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1,300 results for “Sounds”
Figures 4a-4c in Bioacoustic analysis of a compound sound with stridulation and forced air produced by the larva of Phileurus valgus (Olivier, 1789) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figures 4a-4c. Oscillograms (above) and spectrograms (down) of sounds emitted by Phileurus valgus larvae. a) Bioacoustic stridulation (isolated) patterns. b) Bioacoustic sound patterns of compound sound (stridulatory + forced air). c) Bioacoustic pattern of compound sound pulse and some forced air sound pulses (6 pulses after the compound sound). / Oscilogramas (arriba) y espectrogramas (abajo) de los sonidos emitidos por las larvas de Phileurus valgus. a) Patrones bioacústicos de estridulación (aislada). b) Patrones bioacústicos de sonido compuesto (estridulación + aire forzado). c) Patrón bioacústico de pulso de sonido compuesto y algunos pulsos de sonido de aire forzado (6 pulsos después del sonido compuesto).
Figures 3a-3b in Bioacoustic analysis of a compound sound with stridulation and forced air produced by the larva of Phileurus valgus (Olivier, 1789) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figures 3a-3b. Comparison of oscillogram and spectrogram of forced air sound pulse. a. Phileurus valgus. b. Phileurus didymus. / Comparación del oscilograma y el espectrograma del pulso sonoro de aire forzado. a. Phileurus valgus. b. Phileurus didymus.
Figures 1a-1d in Bioacoustic analysis of a compound sound with stridulation and forced air produced by the larva of Phileurus valgus (Olivier, 1789) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figures 1a-1d. Diagrams of the experiments. a-c) Direct interaction (larva-larva) and response experiment. d) Direct manipulation experiment. / Diagramas de los experimentos. a-c) Experimento de interacción directa (larva-larva) y respuesta. d) Experimento de manipulación directa.
Figures 2a-2c in Bioacoustic analysis of a compound sound with stridulation and forced air produced by the larva of Phileurus valgus (Olivier, 1789) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figures 2a-2c. Comparison of numbers of pulses in stridulation, oscillogram (above). Spectrogram (down). a) 4 pulses. b) 10 pulses. c) 12 pulses. / Comparación del número de pulsos en la estridulación, oscilograma (arriba). Espectrograma (abajo). a) 4 pulsos. b) 10 pulsos. c) 12 pulsos.
Figures 4a, b in Bioacoustic study of a sound produced with forced air by larvae of Phileurus didymus (Linnaeus) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figures 4a, b. Comparison of oscillograms. (a) Stridulatory sound (stridulatory teeth) of a Dynastinae beetle larvae. (b) Forced air sounds of P. didymus.
Fig. 3 in Characterization of sounds in maize produced by internally feeding insects: investigations to develop inexpensive devices for detection of Prostephanus truncatus (Coleoptera: Bostrichidae) and Sitophilus zeamais (Coleoptera: Curculionidae) in small-scale storage facilities in sub-Saharan Africa
Fig. 3. Effects of distance on detectability of larval sound impulses. Horizontal axis indicates the mean distance between the larval pouch and the sensor; vertical axis indicates the log10-transformed mean rate of impulses detected at that distance. Bars indicate the standard error of mean transformed rate.
Fig. 1 in Characterization of sounds in maize produced by internally feeding insects: investigations to develop inexpensive devices for detection of Prostephanus truncatus (Coleoptera: Bostrichidae) and Sitophilus zeamais (Coleoptera: Curculionidae) in small-scale storage facilities in sub-Saharan Africa
Fig. 1. Spectral profiles of 4 distinctive types of larval sound impulses detected in cracked corn: HaNb, solid line; Ma, dashed line, Ha, dash-dot-dotted line, and La, dotted line. Horizontal axis indicates frequency in kHz and vertical axis indicates relative spectrum amplitude in dB.
Fig. 2 in Characterization of sounds in maize produced by internally feeding insects: investigations to develop inexpensive devices for detection of Prostephanus truncatus (Coleoptera: Bostrichidae) and Sitophilus zeamais (Coleoptera: Curculionidae) in small-scale storage facilities in sub-Saharan Africa
Fig. 2. Oscillogram of sound impulses recorded 10 cm from pouch containing Sitophilus oryzae larvae. Examples of 3 types of larval sound impulse occur during the 1 s period, and one example each of type (Ha, La, and HaNb) is marked above the impulse. Horizontal axis indicates time in seconds and vertical axis indicates relative signal amplitude.
Sound recordings with a graphene squeeze-film microphone
<p>This dataset contains two recordings of the Super Mario Theme song. </p> <ol> <li>"<span><a href="../api/records/13832687/draft/files/SuperMario_Mic_EntireSong_Paper.wav/content" target="_blank" rel="noopener noreferrer">SuperMario_Mic_EntireSong_Paper.wav</a></span>" was recorded with a reference microphone closely placed to a graphene squeeze-film microphone.</li> <li>"<span><a href="../api/records/13832687/draft/files/SuperMario_DUT_EntireSong_Paper_DownSampledTo48kHzSampFreqSameasMic.wav/content" target="_blank" rel="noopener noreferrer">SuperMario_DUT_EntireSong_Paper_DownSampledTo48kHzSampFreqSameasMic.wav</a></span>" was recorded with a graphene squeeze-film microphone.</li> </ol> <p>More details on the experimental conditions can be found in this preprint: <a href="https://arxiv.org/abs/2406.09566">https://arxiv.org/abs/2406.09566</a></p> <p> </p>
Linked collectors and determiners for: University of Puget Sound, Slater Museum Herbarium Vascular Plants.
Natural history specimen data linked to collectors and determiners held within, "University of Puget Sound, Slater Museum Herbarium Vascular Plants". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ee747f7d-daf9-4613-92f3-d7c2a356cae6">https://bionomia.net/dataset/ee747f7d-daf9-4613-92f3-d7c2a356cae6</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ee747f7d-daf9-4613-92f3-d7c2a356cae6">https://gbif.org/dataset/ee747f7d-daf9-4613-92f3-d7c2a356cae6</a>. Formatted as a Frictionless Data package.
Extract from BluePrint sound installation "In at Midnight and Away by Morning: The Uninvited Guest"
<p>This audio piece is a 3 minute extract of the sound installation "In at Midnight and Away by Morning: The Uninvited Guest" (17 minutes in total) co-created by artist Sara Walmsley and the participants of the BluePrint project. This poetic reflection, which includes sonification of historic and predicted rainfall data*, draws on the lived experience of two communities who, in having to deal with the realities of flooding, are already learning to live with the impacts of climate change. </p> <p> Developed over 8 months (March to October 2024) of collaborative creative workshops and individual ethnographic interviews, this piece weaves together the voices and sounds that tell the stories of two devastating flood events that affected the people and places of Eglinton and Newtownstewart in 2017 and 2022. </p> <p>Within this extract of the sound installation you will hear the melodic, polyphonic harmonies of St. Eugene’s Church choir as they give music to the words of members of their community whose homes were destroyed and lives endangered by flood water. A play, performed by the Visible, Invisible and Self, takes us on a narrative journey through a community which saw their joyful totems of leisure and togetherness tumbled and sodden by a beloved natural element turned wild. A poem, written and performed by those who witnessed the rolling in of a sudden and unexpected storm, represents the voices of the key actors in a devastating event - the flooding victims, the community responders, and the river itself. Finally, we hear throughout the voices of those striving to adapt to our changing climate, those who are responding to the urgency by finding solace, hope, strength and courage in the unending and unsurprising resilience and creativity of our communities.</p> <p>Listeners are invited to remember that as they sit and experience this sound piece, that this is not a piece of art born from an imagined, dystopian future. The events described and represented all happened to real people and real places. As our climate changes at a rapidly accelerating rate, the island of Ireland is particularly vulnerable to an increased frequency of devastating flood events. While this project and this piece of art have aimed to highlight and celebrate the shining threads of hope, inspiration and creativity our communities have shown in meeting the challenges posed by climate change, a note of alarm and urgency has also been sounded. And so, we invite you not just to listen, but to act and demand change that will protect our climate into the future. </p> <p>*<em>Featuring the sonification of monthly average rainfall observation data (1836 – 1924) for County Derry-Londonderry and County Tryone accessed via UK Met Office, and for Derry City and Strabane District and Ireland, monthly average rainfall observations (1976- 2005) and projections (2021 – 2100) for the high emission (RCP 8.5) climate scenario accessed via Met Eireann/ Translate project.</em></p> <p>The BluePrint project is led by the MaREI Centre, University College Cork, with partners the Playhouse, Derry City and Strabane District Council, and Mayo County Council. The BluePrint project is a recipient of the Creative Climate Action fund, an initiative from the Creative Ireland Programme. It is funded by the Department of Tourism, Culture, Arts, Gaeltacht, Sport and Media in collaboration with the Department of the Environment, Climate and Communications. </p> <p>Find out more: <a href="https://www.marei.ie/project/blueprint/">https://www.marei.ie/project/blueprint/</a></p>
Ice Anatomy: A Benchmark Dataset and Methodology for Automatic Ice Boundary Extraction from Radio-Echo Sounding Data
<p>The measurement of ice thickness is of great importance for the accurate estimation of glacier volume and the delineation of their bedrock topography. In particular, this is a crucial factor in forecasting the future evolution of glaciers in the context of a changing climate. In order to derive the ice thickness, the travel time of electromagnetic waves in radargrams acquired by radio-echo sounding (RES) systems is analyzed. This can only be achieved by identifying the ice surface and underlying ice bottom in corresponding radargrams. Manually identifying these two reflection horizons in RES data is a laborious and time-consuming process. Consequently, scientists are attempting to automate this task through the use of techniques such as deep learning. Such automation can significantly reduce the time between a field campaign and the calculation of the glacier's ice thickness distribution. In this paper, we present the first benchmark dataset for delineating the ice surface and bottom boundaries in RES data, to facilitate straightforward comparisons of deep learning models in the future. The ``IceAnatomy'' dataset comprises radargrams and the corresponding manual picks, amounting to a total of over 45,000km of observations. The RES data originates from three sources: FAU, CReSIS, and AWI. The dataset comprises different RES systems as well as different pre-processing methods. In addition, the data was acquired over a large range of geographical and glaciological settings, featuring different thermal regimes present in Antarctica and the Southern Patagonian Icefield. This diversity ensures that the models' behaviors can be analyzed in different scenarios. We define a standardized train-test split for each source in the dataset. This allows us to introduce not only a baseline model trained on the entire training set (the ``omni'' model), but also three source-specific baseline models. The source-specific models are trained exclusively on the subset of the training data acquired by the specified source. The baseline models provide an initial benchmark against which subsequent models can be compared. The source-specific models demonstrate more accurate results than the omni model. For the FAU, CReSIS, and AWI test sets, the source-specific models achieve Mean Meter Errors of 2.1m, 23.1m, and 4.9m for the ice surface and 9.1m, 78.2m, and 29.3m for the ice bottom. In relation to the mean measured ice thickness, these errors equate to 1.2%, 3.1%, and 0.3% for the ice surface and 4.9%, 10.4%, and 1.5% for the ice bottom.</p> <p> </p> <p> For more information, please read the following paper:</p> <p>[Coming soon. Currently under review.]</p> <p>Please also cite this paper if you plan on using the dataset.</p> <p> </p> <p>For the implementation of a baseline model please visit:</p> <p>[Coming soon]</p> <p> </p>
Ambient sound spectrograms between 2015-01-01 and 2021-01-01 for OOI low-frequency hydrophones
<p>Ambient sound spectrograms calculated for the OOI low-frequency hydrophones. The spectrograms are PSD estimates using the welch method and median averaging with an averaging time of 15 minutes and 512 points per segment.</p>
Atmosphéries and the poetics of the in situ: the role and impact of sensors in data-to-sound transposition installations - Supplementary audio material
<p>This audio file wishes to give an insight into NXI Gestatio Design Lab's <em>Atmosphéries</em> research program, and to accompany the publication "Atmosphéries and the poetics of the in situ: the role and impact of sensors in data-to-sound transposition installations". The file consists in a 7-minutes recording of the <em>Meridian Probe</em>, the last instrument designed within the <em>Atmosphéries</em> program, which allows to generate sound from on-site atmospheric data. The recorded extract then acts as a sonic representation of the atmospheric conditions found at the place of exhibition, at the time of recording - in this case, gardens of the Bussy-Rabutin's Castle (France), respectively July 31st, 2021, 17h17. For further information about the <em>Meridian Probe</em>'s design and functioning, we invite you to read the aforementioned publication.</p>
Fig. 1 in Cynolebias parnaibensis, a new seasonal killifish from the Caatinga, Parnaíba River basin, northeastern Brazil, with notes on sound producing courtship behavior (Cyprinodontiformes: Rivulidae)
Fig. 1. Cynolebias parnaibensis, Jacobina do Piauí, Piauí, Brazil. (a) UFPB 6719, holotype, male, 54.5 mm SL. (b) UFPB 6709, paratype, female, 46.2 mm SL.
Fig. 2 in Cynolebias parnaibensis, a new seasonal killifish from the Caatinga, Parnaíba River basin, northeastern Brazil, with notes on sound producing courtship behavior (Cyprinodontiformes: Rivulidae)
Fig. 2. General thump sequence produced by the male of Cynolebias parnaibensis during the courtship behavior (a), and a single thump expanded (the second one above) (b). Oscilogram above and spectogram below (window function Hann, overlap 99%, FFT size 1,200 points).
Europeana Sounds genres dataset
<p><a href="https://pro.europeana.eu/project/europeana-sounds">Europeana Sounds</a> was a project focused on accessing digital audio files. The current dataset aims to provide semistructured data for learning audio-representations using machine learning techniques.</p> <p>The motivation for this dataset is to allow experimentation with audio content and metadata. The current dataset is a subset of a dataset used in <a href="https://arxiv.org/pdf/2003.12265.pdf">this research paper</a> containing 24k audios belonging to different musical genres. Find more information about the Europeana Sounds project and dataset in <a href="http://www.ifs.tuwien.ac.at/~schindler/eusounds_challenge/">this website</a>, together with raw audio features.</p> <p><br> The audio files can be downloaded using the original media URL. Additional data about the object may be obtained in JSON or in RDF. In JSON, by querying with the ID on the <a href="https://pro.europeana.eu/page/record">Europeana Record API</a>. RDF can be obtained by requesting the URI with HTTP Content Negotiation for a well-known RDF serialization format. The URI also provides access to the object's page at Europeana, if HTTP Content Negotiation is not used.</p> <p>The objects were obtained using the Europeana Search API. More information about Europeana APIs can be found <a href="https://pro.europeana.eu/page/apis">here</a>.</p>
Bagoue dataset-Cote d'Ivoire: Electrical profiling, electrical sounding and boreholes data
<p>Bagoue region lies between longitudes 6° and 7° W and latitudes 9° and 11° N in the north of Cote d’Ivoire. The geophysical and boreholes data were collected from National Office of Drinking Water (ONEP) and West-Africa International Drilling Company (FORACO-CI) during the Presidential Emergency Program (PPU) in 2012-2013 and the National Drinking Water Supply Program (PNAEP) in 2014. During the progress of both projects, the electrical methods is the most used especially the resistivity profiling and the electrical sounding methods. Originally, data were used for Groundwater Flow Rate (GFR) prediction using a Support Vector Machines(SVMs).</p>
CNR Ozone Sounding Merged (COSM) Dataset
<p>The <strong>unified database of ozonesounding profiles</strong> was obtained through the merging of three existing ozonesounding datasets, provided by the Southern Hemisphere Additional OZonesondes (SHADOZ), the Network for the Detection of Atmospheric Composition Change (NDACC), and the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). </p> <p>Only a selected set of variables of interest, both data and metadata, were considered to build the unified dataset, due to the heterogeneous formats and varying levels of detail provided by each network, even when referring to measurements shared across different initiatives. These variables are listed in the following Table.</p> <p> </p> <table style="width: 99.9748%; height: 1251.6px;"> <tbody> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>Standard name</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p><strong>Description</strong></p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p><strong>Unit</strong></p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>idstation</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>The name of the station.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>N.A.</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>location_latitude</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Latitude of station.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>deg</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>location_longitude</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Longitude of station.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>deg</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 33.832%; height: 67.2px;"> <p><strong>lacation_height</strong></p> </td> <td style="width: 56.0025%; height: 67.2px;"> <p>Height is defined as the altitude, elevation, or height of the defined platform + instrument above sea level.</p> </td> <td style="width: 4.94483%; height: 67.2px;"> <p>m</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 33.832%; height: 67.2px;"> <p><strong>date_of_observation</strong></p> </td> <td style="width: 56.0025%; height: 67.2px;"> <p>Date when the ozonesonde was launched (in format yyyy-mm-dd hh:mm:ss with time zone).</p> </td> <td style="width: 4.94483%; height: 67.2px;"> <p>N.A.</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>time</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Elapsed flight time since released.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>s</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>pressure</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Atmospheric pressure of each level in Pascals.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>Pa</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>geop_alt</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Geopotential height in meters.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>m</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>temperature</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Air temperature in degrees Kelvin.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>K</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>relative_humidity</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Relative humidity in 1.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>1</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>wind_speed</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Wind speed in meters per seconds.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>m/s</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>wind_direction</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Wind direction in degrees.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>deg</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>latitude</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Observation latitude (during the flight).</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>deg</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>longitude</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Observation longitude (during the flight).</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>deg</p> </td> </tr> <tr style="height: 86.8px;"> <td style="width: 33.832%; height: 86.8px;"> <p><strong>altitude</strong></p> </td> <td style="width: 56.0025%; height: 86.8px;"> <p>Height of sensor above local ground or sea surface. Positive values for above surface (e.g., sondes), negative for below (e.g., xbt). For visual observations, the height of the visual observing platform.</p> </td> <td style="width: 4.94483%; height: 86.8px;"> <p>m (a. s. l.)</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>sample_temperature</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Temperature where sample is measured in degrees Kelvin.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>K</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>o3_partial_pressure</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>The level partial pressure of ozone in Pascals.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>Pa</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>ozone_concentration</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>The level mixing ratio of ozone in ppmv.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>ppmv</p> </td> </tr> <tr style="height: 126px;"> <td style="width: 33.832%; height: 126px;"> <p><strong>ozone_partial_pressure_total_uncertainty</strong></p> </td> <td style="width: 56.0025%; height: 126px;"> <p>Total uncertainty in the calculation of the ozone partial pressure as a composite of the individual uncertainty contribution. Uncertainties due to systematic bias are assumed as random and follow a random normal distribution. The uncertainty calculation also accounts for the increased uncertainty incurred by homogenizing the data record.</p> </td> <td style="width: 4.94483%; height: 126px;"> <p>Pa</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>network</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Source network of the profile.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>N.A.</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>type</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Station classification flag.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>N.A.</p> </td> </tr> <tr> <td style="width: 33.832%;"> <p><strong>vertical_coverage_flag</strong></p> </td> <td style="width: 56.0025%;"> <p>Boolean flag indicating whether the ozone profile reaches the 10 hPa pressure level. Set to 't' if the profile exceeds 10 hPa, 'f' otherwise.</p> </td> <td style="width: 4.94483%;"> <p>N.A.</p> </td> </tr> <tr> <td style="width: 33.832%;"> <p><strong>vertical_completeness_flag</strong></p> </td> <td style="width: 56.0025%;"> <p>Boolean flag indicating whether the ozone profile contains at least one data point every 100 meters throughout its vertical extent. Set to 't' if the profile is vertically complete (i.e., no gaps larger than 100 meters), 'f' otherwise.</p> </td> <td style="width: 4.94483%;"> <p>N.A.</p> </td> </tr> <tr> <td style="width: 33.832%;"> <p><strong>outliers_flag</strong></p> </td> <td style="width: 56.0025%;"> <p>Boolean flag indicating whether the ozone partial pressure profile (<strong>o3_partial_pressure</strong>) contains strong outliers, based on the ±3·IQR method. Set to 't' if no strong outliers are found, 'f' otherwise.</p> </td> <td style="width: 4.94483%;"> <p>N.A.</p> </td> </tr> <tr> <td style="width: 33.832%;"> <p><strong>time_series_completeness_flag</strong></p> </td> <td style="width: 56.0025%;"> <p>Boolean flag indicating whether the time series for a given station includes at least three ozone profiles per month, allowing up to 5% of months without coverage. Set to 't' if this criterion is met, 'f' otherwise.</p> </td> <td style="width: 4.94483%;"> <p>N.A.</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 33.832%; height: 47.6px;"> <p><strong>filter_check</strong></p> </td> <td style="width: 56.0025%; height: 47.6px;"> <p>Profile quality control flag.</p> </td> <td style="width: 4.94483%; height: 47.6px;"> <p>N.A.</p> </td> </tr> </tbody> </table> <p> </p> <p>The dataset is organized into two main tables:</p> <ul> <li><strong>unified_header</strong>, which contains metadata associated with each ozonesounding profile (idstation, date_of_observation, location_latitude, location_longitude, location_height, network, type, filter_check,<strong> </strong>vertical_coverage_flag, vertical_completeness_flag, outliers_flag, time_series_completeness_flag);</li> <li><strong>unified_value</strong>, which includes the actual measurement data (idstation, date_of_observation, time, pressure, geop_alt, temperature, relative_humidity, wind_speed, wind_direction, latitude, longitude, altitude, sample_temperature, o3_partial_pressure, ozone_concentration, ozone_partial_pressure_total_uncertainty).</li> </ul> <p>To improve accessibility and performance, both tables are further subdivided into year-specific subtables, allowing for more efficient querying and data management across temporal ranges.</p> <p>Among the metadata variables included in the unified_header table, <strong>type </strong>and <strong>filter_check</strong> play a key role in characterizing the quality and coverage of the ozonesounding profiles. The <strong>type </strong>variable classifies each station based on the continuity of its time series: stations are grouped into Long Coverage (G), Medium Coverage (Y), or Short Coverage (R), depending on whether they provide at least one profile per month for at least 95% of the months in their time series, spanning:</p> <ul> <li><strong>≥20 years</strong> for Long Coverage,</li> <li><strong>≥10 and <20 years</strong> for Medium Coverage,</li> <li><strong><10 years</strong> for Short Coverage.</li> </ul> <p>The <strong>filter_check </strong>variable is a quality control flag ranging from 0 to 4, summarizing the results of four structural checks applied to each profile: completeness of monthly coverage (at least three ascents per month), vertical coverage (reaching at least 10 hPa), vertical resolution (minimum one data point every 100 meters), and detection of strong outliers (values in ozone profiles beyond ±3·IQR). A higher filter_check value indicates better compliance with these criteria and, consequently, higher data reliability. The individual flags corresponding to each control are also provided in the dataset, allowing users to apply custom quality filters based on their specific research needs.</p> <p>In addition to the dataset, two log files are provided to ensure full transparency of the quality control process and to allow users to trace all data removals and better understand the filtering criteria applied during dataset construction:</p> <ul> <li> <p><strong>delete_outliers.log</strong>: lists all strong outlier values removed from the dataset. Each entry includes the station identifier, the profile date, the pressure level, and the corresponding outlier value of o3_partial_pressure.</p> </li> <li> <p><strong>delete_wrong_profile.log</strong>: contains all ozone profiles that were entirely removed due to being considered erroneous. These profiles typically exhibit values consistently close to zero or deviate significantly from the station’s seasonal climatology. Each entry is catalogued by station and launch date.</p> </li> </ul> <p>Furthermore, an algorithm was implemented able to merge the different datasets by handling their different features and duplicated profiles, i.e. profiles from different networks recorded within a <strong>2-hour time window</strong>. In such cases, the profile that passes the greatest number of quality control (filter_check) tests is retained in the unified dataset. If multiple profiles meet the same number of quality control criteria, the selection is refined using additional indicators of dataset maturity, such as the availability of metadata, documentation, peer-reviewed publications, and especially the presence of <strong>measurement uncertainties</strong> associated with ozone concentration profiles. This last criterion is prioritized, as uncertainties are routinely provided in SHADOZ and, only for a limited number of profiles, in NDACC, while they are generally absent in WOUDC.</p>
Sounds of a little auk colony at Siorapaluk, Greenland (July 2022)
<p>These are audio records of little-auk sounds collected between July 26-29, 2022 near Siorapaluk, Greenland. We used Song Meter Micro sampling at 44.1 kHz. These July records were taken near the village. </p> <p>Time-stamp in the file name corresponds to Local Time.</p> <p>For questions, write to evgeniy.podolskiy@gmail.com.</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.