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90 results for “data retrieval”
Look-up table data used in the GPM Spectral Latent Heating retrieval for the midlatitudes
<h3>Overview</h3> <p>This dataset contains the look-up table (LUT) data used for the midlatitude retrieval of the Global Precipitation Measurement (GPM) Spectral Latent Heating (SLH) V07 product. The LUTs that tie heating profiles to precipitation characteristics are constructed using Local Forecast Model simulations of eight extratropical cyclones around Japan. The LUTs for the following six categories: convective, shallow stratiform, downward increasing (DI) deep stratiform, downward decreasing (DD) deep stratiform, SUB0 (where the 0°C level is near the surface) deep stratiform, and OTHER. The precipitation top height (PTH) serves as an index for LUTs for convective and shallow stratiform, while the maximum precipitation rate (PMAX) is used as an index for LUTs for three deep stratiform and OTHER types. Note that LUTs for DD, DI, and SUB0 are subdivided based on whether the PMAX appears above or at the surface.</p> <p> </p> <h3>Contents</h3> <p>There are two directories named "data" and "fortran90".</p> <p>In the directory named "data", there are the following nine files for each-type LUT:</p> <p> 1. Convective LUT: lut_convective _midlatSLH.grd</p> <p> 2. Shallow stratiform LUT: lut_shallow-stratiform _midlatSLH.grd</p> <p> 3. DD deep strtiform LUT with PMAX above the surface with: lut_DD-stratiform_pmaxABOVEsfc_midlatSLH.grd</p> <p> 4. DD deep strtiform LUT with PMAX at the surface with: lut_DD-stratiform_pmaxATsfc_midlatSLH.grd</p> <p> 5. DI deep strtiform LUT with PMAX above the surface with: lut_DI-stratiform_pmaxABOVEsfc_midlatSLH.grd</p> <p> 6. DI deep strtiform LUT with PMAX at the surface with: lut_DI-stratiform_pmaxATsfc_midlatSLH.grd</p> <p> 7. SUB0 deep strtiform LUT with PMAX above the surface with: lut_SUB0-stratiform_pmaxABOVEsfc_midlatSLH.grd</p> <p> 8. SUB0 deep strtiform LUT with PMAX at the surface with: lut_SUB0-stratiform_pmaxATsfc_midlatSLH.grd</p> <p> 9. OTHER LUT: lut_OTHER_midlatSLH.grd</p> <p>The LUT data are in the plain binary format (single-precision real number, little endian).</p> <p>The LUT values of latent heating, apparent heat source minus radiative heating, and apparent moisture sink are included in each file. Unit is K/h. </p> <p>The convective and shallow stratiform LUTs have 176 PTH bins and 176 vertical level bins, from the surface to 21.875 km at each 125 m.</p> <p>On the other hand, the deep stratiform and OTHER LUTs have 35 PMAX bins defined as follows: 0.2–0.3, 0.3–0.7, 0.7–1, 1–1.5, 1.5–2, 2–2.5, 2.5–3, 3–4, 4–5, 5–6, 6–7, 7–8, 8–9, 9–10, 10–11, 11–12, 12–13, 13–14, 14–15, 15–16, 16–17, 17–18,18–19, 19–20, 20–21, 21–22, 22–23, 23–24, 24–25, 25–26, 26–27, 27–28, 28–29, 29–30, ≥30 mm/h. There are 353 vertical level bins for altitudes standardized by the level of PMAX, PTH, and precipitation bottom height, from -8.8 to 8.8 with an interval of 0.05.</p> <p>Please see Yokoyama et al. (2024, J. Appl. Meteor. Climatol., under review) for details.</p> <p> </p> <p>In the directory named "fortran90", there are two fortran90 programs to read the LUT data. The program named “read_lut_type-pth.f90” is for the convective and shallow stratiform LUTs, while the program named “read_lut_type-pmax.f90” is for the deep stratiform and OTHER LUTs.</p> <p> </p> <p>The data would be updated. </p>
musiXplora: Documentation - Data Retrieval
<h1>musiXplora: Structured Data Access</h1> <p>This documentation provides an overview of structured musiXplora data, that are now persistently accessible on Zenodo utilizing its DOI system. MusiXplora is a linked knowledge base for musicological and organological data developed and maintained by the <strong>Research Center <em>DIGITAL ORGANOLOGY</em></strong> at <strong>Leipzig University</strong>.</p> <p>Currently, the German versions are available only, but it is planned to extend this data after suitable translations were found.</p> <p>Code snippets will are provided for Python, JavaScript, and Bash (cURL), in order to assist with accessing the latest available data and retrieve the desired information efficiently.</p> <p>Rate Limits for Zenodo are listed <a href="https://developers.zenodo.org/#rate-limiting">here</a>.</p> <p>Available musiXplora-IDs with the corresponding latest DOIs are listed here: <a href="https://doi.org/10.5281/zenodo.11581620">musiXplora-Zenodo-Dictionary</a>.</p> <p>For further questions or requests, please refer to: redaktion@musixplora.de</p> <p>Version of Documentation: 0.0.1 (11 June, 2024)</p>
High resolution Sea Surface Wind retrieval over coastal Protected Areas by means of Sentinel-1 data
<p><br> The algorithm used, i.e. SARWIND LG-Mod ver. v4.01 (see reference below), is aimed at producing the Sea Surface Wind (SSW), i.e. Speed and Direction, from a single co-polarized (VV or HH) SAR image. We used EW (Extended Wide) and IW (Interferometric Wide) Swath Mode GRD (Ground Range, Multi-Look, Detected) HR (High Resolution) Sentinel-1 images, with pixel spacings of 40m x 40m and 10m x 10m (azimuth x range) respectively. Associated auxiliary products were obtained from ESA SNAP 5.0 release. SSW fields were provided for the two coastal Protected Areas (PAs) named Camargue and Wadden Sea.</p> <p>Each output folder of the SARWIND LG-Mod results contains useful plots and the estimated SSW field, provided in the file 'SAR_Sigma0_pp_decimationL2P2Tn_gradientOptSobel_LGMod_Results.txt' (pp = VV or HH; n = smoothing/decimation level), which is in the sub-folder 'LG-Mod_Theoretical_Results/Results_MEdegTHxx.xxx_Fisher (where xx.xxx is the final threshold applied). This txt file reports the following 19 columns:</p> <p><br> 1) LAT; 2) LON; 3) AZI; 4) RNG; [Location of the centre of the processed AOI]</p> <p>5) REF_U; 6) REF_V; 7) REF_W; 8) REF_D; [ECMWF reference wind, as U/V components and speed/direction]</p> <p>9) SAR_U; 10) SAR_V; 11) SAR_W; 12) SAR_D; [SARWIND LG-Mod wind estimates, as U/V components and speed/direction]</p> <p>Both REF_D and SAR_D are wind directions (expressed in degrees) with respect to the geographic North (0°=North, 90°=East, 180°=South, 270°=West), that the wind is blowing to.<br> Both REF_W and SAR_W are wind speeds (expressed in m/s).<br> Regarding REF_U/SAR_U and REF_V/SAR_V, note that a positive U component represents wind blowing to the East; a positive V component represents wind blowing to the North.</p> <p>13) SceneCentre_TrueHeading_FF; [Mean angle formed between the geographical South-North direction and the SAR azimuth direction (wrt the centre of the SAR Full-Frame image)]</p> <p>SceneCentre_TrueHeading_FF is a positive clockwise angle. In particular: SceneCentre_TrueHeading_FF is in ]180,360[ [deg].<br> Thus:<br> Descending Pass <-> SceneCentre_TrueHeading_FF is in ]180,270[ [deg]<br> Ascending Pass <-> SceneCentre_TrueHeading_FF is in ]270,360[ [deg]</p> <p>14) ROI_Npoints_UnUsablePointsMasked; [Number of samples used for each SARWIND LG-Mod wind estimation]</p> <p>15) MeanIncAng; 16) MeanNRCS; [Mean incident angle (expressed in degrees) and NRCS of the ROI]</p> <p>17) MeanResultantLength; 18) Alpha2_Est; [Fisher's formula parameters]</p> <p>19) MEdeg [Margin of Error, i.e. accuracy of each wind direction estimate, between 0° and 45°]</p> <p>The accuracy MEdeg is given by the semi-width of the confidence interval, with a confidence level (1-α) fixed, which is assigned to the wind direction estimate. Consequently, lower MEdeg values correspond to better estimates. And, if MEdeg == 45°, wind estimates must be discharged.</p> <p><br> Finally, note also that you can cut an entire row when [SAR_U SAR_V SAR_W SAR_D] == [NaN NaN NaN NaN] (typically, this happens for 'land pixels').</p> <p>% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % REFERENCES: %<br> % %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % The algorithm SARWIND LG-Mod is based on the Ph.D thesis below: %<br> % %<br> % [1] Rana, Fabio Michele (2016) "Exploitation of Satellite %<br> % Synthetic Aperture Radar Data for Geophysical Parameters Retrieval over %<br> % Land and Ocean". Unpublished Ph.D thesis. Politecnico di Bari. %<br> % %<br> % Some applications of the method are described in the following papers: %<br> % %<br> % [2] Fabio M. Rana, Maria Adamo, Guido Pasquariello, Giacomo De Carolis, %<br> % and Sandra Morelli, "LG-Mod: A Modified Local Gradient (LG) Method to %<br> % Retrieve SAR Sea Surface Wind Directions in Marine Coastal Areas," %<br> % Journal of Sensors, vol. 2016, Article ID 9565208, 7 pages, 2016. %<br> % doi:10.1155/2016/9565208. %<br> % %<br> % [3] Rana, F. M., Adamo, M., & Blanda, P. (2018, July). %<br> % LG-Mod Multi-Scale Approach for Sar Sea Surface Wind Directions %<br> % Retrieval. In IGARSS 2018-2018 IEEE International Geoscience and Remote %<br> % Sensing Symposium (pp. 3216-3219). IEEE. %<br> % %<br> % [4] Rana, F. M., Adamo, M., Lucas, R., & Blonda, P. (2019). Sea surface %<br> % wind retrieval in coastal areas by means of Sentinel-1 and numerical %<br> % weather prediction model data. Remote Sensing of Environment, 225, %<br> % 379-391. %<br> % %<br> % Suggestions and comments are always welcome. %<br> % Thanks in advance, %<br> % Fabio Michele Rana %<br> % %<br> % MOB: (+39) 3804114171 %<br> % E-MAILS: fabiomichele.rana@gmail.com; fabiomichele.rana@iia.cnr.it %<br> % %<br> % SKYPE: fabiomichelerana %<br> % %<br> % SARWIND_LG-Mod_v4.01, 2014-2019 %<br> % Author: Fabio M. Rana %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> </p>
Fig. 1 Retrieved data distribution. A in Same information, new applications: revisiting primers for the avian COI gene and improving DNA barcoding identification
Fig. 1 Retrieved data distribution. A Distribution of published primers for the barcode region of the avian COI gene throughout the years. B Number of complete COI sequences available for each bird order
Fig. 3 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 3. Resulting order after multiple sorting according to the columns typeStatus, country, state, city and locality, in that order. This sequence of column names corresponds to the variable Sorting Order in the Gridit software.
Fig. 2 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 2. Columns swapped according to the desired order for the information in the final text. The order of the column names (scientificName, typeStatus, sex, country, etc.) corresponds to the variable Display Order in the Gridit software.
Fig. 1 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 1. Spreadsheet data from some of the specimens of Distictus tibialis (Brullé, 1846) cited in Supeleto et al. (2019). Mandatory columns and column names in the Gridit software marked in red.
Fig. 5 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 5. Data from the spreadsheet in Fig. 4, excluding header and the column scientificName, copied and pasted into a text editor; resulting tabs (cells) replaced with comma. Sequences of "ib" and "?" in each row were grouped together with a preceding number (e.g., "11ib" in row 2) that indicates the total of subsequent repeats. Each row represents a unique collecting event. The final text generated from this file is shown in Table 2.
Fig. 4 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 4. Repetitions in each column identified with the ib code for all columns (strict usage of the gridsetting technique).
Bibliographic data on datasets affiliated to Poznan University of Technology and indexed in Data Citation Index (retrieved by Web of Science service in January 2023))
<p>The file contains the number of datasets published by the researchers affiliated to Poznan University of Technology and indexed in Data Citation Index provided by Web of Science (database updated 10.01.2023). The Search was performed using the name of institution in the 'Affiliation' field. Dataset contains two files in two diffrent formats: plain text and xls.</p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
Open the record for dataset details and reuse information.
Sample Sentinel-1 SAR data for sea ice type retrieval
<p>Sample Sentinel-1 SAR data for sea ice type retrieval processed with thermal noise removal (<a href="https://ieeexplore.ieee.org/document/8126233">https://ieeexplore.ieee.org/document/8126233</a>).</p> <p>Original data is available at ESA Scientific Hub <a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu/</a></p> <p> </p>
Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'
<p><strong>Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval' along with the experimental results.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The licences valid for the elements of this repository are discussed under point "2. Licenses" below.</p> <p><em><strong>1. Structure</strong></em></p> <p>The repository contains the following items:</p> <ol> <li>"data" - the results from our experiments</li> <li>"lib" - some external functions used in the experiments</li> <li>"make_data" - the training and test data</li> <li>"fmt-vgg.py" - the FMT-RAN model</li> <li>"stn.py" - the STN module of ST-RAN</li> <li>"st_ran.py" - the ST-RAN model</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p><strong><em>2. License</em></strong></p> <p>The following licenses apply for the files and folders:</p> <ul> <li>The files "stn.py" and "spatial_transformer_tutorial.py" in the folder "lib" are from the GitHub repository <a href="https://github.com/GHamrouni/stn-tuto">https://github.com/GHamrouni/stn-tuto</a> and therefore are under the copyright of its repository owner Ghassen Hamrouni.</li> <li>All other files are under the <a href="https://mit-license.org/">MIT License</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><em><strong>3. Contact</strong></em></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, <a href="mailto:wuzz@hfuu.edu.cn">wuzz@hfuu.edu.cn</a><br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a>, <a href="http://mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a></p> <p><a href="http://iao.hfuu.edu.cn">Institute of Applied Optimization</a>, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China</p>
Supplementary Material: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
<p>Supplementary material for "The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra" DOI: <a href="https://ui.adsabs.harvard.edu/link_gateway/2024arXiv240307801F/doi:10.48550/arXiv.2403.07801" target="_blank" rel="noreferrer noopener">10.48550/arXiv.2403.07801</a></p> <p>Contents of this record:</p> <ul> <li>The retrieved atmospheric parameters for the population (see supplementary_material.pdf).</li> <li>The retrieval statistics per planet for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns (see supplementary_material.pdf).</li> <li>Planet specific retrieved spectra for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns.</li> <li>Retrieved parameter cornerplots per planet for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns.</li> </ul> <p>(NOTE: the retrieval model and priors for the results displayed in this record are specified in tables 2 and 3 of the paper.)</p>
Data For: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
<p>HDF5 data files for 1249 supraglacial lakes detected in ICESat-2 ATL03 data over Central West Greenland (melt seasons 2019 and 2020) and the Amery Ice Shelf Catchment (melt seasons 2018-19 and 2020-21). Each HDF5 data file is associated with a .jpg "quicklook" file of the same name, showing ATL03 photon elevations with the estimated along-track fits to the lake surface and lakebed and the resulting maximum lake depth, along with the corresponding ICESat-2 ground track over cloud-free concurrent satellite imagery.</p> <p>The data files are structured as following: </p> <div> <div> <div> <div> <div> <pre>group: depth_data/ - dataset: bathymetry_confidence - dataset: lakebed_fit_elevation_meters - dataset: lat - dataset: lon - dataset: surface_fit_elevation_meters - dataset: water_depth_meters - dataset: x_along_track_meters group: fluid_bathymetry_peaks/ - dataset: elevation_meters - dataset: peak_prominence - dataset: x_along_track_meters group: mframe_data/ - dataset: delta_time - dataset: density_ratio_1 - dataset: density_ratio_2 - dataset: density_ratio_3 - dataset: density_ratio_4 - dataset: major_frame_id - dataset: passes_bathymetry_check - dataset: passes_flatness_check - dataset: photon_density_peak_elevation - dataset: q_1_number_peaks - dataset: q_2_prominece - dataset: q_3_elev_spread - dataset: q_4_alignment - dataset: q_s - dataset: x_along_track_meters_end - dataset: x_along_track_meters_start group: photon_data/ - dataset: afterpulse_probability - dataset: fluid_signal_confidence - dataset: geoid_elevation_meters - dataset: lat - dataset: lon - dataset: photon_elevation_above_geoid_meters - dataset: pulse_saturation_level - dataset: x_along_track_meters group: properties/ - dataset: beam_number - dataset: beam_strength - dataset: cycle_number - dataset: granule_id - dataset: gtx - dataset: ice_sheet - dataset: lake_quality - dataset: lat - dataset: lon - dataset: melt_season - dataset: rgt - dataset: sc_orient - dataset: surface_elevation - dataset: time_utc </pre> </div> </div> </div> </div> </div>
The data to create figures in (Sulfur Dioxide Distribution at the Venusian Cloud-top Retrieved from Akatsuki UV Images).
<p>The data to create figures.</p>
Data from: Clathrin-independent endocytic retrieval of SV proteins mediated by the clathrin adaptor AP-2 at mammalian central synapses
<p><span>Neurotransmission is based on the exocytic fusion of synaptic vesicles (SVs) followed by endocytic membrane retrieval and the reformation of SVs. Conflicting models have been proposed regarding the mechanisms of SV endocytosis, most notably clathrin/ AP-2-mediated endocytosis and clathrin-independent ultrafast endocytosis. Partitioning between these pathways has been suggested to be controlled by temperature and stimulus paradigm. We report on the comprehensive survey of six major SV proteins to show that SV endocytosis in mouse hippocampal neurons at physiological temperature occurs independent of clathrin while the endocytic retrieval of a subset of SV proteins including the vesicular transporters for glutamate and GABA depend on sorting by the clathrin adaptor AP-2. Our findings highlight a clathrin-independent role of the clathrin adaptor AP-2 in the endocytic retrieval of select SV cargos from the presynaptic cell surface and suggest a revised model for the endocytosis of SV membranes at mammalian central synapses.</span></p>
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
<p>Files composing the YADL data lake, for the paper "Retrieve, Merge, Predict: Augmenting Tables with Data Lakes (Experiment, Analysis & Benchmark Paper)"</p> <p>We present an in-depth analysis of data discovery for analytics in data lakes, focusing on table augmentation for given machine learning tasks. We analyze alternative methods used in the three key steps: retrieving joinable tables, merging information, and predicting with the resultant table. As data lakes, the paper uses YADL (Yet Another Data Lake) -- a novel dataset developed as a tool for benchmarking this data discovery task -- and Open Data US, a well-referenced real data lake. Through systematic exploration on both lakes, our study outlines the importance of accurately retrieving join candidates, and the efficiency of simple aggregation methods. We report new insights on the benefits of existing solutions and on the their limitations, aiming at guiding future research in this space.</p> <p>Archives provided here follow the notation used for the experiments, which is different from what is reported in the paper. The four YADL versions available here are:</p> <ul> <li>"binary_update" (YADL Binary)</li> <li>"wordnet_full" (YADL Base)</li> <li>"wordnet_vldb_10" (YADL 10k)</li> <li>"wordnet_vldb_50" (YADL 50k)</li> </ul>
Data: Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts
<p>Data for: <br><br>Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts</p> <p>Marian Schönauer<sup>1</sup>, Anneli M. Ågren<sup>2</sup>, Klaus Katzensteiner<sup>3</sup>, Florian Hartsch<sup>1</sup>, Paul Arp<sup>4</sup>, Simon Drollinger<sup>5</sup>, Dirk Jaeger<sup>1</sup></p> <p><sup>1</sup>Department of Forest Work Science and Engineering, University of Göttingen, Göttingen, Germany</p> <p><sup>2</sup>Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden</p> <p><sup>3</sup>Institute of Forest Ecology, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria</p> <p><sup>4</sup>Forestry and Environmental Management, University of New Brunswick, New Brunswick, Canada</p> <p><sup>5</sup>Department of Physical Geography, University of Göttingen, Göttingen, Germany</p>
Prognostic value of a modified pathological staging system for gastric cancer based on the number of retrieved lymph nodes and metastatic lymph node ratio raw data
<p><span>Clinical data from the US Surveillance, Epidemiology, and End Results (SEER) Program from 2010-2015 (https://seer.cancer.gov/) was extracted and analyzed as training set, data from 2016-2017 was adopted as internal validation set. Data from The Cancer Genome Atlas Program (TCGA) (https://portal.gdc.cancer.gov/) and prognosis data from Gastrointestinal surgery Department, Third Affiliated Hospital of Sun Yat-sen University were applied as external validation sets. </span></p> <p><span>Screening criteria for gastric cancer cases were as follow: exclusion of cases with only autopsy or death certificate, cases where initial tumor location was not stomach, patients with stage 0 and stage IV, cases without radical surgery, non-adenocarcinoma cases, death cases within one month after operation, and cases with unknown lymph node information and AJCC TNM stage.</span></p> <p><span>The study analyzed various factors such as age of diagnosis (<50 years, 50-69 years, >69 years), gender, race (white, black, other), AJCC T stage (T1-T4b), AJCC TNM stage (I-III), primary tumor location (stomach body, antrum/pylorus, cardia/fundus, greater gastric recurve, lesser gastric recurve, overlapping area, NOS), Clinical features such as tumor size (≥5cm,<5cm, unknown), tumor grade (I-IV), chemotherapy, radiotherapy, number of lymph nodes retrieved and number of metastases, and lymph node positive rate. The populations of American Indian/Alaskan and Asian/Pacific Islander were classified as "other" due to small sample sizes. Tumor grade was also analyzed, with grades I-IV representing highly differentiated, moderately differentiated, poorly differentiated, and signed-ring cell carcinoma, respectively. Overall survival (OS) is the time from cancer diagnosis to death from any cause, while disease-specific survival (DSS) is the time from cancer diagnosis to death specifically due to the disease.</span></p> <p><strong><span> </span></strong></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.