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9,113 results for “Sampling”
IODP Expedition 371 Sample report
Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.
IODP Expedition 360 Sample report
Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.
Components of spore capture device in "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere"
<p>These STL files enable the 3D printing of the referenced spore capture device. The complete device can be assembled following printing of the: (1) petri dish holder base; (2) lid; and (3) flange. The STL file extension stands for stereolithography, colloquially referred to as Standard Triangle Language or Standard Tessellation Language, and is a popular file format for 3D printing. The 3D models were created, and can be viewed, with CAD software.</p>
Data for "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere"
<p>Colony count data collected from Petri dishes, as described in "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere." See the associated article for more information.</p>
IODP Expedition 397 Sample report
Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.
Sample Records: Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>The project contains several underlying datasets essential for replicating the study's findings. The dataset <strong>01.1_PRIMERPRISMA_IDENTIFICATION.xlsx</strong> includes the initial selection of 6 general terms related to environment and sustainability and 11 specific terms related to disinformation, summarizing the selected keywords, generated Boolean operators, and initial search results, yielding 783 records. The <strong>01.2_PRIMER PRIMA-SCREENING.xlsx</strong> file details the screening process, eliminating duplicates and non-English documents, resulting in 271 retained records. The <strong>01.3_PRIMER PRISMA_INCLUDED.xlsx</strong> file contains results after further screening, retaining 82 documents with expanded bibliometric details. The <strong>02.1_SEGUNDOPRISMA_IDENTIFICATION.xlsx</strong> file documents the second phase of identification using new terms related to climate and disinformation, retrieving 174 records. The <strong>02.2_SEGUNDOPRISMA_SCREENING.xlsx</strong> file includes the screening process for the second phase, reducing records to 75, with an abstract review retaining 2 documents. The <strong>02.3_SEGUNDOPRISMA_INCLUDED.xlsx</strong> file integrates documents from both search phases and other sources, culminating in a final review of 86 documents. The <strong>3.1_Other sources.xlsx</strong> file includes additional relevant sources identified during the review process. Finally, the <strong>4-Final included.xlsx</strong> file contains the final set of 75 publications subjected to the DESLOCIS analysis model.</p>
Sample Records (Analytical procedure): Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>This dataset includes t<span>he online form and the results from the quantitative phase of the study: Disinformation as an obstructionist strategy in climate change mitigation: A review of the scientific literature for a systemic understanding of the phenomenon</span></p> <p>To duplicate the form you can use: https://forms.office.com/Pages/ShareFormPage.aspx?id=6sSEXw03nkuDDHVvi_G1Hw0s3dVrMb1NsO12gDNTB9BUREo4WENRMFFDN1lOSlRSU0xJNkVHWURWUS4u&sharetoken=rg4Qfg19O4UgYzUB084C </p>
Sample Records (PRISMA Checklist and Flow diagram): Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>This dataset includes: the PRISMA Checklist and the <span>PRISMA Flow diagram of the study titled: Disinformation as an obstructionist strategy in climate change mitigation: A review of the scientific literature for a systemic understanding of the phenomenon.</span></p>
East Palestine Soil Sample Analysis - August, 2023
<p>This data come from from Lard et al., in review.</p> <p><span>The 17 congeners measured in this analysis by gas chromatography mass spectrometry are: 1,2,3,4,6,7,8-HpCDD; 1,2,3,4,6,7,8-HpCDF; 1,2,3,4,7,8,9-HpCDF; 1,2,3,4,7,8-HxCDD; 1,2,3,4,7,8-HxCDF; 1,2,3,6,7,8-HxCDD; 1,2,3,6,7,8-HxCDF; 1,2,3,7,8,9-HxCDD; 1,2,3,7,8,9-HxCDF; 1,2,3,7,8-PeCDD; 1,2,3,7,8-PeCDF; 2,3,4,6,7,8-HxCDF; 2,3,4,7,8-PeCDF; 2,3,7,8-TCDD; 2,3,7,8-TCDF; OCDD; OCDF.</span></p> <p><span>Environmentally persistent free radicals were measured by electron paramagnetic resonance analysis.</span></p> <p><span>The ten metals analyzed by inductively coupled plasma mass spectrometry are: B, Ca, Cu, Fe, K, Mg, Mn, P, S, Zn</span></p> <p><span>Total organic carbon was measured by a total carbon analyzer.</span></p>
CyTOF data of PBMC samples of patients with metastatic pancreatic ductal adenocarcinoma
<p>These two CyTOF datasets are a part of the manuscript by M. Baretti "E<span>ntinostat in combination with nivolumab in metastatic pancreatic ductal adenocarcinoma: a phase 2 clinical trial" accepted in Nature Communications. The datasets contain FCS files of PBMCs samples of patients with metastatic pancreatic ductal adenocarcinoma treated with entinostat and nivolumab. PBMC samples were run with myeloid- and lymphoid-oriented panels.<br></span></p>
Model Zoo Dataset Samples for Scalable Weight Space Learning
<p>This dataset contains small versions of model zoo datasets for our ICML 2024 paper "Towards Scalable and Versatile Weight Space Learning". These datasets are intended for testing and rapid pipeline evaluation of the code in the <a title="https://github.com/HSG-AIML/SANE" href="https://github.com/HSG-AIML/SANE">corresponding </a><a href="https://github.com/HSG-AIML/SANE">repository</a>. For full model zoos, please see <a href="modelzoos.cc">modelzoos.cc</a>.</p>
Chirila export of languages in Malaspinas et al sample
<p>Linguistic cognate dataset that underlies the language tree associated with Malaspinas, Anna-Sapfo, et al. "A genomic history of Aboriginal Australia." <em>Nature</em> 538.7624 (2016): 207.</p>
Sample datasets for Galaxy RNA-seq tutorial
<p>This is downsampled dataset from http://dx.doi.org/10.1038/nprot.2016.095. It was prepared as follows:</p> <ol> <li>Mapping data from http://dx.doi.org/10.1038/nprot.2016.095 against hg38 using HISAT2</li> <li>Restricting resulting BAM datasets to chrX:70,000,000-80,000,000</li> <li>Extracting reads using picard SamToFastq tool</li> </ol> <p>File rnaseq_sex.tab contains mapping between accession numbers and sex of the sequences individuals. </p>
SCIAMACHY NO regression fit MCMC samples
<p><strong>SCIAMACHY mesosphere NO data and regression model samples</strong></p> <p>SCIAMACHY mesosphere daily zonal mean NO data and Markov-Chain Monte-Carlo samples from the regression coefficient distributions, derived from and for use with the <a href="https://github.com/st-bender/sciapy"><code>sciapy</code></a> regression module.</p> <p>This data set contains the following files:</p> <ul> <li><code>NO_regress_output_pGM_Lya_ltcs_exp1dscan60d_km32_float32.nc</code>, <code>NO_regress_output_pGM_Lya_ltcs_exp1dscan60d_km32_float64.nc</code> - samples from the regression coefficient distributions (single and double precision)</li> <li><code>NO_regress_quantiles_pGM_Lya_ltcs_exp1dscan60d_km32.nc</code> - the 0.1, 2.5, 16, 50, 84, 97.5, and 99.9 percentiles of the sampled distributions</li> <li><code>scia_nom_dzmNO_2002-2012_v6.2.1_2.2_akm0.002_geomag10_nw.nc</code> - the SCIAMACHY daily zonal mean NO data</li> <li><code>sciapy_regress_tutorial.ipynb</code> - example ipython notebook</li> </ul> <p><strong>MCMC Samples</strong></p> <p>The files <code>NO_regress_output..._float32.nc</code> and <code>NO_regress_output..._float64.nc</code> contain MCMC samples of the model as single and double precision floats. The file <code>NO_regress_quantiles....nc</code> contains the 0.1, 2.5, 16, 50, 84, 97.5, and 99.9 percentiles of the sampled distributions and is provided for convenience. The files contain the following parameters:</p> <ul> <li><code>kernel:log_sigma</code>, <code>kernel:log_rho</code> - the "strength" and "lengthscale" of the Matérn-3/2 Gaussian Process kernel</li> <li><code>mean:offset:value</code> - the constant offset of the NO model in [<span class="math-tex">\(10^6\)</span> cm<span class="math-tex">\(^{-3}\)</span>]</li> <li><code>mean:Lya:amp</code> - the Lyman-<span class="math-tex">\(\alpha\)</span> coefficient of the mean model in [<span class="math-tex">\(10^6\)</span> cm<span class="math-tex">\(^{-3}\)</span> / Lyman-<span class="math-tex">\(\alpha\)</span>]</li> <li><code>mean:GM:amp</code> - the geomagnetic coefficient (AE) in [<span class="math-tex">\(10^6\)</span> cm<span class="math-tex">\(^{-3}\)</span> / nT]</li> <li><code>mean:GM:tau0</code> - the constant lifetime of the geomagnetic lifetime in [d]</li> <li><code>mean:GM:taucos1</code>, <code>mean:GM:tausin1</code> - cosine and sine amplitudes of the yearly geomagnetic lifetime variation in [d]</li> </ul> <p><strong>Daily zonal mean NO data</strong></p> <p>The model was trained on the <a href="http://doi.org/10.5281/zenodo.1009078">SCIAMACHY mesosphere NO dataset</a>, binned into 10° geomagnetic latitude bins using the provided <code>gm_lat</code> variable and using the standard error of the mean as data uncertainties. The data are uploaded as <code>scia_nom_dzmNO_2002-2012_v6.2.1_2.2_akm0.002_geomag10_nw.nc</code> and were prepared by running (after installing <code><a href="https://github.com/st-bender/sciapy">sciapy</a>)</code>:</p> <pre><code class="language-bash">bash> scia_daily_zonal_mean.py -g -b'-90:90:10' -o <daily_zonal_mean_NO.nc> </path/to/SCIAMACHY_NO_NOM_orbits_20??_v6.2.1.nc></code></pre> <p><strong>Regression sampling</strong></p> <p>The samples were generated by running the following command:</p> <pre><code class="language-bash">bash> python -m sciapy.regress <daily_zonal_mean_NO.nc> --proxies Lya:<Lyman-alpha_file.dat>,GM:<AE_file.dat> -A <altitude> -L <geomag_latitude_bin> -w 14 -b 800 -p 1400 -F \"\" -I GM --fit_annlifetimes GM --positive_proxies GM --lifetime_scan=60 --lifetime_prior exp -k -K Mat32 -O0 -m "nom_pGM_Lya_ltcs_exp1dscan60d_km32" -P</code></pre> <p> </p>
MedleyDB Sample
<p>Sample of the MedleyDB dataset: <a href="http://medleydb.weebly.com/">http://medleydb.weebly.com/</a></p> <p>MedleyDB was curated primarily to support research on melody extraction, addressing important shortcomings of existing collections. For each song we provide melody f0 annotations as well as instrument activations for evaluating automatic instrument recognition. The dataset is also useful for research on tasks that require access to the individual tracks of a song such as source separation and automatic mixing.</p>
Figure reproduction for "Accelerating small angle scattering experiments on anisotropic samples using kernel density estimation"
<p>These datasets and a Jupyter notebook reproduce figures in <a href="https://www.nature.com/articles/s41598-018-37345-5">a publication by Saito et al in Scientific Reports</a>. The notebook also serves as a demo for kernel density estimation (smoothing) of 2D data using Python. Details are described in the notebook. If you have no idea about ipynb format, please see HTML version with your web browser instead. It contains exactly the same codes and results as ipynb version.</p>
MADIA_732678_SERGAS_samples_01
<p>MEATADATA ONLY<br> <br> Clinical and biological samples (blood) obtained from Parkinson patients and clinical and biological samples (blood and cerebrospinal fluid_CSF) samples obtained from the Alzheimer patients.</p>
Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS: The Sample
<p>Sample from "Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS". We present a sample of 74,216 M and L dwarfs constructed from two existing catalogs of cool dwarfs spectroscopically identified in the Sloan Digital Sky Survey (SDSS). We cross-matched the SDSS catalog with Gaia DR2 to obtain parallaxes and proper motions and modified the quality cuts suggested by the Gaia Collaboration to make them suitable for late-M and L dwarfs. </p>
Genus interactions from eDNA samples taken in the Klamath mountains
<p>Genus level interactions of organisms discovered in eDNA samples taken in the Klamath mountains in the summer of 2018.</p>
Sample FITS file with non-linear wavelength solution using a Chebyshev model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a Chebyshev model.</p> <p>This data is in its original shape.</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.