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6,452 results for “Application”
Supplementary Information: Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b. Chubb and Min, A&A (2022).
<p>Supplementary information containing additional figures of the journal article 'Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b' by K. L. Chubb and M. Min, published in Astronomy & Astrophysics (2022).</p>
MetaPro: a web-based metabolomics application for MS data batch inspection and library curation
<p>MetaPro is a metabolomics web analysis platform built on the Aird data format with high performance and high compression. This platform includes a series of necessary functions for metabolomics analysis such as quality control, retention time(RT) alignment, target analysis, untarget analysis, manual integration, batch inspection, MS2 library establishment, and report export, providing efficient data analysis, management and visualization capabilities</p>
Dataset - Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model
<p>This dataset accompanies the manuscript titled "Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model", submitted by co-authors to the journal Global Change Biology (GCB) Bioenergy.</p> <p>Wouter Meijninger<sup>1</sup>, Berien Elbersen<sup>1</sup>, Michiel van Eupen<sup>1</sup>, Stephan Mantel<sup>2</sup>, Pilar Ciria Ciria<sup>3</sup>, Andrea Parenti<sup>4</sup>, Marina Sanz Gallego<sup>3</sup> and Paloma Perez Ortiz<sup>3</sup>, Marco Acciai<sup>4</sup>,and Andrea Monti<sup>4</sup><br> Institutes: 1) Wageningen University & Research, 2) ISRIC, 3) CIEMAT, 4) Bologna University,</p> <p><strong>Abstract (Manuscript)</strong></p> <p>In the context of increased pressures on land for food and non-food production it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer-term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions show that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like LPIS data, in regions is one of the challenges in this respect.</p> <p><strong>Readme data files</strong></p> <p><em>Coherence_quarterly_statisitcs_2017_to_2020.zip</em></p> <p>Radar coherence quarterly statistics - Albacete (Spain)</p> <p>Radar coherence data is based on Sentinel-1B<br> Period: 2017 to 2020</p> <p>File naming (.tif files) per year (<em>YYYY</em>):</p> <ul> <li>Mean coherence: <em>mean_YYYY_1to4.tif</em></li> <li>Standard deviation coherence: <em>std_YYYY_1to4.tif</em></li> <li>Range coherence: <em>range_YYYY_1to4.tif</em></li> <li>Mean delta coherence: <em>mean_delta_YYYY_1to4.tif</em></li> <li>Standard deviation delta coherence: <em>std_delta_YYYY_1to4.tif</em></li> <li>Maximum delta coherence: <em>max_delta_YYYY_1to4.tif</em></li> </ul> <p>Each file consists of 4 bands:</p> <ul> <li>band 1: 1st quarter [Jan-Feb-March]</li> <li>band 2: 2nd quarter [April-May-June]</li> <li>band 3: 3rd quarter [July-Aug-Sept]</li> <li>band 4: 4th quarter [Oct-Nov-Dec]</li> </ul> <p>Statistics are based on radar coherence data, which is scaled between >0 and 1<br> No data: 0-values</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>SIGPAC_data_Albacete_2018_to_2020.zip</em></p> <ul> <li>More than 5 year fallow (20m raster files)</li> <li>Land Use Land Cover LULC (20m raster files)</li> </ul> <p>More than 5 year fallow (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.hdr)</li> </ul> <p>Pixel values:<br> 0: Not fallow<br> 1: Fallow more than 5 years</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p>Land Use Land Cover LULC (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>LULC_SIGPAC_Albacete_2018_20m.dat (+ LULC_SIGPAC_Albacete_2018_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2019_20m.dat (+ LULC_SIGPAC_Albacete_2019_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2020_20m.dat (+ LULC_SIGPAC_Albacete_2020_20m.hdr)</li> </ul> <p>Pixel values:</p> <ul> <li>0 - Nan</li> <li>1 - Arable land</li> <li>2 - Vineyards</li> <li>3 - Olives</li> <li>4 - Fruits</li> <li>5 - Nuts</li> <li>6 - Citrus</li> <li>7 - Permanent grassland</li> <li>8 - Forest</li> <li>9 - Rest, small elements</li> <li>10 - Built-up areas</li> <li>11 - Water</li> <li>12 - Roads</li> <li>13 - Unproductive land</li> </ul> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Annual_unused_used_land_maps_Albacete_2017_to_2020.zip</em></p> <p>Derived annual unused/used land maps - Albacete (Spain), based on Random-Forest model<br> Period: 2017-2020<br> File naming (ENVI files):</p> <ul> <li>predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.hdr)</li> </ul> <p>Pixel values:<br> 0 - Used (and/or Nan)<br> 1 - Unused</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Four_year_abandoned_land_Albacete_2017_to_2020.zip</em></p> <p>Four-year abandonment map is based on the 4 annual unused/used land maps<br> File naming (ENVI):</p> <ul> <li>Four_year_abandoned_land_Albacete_2017_to_2020.dat (+ Four_year_abandoned_land_Albacete_2017_to_2020.hdr)</li> </ul> <p>Pixel values:<br> 0 - (Nan)<br> 1 - Used (1 year unused in period 2017 - 2020)<br> 2 - Used (2 year unused in a row in period 2017 - 2020)<br> 3 - Abandoned (3 year unused in a row in period 2017 - 2020)<br> 4 - Abandoned (4 year unused in a row in period 2017 - 2020)</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p>
The Surface Biology and Geology Architecture Study Science and Applications Traceability Matrix (SATM)
<p>This dataset provides a synthesis of the National Academies of Sciences 2017 Earth Science Decadal Survey mission most and very important objectives for the Surface Biology and Geology Earth Observing system consisting of a global visible to shortwave infrared imaging spectrometer and a multi-spectral thermal infrared radiometer. These objectives are traced to performance criteria used for assessing hundreds of potential architecture variants in an architecture study outlined in a paper titled: "Designing an Observing System to Study the Surface Biology and Geology (SBG) of the Earth in the 2020s".</p>
Properties of leak detection sensor, Arduino and application code
<p>The dataset contains properties of a leak detection sensor; absorbance spectra and reflectance in different pH environments, ionic strength, reversibility, FT-IR (Fourier-transform infrared spectroscopy), stability, TGA (Thermogravimetric analysis), and photoisomerization data. The microprocessor and application codes are also included. The information included herein will be helpful for users of the sensor.</p>
Applicability of pea ingredients in baked products: links between formulation, reactivity potential and physicochemical properties
<p>This is the data used for the figures and supplementary information in the following publication:</p> <p>Krause, S., Asamoah, E.A., Huc-Mathis, D., Moulin, G., Jakobi, R., Rega, B. & Bonazzi, C. (2022). Applicability of pea ingredients in baked products: links between formulation, reactivity potential and physicochemical properties. Food Chemistry, 132653. DOI: <a href="https://doi.org/10.1016/j.foodchem.2022.132653">10.1016/j.foodchem.2022.132653</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p>This study aimed to evaluate the applicability of purified pea ingredients (starch and protein isolate) by assessing their potential to form volatile compounds during the different steps of sponge cake development compared to pea flour and wheat flour. While pea flour was highly susceptible to lipid oxidation during batter beating, the combination of purified pea starch and pea protein yielded significantly fewer oxidation markers with known green-beany off-odors. This was due more to the inactivation of lipoxygenase during flour fractionation than to differences in batter structure. However, fractionated ingredients were highly prone to participating in the Maillard reaction and caramelization during baking, leading to a more complex mixture of pyrazines, Strecker aldehydes and furanic compounds with potential malty and roasted notes compared to cakes based on pea flour or wheat flour. These findings confirm that using purified pea fractions can create high-quality products with an attractive composition.</p>
Supplemental material: Operative videos on application of microscope-based augmented reality with intraoperative computed tomography-based navigation for resection of skull base meningiomas
<p>Supplemental material</p> <p>Operative videos:</p> <p>Patient number 9: Microsurgical resection of medial sphenoid wing meningioma using microscope-based augmented reality and intraoperative computed tomography-based navigation</p> <p>Pt 28:Microsurgical resection of right clinoidal meningioma via fronto-temporal craniotomy with microscope-based augmented reality</p> <p>Pt 31:Microsurgical resection of recurrent sphenoid wing meningioma using microscope-based augmented reality with intraoperative computed tomography</p> <p>Pt 36: Microsurgical resection of giant olfactory meningioma via bifrontal approach with use of augmented reality and intraoperative CT-based navigation</p>
Full Datasets for the "Molecular Mimicry Map of SARS-CoV-2" Web Application
<p>A Full Dataset for the "Molecular Mimicry Map of SARS-CoV-2" Web Application</p> <p>This TAR archive file contains the minimally filtered list of potentially cross-reactive epitopes of SARS-CoV-2 and human proteins predicted by the CRESSP analysis pipeline (<a href="https://pypi.org/project/cressp/">https://pypi.org/project/cressp/</a>).</p> <p>Our web application, "Molecular Mimicry Map of SARS-CoV-2" (<a href="https://ahs2202.github.io/3M/">https://ahs2202.github.io/3M/</a>), only visualizes the high-scoring potential cross-reactive epitope pairs (40,000 epitope pairs maximum). However, for the researchers who want to access the complete list of potentially cross-reactive epitope pairs for further analysis, these full datasets can be used.</p>
Contact tracing solutions for COVID-19: applications, data privacy and security :: Suplementary Material
<p>Supplementary Material for the paper "Contact tracing solutions for COVID-19: applications, data privacy and security"</p>
A new remote sensing benchmark dataset for machine learning applications : MultiSenGE
<p>[UPDATE] You can now access MultiSen (GE and NA) collection though this portal : <a href="https://doi.theia.data-terra.org/ai4lcc/?lang=en">https://doi.theia.data-terra.org/ai4lcc/?lang=en</a></p> <p>MultiSenGE is a new large-scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. It contains 8,157 patches of 256 * 256 pixels for Sentinel-2 L2A, Sentinel-1 GRD and a regional LULC topographic regional database. </p> <p>Every file has a specific nomenclature :</p> <ul> <li>Sentinel-1 patches: {tile}_{date}_S1_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>Sentinel-2 patches: {tile}_{date}_S2_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>Ground reference patches: {tile}_GR_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>JSON Labels: {tile}_{x-pixel-coordinate}_{y-pixel-coordinate}.json</li> </ul> <p>where <em>tile</em> is the Sentinel-2 tile number, <em>date</em> the date of acquisition of the patch, <em>x-pixel-coordinate</em> and <em>y-pixel-coordinate</em> are the coordinates of the patch in the tile.</p> <p>In addition, you can find a set of useful python tools for extracting information about the dataset on Github : <a href="https://github.com/r-wenger/MultiSenGE-Tools">https://github.com/r-wenger/MultiSenGE-Tools</a></p> <p>First experiments based on this <em>dataset</em> is in press in ISPRS Annals : <strong>Wenger, R., </strong>Puissant, A., Weber, J., Idoumghar, L., and Forestier, G.: MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 635–640, https://doi.org/10.5194/isprs-annals-V-3-2022-635-2022, 2022.</p> <p>Due to the large size of the dataset, you will only find the associated JSON files on this Zenodo repository. To download the Sentinel-1, Sentinel-2 patches and the reference data, please do so via these links: </p> <ul> <li>Sentinel-1 temporal serie patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz</a></li> <li>Sentinel-2 temporal serie patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz</a></li> <li>Ground reference patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz</a></li> <li>JSON files for each patch: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz</a></li> </ul>
MORPHEMIC Application Monitoring Dataset (Genom)
<p>The application component monitoring dataset for the Genom application under the MORPHEMIC project.</p>
Fig. 3 in Application Of Dna Barcoding In Taxonomy And Phylogeny: An Individual Case Of Coi Partial Gene Sequencing From Seven Animal Species
Fig. 3. Phylogenetic position of Macrobiotus sp., Bayesian inference phylogenetic tree. Sequences obtained by us are written in bold.
Fig. 1 in Application Of Dna Barcoding In Taxonomy And Phylogeny: An Individual Case Of Coi Partial Gene Sequencing From Seven Animal Species
Fig. 1. Phylogenetic position of D. lindholmi and L. a. exigua, Bayesian inference phylogenetic tree. Sequences obtained by us are written in bold.
Lightning Assimilation in the Weather Research and Forecasting (WRF) Model: Technique Updates and Assessment of the Applications from Regional to Hemispheric Scales
<p>Figure 1. The data is proprietary, but it can be purchased from Vaisala Inc. (https:// <a href="http://www.vaisala.com/en/products/systems/lightning-detection">www.vaisala.com/en/products/systems/lightning-detection</a>), and the WWLLN raw data are also available for purchase at <a href="http://wwlln.net">http://wwlln.net</a>.</p> <p>Figure 2. Maps, data is not applicable.</p> <p>Figure 3. Data file: NLDN_WWLLN_Prism_Rainfall_Analysis.xlsx</p> <p>Figure 4. Data file: NLDN_WWLLN_METVARS_T2_Jul_2016.xlsx</p> <p>Figure 5. Data file: CONUSall_METOBS_q_Jul_2016.xlsx</p> <p>Figure 6. Data file: CONUSall_METOBS_ws_Jul_2016.xlsx</p> <p>Figure 7. Created using the R script: Hemi_Rain_ModelOnlyWGPM.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds, AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds, and GPM_WRF_Paired_rain2Hemispheric_July2016.rds.</p> <p>Figure 8. Created using the R script: Hemi_Rain_Aanlysis.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds and AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds.</p> <p>Figure 9. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 10. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 11. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 12. Created using the R script: CreateCPCdataforUSdomain_vs_Prism.R based on the R oject files: Prism_CFC_WRF*.rds</p> <p>Figure 13. Data file: Hemi_lta_METOBS_T2_Jul_2016.xlsx</p> <p>Figure 14. Data file: Hemi_lta_METOBS_q_Jul_2016.xlsx</p> <p> </p>
Parallel generation of extensive vascular networks with application to an archetypal human kidney model
<p>Given the relevance of the inextricable coupling between microcirculation and physiology, and the relation to organ function and disease progression, the construction of synthetic vascular networks for mathematical modelling and computer simulation is becoming an increasingly broad field of research. Building vascular networks that mimic in-vivo morphometry is feasible through algorithms such as constrained constructive optimisation (CCO) and variations. Nevertheless, these methods are limited by the maximum number of vessels to be generated due to the whole network update required at each vessel addition. In this work, we propose a CCO-based approach endowed with a domain decomposition strategy to concurrently create vascular networks. The performance of this approach is evaluated by analysing the agreement with the sequentially generated networks and studying the scalability when building vascular networks up to 200,000 vascular segments. Finally, we apply our method to vascularise a highly complex geometry corresponding to the cortex of a prototypical human kidney. The technique presented in this work enables the automatic generation of extensive vascular networks, removing the limitation from previous works. Thus, we can extent vascular networks (e.g., obtained from medical images) to pre-arteriolar level, yielding patient-specific whole-organ vascular models with an unprecedented level of detail.</p>
TermPicks: A century of Greenland glacier terminus data for use inmachine learning applications
<p>TermPicks_V2.zip contains a single shapefile of the TermPicks marine-terminating terminus trace dataset. Version 2 fixes an issue with missing center x and y points and incorrectly labeled data.</p> <p>Centerline_Retreat_V2.zip contains CSV files of the retreats used in Goliber et al. (in prep). V2 fixes missing data (ex. 291.csv).</p> <p>TermPicks_V1_Coverage.kmz can be opened in Google Earth or any GIS software to view the TermPicks IDs and Landsat Coverage figures.</p> <p>TermPicks_IDs.zip contains a single shapefile of the ID schema for the TermPicks dataset.</p> <p>TermPicks+CALFIN_V3.zip contains a single shapefile of the TermPicks marine-terminating terminus trace dataset and the Calving Front Machine (CALFIN): glacial termini dataset. V2 fixes issues of missing data from the previous version due to an indexing error. V3 fixes an issue with missing center x and y points.</p> <p>When using this dataset, please include the following citation is included:</p> <p>Cheng, D., Hayes, W., Larour, E., Mohajerani, Y., Wood, M., Velicogna, I., & Rignot, E. (2021). Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019. <em>The Cryosphere</em>, <em>15</em>(3), 1663-1675.</p>
Dataset: Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Pavel Čeleda, Lydia Kraus.<br> <em>Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training.</em><br> In Springer Education and Information Technologies. 2022.<br> <a href="https://doi.org/10.1007/s10639-022-11093-6">https://doi.org/10.1007/s10639-022-11093-6</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2307.08582">https://arxiv.org/abs/2307.08582</a></p> <ul> </ul> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2022applications, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and \v{C}eleda, Pavel and Kraus, Lydia}, title = {{Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training}}, journal = {Education and Information Technologies}, publisher = {Springer}, volume = {27}, year = {2022}, issn = {1360-2357}, url = {https://doi.org/10.1007/s10639-022-11093-6}, doi = {10.1007/s10639-022-11093-6}, }</code></pre> <p><strong>Attached content</strong></p> <p>The files included in the ZIP archive are:</p> <ul> <li>`All-discovered-papers.bib` -- a BibTeX export of the Mendeley database of all considered papers discovered by the automated search.</li> <li>`Candidate-papers-reviewer1.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the first investigator.</li> <li>`Candidate-papers-reviewer2.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the second investigator.</li> <li>`Selected-papers.bib` -- a BibTeX export of the Mendeley database of the 35 papers selected for the literature review.</li> <li>`Selected-papers.xlsx` -- an Excel spreadsheet with the extracted information about the selected papers.</li> <li>`Selected-papers.csv` -- a CSV equivalent of the Excel spreadsheet.</li> </ul>
CAESAR-Lisflood existing applications parameter listings - Updated May 2022
<p>A spreadsheet containing a list of all the model parameters known, or published by May 2022 for the CAESAR-Lisflood Landscape Evolution Model (see https://sourceforge.net/projects/caesar-lisflood/ ). These parameters were gathered to the authors best knowledge based on published studies and where known information not in print. Therefore, it may contain omissions or errors and should therefore be used as a guide. </p>
DATASET - Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals
<p>Data for experiments presented in the paper "Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals" </p>
Application of X‑ray Microcomputed Tomography for the Static and Dynamic Characterization of the Microstructure of Oleofoams
<p>Raw, greyscale image stacks collected during a X-Ray tomography analysis on cocoa butter-based oleofoams. The dataset is divided into three subsets: aeration, storage and heating, which contain samples that have been aerated for different amounts of time, samples that have been stored for 3 and 15 months at 20 °C, and finally samples that have been heated to the melting point of the stabilizing crystals, respectively. The dataset contains instructions and the scripts for ImageJ and MATLAB (as text files) to process and measure the bubble size distribution, and the thickness of the continous phase.</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.