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Processed data for the manuscript: Promoting Multi-Task Learning as a General Approach for Deep-Learning-based Hydrological Models

<div> <div>Below is a brief overview of the processed data in this repository:</div> <div>- camels_streamflow: This directory contains streamflow data for CAMELS basins covering the period from January 1, 2015, to December 31, 2021. We have not included the original CAMELS dataset, which contains attributes, meteorological forcing, and streamflow data from January 1, 1980, to December 31, 2014, as it can be easily downloaded from the CAMELS website (https://gdex.ucar.edu/dataset/camels.html) and is too large for us to upload to Zenodo.</div> <div>- modiset4camels: This directory includes multiple versions of basin-mean Evapotranspiration (ET) data retrieved from the MOD16A2 data product. The dataset spans from January 1, 2001, to December 31, 2021, with an 8-day temporal resolution.</div> <div>- nldas4camels: This directory contains basin-mean daily meteorological forcing data from the NLDAS-2 dataset, obtained via Google Earth Engine (GEE). The dataset covers the period from January 1, 2001, to December 31, 2021.</div> <div>- smap4camels: This directory features basin-mean Soil Moisture (SSM) data from the NASA-USDA Enhanced SMAP Global Soil Moisture dataset, covering the period from April 2, 2015, to October 3, 2021. The dataset provides SSM measurements at a 5 cm depth. Additionally, we provide basin-mean daily SMAP L4 data spanning from April 1, 2015, to December 31, 2023.</div> </div>

Data and Scripts used in the Manuscript by Dalponti et. al. GEB

Data and scripts used in the manuscript by Dalponti et al. GEB 2024

Raw data - manuscript - Impact of methyl jasmonate on leaf consumption and development of Tuta absoluta (Lepidoptera: Gelechiidae) on tomato plants

Raw data - manuscript - Impact of methyl jasmonate on leaf consumption and development of Tuta absoluta (Lepidoptera: Gelechiidae) on tomato plants

opencc-by-4.0Aug 2023View details →

The dataset for the manuscript entitled "SUPHRE: A reactive transport model with unsaturated and density-dependent flow"

This is the dataset for the manuscript entitled "SUPHRE: A reactive transport model with unsaturated and density-dependent flow".

Datasets for the manuscript: "Metabolic disruption of zebrafish (Danio rerio) embryos by bisphenol A. An integrated metabolomic and transcriptomic approach"

<h1>Metabolomics datasets for the manuscript: Metabolic disruption of zebrafish (Danio rerio) embryos by bisphenol A. An integrated metabolomic and transcriptomic approach</h1> <h2>Instrumental conditions</h2> LC-MS analyses were carried out using an Agilent Infinity 1200 series LC system coupled with an orthogonal G1385-44300 interface (Agilent Technologies, Waldbronn, Germany) to a 6220 oa-TOF LC/MS mass spectrometer (Agilent Technologies). LC control and separation data acquisition were performed using ChemStation software (Agilent Technologies) that was running in combination with the MassHunter workstation software (Agilent Technologies) for control and data acquisition of the TOF mass spectrometer. For the chromatographic separations, an HILIC TSK Gel Amide-80 column (250 mm length, 2.1 mm inner diameter and 5 μm particle size, Tosoh Bioscience, Tokyo, Japan) was used at 25 °C with gradient elution at a flow rate of 0.15 mL·min−1. Elution gradient was performed using solvent A (acetonitrile) and solvent B (5 mM of ammonium acetate adjusted to pH 5.5 with acetic acid) as follows: 0–8 min, linear gradient from 25 to 30% B; 8–12 min, from 30 to 60% B; 12–17 min, 60% B; 17–20 min, back linearly from 60% to 25% B; and from 20 to 27 min, 25% B. Solvents were degassed for 15 min by sonication before use. Sample injection was performed with an autosampler at 4 °C, and the injection volume was 5 μL. All samples (six replicates per treatment: control, 4.4 μM BPA, 8.8 μM BPA and 17.5 μM BPA) were randomly injected. Several blank samples and calibration standards were also randomly injected to further assess the stability of the instrument among runs. The TOF mass spectrometer operated both in positive and negative mode using the following parameters: capillary voltage 4000 V, drying gas temperature 350 °C, drying gas flow rate 8 L·min−1, nebulizer gas 32 psi, fragmentor voltage 150 V, skimmer voltage 65 V and OCT 1 RF Vpp voltage 300 V. Data were collected in profile mode at 1 spectrum/s (approximately 10 000 transients/spectrum) with an m/z range of 85–1000 working in the extended dynamic range mode (2 GHz) with the mass range set to standard. <h2>List of files</h2> <h3>Negative ionization</h3> <ul> <li>Control x 12 samples - 6 x 2 replicates</li> <li>BPA 1 ppm x 12 samples - 6 x 2 replicates</li> <li>BPA 2 ppm x 12 samples - 6 x 2 replicates</li> <li>BPA 4 ppm x 12 samples - 6 x 2 replicates</li> </ul> <h3>Positive ionization</h3> <ul> <li>Control x 12 samples - 6 x 2 replicates</li> <li>BPA 1 ppm x 12 samples - 6 x 2 replicates</li> <li>BPA 2 ppm x 12 samples - 6 x 2 replicates</li> <li>BPA 4 ppm x 12 samples - 6 x 2 replicates</li> </ul>

Image for manuscript

All images form Material and Methods in "Considering the asymmetrical configuration of a landslide greatly shifts slope stability assessment"

GS2 gyrokinetic simulation data for manuscript "Turbulent thermal equilibration of collisionless magnetospheric plasmas"

A nonlinear simulation dataset produced by the gyrokinetic simulation code GS2 for the entropy mode driven turbulence in the Z pinch configuration. It includes <ul> <li>Raw simulation data containing the electrostatic potential and density profiles, time series of the particle fluxes and energy-exchange between species, and snapshots of distribution functions.</li> <li>Figures of distribution functions, potential and density profiles.</li> <li>Some Python scripts to analyze the data.</li> </ul>

Data for manuscript : Effect of spatial training on space-number mapping: A situated cognition account

From an embodied perspective of cognition, sensorimotor mechanisms play a crucial role in abstract processing, such as the understanding of Arabic numerals. For instance, spatial cognition can influence number processing. These spatial–numerical associations (SNAs) have been thoroughly investigated since the pioneering spatial–numerical association of response codes (SNARC) effect, which demonstrates faster left/right responses to small/large numbers, respectively. While there has been no systematic assessment of SNAs on other planes in three-dimensional space in the literature, recent primary evidence has revealed that SNAs along the transverse and sagittal planes are mutually exclusive with respect to the required spatial reference frames used by the participant. Specifically, under egocentric spatial reference frames, SNAs have been observed only along the sagittal plane, whereas under allocentric reference frames, the reverse pattern has been observed, with SNAs present exclusively along the transverse plane of the body. Given this empirical evidence, we have hypothesized that the subject's ability to switch spatial reference frames to match that of another person could significantly influence the occurrence of SNAs according to the processed plane. Therefore, this study has two aims. The first is to replicate previous seminal findings. The second is to investigate how referential frame switching (RFS) training can affect this organization. While the results of the two experiments reveal a general replication, more importantly, we find that RFS training enables the development of new situated cognition strategies from egocentric perspectives and the generalization of transverse SNAs to other spatial planes from allocentric perspectives.

In situ observations used in GMD Manuscript "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"

Observations taken by Mika Aurela, Tarek S. El-Madany, Marika Honkanen, Anna Kontu, Juha Lemmetyinen, and Susan C. Steele-Dunne and used in the GMD Manuscript "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"

Data for figures of manuscript entitled: "On the Sample Complexity of Quantum Boltzmann Machine Learning"

The zip file contains the data for each of the plots in the figures in the manuscript: "On the Sample Complexity of Quantum Boltzmann Machine Learning." The preprint version of this article can be found on arXiv: https://arxiv.org/abs/2306.14969

Datasets of the manuscript "Mixed-valence state in the dilute-impurity regime of La-substituted SmB6"

You can find here the ARPES and XAS data used in the paper "Mixed-valence state in the dilute-impurity regime of La-substituted SmB6" by M. Zonno et al. .  Data are in Igor Pro binary format. ARPESn5d_21eV and ARPESn5d_67eV are the ARPES n5d data for the two photon energies used. The corresponding Sm concentration scalings are found in ARPES_SmDoping_21eV and ARPES_SmDoping_67eV, respectively.  XAS_07, XAS_13, XAS_20, XAS_30, XAS_70, XAS_90, XAS_97, XAS_100 are the XAS waves for the different Sm concentrations measured. The energy scaling is found in XAS_Energy.

Data and Code for the manuscript "Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space"

This repository includes the data and code to reproduce the results for the study "Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space". The study shows that leaf angle dynamics systematically confound widely applied vegetation indices. Moreover, it is demonstrated that these effects are not random but tightly linked to abiotic environmental conditions. These findings demonstrate both challenges and opportunities for using VIs for vegetation monitoring.

opencc-zeroJul 2024View details →

Transcripts Demonstrating the Application of ChatGPT in the Composition of the Manuscript "Deciphering Cancer Genomes with GenomeSpy: A Grammar-Based Visualization Toolkit" by Lavikka, et al.

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Model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"

This folder contains the model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model" The code for plotting the figures is the notebook Plot_figures.ipynb Fig1/simulation_output/ : Model output necessary for plotting the first figure  The last timestep of each simulation is provided. There is one file for 1D variables (ice volume, ice volume above flotation), and one file for 2D variables (ice sheet thickness for instance). <ul> <li>melt_insoPI_output/ : melt branch, pre-industrial insolation. Results for different CO2 levels</li> <li>growth_insoPI_output/ : growth branch, pre-industrial insolation. Results for different CO2 levels</li> <li>melt_insoMAX_output/ : melt branch, maximum insolation. Results for different CO2 levels</li> <li>growth_insoMIN_output/ : growth branch, minimum insolation. Results for different CO2 levels</li> </ul> compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output Fig1/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level   Fig2/simulation_output/ : Model output necessary for plotting the second figure  The last timestep of each simulation is provided.  <ul> <li>melt_insoPI_enhancedmelt_albfb/ : melt branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</li> <li>growth_insoPI_enhancedmelt_albfb/ : growth branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</li> <li>melt_insoPI_enhancedmelt_fixedalb/ : melt branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</li> <li>growth_insoPI_enhancedmelt_fixedalb/: growth branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</li> </ul> compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output Fig2/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level   Fig3/simulation_output/ : Model output necessary for plotting the third figure  <ul> <li>1xCO2_nocoupling/ : simulation with pre-industrial CO2 levels and insolation and no coupling to the ice sheet model</li> <li>8xCO2_nocoupling/ : simulation with 8xpiCO2 (pre-industrial CO2) levels, pre-industrial insolation and no coupling to the ice sheet model</li> <li>8xCO2_transient_albfb/ : quasi transient simulation, 8xpiCO2 levels,  pre-industrial insolation, coupling with the ice sheet model </li> <li>8xCO2_transient_fixedalb/ : quasi transient simulation, 8xpiCO2 levels,  pre-industrial insolation, coupling with the ice sheet model excluding the albedo-melt feedback</li> </ul>

opencc-by-4.0Feb 2024View details →

Artificially created image files resembling ancient Greek manuscripts in majuscule script

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MS data linked to manuscript: https://doi.org/10.1038/s41598-023-43300-w

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MS data linked to manuscript: https://doi.org/10.3390/cells12071065

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MS data linked to manuscript: https://doi.org/10.3390/pharmaceutics13122121

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MS data linked to manuscript: https://doi.org/10.3390/pharmaceutics15020700

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MS data linked to manuscript: https://doi.org/10.1093/nar/gkac1049

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