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108 results for “environmental monitoring”
Sap-flux and associated environmental data from ash tree monitoring at four urban parks in St. Paul, Minnesota, USA, from May to November of 2023.
We measured the sap flux density of eighteen ash trees (Fraxinus spp.) of varying health and canopy conditions across four urban parks in the City of St. Paul, MN, USA in summer 2023 with a low-cost, compact data logger system we designed in-house. Although many ash trees in the city have either been killed or removed to control the spread of Emerald Ash Borer, chemical insecticide treatments are available for trees that are in early stages infestation. The trees selected for the research have all been receiving insecticide treatment for a few years, but their health and canopy conditions vary. We also have collocated temperature, soil moisture, and precipitation measurements at the same site for summer 2023.
MUDDAT: A SENTINEL-2 IMAGE-BASED MUDDY WATER BENCHMARK DATASET FOR ENVIRONMENTAL MONITORING.
<p>This is a dataset for mapping muddy waters based on Sentinel-2 (L2A products) satellite imagery. The image data are saved as GeoTIFF files and metadata files are provided in json format. There are 19 images in total, based on 16 distinct European Areas of Interest (AOIs), covering a total of 9 countries such as:</p> <ul> <li>Greece</li> <li>Italy</li> <li>France</li> <li>Spain</li> <li>Belgium</li> <li>UK</li> <li>Sweden</li> <li>Finland and</li> <li>Serbia</li> </ul> <p>From the Sentinel-2 L2A products were extracted 10 spectral bands and then resampled to a 10m spatial resolution. All spectral bands used can be found in the Metadata/Source files. The annotated images comprise 3 classes, "Non-muddy", "Muddy" and "Ambiguous". More details about the annotation methodology can be found on the accepted abstract (file: <a href="../api/records/11220437/draft/files/Accepted_Abstract_03_15_2024.pdf/content" target="_blank" rel="noopener noreferrer">Accepted_Abstract_03_15_2024.pdf</a>) or the published paper, that you can find here: <a href="https://doi.org/10.1109/IGARSS53475.2024.10642051" target="_blank" rel="noopener">10.1109/IGARSS53475.2024.10642051</a>.</p>
Interagency Ecological Program: Benthic invertebrate monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 1975-2024.
The Interagency Ecological Program’s (IEP) Environmental Monitoring Program (EMP) was initiated in compliance with the Water Right Decision D-1379 (now mandated by Water Right Decision D-1641) and has monitored benthic invertebrate macrofauna in the upper San Francisco Estuary (SFE) since 1975. The objectives of the EMP are to obtain consistent and accurate monthly data at established monitoring stations and to report this information for the purpose of management and conservation of the upper San Francisco Estuary. While the EMP also collects discrete and continuous water quality data, along with phytoplankton and zooplankton data, this dataset only includes the benthic invertebrate data collected by the EMP from 1975-2024. EMP monitors invertebrate communities in the benthos of the SFE by collecting dredge samples with a Ponar sampler. Sediment and particles smaller than 0.5mm are removed from the sample using a sieve table. The invertebrates present are preserved in formalin, identified to the lowest possible taxonomic level, and enumerated. Currently, samples are collected monthly at 10 sites across the range of salinities found in the SFE. Four replicate dredge samples are collected at each site. The frequency of sampling, number and identity of sampling sites, and number of replicate samples has changed through the 45+ years of monitoring effort, in response to changes in perceived need for data. Links to other EMP datasets can be found on the EMP website: https://emp-des.github.io/emp-reports/data-links.html, or can be found on EDI for searching for "Environmental Monitoring Program" and "San Francisco".
Sacramento-San Joaquin Bay-Delta Continuous (15 Minute) water quality monitoring data collected by the Continuous Environmental Monitoring Program, DWR, 2005- ongoing.
The Continuous Environmental Monitoring Program (CEMP) plays an instrumental role in overseeing real-time water quality in the Sacramento-San Joaquin Delta (the Delta) and Suisun Bay. The program harnesses wireless telemetry to transmit crucial data to the California Data Exchange Center (CDEC), making high-resolution environmental data pertaining to the Delta and Suisun Bay publicly accessible. The extensive dataset captures information at 15-minute intervals from 15 monitoring stations, utilizing YSI 6600 and YSI EXO sondes to obtain standalone water quality measurements. This extensive dataset informs the operations of the California State Water Project, ensuring it adheres to mandated water quality standards set by Water Right Decision 1641. This data compilation incorporates all information since the transition to YSI multiparameter sondes in 2005. It is important to note that the commencement dates and subsequent upgrades vary between stations, leading to slight discrepancies in the dataset's date ranges. Since its inception in the mid-1980s, CEMP has progressively expanded its monitoring capabilities, consistently augmenting the number of monitoring locations and the array of water quality parameters assessed. Its commitment to utilizing the most advanced water quality monitoring technology reaffirms its position as an environmental monitoring leader in the Delta and Suisun Bay. Today, the program oversees 15 water quality stations that reliably capture data every 15 minutes, each day of the year, transmitting this data in real-time. The core tenents of CEMP: • to obtain consistent and accurate data in real-time at established monitoring stations • to provide data necessary to achieve compliance with salinity, flow, and dissolved oxygen standards • to perform data analyses for further understanding of estuarine ecology • to report information to other government agencies, as well as the public, for the purpose of management and conservation of the upper San F
Interagency Ecological Program: Discrete dissolved oxygen monitoring in the Stockton Deep Water Ship Channel, collected by the Environmental Monitoring Program, 1997-2018
Dissolved oxygen levels in the Stockton Deep Water Ship Chanel (SDWSC) have been monitored since 1968 by the Interagency Ecological Program's (IEP) Environmental Monitoring Program (EMP). The SDWSC is located on the San Joaquin River near Stockton, California. Beginning in 1997, 14 stations were routinely monitored typically in summer and fall. Dissolved oxygen impairment can occur in the SDWSC; therefore, two water quality objectives were established. The objectives of the dissolved oxygen monitoring study in the SDWSC are to: (1) determine if dissolved oxygen levels comply with the water quality objectives, (2) monitor long term trends, and (3) detect and document changes along the SDWSC. The EMP collects discrete dissolved oxygen readings near the surface and bottom of the water column during ebb slack tide. The 14 stations are located between Prisoner's Point on the San Joaquin River and ends at the terminus of the channel called Turning Basin. The site locations were selected at the channel markers on the San Joaquin River; therefore, may be referred as station number or channel marker they are located at. Dissolved oxygen and water temperature were recorded 1-meter below surface and 1-meter above the bottom of the channel. Over the period of record the following water quality parameters have been added: water temperature, specific conductance, pH, fluorescence, turbidity, secchi disk and a rating score for the blue-green algae, Microcystis aeruginosa.
Interagency Ecological Program: Discrete water quality monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 1975-2023
\<markdown\> The Interagency Ecological Program’s (IEP) Environmental Monitoring Program (EMP) was initiated in compliance with the Water Right Decision D-1379 (now mandated by Water Right Decision D-1641) and has monitored discrete water quality and nutrients in the upper San Francisco Estuary since 1975. The objectives of the EMP are to obtain consistent and accurate monthly data at established monitoring stations, provide and document information necessary to achieve compliance with salinity, flow, and dissolved oxygen standards, and to report this information for the purpose of management and conservation of the upper San Francisco Estuary. While the EMP also collects biological data, this dataset only includes the discrete water quality and nutrient data collected by the EMP from 1975-2021. Links to other EMP datasets can be found [here](https://emp-des.github.io/emp-reports/data-links.html) \</markdown\>
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the North Shore East Lake Bonney (NELB) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2018-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. The North Shore East Lake Bonney SLIME transect is located approximately 2000 m west of the Priscu Stream inflow to the East Lobe of Lake Bonney. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the North Shore Lake Fryxell (NFRX) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2018-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. The North Shore Lake Fryxell SLIME transect is located approximately 500 m west of the Lake Fryxell Camp. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the South Shore East Lake Bonney (SELB) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2017-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
Soil Lake Inundation Moat Experiment (SLIME): Continuous environmental measurements from the South Shore Lake Fryxell (SFRX) Active Layer and Moat Monitoring Station (ALMMS), McMurdo Dry Valleys, Antarctica (2018-2022, ongoing)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Valleys LTER project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the edges of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. Extensive microbial mats are found across these moatbeds, yet their ecological dynamics remain poorly understood. To study these habitats, we established sampling transects on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. At each transect, we manually sample soils, sediments, microbial mats, and the water column during the austral summer. To complement these efforts, Active Layer and Moat Monitoring Stations (ALMMS) continuously measure key environmental variables, including moatbed temperatures, incoming and underwater photosynthetically active radiation (PAR and UW-PAR), subsurface temperatures, soil volumetric water content, and soil electrical conductivity across a moisture gradient from wet (near the lake shore) to dry (further inland). These measurements help us understand environmental and ecological changes as shoreline soils transition between aquatic and terrestrial habitats, whether through inundation from rising lake levels or drying as moatbeds are exposed. The South Shore Lake Fryxell SLIME transect is located approximately 1500 m west of the F6 Camp. Sensor deployments along the transect follow a wet-to-dry gradient, capturing environmental transitions in real time.
PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing
<p>The urban population continues to grow despite health risks associated with densely populated cities, such as traffic congestion and air pollution. At the same time cities are also further heating up due to climate change. Environmental monitoring is increasingly critical to react quickly to temporarily increased concentrations of, for example, carbon monoxide, nitrogen oxides, ozone, and particulate matter. <br>We introduce a significantly improved version of our PhytoNode, an energy-efficient sensor node designed for phytosensing, that is, using of plants as environmental sensors. We aim for a scalable and sustainable real-time monitoring solution following our vision of an `intelligent plant' as an inexpensive and accurate sensor node. <br>We measure electrical potentials and leaf temperatures of plants to assess their well-being and, in turn, environmental conditions. <br>The PhytoNode achieves long-term energy autonomy by harvesting energy via solar cells and shares data via Bluetooth Low Energy (BLE) communication. We process the gathered time series plant data onboard in real-time using methods of Machine Learning (ML) to analyze the plant's activity and to detect dangerous concentrations of gases. In a few showcasing experiments, we demonstrate the feasibility of both our hardware and software approach for continuous, long-term environmental monitoring based on phytosensing. By embedding engineered devices in living plants as a `plant wearable' that listens to plant responses, we hope to help pushing towards smarter future cities and healthier urban environments. </p> <p> </p> <p>Data repository for our paper "PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing", submitted to the 8th Future of Information and Communication Conference 2025 (FICC 2025). Please refer to the paper for more information.</p>
Testing Smart City environmental monitoring technology using small scale temporary cities
<p>This is the data used for:</p> <blockquote> <p>S. J. Johnston <em>et al</em>., "Testing Smart City environmental monitoring technology using small scale temporary cities," <em>2019 IEEE 5th World Forum on Internet of Things (WF-IoT)</em>, 2019, pp. 578-583, doi: 10.1109/WF-IoT.2019.8767274.</p> </blockquote> <p> </p> <p><em><strong>Abstract:</strong></em></p> <p>Exposure to particulate matter has been identified as a major health problem worldwide. Established measurement<br> techniques require equipment costing many thousands of dollars and specialist expertise to maintain. Ongoing research<br> is investigating the use of low cost <$300 sensors to enable greater temporal-spatial density of readings to be taken. There<br> are questions about the suitability and reliability of these low-cost sensors, queries which can be addressed by deploying<br> and evaluating the sensors in a real world application. We propose festival site as small scale cities to enable a short term<br> deployments and evaluation of sensors. We present data from these devices and experiences gained from using a festival site as a substitute for a city.</p> <p><em><strong>Files:</strong></em></p> <p>1 - timeLapse: mp4 file presenting a time lapse of the measurements realised during the festival<br> 2 - sensor_data: csv file containing the 5 min averaged data from all the sensors deployed and their coordinates used to generate the graph and the maps in the paper</p> <p>3 - workshop.pdf: instructions to run the workshop and code used for the workshop</p>
Dataset for 'Printed ecoresorbable temperature sensors for environmental monitoring'
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “Printed ecoresorbable temperature sensors for environmental monitoring”.</p> <p>This work aims to study the effect of photonic sintering parameters on the temperature behavior of printed zinc resistors, with the aim to fabricate eco-friendly and ecoresorbable temperature sensors on paper. Biodegradable electronic devices have potential in tackling the increasingly pressing challenge of electronic waste and printing methods allow to reduce toxic byproducts of fabrication and wasted material. The sintering method that is optimized here is based on our previous work combining electrochemical and photonic sintering approaches to enable the fabrication of highly-conductive degradable metal tracks. We optimize the sintering parameters to obtain zinc resistors with a high temperature coefficient of resistance and a linear temperature response curve. The data that was collected in the frame of this work is present in this repository. More information about the contents of the dataset is present in the included README files.</p>
Interagency Ecological Program: Phytoplankton monitoring in the Sacramento-San Joaquin Bay-Delta, collected by the Environmental Monitoring Program, 2008-2024
The State Water Resources Control Board (SWRCB) sets water quality objectives to protect beneficial uses of water in the Sacramento-San Joaquin Delta and Suisun Bay. These objectives are met by establishing standards mandated in water right permits issued to the Department of Water Resources and U.S. Bureau of Reclamation by the SWRCB. The standards include minimum Delta outflows, limits to Delta water export by the State Water Project (SWP) and the Central Valley Project (CVP), and maximum allowable salinity levels. In 1971, the State Water Resources Control Board (SWRCB) established Water Right Decision 1379 (D-1379). This Decision contained new water quality requirements for the San Francisco Bay-Delta Estuary. D-1379 was also the first water right decision to provide terms and conditions for a comprehensive monitoring program to routinely determine water quality conditions and changes in environmental conditions within the estuary. The monitoring program described in D-1379 was developed by the Stanford Research Institute through a contract with the SWRCB. Implementation of the monitoring program began in 1972, as SWRCB, DWR, and USBR met to define their individual responsibilities for various elements of the monitoring program. In 1978, amendments to water quality standards were implemented and resulted in Water Right Decision 1485 (D-1485). More recently these standards were again amended under the 1995 Water Quality Control Plan and Water Right Decision 1641 (D-1641) established in 1999. The SWP and CVP are currently operated to comply with the monitoring and reporting requirements described in D-1641. D-1641 requires DWR and USBR to conduct a comprehensive environmental monitoring program to determine compliance with the water quality standards and also to submit an annual report to SWRCB discussing data collected. The phytoplankton monitoring program is one element of DWR’s and USBR’s Environmental Monitoring Program (EMP) conducted under the Interagency Ec
Lake ice surveys, 1874-2022, Adirondack Long-Term Ecological Monitoring Program Project No. 8 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
The objective of this dataset is to document ice-in and ice-out dates on several lakes on the State University of New York College of Environmental Science and Forestry's Huntington Wildlife Forest (HWF). Lakes include: Arbutus, Catlin, Deer, Military, Rich, Wolf and Lodo Pond; some records exist for Long Pond and other water bodies but they are not included here except in some comment fields.
PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System
<p>Cities worldwide are growing, putting bigger populations at risk due to urban pollution. Environmental monitoring is essential and requires a major paradigm shift. We need green and inexpensive means of measuring at high sensor densities and with high user acceptance. We propose using phytosensing: using natural living plants as sensors. In plant experiments we gather electrophysiological data with sensor nodes. We expose the plant <em>Zamioculcas zamiifolia</em> to five different stimuli: wind, temperature, blue light, red light, or no stimulus. Using that data we train ten different types of artificial neural networks to classify measured time series according to the respective stimulus. We achieve good accuracy and succeed in running trained classifying artificial neural networks online on the microcontroller of our small energy-efficient sensor node. To indicate later possible use cases, we showcase the system by sending a notification to a smartphone application once our continuous signal analysis detects a given stimulus.</p> <p> </p> <p>Data repository for our paper "PhytoNodes for Environmental Monitoring: Stimulus Classification based on<br> Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System", submitted to the GoodIT conference. Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>raw_data: </em>The datasets from our plant experiments for the stimuli wind, temperature, red light, blue light, and no stimulus.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. Find the training and testing datasets in the archives folder as well as the trained classifiers in the results folder.</li> <li><em>classification_results.ods: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time).</li> <li><em>TFLite_Models: </em>The trained classifiers in TensorFlow Lite Format.</li> <li><em>00_AI_BLE_MeasuringOnlyWind: </em>Source code for classification on STM-based PhytoNodes (using MCDCNN two-class classifier) and Bluetooth communication. The code is written for the STM32WB55 Nucleo board and can be transferred to the dongle.</li> <li><em>zavrsniProjekt_iOS: </em>Source code of the iOS app used to receive data from the STM-based PhytoNodes.</li> <li><em>Watchplant_application_documentation.pdf: </em>Instructions to build and use the iOS app.</li> </ul>
Environmental nucleic acids: a field-based comparison for monitoring freshwater habitats using eDNA and eRNA
<p>Nucleic acids released by organisms and isolated from environmental substrates are increasingly being used for molecular biomonitoring. While environmental DNA (eDNA) has received attention recently, the potential of environmental RNA as a biomonitoring tool remains less explored. Several recent studies using paired DNA and RNA metabarcoding of bulk samples suggest that RNA might better reflect "metabolically active" parts of the community. However, such studies mainly capture organismal eDNA and eRNA. For larger eukaryotes, isolation of extra-organismal RNA will be important, but viability needs to be examined in a field-based setting. In this study we evaluate (a) whether extra-organismal eRNA release from macroeukaryotes can be detected given its supposedly rapid degradation, and (b) if the same field collection methods for eDNA can be applied to eRNA. We collected eDNA and eRNA from water in lakes where fish community composition is well documented, enabling a comparison between the two nucleic acids in two different seasons with monitoring using conventional methods. We found that eRNA is released from macroeukaryotes and can be filtered from water and metabarcoded in a similar manner as eDNA to reliably provide species composition information. eRNA had a small but significantly greater true positive rate than eDNA, indicating that it correctly detects more species known to exist in the lakes. Given relatively small differences between the two molecules in describing fish community composition, we conclude that if eRNA provides significant advantages in terms of lability, it is a strong candidate to add to the suite of molecular monitoring tools.</p>
A Methodology for the Fast Identification and Monitoring of Microplastics in Environmental Samples using Random Decision Forest Classifiers
<p>This short video shows the results of the application of a classifier for microplastics as described by Hufnagl et al. (2019).</p> <p> </p> <p>If you reuse this video please cite</p> <p> </p> <p>Hufnagl, B., Steiner, D., Renner, Löder, M. G. J., Laforsch, C. and Lohninger, H. <em>A Methodology for the Fast Identification and Monitoring of Microplastics in</em><em> Environmental Samples using Random Decision Forest Classifiers,</em> Analytical Methods, 2019, DOI:10.1039/C9AY00252A</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 from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information
<p><span>Monitoring fish communities is central to the evaluation of ecological health of rivers. Not only presence/absence of species is important to assess, but also the species composition of local fish assemblages is a crucial parameter. Lotic fish communities are traditionally monitored via electrofishing, characterized by a known limited efficiency and high survey costs. The use of environmental DNA-based analyses could serve as a non-destructive alternative, but this approach requires further insights in practical sampling schemes incorporating transport and dilution of the eDNA fragments; as well as optimization of molecular detection in terms of predictive power and quality assurance. By introducing fifteen species known to occur in Belgian waters via a controlled cage experiment, we aim to extend the knowledge on streamreach of eDNA in small rivers and large brooks, as laid out in the European Water Framework Directive's water typology. Introducing fish communities in two transects of a species poor river characterized by contrasting river discharge rates, we found strong and significant correlations between the eDNA relative abundances and the relative biomass per species in the cage community. Despite a decreasing correlation over distance, the underlying community composition remained stable over a distance of 300 m up to 1 km downstream of the cages, depending on the river discharge rate. Such decrease in similarity between relative source biomass and the corresponding eDNA-based community profile with increasing distance downstream from the source, can partly be attributed to variation in species-specific eDNA persistence. Our findings offer novel insights on eDNA behaviour and characterization of riverine fish communities. We conclude that water sampled from a relatively small river offers an adequate snapshot of the total fish community composition occurring within an upstream perimeter ranging between 300 and 1000 meters. The potential application for other river systems is discussed in this study. </span></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.