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ShareScore release 0.9.0
Dataset results
15 results for “Stream Monitoring”
Continuous soil temperature, specific conductance, and volumetric water content measurements from the Von Guerard Stream Active Layer Monitoring Station (ALMS03), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, five Active Layer Monitoring Stations (ALMSs) were established throughout Taylor Valley, Antarctica to support new research foci around the thermal-moisture dynamics of soils that may control habitat conditions and faunal responses to seasonal and annual freezing cycles in this ecosystem. Two ALMSs were established adjacent to streams (Green Creek, Von Guerard Stream), with sensors installed through the active layer from the thalweg out to the shoreline and dry soil beyond. Two ALMSs were similarly established adjacent to water tracks (Wormherder Creek, Water Track B) that are zero-order drainages of snow and ice melt that rarely have surface flow. The remaining station was established in dry soil (F6) to serve as an ambient control. ALMSs measure soil temperature, soil moisture (as volumetric water content; VWC), and specific conductance (as electrical conductivity; EC) through the active layer (soil surface down to the frost table) at several locations from the water’s edge to dry soils. This data package contains measurements from the Active Layer Monitoring Station at Von Guerard Stream (ALMS03).
Raw Data and Codes for the Article "Ultrafast and persistent photoinduced phase transition at room temperature monitored by streaming powder diffraction"
<p>Dataset for the article "Ultrafast and persistent photoinduced phase transition at room temperature monitored by streaming powder diffraction", containing:</p><ul><li>The data and the codes used to generate the figures</li><li>The raw data used for the study, and the codes to analyze it, from raw diffraction images to refinement parameters</li></ul>
Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring
<p><span>Our dataset, Nest Monitoring of the Kagu, consists of around ten days (253 hours) of continuous monitoring sampled at 25 frames per second. Our proposed dataset aims to facilitate computer vision research that relates to event detection and localization. We fully annotated the entire dataset (23M frames) with spatial localization labels in the form of a tight bounding box. Additionally, we provide temporal event segmentation labels of five unique bird activities: Feeding, Pushing leaves, Throwing leaves, Walk-In, and Walk-Out. The feeding event represents the period of time when the birds feed the chick. The nest-building events (pushing/throwing leaves) occur when the birds work on the nest during incubation. Pushing leaves is a nest-building behavior during which the birds form a crater by pushing leaves with their legs toward the edges of the nest while sitting on the nest. Throwing leaves is another nest-building behavior during which the birds throw leaves with the bill towards the nest while being, most of the time, outside the nest. Walk-in and walkout events represent the transitioning events from an empty nest to incubation or brooding, and vice versa. We also provide five additional labels that are based on time-of-day and lighting conditions: Day, Night, Sunrise, Sunset, and Shadows. In our manuscript, we provide a baseline approach that detects events and spatially localizes the bird in each frame using an attention mechanism. Our approach does not require any labels and uses a predictive deep learning architecture that is inspired by cognitive psychology studies, specifically, Event Segmentation Theory (EST). We split the dataset such that the first two days are used for validation, and performance evaluation is done on the last eight days.</span></p>
Simplifying Treatment and Monitoring for HIV (STREAM HIV)
ClinicalTrials.gov study NCT04341779. IPD Sharing: YES. Countries: 1. Publications: 2.
Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring
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Data from: A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams
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Data on monitoring the formation of chemical cocktails in urban streams in response to Freshwater Salinization Syndrome
<p>Data include the concentration of base cations and nutrients over time and over space in urban streams near the University of Maryland campus in College Park, Maryland, USA. Data were collected longitudinally along streams and over 24-hour time periods. Data on the retention and release of base cations and trace elements were also collected through incubation experiments. </p>
EnviroStream: A Stream Reasoning Benchmark for Climate and Ambient Monitoring
<p>Stream Reasoning (SR) focuses on developing advanced approaches for applying inference to dynamic data streams; it has become increasingly relevant in various application scenarios such as IoT, Smart Cities, Emergency Management, and Healthcare, despite being a relatively new field of research.</p> <p>The current lack of standardized formalisms and benchmarks has been hindering the comparison between different SR approaches. <br> We propose a new benchmark, called <em>EnviroStream</em>, for evaluating SR systems on weather and environmental data from two European cities. </p> <p>The benchmark includes queries and datasets of different sizes. We adopt <em>I-DLV-sr</em>, a recently released SR system based on Answer Set Programming, as a baseline experiment. We illustrate how the queries can be modeled via <em>I-DLV-sr</em> input language and report evaluation times. We also assess continuous online reasoning via a web application.</p> <p>############################################################################################</p> <ul> <li>Data can and queries can be also downloaded via the GitHub repository: <a href="https://github.com/DeMaCS-UNICAL/EnviroStream">https://github.com/DeMaCS-UNICAL/EnviroStream</a></li> <li>Real-time data can be visualized via the following link: <a href="https://experiments.demacs.unical.it/">https://experiments.demacs.unical.it/</a></li> </ul>
Data on monitoring the formation of chemical cocktails in urban streams in response to Freshwater Salinization Syndrome
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Acadia National Park, U.S. National Park Service using Lakes and Stream Monitoring Protocol for National Parks in the Northeast Temperate Network, Version 1.1 (2006-2011)
Water quality data collected by Acadia National Park (U.S. National Park Service) staff using the procedures found in the Lakes and Stream Monitoring Protocol for National Parks in the Northeast Temperate Network, Version 1.1. Sampling occurs annually and is scheduled to continue indefinitely. Additional data from non-NPS collaborators are also included (in the pre-2006 sheets). Parameters measured in the field include Secchi transparency and surface temperature. Secchi depth was always measured using a viewing scope with "mask". Color, chlorophyll a, total nitrogen, total phosphorus, DOC, anion & cation samples were collected as grab samples or depth-integrated epilimnetic samples in lakes, and then sent to a central laboratory for analyses. These samples were collected in April, June, August and October. The National Water Quality Lab was used for analysis in 2006 & 2007; the Sawyer Environmental Chemistry Research Lab was used 2008-2011. Values below reporting/detection level indicated in associated "Flag" field. Empty cells with no associated flag indicate no data collected for parameter.
BES LTER Base Cation Data for Core Monitoring Streams across Land Use Gradient
<p>The Baltimore Ecosystem Study LTER has established a network of long-term biogeochemical hydrologic study sites. Sites range from suburban to highly urban. More site information can be found on the BESLTER page at www.beslter.org </p> <p>Descriptions of land use and further site descriptions can be found in Kaushal et al 2017. Tabs represent a different sites, listing the base cation concentrations, as well as a tab summarizing site averages as used in Kaushal et al. 2017.</p>
Data from: Assessing strengths and weaknesses of DNA metabarcoding based macroinvertebrate identification for routine stream monitoring
1) DNA metabarcoding holds great promise for the assessment of macroinvertebrates in stream ecosystems. However, few large-scale studies have compared the performance of DNA metabarcoding with that of routine morphological identification. 2) We performed metabarcoding using four primer sets on macroinvertebrate samples from 18 stream sites across Finland. The samples were collected in 2013 and identified based on morphology as part of a Finnish stream monitoring program. Specimens were morphologically classified, following standardised protocols, to the lowest taxonomic level for which identification was feasible in the routine national monitoring. 3) DNA metabarcoding identified more than twice the number of taxa than the morphology-based protocol, and also yielded a higher taxonomic resolution. For each sample, we detected more taxa by metabarcoding than by the morphological method, and all four primer sets exhibited comparably good performance. Sequence read abundance and the number of specimens per taxon (a proxy for biomass) were significantly correlated in each sample, although the adjusted R2 were low. With a few exceptions, the ecological status assessment metrics calculated from morphological and DNA metabarcoding datasets were similar. Given the recent reduction in sequencing costs, metabarcoding is currently approximately as expensive as morphology-based identification. 4) Using samples obtained in the field, we demonstrated that DNA metabarcoding can achieve comparable assessment results to current protocols relying on morphological identification. Thus, metabarcoding represents a feasible and reliable method to identify macroinvertebrates in stream bioassessment, and offers powerful advantage over morphological identification in providing identification for taxonomic groups that are unfeasible to identify in routine protocols. To unlock the full potential of DNA metabarcoding for ecosystem assessment, however, it will be necessary to address key problems with current laboratory protocols and reference databases.
Data from: Assessing strengths and weaknesses of DNA metabarcoding based macroinvertebrate identification for routine stream monitoring
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Scalable, Asynchronous, Distributed Eigen-Monitoring of Astronomy Data Streams
In this paper, we develop a distributed algorithm for monitoring the principal components (PCs) for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeated images of the night sky every 20 s, thereby generating 30 terabytes of calibrated imagery every night that will need to be co-analyzed with other astronomical data stored at different locations around the world. Event detection, classification, and isolation in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper, we propose a highly scalable and distributed asynchronous algorithm for monitoring the PCs of such dynamic data streams and discuss a prototype web-based system PADMINI (Peer-to-Peer Astronomy Data Mining) which implements this algorithm for use by the astronomers. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e., converges to the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments performed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm.
Scalable Distributed Change Detection from Astronomy Data Streams using Local, Asynchronous Eigen Monitoring Algorithms
This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeat images of the night sky every 20 seconds, thereby generating 30 terabytes of calibrated imagery every night that will need to be coanalyzed with other astronomical data stored at different locations around the world. Change point detection and event classification in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper we propose a highly scalable and distributed asynchronous algorithm for monitoring the principal components (PC) of such dynamic data streams. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e. converges to the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments performed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm.
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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.