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3,481 results for “data set”
On the formulation and implementation of extrinsic cohesive zone models with contact - data set
<p>This data set contains data relating to the paper "On the formulation and implementation of extrinsic cohesive zone models with contact", <a href="https://doi.org/10.1016/j.cma.2022.115545">https://doi.org/10.1016/j.cma.2022.115545</a> , specifically:<br> 1. the meshes used to conduct finite element analyses,<br> 2. the results of those finite element analyses (in the form of vtk files and numpy pickles), and<br> 3. some images of the meshes and the total displacement at the end of the analyses.<br> <br> The corresponding code to generate and read the data is available at https://github.com/nickcollins-craft/On-the-formulation-and-implementation-of-extrinsic-cohesive-zone-models-with-contact (which is the preferred method), or alternatively via https://doi.org/10.5281/zenodo.6939391.</p>
Data set used in glacier algae and filamentous cyanobacteria models
<p>This is a data set for using the glacier algae and filamentous cyanobacteria models (Onuma et al., 2022). The content is as below.</p> <p>- data: observed data (bio-volume, cell count, mineral weight, EC, pH and meteorological conditions) on the bare ice surface in Qaanaaq Ice Cap (CSV files). And, model input and output data (CSV files). About the detailed information on each file, please see the readme files.</p> <p>- python: programs for the visualization (python scripts)</p> <p>- figure: png files created by the python scripts</p> <p>The codes of the glacier algae model can be downloaded below.<br> https://github.com/YukihikoOnuma/SnowAlgaeModel<br> <br> The article regarding the models is as below.<br> https://doi.org/10.1017/jog.2022.76</p>
Point cloud data sets of real and virtual Chenopodium alba
<p>This data set contains:</p> <p>- 5 annotated point clouds of real Chenopodium alba plants obtained from multi-view 2D camera imaging. Annotations consist of 5 classes: leaf blade, petiole, apex, main stem, branch. .txt files contain both 3D coordinates and annotations. .ply files are also provided for raw 3D point data without annotations.</p> <p>- 24 annotated point clouds of virtual Chenopodium alba that were generated by a L-system simulation program. Annotations consist of 3 classes: leaf blade, petiole, stem. 3D coordinates and annotations are in separated .txt files. </p> <p>These files have been used in a companion paper.</p> <p> </p>
Figure Sets and Data Associated with AJ Publication: "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M"
<p>Images for Figure Sets 4, 5, 6, 7, and 9 and data behind the figure for Figure 15 from the AJ publication "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M" (Currently accepted and in press.). Figure sets and file names are described in the fsREADME file. Data behind the figure is described in the dbfREADME file.</p>
Data set: Average daily minimum temperature in January and February in Corsica
<p>Raster providing the average of the daily minimum temperature in Celsius degrees over January and February in Corsica from 1995 to 2003 with a 0.016667x0.0166671 resolution in latitude and longitude.</p> <p>Construction: This raster was constructed from the freely available database (PVGIS © European Communities, 2001-2008) providing, in particular, monthly averages of the daily minimum temperature reconstructed over a grid with 1$\times$1km spatial resolution (Huld et al., 2006). These monthly averages correspond to the period 1995-2003 and were used by Abboud et al. (2019, 2020) to model Xylella fastidious dynamics in South Corsica.</p> <p>Load the raster in the R statistical software (v4.1.2):<br> library(raster)<br> ADMT=raster("average-daily-minimum-temperature_Corsica_Abboud-et-al_Forecasting.grd")<br> print(ADMT)<br> plot(ADMT)</p> <p>Summary information:<br> class : RasterLayer <br> dimensions : 108, 78, 8424 (nrow, ncol, ncell)<br> resolution : 0.016667, 0.016667 (x, y)<br> extent : 8.400708, 9.700734, 41.30018, 43.10021 (xmin, xmax, ymin, ymax)<br> crs : +proj=longlat +datum=WGS84 +no_defs <br> source : average-daily-minimum-temperature_Corsica_Abboud-et-al_Forecasting.grd <br> names : layer <br> values : -0.6748945, 6.75789 (min, max)</p> <p>References:<br> - Abboud, C., Bonnefon, O., Parent, E., and Soubeyrand, S. (2019). Dating and localizing an invasion from post-introduction data and a coupled reaction–diffusion–absorption model. Journal of Mathematical Biology 79, 765–789.<br> - Abboud, C., Parent, E., Bonnefon, O., and Soubeyrand, S. (2022). Forecasting pathogen dynamics with Bayesian model-averaging: Application to Xylella fastidiosa. Preprint.<br> - Huld, T. A., Suri, M., Dunlop, E. D., and Micale, F. (2006). Estimating average daytime and daily temperature profiles within Europe. Environmental Modelling & Software 21, 1650–1661.</p>
Data set from: Rates of Compact Object Coalescences
<p><strong>Data from: Rates of Compact Object Coalescence </strong></p> <p><strong>Brief overview: </strong><br> This Zenodo entry contains the data that has been used to make the figures for the living review <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract">"Rates of Compact Object Coalescence" by Ilya Mandel & Floor Broekgaarden (2021)</a>. To reproduce the figures, download all the <strong>*.csv</strong> files and run the jupyter notebook created to reproduce the results in the publicly available Github directory <a href="https://github.com/FloorBroekgaarden/Rates_of_Compact_Object_Coalescence">https://github.com/FloorBroekgaarden/Rates_of_Compact_Object_Coalescence</a> (the exact jupyter notebook can be found <a href="https://github.com/FloorBroekgaarden/Rates_of_Compact_Object_Coalescence/tree/main/plottingCode/Make_figures_Mandel_and_Broekgaarden_2021_COC_rates_review.ipynb">here</a>)</p> <p>For any suggestions, questions or inquiry, please email one, or both, of the authors: </p> <ul> <li><strong>Ilya Mandel</strong>: <em>ilya.mandel@monash.edu</em> </li> <li><strong>Floor Broekgaarden</strong>: <em>floor.broekgaarden@cfa.harvard.edu</em></li> </ul> <p>We very much welcome suggestions for additional/missing literature with rate predictions or measurements. </p> <p> </p> <p><strong>Extra figures:</strong><br> Extra figures that can be used can be found here:</p> <p><strong>Vertical figures: <a href="https://docs.google.com/presentation/d/1GqJ0k2zpnxBGwIYNeQ0BfsLSU7H2942gspL-PN_iaJY/edit?usp=sharing">https://docs.google.com/presentation/d/1GqJ0k2zpnxBGwIYNeQ0BfsLSU7H2942gspL-PN_iaJY/edit?usp=sharing</a> </strong></p> <p><br> The authors are currently working on making an interactive tool for plotting the rates that will be available soon. In the mean time, feel free to send requests for plots/figures to the authors. </p> <p><strong>Reference</strong><br> If you use this data/code for publication, please cite both the paper: <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract">Mandel & Broekgaarden (2021)</a> (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract</a>) and the dataset on Zenodo through it's doi (see tabs on the right of this zenodo entry) <br> <br> <strong>Details datafiles: </strong></p> <p>The PDF <strong>COC_rates_supplementary_material.pdf</strong> attached (and in the Github repository) describes how each of the rates in the data files of this Zenodo entry are retrieved. The other 26 files are .csv files, where each csv file contains the rates from one specific double compact object type: NS-NS, NS-BH or BH-BH, and specific rate group (isolated binary evolution, gravitational wave observations etc.). The files in this entry are: </p> <p> </p> <ul> <li><strong>Data_Mandel_and_Broekgaarden_2021.zip </strong>all the files below conveniently in one zip file so that you only have to do 1 download. <br> </li> <li><strong>COC_rates_supplementary_material.pdf </strong> # PDF document describing how the rates are retrieved and quoted rom each study<br> </li> <li><strong>BH-BH_rates_CHE.csv</strong> # BH-BH rates for chemically homogeneous evolution </li> <li><strong>BH-BH_rates_flybys.csv </strong> # BH-BH rates for formation from wide isolated binaries with dynamical interactions from flybys</li> <li><strong>BH-BH_rates_globular-clusters.csv</strong> # BH-BH rates for dynamical formation in globular clusters </li> <li><strong>BH-BH_rates_isolated-binary-evolution.csv</strong> # BH-BH rates for isolated binary evolution </li> <li><strong>BH-BH_rates_nuclear-clusters.csv</strong> # BH-BH rates for (dynamical )formation in (active) nuclear star clusters</li> <li><strong>BH-BH_rates_observations-GWs.csv</strong> # BH-BH rates for observations from gravitational waves</li> <li><strong>BH-BH_rates_population-III.csv</strong> # BH-BH rates for population-III stars </li> <li><strong>BH-BH_rates_primordial.csv </strong> # BH-BH rates for primordial formation</li> <li><strong>BH-BH_rates_triples.csv</strong>. # BH-BH rates for formation in (hierarchical) triples </li> <li><strong>BH-BH_rates_young-stellar-clusters.csv</strong> # BH-BH rates for dynamical formation in young/open star clusters <br> </li> <li><strong>NS-BH_rates_CHE.csv</strong> # NS-BH rates for chemically homogeneous evolution </li> <li><strong>NS-BH_rates_flybys.csv </strong> # BH-BH rates for formation from wide isolated binaries with dynamical interactions from flybys</li> <li><strong>NS-BH_rates_globular-clusters.csv</strong> # NS-BH rates for dynamical formation in globular clusters </li> <li><strong>NS-BH_rates_isolated-binary-evolution.csv. </strong># NS-BH rates for isolated binary evolution </li> <li><strong>NS-BH_rates_nuclear-clusters.csv</strong> # NS-BH rates for (dynamical )formation in (active) nuclear star clusters</li> <li><strong>NS-BH_rates_observations-GWs.csv</strong> # NS-BH rates for observations from gravitational waves</li> <li><strong>NS-BH_rates_population-III.csv</strong> # NS-BH rates for population-III stars </li> <li><strong>NS-BH_rates_triples.csv</strong> # NS-BH rates for formation in (hierarchical) triples </li> <li><strong>NS-BH_rates_young-stellar-clusters.csv</strong> # BH-BH rates for dynamical formation in young/open star clusters<br> </li> <li><strong>NS-NS_rates_globular-clusters.csv </strong># NS-NS rates for dynamical formation in globular clusters </li> <li><strong>NS-NS_rates_isolated-binary-evolution.csv </strong> # NS-NS rates for isolated binary evolution </li> <li><strong>NS-NS_rates_nuclear-clusters.csv </strong># NS-NS rates for (dynamical )formation in (active) nuclear star clusters</li> <li><strong>NS-NS_rates_observations-GWs.csv</strong> # NS-NS rates for observations from gravitational waves</li> <li><strong>NS-NS_rates_observations-kilonovae.csv</strong> # NS-NS rates for observations from kilonovae</li> <li><strong>NS-NS_rates_observations-pulsars.csv</strong> # NS-NS rates for observations from Galactic pulsars</li> <li><strong>NS-NS_rates_observations-sGRBs.csv </strong># NS-NS rates for observations short gamma-ray bursts</li> <li><strong>NS-NS_rates_triples.csv </strong># NS-NS rates for formation in (hierarchical) triples </li> <li><strong>NS-NS_rates_young-stellar-clusters.csv</strong> # NS-NS rates for dynamical formation in young/open star clusters </li> </ul> <p> </p> <p><strong>Each csv file contains the following header: </strong><br> ADS year # year of the paper in the ADS entry<br> ADS month # month of the paper in the ADS entry <br> ADS abstract link # link to the ADS abstract <br> ArXiv link # link to the ArXiv version of the paper <br> First Author # name of the first author<br> label string # label of the study, that corresponds to the label in the figure<br> code (optional) # name of the code used in this study <br> type of limit (for plotting, see jupyter notebook for a dictionary) # integer, that is used to map to a certain limit visualization in the plot (e.g. scatter points vs upper limit). </p> <p>Each entry takes two columns in the csv files. One for the rates (quoted under the header 'rate [Gpc^-3 yr^-1]') and one for "notes" where we sometimes added notes about the rates (such as whether it is an upper or lower limit). </p> <p> </p>
Data Set for_Integrating torrefaction of pulp industry sludge with anaerobic digestion to produce biomethane and volatile fatty acids: An example of industrial symbiosis for circular bioeconomy
<p>Industrial symbiosis, which allows the sharing of resources between different industries, could help to improve the overall feasibility of bio-based chemicals production. In that regard, this study focused on integrating the torrefaction of pulp industry sludge with anaerobic digestion. More specifically, anaerobic digestion (AD) of pulp sludge-derived torrefaction condensate (TC) was studied to evaluate the biomethane and volatile fatty acid (VFA) potential. The torrefaction condensate produced at 275 and 300 °C was used in AD. The volatile solid content (VS) was 6.69 and 9.01% for the condensate produced at 275 and 300 °C, respectively. The organic fraction of TC mainly contained acetic acid, 2-furanmethanol, and syringol. The methane yield was in the range of 481–772 mL/g VS for the mesophilic and 401–746 mL/g VS for the thermophilic process, respectively. The VFA yield was in the range of 1.1 to 3.4 g/g VS for mesophilic and from 1.5 to 4.7 g/g VS in thermophilic conditions, when methanogenesis was inhibited. Finally, pulp sludge TC is a feasible feedstock to produce platform chemicals like VFA. However, at higher substrate loading, signs of process inhibition were observed because of the relatively increasing concentration of microbial inhibitors</p>
A data set of monthly global ocean vertical velocity from 1950-2014
<p>This data set provides monthly global ocean vertical velocity from 1950-2014. It was constructed from 41 CMIP6 models (historical experiment). It may be used for investigating the large-scale upwelling and downwelling.</p> <p>Note that this data set has not been widely tested. Please feel free to contact the author if you had any questions or concerns.</p> <p>It will be greatly appreciated if you could send the author an email when you used this data set, so that the author can better improve this data set, and more importantly, provide you with updated data sets or any modifications.</p> <p> </p> <p> </p>
hiPSC 3D immunofluorescence images, test data set 2x2, 10Z
<p>Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a small subset of a larger experiment intended as a test dataset for Fractal: https://github.com/fractal-analytics-platform/fractal</p> <p>3 Channels were imaged:</p> <p>- C01: DAPI, nuclear stain</p> <p>- C02: nanog, antibody staining with Bio-Techne AG, AF1997-SP, Lot KKJ0617121 for the stemness marker nanog</p> <p>- C03: Lamin B1, antibody staining with Abcam, ab16048, Lot GR3244890-2 for the nuclear envelope marker Lamin B1</p> <p> </p> <p>This dataset contains 10 Z levels for 4 field of views for those 3 channels, as well as (manually adjusted) metadata files from the Yokogawa CV7000.</p> <p> </p> <p>The data was acquired in the Pelkmans lab in August 2020. The images have been converted from TIFF into PNG (lossless). </p>
Data set for the manuscript 'Polarization-controlled chromo-encryption'
<p>In this dataset, there are 1 pdf and 2 zip files.</p> <p>The manuscript (<strong><em>Zenodo OpenData for chromo encryption<em>.</em>pdf</em></strong>) contains the figures of simulated and measured spectra.</p> <p>The corresponding raw data can refer to the 2 zip files (<strong><em>Simulated.zip </em></strong>and<strong><em> </em></strong><strong><em>Measured<em>.</em>pdf</em></strong>).</p>
Data set for the publication entitled "Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis"
<p>Set of files containing data used for microbiota analysis by 16S rRNA amplicon sequencing.</p> <p>The study cohort included patients with idiopathic pulmonary fibrosis from four centres in Switzerland, treated with azithromycin or placebo, sampled sequentially by oropharyngeal swab.</p> <p>This work is available in medRxiv and has been submitted</p>
Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"
<p>This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld, M. Finkbeiner, J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”<strong><em>,</em></strong> <em>Sustainable Energy Fuels</em>, <strong>2022</strong>, <strong>6</strong>, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>.</p> <p>It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.</p>
Monitoring open access publishing of NWO funded research (2015-2021) data set
<p>This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (<a href="https://doi.org/10.5281/zenodo.7041897">https://doi.org/10.5281/zenodo.7041897</a>)</p> <p>The report presents statistics on the extent to which publications from the period 2015–2021 funded by NWO are available in Open Access. The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw. This report builds on two earlier reports, published in <a href="https://zenodo.org/record/4446042">2020</a> and <a href="https://zenodo.org/record/5056043">2021</a>, covering publications from the period 2015–2018 and 2015-2020, respectively.</p>
Data set: Australia's hidden radiation - phylogenomic analysis reveals rapid Miocene radiation of blindsnakes
<p>This repository contains the additional raw data to accompany our paper entitled "Australia’s hidden radiation: phylogenomic analysis reveals rapid Miocene radiation of blind snakes."</p> <p>This project is part of the AusARG Initiative funded by BioPlatforms Australia.</p> <p>Raw sequences data can be downloaded from the BioPlatforms downloads portal: https://data.bioplatforms.com/dataset?q=ticket%3ABPAOPS-1196</p> <p><strong>Information about files</strong></p> <ol> <li>ASTRAL_tree_SqCL_AHE.tre - output from ASTRAL-III just with SqCL data + outgroups</li> <li>ASTRAL_tree_SqCL_AHE_Ramphotyphlops.tre - same with above but also including additional <em>Ramphotyphlops </em>genes.</li> <li>mcmctree_1.txt - mcmcfile output from MCMCTree analysis using all SkewT or SkewNormal distribution priors.</li> <li>mcmctree_2.txt - mcmcfile output from MCMCTree analysis using SkewT, SkewNormal, and cauchy distribution priors. **This is the tree used in our publication**</li> <li>mcmctree_strategy1.tre - output phylogeny 1</li> <li>mcmctree_strategy2.tre - output phylogeny 2</li> <li>IQTREE_gcf_scf.nex - gene concordance and site factors for mcmctree_strategy2.tre</li> </ol> <p>tree_data/ folder contains concatenated gene trees (IQTREE) and corresponding shortcut coalescent method (ASTRAL-III) tree.</p> <p>Should there be questions regarding the code and data set, please contact the corresponding author.</p>
Data set for "Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel"
<p>Here we share the relevant data of the manuscript “Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel”.</p> <p>Files:</p> <ul> <li>Opt_Fr_stability_part1.txt</li> <li>Opt_Fr_stability_part2.txt</li> </ul> <p>contain the data used for evaluation of optical frequency transfer stability (Fig. 7 in the paper). The measurements were done with 8-channels K+K phase/frequency recorder. Column 1 contains date, col. 2: time, col. 5: in-loop beatnote phase, col. 6: out-of-loop beatnote phase. The phase is recorded in cycles. In case of out-of-loop beatnote it was divided by factor of two before recording, therefore the data from col. 6 should be multiplied by two to obtain true values of the optical phase fluctuations.</p> <p>File:</p> <ul> <li>RF_stability.txt</li> </ul> <p>contains the data used for evaluation of RF frequency transfer stability (Fig. 8 in the paper). Column 1 contains time in hours, and col. 2 RF phase fluctuations in seconds.</p>
frog scRNA data set objects
<p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p> <p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Briggs et al. 2018 and Qiu et al. 2022.</p>
zebrafish scRNA data set objects
<p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p> <p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Farrel et al. 2018, Wagner et al. 2018 and Qiu et al. 2022.</p>
Nitric oxide (NO) data set (60--160 km) from SCIAMACHY nominal limb scans
<p><strong>Overview</strong><br> Contains the nitric oxide (NO) number densities (in cm<sup>-3</sup>) from 60 km to 160 km retrieved from SCIAMACHY nominal (~0--90 km) limb scans.</p> <p>SCIAMACHY is a UV-visible-near-infrared spectrometer which flies on ESA's Envisat and was operational from 08/2002 to 04/2012 (see Burrows et al., 1995 and Bovensmann et al., 1999 and references therein). The nominal limb mode was carried out daily (apart from outages and a few days dedicated to other measurement modes) from 08/2002 until the end of the mission. The limb scans were performed from ground to about 90 km tangent altitude, and the retrieval was performed on a 2.5° x 2 km latitude--altitude grid from 90°S--90°N and from 60 km--160 km. This data set comprises all SCIAMACHY nominal NO measurements sorted by date and year, each day comprised about 15 orbits. See the accompanying README for the dimension and variable descriptions.</p> <p>The NO retrieval was carried out at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, and is described in Bender et al., 2017. It is adapted from the MLT NO retrieval described in Bender et al., 2013. We used the SCIAMACHY geo-located atmospheric spectra (SCI_NL__1P) version 8.02 provided by ESA via their data browser at<br> https://earth.esa.int/web/guest/data-access/browse-data-products.<br> The spectra were calibrated with ESA's `SciaL1C` command line tool available for download at<br> https://earth.esa.int/web/guest/software-tools/content/-/article/scial1c-command-line-tool-4073.</p> <p>The SCIAMACHY MLT NO data were previously compared to the results from ACE-FTS, MIPAS, and SMR in Bender et al., 2015, showing that all agree within the respective measurement uncertainties. This nominal data set here was not yet validated with other measurements but compares well to the SCIAMACHY MLT NO measurements below 90 km.</p> <p><strong>Acknowledgements</strong><br> The development of the retrieval was funded by the Helmholtz-society under the grant number VH-NG-624. The SCIAMACHY project, which was initiated by Professor Burrows in 1984, was funded by the German Aerospace Agency (DLR), the Netherlands Space Office NSO, formerly NIVR, and the Belgium ministry responsible for space. ESA funded the Envisat project. Professor Burrows of University of Bremen is the Principal Investigator. He and his research team comprising his colleagues in Bremen and international scientific collaborators led the scientific support and development of SCIAMACHY and the scientific exploitation of its data products.</p> <p>The SCIAMACHY instrument is developed by an industrial team headed by companies now known as Airbus SD on the German side and by Dutch Space on the Dutch side and included Belgium companies. The instrument and algorithm development is supported by the activities of the SCIAMACHY Science Advisory Group (SSAG), a team of scientists from various international institutions: University of Bremen (D), SRON (NL), SAO (USA), IASB (B), MPI Chemistry Mainz (D), KNMI (NL), University of Heidelberg (D), IMGA (I), CNRS-LPMA (F). Operational data processing is being performed by ESA and DLR-DFD within the ENVISAT ground segment. Support with respect to mission planning and operations is given by the SCIAMACHY Operations Support Team (SOST). The relevant work at the University of Bremen is funded by the University and State of Bremen.</p>
Mars orbital image (HiRISE) labeled data set
<p>This data set contains 3820 landmarks that were extracted from 168 HiRISE images. The landmarks were detected in HiRISE browse images. For each landmark, we cropped a square bounding box the included the full extent of the landmark plus a 30-pixel margin to left, right, top, and bottom. Each cropped image was then resized to 227x227 pixels.</p> <p><strong>Contents</strong>:</p> <ul> <li>map-proj/: Directory containing individual cropped landmark images</li> <li>labels-map-proj.txt: Class labels (ids) for each landmark image</li> <li>landmark_mp.py: Python dictionary that maps class ids to semantic names</li> </ul> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI: 10.5281/zenodo.1048301</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p> <p> </p>
Mars Target Encyclopedia - LPSC abstracts labeled data set
<p>This data set contains annotated text versions of 2-page abstracts published at the Lunar and Planetary Science Conference in 2015 and 2016.</p> <p>The original PDF abstracts are available at:</p> <ul> <li>https://www.hou.usra.edu/meetings/lpsc2015/programAbstracts/view/</li> <li>https://www.hou.usra.edu/meetings/lpsc2016/programAbstracts/view/</li> </ul> <p>The text files in this archive were extracted using the Apache Tika PDF parsing tool. The text is provided here so that the annotations can be viewed. The text content remains copyright of the original abstract authors.</p> <p>The annotations (entities and relations) are provided in the format used by the brat annotation tool. To view the annotations in a web-based graphical form, install the brat tool (http://brat.nlplab.org/). These annotations were generated using brat v1.3. The annotation files are also human-readable and can be parsed in to be used directly in code.</p> <p><strong>Contents</strong>:</p> <ul> <li>lpsc15/: 62 abstracts</li> <li>lpsc16/: 55 abstracts</li> </ul> <p>Each directory contains a .txt and .ann file for each abstract. The .ann file is in brat standoff format (http://brat.nlplab.org/standoff.html).</p> <p>Additional .conf files are provided to generate color highlighting and keyboard shortcuts. These are used by the brat tool.</p> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI:</p> <p>10.5281/zenodo.1048419</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, Raymond Francis, Thamme Gowda, You Lu, Ellen Riloff, Karanjeet Singh, and Nina Lanza. "Mars Target Encyclopedia: Rock and Soil Composition Extracted from the Literature." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</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.