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9,113 results for “Sampling”
A Methodology for the Fast Identification and Monitoring of Microplastics in Environmental Samples using Random Decision Forest Classifiers
<p>This short video shows the results of the application of a classifier for microplastics as described by Hufnagl et al. (2019).</p> <p> </p> <p>If you reuse this video please cite</p> <p> </p> <p>Hufnagl, B., Steiner, D., Renner, Löder, M. G. J., Laforsch, C. and Lohninger, H. <em>A Methodology for the Fast Identification and Monitoring of Microplastics in</em><em> Environmental Samples using Random Decision Forest Classifiers,</em> Analytical Methods, 2019, DOI:10.1039/C9AY00252A</p>
Sample data for beamModelTester
<p>This sample data for use with <a href="https://github.com/creaneroDIAS/beamModelTester">beamModelTester</a>. The data consists of a HBA observation of CasA taken at LOFAR Station IE613 on the 16th of March 2018 and a model of that observation generated using <a href="https://github.com/2baornot2ba/dreamBeam">dreamBeam</a>. </p>
gwastro/o2-bbh-pe: v2.2 data release of O2 Binary Black Hole posterior samples
<p>This is the v2.2 data release associated with the parameter estimation analysis of the binary black-hole signals from Advanced LIGO-Virgo's second observing run, using the PyCBC Inference toolkit : <a href="https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b">https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b</a> . A companion paper presenting our parameter estimation analysis and the data release is available here : <a href="https://arxiv.org/pdf/1811.09232.pdf">https://arxiv.org/pdf/1811.09232.pdf</a>.</p> <p>The analysis was performed using the PyCBC v1.12.3 code on the gravitational-wave data available at <a href="https://www.gw-openscience.org/catalog/GWTC-1-confident/html/">https://www.gw-openscience.org/catalog/GWTC-1-confident/html/</a> . Descriptions of the gravitational-wave data can be found in the paper <a href="https://arxiv.org/abs/1811.12907">https://arxiv.org/abs/1811.12907</a> .</p> <p>The changes in this release are</p> <ul> <li>An update to the plotting code in <code>data_release_o2_bbh_pe.ipynb</code> for generating Figs. 1, 2, and 3 in the companion paper to take into account cases where a boundary bias may be introduced for plotting probability contours.</li> <li>Addition of a plotting code in <code>data_release_o2_bbh_pe.ipynb</code> that generates a corner plot showing estimates (median and 90% credible interval) and posterior distributions for all the parameters presented in Table 1 of the companion paper.</li> <li>Addition of a notebook <code>o2_bbh_pe_skymaps.ipynb</code> that demonstrates the method for visualizing sky location posteriors as presented in Fig. 4 of the manuscript.</li> </ul> <p>The data and configuration files included remain the same as in the v2.1 release.</p> <p>This release includes :</p> <ul> <li>posterior and prior samples from parameter estimation analyses of the seven binary black-hole events---GW170104, GW170608, GW170729, GW170809, GW170814, GW170818, and GW170823.</li> <li>PSDs used in each of the analyses</li> <li>configuration files and run scripts for running the analyses and generating the data.</li> <li>tutorials for manipulating the data and reconstructing the figures in the companion paper.</li> </ul>
Moisture content and total aflatoxin content of the freshly harvested maize samples
<p>Moisture content and total aflatoxin content of the freshly harvested maize samples. </p> <p>Moisture content of the samples were determined on-site in triplicate using Superpoint handheld moisture analyzer (Supertech Agroline, Hestchaven 5, DK-5400 Bogense, Denmark; ±0.5% accuracy) following the manufacturer’s instructions.</p> <p>Total AF in the samples were quantified by a single step lateral flow immunoassay utilizing the developed Reveal Q+ test strip for Aflatoxin (Neogen Item 8085) read on a calibrated AccuSan Gold reader (Neogen Corporation, 620 Lesher Place, Lansing, MI 48912 USA) (Neogen item 9595) at 18-22<sup>o</sup>C</p>
How Confidence in Prior Attitudes, Social Tag Popularity, and Source Credibility Shape Confirmation Bias Toward Antidepressants and Psychotherapy in a Representative German Sample: Randomized Controlled Web-Based Study
<p>ABSTRACT</p> <p>Background: In health-related, Web-based information search, people should select information in line with expert (vs nonexpert) information, independent of their prior attitudes and consequent confirmation bias.</p> <p>Objective: This study aimed to investigate confirmation bias in mental health–related information search, particularly (1) if high confidence worsens confirmation bias, (2) if social tags eliminate the influence of prior attitudes, and (3) if people successfully distinguish high and low source credibility.</p> <p>Methods: In total, 520 participants of a representative sample of the German Web-based population were recruited via a panel company. Among them, 48.1% (250/520) participants completed the fully automated study. Participants provided <em>prior attitudes</em> about antidepressants and psychotherapy. We manipulated (1) <em>confidence</em> in prior attitudes when participants searched for blog posts about the treatment of depression, (2) <em>tag popularity</em> —either psychotherapy or antidepressant tags were more popular, and (3) <em>source credibility</em> with banners indicating high or low expertise of the tagging community. We measured <em>tag</em> and <em>blog post</em> selection, and <em>treatment</em><em>efficacy ratings</em> after navigation.</p> <p>Results: Tag popularity predicted the proportion of selected antidepressant tags (beta=.44, SE 0.11; <em>P</em><.001) and blog posts (beta=.46, SE 0.11; <em>P</em><.001). When confidence was low (−1 SD), participants selected more blog posts consistent with prior attitudes (beta=−.26, SE 0.05; <em>P</em><.001). Moreover, when confidence was low (−1 SD) and source credibility was high (+1 SD), the efficacy ratings of attitude-consistent treatments increased (beta=.34, SE 0.13; <em>P</em>=.01).</p> <p>Conclusions: We found correlational support for defense motivation account underlying confirmation bias in the mental health–related search context. That is, participants tended to select information that supported their prior attitudes, which is not in line with the current scientific evidence. Implications for presenting persuasive Web-based information are also discussed.</p> <p>Trial Registration: ClinicalTrials.gov NCT03899168; https://clinicaltrials.gov/ct2/show/NCT03899168 (Archived by WebCite at http://www.webcitation.org/77Nyot3Do)</p> <p>J Med Internet Res 2019;21(4):e11081</p> <p>doi:10.2196/11081</p>
AdaptSearch - toy dataset - Trinity 4 samples
<p><a href="https://github.com/abims-sbr/adaptsearch"><strong>AdaptSearch</strong></a> is dedicated to the search of selection within branches and sites of phylogenetic trees built from groups of orthologous genes. The first tools proceeds to data filtering and building of genes groups, while the second part proceeds to phylogeny building (using RAxML) and selection detection (codeML).</p> <p>This dataset can be used as toy example to test the pipeline</p>
Data sample from Israel Electric Corporation (IEC)
<p>The dataset contains logs from IT and OT systems collected from a testbed setup of the IEC.<br> The content is specifically related to:</p> <p>·CCTV and PTZ cameras.</p> <p>·Intrusion detection system (IDS).</p> <p>·Access control system.</p> <p>·Proximity cards and sensors.</p>
Sub-sampled Fastq Files for ChIP-seq datasets from Click-Seq Science Paper (Science 2017, 10.1126/science.aal2066)
<p>Data were downloaded from SRA. We then randomly sub-sampled 20% of the reads using seqtk_sample v 1.2 in galaxy (seed 4).</p> <p>This dataset is a support dataset for the <a href="https://www.embl.de/training/events/2019/EPI19-01/index.html">EMBL Course: Chromatin Signatures During Differentiation: Integrated Omics</a></p>
Sub-sampled Fastq Files from Click-Seq Science Paper (Science 2017, 10.1126/science.aal2066)
<p>Data were downloaded from SRA. We then randomly sub-sampled 20% of the reads using seqtk_sample v 1.2 in galaxy (seed 4).</p> <p>This dataset is a support dataset for the <a href="https://www.embl.de/training/events/2019/EPI19-01/index.html">EMBL Course: Chromatin Signatures During Differentiation: Integrated Omics</a> </p>
Geochemical evidence for high volatile fluxes from the mantle at the end of the Archean: Sample data
<p>This file describes the origin of samples and gives the original xenon data published in refs. 4, 5 and 8. The slopes of the fractionation lines are given in refs. 8 (Table S1), 5 (Tables 1 and S1) and 4 (Table S1). For the barite sample (Ref. 6, Table 1), Xe data were normalized to 132Xe since 130Xe could have been contributed by radioactivity products. In this case Δ129Xe was computed from 128Xe and 131,132Xe data. The Δ129Xe values were computed from the difference between the isotope fractionation slope (‰/u) and the 129Xe/130Xe values in deviation permil (‰) relative to the corresponding atmospheric isotope composition.</p>
14-day smartphone ambulatory assessment of depression symptoms and mood dynamics in a general population sample: comparison with the PHQ-9 depression screening
<p>This dataset contains 14 days of ambulatory assessment (AA) depression symptoms and mood ratings with timestamps, a retrospective Patient Health Questionnaire (PHQ-9) assessment and the demographic variables age and gender.</p> <p>The AA was conducted with users of the mobile mental health / depression screening app "Moodpath". ICD-10 depression symptoms were assessed with the following questions:</p> <p>ICD-10 symptom 1: "depressed mood"<br> q1 = Are you feeling depressed?<br> q2 = Are you feeling hopeless?</p> <p>ICD-10 symptom 2: "loss of interest and enjoyment"<br> q3 = Do you feel like you are not interested in anything right now?<br> q4 = Do you have less pleasure in doing things you usually enjoy?</p> <p>ICD-10 symptom 3: "increased fatigability"<br> q5 = Do you currently have considerably less energy? <br> q6 = Are your everyday tasks making you very tired currently?</p> <p>ICD-10 symptom 4: "reduced concentration and attention"<br> q11 = Is it hard for you to make decisions currently? <br> q12 = Is it hard for you to concentrate currently?</p> <p>ICD-10 symptom 5: "reduced self-esteem and self-confidence"<br> q7 = Is your self-confidence clearly lower than usual?<br> q8 = Are you feeling up to your tasks? </p> <p>ICD-10 symptom 6: "ideas of guilt and unworthiness"<br> q9 = Are you blaming yourself currently? <br> q10 = Do you think you are worth less than others right now?</p> <p>ICD-10 symptom 7: "bleak and pessimistic views of the future"<br> q46 = Are you thinking that you will be doing well in the future? <br> q47 = Are you looking hopefully into the future?</p> <p>ICD-10 symptom 8: "ideas or acts of self-harm or suicide"<br> q16 = Are you thinking about death more often than usual? </p> <p>ICD-10 symptom 9: "disturbed sleep"<br> q13 = Did you sleep badly last night? </p> <p>ICD-10 symptom 10: "diminished appetite"<br> q14 = Do you have less or no appetite today? </p>
Data & Sample Chess
<p>Data & Sample Chess is a game-like figure to enable all project partners of the Horizon 2020 project "MEET" to mark for each work package, which task-related data or sample type has to be provided to other work packages. This figure was used within an interactive session during a DMP-workshop held in May 2019 in Zagreb.</p> <p>The outcomes of this game-like session were summarized and combined into one general figure to identify the internal data paths and dependencies between work packages.</p>
somalier files for thousand genomes high-coverage VCF for 2504 samples
<p>somalier files extracted from the thousand genomes VCF to be used for ancestry prediction with somalier.</p>
Neutron diffraction data of Sikkim and West Bengal samples
<p>This is the data set for the quartz pole figures for six quartzite samples from Sikkim and West Bengal, Indian Himalayas. The data for each sample starts with the sample label: SK222, WB03, WB10, WB11, WB41, and WB76.</p>
Monthly word embeddings for Twitter random sample (English, 2012-2018)
<p>This dataset contains monthly word embeddings created from the tweets available via the statuses/sample endpoint of the Twitter Streaming API from 2012 to 2018. Full details of the creation of the dataset are given in <a href="https://www.aclweb.org/anthology/D19-1007/">Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings</a>. </p> <p>The md5sum of the gzipped tarball file is a76888ffec8cc7aebba09d365ca55ace .</p>
X-ray and ion irradiation effects on azurite, malachite and alizarin pictorial samples
<p>XPS was used to analyze X-ray effects on pellets of azurite, malachite and alizarin pure pigments and on azurite and alizarin tempera paint mock-ups, where the pigments were mixed with egg yolk. X-ray radiation damage was assessed by comparing successive high-resolution spectra acquired on the same spot. The spectra of the pure pigments show low radiation sensitivity and reactivity under the selected X-ray irradiation conditions, although some differences among them were detected. Whereas alizarin does not experience surface chemical changes during the prolonged irradiation experiment, a slow progressive chemical reduction of azurite and malachite (copper-based pigments), together with the formation of cuprite, is observed. On the other side, tempera paint mock-ups are sensitive to X-rays and alterations of the egg yolk binding medium are displayed in a shorter time scale than the induced chemical modifications of the pure pigments. Besides, XPS allows to determine that the azurite pigment and the binder interact chemically. Azurite is partially reduced in the tempera paint from the beginning of the irradiation experiment and the subsequent X-ray exposure induces further reduction</p>
Generic sample set HC1
<p>A typical view of the samples ready to do SEM on them, along tweezers to compare size.</p>
EEG data for generating reference rdFC patterns and sample rdFC patterns
<p>The correlation structure embedded in scalp EEG data is explored through three representative reference rdFC patterns which can be generated from the data in referenceEEG.txt. Three sample EEG data files, each of a triplet of electrodes, serve as examples for comparison with the reference rdFC patterns.</p>
A Sampling Approach to Evaluative Conditioning
<p>In an evaluative conditioning procedure (EC), a conditioned stimulus (CS) is paired with positive or negative unconditioned stimuli (US) and consequently acquires US valence. We introduce a sampling perspective into the EC paradigm by repeatedly giving participants agency over which CSs they want to sample (i.e., for what CSs they want to see conditioning trials). Half of the CSs was paired positively, while the other half was paired negatively. We also introduced a yoked condition, in which participants saw the same information produced by the sampling condition, but did not get to make any decisions. In three experiments, we found that over time a preference for sampling positively paired CSs over negatively paired CSs develops. We replicated standard EC effects and found that a higher number of pairings leads to more positive evaluative shifts, but more so in the sampling than in the yoked condition. This finding shows that evaluative conditioning depends on the learner’s willingness to attend to certain CSs. The results of this set of studies also suggest that agency increases the readiness to process the presented CS-US pairs. This reasoning is supported by memory data that indicate better memory for the number of pairings and paired valence in the sampling condition compared to the yoked condition. Implications for the external validity of the EC paradigm are also discussed.</p>
РИС. 1. Карта района исследований с укаЗанием станций отбора проб (А) и плотности поселениЯ R. venosa (ЭкЗ./м²). FIG. 1. Map of the study area with indication the sampling sites and the population density (A) of R. venosa (specimens/m²). in Rapana venosa (Valenciennes, 1846) Залива ДонуЗлав и прилегаюЩей акватории Чёрного морЯ
РИС. 1. Карта района исследований с укаЗанием станций отбора проб (А) и плотности поселениЯ R. venosa (ЭкЗ./м²). FIG. 1. Map of the study area with indication the sampling sites and the population density (A) of R. venosa (specimens/m²).
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.