Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,152
datasets available to search
ShareScore release 0.9.0
Dataset results
2,152 results for “outputs”
Data Outputs: An analysis of proper nouns in Marian Keyes published novels 1995-2020.xlsx
<p>A data set analysing proper nouns in Marian Keyes' published novels 1995-2020 conducted for my Irish Research Council funded PhD thesis: </p> <ol> <li>Watermelon</li> <li>Lucy Sullivan is Getting Married</li> <li>Rachel's Holiday</li> <li>Last Chance Saloon</li> <li>Sushi for Beginners</li> <li>Angels</li> <li>The Other Side of the Story</li> <li>Anybody Out There?</li> <li>This Charming Man</li> <li>The Brightest Star in the Sky</li> <li>The Mystery of Mercy Close</li> <li>The Woman Who Stole My Life</li> <li>The Break</li> <li>Grown Ups</li> </ol> <p>The results are not statistically significant and therefore not incorporated into my final write up. </p> <p>The list of character names may be useful to incoroprate into a STOP list for anyone else seeking to analyse Keyes' novels via distant reading methods.</p>
Inputs and outputs for exospheric simulations used in the deep neural network for surface reconstruction from simulated exospheric measurements
<p>This dataset contains the inputs and outputs of the exospheric simulations performed for v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".</p>
Inputs and outputs for the training and testing of a deep neural network for surface reconstruction from simulated exospheric measurements
<p>This dataset contains inputs (datasets) and outputs (trainings and tests) used in v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".</p>
CESM2 model output from Eisenman & Armour (2024): Part 4
<p>Description of data: See README.txt</p> <p>Citation for data: I. Eisenman and K. Armour (2024). The radiative feedback continuum from Snowball Earth to an ice-free hothouse. Nature Communications, https://doi.org/10.1038/s41467-024-50406-w</p> <p>See also https://eisenman-group.github.io for links to other fields from these simulations.</p>
CAABA/MECCA model output for evaluating optimized step size control in Rosenbrock solvers for stiff ODEs
<p>This dataset comprehend the simulation output obtained with the CAABA/MECCA model for the optimization of the step size control in the Rosenbrock integrators available from the Kinetic PreProcessor (KPP) version 2.2.3_rs4. This dataset is made avalaible for the peer-review of the manuscript describing the model and the different options of step size control that was implemented. </p>
Data and model output for "Evidence of subsurface control on the coevolution of hillslope morphology and runoff generation"
<p>Data, model output, and scripts supporting the manuscript:</p> <p>Litwin, D. G., & Harman, C. J. (2024) Evidence of subsurface control on the coevolution of hillslope morphology and runoff generation. <em>Water Resources Research</em>, 60, e2024WR037301. https://doi.org/10.1029/2024WR037301</p>
Croco Idealized Model Output for paper: https://doi.org/10.1029/2024JC021204
<p>This is the output of croco idealized model for the JGR-Oceans paper</p>
Paper: "Sensitivity analysis for linear changes of the constraint matrix of a linear program" output
<p>Output generated from the experiments in the paper "Sensitivity analysis for linear changes of the constraint matrix of a linear program"</p>
Example raw quantification outputs used for extracting view data
This data contains the raw quantification outputs from platforms FragPipe, Maxquant, DIA-NN and Spectronaut. We use them as inputs for extracting view data serving as inputs to our newly designed multi-view proteomics framework.
FRACTESUS_UC_S690Q_T0_MCT_output
<p>Fractesus project. Fracture test mini-CT. Master curve output S690Q. UC.</p>
FRACTESUS_UC_S355J2_T0_MCT_output
<p>Fractesus project. Fracture test mini-CT. Master curve output S355J2. UC. </p>
FRACTESUS_UC_S460M_T0_MCT_output
<p>Fractesus project. Fracture test mini-CT. Master curve output S460M. UC.</p>
FRACTESUS_UC_S275JR_T0_MCT_output
<p>Fractesus project. Fracture test mini-CT. Master curve output S275JR. UC. </p>
Data from: Artificial light at night increases growth and reproductive output in Anolis lizards
<p>Since the invention of electric lighting, artificial light at night (ALAN) has become a defining, and evolutionarily novel, feature of human-altered environments especially in cities. ALAN imposes negative impacts on many organisms, including disrupting endocrine function, metabolism, and reproduction. However, we do not know how generalized these impacts are across taxa that exploit urban environments. We exposed brown anole lizards, an abundant and invasive urban exploiter, to relevant levels of ALAN in the lab and assessed effects on growth and reproduction at the start of the breeding season. Male and female anoles exposed to ALAN increased growth and did not suffer increased levels of corticosterone. ALAN exposure induced earlier egg-laying, likely by mimicking a longer photoperiod, and increased reproductive output without reducing offspring quality. These increases in growth and reproduction should increase fitness. Anoles, and potentially other taxa, may be resistant to some negative effects of ALAN and able to take advantage of the novel niche space ALAN creates. ALAN and both its negative and positive impacts may play a crucial role in determining which species invade and exploit urban environments.</p>
Fettweis et al., 2021, TC, Greenland ice sheet and geoengineering: MAR outputs
<p>Monthly MARv3.11.3 outputs used in:</p> <p>Fettweis, X., Hofer, S., Séférian, R., Amory, C., Delhasse, A., Doutreloup, S., Kittel, C., Lang, C., Van Bever, J., Veillon, F., and Irvine, P.: Brief Communication: Reduction of the future Greenland ice sheet surface melt with the help of solar geoengineering, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2020-347, accepted, 2020.</p> <p>The available variables are:</p> <pre><code> name title I J K L LON Longitude 1:73 1:135 ... ... LAT Latitude 1:73 1:135 ... ... SH Surface Height 1:73 1:135 ... ... MSK Ice Sheet Area 1:73 1:135 ... ... SOL Soil Type 1:73 1:135 ... ... FRV Vegetation Class Coverage 1:73 1:135 1:2 ... VEG Vegetation Type Index 1:73 1:135 1:2 ... SMB SMB (mmWE/month) 1:73 1:135 ... 1:12 RU Runoff (mmWE/month) 1:73 1:135 ... 1:12 ME Melt (mmWE/month) 1:73 1:135 ... 1:12 SF Snowfall (mmWE/month) 1:73 1:135 ... 1:12 RF Rainfall (mmWE/month) 1:73 1:135 ... 1:12 SU Sublimation/evaporation (mmWE/m 1:73 1:135 ... 1:12 TT Temperature (degC) 1:73 1:135 ... 1:12 SWD Shortwave downward (w/m^2) 1:73 1:135 ... 1:12 LWD Longwave downward (w/m^2) 1:73 1:135 ... 1:12 SWA Absorbed shortwave radiation (w 1:73 1:135 ... 1:12 AL Albedo 1:73 1:135 ... 1:12 </code></pre> <p><br> </p> <p> </p>
Tutorial output for Tourmaline amplicon sequence processing workflow
<p>Tutorial output for the <a href="https://github.com/aomlomics/tourmaline">Tourmaline</a> amplicon sequence processing workflow.</p> <p>Tourmaline was run on the test data provided in the directory <a href="https://github.com/aomlomics/tourmaline/tree/master/00-data">00-data</a>, which were downloaded along with the rest of the repository using this command:</p> <pre><code>git clone https://github.com/aomlomics/tourmaline</code></pre> <p>Reference data were downloaded and symlinked using these commands:</p> <pre><code>cd tourmaline/01-imported wget https://data.qiime2.org/2021.2/common/silva-138-99-seqs-515-806.qza wget https://data.qiime2.org/2021.2/common/silva-138-99-tax-515-806.qza ln -s silva-138-99-seqs-515-806.qza refseqs.qza ln -s silva-138-99-tax-515-806.qza reftax.qza</code></pre> <p>Paths in 00-data/manifest_pe.csv and 00-data/manifest_se.csv were edited to match the local paths.</p> <p>Output for all modes of the workflow were then generated in series:</p> <pre><code>conda activate qiime2-2021.2 snakemake dada2_pe_report_unfiltered snakemake dada2_pe_report_filtered snakemake dada2_se_report_unfiltered snakemake dada2_se_report_filtered snakemake deblur_se_report_unfiltered snakemake deblur_se_report_filtered</code></pre> <p> </p>
Model output of the Simple Biosphere Model, version 4 (SiB4) used in "Evaluation of carbonyl sulfide biosphere exchange in the Simple Biosphere Model (SiB4)"
<p>Model output of the Simple Biosphere Model, version 4 (SiB4) used in "Evaluation of carbonyl sulfide biosphere exchange in the Simple Biosphere Model (SiB4)". The dataset includes COS ecosystem, vegetation and soil fluxes, as well as CO2 exchange fluxes. COS soil fluxes are calculated through the Ogee et al. (2016) soil model, and COS mole fractions vary spatially and temporally. The site-specific files provide SiB4 output only for the plant functional type that is represented by this site. The plant functional type is included in the file name. Datasets include 3 hourly output between 2000-2020 for site simulations, and monthly output between 2000-2020 for global simulations (at 0.5x0.5 degrees). The SiB4 model code is available online: https://gitlab.com/kdhaynes/sib4_corral</p>
SPH-EXA miniapp Sedov scaling test outputs with v0.3
<p>Output dataset for weak scaling tests on Piz Daint with 1 - 2048 compute nodes.</p> <p>Sedov blast wave test case with 32 million particles per node.</p>
Data and outputs for NESP Project 8.3.1
<p>Data and outputs for <em>NESP Threatened Species Recovery Hub Project 8.3.1: ‘Fire-affected invertebrates: priority species and management response’</em>.</p>
Model output: CICE6 with WIFF
<p>CICE6 model experiments with different versions of wave fracture </p> <p> </p> <p>For further details, see </p> <p>Horvat, C. and Roach, L. (2021) WIFF1.0: A hybrid machine learning parameterization of Wave-Induced sea-ice Floe Fracture. To be submitted to Geoscientific Model Development</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.