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858 results for “monitoring data”
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds. </p>
EV Data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds. </p>
Goose monitoring data from the Meise Botanic Garden, Belgium
<p>This data deposit gives details and routes of the goose monitoring at Meise Botanic Garden, Meise, Belgium. Here you will find details of the survey routes and areas where the geese were counted. </p> <p>These data can be used with the observation data published on the Global Biodiversity Information Facility. However, a snapshot of the data is included here.</p> <p>Groom Q (2019). Waterbirds of the Botanic Garden Meise. Version 3.6. Botanic Garden Meise. Occurrence dataset https://doi.org/10.15468/cgffyq accessed via GBIF.org on 2019-01-01.</p> <p>The files contain the following...</p> <p><strong>0021579-181108115102211.zip</strong></p> <p>The download of data from the Global Biodiversity Information Facility</p> <p>Groom Q (2018). Waterbirds of the Botanic Garden Meise. Version 3.4. Botanic Garden Meise. Occurrence dataset https://doi.org/10.15468/cgffyq accessed via GBIF.org on 2019-01-01.</p> <p><strong>dataonsectors2019.txt; sectorsdbf.txt</strong></p> <p>A tab-delimited text file containing data on the surveyed sectors of the garden.</p> <p><strong>gardenSectors3.shp; ods.dbf</strong></p> <p>A shapefile and data file describing the extents and positions of the surveyed sectors of the Botanic Garden.</p> <p><strong>lakes.zip</strong></p> <p>A shapefile describing the extents of the lakes in the Botanic Garden. This file is not particularly precise and is only used to create maps.</p> <p><strong>route1.zip; route2.zip; route3.zip; route4.zip</strong></p> <p>Shapefiles giving the survey routes within the Botanic Garden. Routes were always walked in a clockwise direction.</p> <p> </p> <p> </p>
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds.</p>
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds.</p>
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds.</p>
Monitoring data from implementation phase
<p>WP6 collected qualitative and quantitative information and data in order to monitor the implementation of the measures of the 11 URBANWASTE pilot cases. Data and information collected are related to the following issues: </p> <ul> <li>Monitoring of actions, items and stakeholders involved</li> <li>Information on materials and flows supporting the LCA analyisis</li> <li>Waste production: weight (daily or 3 sampling weeks) or, in case the weighting of waste is not possible, bins/bags filled in (daily or 3 sampling weeks) and bins/bags dimension.</li> <li>Gender information</li> </ul> <p>All the information collected was reported in the deliverable D6.2 and was also used to support the evaluation of pilot measures (D7.1) and the refinement of the forms related the 11 pilot cities and the 22 measures that are included in deliverable 4.1.</p>
uGIM: week monitorization data of a microgrid with five agents (04/08/2019 - 10/08/2019)
<p>uGIM is a microgrid intelligent management software that can represent individual microgrid’s players using a multi-agent approach. This dataset has data regarding a week (from 04-08-2019 to 10-08-2019) of a microgrid with five players (all offices). All agents have consumption and generation data. One of the agents also has sensor data, such as temperature, movement and humidity.</p> <p>In uGIM, agents are deployed in the player’s facilities using single-board computers. All the data in this dataset is read and stored in five single-board computers. Each agent integrates several resources. In this microgrid deployment, all resources use TCP/IP communication. However, uGIM supports more protocols, such as Modbus/RTU, and Modbus/TCP.</p> <p>uGIM related publications:<br> - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. <a href="http://doi.org/10.1016/j.measurement.2019.107427">https://doi.org/10.1016/j.measurement.2019.107427</a><br> - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. <a href="http://doi.org/10.1109/ACCESS.2020.2985254">https://doi.org/10.1109/ACCESS.2020.2985254</a><br> - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. <a href="http://doi.org/10.14201/gredos.144238">https://doi.org/10.14201/gredos.144238</a><br> - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. <a href="http://doi.org/10.1016/j.jclepro.2019.118154">https://doi.org/10.1016/j.jclepro.2019.118154</a></p> <p> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for independent validation study)
<p>Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.</p> <p>This dataset contains data collected during the independent validation study: derived data from raw accelerometry data, and summary performance data.</p> <p>The full dataset, including raw accelerometry data, is available here: <a href="https://mueller-et-al-2019.s3.amazonaws.com/index.html">https://mueller-et-al-2019.s3.amazonaws.com/index.html</a></p>
The Politecnico di Torino rolling bearing test rig: description of the open-access data for vibration monitoring and diagnostics
<p>Accelerometric measurements from the rolling bearing test rig of the Dynamic and Identification Research Group (DIRG), Department of Mechanical and Aerospace Engineering, Politecnico di Torino.</p> <p>Goals:</p> <p> • Vibration Monitoring, Bearing Diagnostics, Damage detection, Damage localization, Damage classification, Damage assessment.</p> <p>Features:</p> <p> • high-speed spindle driving a hollow shaft supported by a couple of identical roller bearings B1 and B3. B1 is the bearing under analysis and features various damages.</p> <p> • two damage types (indentations on a roller and on the inner ring) and severities (0, 150, 250, 450 µm).</p> <p> • a central, larger roller bearing (B2) is loaded through a sledge generating a controlled radial force measured by a load cell.</p> <p> • lubrication is obtained by oil injection into the hollow shaft.</p> <p> • a K-type thermocouple is used to monitor the temperature (manually recorded).</p> <p> • two triaxial accelerometers are mounted on the supports of bearings B1 and B2.</p> <p>Dataset:</p> <p> • stationary acquisitions at different speed & load combinations (speed: 0, 100, 200, 300, 400, 500 Hz; load: 0, 1000, 1400, 1800 N).</p> <p> • endurance acquisitions of the bearing featuring the 450µm roller indentation. Monitoring of the damage evolution for about 230 hours under the same speed and load condition.</p> <p> </p> <p>The extended description of the dataset can be found in the attached pdf "Description and analysis of open access data" or in:</p> <p>A.P. Daga, A. Fasana, S. Marchesiello, L. Garibaldi, The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data, Mechanical Systems and Signal Processing 120 (2019) 252–273. doi:10.1016/j.ymssp.2018.10.010.</p>
Surface Marine Carbonate System Data (2019-2024) from Volunteer Observing Ship monitoring across the Western Mediterranean Sea
<p><strong><span><span>1.<span> </span></span></span></strong><strong><span>Introduction</span></strong></p> <p><span>We present here a high-resolution dataset which spans five years (February 2019 - February 2024) and is based on weekly physicochemical observations of the surface waters along the western boundary of the Mediterranean Sea. Data were automatically collected by a Surface Ocean Observation Platform (SOOP) operating in underway mode aboard the Volunteer Observing Ship (VOS) MV JONA SOPHIE (formerly RENATE P until November 2021), a container ship managed by Nisa Maritima on the route between the Canary Islands and Barcelona. A total of 92 routes were completed in the Mediterranean Sea during the observation period.</span></p> <p><span>The SOOP CanOA-VOS line, designed and maintained by the QUIMA research group at IOCAG-ULPGC, is part of Spain’s contribution to the Integrated Carbon Observation System (ICOS-ERIC) since 2021 and is recognized as an ICOS Class 1 Ocean Station, ensuring that the measurement equipment and data collection techniques meet ICOS-ERIC's high-quality standards and methodological recommendations. The data collected is also available at the ICOS Data Portal (<a href="https://www.icos-cp.eu/data-products/ocean-release">https://www.icos-cp.eu/data-products/ocean-release</a>).</span></p> <p><strong><span><span>2.<span> </span></span></span></strong><strong><span>Data collection</span></strong></p> <p><span>The dataset includes continuous monitoring of CO<sub>2</sub> levels in both surface ocean and low atmosphere, following protocols to ensure data comparability and quality given by Pierrot et al., (2009). A detailed description is provided by Curbelo-Hernández et al., 2021a, 2021b). An automated CO2 molar fraction (xCO2) measurement system, developed by Craig Nail and commercialized by General Oceanics™, was installed on the ship. This system integrates air and seawater equilibrators with a non-dispersive infrared analyzer by LICOR® for xCO2 detection. The analyzer is regularly calibrated using standard gases provided by the NOAA, traceable to the World Meteorological Organization (WMO). They were in the order of 0 ppm, 250 ppm, 400 ppm and 550 ppm until January 2021, when the gas bottles for standard 2 to 4 were changed for a new set with concentrations in the order of 300 ppm, 500 ppm and 800 ppm. </span></p> <p><span>In addition to xCO2, sea surface temperature (SST) and sea surface salinity (SSS) were monitored using high-precision instruments. The SST was monitored by using a SBE38 thermometer placed at the main seawater intake in the engine room, with a reported error of ±0.01ºC. A SBE45 thermosalinograph and a Hart Scientific HT1523 Handheld Thermometer, with reported errors of ±0.01ºC, were used to monitor the temperature at the entrance of the wet box and inside the equilibrator, respectively. The SBE45 thermosalinograph measured the sea surface salinity (SSS) with an estimated error of ±0.005.</span></p> <p><span>Discrete seawater samples were also collected during three round trips in February 2020, March 2021, and October 2023, for further analysis of total alkalinity and dissolved inorganic carbon. </span><span>A total of 102 discrete samples has been collected in the Mediterranean Sea. Total alkalinity (AT) and total inorganic carbon (CT) were determined using a VINDTA 3C according to Mintrop et al., 2000. AT was analyzed via potentiometric titration with HCl, following the carbonic acid endpoint method (Millero et al., 1993; Dickson and Goyet, 1994), while CT was determined through coulometric titration (Johnson et al., 1993). The VINDTA 3C was calibrated using Certified Reference Material (CRMs) by A. Dickson, ensuring an accuracy of ±1.5 </span><span>μ</span><span>mol kg-1 for AT and ±1.0 </span><span>μ</span><span>mol kg-1 for CT.</span></p> <p><span>The dataset contains some gaps, including a year-long gap from September 2021 to 2022 due to vessel maintenance and shorter gaps due to technical issues, which were addressed during routine maintenance. Some technical issues in 2020 were delayed due to COVID-19 constraints.</span></p> <p><strong><span>3. Dataset content</span></strong></p> <p><span>The dataset includes the following variables: </span></p> <p><span><span>·<span> </span></span></span><span>“Date” (dd/mm/yyyy)</span></p> <p><span><span>·<span> </span></span></span><span>“Time” (hh:mm:ss)</span></p> <p><span><span>·<span> </span></span></span><span>“Latitude”</span></p> <p><span><span>·<span> </span></span></span><span>“Longitude”</span></p> <p><span><span>·<span> </span></span></span><span>“equTemp”: seawater temperature measured inside the equilibrator using a Hart Scientific HT1523 Handheld Thermometer.</span></p> <p><span><span>·<span> </span></span></span><span>“stdVal”: value of the standard gases used for automatically calibrations.</span></p> <p><span><span>·<span> </span></span></span><span>“xCO2”: measured CO2 molar fraction without performing any correlation (raw data).</span></p> <p><span><span>·<span> </span></span></span><span>“atmPress”: Atmospheric pressure (units: atm).</span></p> <p><span><span>·<span> </span></span></span><span>“equPress”: Diffeence in pressure between the atmosphere and the equilibrator (units: atm).</span></p> <p><span><span>·<span> </span></span></span><span>“SST_SBE38”: Sea surface temperature measured with a SBE38 thermometer at the main seawater intake of the vessel.</span></p> <p><span><span>·<span> </span></span></span><span>“Temp_SBE45”: Seawater temperature measured with a SBE45 thermosalinograph just before the water supply to the equilibrator.</span></p> <p><span><span>·<span> </span></span></span><span>“SSS_SBE45”: Sea surface salinity measured with a SBE45 thermosalinograph just before the water supply to the equilibrator.</span></p> <p><span><span>·<span> </span></span></span><span>“xCO2corr_sw”: CO2 molar fraction measured in the sea surface after correction by using standard gases.</span></p> <p><span><span>·<span> </span></span></span><span>“xCO2corr_atm”: CO2 molar fraction measured in low atmosphere after correction by using standard gases.</span></p> <p><span><span>·<span> </span></span></span><span>“pCO2sw”: Partial pressure of CO2 in the sea surface (units: µatm).</span></p> <p><span><span>·<span> </span></span></span><span>“pCO2atm”: Partial pressure of CO2 in low atmosphere (units: µatm).</span></p> <p><span><span>·<span> </span></span></span><span>“fCO2sw”: fugacity of CO2 in the sea surface (units: µatm).</span></p> <p><span><span>·<span> </span></span></span><span>“fCO2atm”: fugacity of CO2 in low atmosphere (units: µatm).</span></p> <p><span><span>·<span> </span></span></span><span>“AT”: Total Alkalinity (µmol kg-1)</span></p> <p><span><span>·<span> </span></span></span><span>“CT”: Total Inorganic Carbon (µmol kg-1)</span></p> <p><span><span>·<span> </span></span></span><span>“pH”: pH in surface seawater at in situ temperature computed in CO2sys using as input variables AT and fCO2sw.</span></p> <p><span><span>·<span> </span></span></span><span>“pH25”: pH in surface seawater at constant temperature of 25ºC computed in CO<sub>2sys</sub> using as input variables AT and fCO2sw.</span></p> <p><span> </span></p> <p><strong><span>Acknowledgement</span></strong></p> <p><span>This research was supported by the Canary Islands Government and the Loro Parque Foundation through the CanBIO project, CanOA subproject (2019–2024), and the CARBOCAN agreement (Consejería de Transición Ecológica y Energía, Gobierno de Canarias). We would like to thank the JONA SOPHIE ship owner, the NISA-Marítima company and the captains and crew members for the support during this collaboration. Special thanks to the technician Adrian Castro-Alamo for biweekly equipment maintenance and discrete sampling of total alkalinity aboard the ship. The SOOP CanOA-VOS line is part of the Spanish contribution to the Integrated Carbon Observation System (ICOS-ERIC; https://www.icos-cp.eu/) since 2021 and has been recognized as an ICOS Class 1 Ocean Station. <span>The participation of D. C-H was funded by the PhD grant PIFULPGC-2020-2 ARTHUM-2</span></span></p>
Data from multi-sensor devices and reference station to monitoring urban air quality
<p>Data from electrochemical and optical sensors.</p> <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td><strong>Electrochemical sensor</strong></td> <td><strong>Optical sensor</strong></td> <td><strong>Probe</strong></td> <td><strong>Reference</strong></td> </tr> <tr> <td> <p>AE = auxiliary electrode (mV)</p> <p>WE = working electrode (mV)</p> <p>N = temperature correction </p> <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> <p>PM1, PM2.5 and PM10 in µg/m³</p> </td> <td> <p>Prs_mbar = pressure (mbar)</p> <p>Temp = temperature (°C)</p> <p>RH = relative humidity (%)</p> </td> <td> <p>DV30 = wind direction @ 30m (°)</p> <p>HR = relative humidity (%)</p> <p>NO, NO2, O3, PM10 and PM2.5 (µg/m³)</p> <p>Precipita = precipitation (mm)</p> <p>TC3 = temperature @ 3m (°C)</p> <p>VV30 = wind speed @ 30m (m/s)</p> </td> </tr> </tbody> </table> <p> </p>
Data for: Temporal consistency and spatial variability in detection: implications for monitoring of macroinvertebrates from shallow groundwater aquifers (Subterranean Biology, 2024)
<p>Original research article: Knüsel M., Alther R., Couton M. & Altermatt F. (2024) Temporal consistency and spatial variability in detection: implications for monitoring of macroinvertebrates from shallow groundwater aquifers. Subterranean Biology 49: 139-161. <a href="https://doi.org/10.3897/subtbiol.49.132515" target="_blank" rel="noopener">https://doi.org/10.3897/subtbiol.49.132515</a></p>
CS#1H Monitoring Data of Subsurface Passage in Managed Aquifer Recharge: Microbial and Organic Composition Analysis
<div>This dataset contains flow cytometry and cultivation-based microbial data, along with measurements of natural organic matter (NOM) characterized by fluorescence, absorbance, and liquid chromatography-organic carbon detection (LC-OCD) from CS#1H. Data were collected over a one-year monitoring period at two sampling locations: the infiltration ditch and the abstraction well. </div>
Data from "Participatory research to monitor lake water pollution"
<p>Data from <strong>Participatory research to monitor lake water pollution</strong></p> <p>Authors: Rachel Aronoff(1*), Anna Dussuet(1,2**) Roger Erismann(1,3), Shannon Erismann(3), Luc Patiny(1,2) and Carlos Vivar-Rios(1,4)</p> <p>Affiliations: </p> <p>1) Hackuarium, Ecublens, Switzerland.</p> <p>2) EPFL, Lausanne, Switzerland.</p> <p>3) Hammerdirt, La Tour-de-Peilz, Switzerland.</p> <p>4) UNIL, Lausanne, Switzerland.</p> <p>* corresponding author: biolab@hackuarium.ch</p> <p>** currently at Integra LifeSciences</p> <p> </p> <p>Article DOI: 10.1002/2688-8319.12094</p> <p>Article: Participatory research to monitor lake water pollution<br> Journal: Ecological Solutions and Evidence </p> <p> </p> <p>This Zenodo Repository includes data for:</p> <p>images - microbial plates and more, from 2016, 2017 and 2020.</p> <p>sequences - in particular the 16S results to ID bacteria, provided in a zipped folder: MCBP_Sequences.zip</p> <p>analysis tool items for quantitation and statistics and the data repository from the GitHub repo: </p> <p>https://github.com/Hackuarium/montreux-water-quality</p> <p> </p> <p> </p> <p> </p> <p> </p>
Monitoring open access publishing of NWO funded research (data)
<p>This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (https://doi.org/10.5281/zenodo.5055609). </p> <p>The report presents statistics on the extent to which publications from the period 2015–2020 funded by NWO are available in Open Access . The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw.</p> <p>This report builds on an <a href="https://doi.org/10.5281/zenodo.4446042">earlier report</a> published in 2020 covering publications from the period 2015–2018.</p> <p> </p>
A river on fiber: high resolution fluvial monitoring with distributed acoustic sensing – Data, Matlab Scripts and App
<p>Matlab software and data associated with Roth et al. (submitted to Seismica, 2025).</p>
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds. </p>
Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data from two monitoring stations Bertha Ganter – Fort McKay and Barge Landing for 20 August 2013 to 2 September 2013. This data was used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125) for model output and observational data comparisons. The same data can be accessed and downloaded from "<a href="https://wbea.org/historical-monitoring-data/">https://wbea.org/historical-monitoring-data/</a>".</p>
Data for: Zebra finch song ecology: monitoring of breeding, observational transects, focal and year-round acoustic recordings, and a large-scale simultaneous playback experiment
<p class="MsoNormal">Male songbirds sing to establish territories and to attract mates. However, increasing reports of singing in non-reproductive contexts and by females show that song use is more diverse than previously considered. Therefore, alternative functions of song, such as social cohesion and synchronisation of breeding, by and large were overlooked even in such well-studied species as the zebra finch (<em>Taeniopygia guttata</em>). In these social songbirds only the males sing and pairs breed synchronously in loose colonies following aseasonal rain events in their arid habitat. As males are not territorial, and pairs form long-term monogamous bonds early in life, conventional theory predicts that zebra finches should not sing much at all; yet they do and their song is the focus of hundreds of lab-based studies. We hypothesise that zebra finch song functions to maintain social cohesion and to synchronise breeding. Here we test this idea using data from five years of field studies, including observational transects, focal and year-round audio recordings, and a large-scale playback experiment. We show that zebra finches frequently sing while in groups, that breeding status influences song output at the nest and at aggregations, that they sing year-round, and that they predominantly sing when with their partner, suggesting that song remains important after pair formation. Our playback reveals that song actively features in social aggregations as it attracts conspecifics. Together, these results demonstrate that birdsong has important functions beyond territoriality and mate choice, illustrating its importance in coordination and cohesion of social units within larger societies.</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.