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2,152 results for “outputs”
Planetary Atmosphere-Interior Model Outputs from Time-Evolution Movie
<p>This array contains the outputs from the movie version of Fig. 2 in Krissansen-Totton et al. (2021, Nature Astronomy). The numpy array can be loaded as follows</p> <p>Outputs = np.load("Output_arrays.npy",allow_pickle=True)</p> <p>The array has format Outputs[i][k][j]</p> <p>where i = 0, ... 5 (total_time, pO2, pCO2, pH2O, Tsurf, and AU)</p> <p>k = 0 , ..., 1437 is the iteration number</p> <p>j = 0 , ... , 2000 is the time index</p> <p>(AU is just a scalar providing orbital separation of that iteration)</p>
Heritage Connector data outputs
<p>Data outputs for the <a href="https://www.sciencemuseumgroup.org.uk/project/heritage-connector/">Heritage Connector project</a>.</p> <p>Contents:</p> <ul> <li>triples (.nt) and csv (.csv) files - the final knowledge graph produced. Includes data from Science Museum Group (SMG) and Victoria & Albert Museum (V&A) collections and Wikidata.</li> <li>triples (.nt) and csv (.csv) files - the knowledge graph produced from the SMG collection and Wikidata.</li> <li>kg_embeddings.zip - knowledge graph embeddings generated by training <a href="https://arxiv.org/abs/1902.10197">RotatE</a> models on the above datasets. Also includes 2D and 3D projections of these embeddings generated using UMAP.</li> </ul>
Output Data: gcam_v5.4
<p>Output data for GCAM official release v5.4.</p> <p>Please cite the GCAM v 5.4 model which produced this dataset when using this data:</p> <ul> <li>Bond-Lamberty et al. 2021. JGCRI/gcam-core: GCAM 5.4 (gcam-v5.4). Zenodo. https://doi.org/10.5281/zenodo.5093192</li> </ul> <p> </p>
Climate model output for "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"
<p>Climate model output associated with the manuscript "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"</p>
Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security
<p>Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)</p>
Model input and output data of the FlexMex model comparison
<p>This data collection includes the input and output data of the FlexMex model experiment (grant number: 03ET4077A-H) funded by the German Federal Ministry for Economic Affairs and Energy (BMWi). The aim of the FlexMex project is to better understand the interrelationships of modelling approaches and model results in the mapping and analysis of technical-structural flexibilities in future electricity systems.</p> <p>The data are separated in the two subfolders InputData and OutputData. For the input data, a distinction is made between scalar and time series data.</p> <p>Please find additional information on the models, test cases and analysis in the ReadMe and the publications cited there.</p>
Turner et al., 2021, urban water supply contributions and GAMUT output data
<p>Output from gamut (Geospatial Analytics for Multisectoral Urban Teleconnections) model supporting Turner et al. (2021) - https://www.nature.com/articles/s41467-021-27509-9</p> <p> </p> <p> </p>
FIG. 1 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 1. — Number of species published by semester from the end of the expedition, with first semester: July-December 2015, and last semester: January-July 2021. The expedition was accomplished in August 2015.
FIG. 4 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 4. —Density map of the shared data from the GBIF portal (https://www.gbif.org) for insects at the scale of the Guiana Shield. Only data with a high occurrence resolution (<1 km) are included.
FIG. 2 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 2. — Number of new (non-marine) animal species of French Guiana's fauna described since 2000. New species collected during the Mitaraka survey are indicated in colour.
FIG. 5 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 5. — Number of holotypes collected according to the different techniques used. Abbreviations: BS, beating sheet; HC, hand collecting; PVB, cross flight intercept trap with a blue LED; PVP, idem, with pink LED; PGL, idem, with GemLight; BPT, blue pan trap; SLAM, sea and land air malaise trap; SW & NS, sweeping & net sweeping; WPT, white pan trap; YPT, yellow pan trap (for a more detailed description of the techniques, see Touroult et al. 2018).
FIG. 6 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 6. — Level of compliance with recommendations for citation of expedition, ABS authorization and deposit of specimens according to two discriminating variables (n = 90 articles).
Model output, Khaled et al., Iron distribution in the aqueous phase (short title)
<p>Data for figures (Khaled et al., The number fraction of iron-containing particles affects OH, HO2 and H2O2 budgets in the atmospheric aqueous phase, Atmos. Chem. Phys.)</p>
Simulation outputs used in "Individual variation in dispersal, and its sources, shape the fate of pushed vs. pulled range expansions"
<p>Simulation outputs used in "Individual variation in dispersal, and its sources, shape the fate of pushed vs. pulled range expansions" (by Maxime Dahirel, Chloé Guicharnaud and Elodie Vercken)</p> <p>This is a copy for archiving purposes of the saved outputs from a NetLogo simulation model of range expansions, used in the analyses described in the following GitHub repository: https://github.com/mdahirel/pushed-pulled-2020-heritability-IBM (Zenodo archive: https://doi.org/10.5281/zenodo.5830993)</p> <p>This .csv file is the final output of the "generate_simulations" code in that repo, and feeds into in the "analyse_simulations" and "supplementary" codes. It is meant to be copied in the NetLogo_output folder of the aforementioned repo after download, in case one:</p> <p>- wants to check our analysis code</p> <p>- and does not want/ have time to re-run the simulations from scratch</p> <p>For any other information, including on the contents of the csv, please see the aforementioned repository</p> <p> </p>
Model output for "Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model"
<p>These data were produced with WRFlux v1.2.1 (https://github.com/matzegoebel/WRFlux/) from a numerical simulation with the community model WRF. Simulations represent the evolution of a convective boundary layer in the atmosphere over an idealized 2D mountain ridge. The data are published in connection with the article "Numerically consistent budgets of potential temperature, momentum and moisture in Cartesian coordinates: Application to the WRF model" in "Geoscientific Model Development" (https://doi.org/10.5194/gmd-15-669-2022).</p> <p>Three-dimensional (x, z, t) fields of five prognostic variables are provided: Potential temperature (T), water vapor mixing ratio (Q), cross-mountain (U), along-mountain (V), and vertical windspeed (W). All fields are averaged in time (30 min averaging interval) and in the along-mountain direction y.</p> <p>The repository contains the following files:</p> <p>grid.nc : variables related to the WRF numerical grid, air density<br> [U,W,T,Q]_flux.nc : resolved and subgrid-scale fluxes<br> [U,W,T,Q]_tendency.nc : resolved and subgrid-scale tendency components<br> UVWT_MEAN.nc : averaged values of the variables themselves<br> plotting.py : python script to approximately reproduce the figures of the paper. Requires the python packages matplotlib, xarray, and netcdf4.</p> <p>Figure 6 in the paper cannot be accurately reproduced with these data since the original figure uses 4D (x, y, z, t) output.</p> <p>For details on the simulation, refer to the article.</p>
The Lagrangian particle trajectory output and the metadata of the Southern Ocean Biogeochemical Divide location for 'Localizing the Southern Ocean Biogeochemical Divide'
<p>1. Trajectory files<br> <br> The Lagrangian particle trajectory output files from virtual particle release experiments at the surface and 500m depth using Connectivity Modeling System (Paris et al. 2013, https://github.com/beatrixparis/connectivity-modeling-system) run offline in the ACCESS-OM2-01 model (Kiss et al., 2020), a global 0.1° ocean sea-ice model, with a JRA55-do repeat year neutral state atmospheric forcing (Stewart et al., 2020).<br> <br> These trajectory datasets are compressed to two .rar format files for 2 depths, which were outputted from the Connectivity Modeling System v2.0 (CMS) in NetCDF format. Trajectory files include latitude, longitude, depth, interpolated along-track salinity and interpolated along-track temperature for each particle which are outputted every five days in the virtual particle tracking experiment. In addition, these datasets also contain the "exitcode" and release date information of each particle. More information please see in the CMS user guide. Other experiment setup files included in each release directory are "nest_1.nml", "runconf.list" and "ibm.list".<br> <br> These datasets are the original data output by the CMS. Limited by multiple nodes and maximum running time on the supercomputer, the surface release experiment is composed of 5 consecutive sub-experiments, and the 500m release experiment is composed of 3 consecutive sub-experiments. Each sub-experiment contains 48 independent output NetCDF files. <br> <br> 2. SOBD files<br> <br> These two .rar SOBD files are original arrays of the percentage of the upper cell minus the lower cell (i.e., the SOBD percentage) at surface and 500m depth (as presented in Fig.3 in Localizing the Southern Ocean Biogeochemical Divide).<br> <br> We provide original arrays in both .csv and .npz formats. More information can be found in the "Readme.txt" file in each .rar file.<br> <br> Citation of associated paper: Y. Xie, V. Tamsitt, L. T. Bach Localizing the Southern Ocean Biogeochemical Divide. <strong><em>to be submitted to Geophysical Research Letters</em></strong></p> <p><br> References:</p> <p>Kiss, A. E., Hogg, A. M., Hannah, N., Dias, F. B., Brassington, G. B., Chamberlain, A., . . . Stewart, K. D. (2020). ACCESS-OM2 v1 . 0 : a global ocean – sea ice model at three resolutions. <strong><em>Geoscientific Model Development</em></strong>,13, 401–442. doi: https://doi.org/10.5194/gmd-13-401-2020</p> <p>Paris, A. C. B., Vaz, A. C., Helgers, J., & Wood, S.(2017).Connectivity Modeling System User 's Guide CMS v 2 . 0. Retrieved from https://github.com/beatrixparis/connectivity-modeling-system</p> <p>Stewart, K. D., Hogg, A. M. C., England, M. H., & Waugh, D. W.(2020).Response of the Southern Ocean Overturning Circulation to Extreme Southern408Annular Mode Conditions. <strong><em>Geophysical Research Letters</em></strong>,47(22), 1–10. doi:10.1029/2020GL091103</p>
Output Data of the FlexMex Analysis Tool
<p>This dataset includes the results of the FlexMex model comparison. It relies in the input dataset published under https://doi.org/10.5281/zenodo.5802178 and the application of the FlexMex Analysis Tool published under https://doi.org/10.5281/zenodo.6010392.</p> <p>For further details please refer to the included file "HowTo_FlexMexAnalysisTool.pdf".</p> <p>The FlexMex project received funding from the German Federal Ministry of Economic Affairs and Climate Action (BMWK), formerly Federal Ministry of Economic Affairs and Energy (BMWi), under grant number 03ET4077A-H.</p>
Network of the European FAIR initiatives: relationships and outputs
<p>This network is an attempt at comprehensively describing the FAIR projects and initiatives in Europe, together with their area of focus, outputs and relationships with one another</p>
Sentiment Analysis outputs based on the combination of three classifiers for news headlines and body text
<p>Sentiment Analysis outputs based on the combination of three classifiers for news headlines and body text covering the Olympic legacy of Rio 2016 and London 2012. Data was searched via Google search engine. It is composed of sentiment labels assigned to 1271 news articles in total.</p> <p><strong>News outlets:</strong></p> <ul> <li>BBC</li> <li>Daily Mail</li> <li>The Telegraph</li> <li>The Guardian</li> <li>Globo</li> <li>Estadao</li> <li>Folha de S. Paulo</li> </ul> <p><strong>Events covered by the articles:</strong></p> <ul> <li>London 2012 Olympic legacy</li> <li>Rio 2016 Olympic legacy</li> </ul> <p>All classifiers were used in texts in English. Text originally published in Portuguese by the Brazilian media were automatically translated.</p> <p><strong>Sentiment classifiers used:</strong></p> <ul> <li>Vader</li> <li>BERT (Trained on Amazon data)</li> <li>BERT (Trained on twitter data - 140)</li> </ul> <p>Each document (spreadsheet - xlsx) refers to one outlet and one event (London 2012 or Rio 2016).</p> <p><strong>How were labels assigned to the texts?</strong></p> <p>These labels are a combination of the three sentiment classifiers listed above. If two of them agree with the same label, then this label would be considered as right. Otherwise, the label ‘other’ was assigned.</p> <p>For news article body text: the proportion of sentences of each sentiment type was used to assign labels to the whole article instead of averaging the sentence scores. For example, if the proportion of sentences with negative labels is greater than 50%, then the article is assigned a negative label.</p> <p><strong>The documents are composed of the following columns:</strong></p> <ul> <li>Rank: the position of the article on Google search ranking</li> <li>Date: date of article's publication (DD/MM/YYYY)</li> <li>Link: article's link</li> <li>Title: article's title</li> <li>Sentiment_Title: final sentiment for article headline</li> <li>Sentiment_Text: final sentiment for article's body text</li> </ul> <p><em>PS: Documents do not include articles' body text. </em></p> <p><strong>Sentiment is presented in labels as follows:</strong></p> <ul> <li>Pos: Positive</li> <li>Neg: Negative</li> <li>Neutral: Neutral</li> <li>other: inconclusive - if each of the 3 classifiers assigned a different label to the article, the label 'other' was used. Therefore, 'other' identifies contradictory results.</li> </ul> <p> </p>
TELL sample output data
<p>This dataset contains sample output data for TELL. The sample dataset includes four years of sample future data (2039, 2059, 2079, and 2099) that comes from IM3's future WRF runs under the RCP 8.5 climate scenario with SSP5 population forcing. Note that the GCAM-USA output used in this simulation is sample data only. As such the quantitative results from this set of sample output should not be considered valid.</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.