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197 results for “beat”
PsPM-trSP1: SCR, and heart beat measurement in response to aversive/neutral IAPS pictures while subjected to auditory distractors
<p>This dataset includes skin conductance response (SCR) and pulse time stamp (HB) measurements for each of 60 healthy unmedicated participants (30 males and 30 females aged 23.7 +/- 4.8 years) in response to the 45 most arousing negative, and 45 least arousing neutral IAPS pictures, each presented for 1 s each, while listening to regular or random distractor sounds, as described in Bach et al. (2015). ITI was selected randomly on each trial from 7.65 s, 9 s, or 10.35 s. The experiment was preceded by a 2-minute resting period and divided into 3 blocks, separated by resting periods. Each resting period begins and ends with an event marker in the psychophysiological recordings.</p>
IPI values of heart beats
<p>InterPuls Interval (IPI) is the time difference between two R-R peak of the heartbeat based on mili-second. Current dataset belongs to 4223 subjects and the IPI values are extracted from ECG datasets provided by PhysioNet. The main file IPI-All.txt contains the value of IPIs and each line represents a subject. In the rest of the files <em>n</em>bit.txt.7z, <em>n </em>is the number of bits in converting the IPI values to binary. For instance, if n=2, then after converting the IPI value to binary, only the least two significant bits were inserted in the 2bit.txt.7z. </p>
Results from retrospective Baltic Sea biodiversity indicator status assessment using BEAT 3.0 tool
<p>Here we present all our results from retrospective Baltic Sea biodiversity indicator data analysis using BEAT 3.0. The BEAT tool (Nygård et al. 2018) is an R coded software (Murray & Nygård 2018, available online: <a href="https://zenodo.org/record/1288315#.XRxNp2cXYg4">https://zenodo.org/record/1288315#.XRxNp2cXYg4</a>) developed for analyzing marine biodivesity status. It follows the strucuture of EU's Marine Strategy Framework Directive. For more detailed metadata about the tool, see Nygård et al. 2018.</p> <p>We used data from various biodiversity indicators in two areas of the Baltic Sea: Bothnian Sea and Gulf of Finland. BEAT integrates indicators to ecosystem components and aggregates them spatially (more details can be found in Nygård et al. 2018). We produced retrospective time series of integrated and aggregated indicators. The yearly assessments are done using the moving average of the indicator status of the past 5 years in order to gain a more robust assessment result. The assessment follows the protocol of biodiversity assessment in the HELCOM Holistic assessment <a href="http://www.helcom.fi/baltic-sea-trends/holistic-assessments">http://www.helcom.fi/baltic-sea-trends/holistic-assessments</a></p> <p>In the results table, all different spatial levels as well as ecosystem components are shown. All indicator results have a value between 0 and 1. If the indicator has a value over 0.6, it is considered to be in a good environmental status. Below are short description of the different columns:</p> <ul> <li>SAUID: ID of the spatial assessment unit (SAU) used. The largest SAU is Baltic Sea with an ID 1. It is divided to smaller SAUs and all individual SAUs have their own ID.</li> <li>SAUlevel: The highest possible level of SAU is the Baltic Sea and it is the level 1. The sea basins (for example Bothnian Sea) are the level 2 and so on.</li> <li>ECID: Ecosystem component ID. All possible indicators have their own ID. See the list of ecosystem components in the input files of the tool (Murray & Nygård 2018).</li> <li>EClevel: Ecosystem component level. The level 1 is biodiversity, in the level 2 it is divided to pelagic habitat, birds, fish, benthic habitat and mammals and so on.</li> <li>EcosystemComponent: this column tells the ecosystem component. It can be higher level e.g. biodversity or an individual indicator for certain taxa.</li> <li>EQR: ecological quality ratio. The status of the certain ecosystem component in a certain SAU. The value varies between 0 and 1. If it is over 0.6, the ecosystem component is considered to be in a good status.</li> <li>Columns H-T: These refer to certain descriptors of Marine Strategy Framework Directive. If the ecosystem component is considered to have a link to a certain descriptor, an EQR value is given.</li> <li>year: year of the assessment. Note: all the yearly values are a moving average of past 5 years.</li> </ul> <p> </p>
Beat Wolf (w2866)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Beat Wolf<br><u>musiXplora-ID</u>: w2866<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/w2866">https://musixplora.de/mxp/w2866</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 1952<br><u>Place of Birth</u>: Schaffhausen<br><u>First Mentioned</u>: 1972<br><u>Sectors</u>: Instrumentenbau, Museum<br><u>Professions (Musical)</u>: Harfenbauer, Restaurator<br><u>Other Places of Activity</u>: Berlin, Nürnberg, Paris, Schaffhausen, Wien<br><br><br><u>Portfolio:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Harfe</td><td><a href="https://musixplora.de/mxp/2001474">2001474</a></td></tr></tbody></table><br><u>Medien:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>VerfasserInnen</td><td>Rezipient</td><td>Harfenarchiv Beat Wolf</td><td><a href="https://musixplora.de/mxp/3080358">3080358</a></td></tr><tr><td>VerfasserInnen</td><td>Verfasser</td><td>MIMUL REST402</td><td><a href="https://musixplora.de/mxp/5014921">5014921</a></td></tr><tr><td>VerfasserInnen</td><td>Verfasser</td><td>Harfen des Berliner Musikinstrumenten-Museums. Bestandskatalog. in Zusammenarbeit mit Beat Wolf, Wolfgang Mertin, Rainer M. Thurau</td><td><a href="https://musixplora.de/mxp/5020706">5020706</a></td></tr></tbody></table><br><u>Tradition:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Interessensbereich</td><td>Nachlassempfänger</td><td>Harfe</td><td><a href="https://musixplora.de/mxp/2001474">2001474</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Beat Grenacher (g2421)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Beat Grenacher<br><u>musiXplora-ID</u>: g2421<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/g2421">https://musixplora.de/mxp/g2421</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 12 November 1941<br><u>Place of Birth</u>: Undefined<br><u>First Mentioned</u>: 1972<br><u>Last Mentioned</u>: 2009<br><u>Sectors</u>: Kirche, Orgelbau<br><u>Professions (Historical)</u>: Intonateur<br><u>Professions (Musical)</u>: Orgelbauer<br><u>Main Place of Activity</u>: Luzern<br><br><br><u>Arbeitsumfeld:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>KollegInnen</td><td>Kollege</td><td>Simon Hebeisen</td><td><a href="https://musixplora.de/mxp/h3872">h3872</a></td></tr><tr><td>KollegInnen</td><td>Kollege</td><td>Jakob Schmidt</td><td><a href="https://musixplora.de/mxp/s5652">s5652</a></td></tr><tr><td>VorgängerInnen</td><td>Nachfolger</td><td>Friedrich Goll</td><td><a href="https://musixplora.de/mxp/g2419">g2419</a></td></tr><tr><td>VorgängerInnen</td><td>Vorgänger</td><td>Simon Hebeisen</td><td><a href="https://musixplora.de/mxp/h3872">h3872</a></td></tr></tbody></table><br><u>Personal:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Leitungsfunktion</td><td>Mitarbeiter</td><td>Orgelbau Goll AG</td><td><a href="https://musixplora.de/mxp/3030806">3030806</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Dataset for "Beating 1 Sievert: Optimal Radiation Shielding of Astronauts on a Mission to Mars" publication in Space Weather journal
<p>Datasets in .fig Matlab format and figures in .jpg format published in Space Weather journal</p> <p>effectiveDoseRF.mat contains the effective dose "response functions" and an example (how2useDoseResponceFunctions.m) of how to use them to assess GCR dose.</p>
Sesquialtera in the Colombian bambuco: Perception and estimation of beat and meter
<p> Supplementary material for the ISMIR2020 paper titled: " Sesquialtera in the Colombian bambuco: Perception and estimation of beat and meter". The data in this repository includes the audio files, transcriptions and annotations of the 10 bambucos used in the study.</p> <p>This work was conducted in the context of the ACMus project. You can find more information about our project <a href="https://acmus-mir.github.io/">here.</a></p> <p>For more information about ACMUS-MIR, our annotated dataset of Colombian music from the Andes region, you can visit our <a href="https://acmus-mir.github.io/acmus-mir/">website</a> and our zenodo <a href="https://zenodo.org/record/3268961#.XShkTo8RVPZ">repository</a>.</p> <p> </p>
Standstill to the beat: Differences in involuntary movement responses to simple and complex rhythms (SOUND STIMULI)
<p>Sound stimuli used in the 2019 "Nordic Championship of Standstill" experiment, presented in the paper titled "Standstill to the beat: Differences in involuntary movement responses to simple and complex rhythms".</p>
Audio files of beat note between WWV-10 carrier wave and GPS reference signal
<p>NAME : Martin Potter, VE3OAT<br> GEOPOSIT : 45 15' 9.023" N 75 35' 27.60" W (Maidenhead FN25eg)<br> START TIME : 1400 UT<br> STATION CONFIG :<br> Multi-band vertical antenna with 18 radials.<br> Receiver Yaesu FTdx1200 in AM mode with audio bandwidth limited to<br> 800-1200 Hz (18 dB/octave roll-off).<br> Trimble Thunderbolt 10 MHz output to HP 3325B sig generator set<br> to 9999 kHz and injected to station antenna.<br> Measured nominal 1 kHz beat note between WWV-10 and sig generator<br> using SpectrumLab software. Audio WAV file recorded by SpectrumLab. <br> <br> Note : On 20 Aug, receiver AGC was ON. On 21 Aug, receiver AGC was<br> OFF and audio output level was set using RF Gain control. </p> <p> </p>
Beating-free quantum oscillations in 2D electron gases with strong spin-orbit and Zeeman interactions
<p>Datafiles for figures in the paper:</p> <p><em>"Beating-free quantum oscillations in 2D electron gases with strong spin-orbit and Zeeman interactions"</em>, Phys. Rev. Research [<em>Accepted</em>]</p> <p>The datafiles relate to Figs. 2, 3, 10, 12, 14, 15, 16, and 17</p> <p>Figs. 1 and 13 contain schematics.</p> <p>Figs, 4, 6, and 8 are generated in a straightforwd manner using Eqs. (27), (34), and (40), respectively, using parameter values supplied in the figure captions.</p> <p>Fig. 5, is generated by Eq. (30) along with Eq. (29), using parameter values supplied in the figure captions.</p> <p>Fig. 7, is generated by Eq. (30) along with Eqs. (35) and (36), using parameter values supplied in the figure captions.</p> <p>Fig. 9, is generated by Eq. (30) along with Eqs. (41) and (42), using parameter values supplied in the figure captions.</p>
Gene embeddings used in GenePT: A Simple But Hard-to-Beat Foundation Model for Genes and Cells Built From ChatGPT
<p>These are the pulled NCBI (and UniProt, when applicable) summaries of genes, as well as the corresponding OpenAI text embeddings (text-embedding-ada-002 and text-embedding-3-large) computed on the summaries. See methods details in Chen and Zou (2024+).</p> <p>The unzipped folder contains four different files: </p> <ol> <li>NCBI_summary_of_genes.json (NCBI gene card summary of human genes)</li> <li>NCBI_UniProt_summary_of_genes.json (NCBI gene card and UniProt protein (when applicable) summary of human genes)</li> <li>GenePT_gene_embedding_ada_text.pickle (a dictionary of numpy array where gene names (upper case) are keys and text-embedding-ada-002 embeddings of the summary in 1. are the values)</li> <li>GenePT_gene_protein_embedding_model_3_text.pickle (a dictionary of numpy array where gene names (upper case) are keys and text-embedding-3-large embeddings of the summary in 1. are the values)</li> </ol> <p>Reference:</p> <p>Chen YT, Zou J. (2024+) GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPT. bioRxiv preprint: <a href="https://www.biorxiv.org/content/10.1101/2023.10.16.562533v1">https://www.biorxiv.org/content/10.1101/2023.10.16.562533v1</a>.</p>
Dataset for "Searching for a source without gradients: how good is infotaxis and how to beat it"
<p>Dataset containing the results presented in "Searching for a source without gradients: how good is infotaxis and how to beat it".</p>
Experimental data and scripts used for the paper "Imperfect symmetry of real annular combustors: beating thermoacoustic modes and heteroclinic orbits"
<p>The folder contains the experimental data, the scripts an the instructions to generate the figures of the paper.</p>
Uruguayan candombe drumming - beat and downbeat tracking data set
<p>Uruguayan Candombe drumming - beat and downbeat data set</p> <p><br>===========<br>Description<br>===========</p> <p>This dataset includes more than 2 hours of Candombe recordings, with annotated beats and downbeats. It features 35 complete performances by renowned players, playing in groups of three to five drums. A total of 26 tambor players from various generations took part, representing the three key traditional Candombe styles. These recordings were created in a studio setting over a period of more than two decades as part of musicological research. The sessions from 1992 and 1995 were produced by Luis Jure, and the session from 2014 was produced by Luis Jure and Martín Rocamora.</p> <p>The audio files are in stereo with a sampling rate of 44.1 kHz and 16-bit precision. An expert annotated the location of beats and downbeats, totaling more than 4700 downbeats. The audio is provided in flac format and the annotations are in .csv files. The values in the first column of the CSV file represent the time instants of the beats. The numbers in the second column indicate both the bar number and the beat number within the bar. For example, 1.1, 1.2, 1.3, and 1.4 represent the four beats of the first bar. Therefore, each label ending in .1 indicates a downbeat. Another set of annotations is provided as .beats files in which the bar numbers are removed.</p> <p><br>========<br>Citation<br>========</p> <p>This dataset was released with the publication of the following paper. So if you find the dataset useful and want to reference it in your publications, please cite it.</p> <p>"Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming”. Leonardo Nunes, Martín Rocamora, Luis Jure, Luiz W. P. Biscainho. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), pages 264-270, Málaga, Spain, 26-30 October, 2015.</p> <p>@inproceedings{Nunes2015,<br> author = {Leonardo Nunes and Martín Rocamora and Luis Jure and Luiz W. P. Biscainho},<br> title = {{Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming}},<br> booktitle = {Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015)},<br> month = {Oct.},<br> address = {Málaga, Spain},<br> pages = {264--270},<br> year = {2015}<br>}</p> <p><br>========<br>Candombe<br>========</p> <p>Candombe is a vital part of Uruguayan popular culture, with thousands of practitioners and its rhythm influencing various genres of popular music. In 2009, UNESCO recognized it as part of the Intangible Cultural Heritage of Humanity. While it originated in Uruguay, Candombe has its roots in the culture brought by African slaves in the 18th century. Over time, it has evolved to incorporate the descendants of European immigrants and has become a part of the entire society. Candombe drumming, with its unique rhythm, is the essential element of this tradition, which also includes dancing, symbolic characters, and costumes.</p> <p>The drum used in Candombe is called tambor, which is Spanish for "drum." There are three different sizes: chico (small), repique (medium), and piano (big). Each size has its own unique sound, ranging from high to low frequency, and its own specific rhythmic patterns. All three drums are played with a stick in the dominant hand and the other one hitting the skin directly. The stick is also used to hit the shell when playing the clave or madera pattern. A minimal ensemble of drums (cuerda de tambores) must have at least one of each of the three drums. During a llamada de tambores, the ensemble usually consists of around 20 to 60 drums. When marching, the players walk forward with short steps synchronized with the beat, which is important for embodying the rhythm, even though it is not audible.</p> <p>===============<br>Acknowledgments<br>===============</p> <p>This work was partially supported by the funding agency Comisión Sectorial de Investigación Científica, Universidad de la República, Uruguay, and by the Department of Culture of the Municipality of Montevideo.</p> <p>This is the complete list of performers, in alphabetical order: Mariano Barroso, Eduardo 'Cacho' Giménez, Eduardo 'Malumba' Giménez, Francisco Giménez, José Luis Giménez, Jorge 'Foqué' Gómez, José Pedro 'Perico' Gularte, Luis 'Pocholo' Maciel, Julio Magariños, Raúl 'Neno' Magariños, Marcelo Magariños, Javier 'Cerdo' Martirena, Wilson Martirena, Eduardo 'Tierra' Nilo, Sergio Ortuño, Fernando 'Lobo' Núñez, Edinson 'Palo' Oviedo, Gustavo Oviedo, Egdardo Pintos, Luis 'Mocambo' Quiroz, Rodolfo 'Pelado' Rodríguez, Fernando 'Hurón' Silva, Juan Silva, Raúl Silva, Waldemar 'Cachila' Silva, and Héctor Manuel Suárez.</p>
Dataset: HeartBeam, Inc. (BEAT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset and software for "Optimizing beat synchronized running to music"
<p>These are the dataset and Matlab scripts that were used for analysis as described in the publication on "<strong>Optimizing beat synchronized running to music"</strong></p>
FIGURE 1. Collecting methods. A–D. Fogging. E. Beating vegetation. F. Berlese funnels. A in Taxonomic Revision Of The Jumping Goblin Spiders Of The Genus Orchestina Simon, 1882, In The Americas (Araneae: Oonopidae)
FIGURE 1. Collecting methods. A–D. Fogging. E. Beating vegetation. F. Berlese funnels. A. The sheets are disposed under the canopy approximately at 1 m above the ground. B. Each sheet was connected to each other with ropes and claspers. C. Setting the machine with a mixture of diesel and pyrethroids. D. The fog is finally directed to the canopy. E. Beating vegetation was the most frequent technique employed during this project. F. Occasionally, some specimens were captured in litter using Berlese funnels. A–D, F. Ecuador. E. Juan Fernández Islands, illustrative image, no specimens of Orchestina were collected there. Credits. C, D. Dimitri Forero. E. Jaime Pizarro.
Beating the shell game with barcodes: Diversity of meiofaunal Caecidae snails (Truncatelloidea, Caenogastropoda) from the Central America
<p>Single gene alignments of COI, 16S rRNA and 28s rRNA sequence data and combined alignments for COI, and16S rRNA as well as COI, 16S rRNA and 28s rRNA sequence data used for phylogenetic inference and species delimitation in the publication Beating the shell game with barcodes: Diversity of meiofaunal Caecidae snails (Truncatelloidea, Caenogastropoda) from the Central America. Original indicates the alignments before gblocks and manual adjustments were used to exclude ambiguous sites.</p>
LDV-beat dataset
<p>The dataset consisted of:(i) one folder and (ii) one .csv file. The folder contain 28 signal aquisitions (one for each subject) in .csv format. Each file is one simoultaneously recording of II lead ECG and common carotid LDV at 500 Hz sampling rate. The LDV-beat dataset.csv contain segment of LDV signal for a length of 75 point each. The LDV segment were labeled as "1" in the "beat" column when the first point was in correspandence of the R peak of the ECG, and labeled as "0" otherwise.</p>
Auditory cortical representation of music favours the perceived beat
<p>Previous research has shown that musical beat perception is a surprisingly complex phenomenon involving widespread neural coordination across higher-order sensory, motor, and cognitive areas. However, the question of how low-level auditory processing must necessarily shape these dynamics, and therefore perception, is not well understood. Here, we present evidence that the auditory cortical representation of music, even in the absence of motor or top-down activations, already favors the beat that will be perceived. Extracellular firing rates in the rat auditory cortex were recorded in response to twenty musical excerpts diverse in tempo and genre, for which musical beat perception had been characterised by the tapping behaviour of 40 human listeners. We found that firing rates in the rat auditory cortex were on average higher on the beat than off the beat. This "neural emphasis" distinguished the beat that was perceived from other possible interpretations of the beat, was predictive of the degree of tapping consensus across human listeners, and was accounted for by a spectrotemporal receptive field model. These findings strongly suggest that the "bottom-up" processing of music performed by the auditory system predisposes the timing and clarity of the perceived musical beat.</p>
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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.