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3,696 results for “Reference”
Database on Certified Reference Materials measured with PAT tools for validation and verification purposes
<p>The H2020 PAT4Nano project aims to develop and demonstrate Process Analytical Technologies (PAT) tools for nanosuspension characterization which have sufficiently high resolution, accuracy, and speed, for real-time industrial process monitoring and control. Real time monitoring is desired for example to obtain: small, high precision, specialty batch of materials, processing monitoring of nucleation/growth/milling of materials at different scales (lab, pilot, production), and for producing feedback loops (adapt T, pH, etc.,) needed for process control.<br> Laser diffraction (LD), Spatially Resolved Dynamic Light Scattering (SR-DLS), Cross-Correlation Dynamic Light Scattering (CC-DLS), Ultrasound Nanoparticle Sizer (UNPS), Raman, and Transmission Electron Microscopy (TEM) are the main PAT tools used in this project. For validation and verification purposes of these measurement techniques, polystyrene and silica samples (200 and 1000 nm particle size) were selected as (Certified) Reference Materials ((C))RMs) by the consortium partners. The results described in this database are particle size measurements using PAT methods in an offline mode. The particle size and particle size distribution data are presented as the D10, D50 and D90 and PDI/span measured with each PAT tool.<br> Raman spectra of the CRMs are presented as well. Here, particle size data was extracted by using chemometric software. Lastly, TEM images of the CRMs are included in the database to cross-correlate and cross-validate the results of the spectroscopic and scattering PAT tools.</p>
Supplemental data files: Beyond the reference: gene expression variation and transcriptional response to RNAi in C. elegans
<p>This dataset holds all non-GEO-hosted supplemental data files for manuscript "Beyond the reference: gene expression variation and transcriptional response to RNAi in <em>C. elegans</em>". Please see the linked preprint/publication for full details.</p> <p>The PDF _guide_to_datafiles.pdf gives details on the format and content of each of the included files.</p>
Datasets of "Differences in the stool metabolome between vegans and omnivores: analyzing the NIST stool reference material" publication
<p>To gain confidence in results of omic-data acquisitions, methods must be benchmarked by validated quality control materials. We here report data combining both untargeted and targeted metabolomics assays for the analysis of four new human fecal reference materials developed by the U.S. National Institute of Standards and Technologies (NIST) for metagenomics and metabolomics measurements. These reference grade test materials (RGTM) were established by NIST based on two different diets and two different samples treatments: homogenized fecal matter from subjects eating vegan diets, stored and submitted in either lyophilized (RGTM 10162) or aqueous form (RGTM 10171); secondly, homogenized fecal matter from subjects eating omnivore diets, stored and submitted in either lyophilized (RGTM 10172) or aqueous form (RGTM 10173). We used four untargeted metabolomics assays (lipidomics, primary metabolites, biogenic amines and polyphenols) and one targeted assay on bile acids.</p>
Fish and crayfish density and count data for Peeks Creek, Macon County, NC, USA 2005-2014, 2019, and 2022 following a catastrophic debris flow, as well as six reference streams
We followed the process of recovery of the fish and crayfish assemblage in Peeks Creek, a high-gradient second order stream in the Little Tennessee River watershed of North Carolina, after a debris flow devastated the channel and its riparian zone. After 15 years, the fish assemblage had recovered, and the channel and riparian zone had stabilized. Of the three major components of the fish assemblage, Rainbow Trout (Oncorhynchus mykiss (Walbaum)), a strong swimmer, reappeared in year 1. Longnose Dace (Rhinichthys cataractae (Valenciennes in Cuvier and Valenciennes)) reappeared in year 3. Mottled Sculpin (Cottus bairdii Girard), a weak swimmer, did not become established until year 6 and only resumed expected abundance in year 9. Appalachian Brook Crayfish (Cambarus bartonii cavatus Hay) numbers recovered quickly, though only one individual was found the year following the debris flow. Unassisted natural recovery occurred after a costly engineered restoration project had been rejected and arguably represents the preferable solution. However, recovery of the fish assemblage may not have been achieved if the stream flowed directly into an impoundment or low gradient river that lacked the source of species for recolonization, or if the stream had been located above a barrier to upstream movement.
Fine woody debris inventory data from reference stands and inventory plots in the Pacific Northwest, 1992 to 2000
These data provide an inventory of the mass of downed fine woody debris stored within various forest types. This data is used to determine total organic matter, carbon, and nutrient stores in forests.
The stations used for counting birds in hayed salt marsh vs reference salt marsh sites, Rowley, MA, PIE LTER.
The birding observation stations used for counting birds in hayed marsh and reference marsh sites near Stackyard Rd. and Patmos Rd, Rowley, Massachusetts.
PIE LTER study of marsh vegetation percent cover at Greenwood Creek, Ipswich, MA effluent enrichment and Clubhead Creek, Rowley, MA reference sites
Vegetation percent cover data along transects through a nutrient enriched marsh receiving wastewater effluent and a reference (unenriched) marsh. Nutrient enrichment comes from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich. The marsh around Clubhead Creek, Rowley, MA was used as a reference.
Presence or absence of marsh plant species along transects through a nutrient enriched marsh receiving wastewater effluent and a reference (unenriched) marsh, Plum Island Ecosystems LTER.
Presence or absence of marsh plant species along transects through a nutrient enriched marsh receiving wastewater effluent and a reference (unenriched) marsh. Nutrient enrichment comes from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich. The marsh around Clubhead Creek, Rowley, MA was used as a reference.
High-marsh epifauna densities within references and ice-rafted sediment deposits, Rowley, MA.
Following a historic bomb cyclone (Winter Storm Grayson) in January of 2018, a large volume of ice-rafted sediment was patchily deposited on the surface of salt marshes in the Great Marsh, MA. In May of 2018, twenty patches of ice-rafted sediments and paired reference sites (i.e., no sediment deposition) were delineated. In May 2018, August 2018, and August 2019, samples were collected to examine how ice-rafted sediments affected vegetation, infauna, and epifauna recovery over time. This specific dataset focuses on epifauna species counts, with the primary species including: Melampus bidentatus, Littorophiloscia vittata, and Orchestia grillus. This dataset is complete and please see our publication (https://doi.org/10.1007/s12237-021-01023-z) for more information.
High-marsh infauna densities within references and ice-rafted sediment deposits, Rowley, MA.
Following a historic bomb cyclone (Winter Storm Grayson) in January of 2018, a large volume of ice-rafted sediment was patchily deposited on the surface of salt marshes in the Great Marsh, MA. In May of 2018, twenty patches of ice-rafted sediments and paired reference sites (i.e., no sediment deposition) were delineated. In May 2018, August 2018, and August 2019, samples were collected to examine how ice-rafted sediments affected vegetation, infauna, and epifauna recovery over time. This specific dataset focuses on infauna species counts, with the primary species including: mites, Manayunkia aestuarina, and Cernosvitotviella immota. This dataset is complete and please see our publication (https://doi.org/10.1007/s12237-021-01023-z) for more information.
Reference Windfarm database CNk2 30
<p>Dataset for TotalControl reference windfarm database simulation of a conventionally neutral boundary layer flow with 30 degree inflow wind direction angle (Casename CNk2 30)</p> <p>Included Python files for loading and visualizing the data. Use the plot_*.py files.</p> <p>Further information, including description of the case and dataset can be found in the deliverable report at: </p> <p><a href="https://cordis.europa.eu/project/id/727680/results">https://cordis.europa.eu/project/id/727680/results</a></p> <p>"Database for reference wind farms part 2: windfarm simulations"</p>
Reference Windfarm database CNk4 30
<p>Dataset for TotalControl reference windfarm database simulation of a conventionally neutral boundary layer flow with 30 degree inflow wind direction angle (Casename CNk4 30)</p> <p>Included Python files for loading and visualizing the data. Use the plot_*.py files.</p> <p>Further information, including description of the case and dataset can be found in the deliverable report at: </p> <p><a href="https://cordis.europa.eu/project/id/727680/results">https://cordis.europa.eu/project/id/727680/results</a></p> <p>"Database for reference wind farms part 2: windfarm simulations"</p>
Reference Windfarm database PDk 30
<p>Dataset for TotalControl reference windfarm database simulation of a pressure-driven high Reynolds number boundary layer flow with 30 degree inflow wind direction angle (Casename PDk 30)</p> <p>Included Python files for loading and visualizing the data. Use the plot_*.py files.</p> <p>Further information, including description of the case and dataset can be found in the deliverable report at: </p> <p><a href="https://cordis.europa.eu/project/id/727680/results">https://cordis.europa.eu/project/id/727680/results</a></p> <p>"Database for reference wind farms part 2: windfarm simulations"</p>
Reference Windfarm database PDk 0
<p>Dataset for TotalControl reference windfarm database simulation of a pressure-driven high Reynolds number boundary layer flow with 0 degree inflow wind direction angle (Casename PDk 0)</p> <p>Included Python files for loading and visualizing the data. Use the plot_*.py files.</p> <p>Further information, including description of the case and dataset can be found in the deliverable report at: </p> <p><a href="https://cordis.europa.eu/project/id/727680/results">https://cordis.europa.eu/project/id/727680/results</a></p> <p>"Database for reference wind farms part 2: windfarm simulations"</p>
Reference Windfarm database CNk8 0
<p>Dataset for TotalControl reference windfarm database simulation of a conventionally neutral boundary layer flow with 0 degree inflow wind direction angle (Casename CNk8 0)</p> <p>Included Python files for loading and visualizing the data. Use the plot_*.py files.</p> <p>Further information, including description of the case and dataset can be found in the deliverable report at: </p> <p><a href="https://cordis.europa.eu/project/id/727680/results">https://cordis.europa.eu/project/id/727680/results</a></p> <p>"Database for reference wind farms part 2: windfarm simulations"</p>
Reference Windfarm database CNk2 60
<p>Dataset for TotalControl reference windfarm database simulation of a conventionally neutral boundary layer flow with 60 degree inflow wind direction angle (Casename CNk2 60)</p> <p>Included Python files for loading and visualizing the data. Use the plot_*.py files.</p> <p>Further information, including description of the case and dataset can be found in the deliverable report at: </p> <p><a href="https://cordis.europa.eu/project/id/727680/results">https://cordis.europa.eu/project/id/727680/results</a></p> <p>"Database for reference wind farms part 2: windfarm simulations"</p>
Monthly reference evapotranspiration for Brazil
<p>The set of monthly ETo data referred to as monthly reference evapotranspiration for Brazil. It has a <strong>spatial resolution</strong> of <strong>30 seconds (~ 1 km²)</strong> and a <strong>temporal resolution of 1 month</strong>. The data set grid is in <strong>GeoTIFF format</strong>, and corresponds perfectly to WorldClim. It uses the <strong>geographic coordinate</strong> reference system, with <strong>WGS84 projection</strong> <strong>(EPSG: 4326)</strong>. The files are named as YEAR MONTH.</p> <p>Reference evapotranspiration (ETo) is a fundamental parameter for hydrological studies and irrigation management. The Penman-Monteith method is the standard for estimating ETo and requires several meteorological elements. Free remote sensing products with evapotranspiration information are rare. The objective of this study was to estimate the monthly ETo from the potential evapotranspiration (PET) made available by the MOD16 product. The monthly ETo estimated by the Penman-Monteith method was considered the standard. For this, data were acquired from the 265 meteorological station of the National Institute of Meteorology (INMET), throughout Brazil, in the period from 2000 to 2014 (15 years). Using machine learning algorithms, MOD16 images and WorldClim information as covariates, the ETo. All machine learning models were effective in improving the performance of the metrics evaluated. Cubist was the model that presented the best metrics for r² (0.91), NSE (0.90) and nRMSE (8.54%) and should be the preferred one for ETo prediction. The use of monthly ETo is recommended, which opens up possibilities for its use in numerous other studies.</p> <p>The article: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245834">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245834</a></p>
Reference Dataset for Benchmarking Organ Doses Derived from Monte Carlo Simulations of CT Exams
<p>This reference dataset contains CT scanner x-ray source characteristics, filtration profile, de-identified patient image data and size characteristics, voxelized patient models, exam characteristics, x-ray tube current data, and organ dose results in tabular form from Monte Carlo (MC) simulations of abdominal/pelvis CT exams of pregnant patients. This dataset can be used for benchmarking MC simulation codes for CT dosimetry.</p>
Virus+ Sequence Masked Mouse Reference Genome (GRCm38)
<p>A version of the mouse genome (<a href="https://www.ncbi.nlm.nih.gov/assembly/327618">GRCm38</a>) masked for all possible viral sequences.</p> <p>See <a href="https://zenodo.org/record/4116107#.X5B7ti9h3UI">Virus+ Masked Human Genome</a> for a masked human reference database.</p> <p>The following commands were used to generate the additional virus sequence masked reference database:</p> <p><strong>1) Download all RefSeq and Neighbor nucleotide records:</strong></p> <p><a href="https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])">https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])</a></p> <p><strong>2) Shred the downloaded viral genomes using shred.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>shred.sh in=refseq_virus_reformated.fasta out=virus_shred.fasta.gz length=85 minlength=75 overlap=30</p> <p><strong>3) Map shredded virus sequence to the GRCm38</strong><strong> genome using bbmap.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmap.sh ref=GRCm38.fa.gz in=virus_shred.fasta.gz outm=map_mouse_all_viruses.sam minid=0.90</p> <p><strong>4) Mask virus sequenced mapped regions from the GRCm38 genome using bbmask.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmask.sh in=GRCm38.fa.gz out=GRCm38_virus_masked.fasta.gz sam=map_mouse_all_viruses.sam</p> <p><strong>5) Remove all N's to further reduce file size using <a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></strong><br> seqkit -is replace -p "n" -r "" GRCm38_virus_masked.fasta.gz > mouse_virus_masked.fasta_Ns_removed.gz</p> <p><strong>Additional References:</strong></p> <ol> <li><a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a></li> <li><a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></li> <li><a href="https://www.ncbi.nlm.nih.gov/genome/viruses/">NCBI Virus Genome RefSeq</a></li> </ol>
Virus+ Sequence Masked Human Reference Genome (hg19)
<p>A version of the human genome (hg19) originally masked for ribosomal, plant, animal, fungal and low-entropy sequences by Brian Bushnell (<a href="https://zenodo.org/record/1208052#.X5BuTy9h3UI">Bushnell Masked Human Genome</a>) additionally masked for all possible viral sequences.</p> <p>The following commands were used to generate the additional virus sequence masked reference database:</p> <p><strong>1) Download all RefSeq and Neighbor nucleotide records:</strong></p> <p><a href="https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])">https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])</a></p> <p><strong>2) Shred the downloaded viral genomes using shred.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>shred.sh in=refseq_virus_reformated.fasta out=virus_shred.fasta.gz length=85 minlength=75 overlap=30</p> <p><strong>3) Map shredded virus sequence to the hg19-masked human genome using bbmap.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmap.sh ref=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz in=virus_shred.fasta.gz outm=map_human_all_viruses.sam minid=0.90</p> <p><strong>4) Mask virus sequenced mapped regions from the hg19-masked human genome using bbmask.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmask.sh in=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz out=human_virus_masked.fasta.gz sam=map_human_all_viruses<br> .sam</p> <p><strong>5) Remove all N's to further reduce file size using <a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></strong><br> seqkit -is replace -p "n" -r "" human_virus_masked.fasta.gz > human_virus_masked.fasta_Ns_removed.gz</p> <p><strong>Additional References:</strong></p> <ol> <li><a href="http://seqanswers.com/forums/showthread.php?t=42552">http://seqanswers.com/forums/showthread.php?t=42552</a> for additional information on the original masking of hg19</li> <li><a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a></li> <li><a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></li> <li><a href="https://www.ncbi.nlm.nih.gov/genome/viruses/">NCBI Virus Genome RefSeq</a></li> </ol>
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.