Find research datasets worth reusing

openCC BYSep 2022View details →

LAGOS-US GEO v1.0: Data module of lake geospatial ecological context at multiple spatial and temporal scales in the conterminous U.S.

The LAGOS-US GEO data package is one of the core data modules of LAGOS-US, an extensible research-ready platform designed to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). The GEO module contains data on the geospatial and temporal ecological setting (e.g., land use, terrain, soils, climate, hydrology, atmospheric deposition, and human influence) quantified at multiple spatial divisions (e.g., equidistant buffers around lakes, watersheds, hydrologic basins, political boundaries, and ecoregions) relevant to the LAGOS-US lake population defined in the LAGOS-US LOCUS module. The database design that supports the LAGOS-US research platform was created based on several important design features: lakes are the fundamental unit of consideration, all lakes in the spatial extent above the minimum size must be represented, and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other two core data modules that are part of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds) and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.

openCC0Feb 2025View details →

Evaluation of stormwater urban ecological infrastructure in Phoenix, Arizona (USA): a case study of a small-scale bioretention basin system

In 2017, Arizona State University finished construction on a pedestrian mall central to its Tempe campus, which included a small-scale bioretention basin system for stormwater management. This study analyzed the flood control and water quality improvement performance of the small-scale bioretention basin system in the Phoenix Metropolitan Area, AZ USA. Flood control efficacy was quantified by calculating discharge from the basin system using water level loggers and measuring soil moisture levels using soil moisture probes. Stormwater runoff samples were collected for twenty-one storm events and analyzed for nitrogen and phosphorus constituent concentrations. Nutrient concentrations at the system inflow and outflow were used to determine percent change in concentration. Water quality improvement performance was compared to results from previous studies on bioretention basin system performance. These data were used to create relevant graphical figures. Results were obtained by performing statistical analysis calculations on the data measurements. The results indicated that the bioretention basin system performed adequately for flood control and water quality improvement, supporting the use of stormwater urban infrastructure systems in arid and semi-arid climates. Further research can reveal how these systems may perform during more severe storm events and offer improvements for future designs.

Landscape-Scale Forest Dynamics in the Luquillo Experimental Forest in Puerto Rico 1936-1989

This study examined landscape-scale forest dynamics in the Luquillo Experimental Forest (Puerto Rico). The analysis was based on vegetation maps created from aerial photographs taken in 1936 and 1989. For details on methods and results, please see the published paper (Foster, D. R., M. Fluet and E. R. Boose. 1999. Human or natural disturbance: landscape-scale dynamics of the tropical forests of Puerto Rico. Ecological Applications 9: 555-572). The Abstract from the paper is reproduced below. "Increasingly ecologists are recognizing that human disturbance has played an important role in tropical forest history and that many assumptions concerning the relative importance of natural processes warrant re-examination. To assess the historical role of broad-scale human versus natural disturbance on an intensively studied tropical forest we undertook a landscape-level analysis of forest dynamics in the Luquillo Experimental Forest (LEF; 10,871 ha) in eastern Puerto Rico. Using aerial photographs (1936 and 1989), GIS, a model of topographic exposure to hurricane winds, and historical data, we sought to: (1) document historical changes in extent, cover and type of forest vegetation, (2) evaluate the distribution of land-use and hurricane impacts, (3) assess the contributions of these processes in controlling current vegetation patterns, and (4) relate these results to ongoing ecological, conservation and natural resource discussions. "With over 1000 m of relief in the LEF, the broad vegetation zones of Tabonuco (below 600 m a.s.l.), Colorado (600-900 m), Dwarf (above 900 m), and Palm forest are determined by environmental gradients. However, over the past 60-100 years forest extent, cover, and type have been transformed: in 1936, 40% of the LEF was unforested or secondary forest and less than 50% had continuous canopy (more than 80% cover); in 1989, less than 97% was continuous forest. Secondary forest and agricultural lands in 1936 were replaced largely by Tabonuco and Colo

Scaling Sarracenia in North America 1900-2100

Scaling in Ecology with a Model System synthesizes central theories from ecology, biogeography, and macroecology through the lens of scale and scaling across spatiotemporal extents and grains, levels of biological organization, and with dimensionless ratios. In this book, we link 25 years of detailed natural history observations and laboratory and field experiments on the “Sarracenia microecosystem” (the northern pitcher plant, Sarracenia purpurea, and its associated food web of microbes and macrobes) with new models and analyses to generate novel insights into ecophysiology and stoichiometry; demography and species distribution models; food webs and trophic dynamics; and tipping points and regime shifts. Our work addresses the Sarracenia microecosystem at a hierarchy of spatial scales: individual pitchers within plants, plants within bogs, and bogs within landscapes, all of which can be treated as replicate “island” ecosystems that can be studied throughout the United States east of the Mississippi River and Canada east of the Rocky Mountains. Through integrated studies of its proteomics, physiology, population dynamics, community ecology, and ecosystem processes, the Sarracenia microecosystem has emerged as a model system for experimental ecology. Our synthetic work clearly illustrates that working with the Sarracenia microecosystem can yield new results and understanding of the importance of ecosystem-wide disturbances and anthropogenically-driven environmental and climatic change. They also show that research with the experimentally tractable Sarracenia microecosystem proceeds much more rapidly than studies of larger, more slowly changing ecosystems such as forests, grasslands, lakes, and streams that are more difficult to replicate and experimentally manipulate.

Plant and litter cover estimates derived from overhead microplot photos in the Cross-Scale Interactions Study (CSIS) at Jornada Basin LTER, 2013-ongoing

This dataset contains plant and litter cover estimates derived from overhead photos collected from microplots in a long-term experiment (2013-present) of cross-scale interactions (CSIs) at the Jornada Basin LTER site in southern New Mexico, U.S.A. Experimental treatments were initiated in 2013 at 15 experimental blocks, each with 4 treatment plots: plant-scale herbicide of mesquite shrubs, patch-scale connectivity modifiers (ConMods), herbicide + ConMods, control without manipulations. Repeat, overhead (downward-looking) photographs of ten "microplots" in each plot were taken for estimation of litter, soil, and vegetation cover in the experimental treatment and control plots over time. Photographs were rotated and then cropped to provide standardized areas, and then were analyzed with USDA SamplePoint software to determine coverage by ~27 plant, litter or other cover classes on a 100 point grid. Raw and corrected cover estimates are provided. This study is ongoing and new data will be added annually. Cover estimates are derived from photos in EDI dataset knb-lter-jrn.210413004.

Litter and soil accumulation estimates derived from lateral microplot photos in the Cross-Scale Interactions Study (CSIS) at Jornada Basin LTER, 2013-2017

This dataset contains litter and soil vertical accumulation estimates derived from lateral photos of microplots in a long-term experiment (2013-2017) of cross-scale interactions (CSIs) at the Jornada Basin LTER site in southern New Mexico, U.S.A. Experimental treatments were initiated in 2013 at 15 experimental blocks, each with 4 treatment plots: plant-scale herbicide of mesquite shrubs, patch-scale connectivity modifiers (ConMods), herbicide + ConMods, control without manipulations. Repeat, lateral (side-looking) photographs of ten "microplots" in each ConMods and herbicide+ConMods plot were taken for estimation of litter, soil, and vegetation cover in the experimental treatment and control plots over time. Photographs were analyzed with SigmaScan software to determine vertical accumulation of litter and soil withing the microplots. This study is complete and ended in 2017. These vertical accumulation estimates are derived from photos in EDI dataset knb-lter-jrn.210413006.

opencc-by-4.0Apr 2024View details →

zenodo56/100

Dataset for algorithmic thinking skills assessment: Results from the virtual CAT large-scale study in Swiss compulsory education

Overview This dataset was collected during a main study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education. As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention. Study Context, Location and Participants To comprehensively investigate algorithmic competencies within compulsory education, exploring their variations and determining the factors influencing them, in Spring 2023 we conducted an experimental study with the virtual CAT's. The sample comprises 129 students (65 girls and 64 boys), selected from nine classes across five public schools in Ticino and Solothurn cantons. Data Collection During the data collection process, session and participant details were manually recorded by the administrator. Each session has been assigned a unique identifier, and specific details, such as the date, canton, school name and type, and the students’ HarmoS grade (HG) level, have been recorded.  Student information are limited to sex and date of birth, with birth dates used to calculate ages, a significant factor in our demographic analysis. To protect student privacy, unique identifiers have been assigned to each participant, keeping the data anonymous and secure. The assessment tool automatically tracked all user interaction within the platform. All data collected have been pseudonymised, aligning with prevailing open science practices in Switzerland (SNSF, 2021).  Data collection was integrated into a validation module of the app.  Data Features The dataset comprises the following files: <ul> <li>STUDENTS_SESSIONS.csv</li> <li>RESULTS.csv</li> <li>LOGS.csv</li> <li>CANTONS.csv</li> <li>ALGORITHMS.csv</li> </ul> These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more. Usage & Ethics In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment.  Initial authorisations were secured from school administrators, teachers, and parents.  Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders. REFERENCES [1] A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a> [2] Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a> [3] Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a> [4] Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a>

zenodo56/100

Sample data for "Machine learning for large-scale forecasting"

This dataset includes sample data for the Netherlands to run the machine learning baseline as described in the paper titled Machine learning for large-scale crop yield forecasting, accessible at <a href="https://doi.org/10.1016/j.agsy.2020.103016">https://doi.org/10.1016/j.agsy.2020.103016</a>. The software implementation of the machine learning baseline is available at: <a href="https://github.com/BigDataWUR/MLforCropYieldForecasting">https://github.com/BigDataWUR/MLforCropYieldForecasting</a>. Notes: The NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU and the UK (see Eurostat, 2016) for more details). Data The dataset consists of 11 CSV files. They are formatted to work as sample inputs to the machine learning baseline. <ol> <li>Crop Area Fractions (NUTS2, NUTS1): We aggregated the predictions of the machine learning baseline from NUTS2 to national (NUTS0) level by weighting them on the modeled crop area. Cerrani and López Lozano (2017) have described in detail the algorithm used to model crop areas for different NUTS levels. The data comes from the MARS Crop Yield Forecasting System (MCYFS) of European Commission's Joint Research Centre (JRC) (see Lecerf et al., 2019).</li> <li>Centroids (NUTS2): Data includes latitude, longitude and distance to coast of the centroids of NUTS2 regions.</li> <li>Meteo Daily Data and Meteo Dekadal Data (NUTS2): The data comes from MCYFS (see EC-JRC, 2020). By default, the implementation uses daily data.</li> <li>Remote Sensing Data (NUTS2, see Copernicus Global Land Service, 2020): Data includes fraction of absorbed photosynthetically active radiation (FAPAR) aggregated to NUTS2.</li> <li>Soil Data: Data includes soil moisture information that can be used to calculate soil water holding capacity. The data comes from MCYFS (see Lecerf et al., 2019).</li> <li>WOFOST data (NUTS2): The World Food Studies (WOFOST) crop model (van Diepen et al., 1989; Supit et al., 1994; de Wit et al.  2019) is a simulation model for the quantitative analysis of the growth and production of annual field crops. It is a mechanistic, dynamic model that explains daily crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by environmental conditions. The crop simulation is fed by weather, soil and crop data. Observed meteorological data is interpolated on a regular 25 km grid using a method based on the distance, altitude and climatic region similarity between the center of grid cells and weather stations (see Van der Goot, 1998). WOFOST runs on the intersection between the 25 km meteorological grid and soil units based on the European soil map (http://esdac.jrc.ec.europa.eu/). In order to have the output data aggregated to administrative regions such as countries or provinces, simulation units are further intersected with the boundaries of these regions. The outputs at soil unit (STU) level are aggregated to grid level in an area weighted manner. Gridded simulations are aggregated to lowest NUTS level 3 considering the arable land area of each grid, derived from GLOBCOVER and CORINE Land Cover (Cerrani and Lopez Lozano, 2017). From NUTS3 to higher levels, crop area fractions for the current year, retrieved from Eurostat, are used to weight and aggregate the output (Cerrani and Lopez Lozano, 2017).</li> <li>GAES data: GAES data includes agro-climatic features of regions, such as elevation and slope (from USGS-EROS, 2021), field size (from Lesiv et al., 2019), irrigated (crop) areas (from EC-JRC, 2020) and crop areas (from EC-JRC, 2020).</li> <li>National yield statistics (NUTS0): These are the official Eurostat national yield statistics (Eurostat, 2020a). We used these yield statistics as reference to compare the machine learning predictions aggregated to NUTS0 and the actual MCYFS forecasts (see van der Velde and Nisini, 2019).</li> <li>Regional yield statistics (NUTS2): We used NUTS2 yield statistics as labels to train and evaluate machine learning algorithms. We got NUTS2 yield statistics from The Central Bureau of Statistics (CBS) of the Netherlands (NL-CBS, 2020).</li> <li>Past MCYFS Yield Forecasts (NUTS0): These are actual forecasts made by MCYFS in the past (see van der Velde and Nisini, 2019). We used the official Eurostat national yield statistics (see point 7 above) as the reference to compare the machine learning predictions aggregated to NUTS0 and MCYFS forecasts.</li> </ol> Crop ID and name mapping 2 : grain maize 6 : sugar beets 7 : potatoes 90 : soft wheat 93 : sunflower 95 : spring barley   Acknowledgements We would like to thank S. Niemeyer from the European Commission’s Joint Research Centre (JRC) for the permission to provide open access to the Netherlands data. Similarly, we would like to thank M. van der Velde, L. Nisini and I. Cerrani from JRC for sharing with us past MCYFS forecasts and Eurostat national yield statistics.

opencc-by-4.0Dec 2020View details →

openCC (other)May 2025View details →

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.

openCC (other)Dec 2025View details →

Summertime methane and carbon dioxide emission rates and associated variables from a national-scale survey of 146 reservoirs in the United States, 2016-2023

Reservoirs are globally important sources of greenhouse gases, but the magnitude of their emissions is highly uncertain. Here we present data for 146 reservoirs from two surveys of reservoir methane and carbon dioxide emissions, one at the regional scale in the midwestern United States and one at the national scale in the conterminous United States, plus data from one reservoir in Washington and another in Puerto Rico. At all reservoirs, ebullitive and diffusive emissions and basic physiochemistry were measured at 15-70 locations during one 22 to 64-hour period during the summers of 2016-2023, with four reservoirs revisited a second time. Concomitant water chemistry measurements were also made at an index site. The dataset is comprised of two geospatial files and seven .csv files containing greenhouse gas emissions, water chemistry, morphology, and other relevant data. These data comprise the largest multi-reservoir emissions dataset ever assembled using consistent measurement methods.

openCC (other)Aug 2025View details →

Long-term demographic dataset for Cladonia perforata, including fine-scale cover, occupancy, and subpopulation area data, 2011-2024

This dataset includes all data pertaining to a long-term demographic study of Cladonia perforata (perforate reindeer lichen), a federally endangered lichen endemic to Florida, including fine-scale cover, occupancy, and population area data, conducted by the Archbold Biological Station Plant Ecology Program. This includes 13 years of data (2011-2024) from nine subpopulation (including seven at Archbold Biological Station, and two at the Lake Wales Ridge Wildlife and Environmental Area, Royce Unit), all located in rosemary scrub habitat within the Lake Wales Ridge metapopulation. This study sought to characterize the fire ecology and long-term population trends for the species, and thus also includes data on prescribed burn severity and time since fire. Data were collected using a stratified random plot design, with occupancy plots (presence/absence within 1.5 meter radius) throughout the subpopulation and a subset of these designated as cover plots only, with this cover data collected as point intercept hits within a 48x48cm area. Cover data also includes microhabitat data – canopy cover in densiometer reading and dominant ground cover. Cover and occupancy data were taken every 3 years for each subpopulation (subpopulations were on different yearly schedules). Subpopulation area was mapped using a submeter GPS unit every 6 years. Subpopulations were resampled for all metrics as soon as possible following a fire, and the sampling schedule was then reset.

openCC0Feb 2022View details →

Fine-scale meteorological observations from walking traverses in two Phoenix Area Social Survey (PASS) 2017 neighborhoods (2019)

This dataset includes human-biometeorological observations from 2.5 km walking traverses with a mobile weather station. The traverses occurred in two 2017 Phoenix Area Social Survey neighborhoods (U18: South Phoenix/Salt River (Audubon) and W15: Camelback Mountain) on one day in June and October, at 12pm and 4pm on each day. Specifically, air temperature, humidity, wind speed, and radiant energy (infrared and solar radiation) in 3-dimensions were measured at 2-second intervals. Additionally, mean radiant temperature was calculated from the radiation measurements. The meteorological observations are spatially referenced with latitude and longitude coordinates. The paths through the neighborhoods were chosen to maximize proximity to PASS 2017 participants’ homes.

Hemlock Woolly Adelgid and Elongate Hemlock Scale Surveys in Connecticut and Massachusetts 1997-2011

In the eastern USA, eastern hemlock (Tsuga canadensis) is the host plant for two invasive insect species - hemlock woolly adelgid (Adelges tsugae) and elongate hemlock scale (Fiorinia externa). We observed the density of adult hemlock woolly adelgid and elongate hemlock scale on eastern hemlock branches on five occasions over 14 years at 142 stands across a latitudinal transect encompassing 7,500 km2 in Connecticut and Massachusetts to assess whether there is a difference in the response to abiotic conditions (winter temperature, summer temperature, summer precipitation) between the two species, and whether the distribution and abundance of each insect species is dependent on biotic interactions with the co-occurring insect species.

Widespread Sampling Biases in Herbaria Revealed from Large-Scale Digitization 1656-2016

Non-random collecting practices may bias conclusions drawn from analyses of herbarium records. Recent efforts to fully digitize and mobilize regional floras offer a timely opportunity to assess commonalities and differences in herbarium sampling biases. We determined spatial, temporal, trait, phylogenetic, and collector biases in ~5 million herbarium records, representing three of the most complete digitized floras of the world: Australia (AU), South Africa (SA), and New England, USA (NE) We identified numerous shared and unique biases among these regions. Shared biases included specimens i) collected close to roads and herbaria; ii) collected more frequently during spring; iii) of threatened species collected less frequently; and iv) of close relatives collected in similar numbers. Regional differences included i) over-representation of graminoids in SA and AU and of annuals in AU; and ii) peak collection during the 1910s in NE, 1980s in SA, and 1990s in AU. Finally, in all regions, a disproportionately large percentage of specimens were collected by a few individuals. These mega-collectors, and their associated preferences and idiosyncrasies, may have shaped patterns of collection bias via ‘founder effects’. Studies using herbarium collections should account for sampling biases and future collecting efforts should avoid compounding these biases.

Repeat overhead photographs of microplots in a cross-scale interactions experiment (CSIS) at Jornada Basin LTER, 2013-ongoing

This dataset contains archived overhead photos collected from microplots in a long-term experiment (2013-present) of cross-scale interactions (CSIs) at the Jornada Basin LTER site in southern New Mexico, U.S.A. Experimental treatments were initiated in 2013 at 15 experimental blocks, each with 4 treatment plots: plant-scale herbicide of mesquite shrubs, patch-scale connectivity modifiers (ConMods), herbicide + ConMods, and control without manipulations. Repeat, overhead (downward-looking) photographs of ten "microplots" in each plot were taken for estimation of litter, soil, and vegetation cover in the experimental treatment and control plots over time. Photographs have been rotated and then cropped to provide standardized areas for this analysis. The photographs are archived by year in Zip files. This study is ongoing and new photos will be added annually. Cover estimates derived from these photos are in EDI dataset knb-lter-jrn.210413005.

Repeat lateral photographs of microplots in a cross-scale interactions experiment (CSIS) at Jornada Basin LTER, 2013-2017

This dataset contains archived lateral photos collected from microplots in a long-term experiment (2013-2017) of cross-scale interactions (CSIs) at the Jornada Basin LTER site in southern New Mexico, U.S.A. Experimental treatments were initiated in 2013 at 15 experimental blocks, each with 4 treatment plots: plant-scale herbicide of mesquite shrubs, patch-scale connectivity modifiers (ConMods), herbicide + ConMods, and control without manipulations. Repeat, lateral (side-looking) photographs of ten "microplots" in each plot were taken for estimation of the vertical accumulation of litter, soil, and vegetation in the experimental treatment and control plots over time. The photographs are archived by year in Zip files. This study is complete and new photos will not be added. Litter/soil accumulation estimates derived from these photos are in EDI dataset knb-lter-jrn.210413007.