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858 results for “monitoring data”
Ixodes Scapularis monitoring data compiled from 7 studies
<p>This dataset lists 289 blacklegged tick population datasets from 7 studies that record abundance. These datasets were found by inputing keywords <em>Ixodes Scapularis</em> and <em>tick </em>in data repositories including Long Term Ecological Research data portal, National Ecological Observatory Network data portal, Google Datasets, Data Dryad, and Data One. The types of tick data recorded from these studies include density (number per square meter for example), proportion of ticks, count of ticks found on people. The locations of the datasets range from New York, New Jersey, Iowa, Massachusetts, and Connecticut, and range from 9 to 24 years in length. These datasets vary in that some record different life stages, geographic scope (county/town/plot), sampling technique (dragging/surveying), and different study length. The impact of these study factors on study results is analyzed in our research.</p> <p>Funding:</p> <p>RMC is supported by the National Institute of General Medical Sciences of the National Institutes of the Health under Award Number R25GM122672. CAB, JP, and KSW are supported by the Office of Advanced Cyberinfrastructure in the National Science Foundation under Award Number #1838807. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation.</p>
Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)
<p>This video is the fourth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)</p> <p>Bio: <strong><a href="https://warwick.ac.uk/fac/sci/wmg/people/profile/?wmgid=1147">Dr Mark Elliott</a> </strong>Mark is an Associate Professor at the Institute of Digital Healthcare, WMG, University of Warwick (UoW). Mark’s core research focuses on human movement and physiology analytics. His research uses signal processing and data science approaches to monitor, measure and model human movement and physiology to infer health status. He is the PI of the WMG Motion Capture Laboratory. His work further extends into the broader area of using wearable and on-the- body sensing devices to make objective measures of human behaviour and behaviour change. Much of Dr Elliott’s research is highly applied and involves collaborating with commercial and NHS partners. He has received funding from EPSRC, Innovate UK and SBRI Healthcare, as well as direct industrial funding. He is currently Data Analytics Theme Lead for the EPSRC funded OATech+ Network and on the steering committee for the EPSRC funded VSimulators facilities at Bath and Exeter.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ChdbggScUgo</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Rinker's Treatment Wetland Water Quality monitoring data (2017 - 2019), South Daytona, Florida
<p><span>Water quality was monitored at a constructed treatment wetland in South Daytona, Florida through </span>a<span> </span><span>two-year</span><span> </span><span>project</span><span> </span><span>(Oct</span><span> </span><span>2017-</span><span> </span>Sept<span> </span><span>2019). </span></p> <p><strong><span>The 2017-2018 INDIAN<span> </span><span>RIVER</span><span> </span><span>LAGOON</span><span> </span><span>NATIONAL</span><span> </span><span>ESTUARY</span><span> </span><span>PROGRAM funded Project</span></span></strong></p> <p><strong><span>Submitted to the IRL Council</span></strong></p> <p><strong><span>Oct 8<sup>th</sup>, 2018</span></strong></p> <p><span> </span></p> <p><strong><span>Project<span> </span>Title</span></strong><span>: <span><span> </span></span>Reed<span> </span><span>Canal</span> <span><span> </span></span>Basin <span><span> </span></span>Stormwater <span><span> </span></span>Improvement <span><span> </span></span>through <span><span> </span></span>Treatment <span><span> </span></span>Wetland</span><span> </span><span>Construction<span> </span>in<span> </span>South<span> </span>Daytona,<span> </span>FL</span></p> <p><strong><span>Project<span> </span>Applicant:<span> </span></span></strong><span>Bethune-Cookman</span><span> </span><span>University</span><span> </span><span>(B-CU)</span></p> <p> </p> <p><strong><span>Amount</span></strong><strong><span> </span></strong><strong><span>of</span></strong><strong><span> <span>Request</span></span></strong><span>:</span><span> </span><span>$181,148</span></p> <h3><span>Other</span><span> </span><span>Funding</span> <span>Sources</span><span> </span><span>and</span> <span>Amount</span><span> </span><span>of</span> <span>Total</span><span> </span><span>Match</span><span>:</span><span> </span><span>$183,095</span></h3> <p><span><span>B-C</span></span>U <span><span> </span></span><span>($74,631);</span> <span><span> </span></span><span>EPA</span> <span><span> </span></span><span>319</span> <span><span> </span></span><span>($11,250);</span> <span><span> </span></span><span>Local</span> <span><span> </span></span><span>wetland/tree/shoreline</span> <span><span> </span></span><span>restoration</span> <span><span> </span></span><span>funds</span> <span><span> </span></span><span>(pending</span></p> <p><span>$60,000); AE-Group ($5,000); </span><span>Volunteers</span><span> </span><span>($2,214);</span><span> </span><span>Project </span>H2O<span> </span><span>Academy</span><span> </span><span>and</span><span> </span><span>partners</span><span> </span><span>($30,000)</span></p> <p><span>The project goals were </span><span>to</span><span> </span><span>design,</span><span> </span><span>construct,</span><span> </span><span>and</span><span> </span><span>assess</span><span> </span>a<span> </span><span>treatment</span><span> </span><span>wetland,</span><span> </span><span>retrofit</span><span> </span><span>Rinker’s</span><span> </span><span>pond</span><span> </span><span>within<span> </span><span>the</span><span> </span><span>city</span></span><span>-owned</span> <span>plat, monitor water quality,</span><span> and</span><span> </span><span>conduct plant</span><span> </span><span>surveys</span><span> </span><span>to</span> test<span> the</span> <span>effectiveness</span><span> </span><span>of </span><span>treatment wetlands</span><span> </span>in<span> stormwater</span> <span>management </span><span>and</span><span> habMayitat quality improvement.</span><span> </span><span>This</span><span> </span><span>project</span><span> </span><span>will</span><span> </span><span>engage</span><span> </span><span>the</span><span> </span><span>residents</span><span> </span><span>into</span><span> </span><span>public</span><span> </span><span>education</span><span> </span><span>programs</span><span> </span>in<span> </span><span>order</span><span> </span><span>to</span><span> </span><span>help</span><span> </span><span>them</span><span> </span><span>enhance</span><span> </span><span>their</span><span> </span><span>awareness</span><span> </span><span>of</span> <span>issues</span><span> </span>of <span>and</span><span> solutions</span><span> </span><span>to</span> <span>stormwater</span><span> </span><span>associated</span> <span>problems.</span></p>
Software and data for: VGWatch: A low-cost stand-alone monitor to measure the valve gape of bivalves in the field
<p>Software and data abstract:</p> <p>This archive includes an Arduino script for utilizing the valve gape monitor outlined in the paper. Additionally, it provides an R script for calibrating the monitor and magnet. Finally, it contains valve gape data from three mussels, collected using the described monitor, along with a script for the analysis.</p> <p> </p> <p>Paper abstract:</p> <p>Valve gape monitors are utilised to measure the extent of bivalve’s shell openings. This measurement is correlated with their filter-feeding activity, and valve closure can serve as an indicator of various environmental stressors, including poor water quality, the presence of predators, and human disturbance. We developed an inexpensive, stand-alone, and reusable valve gape monitor; enabling collection of sufficient and independent spatial replication <em>in situ</em>. The monitor is made using Arduino hardware and software, and we share the design and code. We tested it in the field with three mussels as a proof of concept. The mussels were likely actively feeding most of the time. Two of the mussels were in close vicinity and demonstrated similar valve gape patterns. The monitor enables to measure valve gape in high resolution, providing insights into bivalve biology, ecosystem functioning, and the effects of environmental conditions, including anthropogenic stressors.</p>
Unlabeled AnuraSet: A dataset for leveraging unlabeled data in machine learning models for passive acoustic monitoring
<p>The Unlabeled AnuraSet (U-AnuraSet) is an extension of the original AnuraSet dataset. It consists of soundscape recordings from passive acoustic monitoring conducted in Brazil. The recording sites are identical to those in the original AnuraSet. Each site comprises 2,666 one-minute raw audio files of unlabeled data. The U-AnuraSet is publicly available to encourage machine learning researchers to explore innovative methods for leveraging unlabeled data in the training of models aimed at solving problems such as anuran call identification.</p> <p>If you find the Unlabeled AnuraSet useful for your research, please consider citing it as follows:</p> <p>Cañas, J.S., Toro-Gómez, M.P., Sugai, L.S.M., et al. A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring. Sci Data 10, 771 (2023). https://doi.org/10.1038/s41597-023-02666-2</p>
Open Data in German Forest Information Systems: Towards an EU Forest Resilience Monitor (Original dataset on Forest Resilience Indicators and their compliance with Open data criteria)
<p>This dataset represents the original analysis on which my Master's Thesis in the pioneer master programme at the Universities of Münster, Tallinn (Taltech) and Leuven (KUL), titled "Open Data in German Forest Governance: Towards an EU Forest Resilience Monitor". </p> <ul> <li>The first sheet contains the coding on information systems on <strong>bird species occurrence</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The second sheet contains the coding on information systems on <strong>tree species distribution</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The third sheet contains the coding on information systems on <strong>soil water conditions</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The fourth sheet contains the coding on information systems on <strong>canopy cover</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The fifth sheet contains the coding on information systems on <strong>carbon sequestration</strong> and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The sixth sheet contains the data on the <strong>individual scores per policy level/state per indicator group and the respective averages</strong>. More information on the operationaliation can be found in the methodology section of the thesis. </li> <li>The seventh sheet contains the data on the <strong>individual scores per policy level/state per indicator group and the respective averages, ranked from highest to lowest compliance</strong>. More information on the operationaliation can be found in the methodology section of the thesis. </li> <li>The last sheet gives <strong>information on the coding</strong>. </li> </ul>
FIGURE 7 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 7. Boxplot of yield difference over area index by hiatus class. Note the median for each hiatus class is close to zero. This indicates similar amounts of collection between the earlier year and the later year regardless of the number of years the area has been left to erode.
FIGURE 6. Map showing estimated collection area. Points are field localities and 15 meter buffer shows estimated prospecting areas for 2008 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 6. Map showing estimated collection area. Points are field localities and 15 meter buffer shows estimated prospecting areas for 2008 (yellow), 2009 (green), and 2010 (blue). Precise locality information is available to qualified researchers upon request from JODA's museum program.
FIGURE 9. All field collections from 2013 - early 2019 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 9. All field collections from 2013 - early 2019 to be used for tracking previous collection area. Points outside of JODA boundaries are BLM, USFS, or private localities. Precise locality information is available to qualified researchers upon request from JODA's museum program.
Data and results for manuscript "Imaging groundwater infiltration dynamics in karst vadose zone with long-term ERT monitoring"
<p>This data set contains raw and inverted data from an Electrical Resistivity Tomography (ERT) monitoring experiment conducted over a period of three years at the Rochefort Cave Laboratory (RCL) site in South Belgium. It highlights variable hydrodynamics in the karst vadose zone of Lorette Cave. More conventional hydrological measurements (drip discharge monitoring, soil moisture and water conductivity data sets) are also included in the package, which aims at provide a thorough understanding of the groundwater infiltration. Seasonal changes affect all the imaged areas leading to increases in resistivity in spring/summer attributed to enhanced evapotranspiration, whereas winter is characterised by a general decrease in resistivity associated with a groundwater recharge of the vadose zone. This study provides detailed images of the sources of drip discharge spots traditionally monitored in caves and aims to support modelling approaches of karst hydrological processes.</p>
Data sheet for Monitoring of trimming operation for lightweight composite structure
<p>Each file consists of the following data :</p> <p>- Acceleration measurements in the spindle during machining.</p> <p>- Acceleration measurements in the workpiece during machining.</p> <p>- Spindle power consumption during machining.</p> <p> </p>
EnerGAware monitored energy consumption data
<p>Energy consumption data acquired during the monitoring period, from the houses that were part of the EnerGAware pilot.</p> <p>The data discriminated by dwelling, but it is anonymized.</p>
Data providers package for reporting monitoring results for veterinary medicinal product residues (official data reporting phase)
<p>This data providers package provides the data collection configuration and supporting materials for reporting veterinary medicinal product residues (VMPR) results according to Council Directive 96/23/EC of 29 April 1996 on measures to monitor certain substances and residues thereof in live animals and animal products and repealing Directives 85/358/EEC and 86/469/EEC and Decisions 89/187/EEC and 91/664/EEC. These are to be used for the official data reporting phase.</p> <p>The package includes:</p> <p>Advice on the values to be reported for the mandatory fields specified for this data collection.</p> <p>The Standard Sample Description Version 2 XML schema definition for VMPR reporting.</p> <p>The STX transformation file which automatically assigns sampEventId and sampAnId when this information is not provided.</p> <p>The general and VMPR specific business rules applied for the automatic validation of the submitted datasets.</p> <p>The VMPR specific terminologies to be used for reporting analytical methods and the residues included in the scope of the analytical methods.</p> <p>An example of a reportable dataset following the Quick Start Reporting guide.</p> <p>Excel Mapping tool to convert excel files after mapping into XML document.</p> <p>Guidance on how to use the Excel Mapping tool.</p> <p>Guidance on how to run the validation report after submitting data to the DCF.</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>
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>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </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.