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53 results for “environmental science”
Figure 2. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 2. - Time line of the tasks in UNICORN-project
Figure 1. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 1. - Links and interactions between the work packages
Data on soil variables (with plot IDs) and grassland species traits used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. Journal of Vegetation Science, 35, e13259. Available from: https://doi.org/10.1111/jvs.13259
<p>File <a href="../api/records/10983049/draft/files/Plot_IDs_990ua.txt/content" target="_blank" rel="noopener noreferrer">Plot_IDs_990ua.txt</a> contains the IDs of the 1-m2 plots used for the analysis of grassland vegetation data by Pillar, V.D. (2024) Trait divergence in plant community assembly is generated by environmental factor interactions. The plot data are stored in the sPlot database (PPBio South Brazilian Grassland Database).</p> <p>File <a href="../api/records/10983049/draft/files/E_990ua_21SoilVar.txt/content" target="_blank" rel="noopener noreferrer">E_990ua_21SoilVar.txt</a> contains data on soil variables evaluated in the 250 m transects, but here expanded to the 990 1-m2 plots (each transect was sampled using 10 1-m2 pots).</p> <p>File <a href="../api/records/10983049/draft/files/B_769spp_4t.txt/content" target="_blank" rel="noopener noreferrer">B_769spp_4t.txt</a> is the species trait database collected in the framework of several research projects in the Quantitative Ecology Lab (EcoQua) and Grassland Vegetation Studies Lab (LevCamp) of Universidade Federal do Rio Grande do Sul (UFRGS). Data gaps were filled by compiled from the TRY database and data imputation.</p> <p> </p> <p> </p>
Dataset for Scrollytelling as as Strategy for Socio-Environmental Enagement: A Digital Citizen Science Narrative Approach
<p>Resulting Dataset on the evaluation of Scrollytelling deliverables developed by higher education students based on citizen science projects.</p>
Data for "Citizen science as a valuable tool for environmental review" - Frontiers in Ecology and the Environment - Callaghan et al.
<p>This dataset is the dataset used in Callaghan et al. Citizen science as a valuable tool for environmental review. Frontiers in Ecology and the Environment. The data are Environmental Impact Statement titles, and our coding of those documents. See paper for details.</p>
Data for Bradter, Altringham, Kunin, Thom, O'Connell & Benton: Variable ranking and selection with random forest for unbalanced data. Environmental Data Science
<p>The data are used in 'Bradter, Altringham, Kunin, Thom, O'Connell & Benton: Variable ranking and selection with random forest for unbalanced data. Environmental Data Science' and are described in the ReadMe file and in the manuscript and Supporting information.</p>
Assessing and managing environmental hazards of polymers: historical development, science advances and policy options
<p>Supporting information (open data) from Assessing and managing environmental hazards of polymers: historical development, science advances and policy options</p>
Data from: Designing data science workshops for data-intensive environmental science research
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Ag is STEM: Connecting agriculture to environmental and biological science curriculum
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Ruffed grouse (Bonasa umbellus) drumming surveys, 1987-2017, Adirondack Long-Term Ecological Monitoring Program Project No. 9 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 is to document long-term population trends of ruffed grouse in a northern hardwood ecosystem. The survey area is the Huntington Wildlife Forest, a 6,000 ha field station which receives no hunting pressure. Routes are surveyed starting an hour prior to sunrise on 2-5 mornings each year between April 14 and May 9 (occasionally later), on days when wind and rain are minimal to absent. Counts are standardized relative to weather conditions and timing. Observers count the number of individual ruffed grouse heard drumming (""drummers"") at 32-50 route stations during a 4-minute period. Trends at stations over time as well as overall drummer index are calculated and compared to independent datasets.
Seed Production Survey, 1988-2009, Adirondack Long-Term Ecological Monitoring Program Project No. 26 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York, USA
The purpose of this project is to 1) estimate number of seeds per unit area in a mature northern hardwood/mixed conifer forest stand and 2) document changes over time in selected tree and shrub seed production. Permanent seed traps were established in 1988 in two forest types. Seed traps (13.9 L [5 gal] capacity buckets) are installed 0.5 m off the ground on two metal stakes in the center of each forested plot. Buckets are open to the tree canopy and have small (< 2cm) holes near the bottom edges for drainage. Fifty collection buckets are placed approximately 30 m (100 feet) apart and distributed along painted grid lines in the Huntington Wildlife Forest Natural Area. Twenty-five plots are northern hardwood upland forest (dominated by sugar maple, American beech and yellow birch with some conifers) and 25 plots are in the mixed hardwood/conifer lakeshore forest type (dominated by red maple, yellow birch, red spruce and eastern hemlock). Tree and shrub seeds are collected annually during two periods: July to November (Fall) and November to July (Spring). The spring and autumn collections are based on tree species’ seed phenology. If a bucket was tipped over due to disturbance by a bear or some other factor, it was censored from the survey for that year. Mice or other seed predators that were physically found/present in buckets also resulted in sample censoring. Animal scat or partly-consumed seeds are not censored, as these may have fallen from the tree canopy during seed predators’ normal activities.
White-tailed Deer Population Study, 1962-2008, Adirondack Long-Term Ecological Monitoring Program by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York, USA
From 1962-2008, White-tailed deer (Odocoileus virginianus) were studied at the SUNY ESF Huntington Wildlife Forest (HWF) and adjacent private and public lands in Essex and Hamilton Counties, New York, USA. Social group membership, migration and dispersal, reproductive biology, and many other objectives were studied over the course of the study period. Deer were captured, individually marked with ear tags or streamers, fitted with radio collars (later, GPS collars), and released to be tracked for a variety of research objectives. Deer were located by visual observation, recapture, and/or their location was estimated with ground, air or tower-based radio telemetry. Physical condition of deer was recorded at capture and at subsequent recapture or visual observation select variables were documented (e.g., deer group size; presence of fawns with does). Physiological, demographic, social organization, home range and behavior data were collected. HWF is a no-hunting area but deer could be harvested if they moved to huntable parts of the study area; there was a managed hunt on HWF in 1966-1970 and in 1984 to meet deer density and forest management objectives at that time. Unmarked deer were incorporated into the dataset if they were roadkilled, harvested or otherwise encountered during field activity; these deer did not receive individual identifications but may have been incorporated into select projects.
Continuous Forest Inventory (CFI), 1970-2017, Long-term Forest Property Monitoring by State University of New York College of Environmental Science and Forestry, New York, USA
SUNY College of Environmental Science and Forestry (ESF) based in Syracuse, New York, maintains a series of Continuous Forest Inventory (CFI) permanent plots on their Forest Properties. ESF has over 700 CFI plots located on 5 different properties, four properties in the Adirondack Mountains of northern New York and one property south of Syracuse. Plots cover northern hardwood species including sugar maple, red maple, yellow birch, beech, white ash, red oak, white pine, hemlock, red spruce, and pine/softwood plantations of various species. Data is collected at ten year intervals on each property starting from initial plot establishment. Plot information collected includes: location information, slope, aspect, forest type, cutting history, and photo of plot. Tree information/measurements include (in general, trees greater than 3.6 inches diameter at breast height): tree tag number, species, tree history, diameter at breast height, sawlog height, bole height, total height, crown vigor, crown class, tree location, and tree notes. Data is collected/field checked/edited according to detailed written procedures by ESF professional staff with assistance of students. Data is collected to monitor general forest health, growth rates, mortality, and overall forest metrics. Data is used to calculate standing volumes, stocking of forest trees, carbon stocking in addition to other information. ESF Forest Properties with CFI plots:
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
Data from: A general-purpose spatial survey design for collaborative science and monitoring of global environmental change: the global grid
Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85° because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution ~600 m × 600 m) and require a 64-bit integer representation.
Survey dataset - Environmental Citizen Science: practices and scientists' attitudes at ILTER
<p>The dataset contains survey outcomes from ILTER scientists about their attitudes and actions with regard to Environmental Citizen Science.</p>
Data for: Torii et al.,Observed Kinetics of Enterovirus Inactivation by Free Chlorine Are Host Cell-Dependent, Environmental Science and Technology, 10.1021/acs.est.2c07048
<p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>- Figure 1 (Inactivation curves for E11 by free chlorine, UV, and heat)</p> <p>- Figure 2 (Inactivation curves for CVA9, CVB1, E7, E9, and E13)</p> <p>- Figure S1 (Loss of attachment and the PCR-target by free chlorine treatment)</p> <p>- Figure S2 (Flow cytometric analysis)</p> <p> </p> <p> </p> <p> </p>
Data from: Urban environmental predictors of group size in cliff swallows (Petrochelidon pyrrhonota): A test using community-science eBird data
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Data from: Advanced technologies and data management practices in environmental science: lessons from academia
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Data from: A general-purpose spatial survey design for collaborative science and monitoring of global environmental change: the global grid
Open the record for dataset details and reuse information.
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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.