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428 results for “data standard”
Proof-of-concept AO telemetry data using the AOT standard format
<p>Dataset containing a demonstration of AO telemetry data using the AOT standard format. Contains data from 5 different systems (CIAO, ERIS, GALACSI, NAOMI and PAPYRUS).</p> <p>The data is as follows:</p> <ul> <li>4 CIAO files "CIAO#_*.fits" where # indicates the AT where the data was produced.</li> <li>2 ERIS files, one in LGS (LTAO) mode and another in NGS (SCAO) mode.</li> <li>1 GALACSI file.</li> <li>4 NAOMI files "NAOMI#_*.fits" where # indicates the AT where the data was produced.</li> <li>2 PAPYRUS files, one using a Shack-Hartmann wavefront sensor (SHWFS) and another using a Pyramid wavefront sensor (PWFS).</li> </ul> <p>GALACSI, CIAO, NAOMI and ERIS data were gathered under ESO's program IDs 60.A-9278(B), 60.A-9278(C), 60.A-9278(D) and 60.A-9278(E) respectively. The data was translated via the functions provided in the Python package <em>aotpy</em> (<a href="https://doi.org/10.5281/zenodo.8187230">10.5281/zenodo.8187230</a>).</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004719 (OPTICON–RadioNet Pilot).</p>
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
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>
Data for Measuring the Market Impact of New Auditing Standards in China
<p>Data used in a study of the market impact of new auditing standards in China. The data includes 76 companies listed on the A+H share markets that were subject to the new auditing standards in 2017. It also includes 76 companies listed on the A share market which were matched with the experimental sample using the Propensity-Score Matching method. Files include 2016 & 2017 cumulative abnormal returns (CAR) and volatility (Vol) for the experimental group (A+H shares) and the control group (A shares) and control variables for both groups.</p> <p> </p> <p> </p>
Data release - A Standard Siren Cosmological Measurement from the Potential GW190521 Electromagnetic Counterpart ZTF19abanrhr
<p>Data release accompanying the manuscript</p> <p> "<strong>A Standard Siren Cosmological Measurement from the Potential GW190521 Electromagnetic Counterpart ZTF19abanrhr</strong>" - <a href="https://arxiv.org/abs/2009.14057">Chen et al. (2020)</a></p> <p>assuming an association between the LIGO-Virgo gravitational wave signal <a href="https://www.gw-openscience.org/eventapi/html/O3_Discovery_Papers/GW190521/">GW190521</a> and the electromagnetic signal ZTF19abanrhr as identified by <a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.251102">Graham et al 2020</a>.</p> <p>The posterior samples for the GW analyses are available from <a href="https://doi.org/10.5281/zenodo.4057130">Isi (2020)</a> and <a href="https://dcc.ligo.org/LIGO-P2000158/public">LVC (2020)</a> respectively.</p>
Profile Data for Stylus Device Calibration using Ball Standard
<p>X and Z profile data for the simulation of Stylus device calibration using Ball standard</p> <p>M1000: Number of profile points -20 means: 20 points at the left of the peak</p>
Interaction data for standard tournament for the Axelrod Python project.
<p>This is the data from the tournament described here: http://axelrod-tournament.readthedocs.org/en/latest/standard/strategies.html</p> <p>A description of the format is available here: http://axelrod.readthedocs.org/en/latest/tutorials/further_topics/reading_and_writing_interactions.html</p>
Standard Model effective field theory global fit using electroweak data
<p>These are files that can be used to reproduce the fit results in 10.1007/JHEP09(2016)157 and arXiv:1610.01783<strong>.</strong></p> <p> </p>
A rule based Tibetan part-of-speech (POS) tagger for the creation of gold standard training data
<p>This rule based Tibetan part-of-speech (POS) tagger was prepared in the course of the research project 'Tibetan in Digital Communication' (2012-2015) hosted at SOAS, University of London and funded by the UK's Arts and Humanities Research Council (grant code: AH/J00152X/1). For a description of the tag set see Garrett et al. 2014. and Garrett et al. 2015. For a description of the tagger itself see Garrett et al. 2014. Note that the tagger must be used together with a lexicon (for example Hill & Garrett 2017a). One must use one's own script to tag all words with all tags in the lexicon and then apply the tagger to remove incorrect tags.</p> <p>On the associated corpus of 318,230 words (Hill & Garrett 2017b) the lexical tagger (i.e. simply applying all available tags to all words) tags 141,911 words with the correct unique tag, achieves as accuracy of 1.000 (by definition getting the right tag among others for each word) with an ambiguity of 2.73111. In contrast, the Rule Tagger tags 241,256 words with the correct unique tag, achieves an accuracy of 0.99893 and an ambiguity of 1.38577.</p> <p>Because this tagger does not achieve ambiguity 1.000 it is not suitable for tagging large scale corpora, but instead is useful for the creation of gold standard training data.</p> <p>N.B. In some rare cases the tagger removes all POS-tags.</p>
A lack of open data standards for large infrastructure projects hampers social-ecological research in the Brazilian Amazon
<p>List of papers used in literature review for "A lack of open data standards for large infrastructure projects hampers social-ecological research in the Brazilian Amazon"</p>
Figure 1 in History and development of ABCDEFG: a data standard for geosciences
Figure 1. Simplified overview of the schema extension EFG. Given are the relationships among EFG components and between EFG components and the ABCD schema. EFG elements are given in yellow and elements originating from ABCD in green.
Figure 1 in Why we should develop guidelines and quantitative standards for using genetic data to delimit subspecies for data-poor organisms like cetaceans
Figure 1. Depiction of the divergence of lineages with four times (T1–T4) chosen to illustrate different levels of biological organization. At T1 the yellow lineage is found across the distribution and although there are likely Demographically Independent Populations (DIPs) that differ in frequencies of the blue, yellow, and red lineages, there are no discontinuities. At T2 some lineages may be diagnosable but likely do not yet appear to be separate lineages. At T3 three groups (the blue/green, yellow, and orange/red lineages) meet the subspecies definition (they are diagnosable and appear to be diverging separately). The divergence level is not sufficient that reconvergence can be ruled out. Between T3 and T4, barriers to gene flow change such that the yellow lineage comes into contact with the blue/green and red-dominated lineages. Blue has diverged in a manner by which gene flow does not resume and the green/yellow lineage dies out. The yellow lineage reconverges and persists alongside the red lineage with a small level of gene flow (orange). At T4 the blue lineage is a species evolving separately from the yellow/red species. The yellow/red species has two subspecies that are both diagnosable and partially diverged.
Figure 3 in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data
Figure 3. Flow diagram for subspecies delineation using combined quantitative and qualitative standards. The threshold values assume the user is evaluating a case relying on mtDNA control region data. Percent Diagnosable (PD) is the smallest strata-specific correct classification score in a given comparison (e.g., PD50 in two-strata comparisons in Archer et al. 2017). The second box in the second row (other evidence to meet subspecies definition) allows for subspecies delineation when both conditions are not met using mtDNA. This box could be used either for the case when one condition is met and one unmet or when both just barely miss meeting the standards. For example, consider the case with PD <95% and dA> 0.004. Diagnosability could be achieved with morphological data or nuclear data that are sufficient for subspecies but not for full species.
Figure 2. A in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data
Figure 2. A comparison of the pairs of populations (red triangles), subspecies (green squares) and species (blue circles) estimated by Rosel et al. (2017a). Net nucleotide divergence (dA) is shown on a natural log scale to better illustrate differences between the pairwise comparisons at low levels of divergence. Bars show the central 95th-pecentile of the estimate distributions. The solid vertical line at dA = 0.020 delimits all but one species and correctly excludes all subspecies pairs. The vertical dashed line at dA = 0.004 delimits all populations from the higher taxonomic levels and correctly delimits seven of eleven subspecies. The horizontal dashed lines are two potential thresholds for percent diagnosable (80% and 95%) that are discussed in the text.
Data from Environmental Outcomes of the U.S. Renewable Fuel Standard
<p>Data and figures associated with the paper "Environmental Outcomes of the U.S. Renewable Fuel Standard" by Lark et al.</p>
Sampled ΔH/Δλ and ΔH data from ABFE calculations (using standard atomic masses) of 10 ligands bound to Cyclophilin D
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Included are the ABFE raw free energy samples for multiple replicates (labelled by `run` number) of 10 ligands to bound Cyclophilin D. These ligands are originally detailed by Grädler et al. (https://doi.org/10.1016/j.bmcl.2019.126717). Unlike other datasets in this work, which employed hydrogen mass repartitioning, the ligands here were calculated using standard atomic masses.</p> <p>All samples are provided as a set of `.xvg` files as generated by GROMACS 2021 (https://doi.org/10.5281/zenodo.5849961). The `.xvg` files are labelled as dhdl.N.xvg where N represents the λ state the free energy values were sampled from. The `.xvg` files contain both ΔH/Δλ and ΔH values, please see the header of each files for more information.</p> <p>Samples detailing the partial decoupling of the ligand from the protein-ligand complex are contained within the `complex` folder. These consist of an orientational restraint addition step (found within the `restraints-xvg` folders), charge annihilation step (found within the `coul-xvg` folders), and Van der Waals decoupling step (found within the `vdw-xvg` folders).</p> <p>Samples detailing the partial decoupling of the ligand from solvent are contained within the `ligand` folder and consist of a charge annihilation step (found within the individual `coul-xvg` folders) and a Van der Waals decoupling step (found within the individual `vdw-xvg` folders).</p>
Data of "Durability of self-healing cementitious systems with encapsulated polyurethane evaluated with a new pre-standard test method"
<p>The dataset found here is related to the crack width measurements and water permeability tests performed in a study investigating the durability of self-healing cementitious systems with encapsulated polyurethane evaluated with a new pre-standard test method.</p>
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.