cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7348972
title: Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.

description: For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised Geographic Object-Based Image Analysis (GeOBIA) paired with the Fuzzy C-Means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985-2019 time-period, each spanning five years. Our study site is the George River watershed (GRW), a 42,000 km<sup>2</sup> watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015-2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985-2014 to investigate past changes in hydrological regime. The seven-classification time series showed a homogenization of subwatershed types associated to increases in vegetation productivity and in water content<br> in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evapotranspiration and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km<sup>3</sup>y<sup>−1</sup>) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote sensing based approaches, particularly in areas of discontinuous and sporadic permafrost.
DOI: 10.17864/1947.111
title: Reconstructions of the radiation fluxes at top of atmosphere and net surface energy flux in the period 1985-2015 from DEEP-C project

description: In order to study which mechanisms explain the reduced global surface warming rate since around 2000, and where the excess energy is due to rising greenhouse gas, the radiation fluxes (OLR, ASR and NET) at the top of atmosphere (TOA) prior to the CERES period are reconstructed using satellite observations, atmospheric reanalysis (ERA-Interim) and AMIP5 model simulations. The new approach using the mass-corrected atmospheric energy divergencies (transports) from ERA-Interim is employed to estimate the net surface energy fluxes.
description: See publication references.
description: All files are in netcdf format. The downward net fluxes are defined as positive. The TOA radiation fluxes are from Jan 1985 to Dec 2015. The surface energy flux is the net energy flux from Jan 1985 to Dec 2015. Geodetic weighting can be found at https://ceres.larc.nasa.gov/science_information.php?page=GeodeticWeights. Fmass_v03.0_R.nc and Fmass_v03.0_R_GEOD.nc: the excess/deficit land surface fluxes are redistributed to the oceans zonally (using normal distribution weight in latitude direction). Fmass_v03.0_R_intermediate.nc: the excess/deficit land surface fluxes are redistributed to the hemispheric oceans evenly.
DOI: 10.17871/try.59
title: Global trait maps at 3km resolution
DOI: 10.25900/g76f-0h45
title: OCTOPUS Database v.2: The CRN Denudation Global collection
DOI: 10.25900/ypr0-j711
title: OCTOPUS Database v.2: The SahulArch OSL collection
DOI: 10.5067/atlas/atl06.002
title: ATLAS/ICESat-2 L3A Land Ice Height, Version 2
DOI: 10.5067/aura/tes/tl2panns_l2.007
title: TES/Aura L2 Peroxyacyl Nitrate Nadir Special Observation - Version 7
DOI: 10.5067/byiiuv3pr9l6
title: TROPESS CrIS-JPSS1 L2 Carbon Monoxide for Forward Processing, Standard Product V1
DOI: 10.5067/caliop/calipso/lid_l2_vfm-standard-v4-20
title: CALIPSO Lidar Level 2 Vertical Feature Mask Data V4-20
DOI: 10.5067/ea7g2ttv84rv
title: TROPESS CrIS-SNPP L2 Carbon Monoxide for Forward Stream, Standard Product V1
DOI: 10.5067/gpm/imerg/3b-hh/05
title: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V05
DOI: 10.5067/orbview-2/seawifs/l3m/chl/2018
title: SEAWIFS-ORBVIEW-2 Level 3 Mapped Chlorophyll Data Version R2018.0
DOI: 10.5067/orbview-2/seawifs/l3m/kd/2018
title: SEAWIFS-ORBVIEW-2 Level 3 Mapped Downwelling Diffuse Attenuation Coefficient Data Version R2018.0
DOI: 10.5067/trmm/tmpa/3h/7
title: TRMM (TMPA) Rainfall Estimate L3 3 hour 0.25 degree x 0.25 degree V7
DOI: 10.5067/w0w6l8m6j85x
title: TROPESS CrIS-JPSS1 L2 Peroxyacetyl for Forward Processing, Standard Product V1
DOI: 10.5281/zenodo.13897556
title: Datasets used in van den Akker et al (2024) 'Present day mass loss rates are a precursor precursor for West Antarctic Ice Sheet Collapse

description: This repository contains the default initialization and the continuation runs shown in the paper.
DOI: 10.5281/zenodo.3961473
title: aerosol large particles size distribution collected at Gruvebadet, NY ALESUND, SVALBARD

description: At Gruvebadet aerosol size distribution are measured with a scanning mobility particle sizer spectrometer TSI SMSP 3034, in the range 10-487 nm), and with a Aerodynamic particle sizer TSI APS 3321 i the range 500-20000 nm) since 2010. Data are collected with a frequency of 10 minutes. SMPS 3034 collect data in 54 size channels centred at Dsmps=(micron) (54 channels) 0.0104 0.0111 0.012 0.0129 0.0138 0.0149 0.0160 0.0172 0.0184 0.0198 0.0213 0.0229 0.0246 0.0264 0.0284 0.0305 0.0328 0.0352 0.0379 0.0407 0.0437 0.047 0.0505 0.0542 0.0583 0.0626 0.0673 0.0723 0.0777 0.0835 0.0898 0.0965 0.1037 0.1114 0.1197 0.1286 0.1382 0.1486 0.1596 0.1715 0.1843 0.1981 0.2129 0.2288 0.2458 0.2642 0.2839 0.3051 0.3278 0.3523 0.3786 0.4068 0.4371 0.470 For each channel of SMPS and APS the quantity DN/DlogD is provided Data are stored in monthly files, separately for SMPS, that contains columns of collected measurements in the following order: <strong>column 1</strong> Date time (YYYY-MM-DD HH:MM:SS) in UTC <strong>column 2</strong> DN/DlogD 1st channel <strong>......</strong> <strong>column XX </strong>DN/DlogD last channel where XX is 55 for SMPS 3034 (fine particles) 52 for APS 3321 (coarse particles) We have holes in the data due to instrument failures. In particular we have few months without measurements (empty files). For APS they are: 01/2016, 02/2016, 06-12/2016, 01/2017, 11/2017, 12/2017, 01/2018, 11/2018, 12/2018 .
DOI: 10.5281/zenodo.4320794
title: Data archive for paper "Copula-based synthetic data augmentation for machine-learning emulators"

description: <strong>Overview</strong> This is the data archive for paper "Copula-based synthetic data augmentation for machine-learning emulators". It contains the paper’s data archive with model outputs (see <code>results</code> folder) and the Singularity image for (optionally) re-running experiments. For the Python tool used to generate synthetic data, please refer to Synthia. <strong>Requirements</strong> Singularity &gt;= 3 Portable Batch System (PBS) job scheduler* Today's high-performance computer (e.g. ~ 32 CPUs @ 2 500 MHz with 64 GB of RAM ) *Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=72:00:00</code>). <strong>Usage</strong> To reproduce the results from the experiments described in the paper, first fit all copula models to the reduced NWP-SAF dataset with: <pre><code>qsub hpc/fit.sh</code></pre> then, to generate synthetic data, run all machine learning model configurations, and compute the relevant statistics use: <pre><code>qsub hpc/stats.sh qsub hpc/ml_control.sh qsub hpc/ml_synth.sh</code></pre> Finally, to plot all artifacts included in the paper use: <pre><code>qsub hpc/plot.sh</code></pre> <strong>Licence</strong> Code released under MIT license. Data from the reduced NWP-SAF dataset released under CC BY 4.0.
DOI: 10.5281/zenodo.4434259
title: COSIPY-WRF Daily SMB output 2014-2018

description: Daily average values of
DOI: 10.5281/zenodo.4785390
title: FLEXTRA back-trajectories for Pha Din, Viet Nam, atmospheric observatory

description: FLEXTRA back-trajectories for Pha Din, Viet Nam, atmospheric observatory FLEXTRA is an atmospheric trajectory model available from: https://www.flexpart.eu/wiki/FtAbout The period covered is March and April 2015.<br> A new trajectory was initialised every 4 hours and at different altitudes above model ground (80 m, 420 m, 548 m, 830 m, 1670 m, 2920 m) and at an absolute height above sea level (5500 m).<br> Each back-trajectory was integrated for 10 days backward in time.<br> FLEXTRA was driven with operational analysis/forecast data from the ECMWF IFS operational chain. Input data were available 3-hourly with a horizontal resolution of 1°x1°. Trajectory files (*.gz) are organised by month and for different initial altitudes above ground.<br> The file format is compressed ASCII with data for each trajectory being given in tabulated sections.<br> A sample R code (load.flextra.file.R) for reading the ASCII files is included for reference. For further questions please contact: stephan.henne@empa.ch
DOI: 10.5281/zenodo.5113896
title: Dataset: Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window

description: Uploaded igor pxp files are the data to generate all figures of the results from our publication in Atmospheric Chemistry and Physics with the name of <em>"Time dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling PMF window"</em> by Chen et al. (2021). This study deployed a novel and advanced source apportionment technique on a dataset measured in Magadino. Rolling PMF allows retrieving more realistic, time-dependent and detailed information of the organic aerosol sources. This work highlights the strength of the rolling PMF mechanism by comparing it with the results derived from conventional seasonal PMF. Overall, this comprehensive interpretation of chemical speciation monitor (ACSM) data could be a role model for similar analyses.
DOI: 10.5281/zenodo.6004062
title: ACCESS-AM2 Southern Ocean cloud and radiation data for k-means clustering and analysis

description: The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) data and k-means analysis used for the study described in Fiddes et al. 2022 '<em>Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?' .</em> Included files: modis_cluster_centres_2015-2019.nc - kmeans derived cluster centres for MODIS modis_cluster_labels_2015-2019.nc - kmeans derived cluster labels for MODIS bx400_cluster_labels_2015-2019.nc - kmeans fitted cluster label for model COSP_vars_bx400_2015-2019.nc - model data for analysis The code that performs the analysis/generates this data and has instructions for where to download MODIS data can be found here: https://github.com/sfiddes/code_for_publications_2022/tree/main/ACCESS_cloud_radiation_eval
description: This project was funded by the Australian Government as part of the Antarctic Science Collaboration Initiative program, under the Australian Antarctic Program Partnership, ASCI000002.
DOI: 10.5281/zenodo.6461323
title: Data supporting "Dispersal of bacteria and stimulation of permafrost decomposition by Collembola"

description: This archive contains the processed sequencing data, processed CO<sub>2</sub> flux data and the script used to generate figures and tables presented in "Dispersal of bacteria and stimulation of permafrost decomposition by Collembola" by Monteux S., Mariën J., and Krab E.J., Biogeosciences Discussion 2022 manuscript number BG-2022-98. A more exhaustive repository including bioinformatics processing is found at https://git.bolin.su.se/smonteux/Collembola_vector and the raw DNA data is deposited at ENA under accession number PRJEB51992
DOI: 10.5281/zenodo.6469247
title: Land use and land cover changes in the contiguous United States from 1630 to 2020

description: Through integrating multi-source data including high-resolution remote sensing image-based land use and land cover (LULC) data, model-based land use products, and historical land archives, we reconstructed historical LULC at an annual time scale and 1 km x 1 km resolution in the contiguous United States (CONUS) from 1630 to 2020. Compared to other historical LULC datasets, our data can capture the major characters of LULC as well as provide more accurate information with higher spatial and temporal resolution. The LULC data can be used for regional studies in a wide range of topics including LULC impacts on the ecosystem, biodiversity, water resource, carbon and nitrogen cycles, and greenhouse gas emissions.
DOI: 10.5281/zenodo.6628968
title: Doppler lidar wind profiles from Kumpula

description: This data set contains Doppler lidar wind profiles calculated from VAD (Velocity-Azimuth Display) scans by a Halo Photonics Streamline Doppler lidar between 10 April 2018 and 30 September 2020 at Kumpula, Finland (60.333 N, 25.6 E, 45 m.a.s.l.). A more detailed description of the instrument specification and operating parameters is given in Hirsikko et al. (2014).
DOI: 10.5281/zenodo.6802657
title: Datasets for "Evaluating the performance of a Picarro G2207-i analyser for high-precision atmospheric O2 measurements"

description: These data files contain the data used in the manuscript "Evaluating the performance of a picarro G2207-i analyser for high-precision atmospheric O2 measurements" submitted to Atmospheric Measurement Techniques. WAO_G2207i_O2_calibrated_AM_all : Calibrated O2 measurements from the G2207-i during the no-drying, partial-drying, and full-drying periods at WAO, for both the water-corrected and non-water corrected outputs CRAM_lab_run1_noRT : calibrated O2 measurements for the first run of cylinder gases in the CRAM lab, UEA, without reference tank correction CRAM_lab_run1_wRT : calibrated O2 measurements for the first run of cylinder gases in the CRAM lab, UEA, with reference tank correction applied CRAM_lab_run2_noRT : calibrated O2 measurements for the second run of cylinder gases in the CRAM lab, UEA, without reference tank correction CRAM_lab_run2_wRT : calibrated O2 measurements for the second run of cylinder gases in the CRAM lab, UEA, with reference tank correction applied
DOI: 10.5281/zenodo.7002511
title: Dataset: High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes

description: Dataset used in the article "High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes" The tab-delimited file contains direct surface-atmosphere Volatile Organic Compound fluxes, measured by Eddy Covariance with a Vocus- proton transfer reaction mass spectrometer (Vocus-PTR) at a subarctic fen during 2021. It also contains PAR (Photosynthetic Active Radiation), temperature and flux quality criteria.
DOI: 10.5281/zenodo.7277523
title: Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework"

description: <em>Amonthly_files.tar.gz</em> contains the gridded monthly averaged quantities used in the manuscript Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" for each year between 2000 and 2018. Files containing "simba" in their name contain quantities related to the sea ice mass balance (volume of melt/growth...) Files containing "icemod" in their name contain other quantities related to sea ice properties (thickness, concentration...) In case information is missing, do not hesitate to contact guillaume.boutin@nersc.no , heather.regan@nersc.no or einar.olason@nersc.no This research has been funded by the Norwegian Research Council (Nansen Legacy: grant no. 27673, FRASIL: grant no. 263044, and ARIA: grant no. 302934), JPI Climate and JPI Oceans (MEDLEY project, under agreement with the Norwegian Research Council, grant no 316730), and by Copernicus Marine Environment Monitoring Service (CMEMS) WIzARd project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union<br> Copernicus Marine Environment Monitoring Services (contract no.<br> 69), and the European Space Agency through the Cryosphere Virtual Laboratory (CVL, grant no. 4000128808/19/I-NS).
description: Nomenclature nextsim: sit: sea ice thickness (cell average) [m] sic: sea ice concentration [-] sic_thin: sea ice concentration of newly formed thin ice (young ice) [m] sit_thin: sea ice thickness of newly formed thin ice (young ice, cell average) [m] siu: u-component ("grid-wise") of the sea ice drift velocity siv: v-component ("grid-wise") of the sea ice drift velocity Mass balance component: mlt_bot: sea ice volume (per cell area) melted at the sea ice bottom per day [m/day] mlt_top: sea ice volume (per cell area) melted at the surface of the sea ice per day [m/day] vice_melt: sea ice volume (per cell area) formed(+)/melted(-) per day [m/day] newice: sea ice volume (per cell area) formed in open water (=supercooling) per day [m/day] del_vi: sea ice volume (per cell area) change of old/thick (i.e not young) ice per day [m/day] del_vi_thin: sea ice volume (per cell area) change of thin newly formed (=young) ice per day [m/day] newice: sea ice volume (per cell area) formed by flooding of snow [m/day] total_growth: sea ice volume (per cell area) formed per day [m/day] basal_growth: sea ice volume (per cell area) formed at the bottom per day [m/day] basal_growth_thin: sea ice volume (per cell area) formed at the bottom of thin/newly formed (=young) ice per day [m/day]
description: {"references": ["Boutin, G., \u00d3lason, E. \u00d6., Rampal, P., Regan, H., Lique, C., Talandier, C., Brodeau, L., and Ricker, R.: Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-142, in review, 2022."]}
DOI: 10.5281/zenodo.7324739
title: Model codes and data for ``A control volume finite element model for predicting the morphology of cohesive-frictional debris flow deposits"

description: The code and the dataset can be read/run by using Matlab. The description is as follows: 1. Dataset (field_data) includes transect data of three field debris flow deposits from Coussot et al. (1996). The data can be read and calibrated with the analytical solution by the code field_calibration.m. 2. Dataset (data_T01-T04, T11-T15_DT) includes experimental fan topography data, calibrated parameters, and simulation outputs. 3. Two calibration codes are used for the model parameter calibrations of the two sets of experiments. 4. Function aggradation_DT.m is the CVFEM model for simulating fan morphology. Use CVFEM_exp_simulation.m code to run simulations for the experiments.
DOI: 10.5281/zenodo.7372479
title: Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) in China Seas (2003-2019)

description: This dataset is sea surface partial pressure of CO2 (pCO2) in China seas (0-42°N, 105-132°E) over 2003-2019 with a spatial resolution of 1km and temporal resolution of a month. This is our second version of pCO2 in China seas. The first version was published on the SatCO2 website (http://www.satco2.com/index.php?m=content&c=index&a=show&catid=317&id=188). We produce this dataset by creating a boost machine learning algorithm (XGBoost) based on the gradient boost decision tree (GBDT). The input parameters used in this are sea surface temperature (SST), chlorophyll-a concentration (Chl-a), remote sensing reflectance of three bands (Rrs412, 443, 488 nm), the temperature difference in longitude direction (SST_DIF), and the theoretical background <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>_therm) under corresponding SST. SST_DIF is derived from SST by subtracting the mean value at the same latitude. To calculate <em>p</em>CO<sub>2</sub>_therm, we first assume that the seawater background <em>p</em>CO<sub>2</sub> equals the annual average atmospheric <em>p</em>CO<sub>2</sub> (sea-air <em>p</em>CO<sub>2</sub> balanced under ideal conditions), and the equilibrium temperature is assumed to be the yearly average SST; Then, the background <em>p</em>CO<sub>2</sub> is corrected according to SST to obtain <em>p</em>CO<sub>2</sub>_therm. Air pressure at sea level (SLP) and mole fraction of CO<sub>2</sub> in the air (xCO<sub>2</sub>) are employed when calculating atmospheric <em>p</em>CO<sub>2</sub>. The underway <em>p</em>CO<sub>2</sub> is first gridded to monthly 1 km and then divided into the training and validation sets with the volumes 151009 and 32584 samples. The validation set shows coefficients of determination (R<sup>2</sup>) between the XGBoost model-predicted and in-situ <em>p</em>CO<sub>2</sub> were 0.86, and the root means squared errors (RMSE) for the <em>p</em>CO<sub>2</sub> were 21.1 μatm.
DOI: 10.5281/zenodo.7434851
title: colab_zirc_dims: full results, datasets, and replication code repository

description: Repository for results, full datasets (training and grain measurement), and replication code for the colab_zirc_dims manuscript, with additional copy of the "czd large" training dataset. Updated for manuscript resubmission.
DOI: 10.5281/zenodo.7559671
title: Rainfall data monitored by acoustic sensors in Zurich and Milan during spring and summer 2022

description: The database contains rainfall information obtained from acoustic sensors and rain gauges (meteoblue AG) in the cities of Zurich (Switzerland) and Milan (Italy) during field work conducted in spring and summer 2022. Zurich: Continuous rainfall data is provided at 15 min intervals for April 2022; data_acoustic_Zurich.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm). Milan: Data is provided for 5 rain events in June 2022 at 1 min intervals; data_acoustic_Milan.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm). The locations of the acoustic sensors and rain gauges can be find in the metadata files: Metadata_acoustic.xlsx and Metadata_meteoblue.xlsx The presented-data passed only a primilinary quality control. Further infromation about the senor networks in Milan and Zurich can be found here: https://doi.org/10.5194/nhess-2022-257
description: {"references": ["https://doi.org/10.5194/nhess-2022-257"]}
DOI: 10.5281/zenodo.7715651
title: Hourly dataset of downward longwave radiation at CBSRN

description: This dataset including the hourly downward longwave radiation as well as meteorological elements (e.g., temperature, relative humidity, cloud amount, etc.) observed at seven stations of the China Baseline Surface Radiation Network.
DOI: 10.5281/zenodo.8059335
title: Dataset for Measurement report: Ion clusters as indicator for local new particle formation

description: Data for Measurement report: Ion clusters as indicator for local new particle formation. There are two files, negative_ion_concentrations.csv and positive_ion_concentrations.csv. The former (latter) includes absolute number concentrations for 1.87, 2.16, 2.49, and 2.88 nm negative (positive) ions. The unit for these concentrations is #/cm<sup>-3</sup>. Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.