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9,113 results for “Sampling”
PROGRAMS project. PRM workflows from IDEKO S. Coop. on 2020-09 sample 3
<p>These data is the PRM workflow elaboration of the ones collected from FIDIA machine tool controller during milling operation.</p>
PROGRAMS project. PRM workflows from IDEKO S. Coop. on 2020-09 sample 2
<p>These data is the PRM workflow elaboration of the ones collected from FIDIA machine tool controller during milling operation.</p>
2d U-net models trained to segment human placental maternal/fetal blood volumes and blood vessels from syncrotron micro-CT data along with a sample data volume.
<p>This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at <a href="https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1">https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1</a>. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at <a href="https://github.com/DiamondLightSource/placental-segmentation-2dunet">https://github.com/DiamondLightSource/placental-segmentation-2dunet</a> and is published at <a href="https://doi.org/10.5281/zenodo.4252562">https://doi.org/10.5281/zenodo.4252562</a> </p>
Estimating TOF from ultrasound signal over bare steel sample of 5 mm thickness
<p>These data have been obtained using the CEIT ultrasound testbed exciting a PZT piezoelectric sensor with a +/-15 volts pulse located over a 5 mm bare steel sample. The testbed receives the ultrasound response and estimates the TOF measuring the distance between two consecutive echoes. The aim is to develop an embedded system to measure the loss of thickness produced by corrosion.</p>
Hyperspectral X-ray CT data set of mineralised ore sample with Au and Pb deposits
<p><strong>General data description:</strong></p> <p>This is a hyperspectral (energy-resolved) X-ray CT projection data set of a mineralised ore sample with small gold and galena deposits. It was acquired in a laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The data included contains all the relevant files required for reconstruction, following a hyperspectral scan of a mineralised ore sample. The sample contains a number of mineral phases, of varying concentration, distributed throughout. Some phases (including gold, and lead-based Galena) produce unique absorption edges, which act as spectral identifiers that can be measured by an energy-sensitive detector.</p> <p><strong>File descriptions:</strong></p> <p>The data set consists of one .txt file and three .mat (MATLAB) data files.</p> <p>Au_rock_scan_geometry.txt gives a breakdown of the full sample and detector geometry used when acquiring the raw projections. The number of horizontal detector pixels accounts for the fact that a set of 5 tiled scans of the sample were collected and later stitched together.</p> <p>Au_rock_sinogram_full.mat contains the full 4D sinogram constructed following flat-field normalisation of the raw projection data. The data matrix contains the total number of energy channels acquired during scanning, as well as the conventional elements of vertical/horizontal detector pixel number and total projection angles.</p> <p>commonX.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p> <p>FF.mat contains the 4D flatfield data acquired when no sample was present. This data was used to normalise the projection datasets, as the sinogram was constructed.</p>
Princeton Handbook for Reproducible Neuroimaging: Sample Output
<p>This archive contains sample output files for the <a href="http://doi.org/10.5281/zenodo.3677090">sample data</a> accompanying the <a href="https://brainhack-princeton.github.io/handbook/">Princeton Handbook for Reproducible Neuroimaging</a>. Outputs include the NIfTI images converted using <a href="https://github.com/nipy/heudiconv">HeuDiConv</a> (v0.8.0) and organized according to the <a href="https://bids.neuroimaging.io/">BIDS</a> standard, quality control evaluation using <a href="https://mriqc.readthedocs.io/">MRIQC</a> (v0.15.1), data preprocessed using <a href="https://fmriprep.readthedocs.io/en/stable/">fMRIPrep</a> (v20.2.0), and other auxiliary files. All outputs were created according to the procedures outlined in the handbook, and are intended to serve as a didactic reference for use with the handbook. The sample data from which the outputs are derived were acquired (with informed consent) using the<a href="https://github.com/ReproNim/reproin"> ReproIn</a> naming convention on a Siemens Skyra 3T MRI scanner. The sample data include a T1-weighted anatomical image, four functional runs with the “prettymouth” spoken story stimulus, and one functional run with a block design emotional faces task, as well as auxiliary scans (e.g., scout, soundcheck). The “prettymouth” story stimulus created by <a href="https://doi.org/10.1177%2F0956797616682029">Yeshurun et al., 2017</a> and is available as part of the<a href="https://openneuro.org/datasets/ds002345"> Narratives</a> collection, and the emotional faces task is similar to<a href="https://doi.org/10.1016/j.nicl.2015.05.004"> Chai et al., 2015</a>. The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.</p>
DCSsim (simulated) and DCSsub (sub-sampled) ChIP-seq data for benchmarking DCS tools.
<p>These data are the results from five independent runs of DCSsim and DCSsub for TF, sharp and broad mark signals in 50:50 and 100:0 regulation scenarios.</p> <p> </p> <p>Simulated data from DCSsim: simulated_ChIP-seq_data.zip</p> <ul> <li>Set1: TF 50:50</li> <li>Set2: TF 100:0</li> <li>Set3: Sharp mark 50:50</li> <li>Set4: Sharp mark 100:0</li> <li>Set5: Broad mark 50:50</li> <li>Set6: Broad mark 100:0</li> </ul> <p> </p> <p>Sub-sampled data from DCSsub: sub-sampled_ChIP-seq_data.zip</p> <ul> <li>Set1: Cebpa-ChIP-seq 50:50</li> <li>Set2: Cebpa-ChIP-seq 100:0</li> <li>Set3: H3K27ac-ChIP-seq 50:50</li> <li>Set4: H3K27ac-ChIP-seq 100:0</li> <li>Set5: H3K36me3-ChIP-seq 50:50</li> <li>Set6: H3K36me3-ChIP-seq 100:0</li> </ul>
URLs from tweets for a 2014 sample of Twitter users and for a set of computer scientists
<p>The files in this dataset are used to analyse the tweeting behaviour of computer scientists on Twitter. They comprise</p> <ul> <li>a set of 989,529 tweet-URL pairs (<em>tweets_2014_researcher.tsv.bz2</em>) from 2014 from 6,271 users of the computer scientists sample in https://zenodo.org/record/12942 specified by time, tweet id, user id, and URL,</li> <li>a set of 300,053,850 tweet ids (<em>tweets_2014_sample.tsv.bz2</em>) from the 1% Twitter stream sample from 2014,</li> <li>a set of 671,304 tweet-URL pairs (<em>tweets_2014_sample_6271_users.tsv.bz2</em>) from the 1% Twitter stream sample from 2014 for 6,271 users specified by time, tweet id, user id, and URL,</li> <li>a set of the top 10,000 host names (<em>MAG_hosts_10000.tsv</em>) from the Microsoft Academic Graph data (http://blogs.msdn.com/b/msr_er/archive/2015/06/26/announcing-the-microsoft-academic-graph-let-the-research-begin.aspx), specified by rank, URL count, and host name, and</li> <li>a set of 340 host names of URL shortening services (<em>url_shortening_services.tsv</em>).</li> </ul>
Liftover of Drosoophila melanogaster genotypes, DGRP sample, from dm3 to dm6 reference assembly.
<p>Genotype data for Drosophila melanogaster DGRP collection.<strong> </strong>Variant positions relative to reference genome version dm6.<strong> </strong>Variant identifiers are in NCBI format.<strong> </strong>Processing code (including original data, reference sequences, perl scripts, etc).</p> <p>The initial intention of performing this 'liftover' was to allow comparison with data from a different melanogaster sample which had been analysed against a dm6 reference assembly. At the time of writing I am not aware that a dataset like this one exists.</p> <p>I've uploaded it in case it is useful to anyone in the future - and also as a trial of zenodo.</p> <p>See README.txt and flylifter.sh for more info.</p> <p> </p>
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
<p>We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of negative examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach on three datasets from different languages.</p> <p>The <strong>russian</strong> model.</p> <p>$ python -V; pip show tensorflow numpy scipy scikit-learn gensim | egrep -i '(name|version)'<br> Python 3.5.2 :: Continuum Analytics, Inc.<br> Name: tensorflow<br> Version: 0.12.1<br> Name: numpy<br> Version: 1.12.0<br> Name: scipy<br> Version: 0.18.1<br> Name: scikit-learn<br> Version: 0.18.1<br> Name: gensim<br> Version: 0.13.4.1</p> <p>The <strong>english</strong><strong>-combined</strong> model has been trained using the well-known word embeddings dataset based on Google News: GoogleNews-vectors-negative300.bin on EVALution, BLESS, K&H+N, ROOT09 combined. The <strong>english</strong><strong>-</strong><strong>evalution</strong> model is traned on EVALution only.</p> <p>$ python -V; pip show tensorflow numpy scipy scikit-learn gensim | egrep -i '(name|version)'<br> Python 3.5.2 :: Anaconda custom (64-bit)<br> Name: tensorflow<br> Version: 0.12.1<br> Name: numpy<br> Version: 1.11.3<br> Name: scipy<br> Version: 0.18.1<br> Name: scikit-learn<br> Version: 0.18.1<br> Name: gensim<br> Version: 0.13.4.1</p>
URLs from tweets for a 2014 sample of Twitter users and for a set of computer scientists
<p>The files in this dataset are used to analyse the tweeting behaviour of computer scientists on Twitter. They comprise</p> <ul> <li>a set of 989,529 tweet-URL pairs (<em>tweets_2014_researcher.tsv.bz2</em>) from 2014 from 6,271 users of the computer scientists sample in https://zenodo.org/record/12942 specified by time, tweet id, user id, and URL,</li> <li>a set of 300,053,850 tweet ids (<em>tweets_2014_sample.tsv.bz2</em>) from the 1% Twitter stream sample from 2014,</li> <li>a set of 605,080 tweet-URL pairs (<em>tweets_2014_sample_6694_users.tsv.bz2</em>) from the 1% Twitter stream sample from 2014 for 6,694 users specified by time, tweet id, user id, and URL,</li> <li>a set of the top 10,000 host names (<em>MAG_hosts_10000.tsv</em>) from the Microsoft Academic Graph data (http://blogs.msdn.com/b/msr_er/archive/2015/06/26/announcing-the-microsoft-academic-graph-let-the-research-begin.aspx), specified by rank, URL count, and host name, and</li> <li>a set of 340 host names of URL shortening services (<em>url_shortening_services.tsv</em>).</li> </ul> <p>In addition, the following rankings (based on the odds ratio) of domains, hosts, and URLs that appear in both the researcher dataset and the sample are included:</p> <ul> <li><em>domains_by_odds_ratio.tsv.bz2</em> - a ranking of 61,860 domains,</li> <li><em>hosts_by_odds_ratio.tsv.bz2</em> - a ranking of 80,384 hosts,</li> <li><em>publisher_domains_by_odds_ratio.tsv.bz2</em> - a ranking of 924 publisher domains,</li> <li><em>publisher_urls_by_odds_ratio.tsv.bz2</em> - a ranking of 4,227 publisher URLs.</li> </ul>
Global Pasture Watch - Grassland reference samples based on visual interpretation of VHR imagery and harmonized datasets (2000–2024)
<p>Reference point samples used in the production of the <a href="https://doi.org/10.5281/zenodo.13890401">global maps of annual grassland class and extent for 2000—2022</a><strong> </strong>within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Wath</a> initiative. </p> <p>The reference samples (estabilished by Feature Space Coverage Sampling-FSCS) comprises <strong>2.3M points</strong> visually classified (<em>using Very High Resolution imagery</em>) in:</p> <ol> <li><strong>Cultivated grassland,</strong></li> <li><strong>Natural/semi-natural grassland</strong></li> <li><strong>Other land cover</strong></li> </ol> <p>The file <code>gpw_grassland_fscs.vi.vhr_tile.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> aggregates the samples by visual interpretation units ( 1x1 km) and includes the follow collumns:</p> <ul> <li>cluster_id: Cluster id defined by k-means (FSCS),</li> <li>cluster_distance: Distance from the sample tile to center of the cluster (FSCS),</li> <li>cluster_size: Size of cluster (strata) defined by the FSCS,</li> <li>priority: Priority used by the visual interpretation,</li> <li>tile_id: Sample tile id,</li> <li>imagery: VHR reference images used by the visual interpretation,</li> <li>min_year: Minimum of year covered by the reference samples,</li> <li>max_year: Maximum of year covered by the reference samples,</li> <li>n_years: Number of years covered by the reference samples,</li> <li>n_samples_c1: Number of reference samples for "Cultivated grass" (1),</li> <li>n_samples_c2: Number of reference samples for "Natural / Semi-natural grass" (2),</li> <li>n_samples_c3: Number of reference samples for "Open Shrubland" (2),</li> <li>n_samples_c4: Number of reference samples for "Not grass" (3),</li> <li>n_samples_all: Total number of reference samples,</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_point.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides individual points (with 60-m spatial support) and include the follow collumns:</p> <ul> <li>sample_id: Sample id deribed by MD5 Hash of columns x, y, imagery and year,</li> <li>x: Longitude in WGS84 (EPSG:4326),</li> <li>y: Latitude in WGS84 (EPSG:4326),</li> <li>vi_tile_id: 1-km tile id,</li> <li>tile_id: GLAD tile id (1x1 degree)</li> <li>imagery: VHR Reference image used by the visual interpretation (Google; Bing; Interpolated),</li> <li>ref_date: Reference date of GPW samples (based on VHR image) and of other existing datasets,</li> <li>year: Reference year of GPW samples (based on VHR image) and of other existing datasets,</li> <li>class: Class id (1: Cultivated grassland; 2: Natural/semi-natural grassland; 3: Open shrubland; 4: Other land cover) ,</li> <li>class_label: Class labels (Cultivated grassland; Natural/semi-natural grassland; Open shrubland; Other land cover) ,</li> <li>dataset_name: Existing dataset names (CGLS-LC, EuroCrops, GeoWiki, GeoWiki-feedback, LCMap-Conus, LUCAS, MapBiomas, WorldCereal, GPW) <br>dataset_class: Original land cover class provided by the maintainer of existing dataset</li> <li>esa_worldcover_2020: Land cover class labels extracted from ESA WorldCover 2020,</li> <li>glad_glcluc_yyyy: Land cover class labels extracted from UMD GLAD GLCLUC for the reference date,</li> <li>glc_fcs30d_yyyy: Land cover class labels extracted from GLC_FCS30D for the reference date,</li> <li>gpw_fscs_cluster: K-Means output ranging from 0—9999 according to Feature Space Coverage Sampling (FSCS),</li> <li>ml_cv_group: spatial block CV group (based on vi_tile_id),</li> <li>ml_type: specify if the sample was used for (1) training or (2) calibration.</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_grid.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides the grid samples (with 10-m spatial support) and include the follow collumns:</p> <ul> <li>tile_id: 1-km tile id,</li> <li>bing_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Bing Maps Images,</li> <li>bing_image_start_date: Start date of the Bing Maps Images used in the visual interpretation,</li> <li>bing_image_end_date: End date of the Bing Maps Images used in the visual interpretation,</li> <li>google_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Google Maps Images,</li> <li>google_image_start_date: Start date of the Google Maps Images used in the visual interpretation,</li> <li>google_image_end_date: End date of the Google Maps Images used in the visual interpretation,</li> <li>missing_image_date: No images available,</li> <li>same_image_bing_google: Images from the same date available in Google and Bing Maps.</li> </ul> <p>The dataset was produced through the <a href="https://plugins.qgis.org/plugins/qgis-fgi-plugin/">QGIS plugin Fast Grid Inspection</a>.</p> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="https://zenodo.org/records/13890400">2000-2002</a> <a href="https://zenodo.org/records/13890402">2003-2005</a> <a href="https://zenodo.org/records/13890404">2006-2008</a> <a href="https://zenodo.org/records/13890408">2009-2011</a> <a href="https://zenodo.org/records/13890410">2012-2014</a> <a href="https://zenodo.org/records/13890412">2015-2017</a> <a href="https://zenodo.org/records/13890414">2018-2020</a> <a href="https://zenodo.org/records/13890416">2021-2022</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Grassland reference samples based on VHR imagery (2000–2022):</strong><br><a href="https://doi.org/10.5281/zenodo.11281157">GeoPackage files</a></li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <h3>Support</h3> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></p>
In situ FTIR, EXAFS and HR-STEM data for Pd/TiO2 samples under red-ox conditions
<p>Files Pd_photo-oxidation.xmu.dat and Pd_dep-oxidation.xmu.dat contain the sequence of X-ray absorption spectra during starting from the pre-reduced state (after reduction in H2) during heating in O2 from 50 to 400 for Pd_photo and Pd_dep samples, respectively (synthesized using photodeposition and deposition-precipitation methods). The last two columns in each file correspond to the as-synthesized state of the corresponding sample (before reduction in hydrogen) and reference palladium foil. </p> <p>Pd_photo.ftir.dat and Pd_dep.ftir.dat contain the sequence of the FTIR spectra for the same samples taken at room temperature after sending 35 mbar of CO on pre-oxidized samples.</p> <p>Video files show the evolution of the structure of Pd_dep and Pd_photo samples sample under different atmospheres and temperatures, visualized by in situ HR-STEM microscope.</p>
Sample tweets from May 2022
<p>Dataset containing twitter data, namely hashed twitter id, hashed user id, tweet language, user statistics.</p>
Land use and land cover samples for specific regions of interest in the Brazilian Cerrado agricultural belt
<p>Land use and land cover samples for specific regions of interest in the Brazilian Cerrado agricultural belt in 2019/2020. Total of samples: 957. The process to collect them is described in Chaves, M., & Sanches, I. (2023). Improving crop mapping in Brazil's Cerrado from a data cubes-derived Sentinel-2 temporal analysis. Remote Sensing Applications: Society and Environment, 32, 101014. <a href="https://www.sciencedirect.com/science/article/pii/S2352938523000964">https://www.sciencedirect.com/science/article/pii/S2352938523000964</a> and Chaves, M., Soares, A., Mataveli, G., Sánchez, A., & Sanches, I. (2023). A semi-automated workflow for LULC mapping via Sentinel-2 data cubes and spectral indices. Automation, 4(1), 94-109. <a href="https://www.mdpi.com/2673-4052/4/1/7">https://www.mdpi.com/2673-4052/4/1/7</a>.</p>
IODP Expedition 391 Sample report
Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.
Data for: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3
<p>We present all of the data across our SNR and abundance study for the molecules O2 and O3 for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-20, and the abundance values range in log space in steps of 0.5 and/or 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input O2 and O3 values (abundance case), the retrieved O2 and O3 values (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for O2 and O3. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input O2': str, {'Input O3': str}})</p>
IODP Expedition 397T Sample report
Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.
Dataset: Assessing Background Contamination of Sample Tubes used in Human Biomonitoring by Non-targeted Liquid Chromatography–High Resolution Mass Spectrometry
<p>Data set of the Publication: </p> <div> <div>Krauss, Martin, Carolin Huber, Tobias Schulze, Martina Bartel-Steinbach, Till Weber, Marike Kolossa-Gehring, und Dominik Lermen (2024): Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography–high resolution mass spectrometry. <em>Environment International</em> 183: 108426. <a href="https://doi.org/10.1016/j.envint.2024.108426">https://doi.org/10.1016/j.envint.2024.108426</a>.</div> </div> <p>- raw LC-HRMS data in mzML format for positive and negative mode.</p> <p>- merged MS/MS spectra of whole data set after MZMine 2.53 processing in mgf format.</p> <p> </p>
SEM images of SiO2 and juniper charcoal powders (pure samples and intimate binary mixtures).
<p><strong>Summary:</strong><br>These images are those from SiO2 and juniper charcoal (JChc) powder samples observed with a SEM. The powders were obtained from commercial sources and these samples were prepared at the Bern University (Switzerland) as part of the D-A-CH/CoPhyLab project (https://www.cophylab.space/index.php?id=home)<br><br><strong>Details:</strong><br>¤ SEM images from the sample of pure SiO2:<br> 001-St5607t0_00.tif<br> 002-St5607t0_01.tif<br> 003-St5607t0_03.tif:<br><br>¤ SEM images from the sample of pure juniper charcoal powder:<br> 004-St5607t6_00.tif<br> 005-St5607t6_01.tif<br> 006-St5607t6_04.tif<br> 007-St5607t6_05.tif<br> 008-St5607t6_07.tif:<br><br>¤ SEM images from the sample of the intimate mixture with 90% SiO2 - 10% JChc by mass:<br> 009-St5606t1_00.tif<br> 010-St5607t1_01.tif:</p><p>¤ SEM images from the sample of the intimate mixture with 70% SiO2 - 30% JChc by mass:<br> 011-St5607t2_00.tif<br> 012-St5607t2_02.tif<br><br>¤ Zoom-in on the 70% SiO2 - 30% JChc sample at lignin fragment peppered with smaller JChc and SiO2 particles and agglomerates:<br> 013-St5607t2_03.tif:<br><br>¤ Zoom-in on the 70% SiO2 - 30% JChc sample with apparent large fragment of lignin structure:<br> 014-St5607t2_07.tif:<br><br>¤ SEM images from the sample of the intimate mixture with 50% SiO2 - 50% JChc by mass:<br> 015-St5607t3_00_St5606t3_00.tif<br> 016-St5607t3_03.tif<br><br>¤ SEM images from the sample of the intimate mixture with 30% SiO2 - 70% JChc by mass:<br> 017-St5607t4_00.tif<br> 018-St5607t4_01.tif, <br><br>¤ SEM images from the sample of the intimate mixture with 10% SiO2 - 90% JChc by mass:<br> 019-St5607t5_00.tif<br><br><strong>Addendum:</strong><br>These SEM images are associated with the spectroscopic and photometric data available at the following addresses:<br> https://doi.org/10.26302/SSHADE/EXPERIMENT_CF_20200723_000<br> https://doi.org/10.26302/SSHADE/EXPERIMENT_CF_20200813_000</p>
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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.