdxtt
title: SICEv2.3.2_Svalbard_1000m_2023_05_28.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_28.nc, date: 2023-05-28
DOI: 10.22008/fk2/kule1q/gjomte
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_26.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_26.nc, date: 2023-01-26
DOI: 10.22008/fk2/qkvxc9/dhv7yv
title: SICEv2.3.2_Svalbard_1000m_2023_05_29.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_29.nc, date: 2023-05-29
DOI: 10.58160/73
title: DynaBench: Reaction-Diffusion Grid Medium

description: This package contains the Reaction-Diffusion equation simulations for a medium number of structured (grid) observation locations. It is part of the DynaBench dataset.
DOI: 10.58160/74
title: DynaBench: Reaction-Diffusion Grid High

description: This package contains the Reaction-Diffusion equation simulations for a high number of structured (grid) observation locations. It is part of the DynaBench dataset.
DOI: 10.6084/m9.figshare.23962021.v1
title: Additional file 4 of Reliability and measurement error of sensorimotor tests in patients with neck pain: a systematic review

description: Additional file 4. GRADE Downgrading Reliability. Reasons for downgrading
DOI: 10.6084/m9.figshare.24466951.v1
title: Supplementary Material for: Expression analyses of C-terminal-binding protein 1 (CtBP1) during mouse brain development

description: Introduction: CtBP1 (C-terminal-binding protein 1) is a multi-functional protein with well-established roles as a transcriptional co-repressor in the nucleus and a regulator of membrane fission in the cytoplasm. Although CtBP1 gene abnormalities have been reported to cause neurodevelopmental disorders, the physiological role and expression profile of CtBP1 remains to be elucidated. Methods: In this study, we used biochemical, immunohistochemical and immunofluorescence methods to analyze the expression of CtBP1 during mouse brain development. Results: Western blotting analyses revealed that CtBP1 appeared to be expressed mainly in the central nervous system throughout the developmental process. In immunohistochemical analyses, region-specific nuclear as well as weak cytoplasmic distribution of CtBP1 was observed in telencephalon at embryonic day (E)15 and E17. It is of note that CtBP1 was barely detected in axons, but observed in the nucleus of oligodendrocytes in the white matter at E17. As to cerebellum at postnatal day 30, CtBP1 appeared to be expressed in the nucleus and cytoplasm of Purkinje cells, the nucleus of granule cells and cells in the molecular layer (ML), and the ML per se where granule cell axons and Purkinje cell dendrites are enriched. In addition, CtBP1 was detected in the cerebellar nuclei. Conclusion: The obtained results suggest involvement of CtBP1 in brain function.
DOI: 10.22008/fk2/kule1q/zhavio
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_27.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_27.nc, date: 2023-01-27
DOI: 10.22008/fk2/qkvxc9/zwkpat
title: SICEv2.3.2_Svalbard_1000m_2023_05_30.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_30.nc, date: 2023-05-30
DOI: 10.58160/40
title: DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data

description: This is the meta-repository for the DynaBench dataset - a benchmark dataset for learning physical systems from low-resolution, non-grid data. The benchmark contains simulations of several physical systems (advection, burgers', gas dynamics, kuramoto-sivashinsky, reaction-diffusion, wave). Each system has been simulated several times using a high-resolution numerical solver, from which the observations have been sampled at different resolutions (low, medium, high) as well as two different spatial structures (grid, scattered).
description: The dataset is split into 42 parts (6 equations x 7 combinations of resolution/structure). Each part can be downloaded separately and contains 7000 simulations of the given equation at the given resolution and structure. The simulations are grouped into chunks of 500 simulations saved in the hdf5 file format. Each chunk contains the variable "data", where the values of the simulated system are stored, as well as the variable "points", where the coordinates at which the system has been observed are stored. For more details visit the DynabBench website at https://professor-x.de/dynabench/. The dataset is best used as part of the dynabench python package available at https://pypi.org/project/dynabench/.
description: Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available. The benchmark is available at https://professor-x.de/dynabench.
DOI: 10.58160/79
title: DynaBench: Wave Grid Low

description: This package contains the Wave equation simulations for a low number of structured (grid) observation locations. It is part of the DynaBench dataset.
DOI: 10.22008/fk2/kule1q/xz7rm0
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_28.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_28.nc, date: 2023-01-28
DOI: 10.22008/fk2/qkvxc9/p3p2f7
title: SICEv2.3.2_Svalbard_1000m_2023_05_31.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_05_31.nc, date: 2023-05-31
DOI: 10.22008/fk2/kule1q/b9dcja
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_29.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_29.nc, date: 2023-01-29
DOI: 10.22008/fk2/qkvxc9/p2jj6d
title: SICEv2.3.2_Svalbard_1000m_2023_06_01.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_06_01.nc, date: 2023-06-01
DOI: 10.6084/m9.figshare.24186142.v1
title: Additional file 3 of Depletion of LONP2 unmasks differential requirements for peroxisomal function between cell types and in cholesterol metabolism

description: Additional file 3. This file contains 3 sheets. The first sheet "corrected abundance" presents the AUC (Area Under the Curve) values of the intensities of the corrected signals for all present compounds in the analyzed samples, obtained following our "data processing" (For more details, seeForest et al. J.Proteome Res. 2018, 17, 3657-3670). The second sheet is "statictical analysis". The third sheet "statistics + abundance" present in one glance the data from the first 2 sheets that are combined.
DOI: 10.22008/fk2/kule1q/5awu8r
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_30.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_30.nc, date: 2023-01-30
DOI: 10.22008/fk2/qkvxc9/rvqbjk
title: SICEv2.3.2_Svalbard_1000m_2023_06_02.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_06_02.nc, date: 2023-06-02
DOI: 10.22008/fk2/qkvxc9/smaawp
title: SICEv2.3.2_Svalbard_1000m_2023_06_03.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_06_03.nc, date: 2023-06-03
DOI: 10.6084/m9.figshare.24165118
title: Additional file 2 of The major TMEM106B dementia risk allele affects TMEM106B protein levels, fibril formation, and myelin lipid homeostasis in the ageing human hippocampus

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.24165118.v1
title: Additional file 2 of The major TMEM106B dementia risk allele affects TMEM106B protein levels, fibril formation, and myelin lipid homeostasis in the ageing human hippocampus

description: Supplementary Material 2
DOI: 10.22008/fk2/kule1q/kwegsy
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_31.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_01_31.nc, date: 2023-01-31
DOI: 10.22008/fk2/qkvxc9/kkdbcs
title: SICEv2.3.2_Svalbard_1000m_2023_06_04.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_06_04.nc, date: 2023-06-04
DOI: 10.22008/fk2/kule1q/zwdayd
title: SICEv2.3.2_SouthernArcticCanada_1000m_2023_02_01.nc

description: SICEv2.3.2 output: SICEv2.3.2_SouthernArcticCanada_1000m_2023_02_01.nc, date: 2023-02-01
DOI: 10.22008/fk2/qkvxc9/ab2wdp
title: SICEv2.3.2_Svalbard_1000m_2023_06_05.nc

description: SICEv2.3.2 output: SICEv2.3.2_Svalbard_1000m_2023_06_05.nc, date: 2023-06-05