Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
12
datasets available to search
ShareScore release 0.7.1
Dataset results
12 results for “SEV LTER”
SEV-LTER quadrat plant species cover and height all sites and experiments
This dataset includes plant species cover and height data measured in 1 m x 1 m quadrats at several sites and experiments under the Sevilleta LTER program. Quadrat locations span four distinct ecosystems and their ecotones: creosotebush dominated Chihuahuan Desert shrubland (est. winter 1999), black grama-dominated Chihuahuan Desert grassland (est. winter 1999), blue grama-dominated Plains grassland (est. winter 2002), and pinon-juniper woodland (est. winter 2003). Data on plant cover and height for each plant species are collected per individual plant or patch (for clonal plants) within 1 m x 1 m quadrats. These data inform population dynamics of foundational and rare plant species. In addition, using plant allometries, these non-destructive measurements of plant cover and height can be used to calculate net primary production (NPP), a fundamental ecosystem variable that quantifies rates of carbon consumption and fixation. Estimates of plant species cover, total plant biomass, or NPP can inform understanding of biodiversity, species composition, and energy flow at the community scale of biological organization, as well as spatial and temporal responses of plants to a range of ecological processes and direct experimental manipulations. The cover and height of individual plants or patches are sampled twice yearly (spring and fall) in permanent 1m x 1m plots within each site or experiment. This dataset includes core site monitoring data (CORE, GRIDS, ISOWEB, TOWER), observations in response to wildfire (BURN), and experimental treatments of extreme drought and delayed monsoon rainfall (EDGE), physical disturbance to biological soil crusts on the soil surface (CRUST), interannual variability in precipitation (MEANVAR), intra-annual variability via additions of monsoon rainfall (MRME), additions of nitrogen as ammonium nitrate (FERTILIZER), additions of nitrogen x phosphorus x potassium (NutNet), and interacting effects of nighttime warming, nitrogen addition, and El Ni
SEV-LTER quadrat plant species biomass all sites and experiments
This dataset includes estimated plant aboveground live biomass data measured in 1 m x 1 m quadrats at several sites and experiments under the Sevilleta LTER program. Quadrat locations span four distinct ecosystems and their ecotones: creosotebush dominated Chihuahuan Desert shrubland (est. winter 1999), black grama-dominated Chihuahuan Desert grassland (est. winter 1999), blue grama-dominated Plains grassland (est. winter 2002), and pinon-juniper woodland (est. winter 2003). Data on plant cover and height for each plant species are collected per individual plant or patch (for clonal plants) within 1 m x 1 m quadrats. These data inform population dynamics of foundational and rare plant species. Biomass is estimated using plant allometries from non-destructive measurements of plant cover and height, and can be used to calculate net primary production (NPP), a fundamental ecosystem variable that quantifies rates of carbon consumption and fixation. Estimates of plant species cover, total plant biomass, or NPP can inform understanding of biodiversity, species composition, and energy flow at the community scale of biological organization, as well as spatial and temporal responses of plants to a range of ecological processes and direct experimental manipulations. The cover and height of individual plants or patches are sampled twice yearly (spring and fall) in permanent 1m x 1m plots within each site or experiment. This dataset includes core site monitoring data (CORE, GRIDS, ISOWEB, TOWER), observations in response to wildfire (BURN), and experimental treatments of extreme drought and delayed monsoon rainfall (EDGE), physical disturbance to biological soil crusts on the soil surface (CRUST), interannual variability in precipitation (MEANVAR), intra-annual variability via additions of monsoon rainfall (MRME), additions of nitrogen as ammonium nitrate (FERTILIZER), additions of nitrogen x phosphorus x potassium (NutNet), and interacting effects of nighttime warming, nitroge
SEV-LTER Mean - Variance Experiment Plains Grassland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And How does micro-environmental variation among plots influence how treatments alter soil moisture profiles over three soil depths? This data package includes sensor data from the Mean x Variance experiment in the Plains grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM, which is dominated by the grass species Bouteloua gracilis (blue grama).
SEV-LTER Mean x Variance Experiment Desert Grassland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
Evolutionary Monitoring for the SEV LTER program at the Sevilleta National Wildlife Refuge, New Mexico
This dataset contains collection and specimen archive information for plant and arthropod tissue samples from the Sevilleta National Wildlife Refuge that are currently stored in The Museum of Southwestern Biology’s Division of Genomic Resources at the University of New Mexico. The purpose of the sample collection is to allow researchers in the future to do genetic or genomic work on archived historical samples collected at six-year intervals. Six dominant, foundation plant species are represented. Stored tissues include roots from Bouteloua eriopoda, Bouteloua gracilis, Larrea tridentata, and Machaeranthera pinnatifida, leaves from Juniperus monosperma, Larrea tridentata, and Pinus edulis, and seeds from Larrea tridentata. A few arthropods were collected opportunistically when they adhered to plant material brought back to the lab. Collection dates were from 9 October through 4 November 2019. Samples were collected within four core sites of the Sevilleta National Wildlife Refuge, Socorro, NM: (Plains grassland: core_blue, Desert grassland: core_black, Desert shrubland: core_creosote, and Pinon-juniper woodland: core_PJ).
SEV-LTER Mean Variance Experiment Desert Shrubland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert Shrubland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean Variance Experiment Juniper Savanna Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Juniper Savanna ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean - Variance Experiment Quadrat Plant Species Cover and Height
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Climate Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. This dataset includes plant species cover and height data measured in 1 m x 1 m quadrats at all Mean - Variance experiment sites. Quadrat locations span five important ecosystems of the American southwest: blue grama-dominated Plains grassland (est. fall 2019), black grama-dominated Chihuahuan Desert grassland (est. fall 2020), creosotebush dominated Chihuahuan Desert shrubland (est. fall 2021), juniper savanna (est. fall 2022) and pinon-juniper woodland (est. fall 2023). Data on plant cover and height for each plant species are collected per individual plant or patch (for clonal plants) within 1 m x 1 m quadrats. These data inform population dynamics of foundational and rare plant species. The cover and height of individual plants or patches are sampled twice yearly (spring and fall) in permanent 1m x 1m plots within each site or experiment. This data package includes plant cover and height only -- for species biomass estimates per quad see package knb-lte
SEV-LTER Mean - Variance Experiment Seasonal Biomass Data at the Sevilleta National Wildlife Refuge, New Mexico
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Climate Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. This data package includes species-level plant cover and biomass data from the Mean - Variance Experiment at five sites comprising the major ecosystems of the Sevilleta National Wildlife Refuge: Chihuahuan Desert shrubland, Chihuahuan Desert grassland, Great Plains grassland, Juniper savanna, and pinon-juniper woodland. Species cover and volume in one-meter-squared quadrats are assessed twice-yearly in spring and fall, and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
SEV-LTER Mean x Variance Experiment Pinon Juniper Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Pinon Juniper ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV LTER: Tracking Vegetation Phenology Using PhenoCam Imagery at the Sevilleta National Wildlife Refuge, New Mexico, 2014-2024
As of 03/03/2024, the Sevilleta Long-Term Ecological Research Program is equipped with a total of 65 digital RGB cameras, or PhenoCams, across the Sevilleta National Wildlife Refuge. These cameras are installed on eddy covariance flux towers and at a number of precipitation manipulation experiments to track vegetation phenology and productivity across dryland ecotones. PhenoCams have been paired with eddy covariance flux tower data at the site since 2014, while some Mean-Variance Experiment PhenoCams were installed as recently as June 2023. For information on PhenoCam data processing and formatting, see Richardson et al., 2018, Scientific Data (https://doi.org/10.1038/sdata.2018.28), Seyednasrollah et al., 2019, Scientific Data (https://doi.org/10.1038/s41597-019-0229-9), and the PhenoCam Network web page (https://phenocam.nau.edu/webcam/). The PhenoCam Network uses imagery from digital cameras to track vegetation phenology and seasonal changes in vegetation activity in diverse ecosystems across North America and around the world. Imagery is uploaded to the PhenoCam server hosted at Northern Arizona University, where it is made publicly available in near-real time, every 30 minutes from sunrise to sunset, 365 days a year. The data are processed using simple image analysis tools to yield a measure of canopy greenness, from which phenological metrics are extracted, characterizing the start and end of the growing season. These transition dates have been shown to align well with on-the-ground observations at various research sites. Long-term PhenoCam data can be used to track the impact of climate variability and change on the rhythm of the seasons.
SEV-LTER Plant Traits Database
This dataset contains measurements of morphological (leaf, stem, root, and seed), nutrient, and isotopic traits for plant species growing in the Sevilleta National Wildlife Refuge. Approximately 104 species were sampled in or near four core sites of the SEV-LTER (core_blue, core_black, core_creosote, and core_PJ) plus the Sevilleta Field Station between 2017 and 2021. In addition, seed masses were measured from a 2016-era seed collection provided by Jenny Noble and added to the dataset; for these, site = NA.
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.