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Data for: Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid, Iowa, USA, 2022-2023.
This dataset compiles model outputs, parameter sets, and documentation supporting a techno‑economic analysis (TEA) and life‑cycle assessment (LCA) of co‑digesting beef cattle manure with pretreated mixed prairie biomass to produce renewable natural gas (RNG), with hydroxycinnamic acids (HCA) and digestate‑derived biochar co‑products. It accompanies the study by Katherine Wild, Elmin Rahic, Lisa A Schulte Moore, and Mark Mba Wright "Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid," in Biofuels, Bioproducts, & Biorefining, 2024 (https://doi.org/10.1002/bbb.2710). The integrated simulation and assessment framework quantifies process performance, economics, and greenhouse‑gas intensity across five scenarios representing combinations of alkaline‑ethanol pretreatment for HCA extraction, liquid recirculation fractions, and biochar addition. This data collection includes: stream‑level mass flow/composition tables for each scenario; RNG, biochar, and HCA annual production summaries; literature‑based methane/biogas yield benchmarks; equipment‑level capital costs; TEA assumptions; emission‑factor inventories and displacement credits; and full sensitivity/uncertainty matrices for MFSP and GWP.
Littoral and Pelagic Water Temperatures and Light in Escanaba, McDermott, and Sparkling Lakes, Wisconsin USA 2023-2024
This dataset contains high-frequency temperature (°C) and light (lux) measurements from three north-temperate lakes collected during the summers of 2023 and 2024, as well as locations of sensors in each lake. HOBO (HOBO Pendant temp/light®, Onset Brands, MA) sensors recorded at hourly intervals. Measurements were collected at 0.5m and 1m depth in all lakes (McDermott, Escanaba, and Sparkling) and at select 3m sites in Sparkling Lake. Sampling included pelagic and littoral zones. Each lake contained two pelagic sites instrumented using subsurface buoys. The number of littoral sites varied by lake and year (see Methods). At littoral sites, macrophyte presence or absence was assessed weekly, with presence defined as macrophytes occupying more than 50% of the water column. In Sparkling Lake 3m littoral sites were paired, with sensors placed inside and outside macrophyte beds less than 10m apart. These data were used to quantify thermal habitat breadth (daily temperature range) and to compare estimates based on (1) pelagic-only measurements and (2) combined pelagic + littoral measurements, allowing evaluation of the littoral zone’s contribution to lake thermal assessments. The dataset also supports analyses of macrophyte effects on thermal habitat in littoral zones. Data collection presented in this data package represents a subset of the larger "Walleye Bright Spots" project data.
Microplastic Abundance, Shape, and Color in Passerines Captured at Rushton Woods Preserve Bird Banding Station in Newtown Square, Pennsylvania, USA, April-September 2024
Fecal samples were collected from 5 species of passerine birds between April and September 2024 at the Rushton Woods Preserve Bird Banding Station. Samples were chemically digested and filtered for the purpose of extracting, quantifying, and describing microplastics.
Summer water chemistry, high frequency sensors, zooplankton and benthic macroinvertebrate community composition, periphyton, fish, and macrophyte biomass, along with lake metabolism and greenhouse gas dynamics in six experimental ponds in central Iowa, USA (2020)
This data product contains physical, chemical, and biological data ranging from the minute to daily to weekly scale in six artificial ponds (400 square meter surface area, 2m depth) in central Iowa (USA) 2020. Ponds were paired into three sets of treatment and reference with treatment ponds receiving two nutrient pulses designed to increase ambient phosphorus concentrations ~ 3 - 5%. Nitrogen and phosphorus were added as NH4NO3 and H3PO4, respectively, at a 24:1 molar ratio. The first nutrient pulse occurred on Julian day of year (DOY) 176 corresponding to a 3% increase and the second nutrient pulse occurred on DOY 211 to a 5% increase. Each treatment-reference set had a different food web structure established ranging between low, intermediate, and high complexity based on trophic connectivity and food chain length. Added to this data package is a document titled "2020 Iowa State University Horticultural Farm Experimental Ponds Nutrient Addition Experiment". For experimental set up, context, and a summary table of the data tables archived herein with available variables please review this document. It is added to aid in successful interpretation and to increase ease-of-use. Please email Tyler Butts (tyler.james.butts@gmail.com) for any and all questions regarding context or use of this dataset!
High-frequency winter water temperature and dissolved oxygen at Lake Sunapee, New Hampshire, USA, 2014-2023
The Lake Sunapee Protective Association (LSPA) has been monitoring water quality in Lake Sunapee, New Hampshire, USA, since the 1980s. Beginning in the winter of 2014-2015, the LSPA deployed a string of HOBO temperature sensors at a location near Loon Island (43.391N, 72.058W, where their instrumented buoy is located during the summer months) for under-ice water temperature profile monitoring. A HOBO U26 dissolved oxygen sensor was added to this monitoring string during the winter of 2017-2018 through the winter of 2019-2020. All sensors record data in 15-minute intervals over the winter and are downloaded after ice-off. All data have been QAQC'd to remove obviously errant readings and artifacts of maintenance and flag highly suspicious readings.
Effects of factorial nitrogen, phosphorus, and potassium with micronutrient addition and Host Community on Fungal Endophyte Diversity at Cedar Creek Ecosystem Reserve, Minnesota, USA, 2014
The microbes contained within free-living organisms can alter host growth, reproduction, and interactions with the environment. In turn, processes occurring at larger scales determine the local biotic and abiotic environment of each host that may affect the diversity and composition of the microbiome community. Here, we examine variation in the diversity and composition of the foliar fungal microbiome in the grass host, Andropogon gerardii, across a factorial nitrogen, phosphorus, and potassium addition experiment in Minnesota, USA. We found limited evidence of direct effects of nutrients on endophyte diversity. Instead, the effects of nutrients on endophyte diversity appeared to be mediated by accumulation of plant litter and plant diversity loss. Specifically, nitrogen addition is associated with a 40% decrease in plant diversity and an 11% decrease in endophyte richness. Although nitrogen, phosphorus, and potassium addition increased aboveground live biomass and decreased relative Andropogon cover, endophyte diversity did not covary with live plant biomass or Andropogon cover. Our results suggest that fungal endophyte diversity within this focal host is determined in part by the diversity of the surrounding plant community and its potential impact on immigrant propagules and dispersal dynamics. Our results suggest that elemental nutrients reduce endophyte diversity indirectly via impacts on the local plant community, not direct response to nutrient addition.
Characterization of photosynthetic epilithic biomass on the river bed of the Upper Clark Fork River (Montana, USA) during the algal growing seasons of 2017 and 2018
These data were collected to support monitoring of the Upper Clark Fork River restoration, and data collection was funded by the US NSF Long Term Research in Environmental Biology (LTREB) program and the US NSF EPSCoR funded Montana Consortium for Research on Environmental Water Systems. The LTREB monitoring project consists of monthly or bi-weekly water quality monitoring across a 200-km restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and heavy metal contamination. The original analytical intent for these data was to assess the response of the river algal community to the floodplain restoration. Data characterize epilithic biomass on the river bed including measurements of benthic standing stocks and abundance of pigments associated with primary producers. Metrics of characterization include areal density of chlorophyll a, areal density of phaeophytin, the ratio of carotenoid to chlorophyll absorbance, percent organic matter, areal density of organic matter, and category of biomass composition (filamentous algae vs. other epilithon). Samples were obtained from collecting and scrubbing five rocks at any given site. Biomass estimates were obtained from area corrected Ash Free Dry Mass. Pigment concentrations were obtained through the use of extraction in acetone followed by spectroscopy. Data are from the 2017 and 2018 algal growing seasons. Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.
2017 hydrologic, water quality, and soil quality data from The Jefferson Projects 8 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake’s food web and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project had eight tributary monitoring stations around the lake collecting data on water quality, soil quality and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_ShelvingRock and TS_West. The stations have a sensor payload that may include some or all of the following sensors: EXO2 Multi-parameter sonde, CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter with five 3.0 MHz transducers, Argonaut-SL Doppler current meter, WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and down sampling to an hourly frequency.
Soil nutrient availability from the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016 growing season
This file contains plant-available nitrate (NO3), ammonium (NH4), and phosphate (PO4) in the upper 5-10 cm of organic matter from a burned and unburned site in the southern section of the 2007 Anaktuvuk River fire in northern Alaska. Soil nutrients were assessed using ion-exchange resin membranes incubated in the soil during the growing season of 2016.
Point-frame measurments from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons
This file contains point-frame measurements from a nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016 at a severely burned site and an unburned site. Pin-vegetation contact was recorded using a 0.75 m2 frame with 41 evenly spaced pin-drop points. Data was collected once during the height of the growing season in 2016 (when fertilization began) 2017, 2018 and 2019. This data was used to measure the impact of fertilization and fire on community composition.
Soil nutrient availability from the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2019 growing season
This file contains plant-available nitrate (NO3), ammonium (NH4), phosphate (PO4), and total free primary amines (TFPA )in the upper 5-10cm of organic matter from a burned and unburned site in the southern section of the 2007 Anaktuvuk River fire in northern Alaska. Soil nutrients were assessed using buried resin bags which incubated for 1 month during the peak of the growing season in 2019.
Leaf area index (LAI) recorded from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons
This file contains leaf area index (LAI) measurements from an nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016. LAI was recorded using a handheld plant canopy analyzer (LI-COR 2200C; LI-COR, Lincoln, NE, USA) Data spans 4 years from 2016 (when fertilization began) until 2019. Data was recorded once a year at the peak of each growing season.
Anaktuvuk River, Alaska, USA tussock tundra flowering in response to fire severity, 2008-2015
Eriophorum vaginatum flower counts from annual photographs at the severe, moderate, and unburned Anaktuvuk River, Alaska, USA flux tower sites during peak flowering season (6/17-7/20).
Satellite-derived estimate of artificial lakes in the Phoenix, Arizona, USA Metropolitan area (2000)
Artificial lakes are a prominent example of a local hydrogeomorphic modification in the Phoenix, Arizona metropolitan area. Although lakes are not a natural feature of Sonoran desert ecosystems, numerous artificial lakes are evident in the region. Here we attempt to quantify the heretofore unknown number and extent of artificial lakes. As official GIS layers from local and state agencies had significant misclassifications and omissions, we used two published land cover datasets to estimate the number and areal extent of artificial lakes. We discovered that there are 908-1,390 lakes in the Phoenix area, with the number varying according to level of aggregation. Accurate data on the extent and distribution of these designed ecosystems is vital for water-resources planning and stormwater management. Results, discussion, and additional details are available in document DOI 10.1007/s11252-011-0208-1.
Wetland abundance and distribution changes in Sycamore Creek, Arizona, USA (2014-2019)
The primary objective of this project is to understand how long-term climate variability and change influence the structure and function of desert streams via effects on hydrologic disturbance regimes. Climate and hydrology are intimately linked in arid landscapes; for this reason, desert streams are particularly well suited for both observing and understanding the consequences of climate variability and directional change. Researchers try to (1) determine how climate variability and change over multiple years influence stream biogeomorphic structure (i.e., prevalence and persistence of wetland and gravel-bed ecosystem states) via their influence on factors that control vegetation biomass, and (2) compare interannual variability in within-year successional patterns in ecosystem processes and community structure of primary producers and consumers of two contrasting reach types (wetland and gravel-bed stream reaches). This dataset was collected to understand: (1) the spatial pattern of wetland distribution and abundance; (2) the influence of multi-annual variability in hydrological regime on wetland distribution and abundance; and (3) the mechanism of the resilience of wetland to different disturbances in terms of hydrology (i.e., drying and flooding).
Composited land surface temperature of the greater Phoenix, Arizona, USA metropolitan area and surrounding Sonoran desert derived from cloud-free, summer (June, July, and August) Landsat imagery: 1985-2020
This project calculates land surface temperature (LST) from remotely sensed imagery. The intent is to extend the previous version of the LST data for the CAP LTER study area in central Arizona, USA to include 2020 and update the products so that they are based on a composite of images from each year (all available cloud-free acquisitions from June, July, and August) in the analysis to reduce the potential for outlier images or pixels to impact analyses. The aim is to make updated LST data accessible to stakeholders and researchers studying the greater Phoenix, Arizona, USA metropolitan area. LST is calculated from cloud-free Landsat 5 and 8 imagery (30m resolution) from summer months (June, July, and August) in 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. All images are cropped to the CAP LTER study area boundary.
Long-term monitoring of reptiles and ground arthropods near the Phoenix-Mesa Gateway Airport, Mesa, Arizona, USA, ongoing since 2010
Reptiles and amphibians have been monitored at the Gateway site since 2010. The goals of the project have been to provide undergraduate and graduate students opportunities to learn hands-on wildlife techniques, follow seasonal patterns of herpetofauna and ground arthropods, and serve as a test bed for new projects and technologies, including development of a mobile app for data collection. Live trapping methods include 6 trap arrays of pitfall and funnel traps placed along drift fences. Arrays are checked daily when traps are actively open to trap animals. Lizards are given a unique toe clip code, but all other species are unmarked. Reptiles and amphibians are weighed and measured and released at point of capture. Ground arthropods are counted to the Order-level. Arrays are open typically from March to October and the years vary in trapping effort with arrays open from 2 to 68 days per year. The most common species captured are tiger whiptail (*Aspidoscelis tigris*) and common side-blotched (*Uta stansburiana*) lizards.
Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020
## overview The project extends the long-term, LULC datasets to facilitate environmental change monitoring and social-ecological studies regarding urban sprawl and dynamics, urban heat islands, and outdoor water consumption, among others. Six land-use/land-cover (LULC) maps at 30 m resolution were previously created from 1985 to 2010 at five-year intervals (Zhang and Li 2017). This project updates that suite with maps for 2015 and 2020. As with the prior set, systematic object-based classification was utilized to ensure map consistency and direct comparison capability over time. The maps comprise 11 land-use/land-cover classes with an overall accuracy of 89.1% for 2015 and 89.6% for 2020. ## literature cited - Zhang, Y. and X. Li. 2017. Land cover classification of the CAP LTER study area at five-year intervals from 1985 to 2010 using Landsat imagery ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/dab4db27974f6c8d5b91a91d30c7781d (Accessed 2022-07-13).
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
Long-term composited Modified Normalized Difference Water Index (MNDWI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
Abstract ======== This data package consists of multiple decades of modified normalized difference water index (MNDWI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). By providing a metric by which to reliably identify bodies of open water, these MNDWI data are intended to facilitate analyses of land-based environmental variables (e.g., urbanization, vegetation, land surface temperature) and can also be used to track long-term and seasonal change in the coarse extent of open water as a land-cover type. MNDWI was derived, following the methods of Xu (2006), from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see \'Methods and Protocols\') and accompanying Javascript code. **Citations:** - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18--27. <https://doi.org/10.1016/j.rse.2017.06.031> - Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. *International Journal of Remote Sensing*, *27*(14), 3025--3033. <https://doi.org/10.1080/01431160600589179>
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.