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113 results for “Missouri”
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Missouri River at Hermann, MO, 2022-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site at Missouri River at Hermann, MO, 2022-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/MO_Missouri_River_at_Hermann for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown".Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process. Th
Data from: Invasion timing affects multiple scales, metrics and facets of biodiversity outcomes in ecological restoration experiments (Missouri, 2009-2016)
Vegetation responses to experimental ecological restoration treatments at Tyson Research Centre of Washington University in Missouri, USA. These data include species-level cover responses to various factorial restoration treatments. Treatments were applied starting in 2009 and were measured in 2016. Treatment responses reflect these long term responses, but the dataset is comprised to one time point.
Missouri Lakes and Reservoirs Long-term Limnological Dataset, 1976-2018.
This data set compiles 43 years of limnological data from Missouri lakes and reservoirs collected by the University of Missouri Limnology Lab. Although the dataset includes information from nine different projects, the bulk of the data (~75%) come from the Statewide Lake Assessment Project and the Lakes of Missouri Volunteer Program, both of them funded primarily by Missouri Department of Natural Resources. The Statewide Lake Assessment Project began in 1978 sampling a small set of reservoirs. In 1989 the assessment expanded to include regular annual summer monthly collections between May and August, though monitoring was extended for some reservoirs in certain years. We monitored 240 lakes to create the dataset, which represents over 2600 lake-years. The Lakes of Missouri Volunteer Program began in 1992 monitoring 5 lakes and reservoirs and has expanded to 121 sites on 65 waterbodies. Volunteer community scientists monitor their respective sites approximately 8 times per season (April through September). This dataset represents over 15,000 sample events. Lake Ozarks is a long-term (1976-2014) spatial examination of a single large reservoir during summer. Table Rock Monitoring is another multi-year (1995-2009) spatial examination of a large reservoir, but includes year-round data. The rest of the projects included in the dataset monitored Missouri lakes and reservoirs at various intervals including daily (Woodrail, Daily), weekly (icubed), and biweekly (High Res).
Missouri reservoir profile data including temperature, depth, and oxygen profiles (1989-2022)
This dataset of limnological profiles starts in 1989 and is from 250 reservoirs in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Physical parameters derived from sensors include temperature and oxygen with depth. After 2017, sondes were upgraded to Yellow Springs Instruments (YSI) EXO3s; profiles include a range of physical, chemical, and biological parameters including depth, conductivity, pH, oxidative-reductive potential (ORP), chlorophyll a, phycocyanin (PC), phycoerythrin (PE), and turbidity. Most of the profiles were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of profiles were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few profiles were taken from bridges and docks. The bulk of the data come from the Statewide Lake Assessment Project (SLAP), funded by the Missouri Department of Natural Resources. The profiles are divided into a single file for 1989-2016 and then annual compiled datasets for 2017 and beyond. This data has been quality controlled for basic errors and any data outside of normal factory issued sensor ranges.
Missouri reservoir profile data including depth, temperature, oxygen, photopigments, conductivity, pH, turbidity, and oxidative-reductive potential starting in 2023
This dataset of annual limnological profiles starts in 2023 and is from reservoirs in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Physical parameters derived from sensors include temperature and oxygen with depth. Sondes used were Yellow Springs Instruments (YSI) EXO3s; profiles include a range of physical, chemical, and biological parameters including depth, conductivity, pH, oxidative-reductive potential (ORP), chlorophyll a, phycocyanin (PC), and turbidity. After May 2024, the turbidity sensor was replaced with a phycoerythrin (PE) sensor. Most of the profiles were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of profiles were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat. The bulk of the data come from the Statewide Lake Assessment Project (SLAP), funded by the Missouri Department of Natural Resources. The profiles are divided into single files for each year. This data has been quality controlled for basic errors and any data outside of normal factory issued sensor ranges.
Missouri reservoir water quality data from the Statewide Lake Assessment Program (SLAP), the Lakes of Missouri Volunteer Program (LMVP), and the Reservoir Observer Student Scientists (ROSS) program
This dataset of limnological water quality data continues from Jones et al., 2024, starting in 2017 until 2021. It is from 195 reservoirs, the majority of which are in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: areal pigment absorption coefficient, alkalinity, alpha (light utilization efficiency P-E parameter), ammonium (NH4), ammonium-debt, anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, seston d13C, seston d15N, dissolved turbidity, dissolved organic carbon, Ek (light saturation P-E parameter), FVFM (maximum quantum yield of PSII for photochemistry), gross primary production, microcystin, nitrate & nitrite (NO3), nitrate-debt, particulate nitrogen, particulate phosphorus, phosphorus-debt, pheophytin, particulate carbon, particulate inorganic matter, particulate organic matter, phycocyanin (PHYCO), saxitoxin, Secchi disk depth, silica, soluble reactive phosphorus, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. Most of the data come from the Statewide Lake Assessment Project (SLAP) and the Lakes of Missouri Volunteer Program (LMVP) funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete dep
Missouri reservoir water quality data (2022 - current) from the Statewide Lake Assessment Program (SLAP)
This dataset of limnological water quality data continues from North et al., 2025, starting in 2022 until present. The data is from reservoirs, primarily within the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: ammonium (NH4), anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, dissolved organic carbon, microcystin, nitrate & nitrite (NO3), pheophytin, particulate inorganic matter, particulate organic matter, phycocyanin, saxitoxin, Secchi disk depth, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids, and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. The bulk of the data come from the Statewide Lake Assessment Project (SLAP), funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete depths in the hypolimnion.
Valuing multiple ecosystem services under contrasting land use scenarios, Grand River Basin (Iowa and Missouri, USA), 2016
This dataset includes spatial and tabular data used to evaluate ecosystem service outcomes under three land-use scenarios (baseline, buffered, and productivity-based) within the Grand River Basin, spanning southwest Iowa and northwest Missouri. The dataset comprises geospatial layers and model inputs for land cover, soil characteristics, and hydrology, as well as outputs from the InVEST modeling suite for nutrient delivery ratio, sediment delivery ratio, carbon storage, and pollinator abundance. It also includes financial data used to estimate the net present value of bioenergy grassland systems, including enterprise budgets and biomass yield assumptions. The scenarios simulate the conversion of cropland to native grassland either adjacent to streams (Buffered) or on low-productivity soils (Productivity-based), and the dataset captures associated changes in land cover, ecosystem service indicators, and potential biomass production. This dataset is intended to support further research and planning related to landscape-scale conservation, ecosystem service valuation, and bioenergy development in agricultural regions.
Figure 6 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 6: PCR results using Meloidogyne-specific and M. naasi and M. marylandi-specific primers. DL: DNA Ladder; 1: Meloidogyne spp. (DNA ID:9); 2: M. naasi (DNA ID:9); 3: Meloidogyne spp. (DNA ID:4); 4: M. marylandi (DNA ID:4); and 5: Meloidogyne spp.(DNA ID:4).
Figure 3 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 3: PCR results using Hoplolaimus-specific and H. stephanus, H. columbus and H. galeatus-specific primers. DL: DNA Ladder; 1: Hoplolaimus spp. (DNA ID:10); 2: H. stephanus (DNA ID:10); 3: H. columbus (DNA ID:10); 4 H. galeatus (DNA ID:10); 5: Hoplolaimus spp. (DNA ID:3); 6: H. stephanus (DNA ID:3); 7: H. columbus (DNA ID:3); 8 H. galeatus (DNA ID:3); 9: Hoplolaimus spp. (DNA ID:4); 10: H. stephanus (DNA ID:4); 11: H. columbus (DNA ID:4); and 12 H. galeatus (DNA ID:4).
Figure 2 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 2: Phylogeny of the rDNA ITS region of Hoplolaimus spp. isolated from golf putting greens. Phylogenetic trees were constructed with the neighbor-joining algorithm using the Kimura two-parameter model with Litylenchus spp. (LC383724) as the outgroup. Bootstrap values are based on 1000 resamplings of the data set. DNAID codes correlate to Table 2.
Figure 1 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 1: Distribution of plant-parasitic nematode species sampled from creeping bentgrass putting greens in Missouri and eastern Kansas in 2021 and Indiana in 2022 in two independent pie charts. Samples were collected during the months of April, June, August and October of 2021 and 2022, respectively. "n" indicates total PPNs represented within each chart.
Figure 4 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 4: Scanning-electron micrographs of a lance nematode specimen collected form Site 5. A) four lip annules; B) the presence of an epiptygma; C) 25 longitudinal striae on the basal lip annule; and D) four lateral incisures.
Figure 5 in Depth distribution of plant-parasitic nematodes on bentgrass golf greens in Missouri and Indiana
Figure 5: Phylogeny of molecularly characterized Meloidogyne spp. isolated from golf coursed based on D2/D3 28S genes. phylogenetic trees were constructed with the neighborjoining algorithm using the Kimura two-parameter model with Litylenchus spp. (LC383724) as the outgroup. Bootstrap values are based on 1000 resamplings of the data set and displayed near branch nodes. DNAID codes correlate to Table 2.
Dataset: Southern Missouri Bancorp, Inc. (SMBC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Linked collectors and determiners for: Two new species of freshwater crayfish of the genus Faxonius (Decapoda: Cambaridae) from the Ozark Highlands of Arkansas and Missouri.
Natural history specimen data linked to collectors and determiners held within, "Two new species of freshwater crayfish of the genus Faxonius (Decapoda: Cambaridae) from the Ozark Highlands of Arkansas and Missouri". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6c711c6e-5983-41a5-9ba0-133ac3e85f4a">https://bionomia.net/dataset/6c711c6e-5983-41a5-9ba0-133ac3e85f4a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6c711c6e-5983-41a5-9ba0-133ac3e85f4a">https://gbif.org/dataset/6c711c6e-5983-41a5-9ba0-133ac3e85f4a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: MO herbarium - Missouri Botanical Garden - Amostras Brasileiras Repatriadas - Herbário Virtual REFLORA.
Natural history specimen data linked to collectors and determiners held within, "MO herbarium - Missouri Botanical Garden - Amostras Brasileiras Repatriadas - Herbário Virtual REFLORA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c0a459c6-2ae7-4890-a1e4-38e24e671269">https://bionomia.net/dataset/c0a459c6-2ae7-4890-a1e4-38e24e671269</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c0a459c6-2ae7-4890-a1e4-38e24e671269">https://gbif.org/dataset/c0a459c6-2ae7-4890-a1e4-38e24e671269</a>. Formatted as a Frictionless Data package.
Northern bobwhite adult breeding season and nest survival Missouri 2014-2018
<p>These data and code are associated with the publication in The Journal of Wildlife Management entitled "Northern Bobwhite breeding season and nest survival are greater on native grasslands." We evaluated the influence of vegetation cover type, woody vegetation structure and composition, and habitat management on nest survival and adult survival from May through September in southwest Missouri 2014-2018.</p>
Northern bobwhite adult breeding season and nest survival Missouri 2014-2018
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Data from: Quantifying seed rain patterns in a remnant and a chronosequence of restored tallgrass prairies in north central Missouri
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