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Dataset results
1,600 results for “input”
Low input RNA transcriptomic analysis of spleen and lymph node Fibroblastic Reticular Cells (FRCs) in a murine model of Diffuse Large B-cell Lymphoma (DLBCL)
GEO Series GSE179193. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing.
Improved tagmentation-based whole genome bisulfite sequencing for input DNA from less than 100 cells
GEO Series GSE57062. Oryza sativa. 7 samples. Type: Methylation profiling by high throughput sequencing.
Streamlined low-input transcriptomics through EASY-RNAseq
GEO Series GSE135290. Mus musculus. 23 samples. Type: Expression profiling by high throughput sequencing.
Human SKOV-3 cells, Input DNA vs ChIP DNA
GEO Series GSE29649. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by array.
Low-input, deterministic profiling of single-cell transcriptomes reveals individual intestinal organoid subtypes comprised of single, dominant cell types [C1-HEK]
GEO Series GSE183686. Homo sapiens. 140 samples. Type: Expression profiling by high throughput sequencing.
Low-input mRNA Sequencing Analysis of Epidermal Langerhans cells (LC) from Murine Imiquimod (IMQ)-Induced Psoriasis-Like Dermatitis
GEO Series GSE162274. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Part of the input and output files of Li(FSA)(SN)2 classic MD
Open the record for dataset details and reuse information.
Input data: Can the Hermit Warbler serve as an old-forest indicator species in the Sierra Nevada?
<p>Input data used for occupancy modeling of Hermit Warblers across the Sierra Nevada in 2021.</p>
Example input data GLEAMv4: 2004-01
<p>Example input data for GLEAMv4 for January 2004. GLEAM is a global evaporation model, more info can be found at <a href="https://www.gleam.eu/">GLEAM | Global Land Evaporation Amsterdam Model</a>.</p>
Molecular_dynamics_input_parameter_and_output_files_NAT2
Open the record for dataset details and reuse information.
Carbon input of Flanders' organic fertilizers
<p>Deze dataset bevat de C input waarden voor verschillende types organische meststoffen die worden gebruikt in het BeSOCC model en de Koolstoftool in het bodempaspoort.</p> <p> </p> <p>For access to dataset contact Greet Ruysschaert at greet.ruysschaert@ilvo.vlaanderen.be </p>
SubsurfaceBreaks v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces: Input and Processed Data
<p>This companion dataset relates to the manuscript "<strong>SubsurfaceBreaks</strong> <strong>v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces"</strong>, by Michał Michalak, Christian Gerhards and Peter Menzel.</p> <p>There are several groups of files:</p> <ul> <li>a file with parameters (params.txt) of the generated homoclinal interfaces (slopes) such as dip angle, dip direction, level of noise).</li> <li>files 0-999 are generated using the code from GitHub. (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_synthetic_subsurface_slopes) for generating synthetic slopes. Every slope is in a separate file (.txt files) and it is possible to upload the slope to ParaView for further inspection: Delaunay triangulation, normal vectors and dip vectors have their own .vtu files. The .txt files (0-999) can be uploaded for training using the Python script (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_subsurface_slopes_training_testing_evaluating_revision.ipynb).</li> <li>KSH_input.txt corresponds to real data from Kraków-Silesian Homocline. Every row corresponds to a point representing a geological horizon separating Middle Jurassic geological units: Kościeliska sandstones from ore-bearing clays. This data set can be used to calculate geometric attributes using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes).</li> <li>KSH_input_output_0 corresponds to an output file from processing the KSH_input.txt file using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes). This file should be uploaded to the Python script to identify fault-related features on a real subsurface slope.</li> </ul>
Input data for the IMACLIM-R France model
<p>Data to run the IMACLIM-R France code</p> <p>https://github.com/CIRED/IMACLIM-R_France</p>
Carbon inputs of Flanders' crops
<p>Deze database bevat de C input waarden die gebruikt worden in het BeSOCC model en de Koolstoftool in het bodempaspoort.</p> <p>Voor toegang tot de data kan je Greet Ruysschaert contacteren op greet.Ruysschaert@ilvo.vlaanderen.be.</p>
Input dataset related to NEMO circum-Antarctic configuration for SAM sensitivity tests
<p><strong>Description paper</strong>: Verfaillie, D., Pelletier, C., Goosse, H. <em>et al.</em> The circum-Antarctic ice-shelves respond to a more positive Southern Annular Mode with regionally varied melting. <em>Commun Earth Environ</em> <strong>3, </strong>139 (2022). <a href="https://doi.org/10.1038/s43247-022-00458-x">https://doi.org/10.1038/s43247-022-00458-x</a></p> <p> </p> <p><strong>Source code</strong>: Charles Pelletier, & Deborah Verfaillie. (2021). NEMO circum-Antarctic configuration for SAM sensitivity tests. Zenodo. <a href="https://doi.org/10.5281/zenodo.5780018">https://doi.org/10.5281/zenodo.5780018</a></p> <p> </p> <p><strong>References</strong></p> <p>The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>ERA5:</strong> Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1979 to present. 2018. <a href="https://doi.org/10.24381/cds.adbb2d47">https://doi.org/10.24381/cds.adbb2d47</a>, downloaded from the Copernicus Climate Change Service (C3S); Climate Data Store (CDS) on 01-SEP-2019.</p> <p><strong>ORAS5: </strong>Zuo, H, Alonso-Balmaseda, M, Mogensen, K, Tietsche, S: OCEAN5: The ECMWF Ocean Reanalysis System and its Real-Time analysis component. 2018. <a href="https://doi.org/10.21957/la2v0442">https://doi.org/10.21957/la2v0442</a> downloaded from the <a href="https://www.cen.uni-hamburg.de/en/icdc/data/ocean/easy-init-ocean/ecmwf-oras5.html">ICDC</a> (University of Hamburg) on 01-SEP-2019.</p> <p><strong>BedMachine</strong></p> <p>Morlighem, M. 2020. <em>MEaSUREs BedMachine Antarctica, Version 2</em>. Ice-shelf Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/E1QL9HFQ7A8M">https://doi.org/10.5067/E1QL9HFQ7A8M</a>. Accessed 01-DEC-2019.</p> <p>Morlighem, M., E. Rignot, T. Binder, D. D. Blankenship, R. Drews, G. Eagles, O. Eisen, F. Ferraccioli, R. Forsberg, P. Fretwell, V. Goel, J. S. Greenbaum, H. Gudmundsson, J. Guo, V. Helm, C. Hofstede, I. Howat, A. Humbert, W. Jokat, N. B. Karlsson, W. Lee, K. Matsuoka, R. Millan, J. Mouginot, J. Paden, F. Pattyn, J. L. Roberts, S. Rosier, A. Ruppel, H. Seroussi, E. C. Smith, D. Steinhage, B. Sun, M. R. van den Broeke, T. van Ommen, M. van Wessem, and D. A. Young. 2020. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet, <em>Nature Geoscience</em>. 13. 132-137. <a href="https://doi.org/10.1038/s41561-019-0510-8">https://doi.org/10.1038/s41561-019-0510-8</a></p> <p><strong>Iceberg forcings</strong></p> <p>Jourdain, Nicolas C., Merino, Nacho, Le Sommer, Julien, Durand, Gaël, & Mathiot, Pierre. (2019). Interannual iceberg meltwater fluxes over the Southern Ocean (1.0) [Data set]. <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.3514728">https://doi.org/10.5281/zenodo.3514728</a></p> <p>Merino N., Jourdain, N. C., Le Sommer, J., Goose, H., Mathiot, P. and Durand, G (2018). Impact of increasing Antarctic glacial freshwater release on regional sea-ice cover in the Southern Ocean. <em>Ocean Modelling</em>, 121, 76-89. <a href="https://doi.org/10.1016/j.ocemod.2017.11.009">https://doi.org/10.1016/j.ocemod.2017.11.009</a></p> <p> </p>
Seasonality of stable isotope composition of atmospheric water input at the southern slopes of Mt. Kilimanjaro, Tanzania.
<p>The extensive dataset provides detailed information about different precipitation types and their stable isotope analyses of δD and δ18O of weekly precipitation samples along an altitude gradient rising from 950 m to nearly 4,000 m a.s.l. over 2 years.</p> <p>It provides a broad overview of the main characteristics of water stable isotope compositions of precipitation at the southern slopes of Mt. Kilimanjaro. The data can be used to better characterize groundwater and surface water sources and availability to ecosystems and the people living in the investigated region and provide much-needed information on stable isotope chracteristics of precipitation in equatorial Africa. </p> <p>The associated publication to this data set is available at Hydological Processes:</p> <p>Otte, I, Detsch, F, Gütlein, A, Scholl, M, Kiese, R, Appelhans, T, Nauss, T 2017. Seasonality of stable isotope composition of atmospheric water input at the southern slopes of Mt. Kilimanjaro, Tanzania. Hydrological Processes, Volume 31, Issue 22, 3932-3947. DOI: 10.1002/hyp.11311</p>
Geophysical indices input file for TIE-GCM
<p>A GPI_NCFILE input for the thermosphere-ionosphere-electrodynamics general circulation model (TIE-GCM; Richmond et al., 1992; doi: 10.1029/92GL00401).</p> <p>This file provides Daily F10.7, 81-day centered mean of F10.7, and Kp for the time period 1950-01-01 – 2024-04-18.</p> <p><strong>Netcdf file description</strong></p> <p>netcdf gpi_1950001_2024109 {<br>dimensions:<br> ndays = 27137 ;<br> nkp = 8 ;<br>variables:<br> int year_day(ndays) ;<br> year_day:long_name = "4-digit year followed by 3-digit day" ;<br> double f107d(ndays) ;<br> f107d:long_name = "Penticton observed daily 10.7 cm solar flux (sfu)" ;<br> double f107a(ndays) ;<br> f107a:long_name = "Penticton 81-day average 10.7 cm solar flux (sfu)" ;<br> double kp(ndays, nkp) ;<br> kp:long_name = "GFZ 3-hourly Kp index" ;</p> <p>// global attributes:<br> :title = "Geophysical indices input file for TIE-GCM" ;<br> :history = "Created: Wed May 29 16:34:45 2024" ;<br> :yearday_beg = 1950003 ;<br> :yearday_end = 2024107 ;<br> :author = "Timothy Kodikara (DLR.de)" ;<br> :write_source = "make_tiegcm_gpincfile.py, write_gpi_ncfile.py" ;<br> :Kp_source = "Geomagnetic Observatory Niemegk, GFZ German Research Centre for Geosciences [doi.org/10.5880/Kp.0001]" ;<br> :F107_source = "Observed solar flux provided by the Dominion Radio Astrophysical Observatory and Natural Resources Canada [ftp://ftp.seismo.nrcan.gc.ca/spaceweather/solar_flux/daily_flux_values/fluxtable.txt]" ;<br> :info = "When using this file, please cite the source as https://doi.org/10.5281/zenodo.8017409" ;<br>}</p>
Yerrida Basin Geophysical Modeling - Input data and inverted models.
<p>This companion datasets relates to the manuscript "<strong>Integration of geological uncertainty into geophysical inversion by means of local gradient regularization</strong>", by J. Giraud, M. Lindsay, V. Ogarko, M. Jessell, R. Martin and E. Pakyuz-Charrier, submitted to Solid Earth. The archive contains the input and output geophysical data, starting and inverted models, probabilistic geological model and conditioning volume derived from the calculation of Shannon's entropy. </p>
Run input files for the simulation of a C. jejuni inner membrane model
<p>Run input files for the simulation of a C. jejuni inner membrane model (Chapter 5 of Kahlan Newman's Doctoral Thesis). Full trajectories can be shared on request. </p>
Input data and R code for "Larger male Yellow Warbler ( Setophaga petechia ) occupy smaller home ranges over winter in natural and agricultural sites in western Mexico"
<p>Data used for modeling Yellow Warbler home ranges in western Mexico from 2012 to 2014.</p> <p> </p> <p> </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.