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720 results for “Identification data”
Data from: Identification of two odorant receptors tuned to alarm pheromone in the honey bee <em>Apis mellifera</em>
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Data from: Identification of anti-fungal bioactive terpenoids from the bioenergy crop switchgrass (Panicum virgatum)
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Data for: Species identification and population genetics of the Antarctic fish genera Lepidonotothen and Nototheniops (Perciformes, Notothenioidei)
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Sample extraction and SNP sequencing data for: Identification of sex-linked SNP markers in wild populations of monomorphic birds
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Supporting data and code for: Phylogenetic identification of influenza virus candidates for seasonal vaccines
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Data for: Identification of integrons and gene cassette-associated recombination sites in bacteriophage genomes
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Data for: Russian honey bee genotype identification through enhanced marker panel set
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Data from: Validation of a murine proteome-wide phage display library for identification of autoantibody specificities
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IoT device identification - Multi user data
<p>Artificial multi user observations generated as described in the associated paper Section III.</p> <p>Frequency: 863-870 MHz (center 866,5 MHz)</p> <p>Sample Frequency: 10 MSPS</p> <p>Date of measurement: 15 November 2018</p> <p>Location: Connectivity Lab, Fredrik Bajers Vej 7C, Aalborg University, Denmark</p>
Data from: Genome-wide SNP identification and association mapping for seed mineral concentration in Mung bean (Vigna radiata L.)
<p><span><span>Mung bean (<i>Vigna radiata</i> L.) quality is dependent on seed chemical composition, which in turn determines the benefits of mung bean consumption for human health. While rich in a range of nutritional components, such as protein, macro- and micro- nutrients, carbohydrates and vitamins, mung bean remains less well studied than other legume crops. Mung bean genomics and genetic resources are relatively sparse. To further improve nutritional levels of mung bean grain requires genome-wide marker system tools. The objectives of this research were to develop these tools and conduct nutrient analysis in order to 1) identify single nucleotide polymorphisms (SNPs) using genotyping by sequencing (GBS) and to 2) perform genome-wide association studies (GWAS) for levels of calcium, iron, potassium, manganese, phosphorous, sulfur, and zinc in mung bean grain produced over two years of field experiment. A total of 112 GWAS models were explored using 6,486 high quality SNPs discovered in 92 cultivated mung bean accessions chosen from USDA core collection that represented 13 countries. The data obtained allowed for the identification of 43 associated SNPs and 20 main genomic regions that explained on average 22 % of the overall variation in seed macro- and micro- nutrients concentration on the basis of a multiple-year analysis. Most of the regions discovered in this study provide valuable candidate gene to use in future breeding of new varieties of mung bean with novel nutritional properties. Identification of the <a>underlying genes</a> will help to reveal the genetic control of mung bean seed nutritional property. Other SNPs identified in this study will serve as important resources to enable marker-assisted selection (MAS) in the species <i>V</i>. <i>radiata</i>, including wide and narrow crosses with / between cultivated and wild mung bean.</span></span></p>
Automated identification of the mouse brain's spatial compartments from in situ sequencing data
<p>Example data [1] for testing in situ sequencing graph-based decoding pipeline and decoding results for reproducing publication analyses. Image decoding pipeline and notebooks are available at: <a href="https://github.com/wahlby-lab/graph-iss">https://github.com/wahlby-lab/graph-iss</a></p> <p>------------</p> <p>[1] Ke, Rongqin, et al. "In situ sequencing for RNA analysis in preserved tissue and cells." <em>Nature methods</em> 10.9 (2013): 857.</p>
Data for "Source identification of atmospheric organic vapors in two European pine forests: Results from Vocus PTR-TOF observations"
<p>This file consists of the time series of the measured trace gases, meteorological parameters, and the concentrations of isoprene and monoterpenes in the Landes forest and at the SMEAR Ⅱ station, which have been analyzed in the manuscript "Source identification of atmospheric organic vapors in two European pine forests: Results from Vocus PTR-TOF observations". For more details, please contact the author (haiyan.li@helsinki.fi).</p>
Data from: Friends and Family: a software program for identification of unrelated individuals from molecular marker data. And from: Genetic diversity, relatedness and inbreeding of ranched and fragmented Cape buffalo populations in southern Africa
The identification of related and unrelated individuals from molecular marker data is often difficult, particularly when no pedigree information is available and the data set is large. High levels of relatedness or inbreeding can influence genotype frequencies and thus genetic marker evaluation, as well as the accurate inference of hidden genetic structure. Identification of related and unrelated individuals is also important in breeding programmes, to inform decisions about breeding pairs and translocations. We present Friends and Family, a Windows executable program with a graphical user interface that identifies unrelated individuals from a pairwise relatedness matrix or table generated in programs such as COANCESTRY and GenAlEx. Friends and Family outputs a list of samples that are all unrelated to each other, based on a user-defined relatedness cut-off value. This unrelated data set can be used in downstream analyses, such as marker evaluation or inference of genetic structure. The results can be compared to that of the full data set to determine the effect related individuals have on the analyses. We demonstrate one of the applications of the program: how the removal of related individuals altered the Hardy-Weinberg equilibrium test outcome for microsatellite markers in an empirical data set. Friends and Family can be obtained from https://github.com/DeondeJager/Friends-and-Family.
Data from: Comparative analysis of DNA repeats and identification of novel Fesreba centromeric element in fescues and ryagrasses
<p>Background<br> Cultivated grasses are an important source of food for domestic animals worldwide. Better knowledge of their genomes can speed up the development of new cultivars with better quality and resistance to biotic and abiotic stresses. The most widely grown grasses are tetraploid ryegrass species ( Lolium spp.) and diploid and hexaploid fescue species (Festuca spp.). In this work, we characterized repetitive DNA sequences and their contribution to genome size in five fescue and two ryegrass species, as well as one fescue and two ryegrass cultivars.</p> <p>Results<br> Partial genome sequences produced by Illumina technology were used for genome-wide comparative analyses using RepeatExplorer pipeline. Retrotransposons were found to be the most abundant repeat types in all seven grass species. Athila element of Ty3/gypsy family showed the most striking differences in copy number between fescues and ryegrasses. The sequence data enabled the assembly of an LTR element Fesreba, which is highly enriched in centromeric and (peri)centromeric regions in all species. A combination of FISH with a probe specific to Fesreba element and immunostaining with CENH3 antibody showed their colocalization and indicated a possible role of Fesreba in centromere function.</p> <p>Conclusions<br> Comparative repeatome analysis in a set of fescues and ryegrasses provided new insights into their genome organization and divergence, including the assembly of LTR element Fesreba. A new LTR element Fesreba was identified and found abundant in centromeric regions of the fescues and ryegrasses. It may have a role in the function of their centromeres.</p>
Data from: Identification of top-priority areas to achieve EU Biodiversity Strategy for 2030 key commitments
<p>Data from: Identification of top-priority areas to achieve EU Biodiversity Strategy for 2030 key commitments. The dataset includes all files to run prioritization analyses included in Iulia V. Miu, Laurentiu Rozylowicz, Viorel D. Popescu, Paulina Anastasiu (2020) Identification of areas of very high biodiversity value to achieve the EU Biodiversity Strategy for 2030 key commitments. </p> <p>To run analyses, unzip the files in your computer (drive C) and use Zonation v4 developed by CBIG - CONSERVATION BIOLOGY INFORMATICS GROUP, Department of Biosciences of the University of Helsinki (<a href="https://www.helsinki.fi/en/researchgroups/digital-geography-lab/software-developed-in-cbig#section-52992">https://www.helsinki.fi/en/researchgroups/digital-geography-lab/software-developed-in-cbig#section-52992</a>). Files are available for nationwide prioritization of terrestrial Natural 2000 in Romania, taxa specific prioritization of terrestrial Natural 2000 in Romania (amphibians, mammals, plants, reptiles, invertebrates, and fish), and at biogeographical level prioritization of terrestrial Natural 2000 in Romania (Alpine, Continental, Pannonian, Steppic & Black Sea).</p>
Seek and learn: automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning
<p>1. Animal-borne accelerometers have been used across more than 120 species to infer biologically significant information such as energy expenditure and broad behavioural categories. While the accelerometer's high sensitivity to movement and fast response times present the unprecedented opportunity to resolve fine-scale behaviour, leveraging this opportunity will require overcoming the challenge of developing general, automated methods to analyse the nonstationary signals generated by nonlinear processes governing erratic, impulsive movement characteristic of fine-scale behaviour. 2. We address this issue by conceptualising fine-scale behaviour in terms of characteristic microevents: impulsive movements producing brief (<1 s) shock signals in accelerometer data. We propose a 'seek-and-learn' approach: a novel microevent detection step first locates where shock signals occur ('seek') by searching for peaks in envelopes of acceleration data. Robust machine learning ('learn') employing meaningful features then separates microevents. We showcase the application of our method on tri-axial accelerometer data collected on ten free-living meerkats (Suricata suricatta) for four fine-scale foraging behaviours – searching for digging sites, one-armed digging, two-armed digging, and head jerks during prey ingestion. Annotated videos served as groundtruth, and performance was benchmarked against that of a variety of classical machine-learning approaches. 3. Microevent identification (μEvId) with eight features in a three-node hierarchical classification scheme employing logistic regression at each node achieved a mean overall accuracy of >85% during leave-one-individual-out cross-validation, and exceeded that of the best classical machine learning approach by 9%. μEvId was found to be robust not only to inter-individual variation but also to large changes in model parameters. 4. Our results show that microevents can be modelled as impulse responses of the animal body-and-sensor system. The microevent detection step retains only informative regions of the signal, which results in the selection of discriminative features that reflect biomechanical differences between microevents. Moving-window-based classical machine learning approaches lack this prefiltering step, and were found to be suboptimal for capturing the nonstationary dynamics of the recorded signals. The general, automated technique of μEvId, together with existing models that can identify broad behavioural categories, provides future studies with a powerful toolkit to exploit the full potential of accelerometers for animal behaviour recognition.</p>
Data from: An identification of invariants in life history traits of amphibians and reptiles
<p>While many morphological, physiological, and ecological characteristics of organisms scale with body size, some do not change under size transformation. They are called invariant. A recent study recommended five criteria for identifying invariant traits. These are based on that a trait exhibits a unimodal central tendency and varies over a limited range with body mass (type I), or that it does not vary systematically with body mass (type II). We methodologically improved these criteria and then applied them to life history traits of amphibians, Anura, Caudata (eleven traits), and reptiles (eight traits). The numbers of invariant traits identified by criteria differed across amphibian orders and between amphibians and reptiles. Reproductive output (maximum number of reproductive events per year), incubation time, length of larval period, and metamorphosis size were type I and II invariant across amphibians. In both amphibian orders, reproductive output and metamorphosis size were type I and II invariant. In Anura, incubation time and length of larval period and in Caudata, incubation time were further type II invariant. In reptiles, however, only number of clutches per year was invariant (type II). All these differences could reflect that in reptiles body size and in amphibians, Anura, and Caudata metamorphosis (neotenic species go not through it) and the trend towards independence of egg and larval development from water additionally constrained life history evolution. We further demonstrate that all invariance criteria worked for amphibian and reptilian life history traits, although we corroborated some known and identified new limitations to their application.</p>
Data from: A proteomics approach for the identification of cullin-9 (CUL9) related signaling pathways in induced pluripotent stem cell models
<p>CUL9 is a non-canonical and poorly characterized member of the largest family of E3 ubiquitin ligases known as the Cullin RING ligases (CRLs). Most CRLs play a critical role in developmental processes, however, the role of CUL9 in neuronal development remains elusive. We determined that deletion or depletion of CUL9 protein causes aberrant formation of neural rosettes, an in vitro model of early neuralization. In this study, we applied mass spectrometric approaches in human pluripotent stem cells (hPSCs) and neural progenitor cells (hNPCs) to identify CUL9 related signaling pathways that may contribute to this phenotype. Through LC-MS/MS analysis of immunoprecipitated endogenous CUL9, we identified several subunits of the APC/C, a major cell cycle regulator, as potential CUL9 interacting proteins. Knockdown of the APC/C adapter protein FZR1 resulted in a significant increase in CUL9 protein levels, however, CUL9 does not appear to affect protein abundance of APC/C subunits and adapters or alter cell cycle progression. Quantitative proteomic analysis of CUL9 KO hPSCs and hNPCs identified protein networks related to metabolic, ubiquitin degradation, and transcriptional regulation pathways that are disrupted by CUL9 deletion in both hPSCs and hNPCs. The results of our study build on current evidence that CUL9 may have unique functions in different cell types potentially contributing to the difficulty of identifying CUL9 substrates.</p> <p><strong>Information on data/files</strong>:</p> <p><em>Please first unzip Figure_4_CUL9-IP-LCMSMS-data.zip and Figure_7_iTRAQ-data.zip, then follow the description below.</em></p> <p>CUL9 immunoprecipitation from whole lysates collected for hPSCs were analyzed using LC/MS-MS. IgG was used as a control to determine proteins specifically enriched in the CUL9 IP. This data correlates to Figure 4 of the associated manuscript.</p> <p>Initial CUL9 immunoprecipitation was performed by VG and analyzed by LC-MS/MS at UNC.</p> <p>Fig4-LCMSMS_IPCUL9_Replicate1_UNC</p> <p>Two more immunoprecipitations were performed by VG and analyzed by LC-MS/MS at Vanderbilt University MSRC Proteomics Core. The first run (replicate 1) was used to produce the STRING figure in the associated manuscript, as well as the volcano plot in Supplemental Figure 8.</p> <p>Two Scaffold files contain raw data and our analysis parameters:</p> <ul> <li>Fig4-LCMSMS_IPCUL9_Replicate2_Vanderbilt</li> <li>Fig4-LCMSMS_LCMSMS_IPCUL9_Replicate3_Vanderbilt</li> </ul> <p>Excel files exported from the above Scaffold files contain the raw data in excel format including spectral counts, peptide counts, protein probability, and detailed raw data as it pertains to each identified protein. See these data sets in the folders below:</p> <ul> <li>Fig4-LCMSMS_IPCUL9_Run2_Vanderbilt_CompleteDataSet</li> <li>Fig4-LCMSMS_IPCUL9_Run3_Vanderbilt_CompleteDataSet</li> </ul> <p>CUL9 KO clones were used to identify proteins increased or decreased compared to parental wild-type cell lines using iTRAQ. This data correlates to Figure 8 of the associated manuscript. Two iTRAQ experiments were performed to identify proteins altered in CUL9 KO iPSCs, neural stem cells (NSCs), and NPCs. Each set of experiments was duplicated – each in a different isogenic CUL9 KO clone (Clone 20 or Clone B)</p> <p><strong>iTRAQ labeling of experiment one is as follows:</strong></p> <ol> <li>Label 115: Parental WT NSCs</li> <li>Label 117: CUL9 KO Clone 20 or B NSCs</li> <li>Label 114: Parental WT NPCs</li> <li>Label 116: CUL9 KO Clone 20 or B NPCs</li> </ol> <p>Files containing raw and analyzed data of experiment one are labeled as follows:</p> <ul> <li>Fig7_Experiment1_Clone 002_compared-to-NSC-WT</li> <li>Fig7_Experiment1_Clone001-compared-to-NSC-WT</li> <li>Fig7_Experiment1_Clone001iPSC_compared-to-iPSCWT</li> <li>Fig7_Experiment1_Clone002_compared-to-iPSCWT</li> </ul> <p><strong>iTRAQ labeling of experiment two is as follows:</strong></p> <p>For Clone 20:</p> <ol> <li>Label 115: Parental WT NSCs</li> <li>Label 117: CUL9 KO Clone 20 or B NSCs</li> <li>Label 114: Parental WT iPSCs</li> <li>Label 116: CUL9 KO Clone 20 or B iPSCs</li> </ol> <p>Files containing raw and analyzed data of experiment two are labeled as follows:</p> <ul> <li>Fig7_Experiment2_Clone001NPC_Compared-to-NPC-WT</li> <li>Fig7_Experiment2_Clone001NPC_Compared-to-NSC-WT</li> <li>Fig7_Experiment2_Clone002NPC_Compared-to-NPC-WT</li> <li>Fig7_Experiment2_Clone002NPC_Compared-to-NSC-WT</li> </ul> <p>Please note that the first tab of each excel document contains the full raw data set before analysis. All other tabs contain analyzed data comparing the proteins identified in two cell lines. Analyzed tabs are labeled to indicate which cell lines are being compared. Please note that the CUL9 isogenic clones Clone 20=Clone#1 and Clone B=Clone#2 as referenced in the associated manuscript.</p> <p>Detailed information about the protocols used for collection and analysis of this data can be found in the supplemental information within the associated manuscript.</p>
Data from: Development of a Chinook salmon sex identification SNP assay based on the growth hormone pseudogene
Genotypic sex identification assays can provide valuable information about fish populations when phenotypic sex determination is difficult. Here we describe the development of a TaqMan® assay (Ots_SexID) designed to identify the genotypic sex of Winter-Run Chinook salmon collected from the Sacramento River and spawned at the Livingston Stone National Fish Hatchery. The TaqMan® assay targets a region previously examined in the growth hormone pseudogene. Accuracy of the marker was assessed by comparing genotypic sex assignments for Chinook salmon spawned at Livingston Stone National Fish hatchery in 2012 (n = 84) to phenotypic sex recorded during spawning. Genotypic sex was observed to be concordant with phenotypic sex identified using Ots_SexID in 83/84 individuals, suggesting that the assay could be used to predict phenotypic sex with ~99% accuracy. To evaluate the utility of the TaqMan® assay in other parts of the species' range, we examined collections from 29 other populations ranging from Alaska to California. Sex assignments based on the assay were generally concordant with observed phenotypes, but there were some strong exceptions. These results suggest that the new assay will be very useful in Sacramento River Winter-Run Chinook salmon, but also highlight the importance of thoroughly testing any sex identification assay prior to application in a population of interest.
Data from: Particle backtracking improves breeding subpopulation discrimination and natal-source identification in mixed populations
We provide a novel method to improve the use of natural tagging approaches for subpopulation discrimination and source-origin identification in aquatic and terrestrial animals with a passive dispersive phase. Our method integrates observed site-referenced biological information on individuals in mixed populations with a particle-tracking model to retrace likely dispersal histories prior to capture (i.e., particle backtracking). To illustrate and test our approach, we focus on western Lake Erie's yellow perch (Perca flavescens) population during 2006–2007, using microsatellite DNA and otolith microchemistry from larvae and juveniles as natural tags. Particle backtracking showed that not all larvae collected near a presumed hatching location may have originated there, owing to passive drift during the larval stage that was influenced by strong river- and wind-driven water circulation. Re-assigning larvae to their most probable hatching site (based on probabilistic dispersal trajectories from the particle backtracking model) improved the use of genetics and otolith microchemistry to discriminate among local breeding subpopulations. This enhancement, in turn, altered (and likely improved) the estimated contributions of each breeding subpopulation to the mixed population of juvenile recruits. Our findings indicate that particle backtracking can complement existing tools used to identify the origin of individuals in mixed populations, especially in flow-dominated systems.
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.