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8,460 results for “MONITORING”
Evaluation of different settlement substrates for field-based collection of Ostrea edulis. Spatial variation in oyster spat settlement along the Swedish west coast was also monitored.
<p>Aim: Evaluate different types of settlement substrates for field-based collection of <em>Ostrea edulis</em>. Document spatial variation in settlement of oyster spat on coupelle collectors along the Swedish west coast.</p> <p>Different types of substrates (shells of different species and materials with different structures) were placed in the sea and the survival and number of spat attached to the substrates were evaluated. Growth and species identification (<em>O. edulis</em> versus <em>M. gigas</em>) were documented. Data was collected from the Swedish west coast in 2020.</p>
Data, code, and supplementary materials for Pearman P. B., Broennimann, O., et al. Monitoring species genetic diversity in Europe varies greatly and overlooks potential climate change impacts. Nature Ecology & Evolution
<p>The repository contains several archives of digital materials that were used and/or produced in the analyses presented in Pearman, P. B. and Broennimann et al. Monitoring species genetic diversity in Europe varies greatly and overlooks potential climate change impacts. <strong>Nature Ecology & Evolution</strong>, likely 2023. These archives include (1) Supplementary Materials files ; (2) Data and code to generate country-level maps and plots; and (3) data and code to generate all maps of species and joint climate niche marginality, all in G-zipped tar archives. Readme files are available in each archive to guide running of the scripts and identification of objects in the Supplementary Materials. Please see the paper for all co-authors names, and the methods, the results obtained, and discussion of their implications.</p> <p>This work is dedicated to the memory of our friend and colleague Michael Bruford (1963-2023).</p>
Figura 1 in IDENTIFICATION OF PRIORITY SEASONS AND PESTICIDES FOR WATER MONITORING: A CASE STUDY IN ESPÍRITO SANTO
Figura 1: Distribuição espacial da produção cafeeira no estado do Espírito Santo. Destaque para as bacias dos rios Barra Seca/Mucuri e Foz do Rio Doce, onde estão inseridos os cinco maiores municípios produtores.
Figura 2 in IDENTIFICATION OF PRIORITY SEASONS AND PESTICIDES FOR WATER MONITORING: A CASE STUDY IN ESPÍRITO SANTO
Figura 2: Distribuição temporal dos estágios fenológicos do café, das condições climáticas, épocas de maior incidência das pragas e doenças, durante os 12 meses do ano no estado do Espírito Santo. No degradê de cores, as mais escuras representam períodos de maior ocorrência das pragas e doenças, assim como maiores pluviosidades e temperaturas, torando-se mais claras conforme se reduz a incidência e intensidade dos eventos.
Figura 3 in IDENTIFICATION OF PRIORITY SEASONS AND PESTICIDES FOR WATER MONITORING: A CASE STUDY IN ESPÍRITO SANTO
Figura 3: Distribuição temporal das condições climáticas e épocas de aplicação dos ingredientes ativos autorizados classificados entre prioritários e não prioritários no Estado do Espírito Santo. No degradê de cores para as condições climáticas, a cor mais escura representa o período de altas temperaturas e pluviosidades, se tornando mais clara conforme estas condições se tornam menos intensas.
Data assimilation experiments inform monitoring needs for near-term ecological forecasts in a eutrophic reservoir: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Beaverdam Reservoir forecasting data assimilation experiments using the FLARE (Forecasting Lake And Reservoir Ecosystems) system: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations and meteorological data, forecasts.zip contains forecast parquet files generated from the BVR FLARE DA experiment workflow, and scores.zip contains forecast skill metrics required for analysis. Within the forecasts and scores folders, there are four runs that were conducted with different parameter tuning and uncertainty quantification. The "all_UC" folder includes forecasts run with process, driver, parameter, and initial condition uncertainty quantification. The "IC_off" folder includes forecasts run without initial conditions uncertainty included (i.e., only process, driver, and parameter uncertainty). The "constant_bad_pars" folder includes forecasts run with constant parameters (but daily updating of initial conditions) that were not tuned for Beaverdam Reservoir before forecasts were generated. Finally, the "tuned_bad_pars" folder includes forecasts that were run with daily updating of initial conditions and parameters, but the parameters started out at random values that were not tuned for Beaverdam Reservoir.</p>
Robotic Monitoring of Forests: a Dataset from the EU habitat 9210* in the Tuscan Apennines (Central Italy)
<p>Data collected between the 27th and the 28th of April 2022, in Chiusi Della Verna, Arezzo 52010 (AR), Italy, inside the Natura 2000 SAC IT5180101. The data has been acquired mainly by the legged robot ANYmal C guided by a team of both roboticists and plant scientists. </p><p>The dataset contains four different sets of data: </p><p>1) species data - photos of four indicator species of the habitat 9210 (3 typical species and 1 early warning species).</p><p>2) mapping data - three dimensional point clouds of the habitat environment.</p><p>3) autonomous monitoring mission data - photos and videos taken by the robot during the surveys, robot status, and external videos of the autonomous mission.</p><p>4) teleoperated monitoring mission data - photos and videos taken by the robot during the surveys, robot status, and external videos of the teleoperated mission.</p><p>Researchers from a variety of disciplines can benefit from using this dataset because of its multidisciplinary scope. On the one hand, robotic engineers could, for instance, benchmark the performance of the robots and test or validate their own methods using the point clouds and the information about the robot state. On the other hand, botanists could evaluate the accuracy of this data as well as the habitat's conditions using the plant videos and images that the robot captured, or computer scientists could test their AI algorithms for identifying and classifying different species using these data.</p>
Adjusting for bias in biodiversity monitoring data
<p>Supporting information to Boyd et al. (2023, <i>Ecology</i>, forthcoming). Includes code to conduct bias adjustments and data supporting the article. </p>
Figure 10. - Different fish abundance dynamic profiles between 1983 and 2014 in Long term monitoring of coral and fish assemblages (1983-2014) in Tiahura reefs, Moorea, French Polynesia
Figure 10. - Different fish abundance dynamic profiles between 1983 and 2014 on the outer slope at Tiahura sector in Moorea.
Figure 6 in Long term monitoring of coral and fish assemblages (1983-2014) in Tiahura reefs, Moorea, French Polynesia
Figure 6. - Commercial fish abundance at Tiahura sector in Moorea for the barrier reef and outer slope. Second-degree polynomial models were fitted to data (*: 0.01 <p ≤ 0.05, **: 0.001 <p≤ 0.01).
Figure 4 in Long term monitoring of coral and fish assemblages (1983-2014) in Tiahura reefs, Moorea, French Polynesia
Figure 4. - Total fish abundance at Tiahura sector in Moorea for the fringing reef. Second degree polynomial models were fitted to data (*: 0.01 <p ≤ 0.05, **: 0.001 <p ≤ 0.01).
Figure 9 in Long term monitoring of coral and fish assemblages (1983-2014) in Tiahura reefs, Moorea, French Polynesia
Figure 9. - Herbivorous fish species richness at Tiahura sector in Moorea for the three habitats. Linear models were fitted to data (**: 0.001 <p ≤ 0.01; ***: p ≤ 0.001).
Figure 5 in Long term monitoring of coral and fish assemblages (1983-2014) in Tiahura reefs, Moorea, French Polynesia
Figure 5. - Total fish species richness at Tiahura sector in Moorea for the fringing reef. Linear models were fitted to data (***: p ≤ 0.001).
Figure 8 in Long term monitoring of coral and fish assemblages (1983-2014) in Tiahura reefs, Moorea, French Polynesia
Figure 8. - Herbivorous fish abundance at Tiahura sector in Moorea for the barrier reef and outer slope. Linear models were fitted to data (**: 0.001 <p ≤ 0.01).
Figure 3 in Long term monitoring of coral and fish assemblages (1983-2014) in Tiahura reefs, Moorea, French Polynesia
Figure 3. - Temporal dynamics in coral percentage cover on the outer slope of Tiahura sector from 1979 to 2011. Stars denote the five main disturbances that affected the reef over the study period (COTS: Acanthaster planci outbreak). Dotted lines correspond to linear interpolation of coral percentage cover.
Capnography-Assisted Learned Monitored (CALM) Breathing Therapy for COPD
ClinicalTrials.gov study NCT04786184. IPD Sharing: YES. Countries: 1. Publications: 1.
Monitoring Movement and Health Study
ClinicalTrials.gov study NCT03084302. IPD Sharing: YES. Countries: 1. Publications: 7.
Development of Continuous Glucose Monitoring System Cohort for Personalized Diabetes Prevention and Management Platform
ClinicalTrials.gov study NCT04369833. IPD Sharing: NO. Countries: 1. Publications: 11.
Log2Lose: Incenting Weight Loss and Dietary Self-monitoring in Real-time to Improve Weight Management Among Adults With Obesity
ClinicalTrials.gov study NCT04770909. IPD Sharing: YES. Countries: 1. Publications: 2.
Alcohol Biosensor Monitoring for Alcoholic Liver Disease
ClinicalTrials.gov study NCT03533660. IPD Sharing: YES. Countries: 1. Publications: 1.
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.