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datasets available to search
ShareScore release 0.9.0
Dataset results
8 results for “iScience”
Song et al., 2022_iScience_DATASET
<p>This dataset contains the underlying data (energy sector data for India) for the book chapter Song et al., 2022 published in Science in May 2022.</p>
DS1_LH_ Abdel Aziz et al_iScience_2020_photoelectrochemistry data
<p>Cyclic Voltammetry, Linear sweep voltammetry, Photocurrent action spectrum and Spectroelectrochemistry data</p>
DS13_LH_Abdel Aziz et al_iScience_2020_SECM data
<p>Scanning electrochemical microscopy data</p>
Data and code for "Multi-omics Reveals Microbiome, Host Gene Expression, and Immune Landscape in Gastric Carcinogenesis" by Park et al., iScience 2022
<p>This repository is a part of the supplementary document in Park et al., "Multi-omics Reveals Microbiome, Host Gene Expression, and Immune Landscape in Gastric Carcinogenesis" published in iScience 2022.</p> <p>Abstract: To date, there has been no multi-omic analysis characterizing the intricate relationships between the intragastric microbiome and gastric mucosal gene expression in gastric carcinogenesis. Using multi-omic approaches, we provide a comprehensive view of the connections between the microbiome and host gene expression in distinct stages of gastric carcinogenesis (i.e., healthy, gastritis, cancer). We uncover associations specific to disease states. For example, uniquely in gastritis, Helicobacteraceae is highly correlated with the expression of <em>FAM3D</em>, which has been previously implicated in gastrointestinal inflammation. Additionally, in gastric cancer but not in adjacent gastritis, Lachnospiraceae is highly correlated with the expression of <em>UBD</em>, which regulates mitosis and cell cycle time. Furthermore, lower abundances of B cells in gastric cancer compared to gastritis may suggest a previously unidentified immune evasion process in gastric carcinogenesis. Our integrative analysis provides the most comprehensive description of microbial, host transcriptomic, and immune cell factors of the gastric carcinogenesis pathway.</p>
Dataset from Maith, O., Baladron, J., Einhäuser, W., & Hamker, F. H. (2023). Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model. Submitted to iScience.
<p>This dataset contains all analyzed data from the study "Maith, O., Baladron, J., Einhäuser, W., & Hamker, F. H. (2023). Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model. Submitted to iScience.". It includes the behavioral data of 20 human participants (folder "psychExp") and of simulations of a neuro-computational basal ganglia model (folder "simulations") of the study.</p> <p>To replicate the results of the study, the dataset can be analyzed using the code provided separately under the following identifier: https://doi.org/10.5281/zenodo.6555886. The dataset is organized in the directory structure required for this purpose.</p> <p>For the human participants, only preprocessed eye-tracking and general behavioral data (.mat files) and the final analyzed behavioral data (output files) generated with the script "get_vps_outputs.m" (folder psychExp/..../3_srcAna/) are available. For more information about preprocessing steps as well as raw data of the eye-tracking experiment, please contact us by email (click <a href="https://www.tu-chemnitz.de/urz/mail/adrx.html?1-d29sZmdhbmcuZWluaGFldXNlci10cmV5ZXJAcGh5c2lrLg==">here</a>).</p>
Data subset from Sorbie, Jimenez and Benakis, iScience 2022
<p>This repository contains a subset of the raw fastq files from Sorbie, Jimenez and Benakis, iScience, 2022, as well as the processed datasets. This data is provided to test our pipeline and provide a citable link to the processed dataset. </p>
vanBeest iScience 2024: The direct and indirect pathways of the basal ganglia antagonistically influence cortical activity and perceptual decisions.
<p>Data for the paper 'The direct and indirect pathways of the basal ganglia antagonistically influence cortical activity and perceptual decisions.' by van Beest et al. 2024, iScience: https://doi.org/10.1016/j.isci.2024.110753</p> <p>Code to analyze data is available via https://github.com/EnnyvanBeest/vanBeest_iScience2024.</p> <p> </p> <p> </p>
360Figure-iscience
<p>Video describing the work published by G. Zambito et al in iScience in 2020.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.