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8,460 results for “MONITORING”
Phytoplankton Dataset: Supporting HAB monitoring based on cost-effective open-hardware solutions
<p>ASTRAL Phytoplankton monitoring dataset which has supported technology validation based on images from single-specie phytoplankton sample and prototype deployment within IMTA labs</p>
Table 4 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U. S. A.
<p><i>Table 4.</i> Predictor environmental variables in generalized additive models (GAM) of harbor porpoise echolocation activity and their significance, with deviance explained of entire model.</p><table><tbody><tr><th></th><th>Reef</th><th>Reef</th><th>Offshore</th><th>Offshore</th></tr></tbody><tbody><tr><th>Predictor</th><td>PPM</td><td>BPM</td><td>PPM</td><td>BPM</td></tr><tr><th>Julian day</th><td><0.001c</td><td><0.001c</td><td><0.001c</td><td><0.001c</td></tr><tr><th>Diel phase:</th><td></td><td></td><td></td><td></td></tr><tr><th>Morning</th><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><th>Day Evening Night</th><td>— — 0.01a</td><td>— — —</td><td>— 0.003b 0.04a</td><td>— <0.001c</td></tr><tr><th>Tidal phase</th><td>0.05</td><td>—</td><td>0.05</td><td>—</td></tr><tr><th>Julian day <i>×</i> Diel</th><td></td><td></td><td></td><td></td></tr><tr><th>phase: Morning Day Evening Night Julian day <i>×</i> Tidal</th><td>— — <0.001c <0.001c 0.02a</td><td>— 0.04a 0.001b <0.001c <0.001c</td><td>— <0.001c <0.001c <0.001c —</td><td><0.001c <0.001c — —</td></tr><tr><th>phase</th><td></td><td></td><td></td><td></td></tr><tr><th>Diel phase <i>×</i> Tidal</th><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><th>phase</th><td></td><td></td><td></td><td></td></tr><tr><th>Deviance explained</th><td>6.9%</td><td>13.7%</td><td>11.5%</td><td>13.2%</td></tr></tbody></table><p><sup>a</sup> Significant at the 0.05 probability level.</p><p><sup>b</sup> Significant at the 0.01 probability level.</p><p><sup>c</sup> Significant at the 0.001 probability level.</p>
Table 3 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U. S. A.
<p><i>Table 3.</i> Simultaneous DMON site deployment data: For the periods when recording devices were present at both the reef and offshore sites and simultaneously recording, the number and percent of porpoise positive minutes (PPMs), and buzz positive minutes (BPMs) detected at each site are given, as well as the number and percent (displayed in bold) that were detected at both sites simultaneously during the same 10 min time period.</p><table><tbody><tr><th>Detection type</th><th>Location</th><th>Number of detections</th><th>Percent of total detection type</th></tr></tbody><tbody><tr><th>PPMs</th><td>Reef Offshore Present at both</td><td>1,297 514 521</td><td>55.6% 22.0% <b>22.3%</b></td></tr><tr><th>BPMs</th><td>Reef Offshore Present at both</td><td>546 207 43</td><td>68.6% 26.0% 5.4%</td></tr></tbody></table>
Table 2 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U. S. A.
<p><i>Table 2.</i> Recorded data set for the entire time of investigation, separated for each station by diel phase and their totals. PPM: porpoise positive minute; BPM: buzz-positive minute.</p><table><tbody><tr><th></th><th>Diel</th><th>Recorded</th><th>Porpoise positive</th><th>PPM (% per complete</th><th>Click only</th><th>Buzz positive</th><th>BPM (%</th></tr></tbody><tbody><tr><th>Site</th><td>phase</td><td>minutes</td><td>minutes (PPM)</td><td>observation period)</td><td>minutes</td><td>minutes (BPM)</td><td>per PPM)</td></tr><tr><th>Reef</th><td>Morning</td><td>278</td><td>92</td><td>33.1</td><td>66</td><td>26</td><td>28.3</td></tr><tr><th></th><td>Day</td><td>3,163</td><td>1,239</td><td>39.2</td><td>876</td><td>363</td><td>29.3</td></tr><tr><th></th><td>Evening</td><td>286</td><td>104</td><td>36.4</td><td>68</td><td>36</td><td>34.6</td></tr><tr><th></th><td>Night</td><td>1,793</td><td>622</td><td>34.7</td><td>436</td><td>186</td><td>29.9</td></tr><tr><th>Offshore</th><td>Morning</td><td>353</td><td>62</td><td>17.6</td><td>40</td><td>22</td><td>35.5</td></tr><tr><th></th><td>Day</td><td>4,075</td><td>730</td><td>17.9</td><td>593</td><td>137</td><td>18.8</td></tr><tr><th></th><td>Evening</td><td>351</td><td>78</td><td>22.2</td><td>64</td><td>14</td><td>17.9</td></tr><tr><th></th><td>Night</td><td>3,019</td><td>550</td><td>18.2</td><td>370</td><td>180</td><td>32.7</td></tr></tbody></table>
Microplastic Dataset: Supporting microplastic monitoring based on cost-effective open-hardware solutions
<p>ASTRAL Dataset related to microplastic monitoring based on the open-hardware initiative PlanktoScope</p>
Biosensor Dataset: Supporting research on biosensor technology for overall water quality monitoring
<p>Dataset regarding vision-based and MEMS-based biosensor technology validation within ASTRAL IMTA labs.</p>
AI-based real-time animal management system for flock monitoring in the husbandry of fattening turkeys
<p>The dataset was collected in a commercial turkey barn using a self-developed AI-based real-time animal management system to assess the behavior of turkeys. The data covers turkeys from the 6th to the 18th week of life.</p>
Data set for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study"
<p>Dataset has been created for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study" paper.</p> <p>We have created a smartphone based behaviour monitoring and recommendation system to aid patients recruited in a weight loss intervention programme.<br>The main interaction element for the patients is <strong>EghiFit</strong> application which was used in context of this dataset for data acquisition and secure transmission to our servers.</p> <p>The data consists of application usage per patient, steps achieved, nutritional information of meals logged, heart rate data, interaction data and more.<br>For more information about the dataset, please take a look at <strong>readme.md</strong> file and our paper.</p>
Monitoring of plant cover for the experiment RESTAURSOL (Lil'O, Ile Saint Denis, France)
<p>The data set includes 6240 horizontal high resoltion photographies of plant cover (2292*2292, 72 ppp) for the 6 treatments of RESTAURSOL experiment. This experimentent compares different soil engineering techniques using urban wastes or topsoil extracted in rural aeras that may be implemented for the reclamation of urban soils degraded by past industrial use. It is located in Lil'O site (Ile Saint-Denis, France) :</p> <p>The following reclamation techniques are compared to a control plot with untreated degraded soil :</p> <p>1- Decompaction, 2- Decomp. + compost, 3- Decomp. + excavated materials, 4 - Decomp. + excavated materials + compost, 5 - Decomp. + topsoil</p> <p>The 6 treatments are replicated over 4 blocks, giving a total of 24 experimental plots.</p> <p>A detailled presentation of the experiment and of some results (published during the 12e SUITMA conference) are given in the [RESTAUR'SOL presenttion.pdf] file attached to the data base. </p> <p>The photographies were taken for each plots over 4 squares 1*1 m2 sub-divided in 4 squares 0.5*0.5 m2,</p> <p>For each date of the monitoring. there are 24*4 = 96 (1 *1 m2) and 24*4*4 = 384 (0.5*0.5 m2) hence a total of 480 photos </p> <p>The indexation of the photos is detailed in the [Indexation photos plant cover RESTAURSOL.xls] file</p> <p>The field surveys were carried out for the 13 following dates :</p> <p>01/10/2021; 10/11/2021; 09/12/2021; 08/02/2022; 11/03/2022; 06/04/2022; 13/05/2022; 07/06/2022; 14/02/2023; 14/03/2023; 12/04/2023; 16/05/2023; 14/06/2023</p> <p> </p>
Reproducibility Case Study and Survey: Machine Learning-based Additive Manufacturing Process Monitoring and Quality Prediction
<p><span>Machine learning (ML)-based monitoring systems have been extensively developed to enhance the print quality of additive manufacturing (AM). However, the reproducibility of the proposed ML-based AM monitoring systems in published works has not been investigated due to a lack of evaluation methods. In the paper 'Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing,' we propose a reproducibility investigation pipeline and conduct two case studies to validate the pipeline. This dataset records the data generated by one of the case studies. This dataset also contains the reproducibility survey results.</span></p>
Quantification of preferential flow in single fracture using electrical monitoring
Open the record for dataset details and reuse information.
Operational Dataset for Phased Array Radar Network for Natural Hazard Monitoring and Warnings in Urban Environments over the Greater Bay Area, China
<p>This is the dataset for the BAMS paper: Operational Phased Array Radar Network for Natural Hazard Monitoring and Warnings in<br>Urban Environments over the Greater Bay Area, China</p> <p>Due to policy restrictions, long-term data cannot be openly shared. Access to these data requires further arrangements through a formal agreement. For inquiries regarding data access, please contact us to discuss the terms and conditions.</p>
The groundwater-level anomalies observed by 446 monitoring wells in the North China Plain during 2003-2017
<p>The groundwater level anomalies observed by 446 monitoring wells in the North China Plain (NCP) during 2003-2017. The monthly GWL data were obtained from the Ministry of Water Resources of China and the groundwater yearbooks. The wells were relatively evenly distributed in NCP region . The GWL anomalies were calculated by subtracting the mean GWL values during 2003-2017 from the monthly time series values of each well. Unit: meter</p>
Six years of high-resolution monitoring data of 40 borehole heat exchangers
<p>This dataset provides six years of monitoring data of the ground heat exchanger that supplies heat and cold to the E.ON Energy Research Center located in Aachen, Germany. The data belong to the following publication:</p> <blockquote> <p>Heim, E., Stoffel, P., Müller, D., & Klitzsch, N. (2024). <em>Six years of high-resolution monitoring data of 40 borehole heat exchangers</em>. Scientific Data, 11(1), 1334. <a href="https://doi.org/10.1038/s41597-024-04241-9" rel="nofollow">https://doi.org/10.1038/s41597-024-04241-9</a></p> </blockquote> <p>The ground heat exchanger consists of 40 double-U-loop borehole heat exchangers (BHE), that are arranged in three subfields. Each subfield is connected to an underground vault, in which sensors for the inlet fluid temperature, the outlet fluid temperature and the volume flow of each BHE are placed. The sensors record data in 30-second intervals. Coherent data is provided from July 1, 2018 to June 30, 2024 in two time resolutions and processing levels:</p> <ul> <li><strong>Raw data</strong>: The raw data in its initial resolution, aligned to coherent 30-second timestamps.</li> <li><strong>Prepared data</strong>: The raw data was resampled to 5 minute intervals using the weighted mean, with the volume flow as weight. Moreover, data periods that are not representative of the thermal exchange process in the BHE were masked (e.g., no-flow periods).</li> </ul> <p>Both raw and prepared data are provided as .csv files for each month individually. Temperature measurements are given in °C, the volume flow rate in l/min. </p> <p>In addition, a <strong>supporting file</strong> indicating the BHE locations is provided. It also contains the length of the horizontal connecting pipes going from the underground vaults (where the sensors are placed) to the BHE heads. </p> <p><strong>Code </strong>explaining how to open and work with the data is provided in a separate github repository:<strong> </strong><a href="https://github.com/elimh/ERC_BHEfield_Data_Code">https://github.com/elimh/ERC_BHEfield_Data_Code</a></p>
TSE surveillance and Atypical scrapie intensified monitoring data
<p>GENERAL_DATA_AS_REPORT.xls: statutory TSE surveillance data of sheep and goats 2013-2019 from 22 countries.</p> <p>MANDATE_DATA_RAW_AS_REPORT.xls: raw data collated by the EC for the mandate EFSA-Q-2020-00662, including the results of the intensified monitoring implemented in flocks/herds from 22 countries in which a case of atypical scrapie has been confirmed</p> <p>MANDATE_DATA_ANALYSIS_AS_REPORT.xls: : data collated by the EC for the mandate EFSA-Q-2020-00662, including the results of the intensified monitoring implemented in flocks/herds from 22 countries in which a case of atypical scrapie has been confirmed, aggregated for analysis.</p>
Image 6 in First record of the Borneo Earless Monitor Lanthanotus borneensis (Steindachner, 1877) (Reptilia: Lanthanotidae) in West Kalimantan (Indonesian Borneo)
Image 6. Localities surveyed in Landak District of West Kalimantan overlaid with 2007 forest cover.
Image 2 in First record of the Borneo Earless Monitor Lanthanotus borneensis (Steindachner, 1877) (Reptilia: Lanthanotidae) in West Kalimantan (Indonesian Borneo)
Image 2. Image of rocky, forest habitat near the shallow stream bed where the specimen was found.
Austrian Science Fund (FWF) Open Access Compliance Monitoring 2020
<p><strong>I. Executive summary</strong></p> <p>The Austrian Science Fund (FWF), which is Austria's main funding organisation for basic research, encourages and helps all project leaders and project staff members to make their peer-reviewed research results freely available on the Internet. Any exceptions must be clearly indicated and justified. For projects that started after 1 January 2015, open access has been compulsory for all peer-reviewed publications. All principal investigators in FWF-funded projects are obliged to submit a final report within four months of completing their projects.</p> <p>In 2020, a total of 483 final reports were submitted to the FWF, 32 of which could not report any publications so far. The publications and other data mentioned in those reports are archived and analysed by the FWF. This report examines the state of compliance with open access requirements for peer-reviewed publications on the basis of final project reports submitted in the year 2020.</p> <p>2020 shows a significant decline in the number of publications compared to 2019. This decline can be explained by the fact that 15% fewer final project reports were received as a result of weaker funding years in the mid-2010s as well as an increased shift of final report submissions from 2020 to later points in time (‘corona effect’). Especially in the programmes with the highest number of publications (Doctoral Programmes, Special Research Programmes) as well as in programmes with many projects (Stand-Alone Projects, International Projects), there were fewer final project reports submitted, which has a massive impact on the total number of publications. It must be taken into account that the method (recording the number of publications via final project reports) is a look into the past, as the final project reports show publications that were produced in previous years during the project term. The publication activity in relation to the year of publication itself is relatively constant according to databases such as Dimensions. Due to the automation of the monitoring processes and the introduction of Plan S of cOAlition S, the category of ‘Other open access’ (self-archiving in a non-maintained repository, homepage, or archiving of preprints), which was previously determined manually, is no longer taken into account, and thus the open access share of peer-reviewed publications has decreased compared to previous years.</p> <p>Main findings:</p> <p>-A total of 6,196 publications were listed in the final project reports submitted in 2020.</p> <p>-Of those publications, 4,809 were conclusively identified as peer-reviewed.</p> <p>-Regarding compliance with the FWF's Open Access Policy, the analysis shows that 84% of the peer-reviewed publications listed in the final project reports submitted in 2020 were openly accessible (2015: 83%, 2016: 92%, 2017: 90%, 2018: 92%, 2019: 89%).</p> <p>-The most frequently chosen option was hybrid open access (40%). The share of old open access was 21%, the share of green open access 23%, and no open access 16%.</p> <p>-The majority of peer-reviewed publications submitted were journal articles (89%), 88% of which were openly accessible.</p> <p>-The lowest rate of compliance with the FWF's Open Access Policy could be found in editions, contributions to edited volumes, and monographs (38%).</p> <p>-The absolute number of publications listed in final reports in 2020 included 362 publications that were mentioned several times in different projects; however, those repetitions did not ultimately affect the relative share of open access publications (83%).</p> <p><strong>II. Bias</strong></p> <p>Most of the publications were submitted to the FWF via a research documentation system (provided by ©Researchfish), in which FWF-funded researchers are able to enter and update their research data on an ongoing basis. The last check of the open access status was performed in March 2020. In some cases, the embargo period may have already expired, and publications labelled ‘no open access’ in the FWF’s database may now be accessible through green open access.</p> <p>Whenever the status ‘peer-reviewed’ could not be clearly identified according to the FWF’s guidelines, the publications were classified as non-peer reviewed.</p> <p>Openly available non-peer reviewed publications were entered as 'openly available’.</p> <p> </p>
Use of the Animal Welfare Assessment Grid to monitor the ef-fects of enrichment on macaque welfare.
<p>The data pubished relates to a research project that was undertaken to identify the effect of novel enrichments on the welfare of rhesus macaques housed in a breeding facility in the UK. </p>
Pin-tailed whydah and Cape robin-chat calls for passive acoustic monitoring
<p>We provide the audio data (.wav) used to train neural network classifiers along with the corresponding labelled files (.svl). The .svl files are natively read using Sonic Visualiser (https://www.sonicvisualiser.org/) but can directly be read using Python as these are XML files.</p> <p>This is a three class classification dataset. The recordings were obtained using an AudioMoth which was placed at one location in Intaka Island Nature Reserve, Cape Town, South Africa. The recorder was attached to a tree at approximately 1.5 meters from the ground. The sampling rate was set to 48000Hz with a bit rate of 768kbps. The recordings took place in January 2021. While further recordings exist we only provide a small subset here. Additional data can be requested.</p> <p><strong>Files provided</strong></p> <p>Audio.zip - contains audio files (.wav)</p> <p>Annotations.zip - contains the corresponding labels (.svl) for Sonic Visualiser</p> <p><strong>Class description</strong></p> <p>CRC: calls of the Cape robin-chat (Cossypha caffra)</p> <p>PTW: calls of the pin-tailed whydah (Vidua macroura)</p> <p>NOISE: any sound event that does not contain a Cape robin-chat or pin-tailed whydah call</p> <p> </p> <p>Parts of this data was used in two MSc dissertations:</p> <ul> <li>"Acoustic Data Augmentation for Small Passive Acoustic Monitoring Datasets", Aime Nshimiyimana, African Centre of Excellence in Data Science (ACE-DS) of the University of Rwanda, College of Business and Economics</li> <li>"Pre-training neural networks on Xeno-Canto and eBird for bioacoustic classification models", Mikwa Boris Tamanjong, African Centre of Excellence in Data Science (ACE-DS) of the University of Rwanda, College of Business and Economics</li> </ul>
ScienceDex guides
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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.