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2,152 results for “outputs”
Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM - output files and plot scripts
<p>This archive outputs_plotting.zip contains the raw output files (STDOUT) from the scaling runs performed for the paper "Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM" submitted to GMD (gmd-2019-167). Further the scripts to extract timing information from the raw output files and plot scripts are included.</p> <p>The archive SST-DA_plotting.zip contains the scripts to compute RMS errors for the free ensemble run (output file in gmd_N46_free.zip) and the SST assimilation run (gmd_N46_sst.zip) and to plot these. The two output files contain each a Netcdf file with the ensemble mean state information and the stdout file from the model run.</p>
Mass spectrometry output SILAC labelled (F/Y) biological replicate 2 - anti-HLA-A29 antibody DK1G8
<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 with anti-HLA-A29 antibody DK1G8. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
Mass spectrometry output SILAC labelled (F/Y) biological replicate 2 - 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>
Flow of Agricultural Nitrogen, version 2 (FANv2): Model input and output data
<p>This upload includes data associated with the manuscript "An improved mechanistic model for ammonia volatilization in Earth system models: Flow of Agricultural Nitrogen, version 2 (FANv2)" submitted to Geoscientific Model Development. The dataset includes an input file for use with the Community Land Model, and an output file with the simulated ammonia emissions for the agricultural sector. The emissions are monthly averages from the simulation for 2010-2015. Additional information is given in the readme file.</p>
Outcomes over output: a user-centric approach to building successful systems
<p><strong>Opening Plenary and keynote: Jeff Gothelf: </strong>Outcomes over output: a user-centric approach to building successful systems<br> Chair: <strong>Jessica Lindholm</strong>, Chalmers University of Technology</p> <p> </p>
Input and output data from numerical simulations associated to paper Biggs and Annen (2019)
<p>Input and output files produced by code Heat2D_Car and used to produce the results published in Biggs and Annen (2019)</p>
Dedalus output from 2D incompressible MHD fluid on a magnetized beta plane
<p>This dataset includes all output from simulations of the 2D incompressible MHD fluid on a magnetized beta plane that were used in the paper:</p> <blockquote> <p>Parker, J. B. and Constantinou, N. C. (2019). Magnetic eddy viscosity of mean shear flows in two-dimensional magnetohydrodynamics. Phys. Rev. Fluids, 4, 083701. DOI: 10.1103/PhysRevFluids.4.083701</p> </blockquote> <p>Simulations were performed using the spectral code Dedalus.</p>
Dispa-SET Output files for the JRC report "Power System Flexibility in a variable climate"
<p>Here you can find the model results of the <a href="https://doi.org/10.2760/75312">report</a>:</p> <pre><code>De Felice, M., Busch, S., Kanellopoulos, K., Kavvadias, K. and Hidalgo Gonzalez, I., Power system flexibility in a variable climate, EUR 30184 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-18183-5 (online), doi:10.2760/75312 (online), JRC120338. </code></pre> <p>This dataset contains both the raw GDX files generated by the GAMS (<www.gams.com>) optimiser for the <a href="http://www.dispaset.eu">Dispa-SET model</a>. Details on the output format and the names of the variables can be found in the Dispa-SET documentation. A markdown notebook in R (and the rendered PDF) contains an example on how to read the GDX files in R.</p> <p>We also include in this dataset a data frame saved in the <a href="https://parquet.apache.org/">Apache Parquet format</a> that can be read both <a href="http://arrow.apache.org/blog/2019/08/08/r-package-on-cran/">in R</a> and <a href="https://arrow.apache.org/docs/python/parquet.html">Python</a>.</p> <p>A description of the methodology and the data sources with the references can be found into the report.</p> <p><strong>Linked resources</strong></p> <ul> <li>Input files:<strong> </strong>https://zenodo.org/record/3775569#.XqqY3JpS-fc</li> <li>Source code for the figures: https://github.com/energy-modelling-toolkit/figures-JRC-report-power-system-and-climate-variability</li> </ul> <p><strong>Update</strong></p> <p>[29/06/2020] Updated new version of the Parquet file with the right data in the column `climate_year`</p>
SDG11_NL_Outputs
<p>This list of Digital Object Identifiers (DOI) represents the results of the Sustainable Development Goal (SDG) content classifier for Goal No.11 Sustainable Cities and Communities. 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>
SDG7_NL_Outputs
<p>This list of Digital Object Identifiers (DOI) represents the results of the Sustainable Development Goal (SDG) content classifier for Goal No.7 Affordable and Clean Energy. 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>
Output of Tax4Fun of predicted functional profiles. KO IDs and a KO abundance in every samples after a process of normalization.
<p>Supplementary Material Chapter 1.</p> <p> </p> <p>Table # 3: Output of Tax4Fun of predicted functional profiles. KO IDs and a KO abundance in every samples after a process of normalization.</p>
CAS FGOALS-f3-L model output
<p>The file includes the model output of the CAS FGOALS-f3-L which associated with the assessing of the aerosol effective radiative forcing.</p>
Clouds and Radiation in a mock-Walker Circulation model output
<p>Data used in the creation of figures for Silvers and Robinson, Clouds and Radiation in a mock-Walker Circulation. 2020, JAMES. </p> <p>These experiments are described in detail in the paper. They use the AM4.0 physics package on a doubly periodic domain with a warm patch of SST that is fixed in time at the center of the domain. Grid-Spacings of 1km, 2km, 25km, and 100km are explored on different domain sizes. The data set focuses on analysis of the atmospheric circulation and cloud response to this SST warm region.</p> <p>For questions please contact Levi Silvers at levi.silvers@stonybrook.edu, thomas.robinson@noaa.gov</p> <p>Directory contents are described below:</p> <p>#=========================================================================================<br> # data for the P100, P100L, P25, P25L, E25 experiments are in the following directories <br> #=========================================================================================<br> # P100<br> c8x40L33_am4p0_100km_wlkr_ent0p9<br> c8x40L33_am4p0_100km_wlkr_ent0p9_lwoff<br> # P100L<br> c8x160L33_am4p0_100km_wlkr_ent0p9<br> c8x160L33_am4p0_100km_wlkr_ent0p9_lwoff<br> # P25<br> c8x160L33_am4p0_25km_wlkr_ent0p9<br> c8x160L33_am4p0_25km_wlkr_ent0p9_lwoff<br> # P25L<br> c8x640L33_am4p0_25km_wlkr_ent0p9<br> c8x640L33_am4p0_25km_wlkr_ent0p9_lwoff<br> # E25<br> c8x160L33_am4p0_25km_wlkr_ent0p9_noconv<br> c8x160L33_am4p0_25km_wlkr_ent0p9_noconv_lwoff</p> <p># within the directories for each of these experiments: P100, P100L, P25, P25L, E25<br> # there are two files: 1979th1983_daily.nc and 1980th1983.atmos_month_tmn.nc</p> <p># within each of these files are the following variables: <br> 1979th1983_daily.nc<br> 'prec_conv'<br> 'prec_ls'<br> 'precip'</p> <p>1980th1983.atmos_month_tmn.nc<br> 'ps'<br> 'temp'<br> 'ucomp'<br> 'w'<br> 'sphum'<br> 'cld_amt'<br> 'z_full'<br> 'precip'<br> 'prec_conv'<br> 'prec_ls'<br> 'rh'<br> 'tot_liq_amt'<br> 'tot_ice_amt'<br> 'tdt_lw'<br> 'evap'<br> 'shflx'</p> <p><br> #=========================================================================================<br> # data for the E1 and E2 experiments are in the following directories <br> #=========================================================================================<br> # E1 <br> c10x4000L33_am4p0_1km_wlkr_4K<br> c10x4000L33_am4p0_1km_wlkr_4K_lwoff<br> # E2 <br> c50x2000L33_am4p0_2km_wlkr_4K<br> c50x2000L33_am4p0_2km_wlkr_4K_lwoff</p> <p># within each of these directories are two files: 1979_6mn.atmos_month.nc and 1979.6mn.atmos_daily_selvars.nc<br> # these files contain the following variables: </p> <p># 1979_6mn.atmos_month.nc<br> tdt_lw<br> t_surf<br> evap<br> shflx<br> tot_ice_amt<br> tot_liq_amt<br> rh<br> precip<br> z_full<br> cld_amt<br> sphum<br> ucomp<br> temp <br> w<br> ps<br> shflx</p> <p># 1979.6mn.atmos_daily_selvars.nc<br> t_surf<br> ps<br> precip</p> <p>Acknowledgment: Aparna Radhakrishnan supported the provision of this Zenodo DOI. <br> </p>
Raw data and analysis outputs for accompanying doi: 10.5281/zenodo.4064305
<p>These are the raw data and analysis outputs accompanying the GitHub repository archived here: 10.5281/zenodo.4064304</p> <p> </p>
Key input and output data for the multi-model analysis "Open Source Energiewende"
<p>This repository contains key input and output data of the multi-model analysis carried out in the project "Open Source Energiewende", financed by the German Federal Ministry for Economic Affairs and Energy.</p> <p>The results are presented and discussed in the paper "Power sector effects of cheaper stationary batteries: insights from an open multi-model analysis".</p> <p>The model codes are availabe in individual repositories, which are provided in the paper.</p>
Model output for: Attributing causes of future climate change in the California Current System with multi-model downscaling
<p>Regional Ocean Modeling System outputs from dynamic downscaling of Coupled Model Intercomparison Project climate forcings in the California Current system, including projections with full climate forcings, as well as attribution experiments with only changes in wind, heat fluxes and other properties changing stratification, and boundary biogeochemical forcings. Output variables include euphotic zone integrated net primary productivity, and incident photosytnehtically available radiation, and ocean temperature, salinity, vertical velocity, and dissolved oxygen and nitrate concentrations at select depths.</p>
Model output data for Marine Wild-Capture Fisheries after Nuclear War
<p>This is the model data material for Scherrer at al., PNAS. [Scherrer K. J. N., et al. Marine wild-capture fisheries after nuclear war, PNAS in press]. Input files include time series of gridded global oceanic Sea Surface Temperature and Net Primary Productivity (output from the CESM model) and socioeconomic input for the global fisheries model (BOATS). Output files include globally integrated time series (used in Figs. 1-3 and 6) and global gridded model output (used in Figs. 4-5) for each of the five ensemble member runs.</p>
Data archives for all sessions, consisting of probes and instructional sessions outputs for each of the three research participants
<p>Three participants, identified with fictitious names, were exposed to Instructional and Probe sessions to induce and test for Bidirectional Naming (BiN). This Dataset is composed by the records of both types of individual sessions, as recorded originaly by the computer and the experimenter. In the begginig of the files there are some general information, and below are the data for trials. Each line represents a trial and informs the response latency, the stimuli presented and the response given, as well as the expected response.<br> The data is in portuguese, but the authors are at disposal for any doubts. The name of the file indicates the participant and the study phase.</p>
quishqa/MUNICH_VEIN_SP: MUNICH and VEIN input and output data for Sao Paulo
<p>Here are the input and output data to create the results for the paper Simulation of O3 and NOX in Sao Paulo street urban canyons with VEIN (v0.2.2) and MUNICH (v1.0)</p>
Imperial College London COVID-19 outputs Jan-Sep 20
<p>Bibliographic records of Imperial College London COVID-19 research outputs.</p> <p>Includes journal articles, preprints, datasets, reports and software/code. </p> <p>Publication dates 01.01.2020 - 30.09.2020.</p> <p> </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.