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Dataset results
2,152 results for “outputs”
Distinct TNF Receptors Dictate Stem Cell Fitness Versus Lineage Output in Clonal Hematopoiesis of Aging
GEO Series GSE189406. Mus musculus. 28 samples. Type: Expression profiling by high throughput sequencing.
Prospective isolation of radiation induced erythroid stress progenitors reveals unique transcriptomic and epigenetic signatures enabling increased erythroid output
GEO Series GSE139336. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Identifying the transcriptional output associated with distal regulatory elements where ASCL1 interacts with mSWI/SNF remodelers to regulate neural differentiation [RNA-seq]
GEO Series GSE214382. Homo sapiens. 23 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Supplementary tables for "Differential neuropeptide modulation of premotor and motor neurons in the lobster cardiac ganglion underlies changes in patterned neuronal output"
<p>Supplementary tables for "Differential neuropeptide modulation of premotor and motor neurons in the lobster cardiac ganglion"</p>
Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model"
<p>Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model" in JGR-Atmospheres (2020). </p> <p>Output are NetCDF files containing annual means (named 'yearmean', 2007-2013), or multi-annual monthly means ('ymonmean', 2007-2012) of various variables that are of interest and/or used for analysis in this study. The file name starts with the variable name. Fields are global, at a resolution of 0.9 x 1.25 degrees latitude/longitude.</p> <p>The test simulations are named (as discussed in the paper):</p> <p>cam4_clm5<br> cam5_clm5<br> cam6_noicenucl_clm5<br> cam6_noclubb_clm5<br> cam6_mg1_clm5<br> cam6</p>
Mass spectrometry output SILAC labelled (F/Y) biological replicate 1 - anti-HLA-ABC antibody W6/32
<p>Mass spectrometry output from SILAC labelled (F/Y) ERAP2 WT versus (CRISPR Cas9-mediated) ERAP2-KO lymphoblastoid cell line from a Birdshot Uveitis patient (ERAP1 hap10/10 ERAP2 hapA/A). Peptides were eluted from immuno-purifications from HLA-A29-negative (DK1G8-negatively selected) fractions with anti-HLA-ABC antibody W6/32. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
Input parameters, output data, and script to calculate strain and velocity from fault source parameters
<p>This archive contains two data tables, one script, and one dataset to accompany the paper:</p> <p><strong>Strain and velocity across the Great Basin derived from 15-ka fault slip rates: Implications for continuous deformation and seismic hazard in the Walker Lane</strong></p> <p><em>Nadine G. Reitman<sup>1 </sup> and Peter Molnar<sup>1,2</sup></em></p> <p><sup>1</sup>Department of Geological Sciences, University of Colorado Boulder</p> <p><sup>2</sup>Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado Boulder</p> <p> </p> <p>The data tables contain the input source fault parameters (fault strike, dip, rake, length, location, and slip rate) and the contribution to strain calculated for each fault relative to 317 degrees using the associated script. The script uses the methods of Kostrov (1974) and Haines (1982) to calculate strain and velocity across the Great Basin using slip rates reported in the USGS National Seismic Hazard Map (2020 update of the 2014 data, Petersen et al, 2014) and Pérouse & Wernicke (2017). The script is written in Python3 and requires the user to have some knowledge of how to run Python code. The included dataset (hazfaults_clean.p) contains the data to run the script for the USGS fault sources. </p> <p>Please contact nadine.reitman@colorado.edu with questions.</p>
Data repository of model outputs in Ferrier and Perron (2020), "The importance of hillslope scale in responses of chemical erosion rate to changes in tectonics and climate"
<p>This is a repository of model outputs in Ferrier and Perron (2020), "The importance of hillslope scale in responses of chemical erosion rate to changes in tectonics and climate" at the Journal of Geophysical Research - Earth Surface. See the readme file for descriptions of the data contained in each file.</p>
Tanuma and Tozuka (2020) Model outputs
<p>Model outputs used in the manuscript entitled "Influences of the Interdecadal Pacific Oscillation on the locally amplified Ningaloo Niño" by Natsumi Tanuma and Tomoki Tozuka that was submitted to Geophysical Research Letters.</p>
Aggregated WRF-Chem outputs in "Evaluation of cloud and precipitation response to aerosols in WRF-Chem with satellite observations"
<p>This dataset contains the aggregated output of WRF-Chem simulations by Zhoukun Liu. </p> <p>The "MOR" in file name denotes the data from simulation which used Morrison scheme and "LIN" denotes the data from simulation which used Lin scheme.</p> <p>In "MOR_110km_core.nc" and "LIN_110km_core.nc", the variables are averaged over the cloudy pixels that have the highest 10% of cloud optical thickness in each 110km scene.</p> <p>In "MOR_110km_incloud.nc" and "LIN_110km_incloud.nc", the variables are averaged over all the cloudy pixels in each 110km scene.</p> <p>"MOR_110km_R.nc" and "LIN_110km_R.nc" contains the precipitaiton characteristics from models for each 110km scene. </p> <p> </p>
SDG16_NL_Outputs
<p>This list of Digital Object Identifiers (DOI) represents the results of the Sustainable Development Goal (SDG) content classifier for Goal No.16 Peace, Justice and Strong Institutions. All DOIs contain at least one author whose is affiliated with a research organisation in the Netherlands. The classifier was created as part of a unique collaboration between Springer Nature, Digital Science, and VSNU/UKB. For further information, see below.</p> <p>All data in the Excel was sourced from Dimensions, an inter-linked research information system provided by Digital Science (https://www.dimensions.ai) The data has been released for strictly non-commercial use under a CC-BY-NC-SA 4.0 license. The data may be analysed for non-commercial reports or studies related to the SDGs until December 2022. Thereafter further reuse of the data requires Digital Science's approval. </p> <p>Background information: Springer Nature, together with Digital Science, and The Association of Universities in the Netherlands (VSNU) and the Dutch Consortium of University Libraries and The National Library of The Netherlands (UKB) created a model from a selection of Sustainable Development Goals (SDG) focusing on societal aspects in the United Nations (UN) Sustainability Agenda. Keyword search strings for five goals were defined, with input from the project partners, in order to produce training sets based on publications from the Dimensions platform. Using improved search strings instead of a manual build-up of respective sets of SDG related publications, the created training sets were used to apply Natural Language Processing and Machine Learning resulting in a classification scheme based on five UN SDGs.</p>
SDG4_NL_Outputs
<p>This list of Digital Object Identifiers (DOI) represents the results of the Sustainable Development Goal (SDG) content classifier for Goal No.4 Quality Education. All DOIs contain at least one author whose is affiliated with a research organisation in the Netherlands. The classifier was created as part of a unique collaboration between Springer Nature, Digital Science, and VSNU/UKB. For further information, see below.</p> <p>All data in the Excel was sourced from Dimensions, an inter-linked research information system provided by Digital Science (https://www.dimensions.ai) The data has been released for strictly non-commercial use under a CC-BY-NC-SA 4.0 license. The data may be analysed for non-commercial reports or studies related to the SDGs until December 2022. Thereafter further reuse of the data requires Digital Science's approval. </p> <p>Background information: Springer Nature, together with Digital Science, and The Association of Universities in the Netherlands (VSNU) and the Dutch Consortium of University Libraries and The National Library of The Netherlands (UKB) created a model from a selection of Sustainable Development Goals (SDG) focusing on societal aspects in the United Nations (UN) Sustainability Agenda. Keyword search strings for five goals were defined, with input from the project partners, in order to produce training sets based on publications from the Dimensions platform. Using improved search strings instead of a manual build-up of respective sets of SDG related publications, the created training sets were used to apply Natural Language Processing and Machine Learning resulting in a classification scheme based on five UN SDGs.</p>
SDG3_NL_Outputs
<p>This list of Digital Object Identifiers (DOI) represents the results of the Sustainable Development Goal (SDG) content classifier for Goal No.3 Good Health and Well-Being. All DOIs contain at least one author whose is affiliated with a research organisation in the Netherlands. The classifier was created as part of a unique collaboration between Springer Nature, Digital Science, and VSNU/UKB. For further information, see below.</p> <p>All data in the Excel was sourced from Dimensions, an inter-linked research information system provided by Digital Science (https://www.dimensions.ai) The data has been released for strictly non-commercial use under a CC-BY-NC-SA 4.0 license. The data may be analysed for non-commercial reports or studies related to the SDGs until December 2022. Thereafter further reuse of the data requires Digital Science's approval. </p> <p>Background information: Springer Nature, together with Digital Science, and The Association of Universities in the Netherlands (VSNU) and the Dutch Consortium of University Libraries and The National Library of The Netherlands (UKB) created a model from a selection of Sustainable Development Goals (SDG) focusing on societal aspects in the United Nations (UN) Sustainability Agenda. Keyword search strings for five goals were defined, with input from the project partners, in order to produce training sets based on publications from the Dimensions platform. Using improved search strings instead of a manual build-up of respective sets of SDG related publications, the created training sets were used to apply Natural Language Processing and Machine Learning resulting in a classification scheme based on five UN SDGs.</p>
Input and Output Data of SGRIST
<p>Three SCM cases as in the manuscript.</p>
RAM-SCBE simulation outputs
<p>These simulations were performed for, and used in the paper “Simulating the Ion Precipitation from the Inner Magnetosphere by H-band and He-band Electro Magnetic Ion Cyclotron (EMIC) Waves” by P. R. Shreedevi , Yiqun Yu , Binbin Ni, Anthony Saikin and Vania K. Jordanova</p>
Example 1 Cufflinks output (brain.fpkm_tracking)
<p>Example 1 Cufflinks output (brain.fpkm_tracking).</p> <p> </p> <p>DIGGER: https://exbio.wzw.tum.de/digger/</p>
Outputs of the LBM and CAM5 experiments
<p>We used two types of numerical model to validate the diagnosing results and estimate the relative contributions of CP El Niño and pIOD. One was an ideal linear baroclinic model (LBM) with a T42 resolution in the horizontal direction and 20 sigma levels in the vertical direction (Watanabe & Kimoto, 2000). The other was an atmospheric general circulation model (AGCM) of the NCAR Community Atmosphere Model version 5 (CAM5), which had a 1.9° × 2.5° finite volume grid on the 26 hybrid sigma levels (Neale et al., 2012).</p> <p>The three CAM5 numerical experiments are 1. the TSEIO run, 2. the CP run, 3. the TSEIO+CP run. The climatology variables in the control run (abbreviated as "CTL") are also provided. All the model outputs were first interpolated onto the same spatial grid as NCEP‐DOE R2. </p> <p>Here are the outputs of the LBM and CAM5 experiments. Each output data is written in binary format (.bin/.dat) with a data descriptor file (.ctl).</p> <p>The file of LBM is named as "Model name"_"output"_"variable"."format".</p> <p>The file of CAM5 is named as "Model name"_"Sensitivity experiment name"_"variable"_"Season (e.g., ASO)"_"Selected experiment times (e.g., 0006-0018 or Clim.)"."format".</p>
Delft3D input and output files
<p>Supplementary data for:</p> <p>Hepkema et al., Oblique sand ridges in confined tidal channels due to Coriolis and frictional torques (submitted to Ocean Dynamics, 2020)</p> <p>Two .mat (matlab) files with the bathymetric output of the D3D runs. One where the Coriolis effect is taken into account and one where it is not.</p> <p>The zip file contains all the necessary inputfiles to rerun the Delft3D model. </p>
PRISM output for all genomes analyzed
<p>PRISM 4 output, in JSON format, for 10,121 genomes, including 3,759 dereplicated complete genomes and 6,362 metagenome-assembled genomes (MAGs).</p>
FESOM output supporting stronger variability in the Arctic Ocean in a warming climate
<p>FESOM simulation results used in the manuscript "Stronger variability in the Arctic Ocean induced by sea ice decline in a warming climate".</p>
ScienceDex guides
Understand access before you commit
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.