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8,460 results for “MONITORING”
Simulated datasets of twelve satellites capable for SIF monitoring.
<p>The simulated datasets of twelve satellites capable for SIF monitoring, which were used in the paper entitled<em><strong> Investigating the potential accuracy of spaceborne solar-induced chlorophyll fluorescence retrieval for twelve capable satellites based on simulation data</strong></em><strong>.</strong> </p> <p>The datasets contain the test and training datasets, the corresponding wavelengths, as well as the SIF intensities at 685 (SIF_685_true) and 740 nm (SIF_740_true) simulated by SCOPE.</p>
The use of environmental DNA to monitor impacted coastal estuaries
<p>Environmental DNA (eDNA) metabarcoding is increasingly being used to assess community composition in coastal ecosystems. In this study, we chose to examine temporal and spatial changes in the aquatic community of Manly Lagoon – one of the most heavily developed and polluted estuaries in eastern Australia. Based on metabarcoding of the 16S mitochondrial gene (for fish) and the 18S nuclear gene (for macroinvertebrates), we identified seasonal differences in fish and macroinvertebrate community composition as well as species richness, which correlated, in some cases, with environmental parameters (sea surface temperature and freshwater input). Moreover, given the greater taxonomic resolution of fish versus macroinvertebrate assignments, we identified several known migratory fish species of management importance that contributed significantly to the overall patterns observed. Overall, our data support the use of eDNA metabarcoding to track fish assemblages shifting in response to environmental drivers in polluted estuaries with increased sampling and consultation with historical data.</p>
Data derived state probabilities for Z. noltei monitoring study. Observed variables were shoot density at four sites in this study
<p>1. In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well-known situation. Model transferability and adaptability may be extremely beneficial - approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context.</p> <p>2. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer-reviewed literature to identify which components needed adjustment including parameterisation and quantification of the model, and desired outcomes. We adopted both linguistic labels and scenario-based elicitation to elicit from experts the conditional probabilities used to quantify the DBN.</p> <p>3. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterised the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses.</p> <p>4. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximise model reuse and minimise re-development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.</p>
Spatiotemporal monitoring of the rare Northern dragonhead, Dracocephalum ruyschiana (Lamiaceae): SNP genotyping and environmental niche modelling herbarium specimens
<p><strong>Aim: </strong>We have studied spatiotemporal genetic change in the Northern dragonhead, a plant species that has experienced a drastic population decline and habitat loss in Europe. We add a temporal perspective to the monitoring of dragonhead in Norway by genotyping herbarium specimens up to 200 years old. We also assess whether dragonhead has achieved its potential distribution in Norway. Location: Europe (mainly Norway)</p> <p><strong>Methods:</strong> We have applied a microfluidic array consisting of 96 SNP markers on 130 herbarium specimens collected from 1820 to 2008, mainly from Norway (83) but also beyond (47). We have compared our new genotype data with existing data from modern samples. We have modelled the species' environmental niche and potential distribution in Norway using sample metadata and observational records.</p> <p><strong>Results: </strong>The SNP array successfully genotyped all included herbarium specimens. The captured genetic diversity was negatively correlated with distance from Norway. The historical-modern comparison revealed similar genetic structure and diversity across space and limited genetic change through time in Norway. The ENM suggests that dragonhead is anchored in warmer and drier habitats.</p> <p><strong>Main conclusions: </strong>With appropriate design procedures, the SNP array technology is promising for genotyping old herbarium specimens. We found no signs of any regional bottleneck. The regional areas in Norway have remained genetically divergent, however, both from each other and more so from populations outside of Norway, rendering continued protection of the species in Norway relevant. The ENM suggests that dragonhead has not fully achieved its potential distribution in Norway.</p>
Dataset from: Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring
<p>Estimating habitat and spatial associations for wildlife is common across ecological studies, and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inferences in an effort to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eularian) data for seven species collected simultaneously in eastern Washington (2018 – 2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differ depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, though specific habitat relationships differed. We hypothesized the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.</p>
Self-Supervised Learning for Avian Diversity Monitoring SC22
<p><strong>Clusters </strong>The clusterization generated from the output of the pre-trained backbone</p> <p><strong>Morton Spectrograms</strong> The original spectrograms with which we trained the model to check the clusterization </p> <p><strong>Pretrained Model</strong> The pre-trained model</p> <p><strong>Single Image Attentional Maps</strong> The attentional maps and masks generated for a single image</p> <p><strong>features attentional maps and names</strong> Features, attentional maps and names of all the Spectrogram Images</p>
Data of carcass monitoring in rewilding mountain landscapes
<p><span>Rural abandonment and subsequent vegetation regeneration ('passive rewilding') are expected to increase worldwide, producing cascades of dynamic socioeconomic, landscape and biological changes. Although landscape characteristics strongly influence the structure and functioning of scavengers, little is known about the ecological consequences of passive rewilding due to woody encroachment (i.e., 'landscape closure') on scavenging assemblages. We investigated differences in 'scavenger assemblage composition' (species richness and abundances) and 'scavenging efficiency' (scavenging frequency, detection and consumption times and consumption rates) in a mountain agroecosystem (Pyrenees) undergoing passive rewilding.</span> <span>We monitored 178 carcasses in three landscapes: 'open', 'shrubland' and 'forest', and evaluated the effects of landscape type on 'scavenger assemblage composition' and 'scavenging efficiency' at the community and species levels, while accounting for the influences of carcass size, type and placement time. We also examined whether the locally most abundant and efficient scavenger (i.e., the griffon vulture <em>Gyps fulvus</em>) affects scavenging patterns. We found that landscape type was the main factor governing scavenging dynamics. Overall and average scavenger richness were similar in open and shrubland landscapes, while forests contained the lowest number of scavengers, mainly comprising mammals. Unlike mammals, avian scavenging frequency decreased as vegetation cover increased, especially for obligate scavengers (i.e., vultures). Scavenger abundances were highest in open landscapes, and carcasses were detected and consumed more rapidly in these landscapes. Carcass size did not influence detection and consumption times, although it did affect average scavenger richness, abundances and consumption rates. Consumption rates were higher in open landscapes and were strongly associated with the presence of griffon vultures. Interestingly, we found that griffon vultures influenced scavenging dynamics via facilitation processes. However, woody encroachment could </span><span>reduce the scavenging role of this species</span><span>, while favoring mammalian facultative scavengers. Finally, our findings highlight the pivotal role of griffon vultures, mediated by landscape characteristics, in reducing carcass persistence.</span></p>
Multisensor monitoring data of Hochebenkar Rock Glacier
<p>HRG_DSM_stack.tif: Area-wide monitoring time series of Hochebenkar Rock Glacier (Oetz valley, Tyrol, Austria) based on multitemporal digital surface models (DSMs) derived from</p> <ul> <li>photogrammetry using historical arerial imagery (1953, 1971, 1977, 1990, 1997)</li> <li>airborne laser scanning (2006, 2009, 2010, 2011, 2017)</li> <li>and unmanned aerial vehicle-based laser scanning (2018, 2019, 2020, 2021).</li> </ul> <p>The airborne laser scanning data from 2006 and 2017 were provided by the Federal Government of Tyrol and are licenced under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.de) and must be used according to the following conditions of use: https://www.tirol.gv.at/data/nutzungsbedingungen/</p> <p>HRG_HS_stack.tif: Shaded reliefs computed from the multitemporal DSMs, which were used for applying the image correlation technique to derive displacement vectors.</p> <p>HRG_DDSM_stack.tif: Differential digital surface models computed by subtracting the subsequent DSMs. The uncertainty of the individual DDSMs are masked.</p> <p>Shapefiles in HRG_velocity_vectors.zip: Mean velocity vectors (m/yr) computed from the displacement vectors by dividing the vector length by the time period between the acquisition campaigns.</p> <p>All datasets are provided in the Austrian GK West projection (EPSG 31254). The spatial resolution of the raster datasets is 1 metre.</p>
Machine Learning Constructs Color Features to Accelerate Development of Long-Term Continuous Water Quality Monitoring
<p>This is a machine learning method for predicting the concentration of colored pollutants based on RGB and kmeans methods. This dataset includes raw images of pollutants as well as characteristic data of pollutants, as well as code for the model. You can see the contents of the zip file for details.</p>
Datasets for reproducing "Robust Modelling of Internet Delay and Smart Monitoring Schemes for the Automation of Overlay Networks"
<p>This upload contains the datasets necessary to reproduce the figures and the results of my PhD thesis titled "<a href="https://tel.archives-ouvertes.fr/tel-03666771/document">Robust Modelling of Internet Delay and Smart Monitoring Schemes for the Automation of Overlay Networks</a>" (2020).</p> <p>These datasets are derived from public sources: <a href="https://www.caida.org/projects/manic/">CAIDA MANIC</a> and <a href="https://atlas.ripe.net/">RIPE Atlas</a>.</p>
[Dataset] In situ laser-ultrasonic monitoring of elastic parameters during natural aging in an Al-Zn-Mg-Cu alloy (AA7075)
<p>Datasets generated and analyzed during the research study titled "In situ laser-ultrasonic monitoring of elastic parameters during natural aging in an Al-Zn-Mg-Cu alloy (AA7075)".<br> The files ending in ".xls" or ".xlsx" are stored in the Microsoft Excel table format, while the ".mat" files can be opened with Matlab.<br> Further questions about the data structure can be directed to the corresponding authors.</p>
Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the fifth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</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/egINC9hJifI</p>
Part design aiming at dataset generation for training a vision based quality monitoring system of a circular economy cell
<p>Part design aiming at dataset generation</p> <p> </p> <p>Related to the publication: Stavropoulos, P., Papacharalampopoulos, A., Athanasopoulou, L., Kampouris, K., & Lagios, P. (2022). Designing a digitalized cell for remanufacturing of automotive frames. <em>Procedia CIRP</em>, <em>109</em>, 513-519.</p> <p> </p> <p>Contents of the ZIP file: Different configurations as CATPart files</p> <p> </p> <p>It can open with the help of CATIA software</p>
Figure 20 in The diversity of polychaetes (Annelida: Polychaeta) in a longterm pollution monitoring study from the Levantine coast of Turkey (Eastern Mediterranean), with the descriptions of four species new to science and two species new to the Mediterranean fauna
Figure 20. Anobothrus amourouxi ESFM-POL/2017-10: (a) anterior part, dorsal view; (b) anterior end, dorsal view; (c) lateral view of body; (d) notochaetae with serrate tip on TU8; (e) abdominal uncini on AU1; (f) thoracic uncini on TU1. Scale bars: a = 1.7 mm; b = 1.1 mm; c = 2.2 mm; d = 36 µm; e = 22 µm; f = 23 µm. Abbreviation. TU: thoracic unciniger. The arrow in (a) indicates a white band on dorsum of uniciniger 3.
Figure 18 in The diversity of polychaetes (Annelida: Polychaeta) in a longterm pollution monitoring study from the Levantine coast of Turkey (Eastern Mediterranean), with the descriptions of four species new to science and two species new to the Mediterranean fauna
Figure 18. Barantolla cryptogenica sp. nov. paratype (ESFM-POL/2017-211): (a) anterior end with everted nuchal organ, lateral view; (b) nuchal organ; (c) notopodial hooks, thorax; (d) neuropodial hook, abdomen. Scale bar: a = 190 µm; b = 30 µm; c = 16 µm; d = 25 µm.
Figure 17 in The diversity of polychaetes (Annelida: Polychaeta) in a longterm pollution monitoring study from the Levantine coast of Turkey (Eastern Mediterranean), with the descriptions of four species new to science and two species new to the Mediterranean fauna
Figure 17. Barantolla cryptogenica sp. nov. paratype (ESFM-POL/2017-211): (a–d) neuropodial hooks, abdomen. Scale bar: a = 4 µm; b = 2 µm; c = 9 µm; d = 4 µm.
Figure 16 in The diversity of polychaetes (Annelida: Polychaeta) in a longterm pollution monitoring study from the Levantine coast of Turkey (Eastern Mediterranean), with the descriptions of four species new to science and two species new to the Mediterranean fauna
Figure 16. Barantolla cryptogenica sp. nov. paratype (ESFM-POL/2017-211): (a) anterior end, lateral view; (b) anterior end with everted proboscis, lateral view; (c) prostomium with everted nuchal organs, dorsal view; (d) nuchal organ, dorso-lateral view; (e) lateral organ, thorax. Scale bar: a = 0.20 mm; b = 100 µm; c = 70 µm; d = 18 µm; e = 8 µm.
Figure 15 in The diversity of polychaetes (Annelida: Polychaeta) in a longterm pollution monitoring study from the Levantine coast of Turkey (Eastern Mediterranean), with the descriptions of four species new to science and two species new to the Mediterranean fauna
Figure 15. Barantolla cryptogenica sp. nov. paratype (ESFM-POL/2017-211): (a) anterior end, lateral view; (b) limbate chaetae, thorax; (c) notopodial hooks, thorax; (d) neuropodial hooks, thorax; (e) notopodial hooks, abdomen; (f) neuropodial hooks, abdomen. Scale bar: a = 0.35 mm; b = 26 µm; cf = 55 µm.
Figure 13 in The diversity of polychaetes (Annelida: Polychaeta) in a longterm pollution monitoring study from the Levantine coast of Turkey (Eastern Mediterranean), with the descriptions of four species new to science and two species new to the Mediterranean fauna
Figure 13. Paraonis lobulata sp. nov. paratype and non-type specimens: (a) antero-dorsal view of body (ESFM-POL/2018-124); (b) cheek organ (ESFM-POL/2018-124); (c) dorso-lateral view of prostomium and peristomium (ESFM-POL/2018-124); (d) Lateral view of prostomium and peristomium (ESFM-POL/ 2019-14); (e) dorso-lateral view of prostomium (ESFM-POL/2017-157); (f) dorsal view of prostomium (ESFM-POL/2017-157). Scale bar: a = 154 µm; b = 9 µm; c = 62 µm; d = 46 µm; e = 34 µm; f = 81 µm. Abbreviations. bran: branchia; cb: ciliary band; co: cheek organ; dcb: dorsal ciliary band; lso: lateral sense organ; nacb: nuchal organ associated ciliary band; no: nuchal organ; np: notopodial papilla; ntpcl: notopodial postchaetal lobe; per: peristomium; sf: skin fold.
Figure 11 in The diversity of polychaetes (Annelida: Polychaeta) in a longterm pollution monitoring study from the Levantine coast of Turkey (Eastern Mediterranean), with the descriptions of four species new to science and two species new to the Mediterranean fauna
Figure 11. Paraonis lobulata sp. nov. holotype (ESFM-POL/2019-11): (a) general view of body, dorsal view; (b) antero-dorsal view of body; (c) modified neurochaetae on chaetigers 49 and 50. Scale bar: a = 193 µm; b = 180 µm; c = 51 µm.
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.