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10,331 results for “Signaling”
Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: multi-messenger signals
<p>1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27. Run with SFHo EOS, M1 with 12 energy groups.</p> <p>For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020).</p> <p>Includes the multi-messenger data from the STIR simulations. The filename indicates the turbulent mixing parameter a and progenitor mass m of the simulation. Columns are time [s], shock radius [cm], explosion energy [ergs], electron neutrino mean energy [MeV], electron neutrino rms energy [MeV], electron neutrino luminosity [10^51 ergs/s], electron antineutrino mean energy [MeV], electron antineutrino rms energy [MeV], electron antineutrino luminosity [10^51 ergs/s], x neutrino mean energy [MeV], x neutrino rms energy [MeV], x neutrino luminosity [10^51 ergs/s], gravitational wave frequency from eigenmode analysis of the protoneutron star structure [Hz]. Note that the x neutrino luminosity is for <strong>one</strong> neutrino flavor - to get the total mu/tau neutrino and antineutrino luminosities requires multiplying this number by 4.</p> <p>v1.1 - removed unnecessary duplicate files</p> <p>v1.2 - upload failed. Obsolete.</p> <p>v1.3 - packaging alpha values as separate tar files for easy download.</p>
Dataset covidgilance signals
<p>Research datasets about top signals for covid 19 (coronavirus) for study into Google Trends (GT) and with SEO metrics</p> <p> </p> <p>Website</p> <p>The study is currently published on <a href="https://covidgilance.org">https://covidgilance.org</a> website (in french)</p> <p> </p> <p>Datasets description</p> <p><code>covid signals -> |selection| -> 4 dataset -> |serp.py| -> 4 serp datasets -> |aggregate_serp.pl| -> 4 aggregated dataset of serp -> |prepare datasets| -> 4 ranked top seo dataset</code></p> <p> </p> <p>Original lists of signals (mainly covid symptoms) - dataset</p> <p><strong>Description:</strong> contain the original relevant list of signals for covid19 (here list of queries where you can see, in GT, a relevant signal during the covid 19 period of time)<br> <strong>Name:</strong> covid_signal_list.tsv</p> <p><strong>List of content:</strong></p> <p><strong>- id:</strong> unique id for the topic<br> <strong>- topic-fr:</strong> name of the topic in French<br> <strong>- topic-en:</strong> name of the topic in English<br> <strong>- topic-id:</strong> GT topic id<br> <strong>- keyword fr:</strong> one or several keywords in French for GT<br> <strong>- keyword en:</strong> one or several keywords in English for GT<br> <strong>- fr-topic-url-12M:</strong> link to 12-months French query topic in GT in France<br> <strong>- en-topic-url-12M:</strong> link to 12-months English query topic in GT in US<br> <strong>- fr-url-12M:</strong> link to 12-months French queries in GT in France<br> <strong>- en-url-12M:</strong> link to 12-months English queries topic in GT in US<br> <strong>- fr-topic-url-5M:</strong> link to 5-months French query topic in GT in France<br> <strong>- en-topic-url-5M:</strong> link to 5-months English query topic in GT in US<br> <strong>- fr-url-5M:</strong> link to 5-months French queries in GT in France<br> <strong>- en-url-5M:</strong> link to 5-months English queries topic in GT in US</p> <p> </p> <p>Tool to get SERP of covid signals - tool</p> <p><strong>Description:</strong> query google with a list of covid signals and obtain a list of serps in csv (tsv in fact) file format<br> <strong>Name:</strong> serper.py</p> <p><code>python serper.py</code></p> <p> </p> <p>SERP files - datasets</p> <p><strong>Description</strong> Serp results for 4 datesets of queries <strong>Names:</strong> simple version of covid signals from google.ch in French: serp_signals_20_ch_fr.csv<br> simple version of covid signals from google.com in English: serp_signals_20_en.csv<br> amplified version of covid signals from google.ch in French: serp_signals_covid_20_ch_fr.csv<br> amplified version of covid signals from google.com in English: serp_signals_covid_20_en.csv</p> <p>amplified version means that for each query we create two queries one with the keywords "covid" and one with "coronavirus"</p> <p> </p> <p>Tool to aggregate SERP results - tool</p> <p><strong>Description:</strong> load csv serp data and aggregate the data to create a new csv file where each line is a website and each column is a query. <strong>Name:</strong> aggregate_serp.pl</p> <p>`perl aggregate_serp.pl> aggregated_signals_20_en.csv</p> <p> </p> <p>datasets of top website from the SERP results - dataset</p> <p><strong>Description</strong> a aggregated version of the SERP where each line is a website and each column a query<br> <strong>Names:</strong><br> aggregated_signals_20_ch_fr.csv<br> aggregated_signals_20_en.csv<br> aggregated_signals_covid_20_ch_fr.csv<br> aggregated_signals_covid_20_en.csv</p> <p><strong>List of content:</strong></p> <p><strong>- domain:</strong> domain name of the website<br> <strong>- signal 1:</strong> Position of the query 1 (signal 1) in the SERP where 30 indicates arbitrary that this website is not present in the SERP<br> <strong>- signal ...:</strong> Position of the query (signal) in the SERP where 30 indicates arbitrary that this website is not present in the SERP<br> <strong>- signal n:</strong> Position of the query n (signal n) in the SERP where 30 indicates arbitrary that this website is not present in the SERP<br> <strong>- total:</strong> average position (total of all position /divided by the number of queries)<br> <strong>- missing:</strong> Total number of missing results in the SERP for this website</p> <p> </p> <p>datasets ranked top seo - dataset</p> <p><strong>Description</strong> a ranked (by weighted average position) version of the aggregated version of the SERP where each line is a website and each column a query. TOP 20 have more information about the type and HONcode validity (from the date of collect: September 2020)</p> <p><strong>Names:</strong><br> ranked_signals_20_ch_fr.csv<br> ranked_signals_20_en.csv<br> ranked_signals_covid_20_ch_fr.csv<br> ranked_signals_covid_20_en.csv</p> <p><strong>List of content:</strong></p> <p><strong>- domain:</strong> domain name of the website<br> <strong>- signal 1:</strong> Position of the query 1 (signal 1) in the SERP where 30 indicates arbitrary that this website is not present in the SERP<br> <strong>- signal ...:</strong> Position of the query (signal) in the SERP where 30 indicates arbitrary that this website is not present in the SERP<br> <strong>- signal n:</strong> Position of the query n (signal n) in the SERP where 30 indicates arbitrary that this website is not present in the SERP<br> <strong>- avg position:</strong> average position (total of all position /divided by the number of queries)<br> <strong>- nb missing:</strong> Total number of missing results in the SERP for this website<br> <strong>- % presence:</strong> % of presence<br> <strong>- weighted avg postion:</strong> combination of avg position and % of presence for final ranking<br> <strong>- honcode:</strong> status of the Honcode certificate for this website (none/valid/expired)<br> <strong>- type:</strong> type of the website (health, gov, edu or media)</p>
Estimating TOF from ultrasound signal over bare steel sample of 5 mm thickness
<p>These data have been obtained using the CEIT ultrasound testbed exciting a PZT piezoelectric sensor with a +/-15 volts pulse located over a 5 mm bare steel sample. The testbed receives the ultrasound response and estimates the TOF measuring the distance between two consecutive echoes. The aim is to develop an embedded system to measure the loss of thickness produced by corrosion.</p>
Mouse olfactory bulb intrinsic signal imaging for NNMF decomposition
<p>This dataset supplements our recent paper about automatic image segmentation using Non-negative Matrix Factorisation (NMF):</p> <p>Jan Soelter, Jan Schumacher, Hartwig Spors, Michael Schmuker (2014). Automatic segmentation of odor maps in the mouse olfactory bulb using regularized non-negative matrix factorization. <em>NeuroImage</em> 98:279-288.</p> <p>http://dx.doi.org/10.1016/j.neuroimage.2014.04.041</p>
Signal feeds for creating the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Wav files for the comparison of wave field synthesis, surround, and stereo listening test, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Environmental DNA captures signals of the internal structure of a pond metacommunity
<p>R Code for the study of "<strong>Environmental DNA captures signals of the internal structure of a pond metacommunity"</strong></p>
Aircraft Marshaling Signals Dataset of FMCW Radar and Event-Based Camera for Sensor Fusion
<p><strong>Dataset Introduction</strong></p><p>The advent of neural networks capable of learning salient features from variance in the radar data has expanded the breadth of radar applications, often as an alternative sensor or a complementary modality to camera vision. Gesture recognition for command control is arguably the most commonly explored application. Nevertheless, more suitable benchmarking datasets than currently available are needed to assess and compare the merits of the different proposed solutions and explore a broader range of scenarios than simple hand-gesturing a few centimeters away from a radar transmitter/receiver. Most current publicly available radar datasets used in gesture recognition provide limited diversity, do not provide access to raw ADC data, and are not significantly challenging. To address these shortcomings, we created and make available a new dataset that combines FMCW radar and dynamic vision camera of 10 aircraft marshalling signals (whole body) at several distances and angles from the sensors, recorded from 13 people. The two modalities are hardware synchronized using the radar's PRI signal. Moreover, in the supporting publication we propose a sparse encoding of the time domain (ADC) signals that achieve a dramatic data rate reduction (>76%) while retaining the efficacy of the downstream FFT processing (<2% accuracy loss on recognition tasks), and can be used to create an sparse event-based representation of the radar data. In this way the dataset can be used as a two-modality neuromorphic dataset.</p><p><strong>Synchronization of the two modalities</strong></p><p>The PRI pulses from the radar have been hard-wired to the event stream of the DVS sensor, and timestamped using the DVS clock. Based on this signal the DVS event stream has been segmented such that groups of events (time-bins) of the DVS are mapped with individual radar pulses (chirps).</p><p><strong>Data storage</strong></p><p>DVS events (x,y coords and timestamps) are stored in structured arrays, and one such structured array object is associated with the data of a radar transmission (pulse/chirp). A radar transmission is a vector of 512 ADC levels that correspond to sampling points of chirping signal (FMCW radar) that lasts about ~1.3ms. Every 192 radar transmissions are stacked in a matrix called a radar frame (each transmission is a row in that matrix). A data capture (recording) consisting of some thousands of continuous radar transmissions is therefore segmented in a number of radar frames. Finally radar frames and the corresponding DVS structured arrays are stored in separate containers in a custom-made multi-container file format (extension .rad). We provide a (rad file) parser for extracting the data out of these files. There is one file per capture of continuous gesture recording of about 10s.</p><p>Note the number of 192 transmissions per radar frame is an ad-hoc segmentation that suits the purpose of obtaining sufficient signal resolution in a 2D FFT typical in radar signal processing, for the range resolution of the specific radar. It also served the purpose of fast streaming storing of the data during capture. For extracting individual data points for the dataset however, one can pool together (concat) all the radar frames from a single capture file and re-segment them according to liking. The data loader that we provide offers this, with a default of re-segmenting every 769 transmissions (about 1s of gesturing).</p><p><strong>Data captures directory organization (</strong><a href="https://zenodo.org/api/records/10359770/draft/files/radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z/content">radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z</a><strong>)</strong></p><p>The dataset captures (recordings) are organized in a common directory structure which encompasses additional metadata information about the captures.</p><p>dataset_dir/<stage>/<room>/<person>-<gesture>-<distance>/ofxRadar8Ghz_yyyy-mm-dd_HH-MM-SS.rad</p><p>Identifiers</p><ul><li>stage [train, test].</li><li>room: [conference_room, foyer, open_space].</li><li>subject: [0-9]. Note that 0 stands for no person, and 1 for an unlabeled, random person (only present in test).</li><li>gesture: ['none', 'emergency_stop', 'move_ahead', 'move_back_v1', 'move_back_v2', 'slow_down' 'start_engines', 'stop_engines', 'straight_ahead', 'turn_left', 'turn_right'].</li><li>distance: ['xxx', '100', '150', '200', '250', '300', '350', '400', '450'] (in cm). Note that xxx is used for none gestures when there is no person present in front of the radar (i.e. background samples), or when a person is walking in front of the radar with varying distances but performing no gesture.</li></ul><p>The test data captures contain both subjects that appear in the train data as well as previously <i>unseen</i> subjects. Similarly the test data contain captures from the spaces that train data were recorded at, as well as from a new <i>unseen</i> open space.</p><p><strong>Files List</strong></p><p><a href="https://zenodo.org/api/records/10359770/draft/files/radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z/content">radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z</a></p><p>This is the actual archive bundle with the data captures (recordings).</p><p><a href="https://zenodo.org/api/records/10359770/draft/files/rad_file_parser_2.py/content">rad_file_parser_2.py</a></p><p>Parser for individual .rad files, which contain capture data.</p><p><a href="https://zenodo.org/api/records/10359770/draft/files/loader.py/content">loader.py</a></p><p>A convenience PyTorch Dataset loader (partly Tonic compatible). You practically only need this to quick-start if you don't want to delve too much into code reading. When you init a DvsRadarAircraftMarshallingSignals class object it automatically downloads the dataset archive and the .rad file parser, unpacks the archive, and imports the .rad parser to load the data. One can then <i>request from it </i>a training set, a validation set and a test set as torch.Datasets to work with<i>.</i> </p><p><a href="https://zenodo.org/api/records/10359770/draft/files/aircraft_marshalling_signals_howto.ipynb/content">aircraft_marshalling_signals_howto.ipynb</a></p><p>Jupyter notebook for exemplary basic use of loader.py</p><p><strong>Contact</strong></p><p>For further information or questions try contacting first M. Sifalakis or F. Corradi.</p><p> </p>
Vibrational signals produced by wing buzzing in Cacopsylla pyrisuga males (Hemiptera: Psyllidae)
<p>High-speed camera (video files) and laser vibrometer (audio files) recordings of Cacopsylla pyrisuga males producing vibrational signals - a dataset accompanying the publication</p> <p>Polajnar J., Kvinikadze E., Harley A.W., Malenovský I. (2024) Wing buzzing as a mechanism for generating vibrational signals in psyllids (Hemiptera: Psylloidea). Insect Science. See the publication for details about the methodology used.</p> <p>The dataset additionaly includes tracked points at wing and abdomen tips from two videos, and an R script with instructions to read this data.</p>
Additional Data: Poised PABP-RNA hubs implement signal-dependent mRNA decay in development
<p>This repository contains processed data resulting from iCLIP experiments that were analysed in the following paper:"<strong>Poised PABP-RNA hubs implement signal-dependent mRNA decay in development</strong>"<br>The paper is published at Nature Structural and Molecular BIology.</p> <h2><br>Archived data</h2> <p>Data archived in this repository include:</p> <ol> <li>Data derived from iCLIP experiments targeting LIN28A, PABPC1, and PABPC4, that were analysed in the manuscript (see iCLIP.zip). Raw data is available from ENA, with the accession code PRJEB60519. <ol> <li>Sample descriptions are given in iCLIP-SampleAnnotation.csv</li> <li>Crosslink files in BED6 format (individual replicates and merged replicates)</li> <li>Peak files generated with the Clippy peak caller in BED6 format</li> <li>K-mer enrichment around high-confidence crosslink sites in the 3'-UTRs, calculated by the PEKA software</li> </ol> </li> <li>Expression values (salmon quantfiles) for 3'-seq experiments, specified in "QuantseqExperimentsAnnotation.tsv", are available in "SalmonQuantfiles.zip". Raw data is available from ENA, with the accession code PRJEB60519.</li> <li>Source code of the nextflow pipeline, which was used on the iMaps webserver to analyse iCLIP data and produce the files archived here (see imaps-nf-0.30.zip).</li> <li>A list of naive genes, that were analysed in the manuscript (see NaiveGeneIds.csv).</li> </ol> <h2>Details on iCLIP data generation</h2> <p>iCLIP data for LIN28A-WT (in 2iL and FGF2 treated cells), LIN28A-S200A (in FGF2 treated cells) as well as for PABPC1 and PABPC4 (in LIN28A KO cells with and without LIN28A overexpression), were analysed on iMaps Goodwright server (<a href="https://imaps.goodwright.com/">https://imaps.goodwright.com/</a>). The LIN28A iCLIPs were analysed on 18th of July, 2022; the PABPC iCLIPs were analysed on 26th of December, 2022. The code and settings used in the pipeline (release v0.30) can be viewed at <a href="https://github.com/goodwright/imaps-nf">https://github.com/goodwright/imaps-nf </a>, and is also archived here - (imaps-nf-0.30.zip)<br> </p> <ul> <li>First, reads were demultiplexed using Ultraplex and barcodes were trimmed from the reads. The default Ultraplex settings were applied, as denoted below:</li> </ul> <blockquote> <p>adapter='AGATCGGAAGAGCGGTTCAG'<br>adapter2='AGATCGGAAGAGCGTCGTG'<br>barcodes='barcode.csv',<br>final_min_length=20<br>fiveprimemismatches=1<br>ignore_no_match=False<br>ignore_space_warning=False<br>inputfastq='MOD4878A1-merged.fastq.gz',<br>keep_barcode=False,<br>min_trim=3,<br>outputprefix='demux',<br>phredquality=30,<br>phredquality_5_prime=0,<br>sbatchcompression=False,<br>threads=10,<br>threeprimemismatches=0,<br>ultra=False</p> </blockquote> <p> </p> <ul> <li>TrimGalore was used to run FASTQC and quality trim the reads and remove reads with length less than 10 nt:</li> </ul> <blockquote> <p>trim_galore --fastqc --length 10 -q 20 --cores 8 --gzip file.fastq.gz</p> </blockquote> <p> </p> <ul> <li>Reads were then premapped to rRNA, tRNA sequences referred to as small RNA, smRNA, using mouse genome build (GRCm39 GENCODE M28 annotation) with Bowtie v1.3.0 (Langmead et al., 2009)</li> </ul> <blockquote> <p>bowtie --threads 12 --sam -x $INDEX -q --un file.unmapped.fastq -v 2 -m 100 --norc --best --strata file.fq.gz 2</p> </blockquote> <p> </p> <ul> <li>Reads that did not map with Bowtie were then aligned with STAR v2.7.9a (Dobin et al., 2013) to mouse genome build (GRCm39 GENCODE M28 annotation).</li> </ul> <blockquote> <p>STAR \<br>--genomeDir star \<br>--readFilesIn file.unmapped.fastq.gz \<br>--runThreadN 12 \<br>--outFileNamePrefix 1_R1. \<br>\<br>--sjdbGTFfile Homo_sapiens_filtered.gtf \<br>--outSAMattrRGline 'ID:1_R1' 'SM:1_R1' \<br> --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --quantMode TranscriptomeSAM --outFilterMultimapNmax 1 --outFilterMultimapScoreRange 1 --outSAMattributes All --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterType BySJout --alignIntronMin 20 --alignIntronMax 1000000 --outFilterScoreMin 10 --alignEndsType Extend5pOfRead1 --twopassMode Basic</p> </blockquote> <p> </p> <ul> <li>PCR-duplicates were removed using UMI-tools (Smith, Heger and Sudbery, 2017)</li> </ul> <blockquote> <p>java -jar /UMICollapse/umicollapse.jar \<br> bam \<br> -i file.Aligned.sortedByCoord.out.bam \<br> -o file.dedup.bam \<br> --umi-sep rbc:</p> </blockquote> <p> </p> <ul> <li>The nucleotide preceding each sequencing read was assigned as the crosslink event.</li> </ul> <p> </p> <ul> <li>Peaks of crosslinking signal were identified with Clippy v1.4.1, using the default settings.</li> </ul> <p> </p> <ul> <li>Obtained peaks and crosslink sites were used to run PEKA v1.0.0 (Kuret et al., 2022), using the default settings.</li> </ul> <p> </p> <ul> <li>For Clippy and PEKA, the GENCODE primary assembly annotation M28 was filtered to retain only entries with transcript support level 1 or 2, in genes where such transcripts were available, and used to produce a segmentation file with the <em>get_segments</em> function from the iCount tool (Curk, 2019).</li> </ul> <p> </p> <ul> <li>All files generated during data processing are available from the iMaps Goodwright webserver for analysis of CLIP data (see <a href="https://imaps.goodwright.com/collections/882635250203/">https://imaps.goodwright.com/collections/882635250203/</a> and <a href="https://imaps.goodwright.com/collections/340215254997/">https://imaps.goodwright.com/collections/340215254997/</a> for LIN28A and PABPC1/4 iCLIPs, respectively).</li> </ul> <h2>Source data</h2> <p>Raw sequencing reads, from which the data enclosed here were derived, are accessible at ENA (PRJEB60519).<br>The raw sequencing reads and all data produced by the analysis pipeline is also available at the iMaps webserver (see <a href="https://imaps.goodwright.com/collections/882635250203/">https://imaps.goodwright.com/collections/882635250203/</a> and <a href="https://imaps.goodwright.com/collections/340215254997/">https://imaps.goodwright.com/collections/340215254997/</a> for LIN28A and PABPC1/4 iCLIPs, respectively); and on the updated Flow webserver (see <a href="https://app.flow.bio/projects/882635250203/">https://app.flow.bio/projects/882635250203/</a> and <a href="https://app.flow.bio/projects/340215254997/">https://app.flow.bio/projects/340215254997/ </a>for LIN28A and PABPC1/4 iCLIPs, respectively).</p> <h2>Downstream computational analysis of enclosed data</h2> <p>The code, used to analyse the data enclosed here and train the CNN to predict transcript stability in naive-to-primed transition based on 3'UTR nucleotide sequence, is available at GitHub (<a href="https://github.com/ulelab/LIN28A_RNPreassembly_bioinformatics">https://github.com/ulelab/LIN28A_RNPreassembly_bioinformatics</a>) and archived on Zenodo (<a href="../doi/10.5281/zenodo.10054297">https://zenodo.org/doi/10.5281/zenodo.10054297</a><strong>).</strong></p>
Source data for publication "Bacteria use exogenous peptidoglycan as a danger signal to trigger biofilm formation"
<p><strong>This dataset contains the source data for the figures in the following publication: </strong></p> <p><strong>Title: </strong>Bacteria use exogenous peptidoglycan as a danger signal to trigger biofilm formation</p> <p><strong>Authors: </strong>Sanika Vaidya, Dibya Saha, Daniel K.H. Rode, Gabriel Torrens, Mads F. Hansen, Praveen K. Singh, Eric Jelli, Kazuki Nosho, Hannah Jeckel, Stephan Göttig, Felipe Cava, Knut Drescher</p> <p><strong>Journal: </strong>Nature Microbiology, 2025</p> <p> </p> <p><strong>Description of the dataset: </strong></p> <p>The data is organized by figures in the publication receferenced above. For each figure, there is a XLSX-file that contains the processed data and a ZIP-archive that contains the raw image data. The ZIP-archive also contains readme documents with more detailed descriptions for every set of raw image data. </p> <p>Example: For Figure 1 in the main text of the publication, there following data are available:</p> <ul> <li>raw data: Figure_01.zip</li> <li>processed data: Processed_Source_Data_Figure1.xlsx</li> </ul> <p>Similarly, XLSX-files and ZIP-archives are available for the main text Figures 1-6. For the Extended Data (ED) Figures 1-10, there are XLSX-files available that present the data shown in each figure. Only some of the Extended Data Figures present results based on image data - therefore raw image data ZIP-archives are only available for ED Figures 3, 6, 7, 8, 9, 10.</p>
Data and script: Community size can affect the signals of ecological drift and niche selection on biodiversity
<p>Updated version of the code. Data files are the same. This is the final version of the code, associated with a manuscript published in Ecology (doi: 10.1002/ecy.3014). A preprint is also available: https://www.biorxiv.org/content/10.1101/515098v1.abstract</p> <p>This is a unique dataset on insect communities sampled identically in a total of 200 streams in climatically highly different regions (100 in Brazil and 100 in Finland). The sampling design included 5 streams (communities) per watershed and provided us replicates of metacommunities (watersheds). Data also include information on in-stream variables (such as current velocity (m/s), depth (cm), stream width (cm), % of sand (0.25-2 mm), gravel (2-16 mm), pebble (16-64 mm), cobble (64-256 mm), and boulder (256-1024 mm), % of canopy cover by riparian vegetation, pH, conductivity, total nitrogen, and total phosphorus) and catchment level variables (such as average slope, % of native forest cover, pasture, agriculture, planted forests, urban areas, mining, water bodies, bare soil, secondary forest cover, and mixed land uses).</p> <p>In addition to the dataset, here we also provide and R code used to investigate the relationship between beta diversity and community size. This code calculates 4 types of beta-diversity metric for each of 100 watersheds (5 streams) in Brazil and Finland. Beta diversity: Sorensen and Bray-Curtis dissimilarity between all pairs. Beta deviation from null models: Raup-Crick (vegan version) and Bray-Curtis beta-deviation (based on the scripts by Chris Catano and Jonathan Myers). These beta diversity metrics are modelled against community size, environmental heterogeneity and spatial extent.</p> <p> </p>
Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)
<p>Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen.</p> <p>Contents of set 2:<br> - Data set and materials used for the human-robot interaction experiment and for data analysis</p> <p>Files:<br> - "Questionnaire.pdf": Questionnaire used for data collection.<br> - "Video links.txt": Weblinks to stimuli videos used.<br> - "Data set.xls": Collected raw data.<br> - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.<br> - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.<br> - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.<br> - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM.</p> <p>The experiment procedure is described in the paper.<br> The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201</p>
Dataset: Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample
<p>Original measurement and processed data of the study <em>Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample </em>submitted for review to Radiation Measurements. The data are structured as follows:</p> <ol> <li><strong>Measurement data </strong></li> <li><strong>Processed data</strong></li> </ol> <p>Experiments were carried out at the Archéosciences Bordeaux (UMR 6034, CNRS - Université Bordeaux Montaigne; former IRAMAT-CRP2A) in Bordeaux (France) and at the Département des sciences de la Terre of the Université du Québec à Montréal (Canada). The subfolders are organised by the laboratory where the experiments were carried out: spectrometer measurements in Montréal (00_Montreal_Spectrometer) and spatially resolved measurements (camera) in Bordeaux (10_Bordeaux_Camera). </p> <p><strong>Measurement data </strong>contains sequence files used to run the experiments (so-called *.lseq files) as well as the raw, unaltered measurement output in the form of files with the ending *.xsyg and *.tiff. For the camera measurements in Bordeaux, the system returned a couple of single TIFF files. We merged those files in two files, one for <em>RF<sub>nat</sub></em> and <em>RF<sub>reg</sub></em>, for convenience reasons. The data are, however, unprocessed. </p> <p><strong>Processed data</strong> is organized like the measurement data folder containing all kinds of semi-automated processed data (PDF files, images). All data were processed with the R (R Core Team, 2021) package 'Luminescence' (Kreutzer et al., 2012; 2021) and an <em>ImageJ </em>macro detailed in Mittelstraß and Kreutzer (2021)</p> <p> </p> <p><strong>References</strong></p> <p>Kreutzer, S., Schmidt, C., Fuchs, M.C., Dietze, M., Fischer, M., Fuchs, M., 2012. Introducing an R package for luminescence dating analysis. Ancient TL 30, 1–8.</p> <p>Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Smedley, R.K., Christophe, C., Zink, A., Durcan, J., King, G.E., Philippe, A., Guérin, G., Riedesel, S., Autzen, M., Guibert, P., Mittelstrass, D., Gray, H.J., 2021. Luminescence: Comprehensive luminescence dating data analysis. CRAN. https://doi.org/10.5281/zenodo.4729933</p> <p>Mittelstraß, D., Kreutzer, S., 2021. Spatially resolved infrared radiofluorescence: single-grain K-feldspar dating using CCD imaging. Geochronology 3, 299–319. https://doi.org/10.5194/gchron-3-299-2021</p> <p>R Core Team, 2021. R: A language and environment for statistical computing. https://www.r-project.org</p> <p> </p> <p> </p>
Radiofrequency ultrasound signals from bovine cartilage samples degraded with trypsin and collagenase
The folder Repository_RF_data contains the radiofrequency (RF) data acquired with an ArtUS EXT-1H system (Telemed, Italy) equipped with a 192 elements linear probe L15-7H40-A5 working in the frequency range 7.5-15 MHz, in the matlab format ".mat". Data were collected at 15 MHz, with a sampling rate of 40 MHz, adjusting the focus in the middle of the samples. The analysed samples were bovine cartilage samples, divided in three groups: - Control group: cartilage sample without any chemical treatment. - Trypsin group: cartilage samples immersed in a trypsin solution for 4h. - Collagenase group: cartilage samples immersed in a collagenase solution for 24h. The folder Repository_RF_data includes 2 matlab variables: - trypsin.mat = data acquired from 6 samples before and after the trypsin treatment - collagenase.mat = data acquired from 6 samples before and after the collagenase treatment Each variable is a TxN cell, where T is the time point of evaluation and N is the number of samples. The first row of each variable corresponds to the time zero of treatment, that is the control group; while the second row includes measurement at the final time point of treatment (4h for trypsin an 24h for collagenase). In particular, a single RF frame was acquired for all the analyses. Each recorded RF frame resulted in a matrix in which the columns (57) represented the number of RF scanning lines in a specific RF window, while the rows (727) constituted the number of samples in a single scanning line. For the details, see the articles published on Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Sorriento A, Cafarelli A, Valenza G, Ricotti L. Ex-vivo quantitative ultrasound assessment of cartilage degeneration. Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2976-2980. doi: 10.1109/EMBC46164.2021.9630198. PMID: 34891870.
Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models
<p>This data set contains the simulations and data analysis files used in the publication: "<em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models</em>", by D. Cortés-Ortuño, K. Fabian and L. V. de Groot.</p> <p>The data set includes:</p> <ul> <li>Scripts and output files from MERRILL simulations</li> <li>Jupyter notebooks with data analysis</li> <li>Figures</li> </ul> <p>A preprint of this work can be found in:</p> <p>David Cortés-Ortuño, Karl Fabian and Lennart V. de Groot. <em>Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models.</em> DOI: 10.1002/essoar.10510574.1. Earth and Space Science Open Archive. <a href="https://doi.org/10.1002/essoar.10510574.1">https://doi.org/10.1002/essoar.10510574.1</a></p> <p>The README file in this dataset (in markdown format) contains full details about the simulations. The dataset also contains pre-computed data files to calculate the inversions and produce the figures and analyze the inversion data without processing the vbox files.</p> <p>To cite this dataset you can use the following bibtex entry:</p> <pre><code>@Misc{Cortes2022, author = {Cortés-Ortuño, David and Fabian, Karl and de Groot, Lennart V.}, title = {{Data set for: Mapping magnetic signals of individual magnetite grains to their internal magnetic configurations using micromagnetic models}}, publisher = {Zenodo}, year = {2022}, doi = {10.5281/zenodo.6501818}, url = {https://doi.org/10.5281/zenodo.6501818}, } </code></pre> <p> </p>
Temperature-dependent Lamb wave signals in highly anisotropic CFRP
<p>The dataset contains signals of propagating Lamb waves in highly anisotropic carbon fibre reinforced polymer (CFRP). The reinforcement is unidirectional along 0 deg. A detailed description of the material and its parameters is given in [1]. The arrangement of piezoelectric actuator A and sensors S1-S7 is shown in figure "plate_angular_pzt_arrangement_50x50.png". Sensors are placed at propagation angles from 0 deg to 90 deg with a step of 15 deg. It should be noted that two piezoelectric transducers bonded to both sides of the plate were used as the actuator. It allowed for exciting Lamb waves with dominant A0 and S0 modes, respectively. Hence, there are two respective zip files with data.</p> <p>The following parameters were used during measurements:</p> <ul> <li>Temperatures: T=[50,40,30,20,10,0,-10,-20,-30,-40,-50];</li> <li>Number of cycles in Hann windowed signals: no_of_cycles=[2,2.5,3];</li> <li>Carrier frequencies of excitation signals [kHz]: frequencies=[20:10:250];</li> <li>Number of averages: 50;</li> <li>Sampling frequency: 10 MHz.</li> </ul> <p>The following equipment was used in the experiment:</p> <ul> <li>Environmental chamber by Angelantoni Test Technologies, model MyDiscovery 600 C;</li> <li>National Instruments waveform generator PXIe-5413;</li> <li>Krohn-Hite voltage amplifier model 7500;</li> <li>Cedrat Technologies LWDS amplifier (used as a charge amplifier);</li> <li>National Instruments oscilloscope PXIe-5105.</li> </ul> <p>Files in CSV format contain environmental chamber data (temperature programme, actual temperature and humidity over time, etc.). This can be read and plotted in Matlab by running the script “Read_plot_environmental_chamber.m”. There is another file “Read_plot_environmental_chamber_plus_DS18B20_RH20_KROHN_A0.m” in which temperature was registered also by DS18B20 digital sensor. It loops over all measurements so that it can be used also for reading signals from “niscope_avg_waveform.mat” in respective subfolders. In particular, sensor signals are stored in the “niscope_avg_waveform” variable, a matrix of dimensions 8192x7.</p>
Unsupervised New Physics detection at 40 MHz: h+ -> tau nu Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of h+ -> tau nu decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: h^0 -> tau tau Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of h^0 -> tau tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: LQ -> b tau Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of Leptoquarks -> b tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: A -> 4 leptons Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of A -> 4 leptons decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
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.