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477
datasets available to search
ShareScore release 0.9.0
Dataset results
477 results for “input data”
ltra-low-input native ChIP-seq and Whole genome bisulfite Sequencing data of four types mouse spermatogenesis
GEO Series GSE137744. Mus musculus. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
miRTrace quality control of small RNA-Seq data prepared from low-input, degraded and contamianted HEK-293T RNA samples
GEO Series GSE118437. Drosophila melanogaster; Homo sapiens. 7 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Input Strategy for Improving Analysis of ChIP-exo Data and Beyond [RNA-Seq]
GEO Series GSE79564. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Input data set for Professor/Apprentice
<p>Example MC data set of rivet runs and yoda output. This is educational material to be used as input to professor/apprentice.</p>
Input data files for RSS-NET analysis of simulated GWAS summary statistics and B cell regulatory network
<p>Details of these data files are provided in https://suwonglab.github.io/rss-net/wtccc_bcell.</p> <p>Contact:<code> xiangzhu[at]psu.edu </code></p>
Input data for manuscript "An open-source low-cost sensor for SNR-based GNSS reflectometry: Design and long-term validation towards sea level altimetry"
<p>Input data for Fagundes et al. (2021), "An open-source low-cost sensor for SNR-based GNSS reflectometry: Design and long-term validation towards sea level altimetry", GPS Solutions (in press). <a href="https://www.researchgate.net/publication/341946011">preprint</a></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>
Input forcing data for CLM4.5 or CLM5 at Soltis Center in Costa Rica
<p>Input forcing data for CLM4.5 or CLM5 at Soltis Center in Costa Rica</p>
Input and Output Data of SGRIST
<p>Three SCM cases as in the manuscript.</p>
Example CCE worldtube input data
<p>This is an example worldtube file for evaluating the installation and functionality of a Cauchy Characteristic extraction scheme for waveform extraction from finite-radius GR simulation. This file was generated by a SpEC run of an equal-mass non-spinning binary black hole simulation by Mark Scheel. The techniques used to simulate the binary black hole system are similar to those used in <a href="https://arxiv.org/pdf/1904.04831.pdf">Boyle et. al. 2019</a>, in particular the GR simulation techniques published in <a href="https://arxiv.org/abs/gr-qc/0512093">Lindblom et. al. 2006</a>. This is a suitable input file for instance, for SpECTRE or SpEC CCE systems.</p>
Data from: Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions
Crop growth models and remote sensing are useful tools for predicting crop growth and yield, but each tool has inherent drawbacks when predicting crop growth and yield at a regional scale. To improve the accuracy and precision of regional corn yield predictions, a simple approach for assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) products into a crop growth model was developed, and regional yield prediction performance was evaluated in a major corn-producing state, Illinois, USA. Corn growth and yield were simulated for each grid using the Crop Environment Resource Synthesis (CERES)-Maize model with minimum inputs comprising planting date, fertilizer amount, genetic coefficients, soil, and weather data. Planting date was estimated using a phenology model with a leaf area duration (LAD)-logistic function that describes the seasonal evolution of MODIS-derived leaf area index (LAI). Genetic coefficients of the corn cultivar were determined to be the genetic coefficients of the maturity group [included in Decision Support System for Agrotechnology Transfer (DSSAT) 4.6], which shows the minimum difference between the maximum LAI derived from the LAD-logistic function and that simulated by the CERES-Maize model. In addition, the daily water stress factors were estimated from the ratio between daily leaf area/weight growth rates estimated from the LAD-logistic function and that simulated by the CERES-Maize model under the rain-fed and auto-irrigation conditions. The additional assimilation of MODIS data-derived water stress factors and LAI under the auto-irrigation condition showed the highest prediction accuracy and precision for the yearly corn yield prediction (R2 is 0.78 and the root mean square error is 0.75 t ha-1). The present strategy for assimilating MODIS data into a crop growth model using minimum inputs was successful for predicting regional yields, and it should be examined for spatial portability to diverse agro-climatic and agro-technology regions.
DEVOLUTION input files and subclone annotation & single cell data.
<p>Related data files for the paper:</p> <p>"Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma by" by Jenny Karlsson et al.</p> <p>https://www.researchsquare.com/article/rs-2104205/v1</p> <ul> <li>DEVOLUTION input segment files.</li> <li>Subclones obtained from DEVOLUTION.</li> <li>Single cell data for patients and PDX models.</li> <li>A manual of how to create phylogenetic trees based on the data.</li> </ul>
The input files and associated data products for "Modeling High Mass X-ray Binaries to Double Neutron Stars through Common Envelope Evolution"
<p>Simulations were made using the version 12115 of the MESA code together with the x86_64-linux-20190830 MESA SDK. "template.zip" provides the MESA inlist files to reproduce our simulations. "CE_1.zip" provides our simulated results for a grid of binary systems with common envelope ejection efficiencies set to be 1.0. Different folders indicate the binary systems with different initial parameters. Inside each folder information can be found for binary properties in the "history.data" file. Each folder also contains the "result.txt" file with the terminal output of the simulation. "CE_3.zip", "CE_0.3.zip" and "CE_0.1.zip" are the same as "CE_1.zip" but with common envelope ejection efficiencies set to be 3.0, 0.3 and 0.1, respectively.</p>
Data from: Getting to the root of organic inputs in groundwaters: stygofaunal plant consumption in a calcrete aquifer
<p>Groundwater environments interact with and support subterranean biota as well as superficial aquatic and terrestrial ecosystems. However, knowledge of subterranean energy flows remains incomplete. Cross-boundary investigations are needed to better understand the trophic structures of groundwater ecosystems and their reliance on carbon inputs from aboveground. In this study we used carbon and nitrogen stable isotope analyses combined with radiocarbon fingerprints to characterise organic flows in groundwater ecosystems. We coupled these data with DNA metabarcoding of the gut contents of consumers to further elucidate organic matter sources and shifts in diet preferences. Samples were collected from the arid zone Sturt Meadows calcrete aquifer under low rainfall (LR) and high rainfall (HR) conditions. Bayesian modelling of Δ<sup>14</sup>C, δ<sup>13</sup>C and δ<sup>15</sup>N data indicated that primary consumers (copepods) incorporated mainly particulate organic carbon (POC) under LR but during HR shifted to root derived material (either exudates or direct root grazing). By contrast, diets of secondary consumers (amphipods) were dominated by root material under both LR and HR. Our DNA metabarcoding-based results indicate that amphipods relied primarily on root inputs from perennial trees (likely <i>Eucalyptus</i> and <i>Callitris</i>) during the dry season (LR). Under HR, diets of both amphipods and copepods also included organic material derived from a broad range of more shallow rooted shrubs, and ephemeral herbs and grasses. Our findings illustrate the complexity of functional linkages between groundwater biota and terrestrial surficial ecosystems in environments where aboveground productivity, diversity and organic matter flux to groundwater are intimately linked to often episodic rainfall.</p>
MD Analysis Pre-Processed Data Input Files: Towards a New Model for the TREX1 Exonuclease, Hemphill et al.
<p>All atoms molecular dynamics simulations were performed for TREX1 apoenzyme, TREX1-ssDNA complex, and TREX1-dsDNA complex, with four replicate simulations per system. Trajectory and initial condition output files were pre-processed in VMD to generate time-step sampled mol2 files of the macromolecules in each simulation. The mol2 files were used to calculate phi/psi angles of the protein backbone, and to extract all-atoms coordinate information over time, and this information was organized into an RData file for each simulation system, including all simulation replicates. These RData files are provided, and they were analyzed with the scripts described in the affiliated manuscript. </p>
Taylor Valley meteorological input data for ICEMELT model
<p>These input files were too large to be included in ICEMELT-Cross GitHub repository (https://doi.org/10.5281/zenodo.6808770). To run the ICEMELT model, copy and paste the contents of this repository into the 'input' directory.</p>
Input data for deep learning model-analog
<p>This repository contains input data required to run the Deep Learning Model-Analog (<a href="https://github.com/kinyatoride/DLMA">GitHub</a>), as presented in the paper titled "Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts" by Toride et al. A preprint is available at <a href="https://arxiv.org/abs/2404.15419">https://arxiv.org/abs/2404.15419</a>.</p> <p>The <code>cesm2</code> directory contains the Community Earth System Model Version 2 Large Ensemble (<a href="https://doi.org/10.26024/kgmp-c556" rel="nofollow">CESM2-LE</a>), while the <code>real</code> directory contains the Ocean Reanalysis System 5 (<a href="https://doi.org/10.24381/cds.67e8eeb7" rel="nofollow">ORAS5</a>) datasets. These datasets have been processed to provide detrended monthly anomalies and have been interpolated to two different resolutions: 2° × 2° and 5° × 5°. The 5°×5° files are used as input, while the 2°×2° files are used for analog forecasting.</p>
CMAQ 5.0 Adjoint Input Data
<p>The data set that is used for testing the adjoint model.</p>
Nutrient loading from a sustainably certified aquaculture operation dwarfs annual nutrient inputs from a large multi-use watershed, Lake Yojoa, Honduras (DATA)
<p><strong>Dataset for manuscript entitled: "Nutrient loading from a sustainably certified aquaculture operation dwarfs annual nutrient inputs from a large multi-use watershed, Lake Yojoa, Honduras"</strong></p> <p><strong>Abstract: </strong>Net-pen aquaculture is a popular and increasingly prevalent method for producing large quantities of low-fat protein in freshwater ecosystems across the tropics. While there are numerable social and economic advantages associated with aquaculture, there are also challenges related to the environmental sustainability of aquaculture operations which supply pens with externally produced feed. For example, excessive nutrient loading which can drive rapid eutrophication in aquatic ecosystems is a major risk. In this study we compare the estimated annual nutrient loads from the six principal tributaries that contribute to Lake Yojoa to the estimated nutrient load of a large net-pen Tilapia operation located in the central region of the lake. The Tilapia farm was responsible for ~ 86% of the nitrogen (N) and ~95% of the phosphorus (P) contributions to Lake Yojoa for the year of our study. This disproportionate nutrient loading of both N and P suggests that this single aquaculture operation, more so than changes in nutrient inputs from the watershed, was responsible for the previously documented deterioration of Lake Yojoa. This study shows the potential for net-pen aquaculture to have disproportionately negative impacts on freshwater ecosystems, even when operations meet the current sustainability certifications standards. We suggest shifts in metrics that could improve the impact of the certification process so that best practices can reduce the impact of net-pen aquaculture on freshwater ecosystems and arrive at the intended goal of long-term environmental sustainability.</p> <p>Data files: </p> <ul> <li>rating_curves_2018_2019: Rating cruves for tributaries Helado, Balas, Varsovia, and Raices</li> <li>sensor_2018_2019: Pressure transducer data for Healdo, Balas, Varsovia, and Raices</li> <li>Staff_TS_27MAY21: Manual stage measurements for Helado, Balas, Varsovia, and Raices</li> <li>13OCT21_Rivers: In stream nutrient data for all tributaries</li> <li>q_cacao_2019: Discharge for Cacao calculated using Manning's equation (methods provided in text)</li> <li>q_yure_2019: Discharge for Yure calculated using Manning's equation (methods provided in text)</li> <li>N_P_aqua: Nutrient contributions from aquaculture (calculations provided in Supplemental Text 4)</li> <li>precip: Yojoa watershed precipitation</li> </ul> <p> </p>
input file/output data for Liu et al.,2021
<p>This is the input file, fault kinematic table, and full model output.</p> <p>MODEL_DATA_0100-MODEL_DATA_0500 are modeling results of the <em>basin</em> stage.<br> MODEL_DATA_AFTER_PHASE_01-MODEL_DATA_AFTER_PHASE_08 are modeling results of <em>margin</em> stage.<br> MODEL_DATA_1700-MODEL_DATA_10000 are modeling results of the<em> post-rift </em>stage.</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.