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.
3,575
datasets available to search
ShareScore release 0.7.1
Dataset results
3,575 results for “2009”
Stream Suspended Sediment and Particulate Organic Matter at Harvard Forest 2009-2010
In addition to conveying water and nutrients and providing habitat to a variety of ecosystems, streams transport downstream mineral sediment and other particulate matter washed in from hillslopes and eroded from its channel and banks. At high levels, suspended sediment can be a devastating pollutant for aquatic organisms. The amount of suspended material in a stream varies tremendously with discharge; typically, suspended sediment increases with discharge, as stormwater runoff and overland flow carry particles from the hillslopes into the channel. Suspended sediment can also be a function of land use and vegetation, both of which affect the infiltration capacity of the landscape; more infiltration generally means less surface runoff and thus less sediment. Forested watersheds such as the Bigelow Brook watershed will typically have less suspended sediment than similar watersheds in urban environments. By analyzing how suspended sediment varies with discharge, I will be able to compare the relative effectiveness of overland flow of stormwater in washing materials into the streams. It is also possible that tree loss due to the wooly adelgid, ice storms, or fire in the watershed may increase the amount of sediment to Bigelow Brook, as a loss in tree canopy may result in more soil erosion due to rain splash and more water overall reaching the stream. For this reason, I hope to continue monitoring sediment in Bigelow Brook for an extended period of time to record any significant changes due to changing vegetation. Furthermore, by determining how much of the suspended sediment consists of particulate organic matter (using standard LOI techniques), I will be able to estimate the net carbon export from the two watersheds via that pathway. Preliminary, back-of-the-envelope calculations suggest that as much as 3-5% of the total annual carbon export leaves the Harvard Forest watershed via stream-transported particulate organic matter. To this end, I propose to measure suspended
Forest Inventory for Tree Demography and Carbohydrate Reserves at Harvard Forest 2009-2011
The objective of this study is to establish a network of forest plots spanning the eastern U.S. focused on understanding how regional scale climate patterns affect patterns of tree demography (e.g. growth, mortality, dieback). Within each research site we also aim to understand the landscape-scale variability in demographic rates and how these rates are affected by edaphic variables by aligning plots along primary environmental gradients (elevation, soils, hydrology, fire return interval). The demographic data obtained will also be compared to regional and landscape-scale patterns in carbon reserves in adults and sapling by sampling root and stem concentrations of nonstructural carbohydrates (TNC) and relate these to demographic patterns, life-history traits, and plant C:N ratios. Besides addressing important ecological questions directly, this study is designed to improve the representations of vegetation dynamics and carbohydrate reserves in regional and landscape-scale forest ecosystem models--two of the least data-constrained processes in such models--by parameterizing and validating the modules for these processes in the Ecosystem Demography model (ED v2.1). This data set contains two years of census data for eight mapped plots distributed across Harvard Forest along the aforementioned primary environmental gradients.
Eddy Covariance and Meteorological Data in the Clearcut Site at Harvard Forest 2009-2012
Clearcutting and other forest disturbances perturb carbon, water, and energy balances in significant ways, with corresponding influences on Earth’s climate system through biogeochemical and biogeophysical effects. Observations are needed to quantify the precise changes in these balances as they vary across diverse disturbances of different types, severities, and in various climate and ecosystem type settings. This dataset reports eddy covariance and micrometeorological measurements of surface-atmosphere exchanges that can be combined with related datasets from vegetation inventories and chamber-based estimates of soil respiration and leaf gas exchange to collectively quantify and understand how carbon, water, and energy fluxes changed during the first four years following forest clearing in a temperate forest environment of the northeastern US. Associated publications show rapid recovery with sustained increases in gross ecosystem productivity (GEP) over the first three growing seasons post-clearing, coincident with large and relatively stable net emission of CO2 because of overwhelmingly large ecosystem respiration. The rise in GEP was attributed to vegetation changes not environmental conditions (e.g. weather), but attribution to the expansion of leaf area versus changes in vegetation composition remains unclear. Soil respiration was estimated to contribute 44% of total ecosystem respiration during summer months and coarse woody debris accounted for another 18%. Evapotranspiration also recovered rapidly and continued to rise across years with a corresponding decrease in sensible heat flux. Gross shortwave and longwave radiative fluxes were stable across years except for strong wintertime dependence on snow covered conditions and corresponding variation in albedo. Overall, these findings underscore the highly dynamic nature of carbon and water exchanges and vegetation composition during the regrowth following a severe forest disturbance, and sheds light on both the
Partitioning the Components of Soil Respiration in a Trenching Experiment at Harvard Forest 2009-2010
Total soil respiration (Rt) is a combination of autotrophic (Ra) and heterotrophic respiration (Rh). Several methods have been developed to tease out the components of Rt, such as isotopic analyses, and removing Ra input through tree girdling and root exclusion experiments. Trenching involves severing the rooting system surrounding a plot to remove the Ra component within the plot. This method has some potential limitations. Reduced water uptake in trenched plots could change soil water content, which is one of the environmental controllers of Rt in many ecosystems. Eliminating root inputs could reduce heterotrophic decomposition of SOM via lack of priming. On the other hand, the severed dead roots may temporarily increase available carbon substrate for Rh. We utilized the trenching method to partition the autotrophic and heterotrophic components of soil respiration in an oak dominated forest with the footprint of the LPH tower.
MCR LTER: Coral Reef: Benthic Photosynthetically Active Radiation (PAR), ongoing since 2009
These data contain a time-series from sequential deployments of an ALW-CMP PAR sensor recording quanta of photosynthetically active radiation (PAR) underwater between wavelengths 400 nm to 700 nm every two seconds. The sensor window is wiped free of algae periodically to prevent fouling. Deployments are three to six months in length. The sensor was mounted at 17 m depth on the fore reef, and at 2 m depth on the Backreef. PAR irradiance is measured as quanta in micromoles of photons per square meter per second.
Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Littoral Plots 2001 - 2009
In 2001 - 2004 the abundance of coarse wood and other aspects of the physical structure of the littoral zone were surveyed along transects that followed the 0.5 m depth contour at 488 sites in Vilas County. These data were collected as part of the "cross-lake comparison" segment of the Biocomplexity Project (Landscape Context - Coordinated Field Studies). The study explored the links between terrestrial and aquatic systems across a gradient of residential development and lake landscape position. Specifically, this project attempted to relate the abundance of Coarse Wood in the littoral zone with abiotic, biotic and anthropogenic features of the adjacent shoreline. Each of the 488 sites was a 50 m stretch of shoreline. The transects started and ended at the beginning and end of the site; the length of each transect, therefore, varied. Logs which were at least 150 cm in length were counted; more detailed descriptions were taken of logs at least 10 cm in diameter and 150 cm long. Information on littoral and shoreline substrate was also collected. Sampling Frequency: each site sampled once Number of sites: 488 sites on 61 Vilas County lakes were sampled from 2001-2004 (approximately 15 different lakes each year; eight sites per lake).
Biocomplexity at North Temperate Lakes LTER; Coordinated Field Studies: Coarse Woody Habitat Data 2001 - 2009
These data were collected to test for changes in the population dynamics and the food webs of the fish populations of Little Rock and Camp lakes, Vilas County, WI, USA. Little Rock Lake was the site of a whole-lake removal of coarse woody habitat in 2002 and Camp Lake was the site of a whole-lake coarse woody habitat addition in 2004. Sampling began in May of 2001 and ended in August of 2006. Some sampling was repeated from 2007 to 2009. Number of sites: 4. Two lakes with reference and treatment basin in each lake.
Biocomplexity at North Temperate Lakes LTER; Whole Lake Manipulations: Rainbow Smelt Removal 2001 - 2009
Rainbow smelt (Osmerus mordax) are a harmful invasive species in lakes of northern Wisconsin. Smelt were first detected in Sparkling Lake, Vilas county, WI in 1980 and their population has since increased dramatically. We attempt to remove rainbow smelt from Sparkling Lake through a combined strategy of harvest and predation. If successful, such a strategy might be employed to restore other Wisconsin lakes invaded by smelt to a more natural species assemblage without resorting to piscicides. The data sets presented here report the harvest component of smelt removal. An assessment of the rainbow smelt population, supplementing annual LTER data, was performed during the late summer of 2001. The spring removal effort began in 2002 at ice out using multiple gear types. In 2002, the removal effort also continued from mid to late summer using horizontal gill nets. However, from 2003-2009 we took advantage of smelt spawning behavior and our efforts were condensed to a spring removal at ice-off and we utilized only fyke nets. The total weight of each catch was recorded and length-weights as well as sex ratios were documented for a subset of the catch from each removal event. The removal effort resulted in the removal of the majority of the adult population multiple times. However, smelt are a robust species and the population continuously rebounded from large removal years. As a result, catches have fluctuated from 16kg to nearly two tons. We have observed an overall reduction in fish size and an increase in the proportion of males to females. Sampling Frequency: annually
North Temperate Lakes LTER: Sparkling Lake Littoral Fish 2009 - 2010
Sparkling Lake littoral fish caught from 2009-2010. We sampled the Sparkling Lake littoral fish community bimonthly during summer months using electrofishing, angling, and fyke nets were from 2009-2010. Sampling included length, weight, scales, and diet.
Lake Mendota at North Temperate Lakes LTER: Snow and Ice Depth 2009-2010
Ice core data collected by Yi-Fang (Yvonne) Hsieh and collaborators for her PhD project, “Modeling Ice Cover and Water Temperature of Lake Mendota.” Part of the project was the development of a 3D hydrodynamic-ice model that simulated both temporal and spatial distributions of ice cover on Lake Mendota for the winter 2009-2010. The parameters from these ice core data were used as model inputs to run model simulations. Parameters measured include: blue ice, white ice, snow depth, and total ice. On February 13, 2009, ice cores were taken on Lake Mendota at four different stations. From January 14, 2010 through March 3, 2010 ice cores were taken on Lake Mendota at 31 different stations. In addition, ice cores were taken on other Yahara Lakes during February of 2009: Lake Kegonsa (4 stations_February 6), Lake Waubesa (4 stations_February 7), Lake Wingra (2 stations_February 8), and Lake Monona (4 stations_February 8). Only total ice measurements are reported for 2009. Included in this data set are the ice core data, and geospatial information for ice coring stations. Documentation: Hsieh, Y.-F., 2012a. Modeling ice cover and water temperature of Lake Mendota. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 157.
Microbial Observatory at North Temperate Lakes LTER Time series of bacterial community dynamics in Lake Mendota 2000 - 2009
With an unprecedented decade-long time series from a temperate eutrophic lake, we analyzed bacterial and environmental co-occurrence networks to gain insight into seasonal dynamics at the community level. We found that (1) bacterial co-occurrence networks were non-random, (2) season explained the network complexity and (3) co-occurrence network complexity was negatively correlated with the underlying community diversity across different seasons. Network complexity was not related to the variance of associated environmental factors. Temperature and productivity may drive changes in diversity across seasons in temperate aquatic systems, much as they control diversity across latitude. While the implications of bacterioplankton network structure on ecosystem function are still largely unknown, network analysis, in conjunction with traditional multivariate techniques, continues to increase our understanding of bacterioplankton temporal dynamics.
Microbial Observatory at North Temperate Lakes LTER Spatial and temporal cyanobacterial population dynamics in Lake Mendota 2009 - 2011
Toxic cyanobacterial blooms threaten freshwaters worldwide but have proven difficult to predict because the mechanisms of bloom formation and toxin production are unknown, especially on weekly time scales. Water quality management continues to focus on aggregated metrics, such as chlorophyll and total nutrients, which may not be sufficient to explain complex community changes and functions such as toxin production. For example, nitrogen (N) speciation and cycling play an important role, on daily time scales, in shaping cyanobacterial communities because declining N has been shown to select for N fixers. In addition, subsequent N pulses from N2 fixation may stimulate and sustain toxic cyanobacterial growth. Herein, we describe how rapid early summer declines in N followed by bursts of N fixation have shaped cyanobacterial communities in a eutrophic lake (Lake Mendota, Wisconsin, USA), possibly driving toxic Microcystis blooms throughout the growing season. On weekly time scales in 2010 and *2011, we monitored the cyanobacterial community in a eutrophic lake using the phycocyanin intergenic spacer (PC-IGS) region to determine population dynamics. In parallel, we measured microcystin concentrations, N2 fixation rates, and potential environmental drivers that contribute to structuring the community.
LTREB: Lake Myvatn Predation experiments at Myvatn, Iceland during 2009 and 2011
Changes in one prey species' density can indirectly affect the abundance of another prey species if a shared predator eats both species leading to positive or negative indirect effects. In some cases, indirect effects may occur when prey move into a habitat, such as when riparian predator populations grow in response to adult aquatic insects and increase predation on terrestrial prey. However, predators could instead switch to aquatic insects or become satiated, reducing predation on terrestrial prey. To determine the net indirect effect of aquatic insects on terrestrial arthropods via generalist spider predators, we conducted a field experiment using enclosures on the shoreline of an Icelandic lake with numerous aquatic midges. Midge abundance and wolf spider density were altered to mimic midge influx and a wolf spider numerical response. At all predator densities, the presence of midges decreased rates of predation on terrestrial prey. When midges were absent, predation was 30percent greater at high spider density. But when midges were present, predation of sentinel prey was equal across spider densities, negating the influence of increased predator density. In lab mesocosms, prey survivorship increased greater or equal 50percent where midges were present and rapidly saturated; the addition of 5, 20, 50 and 100 midges equivalently reduced spider predation, supporting predator distraction rather than satiation as the root cause. Our results demonstrate a strong positive indirect effect of midges, and broadly support the concept that predator responses to alternative prey are a major influence on the magnitude and direction of predator-mediated indirect effects.
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format
The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Zooplankton community composition and trait data for Green Lakes Valley, 2009 - ongoing.
Starting in 2012 zooplankton sampling at Green Lake 4 was included in the long term monitoring data set at Niwot Ridge. Immediately after the ice has completely melted from the lakes, zooplankton samples are taken once a week for six consecutive weeks at the deepest portion of the lake from an inflatable raft. Zooplankton were sampled at the deepest location of each lake by pulling a conical net (Wisconsin net) vertically through the water column (i.e., vertical tow sample). For each zooplankton sample obtained, adult organisms were identified to species, or lowest taxonomic level (Chydoridae sp. and Bosminidae sp.). Larvae of cladocerans were counted together as neonates; calanoid and cyclopoid copepodites were counted together as nauplii. Individual body lengths of the first 50 -100 (when possible) individuals of each taxon were recorded using a calibrated eyepiece micrometer and means reported.
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2009 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Red knot occurrence, prey density, island morphology, and climate change in the Virginia Barrier Islands (2009-2023)
Global climate change is reshaping dynamic coastal ecosystems, with uncertain consequences for migratory shorebirds such as the federally threatened red knot (Calidris canutus rufa) that rely on coastal staging sites during migration. Understanding how sea-level rise and changing climate drivers affect red knot foraging ecology is critical for informing conservation and management at coastal staging sites. We integrated long-term biological, geomorphological, and climatological data to examine the direct and indirect pathways influencing red knots and their prey at intertidal foraging sites on the Virginia Barrier Islands during spring migration (May 21 - 28, 2009-2023). Using piecewise structural equation modeling, we tested hypothesized two causal networks linking 1) red knot occurrence and 2) densities of their main invertebrate prey to habitat characteristics, island morphology, geomorphic change, and climate drivers of ecosystem change. Red knots were indirectly affected by geomorphic change and climate drivers through bottom-up effects on invertebrate communities mediated by island morphology. Accelerated shoreline change narrowed islands, reducing invertebrate density and richness and indirectly decreasing red knot occurrence. Storms interacted with global climate oscillations to drive erosion or accretion of beaches, with variable effects on invertebrate density and red knot occurrence. Invertebrate responses were taxon-specific: shoreline change directly increased blue mussel density but indirectly reduced coquina clam and crustacean densities by narrowing island width, while storms impacts on crustacean density were mediated by beach width. Our findings suggest that accelerated ecosystem change under future climate scenarios may alter foraging conditions for red knots and other migratory shorebirds in the Virginia Barrier Islands, with broader implications for long-term population resilience.
Calibrated Relocations for the TXAR Catalog (2009–2016)
<p>This dataset contains the relocated earthquake catalog for the Southern Delaware Basin, as described in the published research paper titled <strong>"Insights into Temporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations from the TXAR Catalog (2009–2016)"</strong>.</p> <p>The earthquake relocation was performed using <strong>Hypocentroidal Decomposition</strong> technique, which provided improved spatial resolution for 73 events of magnitude 1.5 or greater in the TXAR catalog. The virtual hypocentroid used for relocating the TXAR catalog events was inverted from a core cluster of 116 post-2020 events recorded by the Texas Seismological Network. This relocated catalog includes key hypocentral parameters for each event—latitude, longitude, and depth—along with origin time, associated uncertainty estimates, and magnitude.</p> <p>This dataset serves as a critical resource for understanding the temporal and spatial patterns of induced seismicity related to anthropogenic activities, such as shallow fluid injection, in the Southern Delaware Basin before the operation of local seismic networks in the region. It is well-suited for use in further seismic hazard assessments, modeling studies, and comparisons with other induced seismicity datasets.</p> <h3>Citation:</h3> <p>Asiye Aziz Zanjani, Heather R. DeShon, Vamshi Karanam, Alexandros Savvaidis; <strong>Insights into Temporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations from the TXAR Catalog (2009–2016)</strong>. <em>The Seismic Record</em>, 2024; 4(2): 140–150. DOI: <a href="https://doi.org/10.1785/0320240011" target="_new" rel="noopener">https://doi.org/10.1785/0320240011</a></p>
Dataset for the IntoValue 1 + 2 studies on results dissemination from clinical trials conducted at German university medical centers completed between 2009 and 2017
<p>The IntoValue dataset contains clinical trials conducted at one of 35 German UMCs and registered on ClinicalTrials.gov or the German Clinical Trials Registry (DRKS). All trials were reported as complete between 2009 and 2017 on the trial registry at the time of data collection. The dataset also includes a results publication found via manual searches; if multiple results publications were found, the earliest was included.</p> <p>Trials were associated with a German UMC by searching for trials with a UMC listed as responsible party or lead sponsor, or with a principle investigator (PI) from a UMC ('lead_city'). Version 1 additionally includes trials with a UMC only as a facility (`facility_city`). A lookup table of regular expressions used to identify German UMCs is available at <a href="https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv">https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv</a>.</p> <p>Trials include all interventional studies and are not limited to investigational medical product trials, as regulated by the EU's Clinical Trials Directive or Germany's Arzneimittelgesetz (AMG) or Novelle des Medizinproduktegesetzes (MPG).</p> <p>DRKS data were searched (pre-filtered for completion years and study status as well as Germany as 'Country of recruitment') and downloaded as CSVs from the DRKS website (<a href="https://www.drks.de/">https://www.drks.de/</a>). ClinicalTrials.gov data were downloaded downloaded as pipe files from Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) (<a href="https://aact.ctti-clinicaltrials.org/pipe_files">https://aact.ctti-clinicaltrials.org/pipe_files</a>). DRKS and ClinicalTrials.gov use different terminology for various trial aspects, such as phase and masking; these different levels are captured in the data dictionary as `levels_drks` and `levels_ctgov`. For later analyses requiring parity across registries, levels for some variables were collapsed and a lookup table is provided in `iv_data_lookup_registries.csv`.</p> <p>These data were generated and used for two publications (Wieschowski et al., 2019; Riedel et al. 2021) and therefore comprises two versions (indicated as `iv_version`).</p> <p>For version 1, registry data was collected on April 17, 2017 from ClinicalTrials.gov and on July 27, 2017 for DRKS and was limited to trials with a completion date on DRKS and primary completion date on ClinicalTrials.gov between 2009 and 2013. Version 1 manual searches for results publications were conducted from 2017-07-01 to 2017-12-01.<br> For version 2, registry data was collected on June 3, 2020 and was limited to trials with a completion date on DRKS and ClinicalTrials.gov between 2014 and 2017. Version 2 manual searches for results publications were conducted from 2020-07-01 to 2020-09-01.</p> <p>Raw registry data for versions 1 and 2 is available in `raw-registries.zip`.</p> <p>Publication identifiers (DOI, PMID, URL) were manually entered during the publication search and then further enhanced using the API of Internet Archive's open-source Fatcat catalog of research publications, to add PMIDs based on DOIs, and vice versa.</p> <p>Manual search steps differed slightly in the two versions and are indicated and described in `identification_step`.<br> Version 1 includes trials with a German UMC as either a `lead_city` or a `facility_city`, whereas version 2 is limited to trials a German UMC as a `lead_city`.</p> <p>Each row indicates a single trial registration. Due to changes in completion dates, some trials are duplicated between versions as indicated in `is_dupe`. Cross-registered trials were manually deduplicated, and some cross-registered duplicates remain (e.g., DRKS00004156 and NCT00215683) and are not indicated in the dataset.</p> <p>All dates are provided as `yyyy-mm-dd`.</p> <p>Additional documentation on each variable (type, description, levels) is provided in `iv_data_dictionary.csv`.</p> <p>Additional information on the project and methods for generating the dataset is available in associated publications and at the project's OSF page (<a href="https://osf.io/98j7u/">https://osf.io/98j7u/</a>). Code for the project is available at <a href="https://github.com/quest-bih/IntoValue2">https://github.com/quest-bih/IntoValue2</a>.</p> <p><strong>References:</strong></p> <p>Wieschowski, S., Riedel, N., Wollmann, K., Kahrass, H., Müller-Ohlraun, S., Schürmann, C., Kelley, S., Kszuk, U., Siegerink, B., Dirnagl, U., Meerpohl, J., & Strech, D. (2019). Result dissemination from clinical trials conducted at German university medical centers was delayed and incomplete. Journal of Clinical Epidemiology, 115, 37–45. <a href="https://doi.org/10.1016/j.jclinepi.2019.06.002">https://doi.org/10.1016/j.jclinepi.2019.06.002</a></p> <p>Riedel, N., Wieschowski, S., Bruckner, T., Holst, M. R., Kahrass, H., Nury, E., Meerpohl, J. J., Salholz-Hillel, M., & Strech, D. (2021). Results dissemination from completed clinical trials conducted at German university medical centers remained delayed and incomplete. The 2014-2017 cohort. Journal of Clinical Epidemiology, 0(0). <a href="http://doi.org/10.1016/j.jclinepi.2021.12.012">https://doi.org/10.1016/j.jclinepi.2021.12.012</a><br> </p>
Monthly aggregated GLASS FAPAR V6 (250 m): 95th percentile monthly time-series (2009)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
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.