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1,940 results for “data sample”
Raw data for: Identifying archived insect bulk samples using DNA metabarcoding: A case study using the long-term Rothamsted Insect Survey
<p>These are the raw files from the Illumina MiSeq for the 12 libraries sequenced.</p>
Additional data for "Settling velocities of tire and road wear particles: Analyzing finely graded density fractions of samples from a road simulator and a highway tunnel"
<p>This repository provides additional files for the publication "Settling velocities of tire and road wear particles: Analyzing finely graded density fractions of samples from a road simulator and a highway tunnel" by Stefan Dittmar, Steffen Weyrauch, Thorsten Reemtsma, Paul Eisentraut, Korinna Altmann, Aki Sebastian Ruhl and Martin Jekel (DOI: <em>tba</em>).</p> <p>It contains:<br>- single particle raw data from settling experiments (<strong>1_settling_data.zip</strong>)<br>- cumulative distributions of settling velocity and particle size for each investigated density fraction of both samples (<strong>2_measured_distributions.zip</strong>)<br>- composed cumulative distributions of settling velocity and particle size for incorporated tire material/TRWP from both samples (<strong>3_composed_distributions.zip</strong>)<br>- Python script to compute composed distributions (similar to 3) corrected for water properties differing from the experimental conditions (T=15 °C, salinity=0 g/kg)<br>(<strong>4_correct_temperature_salinity.zip</strong>)</p> <p>Please consider the included readme files (<strong>0_README</strong>{...}<strong>.txt</strong>) and revisit main publication and supplement information for further context on this data set, especially when you want to implement the data (e.g. from <strong>3_composed_distributions</strong>) or generate corrected composed distributions via the Python script <strong>4_correct_temperature_salinity.py</strong> yourself to be used in future studies.</p> <p>Please contact Stefan Dittmar (stefan.dittmar@tu-berlin.de), if you have questions in that regard.</p> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p> </p> <p> </p> </div> </div> </div> </div> </div> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p> </p> <p> </p> </div> </div> </div> </div> </div>
Data from: Taxon sampling and reverse successive weighting
[No abstract entered]
CS#2: Toxicity data on samples grabbed from 5 DWTPs in Milan
<p>Toxicity data on samples grabbed in 5 different DWTPs Milan performed by BDS using CALUX bioassays</p>
Raw Acoustic data samples from hydrophone SLim Towed Array towed by marine robot acquired during STO-CMRE sea trials
<p>Sample raw acoustic datasets acquired from hydrophone array SLiTA (SLim Towed Array, specific for AUV applications) towed by marine autonomous vehicles (e.g, Autonomous Underwater Vehicles, AUVs) [*] during three STO-CMRE sea trials:</p> <ul> <li>PORTOPALO (2013), at Portopalo di Capo Passero, Sicily, Italy, on 18-19 February 2013</li> <li>COLLAB13, at Palmaria Island, La Spezia, Italy, on 30 June - 7 July 2013</li> <li>COLLAB14, at Massa, Italy, on 29-31 October 2014</li> </ul> <p>Access to this dataset is restricted to NATO and H2020 INFORE consortium. This data set is provided to INFORE consortium partners for the purposes of executing tasks under the INFORE Grant Agreement and may not be used for other purposes or further distributed.</p> <p>The creation of derived products, as well the use in scientific publications must be pre-approved by NATO STO CMRE and acknowledged.</p> <p>These data are available to partners of NATO Nations after the execution of the project on the basis of existing NATO policies and rules for data release, other memorandum of understanding and agreements, and on the basis of CMRE terms and conditions for data reuse.</p> <p>Acoustic raw data from SliTa array header specification is described in the Table below,</p> <table> <thead> <tr> <th> <p><strong>Field Name</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Description</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>headersize</p> </td> <td> <p>int</p> </td> <td> <p>Size of header in bytes</p> </td> </tr> <tr> <td> <p>dataFormat</p> </td> <td> <p>int</p> </td> <td> <p>0 means 2’s complement; 1 means Offset Binary</p> </td> </tr> <tr> <td> <p>fs</p> </td> <td> <p>float</p> </td> <td> <p>Sampling Frequency [Hz]</p> </td> </tr> <tr> <td> <p>inputRange</p> </td> <td> <p>int</p> </td> <td> <p>Flag to determine the max voltage.<br> {3=10V, 2=5V, else 2.5V}</p> </td> </tr> <tr> <td> <p>gainHydPreamp</p> </td> <td> <p>float</p> </td> <td> <p>Preamplifier fain</p> </td> </tr> <tr> <td> <p>gainA2dAmp</p> </td> <td> <p>float</p> </td> <td> <p>A/D gain</p> </td> </tr> <tr> <td> <p>dataWidth</p> </td> <td> <p>int</p> </td> <td> <p>Flag to determine the number of bit per sample<br> {3=24bit, 2=20bit, 1=18bit, else 15bit}</p> </td> </tr> <tr> <td> <p>acqLength</p> </td> <td> <p>float</p> </td> <td> <p>Size (in seconds) on each block of data returned by A/D</p> </td> </tr> <tr> <td> <p>octave</p> </td> <td> <p>int</p> </td> <td> <p>Determine the array spacing:<br> {1=0.21m, 2=0.42, 3=0.84, 4=1.05m}</p> </td> </tr> <tr> <td> <p>pc_day</p> </td> <td> <p>int</p> </td> <td> <p>Day from PC time</p> </td> </tr> <tr> <td> <p>pc_month</p> </td> <td> <p>int</p> </td> <td> <p>Month from PC time</p> </td> </tr> <tr> <td> <p>pc_year</p> </td> <td> <p>int</p> </td> <td> <p>Year from PC time</p> </td> </tr> <tr> <td> <p>pc_hr</p> </td> <td> <p>int</p> </td> <td> <p>Hour from PC time</p> </td> </tr> <tr> <td> <p>pc_min</p> </td> <td> <p>int</p> </td> <td> <p>Minute from PC time</p> </td> </tr> <tr> <td> <p>pc_sec</p> </td> <td> <p>int</p> </td> <td> <p>Seconds from PC time</p> </td> </tr> <tr> <td> <p>gps_month</p> </td> <td> <p>int</p> </td> <td> <p>Month from GPS</p> </td> </tr> <tr> <td> <p>gps_day</p> </td> <td> <p>int</p> </td> <td> <p>Day from GPS</p> </td> </tr> <tr> <td> <p>gps_year</p> </td> <td> <p>int</p> </td> <td> <p>Year from GPS</p> </td> </tr> <tr> <td> <p>oex_hr</p> </td> <td> <p>int</p> </td> <td> <p>Hour from Frontseat PC</p> </td> </tr> <tr> <td> <p>oex_min</p> </td> <td> <p>int</p> </td> <td> <p>Minute from Frontseat PC</p> </td> </tr> <tr> <td> <p>oex_sec</p> </td> <td> <p>double</p> </td> <td> <p>Seconds from Frontseat PC</p> </td> </tr> <tr> <td> <p>lat_deg</p> </td> <td> <p>int</p> </td> <td> <p>Latitude [degrees]</p> </td> </tr> <tr> <td> <p>lat_min</p> </td> <td> <p>double</p> </td> <td> <p>Latitude [minutes]</p> </td> </tr> <tr> <td> <p>lon_deg</p> </td> <td> <p>int</p> </td> <td> <p>Longitude [degrees]</p> </td> </tr> <tr> <td> <p>lon_min</p> </td> <td> <p>double</p> </td> <td> <p>Longitude [minutes]</p> </td> </tr> <tr> <td> <p>heading</p> </td> <td> <p>float</p> </td> <td> <p>Size of header in bytes</p> </td> </tr> <tr> <td> <p>cog</p> </td> <td> <p>float</p> </td> <td> <p>0 means 2’s complement; 1 means Offset Binary</p> </td> </tr> <tr> <td> <p>depth</p> </td> <td> <p>float</p> </td> <td> <p>Sampling Frequency [Hz]</p> </td> </tr> <tr> <td> <p>altitude</p> </td> <td> <p>double</p> </td> <td> <p>Flag to determine the max voltage.<br> {3=10V, 2=5V, else 2.5V}</p> </td> </tr> <tr> <td> <p>sog(dm/s)</p> </td> <td> <p>int</p> </td> <td> <p>Preamplifier fain</p> </td> </tr> <tr> <td> <p>sow(dm/s)</p> </td> <td> <p>int</p> </td> <td> <p>A/D gain</p> </td> </tr> <tr> <td> <p>track_stat</p> </td> <td> <p>int</p> </td> <td> <p>Flag to determine the number of bit per sample<br> {3=24bit, 2=20bit, 1=18bit, else 15bit}</p> </td> </tr> <tr> <td> <p>fix_type</p> </td> <td> <p>int</p> </td> <td> <p>Size (in seconds) on each block of data returned by A/D</p> </td> </tr> <tr> <td> <p>pps_output</p> </td> <td> <p>int</p> </td> <td> <p>Determine the array spacing:<br> {1=0.21m, 2=0.42, 3=0.84, 4=1.05m}</p> </td> </tr> </tbody> </table> <p>[*] Alain Maguer, Rodney Dymond, Piero Guerrini, Luigi Troiano, Vittorio Grandi, Alberto Figoli, Claudio Olivero, Alessandro Sapienza, Stefano Fioravanti, John Potter Receiving and transmitting acoustic systems for AUV/gliders. Proceedings of the 3rd International Conference and Exhibition on Underwater Acoustic Measurements: Technologies and Results, 21-26 June, 2009, Nafplion, Greece. <a href="https://openlibrary.cmre.nato.int/bitstream/handle/20.500.12489/651/NURC-PR-2009-004.pdf">https://openlibrary.cmre.nato.int/bitstream/handle/20.500.12489/651/NURC-PR-2009-004.pdf</a></p>
FIGURE 7 in Kinorhyncha from the Iberian Peninsula: new data from the first intensive sampling campaigns
FIGURE 7. Depth records for kinorhynch species collected in this study. Data are not available for Echinoderes worthingi and Pycnophyes sp. 2.
FIGURE 4. Scanning electron micrographs. A in Kinorhyncha from the Iberian Peninsula: new data from the first intensive sampling campaigns
FIGURE 4. Scanning electron micrographs. A, dorsal view of Pycnophyes dentatus. B, ventral view of Pycnophyes aulacodes. C, lateroventral view of Echinoderes cantabricus. D, Ventral view of Campyloderes cf. vanhoeffeni. E, ventral view of Pycnophyes carinatus. F, P. aulacodes, male, ventral view; midsternal plate with a midventral pointed projection. G, ventral view of Semnoderes armiger. H, E. cantabricus, dorsal view; midlateral tube on segment 1. I, P. carinatus, female, ventral view; placids and shape of the midsternal and episternal plates. J, P. dentatus, ventral view; cuticular structures on segment 10. K, S. armiger, ventral view; detail of cuspidate and acicular spines from segment 9.
sample data for project
<p>some details</p>
Sample text data
<p>This is just a sample text data.</p>
ABS richt model waste characterization for different sampling strategies - NMR data
<p>The purpose of this analysis is the development of an efficient sampling protocol for plastic waste streams. </p> <p>A model waste from different polymers was formulated, rich in ABS and containing PS, PP and PE in smaller proportions. Additionally, one bromine containing flame retardant is added to a final concentration of either 500ppm or 50ppm. Different sampling approaches were followed including extrusion and/or cryogenic grinding as a homogenization step. Each approach was assessed via various analytical techniques as to homogenization efficiency. </p> <p>This dataset contains raw NMR data of the model waste from the different sampling approaches. The content is:</p> <ul> <li> <p>One Excel file containing NMR data of the model waste, wherein the approach was based on extrusion and measurement protocol</p> </li> <li> <p>One Excel file containing NMR data of the model waste, wherein the approach was based on cryogenic grinding and measurement protocol</p> </li> <li> <p>One Readme file containing further information about the methodology and nomenclature </p> </li> </ul> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</p>
Collection of Biological and Environmental Samples and Clinical Data From Anonymous Adult Men and Women for Quality Control and Methods Development and Evaluation (ASCA)
ClinicalTrials.gov study NCT02940015. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Gene expression data from meningioma samples and healthy meningial cell line
GEO Series GSE88720. Homo sapiens. 15 samples. Type: Expression profiling by array.
Affymetrix SNP array data for 31 Tibetan samples
GEO Series GSE21661. Homo sapiens. 31 samples. Type: Genome variation profiling by SNP array; SNP genotyping by SNP array.
Gene expression data of organoids derived from human gastric tumor and normal stomach samples.
GEO Series GSE112369. Homo sapiens. 62 samples. Type: Expression profiling by array.
Whole-transcript and exon-level expression data for human primary and metastatic prostate cancer samples and control normal adjacent benign prostate
GEO Series GSE21034. Homo sapiens. 370 samples. Type: Expression profiling by array.
Expression data from human colonic biopsy sample
GEO Series GSE10714. Homo sapiens. 33 samples. Type: Expression profiling by array.
Affymetrix SNP array data for Cryptomeria japonica samples [Axiom_Cj70K_v1]
GEO Series GSE95616. Cryptomeria japonica. 473 samples. Type: Genome variation profiling by SNP array; SNP genotyping by SNP array.
Analyzing Flow Cytometry or Targeted Gene Expression Data Influences Clinical Discoveries — Profiling Blood Samples of Pancreatic Ductal Adenocarcinoma Patients
GEO Series GSE241957. Homo sapiens. 88 samples. Type: Other.
Expression data from human colonic biopsy samples (adenoma-carcinoma)
GEO Series GSE37364. Homo sapiens. 94 samples. Type: Expression profiling by array.
Expression data from murine vaginal samples following adjuvant treatment
GEO Series GSE27149. Mus musculus. 47 samples. Type: Expression profiling by array.
ScienceDex guides
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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.