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Data of the paper "Estimating the groundwater flux through Active-DTS (heat tracer experiment) and FVPDM (tracer experiment) : a field comparison" (Hydrogeology Journal)
No full textThis dataset contains the data presented in the paper "Estimating the groundwater flux through Active-DTS (heat tracer experiment) and FVPDM (tracer experiment) : a field comparison" (Hydrogeology Journal)
Please read the file called "READ ME" that describes the dataset and informations about the experimental setu
Small-angle scattering and sorption data in SBA-15 materials
No full textThis data contains small angle x-ray scattering patterns and sorption isotherms measured on SBA-15 samples. Water sorption at 40C, nitrogen sorption at 77K, argon sorption at 87K
DADE dataset
No full textThe DADE dataset, short for "Driving Agents in Dynamic Environments", is a synthetic dataset designed for the training and evaluation of methods for the task of semantic segmentation in the context of autonomous driving agents navigating dynamic environments and weather conditions. This dataset was generated using the CARLA simulator (version 0.9.14).
The dataset is divided into two sub-datasets: the subset 1, containing 100 video sequences, with static weather conditions (clear day) and the subset 2, containing 300 video sequences, with dynamic weather conditions.
The dataset is composed of temporal frames (video sequences) and includes the following information: RGB images, semantic segmentation ground truths, GNSS (Global Navigation Satellite System) position data, weather information
Replication Data for: EEG-fMRI mind blanking during SART
No full textThis dataset contains all necessary material to replicate the EEG-fMRI mind blanking study conducted in collaboration between the Monash University and the University of Liege.
Specifiecally, it contains the extract ROI timeseries post preprocessing, as well as all data extracted and analyzed from the ROI
Replication Data for: Association between executive functions and COMT Val108/158Met polymorphism among healthy younger and older adults: a preliminary study
No full textThe Dataset includes behavioral data from 100 participants (55 younger and 45 older). The data were used in a study examining whether genetic polymorphism for catechol-O-methyltransferase (COMT Val158Met) is related to performance on updating, shifting and inhibition tasks during aging. The Dataset is made available to comply with FAIR principles
LASLAfiles_Latin_APNformat
No full textWe recommend to access the Dataverse using the "Tree View". The folder 'LASLA Files APN format' contains the LASLA Latin Files in a APN format as described in the documentation found in the subfolder 'APN_Files_Documentation'. The subfolder 'APN_Files_Total' contains all the APN files of the LASLA corpus. The Subfolder 'APN_Files_for_Hyperbase' contains the files that are used to create the datasets that can be searched on HyperbaseWeb (http://hyperbase.unice.fr/hyperbase/).
The LASLA files have been converted to the CoNLL-U format and enriched with the links to the LiLa Knowledge Base by the LiLa team: the files are available on Zenodo (https://doi.org/10.5281/zenodo.5961377) and Github (https://github.com/CIRCSE/LASLA). The files in this repository are shared under the license CC BY-NC-SA 4.0
DATASET of the paper: "Innovative use of passive and active Distributed Temperature Sensing for estimating infiltration rates in a Managed Aquifer Recharge framework" (Journal of Hydrology)
No full textThis dataset contains the data presented in the paper: "Innovative use of passive and active Distributed Temperature Sensing for estimating infiltration rates in a Managed Aquifer Recharge framework". The explanations related to each datafile are contained in the file README.txt
Supplemental data for the article "Impact of Antenatal Exposure to a Mixture of Endocrine Disruptors on Attentional and Executive Functions in Children"
No full textSupplemental data for the Article: "Impact of Antenatal Exposure to a Mixture of Endocrine Disruptors on Attentional and Executive Functions in Children"
Objective:
Numerous studies indicate negative associations between early life exposure to endocrine disrupting chemicals and various aspects of neurodevelopment. However, few have focused on specific cognitive processes. Additionally, toxicants are often analysed individually, without accounting for their combined effects. This study aimed at investigating the impact of prenatal exposure to a mixture of endocrine disruptors on attention and executive functions in young children and comparing their effects to those reported in the literature.
Methods:
Two polychlorinated biphenyls (PCBs) and four perfluoroalkyl substances (PFASs) were measured in the cord blood from fifty-five children enrolled in a longitudinal Belgian cohort study. At six years of age, attentional and executive functions were assessed using specific neuropsychological tests. Associations between a mixture of toxicants and cognitive performance were analyzed using the principal components approach and weighted quantile sum regression, while accounting for sex differences.
Results:
Higher prenatal exposure to PCB mixtures was significantly associated with an increased number of omissions in the Divided Attention test. In sex-stratified analyses, this association remained significant but was observed only in boys. Additionally, boys exhibited reduced working memory and planning abilities following exposure to a mixture of PCBs and PFASs. In contrast, antenatal exposure to a mixture of PCBs and PFASs in girls was associated with reduced behavioral regulation, including inhibition control, as assessed by parent-reported questionnaires screening executive functioning in daily life.
Conclusion:
These results support associations between antenatal exposure to a mixture of endocrine disruptors and attention and executive development, emphasizing a sex-specific effect.</p
Weights and training data for SKiNN (Stellar Kinematics Neural Network)
No full textWeights and training data for the Stellar Kinematics Neural Network (SKiNN).
The SKiNN software can be found at https://github.com/mattgomer/SKiNN and is also archived on Software Heritage
The full usage of SKiNN is described in the article Accelerating galaxy dynamical modeling using a neural network for joint lensing and kinematic analyses
Matthew R. Gomer, Sebastian Ertl, Luca Biggio, Han Wang, Aymeric Galan, Lyne Van de Vyvere, Dominique Sluse, Georgios Vernardos, Sherry H. Suyu
A&A 679 A59 (2023)
DOI: 10.1051/0004-6361/202347507. Here is the abstract of the paper explaining the scientific context :
Strong gravitational lensing is a powerful tool to provide constraints on galaxy mass distributions and cosmological parameters, such as the Hubble constant, H0. Nevertheless, inference of such parameters from images of lensing systems is not trivial as parameter degeneracies can limit the precision in the measured lens mass and cosmological results. External information on the mass of the lens, in the form of kinematic measurements, is needed to ensure a precise and unbiased inference. Traditionally, such kinematic information has been included in the inference after the image modeling, using spherical Jeans approximations to match the measured velocity dispersion integrated within an aperture. However, as spatially resolved kinematic measurements become available via IFU data, more sophisticated dynamical modeling is necessary. Such kinematic modeling is expensive, and constitutes a computational bottleneck that we aim to overcome with our Stellar Kinematics Neural Network (SKiNN). SKiNN emulates axisymmetric modeling using a neural network, quickly synthesizing from a given mass model a kinematic map that can be compared to the observations to evaluate a likelihood. With a joint lensing plus kinematic framework, this likelihood constrains the mass model at the same time as the imaging data. We show that SKiNN’s emulation of a kinematic map is accurate to a considerably better precision than can be measured (better than 1% in almost all cases). Using SKiNN speeds up the likelihood evaluation by a factor of ~200. This speedup makes dynamical modeling economical, and enables lens modelers to make effective use of modern data quality in the JWST era.
More precisely, this dataset contains the training data described in section 3.1 of the paper and the resulting weights to use the SKiNN after training. The training data (4000 pairs) consist a parameters.npy file, that is sets of parameters - describing the PEMD mass, the elliptcial sersic light and the kinematics parameters - and a vrms_maps.npy file containing the corresponding kinematic map - created using the JAM software and the MGE method. The weights is a single file weights.ckpt which needs to be imported as described in the setup of SKiNN to enable the creation of a kinematic map from a set of parameters describing the galaxy properties.
NB: the parameters are ordered as following: q_mass (axis ratio of the mass profile), q_light (axis ratio of the light profile), theta_E (Einstein radius), n_sersic (Sersic index of the light), R_sersic (Sersic radius of the light), r_core (Core radius set to 0.08 arcsec, see paper section 3.1 for more information), gamma (mass profile slope), b_ani (anisotropy), i (inclination).
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Variations of autonomic arousal mediate the reportability of mind-blanking occurrences
No full textMind-Blanking (MB) is a state of complete absence of any mental content, only recognized by a post-hoc realization that "I was thinking of nothing". Previous work has identified unique brain physiology and network configurations which promote MB reportability. These indices seem to reflect decreased levels of physiological arousal as expressed in terms of reduced cortical excitation. Our project aims to provide a direct link between physiological and cortical arousal and MB occurrence. We will measure MB reports under 3 arousal conditions, baseline, after sleep deprivation (low), and after high-intensity exercise (high). Therefore, using multimodal physiological recordings, experience sampling, and a data-driven decoding approach, we want to examine:
1. how MB response distributions are affected by different levels of arousal
2. if there is a specific brain-body pattern that promotes MB occurrences.
This dataset contains all raw data (EEG, ECG, EDA, RSP, EYE -- resampled to EEG sampling rate) in BIDS format, all results dataframes and all machine learning models used in "Variations of autonomic arousal mediate the reportability of mind-blanking occurrences"