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Sky images recorded during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Scattered sunlight measurements can be strongly affected by clouds, mainly due to multiple scattering effects that give rise to large uncertainties in the light path retrieval. In order to estimate cloud cover, we recorded images of the sky every 5 minutes to evaluate a cloud index, from 0 (clear sky) to 10 (completely overcast).</p> <p>This dataset presents the sky images in PNG (Portable Network Graphics) format recorded on board the R/V Akademik Tryoshnikov during the austral summer of 2016/17 as part of the circumnavigation expedition (ACE). Data coverage is from December 2016 until April 2017.</p> <p>These images form a supporting dataset to optical spectroscopy data (Benavent et al., 2020; DOI 10.5281/zenodo.3827443).</p> <p>The ship’s position can be matched with these images using the corrected cruise track (Thomas and Pina Estany, 2019; DOI: 10.5281/zenodo.3483166) or the GPS data provided with the spectroscopy data set (Benavent et al., 2020; DOI 10.5281/zenodo.3827443).</p> <p><strong>Dataset contents</strong></p> <ul> <li>YYYY-MM-DD_hh.mm.ss.png, data file, portable network graphics</li> <li>README.txt, metadata, text</li> </ul> <p>where YYYY-MM-DD_hh.mm.ss is the date and time at which the file was saved in UTC.</p> <p><strong>Dataset license</strong></p> <p>This dataset of raw optical sky images from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals
<p>This repository contains Electrogastrography signals termed Electrogastrograms (<a href="https://en.wikipedia.org/wiki/Electrogastrogram">EGG</a>) recorded with surface Ag/AgCl electrodes placed over stomach and pre-processed in 20 healthy individuals (8 Females and 12 Males). The method for EGG recording and pre-processing together with subjects' data can be found in <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a>.</p> <p>For each subject, EGG was recorded from three locations before (fasting state) and after (postprandial state) a commercial oat meal (274 kcal). Two 20 minutes recordings (files) are obtained for each subject - fasting and postprandial.</p> <p>Naming convention for files: <strong>subjects ID _ type of recording (fasting / postprandial)</strong>.</p> <p>Sample rate was set at 2 Hz and <a href="https://en.wikipedia.org/wiki/Analog-to-digital_converter">A/D card</a> had 16 bits resolution. Gain of the amplifier was set at 1000. Overall, file size is 7200 samples (2400 samples for each channel). All signals were filtered with 3<sup>rd</sup> order band-pass <a href="https://en.wikipedia.org/wiki/Butterworth_filter">Butterworth filter</a> with cut-off frequencies of 0.03 Hz and 0.25 Hz. In order to avoid phase distortion, zero-phase digital filtering was performed in <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> R2013a by <a href="https://www.mathworks.com/help/signal/ref/filtfilt.html">filtfilt()</a> function. <a href="https://www.gnu.org/software/octave/">GNU Octave</a> code for analysis of EGG signals with statistical calculations presented in <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a> is also provided (<a href="https://zenodo.org/record/3878435/files/eggAnalysis.m?download=1">eggAnalysis.m</a>).</p> <p>For convenient test download and appropriate preview, we provided all signals in <a href="https://en.wikipedia.org/wiki/Zip_(file_format)">.zip</a> and sample signal for ID1 in <a href="https://en.wikipedia.org/wiki/Text_file">.txt</a> form.</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/EGG-database.zip?versionId=84315b6b-58da-4655-83f4-8f1d43c3b02c">EGG-database.zip</a>, data files, text format</li> <li><a href="https://zenodo.org/record/3878435/files/eggAnalysis.m?download=1">eggAnalysis.m</a>, GNU Octave code</li> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/README.txt">README.txt</a>, metadata for data files, text format</li> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/ID1_fasting.txt?versionId=47d0bd09-1a87-42f2-a3e5-ef0c4b4a18e2">ID1_fasting.txt</a> and <a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/ID1_postprandial.txt?versionId=c8936a32-2896-44d6-bf3d-2ee37887766c">ID1_postprandial.txt</a>, sample data files for subject ID1, text format</li> </ol> <p><strong>Data files contain numerical values with decimal point according to the following structure</strong></p> <ol> <li>column - CH1* (recorded samples from channel 1)</li> <li>column - CH2* (recorded samples from channel 2)</li> <li>column - CH3* (recorded samples from channel 3)</li> </ol> <p>* For exact anatomical locations for EGG channels CH1, CH2, and CH3, please refer to <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a>.</p> <p>If you find these signals useful for your own research or teaching class, please cite both relevant paper and dataset as:</p> <ol> <li> <p>Popović, N.B., Miljković, N. and Popović, M.B., 2019. Simple gastric motility assessment method with a single-channel electrogastrogram. <em>Biomedical Engineering/Biomedizinische Technik</em>, <em>64</em>(2), pp.177-185, doi: <a href="https://doi.org/10.1515/bmt-2017-0218">10.1515/bmt-2017-0218</a>.</p> </li> <li> <p>Popović, N.B., Miljković, N. and Popović, M.B., 2020. Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals [Data set]. <em>Zenodo</em>, doi: <a href="https://doi.org/10.5281/zenodo.3730617">10.5281/zenodo.3730617</a>.</p> </li> </ol> <p>DISCLAIMER: The GNU Octave code is provided without any guarantee and it is not intended for medical purposes.</p>
Infrasound array data recorded in July-August, 2019, at Mt. Etna (Italy) during the VOSSIA field experiment
<p>We present infrasound data recorded by two infrasound arrays installed at Mt. Etna (Italy) within the framework of the VOSSIA (Volcanic emissions analysis through Seismic and Infrasound Advanced monitoring) project. VOSSIA was supported by the Trans-National Access component of the EUROVOLC project (European Network of Observatories and Research Infrastructures for Volcanology, EU Horizon 2020 Research Infrastructure Project grant No 731070).</p> <p>This data repository includes continuous raw waveforms recorded by two 6-element, small-aperture, infrasound arrays during July-August, 2019. The arrays, ENEA and ENCR, were installed on Mt. Etna in proximity of the summit Nord East and South East craters, respectively. ENEA was equipped with Chaparral M60 sensors (<a href="http://chaparralphysics.com/specs/specs_model60UHP.pdf">http://chaparralphysics.com/specs/specs_model60UHP.pdf</a>), while IST2018 microphones (<a href="https://doi.org/10.1016/j.jvolgeores.2019.106668">https://doi.org/10.1016/j.jvolgeores.2019.106668</a>) were installed at ENCR. Data at both arrays were recorded with a sampling frequency of 100 Hz and 24-bit resolution using DiGOS Datacube<sup>3</sup> digitizers (<a href="https://digos.eu/seismology-and-cubes/">https://digos.eu/seismology-and-cubes</a>).</p> <p>Waveform data are provided as day-long files in MSEED format (<a href="https://ds.iris.edu/ds/nodes/dmc/data/formats/">https://ds.iris.edu/ds/nodes/dmc/data/formats/</a>).</p> <p>We also provide metadata including:</p> <p>1) Station coordinates (.csv file station_coords.csv);</p> <p>2) Instrument_response.rar: Instrument response information in different formats (individual RESP files, SEED DATALESS, xlm DATALESS)</p>
Wild for Orchids Citizen Science Campaign Records 2020 - 2023
<p>This dataset represents the geographic distribution of wild orchids in the Maltese Islands, as recorded by the Wild For Orchids Citizen Science Initiative between January 2020 and December 2023. It includes data obtained through citizen science contributions and has undergone rigorous two-stage quality control for species identification and GPS accuracy. Species identification was carried out according to Mifsud (2018). The location data of each records is provided as a shapefile format projected in ETRS89-extended / LAEA Europe (EPSG:3035), and exact GPS location were transformed in 1 km square grid according to the <span>European Forum for Geography and Statistics (EFGS).<br></span></p> <p><span>Wild for Orchids is a Citizen Science Initiative designed and managed by Green House Malta.</span></p>
Audio tagging of avian dawn chorus recordings in California, Oregon, and Washington
<p><strong>General Summary</strong></p> <p>This acoustic data collection includes 1,575 5-minute soundscape recordings randomly selected from passive acoustic recordings made at 525 sites during 2022 on federally managed lands in western California, Oregon, and Washington, USA. We fully labeled 141 recordings (11.75 hrs) with 39,717 annotations for 118 sound types, including 58 avian species, two mammalian species, six aggregated biotic sounds, and eight non-biotic sound types. An additional 215 recordings were partially annotated with 1,466 annotations. The remaining unlabeled recordings have been included to facilitate novel research applications and methodological evaluations. Beyond the labeled soundscape recordings, we have included township and range identifications and 38 environmental covariates for each recording location.</p> <p><strong>Data Collection</strong></p> <p>Lesmeister et al. (2021) collected passive acoustic recordings during 2022 in support of long-term monitoring of federally threatened northern spotted owl (<em>Strix occidentalis caurina) </em>populations under the Northwest Forest Plan Effective Monitoring Program (U. S. Fish and Wildlife Service 1990, U. S. Department of Agriculture and U. S. Department of the Interior 1994). These data were collected at 643 hexagons that were randomly selected from a tessellation of 5 km2 hexagons covering the entire range of the northern spotted owl (Northern California, Oregon, Washington) under a selective constraint that hexagons contain ≥ 50 % forest-capable lands (<em>def.</em> forested lands or lands capable of developing closed-canopy forests) and be ≥ 25% federal ownership (Davis et al., 2011).</p> <p>Each hexagon was sampled by four Song Meter 4 (SM4) acoustic recording units (Wildlife Acoustics, Maynard, MA) deployed in a standardized spatial arrangement, such that recorders on a site were placed ≥ 500 m apart and were ≥ 200 m from the edge of the sampling hexagon boundary. Recorders were mounted to small trees (15 – 20 cm diameter at breast height) approximately 1.5 m above the ground and were placed on mid-to-upper slopes and ≥ 50 m from roads, trails, and streams. The SM4 devices each have two built-in omnidirectional microphones with a signal-to-noise ratio of 80 dB, typical at 1 kHz, and a recording bandwidth of 20 Hz – 48 kHz. Each device recorded ~11 hours of audio daily for six weeks from March to August at a sampling rate of 32 kHz. The daily recording schedule included a 4-hour window from two hours before sunrise to two hours after sunrise, a 4-hour window from one hour before sunset to 3 hours after sunset, and 10-minute recordings outside the two longer recording blocks at the start of every hour.</p> <p><strong>Data Sampling</strong></p> <p>The goal of this project was to develop a tagged audio dataset (hereafter project dataset) focused on the avian dawn chorus, which is an ecologically important period for the study of avian behavior (McNamara et al. 1987, Staicer et al. 1996, Zhang et al. 2015) and monitoring avian biodiversity (Bibby et al. 2000), but remains a challenging problem for acoustic classification systems (Duan et al. 2013, Stowell 2022). Passive acoustic monitoring on our sites occurs throughout the day. We filtered the full dataset to recordings collected between May and August during the hour immediately after sunrise. From the recordings meeting our filtering criteria, we randomly selected three 5-minute files from each site, which were assigned ordinal labels 'A, 'B,' or 'C.' The final project dataset comprised 131.25 hours of acoustic data.</p> <p><strong>Annotation Protocol</strong></p> <p>We randomly selected 141 sites from the project dataset and fully annotated each recording at a 2-second resolution. We applied labels to each 2-second window of the selected recordings following a predefined sound phonology library (available in the 'metadata.tsv' file), which concatenated the 2021 eBird taxonomy codes (Clements list; Clements et al. 2022) with standardized sonotype codes that incremented depending on the species repertoire (i.e., 'call_1,' 'song_1,' 'drum_1'). For example, 'herthr_song_1' is the label for Hermit Thrush, song_1. Unknown signals were labeled 'unknown,' and clips with no biotic signals (or noise classes of interest documented in metadata.tsv) were labeled 'empty.' Windows were labeled 'complete' and considered fully annotated when every signal was assigned an annotation. Files were deemed fully annotated when every 2-second window contained the 'complete' label.</p> <p><strong>Environmental Covariates</strong></p> <p>Sampling locations will not be published to afford protections for Federally Threatened or Endangered species which may occur on our sites. However, we provide the State, Township, and Range for each sampling location along with the site-specific values for 38 forest structure, topographic, and climatic environmental covariates developed by the Landscape Ecology, Modeling, Mapping, and Analysis group in the Pacific Northwest (<a href="https://lemma.forestry.oregonstate.edu/data">https://lemma.forestry.oregonstate.edu/data</a>; Ohmann and Gregory 2002). State, Township, and Range values are sufficient to explore geographic variation in species- or community-specific call and song phenology and the extracted environmental covariates may provide useful contextual information for novel machine-learning developments (Liu et al. 2018). </p> <p><strong>Description of Data Format</strong></p> <p>The fully annotated audio files can be accessed by downloading and extracting "annotated_recordings.zip." Partially annotated and non-annotated audio files can be accessed by downloading and extracting "additional_recordings_part_1.zip" or "additional_recordings_part_2.zip." Acoustic file names contain site and replicate indicators, such that file "Site_001_Rep_A.wav' was recorded on site 1 and is the A replicate random draw from the available set of dawn chorus recordings. The site and replicate numbers link to additional recording information in "files.tsv," annotations in "annotations.tsv" and "partial_annotations.tsv," as well as site and replicate specific environmental characteristics in "environmental_characteristics.tsv."</p> <p>Metadata describing sound classes and environmental characteristics can be found in "metadata.tsv," and "environmental_characteristics_metadata.tsv."</p> <p><strong>Acknowledgments</strong></p> <p>Acoustic data collection was funded and collected by the US Forest Service and the US Bureau of Land Management. Annotation work was funded by Google. We would also like to thank the many biologists that collected and processed the data compiled here. The use of trade or firm names in this publication is for reader information and does not imply endorsement by the U.S. Government of any product or service.</p>
Two Wearable Sensor Datasets recording the Countermovement Jump
<p>These datasets come from two independent studies using wearable inertial sensors to estimate countermovement jump performance. The participants were healthy sports science students, free of injury, all of whom had given their prior written consent. Ethical approval was given by the governing institutions’ ethics committees, which included further analysis of the data.</p> <ul> <li><strong>Smartphone Dataset:</strong> <ul> <li>119 valid jumps</li> <li>Peak power 40.7 +/- 8.9 W/kg</li> <li>22 males, 10 females (26.5 +/- 4.1 yrs; standing height 1.74 +/- 0.08 m; body mass 70.0 +/- 10.9 kg)</li> <li>Redmi 9T phone (Xiaomi Technology, Beijing, China)</li> <li>128 Hz sampling frequency</li> <li>Accelerometer & gyroscope</li> <li>Handheld at sternum level</li> <li>Mascia, G.; De Lazzari, B.; Camomilla, V. Machine learning aided jump height estimate democratization through smartphone measures. Frontiers in Sports and Active Living 2023, 5, 1112739. <a href="https://doi.org/10.3389/fspor.2023.1112739">https://doi.org/10.3389/fspor.2023.1112739</a>.</li> </ul> </li> <li><strong>Accelerometer Dataset:</strong> <ul> <li>347 valid jumps</li> <li>Peak power 45.1 +/- 7.6 W/kg</li> <li>48 males, 25 females (21.6 +/- 3.3 yrs; standing height 1.75 +/- 0.10 m; body mass 71.2 +/- 15.1 kg)</li> <li>Trigno sensor (Delsys Inc, MA, USA)</li> <li>250 Hz sampling frequency</li> <li>Accelerometer</li> <li>Taped to lower back (L4)</li> <li>White, M.G.E.; Bezodis, N.E.; Neville, J.; Summers, H.; Rees, P. Determining jumping performance from a single body-worn accelerometer using machine learning. PLOS ONE 2022, 17, e0263846. <a href="https://doi.org/10.1371/journal.pone.0263846">https://doi.org/10.1371/journal.pone.0263846</a></li> </ul> </li> </ul> <p>MATLAB .mat files</p> <p>This repository was used by the paper currently under review for the open journal Mathematics:</p> <p>White, M.; De Lazzari, B.; Bezodis, N., Camomilla, V. Title. Mathematics 2024, 1, 0. Wearable Sensors for Athletic Performance: A Comparison of Discrete and Continuous Feature Extraction Methods for Prediction Models</p>
Occurrences records of Herichthys labridens (Cichliformes: Cichlidae), with associated habitat information, in the Media Luna spring, San Luis Potosí, Mexico
<h2><strong>Introduction</strong></h2> <blockquote> <p>Occurrence records of the endemic cichlid <em>Herichthys labridens</em>, by adult and juvenile life stages, during three summer events (years of 1999, 2009, and 2019), in the Media Luna spring, San Luis Potosí Mexico. </p> </blockquote> <h2><strong>Material and Methods </strong></h2> <blockquote> <p>The occurrence records, ordered by adult and juvenile life stages, were obtained from two sources. For the summer of 1999, data were downloaded from the literature (Palacio-Núñez et al., 2010). For subsequent events, we recorded new data from 66 underwater transects distributed among 14 sectors (S1 to S14) in the Media Luna spring. We followed the method of Palacio-Núñez (2007), which maintained the transect location and sector boundaries of the summer of 1999 (Fig. 1a). The 20 m² transects were placed transversely to the current, from the edge to the central part of the canal (Fig. 1b). This sampling design was selected to meet two basic assumptions for studies of spatial distribution and habitat suitability: (1) the observations within the area are true and, (2) these observations delimit the initial position of the recorded individuals (Buckland & Elston, 1993). The analysis of the spatial information of the sectors, the underwater transects, and the delimitation of the water surface was performed using the QGIS® software version 3.4.8 (Menke, 2019).</p> <p> </p> <p><strong>Figure 1</strong>. <a href="https://zenodo.org/api/records/14231104/draft/files/Sector%20boundary_Transect%20location%20and%20sampling_Media%20Luna%20spring.jpeg/content" target="_blank" rel="noopener noreferrer">Sector boundary_Transect location and sampling_Media Luna spring.jpeg</a>. (a) Location of the transects in the Media Luna spring, Mexico. (b) Design scheme of the sampling transect; a CPVC pipe was used to give width to the edges of the transect and a nylon rope was attached to each side of the pipes to demarcate the length of the transect. Floating rubber buoys were added to the transects (at the edge towards the center of the canal) to prevent them from sinking into the sediment and to locate them among the vegetation. Transect scheme: Jorge Palacio-Núñez.</p> <p><br>In the summer events where we worked in field, we recorded the spatial location (i.e., GPS coordinates) of each individual and its life stage by direct observation with snorkel equipment and using a Garmin etrex device. The recorded information included the data of water depth and related underwater coverage. It is important to mention that, to prevent a repeat observation of the same organism or to ommit any individual, the transect was swaped slowly and in one direction only (i.e., from the center of the canal to the shore). We also used underwater cameras to validate the information. In adittion, the characterization of <em>H. labridens</em> individuals by life stage was performed by approximate size. For this purpose, previous studies on the life history and biology of the species were reviewed (Miller et al., 2005; De La Maza-Benignos & Lozano-Vilano, 2013). It is worth mentioning that, during fieldwork, we avoided manipulation, damage, or unnecessary capture of the fish (e.g., Prchalová et al., 2009).</p> <p><br>The databases by life stage were organized for each summer event, where, each observation record was included along with the associated habitat conditions. Subsequently, we depurated each database to remove atypical spatial data, data without information, incomplete data, or data with duplicate coordinates (García-Roselló et al., 2014). Then, we performed spatial filtering of the remaining records to validate those that were within the study area, and to prevent that two or more points were within 0.1 m of each other. These steps of our analysis were performed using the software Qgis® version 3.28.4 and Rstudio® (Rstudio team, 2020). Subsequently, with the data set that included fish records, water depth, and underwater coverage variables, we performed a final environmental filter to rule out atypical records. This exploration was performed in Rstudio ® using the outliers function, starting from the lowest and highest quantiles.</p> </blockquote> <h2><strong>Results</strong></h2> <blockquote> <p>The final filtered databases were organized by life stage and summer event:</p> <p><strong>Adult: </strong></p> <table> <tbody> <tr> <td>Summer event</td> <td>Database</td> </tr> <tr> <td>1999</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Adult_1999_Palacio-N%C3%BA%C3%B1ez%20et%20al.,%202010.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Adult_1999_Palacio-Núñez et al., 2010.csv</a></td> </tr> <tr> <td>2009</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Adult_2009_Field%20work.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Adult_2009_Field work.csv</a></td> </tr> <tr> <td>2019</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Adult_2019_Field%20work.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Adult_2019_Field work.csv</a></td> </tr> </tbody> </table> <p><strong> Juvenile:</strong></p> <table> <tbody> <tr> <td>Summer event</td> <td>Database</td> </tr> <tr> <td>1999</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Juvenile_1999_Palacio-N%C3%BA%C3%B1ez%20et%20al.,%202010.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Juvenile_1999_Palacio-Núñez et al., 2010.csv</a></td> </tr> <tr> <td>2009</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Juvenile_2009_Field%20work.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Juvenile_2009_Field work.csv</a></td> </tr> <tr> <td>2019</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Juvenile_2019_Field%20work.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Juvenile_2019_Field work.csv</a></td> </tr> </tbody> </table> </blockquote> <p> </p> <blockquote> <p>These occurrence records for <em>H. labridens </em>are ready to be used in ecological niche modeling and spatial distribution studies. Also, these records can be used for other ecological and spatial studies, because each record (i.e., individual) included geoespatial coordinates, sector, location, and transect number. Also, we recorded information about the conditions of underwater coverage and water depth, which were asociated to each ocurrence record.</p> <p>For more information about several R codes where the previous databases can be used, visit the following repository URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Also, to download the UC and WDp variables to run the spatial and ecological modeling, visit the following repository URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p> </blockquote>
Record Label & Music Publishing Turnover in Europe
<p>Imputed and forecasted values of the recording and music publishing industry from the <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=sbs_na_1a_se_r2&lang=en">Annual detailed enterprise statistics for services (NACE Rev. 2 H-N and S95)</a> Eurostat folder.</p>
Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects
<p><strong>Study Sample and Ethics Statement</strong></p> <p>The sample consisted of 71 university students, average age 20.38 years (<em>SD</em> = 2.96), 78.8% female. Subjects with previous cardio-vascular disorders and irregular ECG were excluded. The study has been approved by the Institutional Review Board of the Department of Psychology, University of Belgrade No. 2018-19. All participants signed Informed Consents in accordance with the Declaration of Helsinki.</p> <p>In the course of visual examination, it was decided to discard ECG from one subject due to the presence of bigeminial arythmia, so further analysis was performed on 70 subjects instead of 71.</p> <p><strong>Measurement Setup</strong></p> <p>BIOPAC sensors (Biopac Systems Inc., Camino Goleta, CA, USA) were used for recording biosignals in another study (<a href="http://empirijskaistrazivanja.org/wp-content/uploads/2021/04/EIP2020_conf_proceedings.pdf#page=17">Bjegojević et al., 2020</a>). Here, we used only ECG signals recorded in sitting relaxed position from standard bipolar Lead I using the BIOPAC MP150 unit with AcqKnowledge software and ECG 100C module with surface H135SG Ag/AgCl electrodes (Kendall/Covidien, Dublin, Ireland). In order to decrease skin-electrode impedance, the skin was cleaned with Nuprep gel (Weaver & Co., Aurora, USA) to reduce skin-electrode impedance. The sampling frequency was set at 2000 Hz and the gain was set to 1000.</p> <p>ECG signals were recorded during relaxation in a sitting position and data were recorded during 2 min long intervals. More information is available in the article [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>].</p> <p><strong>Dataset, Code, and Feature Extraction Instructions</strong></p> <ol> <li><a href="https://zenodo.org/record/5736849/files/analysisECG.R?download=1">analysisECG.R</a>, function with analysis procedures written in <a href="https://www.r-project.org/">R programming language</a></li> <li><a href="https://zenodo.org/record/5736849/files/anec12919-sup-0001-supinfo.pdf?download=1">anec12919-sup-0001-supinfo.pdf</a>, detailed ECG processing and feature extraction procedure (also available as <a href="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fanec.12919&file=anec12919-sup-0001-Supinfo.docx">supplementary material</a> for article [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>])</li> <li><a href="https://zenodo.org/record/5736849/files/ecg_70.txt?download=1">ecg_70.txt</a>, .txt data file, text format</li> <li><a href="https://zenodo.org/record/5736849/files/mainECG.R?download=1">mainECG.R</a>, a main program written in R programming language</li> <li><a href="https://zenodo.org/record/5736849/files/R-studio-version-info.txt?download=1">R-studio-version-info.txt</a>, the version of <a href="https://www.rstudio.com/">R Studio</a> where the code was tested</li> <li><a href="https://zenodo.org/record/5736849/files/R-version-info.txt?download=1">R-version-info.txt</a><a href="https://zenodo.org/api/files/aa8d999e-1b08-44b5-883f-0540afe8feb8/R-version-info.txt"> </a>, the version of R programming language where the code was tested</li> </ol> <p>For ECG-based feature extraction, we used the following R packages:</p> <ol> <li><strong>signal</strong> - Signal Processing Functions (signal developers (2014). <em>signal: Signal processing</em>. <a href="http://r-forge.r-project.org/projects/signal/">http://r-forge.r-project.org/projects/signal/</a>)</li> <li><strong>pracma</strong> - Practical Numerical Math Functions ( Borchers, H. W. (2019). <em>Package ‘pracma’: Practical numerical math functions</em>. R package version, 2(1). <a href="https://CRAN.R-project.org/package=pracma">https://CRAN.R-project.org/package=pracma</a>)</li> </ol> <p>Please, note that the results of personality trait tests are not available in the current dataset. We are planning to open them in our future research. For more information and planned availability in open access, please, contact the corresponding author of [<a href="https://onlinelibrary.wiley.com/doi/10.1111/anec.12919">1</a>] by e-mail (<a href="mailto:nadica.miljkovic@etf.bg.ac.rs">nadica.miljkovic@etf.bg.ac.rs</a>).</p> <p><strong>Citing Instruction</strong></p> <p>If you find these signals and code useful for your own research or teaching class, please cite relevant dataset and supporting publications:</p> <ol> <li> <p>Boljanić, T., Miljković, N., Lazarević, L. B., Knežević, G., & Milašinović, G. (2021). Relationship between electrocardiogram-based features and personality traits: Machine learning approach. <em>Annals of Noninvasive Electrocardiology</em>, 00, e12919. <a href="https://doi.org/10.1111/anec.12919">https://doi.org/10.1111/anec.12919</a></p> </li> <li> <p>Bjegojević, B., Milosavljević, N., Dubljević, O., Purić, D., & Knežević, G. (2020). <a href="http://empirijskaistrazivanja.org/wp-content/uploads/2021/04/EIP2020_conf_proceedings.pdf#page=17">In pursuit of objectivity: Physiological measures as a means of emotion induction procedure validation</a>. <em>XXIVI Scientific Conference on Empirical Studies in Psychology</em>, p. 17-19.</p> </li> <li> <p>Boljanić, T., Miljković, N., Lazarević B. Lj., Knežević, G., & Milašinović, G. (2021). Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects (Version 1) [Data set]. <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.5599239">https://doi.org/10.5281/zenodo.5599239</a></p> </li> </ol>
Synthetic Simulations Of Extracellular Recordings (SSOER) Dataset
<p>This dataset contains synthetic data from simulations (for a total duration of 10 minutes) including the activity of one multi-unit and two single-units for different firing rates and signal-to-noise ratio levels. It is intended to be used as a standardized dataset to evaluate spike sorting algorithms.</p> <p>Recordings were taken using a sampling rate of 24 kHz, and are comprised of spikes from a database with 594 different average spike shapes, taken from real recordings from monkey neocortex and basal ganglia.</p> <p>This dataset is comprised of two files: <em>data.npy</em> and <em>labels.csv</em>.</p> <ul> <li><em>data.npy</em> contains 14,400,000 sampled voltage values, from a single channel, taken at a sampling rate of 24 kHz. </li> <li><em>labels.csv</em> contains the timestep, spike class, amplitude (SNR), and firing rate associated with each spiking event.</li> </ul> <p>The original samples used to construct this dataset where previously constructed and made available in [1]. This dataset is an amalgamation of simulation files, which were previously publicly accessible at: <a href="http://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/software">http://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/software</a>. Consequently, when using or making modifications to this dataset, in addition to acknowledging this record, [1] must also be acknowledged, as per the original author's request.</p> <p>[1] J. Martinez, C. Pedreira, M. J. Ison, and R. Quian Quiroga, “Realistic simulation of extracellular recordings,” Journal of Neuroscience Methods, vol. 184, no. 2, pp. 285–293, Nov. 2009, doi: 10.1016/j.jneumeth.2009.08.017.</p>
Jingju a Cappella Recordings Collection
<p>The <strong>Jingju a Cappella Recordings Collection</strong> (<strong>JaCRC</strong>) is part of the <strong><a href="https://compmusic.upf.edu/corpora">Jingju Music Corpus</a></strong> created in the <a href="http://compmusic.upf.edu/">CompMusic project</a> at the Music Technology Group, Universitat Pompeu Fabra, Barcelona (MTG). The <strong>JaCRC</strong> was created for different research tasks, mostly concerning melodic characteristics of jingju arias and pronunciation in jingju, and parts of the collection have been used in several publications. The <strong>JaCRC </strong>contains 314 recordings of jingju a cappella singing, plus 76 recordings of the jinghu accompaniment for their corresponding vocal tracks. Except for 53 of them (see CONTENT below), all of the recordings were newly created for this collection. The <strong>JaCRC </strong>also contains the manual segmentation of 217 vocal recordings and lyrics files for 156, 67 of which include annotations for start and end of each lyrics line in a related music score (see the README file). The dataset is released under a Creative Commons license (see LICENSE below).</p> <p>The content of the <strong>JaCRC</strong> was previously published in three different parts (<a href="https://doi.org/10.5281/zenodo.780559">part 1</a>, <a href="https://doi.org/10.5281/zenodo.842229">part 2</a>, <a href="https://doi.org/10.5281/zenodo.1244732">part 3</a>). This new release puts all the data together under an unified structure in order to ease its usability.</p> <p><br> <strong>CONTENT</strong></p> <p>The main body of the <strong>JaCRC </strong>are 239 a cappella recordings of jingju arias. Among those, the main contribution of the collection are the 186 newly created a cappella recordings by professional or semi-professional actors. Some of the recordings contain incomplete arias because the performer decided to stop according to their own will. The aria is then completed in subsequent recording(s). In few occasions, the performer decided to record a second version of the same aria. Both versions are included in the collection.</p> <p>The performers for 76 of these recordings sung over a jinghu accompaniment played live in a different room. These accompaniments were also recorded and added to the <strong>JaCRC</strong>.</p> <p>To complement the collection, recordings from existing sources were also integrated to the <strong>JaCRC</strong>. 15 a cappella recordings were obtained from commercial releases by subtracting the instrumental accompaniment, published in separate tracks to be used as accompaniment by amateur singers, from the mixed track. These recordings are not included in the <strong>JaCRC </strong>for copyright issues, but can be shared for research purposes only (see CONTACT below). However, the metadata and the segmentation files for these 15 recordings have been included in the <strong>JaCRC</strong>. Besides, 53 a cappella jingju recordings from <a href="http://isophonics.net/SingingVoiceDataset">Singing Voice Audio Dataset</a> were included here with permission of their authors (see LICENSE and USE below).</p> <p>With the goal of developing technologies to aid learning of jingju singing, 75 recordings were created from amateur performers, both children and adults. These amateur performers, considered as ‘students,’ sung trying to imitate a reference model, considered as ‘teacher.’ The ‘teacher’ would be either present in the session, and their performances were also recorded, or an existing recording of the <strong>JaCRC </strong>was played as model. The 16 recordings of the teachers are part of the <strong>JaCRC </strong>and the anonymized recordings of the students are included in the <strong>JaCRC</strong>.</p> <p>All the artists recorded for the <strong>JaCRC </strong>manifested their written consent to the MTG for the public release of these recordings under Creative Common license.</p> <p>In order to be used for different research tasks, 142 recordings were manually segmented to the phrase and syllable level. Among these, 81 recordings, including those 16 ones used as ‘teacher’ recordings, were further segmented to the phoneme level. All ‘student’ recordings were also segmented to the phrase, syllable and phoneme level. These segmentations are included in the <strong>JaCRC </strong>as <a href="https://www.fon.hum.uva.nl/praat/">Praat</a> TextGrid files.</p> <p>For 156 recordings there are corresponding csv files containing the lyrics performed in the recording, one line per row. Among these, 67 csv files also contain annotations for the boundaries of each lyrics line in a related music score. The boundaries are annotated as offset according to the <a href="https://web.mit.edu/music21/">music21 toolkit</a>. The related music scores can be found in the <a href="https://doi.org/10.5281/zenodo.1285612">Jingju Music Scores Collection</a> with the same name as the one annotated in the csv files.</p> <p><br> <strong>COVERAGE</strong></p> <p>As part of the Jingju Music Corpus, the <strong>JaCRC </strong>was gathered with the purpose of studying the most representative characteristics of jingju vocal music, and therefore the most representative instances of the main elements of jingju vocal music, that is, role type, shengqiang and banshi, are well covered in the collection. Below some statistics about the coverage of these elements in the JaCRC are given. The numbers in brackets correspond to the number of recordings that include (not always exclusively) that element and its percentage with respect to the total 254 recordings in the collection. The numbers include the 15 recordings from commercial realeases not available in the collection (see CONTENT above).</p> <p>Regarding role types, the <strong>JaCRC </strong>includes 5 different ones. The two most extensively covered ones are dan (127, 50.0%), including male dan (27) and huadan (2), and laosheng (108, 42.5%), including female laosheng (8). The other role types included in the JaCRC are jing (17, 6.7%), most of them of female jing (16), xiaosheng (1, 0.4%) and chou (1, 0.4%).</p> <p>The two main shengqiang in jingju are extensively covered in the <strong>JaCRC</strong>, namely xipi (153, 60.2%) and erhuang (62, 24.4%). Besides, other 7 shengqiang are also present in the collection, namely sipingdiao (14, 5.5%), nanbangzi (11, 4.3%), fan’erhuang (8, 3.1%), fansipingdiao (2, 0.8%), fanxipi (4, 1.6%), gaobozi (1, 0.4%), and handiao (1, 0.4%).</p> <p>As for banshi, there are instances of 18 different ones included in the <strong>JaCRC</strong>. The 7 more extensively represented banshi are yuanban (76, 29.9%), liushui (63, 24.8%), manban (46, 18.1%), erliu (40, 15.7%), sanban (34, 13.4%), yaoban (34, 13.4%), and daoban (27, 10.6%). Other banshi also included in the collection are kuaiban (17, 6.7%), huilong (8, 3.1%), sanyan (7, 2.8%), kuaisanyan (7, 2.8%), mansanyan (3, 1.2%), zhongsanyan (3, 1.2%), pengban (2, 0.8%), gunban (1, 0.4%), duoban (1, 0.4%), shuban (1, 0.4%), and kuaisanban (1, 0.4%).</p> <p>In terms of content, the <strong>JaCRC </strong>contains recordings of 142 arias from 74 different plays.</p> <p>Finally, the recordings in the <strong>JaCRC </strong>are performed by 23 artists, including 8 professional actors, 2 graduated jingju students, 3 undergraduate jingju students in their 4th year, and 10 amateur performers. In terms of role types, there are 10 laosheng performers, one of them being the one who also performs the xiaosheng and jing recordings, and another one also performing the chou recording, 8 dan, one of them also performing the huadan recordings, 3 male dan, 1 female laosheng and 1 female jing.</p> <p><br> <strong>ANNOTATIONS</strong></p> <p>All the annotation files are named in the same exact manner as its corresponding recording, so that they can be easily matched. Besides, the metadata and information csv files indicate which annotations are available for which recordings.</p> <p>There are two types of annotations: segmentation and lyrics.</p> <p>The segmentation annotations were done manually and in three phases, corresponding to the subfolders in the “JaCRC-annotations” folder numbered ‘1,’ ‘2’ and ‘3.’ All the segmentations were done using the software <a href="https://www.fon.hum.uva.nl/praat/">Praat</a> and are available in the <strong>JaCRC </strong>as TextGrid files. The phoneme annotations follow the Extended Speech Assessment Methods Phonetic Alphabet (<a href="https://en.wikipedia.org/wiki/X-SAMPA">X-SAMPA</a>). Below is a description of the annotations contained in each of the subfolders:</p> <p>“1-phrase-syllable-phoneme” folder: all the recordings whose annotations are contained in this folder were segmented at least to the phrase (lyrics line), syllable and phoneme levels. Since the annotations were done for different research tasks, the TextGrid files might contain different numbers of tiers, but all of them have a tier named ‘line’ for the phrase level segmentation with lyrics line in Chinese characters as labels, a tier named ‘pinyin’ for the syllable level segmentation with syllables in the pinyin romanization system as labels, and a ‘details’ tier for phoneme segmentation and labels in X-SAMPA. In order to ease access to these annotations, tab-separated values files were generated from the TextGrid files and also included as txt files in this folder. The files that add “_phrase” to the recording’s name contain the phrase level annotations in pinyin. Those that add “_phrase_char” contain the same phrase level annotations, but in Chinese characters. Those that add “_syllable” contain the syllable level annotations in pinyin. And those that add “_phoneme” contain the phoneme level annotations in X-SAMPA.</p> <p>“2-phrase-syllable” folder: same case as in the previous folder, but without phoneme level annotations. In these TextGrid files, the phrase level annotations are still in tiers named ‘line,’ and the syllable level ones are in tiers named ‘dianSilence.’</p> <p>“3-students” folder: same case as in “1-phrase-syllable-phoneme” folder. In these TextGrid files, the phrase level annotations are still in tiers named ‘line,’ the syllable level ones are in tiers named ‘dianSilence,’ and the phoneme level ones in tiers named ‘details.’</p> <p>The lyrics annotations consist of csv files (semicolon as separator) containing the lyrics of their corresponding recordings in their original Chinese script. Each row corresponds to a lyrics line. The first three columns contain information for “Role type,” “Shengqiang” and “Banshi” (see the README file). In the fourth one, under the heading “Couplet line,” “s” (from shangju) indicates that the corresponding lyrics line is an opening line, “x” (from xiaju) indicates that it is a closing line, and “k” indicates is a kutou line. The fifth column, “Lyrics line,” contains the lyrics. If there is a matching music score in the <a href="https://doi.org/10.5281/zenodo.1285612">Jingju Music Scores Collection</a> (JMSC) for the aria performed in the corresponding recording, the sixth column, “Matched score lyrics line,” contains the lyrics for the same as they appear in the score. The seventh column, “Score XML” contains the name of the music score file in the JMSC. Finally, the eight and ninth columns, “Start” and “End,” contain the starting and ending boundaries of the lyrics line in the score. The boundaries are given as note offsets, according to <a href="https://web.mit.edu/music21/">music21</a>. With this information, the notation of each line can be retrieved from the score.</p> <p>For a thorough description of the <strong>JaCRC</strong>, including metadata and information, naming convention and sources, please see the README file.</p> <p><br> <strong>LICENSE</strong></p> <p>All the recordings newly created for the <strong>JaCRC</strong>, that is, all of them except for those from the Singing Voice Audio Dataset, are published under a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International License</a>.</p> <p>For the license of the recordings from the Singing Voice Audio Dataset (those whose source in the metadata and information csv files is “SVAD”), included in the JaCRC with permission of the authors, please refer to its <a href="http://isophonics.net/SingingVoiceDataset">website</a>.</p> <p><br> <strong>REFERENCING THE JaCRC</strong></p> <p>If you use the recordings of the <strong>JaCRC </strong>in your research, please reference it in your publications using the text proposed in this website in the section “Cite as.”</p> <p>If you use the recordings from the Singing Voice Audio Dataset (those whose source in the metadata and information csv files is “SVAD”), please also include the following reference in your publications:</p> <blockquote> <p>Dawn A. A. Black, Ma Li and Mi Tian. "Automatic Identification of Emotional Cues in Chinese Opera Singing", in Proc. of 13th Int. Conf. on Music Perception and Cognition and the 5th Conference for the Asian-Pacific Society for Cognitive Sciences of Music (ICMPC 13-APSC0M 5 2014), Seoul, South Korea, August 2014.</p> </blockquote> <p><br> <strong>CONTACT</strong></p> <p>For more information, or to request access to the recordings from commercial sources, that can be shared only for research purposes, please contact Rafael Caro Repetto (rafael.caro at upf.edu).</p> <p><br> <strong>ACKNOWLEDGEMENTS</strong></p> <p>We express our deepest gratitude to all the professional and amateur performers who so generously contributed with their time and their art to the <strong>JaCRC</strong>.</p> <p>The creation of the <strong>JaCRC </strong>was funded by the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013), as part of the CompMusic project (ERC grant agreement 267583).</p>
Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - images
<p>This resource contains images that are framegrabs from video recorded by submarine deployed by the MY Arctic Sunrise during their Antarctica expeditions. The first took place in 2018 and focused within the Gerlache Strait and along the western Antarctic Peninsula and the Antarctic Sound in January 2018. Dives were conducted beginning 19th to 27th January 2018. This resource supplement the images for “Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - data”</p>
Climate records for Bulu, Ndian Division, SW Cameroon
<p>The file ‘Bulu-Ndian_Climate_1984-2017.csv' (format: comma-separated values) contains year, month, and day number in the first columns, followed by maximum (‘<em>maxT</em>’, <sup>o</sup>C) and minimum (‘<em>minT</em>’, <sup>o</sup>C) temperatures, volume of evaporated radiometer water (‘<em>radi</em>’, ml/day) and rainfall (‘<em>rain</em>’, mm/day) at Bulu, Ndian Department, SW Cameroon (4<sup>o</sup>55’53.47” N, 8<sup>o</sup>51’32.70” E; 51 m elevation [Google Earth]). This location is close to eastern border of Korup National Park, and 7.5 km SW of the town of Mundemba. The station is operated by PAMOL Plantations Plc, Cameroon. Readings were taken manually at 07:00 h each day, a record of the previous 24 hours. The date entered into the file was accordingly that of the previous day. Rainfall was measured in a standard copper collecting gauge, temperature read on thermometers inside a Stevenson screen, and radiation measured using Gunn-Bellani radiometers (Baird and Tatlock, London), one for each of two successive periods of time.</p> <p>To convert ‘<em>radi</em>’ [V] to radiation in W/m<sup>2</sup> [R] the following calibration equations can be applied for the radiometers over their corresponding periods of operation:</p> <ol> <li>R = 24.1 + 12.3∙V (01.01.1984 - 02.04.2009)</li> <li>R = 21.1 + 14.7∙V (30.05.2011 – 31.05.2017)</li> </ol> <p>(Between 03.04.2009 and 29.05.2011 no data were recorded (values ‘NA’). This was due to accidental breakage of the original instrument and a delay whilst a suitable replacement was found and installed.)</p> <p>The details of the calibrations of the two radiometers are explained in an appendix to the following related paper in preparation: “Mast fruiting in <em>Microberlinia bisulcata</em> and further evidence for the nutrient resource limitation hypothesis”, by Newbery, D. M., Schwan, S. Chuyong, G. B., Neba, G. A., Etta, C., Norghauer, J. M. and Worbes, M. [Bibliographic details subject to updating.]</p> <p>We acknowledge the help and support of their Technical Officers Cyprain Lantang and Daniel Tabi at Bulu, and the assistance of Marlise Zimmermann (IPS, Bern) with entering the data into computer files from photographed pages of the climate record books. The data were checked and curated by D. M. Newbery.</p> <p>These climate data have been of invaluable use to our ecosystem and vegetation research of the forests of Southern Korup National Park, the main field site for which lies c. 12 km NW of Bulu. They are also of interest for regional climate mapping because other stations in this part of western Central Africa are very sparse and their records incomplete.</p> <p>Colbertson E. Etta, PAMOL Plantations Plc, Lobe Oil Palm Estate, PMB 03, Ekondo Titi, SW Region, Cameroon. George B. Chuyong, Department of Plant Science, University of Buea, P. O. Box 63, Buea, SW Region, Cameroon. David M. Newbery, Institute of Plant Sciences, University of Bern, Altenbergrain 21, CH-3013, Bern, Switzerland.</p>
Ultrafast imaging recordings from the axon initial segment of neocortical layer-5 pyramidal neurons.
<p>This dataset contains imaging and whole-cell electrophysiological recordings from neocortical layer-5 pyramidal neuron from brain slices of the mouse.</p> <p>Electrophysiological recordings (at 20 kHz) are from the soma. Imaging data (10 kHz) are from lines along the axon initial segment (distal>proximal) with 500 nm pixel resolution. These correspond to:</p> <ul> <li>Sodium imaging (Figures 1 and S6).</li> <li>Voltage imaging (Figures 2,4,5,S4,S7)</li> <li>Calcium imaging (Figures 3,S3,S8).</li> </ul> <p>This dataset is used in the paper available online:</p> <p>Filipis L, Blömer LA, Montnach J, De Waard M, Canepari M. Nav1.2 and BK channels interaction shapes the action potential in the axon initial segment. bioRxiv, 2022. doi: 10.1101/2022.04.12.488116.</p>
Architectural Atmospheres — Literature Record
<p>This dataset is an output of the RESONANCES project. <br>It collects bibliographic entries about four topics:</p> <ul> <li>architectural atmospheres</li> <li>phenomenology in architecture</li> <li>emotions, embodiment, and empathy in architecture</li> <li>the biological basis of atmospheric perception.</li> </ul>
Kinematic and Electromyographic Recordings during Dynamic, Repetitive, Low-Force Movements
<p>Experimental recordings of kinematic (XSens Awinda, Full-Body) and electromyographic (Delsys Trigno, 8 Right Arm Muscles) data during 4 repetive upper limb exercises with a 1.5Kg dumbell: A) elbow counter-gravity flexion and gravity-assisted extension, with torso tilted forwards; B) shoulder counter-gravity abduction and gravity-assisted adduction; C) shoulder counter-gravity flexion and gravity-assisted extensio; D) composite sequence of elbow/shoulder flexion/extension motions, executed until self-reported fatigue.</p> <p>A total of 17 healthy volunteers (11 Male; 6 Female; 23.82 ± 2.79 years old, 69.06 ± 14.75 Kg) were recruited. Each participant willingly agreed to participate in the study and gave their signed, informed consent, following the standard set by the declaration of Helsinki and the Oviedo Conventions.</p> <p>Additionally, self-reported fatigue after each exercise, according to Borg's Perceived Exertion Scale (Borg, 1998), is provided for all subjects.</p> <p>For a detailed description of the experimental protocol and instrumentation, refer to associated research paper (submission under review).</p>
BirdVox-full-night: a dataset for avian flight call detection in continuous recordings
<p>BirdVox-full-night: a dataset for avian flight call detection in continuous recordings<br> ======================================================================================<br> Version 3.0, March 2018.</p> <p><br> Created By<br> ----------</p> <p>Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew Farnsworth (1), Steve Kelling (1), and Juan Pablo Bello (2, 3).</p> <p>(1): Cornell Lab of Ornithology (CLO)<br> (2): Center for Urban Science and Progress, New York University<br> (3): Music and Audio Research Lab, New York University</p> <p>https://wp.nyu.edu/birdvox</p> <p> </p> <p>Description<br> -----------</p> <p>The BirdVox-full-night dataset contains 6 audio recordings, each about ten hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured on the night of September 23rd, 2015, by six different sensors, originally numbered 1, 2, 3, 5, 7, and 10.</p> <p>Andrew Farnsworth used the Raven software to pinpoint every avian flight call in time and frequency. He found 35402 flight calls in total. He estimates that about 25 different species of passerines (thrushes, warblers, and sparrows) are present in this recording. Species are not labeled in BirdVox-full-night, but it is possible to tell apart thrushes from warblers and sparrrows by looking at the center frequencies of their calls. The annotation process took 102 hours.</p> <p>The dataset can be used, among other things, for the research,<br> development and testing of bioacoustic classification models, including the reproduction of the results reported in [1].</p> <p>For details on the hardware of ROBIN recording units, we refer the reader to [2].</p> <p>[1] V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection. Proc. IEEE ICASSP, 2018.</p> <p>[2] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016.</p> <p>@inproceedings{lostanlen2018icassp,<br> title = {BirdVox-full-night: a dataset and benchmark for avian flight call detection},<br> author = {Lostanlen, Vincent and Salamon, Justin and Farnsworth, Andrew and Kelling, Steve and Bello, Juan Pablo},<br> booktitle = {Proc. IEEE ICASSP},<br> year = {2018},<br> published = {IEEE},<br> venue = {Calgary, Canada},<br> month = {April},<br> }</p> <p> </p> <p>Data Files<br> ------------</p> <p>The BirdVox-full-night_flac-audio folder contains the recordings as FLAC files, sampled at 24 kHz, with a single channel (mono).</p> <p> </p> <p>Metadata Files<br> --------------</p> <p>The BirdVox-full-night_csv-annotations folder contains JAMS files, where each row correspond to a different location in the time frequency domain (columns "Time (s)" and "Freq (Hz)").</p> <p>The approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) and UTC timestamps corresponding to the start of the recording for each sensor are included as CSV files in the main directory.</p> <p> </p> <p>Please acknowledge BirdVox-full-night in academic research<br> ----------------------------------------------------------</p> <p>When BirdVox-full-night is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018.</p> <p>The creation of this dataset was supported by NSF grants 1125098 (BIRDCAST) and 1633259 (BIRDVOX), a Google Faculty Award, the Leon Levy Foundation, and two anonymous donors.</p> <p> </p> <p>Conditions of Use<br> -----------------</p> <p>Dataset created by Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello.</p> <p>The BirdVox-full-night dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-full-night dataset or any part of it.</p> <p> </p> <p>Feedback<br> -----------</p> <p>Please help us improve BirdVox-full-night by sending your feedback to:<br> vincent.lostanlen@gmail.com and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p>Acknowledgements<br> ----------------</p> <p>Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes.</p> <p>We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.</p>
Aquatic Mollusca (Gastropoda and Bivalvia) from the Malaysian Borneo: The inconsistency records
<p>This data set contains the available taxonomic inconsistencies of aquatic Mollusca (Gastropoda and Bivalvia) reported from Malaysian Borneo (East Malaysia) comprised of two provinces namely Sarawak and Sabah along with the federal territory of Labuan. </p>
Catalog of synthetic seismic records from mineral physics and travel-time tables from Waszek et al., 2021, Nature Geoscience
<p>This release is associated with the accepted publication in Nature Geoscience:</p> <p>Waszek L., Tauzin B., Schmerr N., Ballmer M. and Afonso J.C. A poorly mixed mantle transition zone and its thermal state inferred from seismic waves. Nature Geoscience, 2021.</p> <p>This dataset must be used in conjunction with the NoLimit software package (https://zenodo.org/record/5512805).</p> <p>Both the software and dataset allow the prediction of synthetic seismic waveforms for SS and PP-precursors from mineral physics models, as well as their processing for reconstructing the surface of seismic boundaries associated with major mineralogical phase transitions in the Earth’s mantle (namely, the 410-km and 660-km depth discontinuities).</p> <p>For technical reasons (storage and quick access), the catalog is downsampled with respect to the one in Waszek et al. (2021), and it is provided with the HDF5 format. For more advanced applications such as changing mantle composition, or generating waveforms for deeper earthquakes, please contact Benoit Tauzin (benoit.tauzin@univ-lyon1.fr) and Lauren Waszek (lauren.waszek@jcu.edu.au).</p> <p>The dataset includes:</p> <p>* A fixed mantle composition, which is a mechanical mixture of basalt and harzburgite with a fraction of basalt f=0.2.<br> * A downsampled catalog of synthetic waveforms for event depths between 0 and 80 km by step of 10 km (enough for reproducing the processing of observed SS and PP precursors waveforms).<br> * Adiabatic temperature gradients with potential temperature Tpot between 1200 and 2100 K by step of 100 K.</p> <p>This catalog and associated travel-time tables will allow any user to generate synthetic waveforms for any moment tensor, and events within the pre-defined depth interval.<br> </p> <p><strong>How to cite this material?</strong></p> <p>Any use of the datasets or software must refer to:</p> <p>The reference paper: Waszek L., Tauzin B., Schmerr N., Ballmer M., Afonso J.C. A poorly mixed mantle transition zone and its thermal state inferred from seismic waves. Nature Geoscience. 2021.<br> <br> Software: Tauzin, Benoit, & Waszek, Lauren. (2021). NoLiMit MATLAB package v1.0. Non-Linear Bayesian partition Modeling of the Earth's Mantle Transition zone (Version 1). Zenodo. https://doi.org/10.5281/zenodo.5512805<br> <br> Datasets: Tauzin, Benoit, Waszek, Lauren, & Afonso, Juan Carlos. (2021). Catalog of synthetic seismic records from mineral physics and travel-time tables from Waszek et al., 2021, Nature Geoscience (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5512035</p>
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
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