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1,079 results for “source data”
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach.
<p>The products can be vizualized at <a href="https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer">https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer</a></p> <p>- FORMS-H: Canopy height map of France at 10 m resolution. The units are in centimeter (10^-2 m).</p> <p>- FORMS-B: Above-ground biomass density map of France at 30 m resolution. The units are in Mg ha-1</p> <p>- FORMS-V: Wood volume density map of France at 30 m resolution. The units are in m3 ha-1</p> <p>Please refer to the paper <a href="https://doi.org/10.5194/essd-15-4927-2023">https://doi.org/10.5194/essd-15-4927-2023</a> for further details.</p>
Source data for manuscript "Real-time microscopy of the relaxation of a glass".
<p>Source data for manuscript "Real-time microscopy of the relaxation of a glass" (DOI: 10.1038/s41567-023-02125-0), including:</p> <p>- AFM source images</p> <p>- data points for all plots in the manuscript</p>
Supplementary Movies and Source Data for: Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation
<p>Supplementary Movies and raw data for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation":</p> <p>Source_Data.zip: Supplementary Code, Supplementary Data and Weka Analysis</p> <p>Lan_supplementary_movies_AVI.zip: Supplementary movies as AVI</p> <p>Lan_supplementary_movies_MP4.zip: Supplementary movies as MP4</p> <p>Lan_raw_movies.zip: Raw TIFF stacks of the movies.</p> <p>Lan_supplementary_movies.zip: Old version of the movies.</p>
Covid-19 Vaccine Monitoring project (CVM)-Electronic Health Record data sources Codelist
<p>This is the code list that was used to identify outcomes and covariates (those tagged as in narrow) in electronic health records of participating data sources in the the CVM study which was addressing the following questions</p> <p> </p> <p>1)<strong> To create and assess readiness of electronic health record data sources for rapid evaluation of safety signals by </strong></p> <ul> <li> <p>Providing an overview of the methods for identification of COVID-19 vaccine exposure in the data sources </p> </li> <li> <p>Monitoring the number of individuals exposed to any COVID-19 vaccine and to compare this to COVID-19 vaccine exposure (benchmark: ECDC vaccine tracker)1 </p> </li> <li> <p>Generation of updated background rates for AESIs </p> </li> </ul> <p><strong>2) To conduct rapid safety assessment studies using electronic healthcare records and support EMA safety assessments. </strong></p> <p>The protocol for this study is publicly available www.encepp.eu/encepp/viewResource.htm?id=42637. The report with results using the code list is publicly available on Zenodo as well. </p> <p> </p> <p> </p> <p> </p>
Source data for "Feed-forward metabotropic signaling by Cav1 Ca2+ channels supports pacemaking in pedunculopontine cholinergic neurons"
<p><strong>Fig.1A_ChAT.tif</strong></p><p>Confocal image (green channel, anti-ChAT staining) for Fig.1A</p><p> </p><p><strong>Fig.1A_tdTomato.tif </strong></p><p>Confocal image (red channel, tdTomato) for Fig.1A</p><p> </p><p><strong>Fig.1B_ChAT.tif</strong></p><p>Confocal image (green channel, anti-ChAT staining) for Fig.1B</p><p> </p><p><strong>Fig.1B_tdTomato.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.1B</p><p> </p><p><strong>Fig.1C_DIC.png</strong></p><p>Differential interference contrast micrograph for Fig.1C left</p><p> </p><p><strong>Fig.1C_Fluo.png</strong></p><p>Epifluorescent illumination micrograph for Fig. 1C right</p><p> </p><p><strong>Fig.1DEH.xlsx</strong></p><p>Numerical data for the charts in Fig. 1D, Fig.1E, Fig.1H</p><p> </p><p><strong>Fig.1F.tif</strong></p><p>MAX projection of z-stack of 2PLSM images (red channel, Alexa 594) used to generate Fig.1F </p><p> </p><p><strong>Fig.1F_inset.tif</strong></p><p>2PLSM image (green channel, Fura-2) for the right inset of Fig.1F</p><p> </p><p><strong>Fig.2A_inset.tif</strong></p><p>Confocal image (green channel, GFP) for the higher magnification inset of Fig.2A</p><p> </p><p><strong>Fig.2A.tif</strong></p><p>Confocal image (green channel, GFP) for Fig.2A</p><p> </p><p><strong>Fig.2B_bottom.tif</strong></p><p>Confocal image (green channel, GFP) for Fig.2B (bottom and overlay panels)</p><p> </p><p><strong>Fig.2B_top.tif</strong></p><p>Confocal image (red channel, td Tomato) for Fig.2B (top and overlay panels)</p><p> </p><p><strong>Fig.2CE.xlsx</strong></p><p>Numerical data for the charts in Fig. 2C, Fig. 2E</p><p> </p><p><strong>Fig.3B.tif</strong></p><p>Confocal image (green channel, MitoGCaMP6) for Fig.3B and overlay in Fig.3D</p><p> </p><p><strong>Fig.3C.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.3C and overlay in Fig.3D</p><p> </p><p><strong>Fig.3E.tif</strong></p><p>2PLSM image (green channel, MitoGCaMP6) for Fig.3E</p><p> </p><p><strong>Fig.3GIJ.xlsx</strong></p><p>Numerical data for the charts in Fig. 3G, Fig. 3I, Fig.3J</p><p> </p><p><strong>Fig.4B.tif</strong></p><p>2PLSM image (green channel, MitoGCaMP6) for Fig.4B</p><p> </p><p><strong>Fig.4DFG.xlsx</strong></p><p>Numerical data for the charts in Fig.4D, Fig.4F, Fig.4G</p><p> </p><p><strong>Fig.5A.tif</strong></p><p>Confocal image (green channel, PercevalHR) for Fig.5A and overlay in Fig.5C</p><p> </p><p><strong>Fig.5B.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.5B and overlay in Fig.5C</p><p> </p><p><strong>Fig.5D.tif</strong></p><p>2PLSM image (green channel, PercevalHR) for Fig.5D</p><p> </p><p><strong>Fig.5GHJ.xlsx</strong></p><p>Numerical data for the charts in Fig.5G, Fig.5H, Fig.5J</p><p> </p><p><strong>Fig.6BCD.xlsx</strong></p><p>Numerical data for the charts in Fig.6b, Fig.6C, Fig.6D</p><p> </p><p><strong>Fig.7A.tif</strong></p><p>Confocal image (green channel, mito-roGFP) for Fig.7A and overlay in Fig.7C</p><p> </p><p><strong>Fig.7B.tif</strong></p><p>Confocal image (red channel, tdTomato) for Fig.7B and overlay in Fig.7C</p><p> </p><p><strong>Fig.7D.tif</strong></p><p>2PLSM image (green channel, mito-roGFP) for Fig.7D</p><p> </p><p><strong>Fig.7F.xlsx</strong></p><p>Numerical data for the charts in Fig.7F</p>
The dataset from a submitted journal entitled "Characterization of the Mamasa earthquake source in West Sulawesi based on the earthquake relocation data, gravity data, and coulomb stress change of Palu earthquake series"Dataset for paper
<p>This dataset consists of four files, namely:<br> 1. Coulomb Stress Input file. This data is input data for Coulomb 3.3 software<br> 2. Double Couple Percentage. This table is used for the Spatio-temporal Compensated Linear Vector Dipole (CLVD) analysis<br> 3. Gravity data. This data consists of coordinates, altitude, and Complete Bouguer Anomaly.<br> 4. Residual comparison of before and after the relocation. This table is to ensure that our relocation is successful</p>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
Genetic data and underlying taxa and GenBank sources of diatoms used in phylogenetic analysis for the diatom genus Nupela
<p>Supplementary material for the manuscript: Kulikovskiy M., Maltsev Y., Glushchenko A., Gusev E., Kapustin D., Kuznetsova I., Kociolek J.P. Preliminary molecular phylogeny of the diatom genus <em>Nupela</em> with the description of a new species and consideration of the interrelationships of taxa in the suborder Neidiineae D.G. Mann sensu E.J. Cox. Fottea</p> <p>Molecular investigation of diatom genera <em>Nupela</em> and <em>Brachysira</em> is conducted using strains from Indonesia and Vietnam. New species from the genus <em>Nupela indonesica</em> sp. nov. is described using combined approach. <em>Nupela lesothensis</em> (Schoeman) Lange-Bertalot is investigated using molecular data too. Phylogenetic analysis shows that <em>Nupela</em> and <em>Brachysira</em> are not closest genera. Morphology of <em>Nupela</em> and it differences from <em>Brachysira</em> is discussed. The genus <em>Nupela</em> is differs from all other diatom taxa by having coalescent hymenes ouside of areolae but not inside. Facultative development of raphe between different <em>Nupela</em> species is discussed.<br> SUPPLEMENT S1. Taxa and DNA sequence data used in phylogenetic analysis.<br> SUPPLEMENT S2. Final alignment of 2-gene DNA sequence data used for phylogenetic analysis in FASTA format.<br> SUPPLEMENT S3. Maximum Likelihood tree of <em>Nupela</em> species (indicated in bold) constructed from a concatenated alignment of 163 partial rbcL and partial 18S rDNA sequences of 1806 characters. Values near the horizontal lines (slash) are bootstrap support from RAxML analyses (<50 are not shown). Species from the centric diatoms were used as an outgroup. Families indicated according COX (2015).</p>
Source data for road transportation applications (road surface assessment, authentication of automotive vehicles)
<p>This data set records the driving using an Inertial Measurement Units of 12 different vehicles on the road infrastructure of the European Commission Joint Research Centre.</p> <p>The data set is described more in detail in the paper:</p> <p>Baldini, G.; Geib, F.; Giuliani, R. Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units. <em>Sensors</em> <strong>2019</strong>, <em>19</em>, 5283.</p> <p><a href="https://doi.org/10.3390/s19235283">https://doi.org/10.3390/s19235283</a></p> <p>Please, cite this paper if you use this data set.</p>
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
BenjaminSMoss/Sheets-project-source-data: Photocatalyst Sheets Source Data NM19124170A
<p>Source data in opj format (OriginPro) from our publication in Nature Materials (2020) entitled Linking in-situ charge accumulation to electronic structure in doped SrTiO3 reveals design principles for hydrogen evolving photocatalysts. by Benjamin Moss, Qian Wang, Keith T. Butler, Ricardo Grau-Crespo, Shababa Selim, Anna Regoutz , Takashi Hisatomi , Robert Godin, David J. Payne, Andreas Kafizas, Kazunari Domen, Ludmilla Steier* and James R. Durrant</p>
source_data_flood_attribution
<p>The dataset includes source data for the journal article:</p> <p>Inga Sauer, Ronja Reese, Christian Otto, Tobias Geiger, Sven Willner, Benoit Guillod, David Bresch, and Katja Frieler. “Climate Signals in River Flood Damages Emerge under Sound Regional Disaggregation,” <a href="https://doi.org/10.21203/rs.3.rs-37259/v1">10.21203/rs.3.rs-37259/v1</a></p> <p>It provides data data required for hazard and exposure modeling required for damage modeling provided in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).</p> <p>Hazard modeling: provided are spatially explicit flooded areas and flood depth on a 150arcsec resolution and discharge on a 0.25 degree resolution the file names follow the structure: variable_resolution_ghm_climateforcing_protectionstandard.nc (for flood depth (flddph) and flooded fraction (fldfrc)) and variable_ghm_climateforcing.nc</p> <p>Exposure modeling:</p> <p>The file gdp_1850_2100_150arcsec.nc contains yearly gridded-GDP on a 150 arcsec resolution converted to PPP 2005 USD. Between 2000 and 2010 there is a transition between observed GDP and the future socio-economic development scenario SSP2.</p> <p>The application of the datasets for damage modeling and for the reproduction of the article data is described here:</p> <p>https://github.com/ingajsa/flood_attribution_paper</p> <p>More information on flood modeling can be found at: 10.5281/zenodo.1241051</p> <p>For the gridded_GDP, see also: https://doi.org/10.5880/pik.2017.003</p>
Data from Potential source areas for atmospheric lead reaching Ny-Ålesund from 2010 to 2018
<p>date reports the sampling data in YYYY-MM-DD format and volume the sampling volume in m3.<br> pb_sign is = for Pb concentrarion data above limit of quantification (LoQ) and < for data below LoQ.<br> pb_val is numeric and it is the measured Pb concentration or LoQ in pg/m3.<br> pb is text and it is the measured Pb concentration or <LoQ in pg/m3.<br> al_ef is the enrichment factor (EF) EF(Pb/Al)c in comparison to the upper continental crust (UCC, Wedepohl 1995).<br> pb20x20y is the value measured for 20xPb / 20yPb isotope ratio.<br> u20x20y is the 95-confidence level uncertainty for the measured 20xPb / 20yPb isotope ratio value.<br> Missing values are reported as NA.<br> Wedepohl 1995: Wedepohl, K.H., 1995. The composition of the continental crust. Geochim. Cosmochim. Acta 58A, 959–960. https://doi.org/10.1180/minmag.1994.58A.2.234</p>
Data set on the main text of "A bright and fast source of coherent single photons"
<p>The data set that is presented in the main text is uploaded to the repository. Please note that all the data is scaled according to the axis on the paper, that means if the axis has a multiplication by 1e3 then the data is divided by 1e3.</p> <p>Each file is named after the corresponding subfigure.</p> <p>The preprint version of the article can be found in: https://arxiv.org/abs/2007.12654</p>
Data from: Assessing the contributions of intraspecific and environmental sources of infection in urban wildlife: Salmonella enterica and white ibis as a case study
Conversion of natural habitats into urban landscapes can expose wildlife to novel pathogens and alter pathogen transmission pathways. Because transmission is difficult to quantify for many wildlife pathogens, mathematical models paired with field observations can help select among competing transmission pathways that might operate in urban landscapes. Here we develop a mathematical model for the enteric bacteria Salmonella enterica in urban-foraging white ibis (Eudocimus albus) in south Florida as a case study to determine (i) the relative importance of contact-based versus environmental transmission among ibis and (ii) whether transmission can be supported by ibis alone or requires external sources of infection. We use biannual field prevalence data to restrict model outputs generated from a Latin hypercube sample of parameter space and select among competing transmission scenarios. We find the most support for transmission from environmental uptake rather than between-host contact and that ibis–ibis transmission alone could maintain low infection prevalence. Our analysis provides the first parameter estimates for Salmonella shedding and uptake in a wild bird and provides a key starting point for predicting how ibis response to urbanization alters their exposure to a multi-host zoonotic enteric pathogen. More broadly, our study provides an analytical roadmap to assess transmission pathways of multi-host wildlife pathogens in the face of scarce infection data.
South Africa higher education data 2 - Data sources
<p>GIS-based map visualisation of the data sources providing open data on South African higher education data. Generated as part of research conducted for the 'Use of open data in the governance of South African higher education' research project, in the IDRC/WWWF 'Exploring Emerging Impacts of Open Data in the South' initiative.</p>
Localisation of a real vs. binaural simulated point source -- data
<p>This data set contains stimuli and results from an experiment that compared the localisation of a real point source realised by a loudspeaker to the localisation of a binaural simulation of the same source using head related impulse responses (HRIRs) or binaural room impulse responses (BRIRs). The results are published in [1].</p> <p>The corresponding binaural room scanning (BRS) files for the binaural simulation can be found in the file `brs.zip`, the employed noise stimulus in `stimuli.zip`. The file `results.zip` contains the results of all 11 listeners to the localisation task and the file `results_head_movements.zip` the recoreded head movements the listeners performed during the task. The file analysis.zip` contains average results and a plotting script.</p>
Coloration of a point source in Wave Field Synthesis revisited -- data
<p>This database entry contains stimuli and results from the follow up experiment to [1, 2]. In the experiment different Wave Field Synthesis (WFS) systems synthesising a point source were rated in terms of their perceived coloration compared to a real point source. This was done for different audio material, namely pink noise, speech, and music and different listener positions. The different WFS systems consisted of a circular loudspeaker array with a radius of 3m and a linear loudspeaker array with a length of 3m, but different number of employed loudspeakers. To control for the exact listening position, allow instantaneous switching between listening positions, and allow for very high numbers of loudspeakers in the WFS systems the experiment was performed with binaural synthesis without head tracking.</p> <p>The corresponding binaural room scanning (BRS) files for the binaural simulation can be found in the file `brs.zip`, the employed noise and speech stimuli in `stimuli.zip` (note that we cannot release the employed music stimulus, which was a twelve second clip from the electronic song “Luv deluxe” by “Cinnamon Chasers”). The file `results.zip` contains the results of all 16 listeners and the file analysis.zip` calculated average values and a plotting script.</p> <p>[1] Wierstorf, H., Hohnerlein, C., Spors, S., Raake, A. (2014), “Coloration in wave field synthesis,” 55th International Aes Conference, Paper 5-3</p> <p>[2] Wierstorf, H., Hohnerlein, C. (2016), “Coloration of a point source in Wave Field Synthesis -- data,“ [Data set]. Zenodo. http://doi.org/10.5281/zenodo.164589</p>
Coloration of a point source in Wave Field Synthesis -- data
<p>This database entry contains stimuli and results from the experiments described in [1]. In the experiment different Wave Field Synthesis (WFS) systems synthesising a point source were rated in terms of their perceived coloration compared to a real point source. This was done for different audio material, namely pink noise, speech, and music and different listener positions. The different WFS systems consisted always of a circular loudspeaker array with a radius of 3m, but different number of employed loudspeakers. To control for the exact listening position, allow instantaneous switching between listening positions, and allow for very high numbers of loudspeakers in the WFS systems the experiment was performed with binaural synthesis without head tracking.</p> <p>The corresponding binaural room scanning (BRS) files for the binaural simulation can be found in the file `brs.zip`, the employed noise and speech stimuli in `stimuli.zip` (note that we cannot release the employed music stimulus, which was a twelve second clip from the electronic song “Luv deluxe” by “Cinnamon Chasers”). The file `results.zip` contains the results of all 16 listeners and the file analysis.zip` calculated average values and a plotting script.</p> <p>[1] Wierstorf, H., Hohnerlein, C., Spors, S., Raake, A. (2014), “Coloration in wave field synthesis,” 55th International Aes Conference, Paper 5-3</p>
The first 500-meter, long-term winter wheat grain protein content dataset for China from multi-source data
<p>In China, the demand for precise perception of wheat Grain Protein Content (GPC) has gained increased urgency, driven by the rising demands in the food consumption market and intensifying international market competition. However, due to the lack of extensive, prolonged high-resolution benchmark data, previous GPC studies have primarily focused on experimental fields, small geographic units, and limited temporal scopes. Additionally, the diversified geographical landscape in China introduces spatiotemporal heterogeneity and intricacy to the influence of wheat GPC, further amplifying the challenges of large-scale GPC estimation. To address this challenge and the data gap, the first 500-meter spatial resolution, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500) was created by integrating multi-source data from ERA5 and MODIS.</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.