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5,738 results for “Standards”
UCSB SONGS Mitigation Monitoring: Reef Performance Standard – Fish Production
These data describe annual estimates of the somatic and gonadal tissue production in five species of reef fishes at an artificial reef (Wheeler North Reef in Orange County, CA) and two natural reference reefs (San Mateo Kelp in Orange County, CA and Barn Kelp in San Diego County, CA). Data were collected from 2009 – 2023 as part of a comprehensive mitigation program aimed at evaluating the ability of Wheeler North Reef to compensate for losses of kelp forest habitat and associated biota caused by the operation of the San Onofre Nuclear Generating Station (SONGS).
SGS-LTER Standard Production Data: 1983-2008 Annual Aboveground Net Primary Production on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-2008, ARS Study Number 6 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/325/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/700/1. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of the long-term ANPP study is to monitor long-term net above ground primary production of the shortgrass steppe community by species. There are 6 sites: ridgetop (ridge), midslope (mid), swale, ESA (replicate 1 not 2), Section 25 (SEC 25), and owl-creek (OC). Each site is located in a different landscape position or soil type on the shortgrass steppe and may be grazed or not. Ridgetop, midslope and swale are grazed and are sampled along a catena. Section 25 is grazed and is located in an upload grassland. ESA is an ungrazed upland grassland an is the control from the Ecosystem Stress Area experiment. Owl Creek is ungrazed and is located in the lowland along the owl creek drainage. There are 3 transects with 5 plots in each transect. Plots in the grazed
Standardized ring-width chronologies of thinleaf alder growth along the Tanana River floodplains spanning 1968-2006.
This dataset contains landscape level and site level standardized ring-width chronologies (1968-2006) of thinleaf alder growth along the Tanana River floodplains. The raw tree ring-width series from each alder disk was individually detrended to remove age-related growth trends. The chronologies represent the high-frequency variation in ring-width to be analyzed at the inter-annual time scale.
Total dissolved organic carbon measurements at standard depths in the water column from nine CalCOFI cruises, 2008-2017.
Water column bottle samples at multiple depths are taken during CalCOFI cruises (since 2006, ongoing) at various CTD stations, and measurements of total organic carbon (TOC) are performed onshore in the lab. TOC includes both dissolved and particulate organic carbon (DOC and POC, respectively). In open ocean waters, POC is subtracted from TOC, and likely provides an accurate estimate of DOC because particles are typically small and homogeneously distributed in the sample. In coastal waters, and at stations where relatively high chlorophyll concentrations are present, the TOC measurement is not easily converted to DOC by subtracting POC values. Experience has shown that particles in these regions are large and inhomogeneously distributed. Therefore, samples collected in the CCE are reported as TOC, expressed as micromoles of carbon per liter of sea water.
CAT 4.6 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly
<p>Taxonomic binning of the gold standard pooled assembly<br> <strong>Software: </strong>CAT<br> <strong>SoftwareVersion: </strong>4.6<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/dutilh/CAT<br> <strong>ReferenceDatabase:</strong> prebuilt 2018-12-12<br> <strong>Taxonomy:</strong> NCBI 2018-12-12<br> <strong>ShortReadsUsed:</strong> False<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> CAT contigs -c anonymous_gsa_pooled.fasta -d CAT_prepare_20181212/2018-12-12_CAT_database/ -t CAT_prepare_20181212/2018-12-12_taxonomy/ --tmpdir tmp --nproc 16</p>
PhyloPythiaS+ 1.4 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly
<p>Taxonomic binning of the gold standard pooled assembly<br> <strong>Software: </strong>PhyloPythiaS+<br> <strong>SoftwareVersion: </strong>1.4<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://github.com/algbioi/ppsp<br> <strong>DockerImage:</strong> cami/ppsp:1.4<br> <strong>IsBiobox:</strong> False<br> <strong>ReferenceDatabase:</strong> RefSeq 93, SILVA 132<br> <strong>Taxonomy:</strong> NCBI 2018-02-26<br> <strong>ShortReadsUsed:</strong> False<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> run_ppsp.py --pipelineDir ppsp_pipepline --inputFastaFile anonymous_gsa_pooled.fasta --databaseFile ncbi_taxonomy --refSeq refseq93 --s16Database SILVA_132 --mgDatabase reference_NCBI201502/mg5</p>
MetaBAT 2.12.1 genome binning of the CAMI 2 Mouse Gut Toy data set, samples 0-63, gold standard pooled assembly
Genome binning of the gold standard pooled assembly <br><strong>Software: </strong>MetaBAT<br><strong>SoftwareVersion: </strong>2.12.1<br><strong>DataURL: </strong> https://data.cami-challenge.org/participate<br><strong>SoftwareURL:</strong> https://bitbucket.org/berkeleylab/metabat<br><strong>ShortReadsUsed:</strong> True<br><strong>LongReadsUsed:</strong> False<br><strong>CommandUsed:</strong> bowtie2-build anonymous_gsa_pooled.fasta anonymous_gsa_pooled.fasta<br>for i in {0..63}; do bowtie2 -q --threads 30 --fr -x anonymous_gsa_pooled.fasta --interleaved sample_${i}/anonymous_reads.fq -S anonymous_reads_sample_${i}.sam ; done<br>for i in {0..63}; do samtools view -b sample_${i}.sam -o anonymous_reads_sample_${i}.bam & done<br>for i in {0..63}; do samtools sort anonymous_reads_sample_${i}.bam -o anonymous_reads_sample_${i}.sorted.bam ; done<br>for i in {0..63}; do samtools index anonymous_reads_sample_${i}.sorted.bam ; done<br>runMetaBat.sh -l anonymous_gsa_pooled.fasta anonymous_reads_sample_*.sorted.bam
Kraken 2.0.8 beta taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly
<p>Taxonomic binning of the gold standard pooled assembly<br> <strong>Software: </strong>Kraken<br> <strong>SoftwareVersion: </strong>2.0.8 beta<br> <strong>DataURL: </strong> https://data.cami-challenge.org/participate<br> <strong>SoftwareURL:</strong> https://ccb.jhu.edu/software/kraken2/<br> <strong>ReferenceDatabase:</strong> built 2019-05-22<br> <strong>Taxonomy:</strong> NCBI 2019-05-22<br> <strong>ShortReadsUsed:</strong> False<br> <strong>LongReadsUsed:</strong> False<br> <strong>CommandUsed:</strong> kraken2-build --standard --db kraken2db_std --use-ftp<br> kraken2 --db kraken2db_std --threads 16 --output 19122017_mousegut_scaffolds.kraken --report 19122017_mousegut_scaffolds.kreport anonymous_gsa_pooled.fasta<br> cat 19122017_mousegut_scaffolds | awk '{print $2 "\t" $3}' > 19122017_mousegut_scaffolds.cami</p>
Notified bodies, horizontal specifications and harmonised standards for construction materials third party assessment according to the CPR - EU 305/2011
<p>This dataset was extracted from NANDO, the website of the European Commission for notified bodies. It contains the list of notified bodies, horizontal specifications and harmonised standards for construction materials third party assessment according to the CPR - EU 305/2011</p>
Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 mm Macro lens and processed with Agisoft Photoscan. The visual comparison of the mollusc shows a similar level of detail between photogrammetry and MechScan for the external surfaces, with still a bit more detail for the MechScan. The HDI Advance has a much lower resolution.
Fig. 5.6 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 5.6. Decimation of a 3D model. The four parts show a 3D model in various degrees of reducing the amount of faces. In the left upper corner is the original and rotating clockwise are the models at 50%, 75% and 90% decimation. Until 75% there is hardly any difference noticeable, while at 90% the cracks become less deep and the faces become more visible.
Fig. 6.2 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.2. Texture comparison between photogrammetry and MechScan (above) and an actual picture captured by a Canon 700D with 100 mm macro lens of the shell below.
Fig. 5.3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 5.3. Micro-contrast enhancement in DxO OpticsPro 11. A crop of the original image is on the left, one of the post-processed pictures on the right.
Fig. 5.4 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 5.4. Micro-contrast enhancement in DxO OpticsPro 11. The original image is on the left, the postprocessed picture on the right. The post-processed picture looks more crisp and shows more details than the original one as the washed-out appearance has gone.
Fig. 6.14. Ishango rod. The left 3D model was acquired with a in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.14. Ishango rod. The left 3D model was acquired with a µCT many years ago. The middle one is scanned with the MechScan structured light scanner. The right one is the combination of both the µCT scan, the structured light scan and the texture of the photogrammetry model.
Fig. 5.1 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 5.1. Relighting in DxO OpticsPro 11. The original image is on the left, the post-processed picture on the right. The underexposed image is now corrected without the need to take new images.
Fig. 4.9 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 4.9. Pre-Columbian handle of an incense shovel from the Royal Museum of Art and History collections. UV fluorescence photogrammetry model. In this case, fluorescence enables to enhance the glue (fluorescing in green). https://sketchfab.com/models/2d82a98be64c48b89cada459b81bd0ab
Fig. 4.7 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 4.7. Enhancing the legibility of a specimen. The picture on the left represents the specimen captured under white light, while the picture on the right displays the specimen under UV light. Part of the reflections is reduced under UV light allowing to display more contrasted structures.
Fig. 4.5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 4.5. Detail of the Halszkaraptor fossil from Mongolia. In white light on the left, in UV fluorescence on the right. The UV fluorescence image displays restorations of the fossils and treatment applied to preserve it.
Fig. 3.20. 3D in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.20. 3D model of a Dorylus ant (size: 1.5 cm) based upon focus stacked images, textured model is on the left, the view of only the mesh is on the right. The VCM option in Agisoft Photoscan is chosen to include small detail in the 3D model. The tibia spurs are clearly marked. https://sketchfab.com/models/da9aa414bfa64caabfe5c552368b16f0
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.