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72 results for “Global synthesis”
Global dataset of nitrogen fixation rates across inland and coastal waters based on a coordinated synthesis effort
Biological nitrogen fixation converts inert di-nitrogen gas into bioavailable nitrogen and can be an important source of bioavailable nitrogen to organisms. This dataset synthesizes the aquatic nitrogen fixation rate measurements across inland and coastal waters. Data were derived from papers and datasets published by April 2022 and include rates measured using the acetylene reduction assay (ARA), 15N2 labeling, or the N2/Ar technique. The dataset is comprised of 4793 nitrogen fixation rates measurements from 267 studies, and is structured into four tables: 1) a reference table with sources from which data were extracted, 2) a rates table with nitrogen fixation rates that includes habitat, substrate, geographic coordinates, and method of measuring N2 fixation rates, 3) a table with supporting environmental and chemical data for a subset of the rate measurements when data were available, and 4) a data dictionary with definitions for each variable in each data table. This dataset was compiled and curated by the NSF-funded Aquatic Nitrogen Fixation Research Coordination Network (award number 2015825).
Data set for Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types
<p>Dataset used in the publication: " Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types". The dataset gathers estimates of ecosystem standing stocks (biomass, organic carbon, detritus), fluxes (GPP, ER, NEP) and process rates (decomposition and carbon uptake rates) for eight broad ecosystem types (forest, grassland, agroecosystem, desert, stream, lake, pelagic and benthic marine ecosystems) in five broad climatic zones (arctic, boreal, arid, temperate, tropical, arid).</p> <p>The scripts to produce the figures and the statistics of the publication are released along with the txt version of the data, which file is uploaded when running the script.</p>
ZooBase: A global synthesis of marine zooplankton species occurrences.
<p><em><strong>Description of the methods used to implement the present ZooBase dataset (extarct from Section A.2 from the Methods of Benedetti et al., 2021).</strong></em></p> <p>A new dataset of global zooplankton species occurrences was compiled in a comparable fashion to that put together for phytoplankton (Righetti et al., 2020). Prior to retrieving the occurrence data online, we first identified the phyla (Order/Class/Family) that comprise the bulk of extant oceanic zooplankton communities: Copelata (i.e. appendicularians), Ctenophora, Cubozoa (i.e. box jellyfish), Euphausiidae (i.e. krill), Foraminifera, Gymnosomata (i.e. sea angels, pteropods), Hydrozoa (i.e. jellyfish), Hyperiidea (i.e. amphipods), Myodocopina (i.e. ostracods), Mysidae (i.e. small pelagic shrimps resembling krill), Neocopepoda, Podonidae and <em>Penilia</em> <em>avirostris</em> (i.e. cladocerans), Sagittoidea (i.e. chaetognaths), Scyphozoa (i.e. jellyfish), Thaliacea (i.e. salps, doliolids and pyrosomes), Thecosomata (i.e. pteropods), and four families of pelagic Polychaeta (i.e. worms) that are often found in the zooplankton and whose species are known to display holoplanktonic lifecycles (Tomopteridae, Alciopidae, Lopadorrhynchidae, Typhloscolecidae). The presence data associated with species belonging to these groups were retrieved from OBIS and GBIF between the 12/04/2018 and the 18/04/2018 using online queries via the R packages RPostgreSQL, robis and rgbif. Since the Neocopepoda infra-class comprise several thousands of benthic and parasitic taxa (https://copepodes.obs-banyuls.fr/en/), a preliminary selection of the non-parasitic planktonic species had to be carried out prior to the online downloading using the species list of Razouls et al. (https://copepodes.obs-banyuls.fr/en/) as a reference. The spatial distributions of the groups cited above were first inspected using GBIF’s and OBIS’s online mapping tools to evaluate the potential number of overlapping observations between the two databases. As a result of their relatively low contributions to total observations/diversity, and very high overlap between databases, the occurrences of Cladocera and Polychaeta were retrieved from OBIS only (which usually harbours more occurrences). On top of the data collected from OBIS and GBIF, the copepod occurrences from Cornils et al. (2018) and the pteropod occurrences from the MAREDAT initiative (Buitenhuis et al., 2013) were added to the dataset. We discarded records that: (i) presented at least one missing spatial coordinate, (ii) were associated with an incomplete sampling date (d/m/y), (iii) were associated with a year of collection older than 1800, (iv) were not associated with any sampling depth, (v) were not identified down to the species level. Occurrences associated with grid cells shallower than 10m were removed (bathymetry data from ETOPOv1 at a 15min resolution, downloaded using the 'marmap' R package). Finally, every species name was then carefully examined and compared to the taxonomic reference list of the World Register of Marine Species (WoRMS; <a href="http://www.marinespecies.org">http://www.marinespecies.org</a>) for all taxa. The AphiaID and the Status were retreived from WoRMS based on the ScientificName. To remove the duplicate occurrences due to the highly overlapping source archives (GBIF and OBIS), a unique occurrenceID was given to each record based on rounded spatial coordinates (closest 0.1°x0.1°), rounded depth layer (10m depth layers), month and year of the occurrence and the acccpted species name (e.g., AphiaID).</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
Synthesis data for manuscript: Global stocks and capacity of mineral-associated soil organic carbon
<p>Supporting synthesis data for manuscript: Georgiou K., Jackson R. B., Vindušková O., Abramoff R. Z., Ahlström A., Feng W., Harden J. W., Pellegrini A. F. A., Polley H. W., Soong J. L., Riley W. J., Torn M. S. Global stocks and capacity of mineral-associated soil organic carbon. <em>Nature Communications</em>, 2022.</p> <p>We performed an observational synthesis of soil fractionation data constituting 1,144 globally-distributed soil profiles from 78 studies that reported fractionation and bulk measurements of organic carbon across depths. This dataset includes measurements of mineral-associated, particulate, and bulk soil organic carbon, as well as ancillary data on edaphic, climate, and vegetation characteristics. We also performed a separate observational synthesis of soil carbon accrual from manipulation and chronosequence studies, which included changes in carbon stocks or concentrations, bulk density, experimental duration, and edaphic properties. This latter synthesis included 103 observations from 34 studies that spanned crop, pasture, grassland, and forest ecosystems across climates and soil types. Further details for both syntheses can be found in the methods and supplementary materials of the associated manuscript.</p>
A global synthesis of high-resolution stable isotope data from benthic foraminifera of the last deglaciation
<p>In paleoceanography, carbon and oxygen stable isotope ratios from benthic foraminifera are used as tracers of physical and biogeochemical properties of the deep ocean. We present the first version of the Ocean Carbon Cycling working group database, of stable isotope ratios of oxygen and carbon from benthic foraminifera from deep ocean sediment cores from the Last Glacial Maximum (LGM, 23-20 ky before present (BP)) to the Holocene (<10 ky BP) with a particular focus on the early last deglaciation (20-15 ky BP). It includes 287 globally distributed coring sites, with metadata, isotopic and chronostratigraphic information, and age models. A quality check was performed for all data and age models. Sites with at least millennial resolution were preferred, because the main goal is to resolve ocean changes associated with the last deglaciation on at least millennial timescales. Software tools were produced to access and analyze the data, and are included with this publication. Deep water mass structure as well as differences between the early deglaciation and LGM are captured by the data in the compilation, even though its coverage is still sparse in many ocean regions. We find high correlations among time series calculated with different age models at sites that allow such analysis. The database provides a useful dynamical approach to map physical and biogeochemical changes of the ocean throughout the last deglaciation.</p>
Dataset supplementing Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology. DOI: 10.1111/gcb.13714</p> <p> </p> <p>Please cite the above article if you use any of the included data or code.</p> <p> </p> <p>Files are described in README.md.</p>
A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1
<p>A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies. GSHA covers 21,568 watersheds from 13 agencies for as long as 43 years based on the discharge observations scraped from the web. GSHA includes yearly streamflow characteristics derived from daily discharge observations, daily meteorological variables (including precipitation, 2-m air temperature, long- and shortwave radiation, wind speed, actual and potential evapotranspiration (AET and PET)), daily or weekly water storage terms (4 layers of soil moisture, groundwater, and snow depth water equivalence), daily vegetation index (leaf area index (LAI)), yearly LULC characteristics (urban, cropland, and forest fraction), and yearly reservoir information (degree of regulation (DOR) and reservoir capacity). For each meteorological variable, multiple independent data sources are incorporated to provide uncertainty estimates. Static attributes like land physiography, soils, and geology are not additionally extracted, as similar efforts have been made by other researchers, so we directly matched our gauge locations to the HydroATLAS dataset by providing the river ID match table.</p> <p>For more details of GSHA, please refer to a companion research article submitted to ESSD.</p> <p>Please access the variables in version 1.0. Monthly streamflow indices files do not include Chinese basins.</p> <p>Citation: <strong> </strong>Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-256, in review, 2023.</p> <p> </p>
Supporting material for von Fromm et al (2024) Global synthesis of SOC abundance and persistence profile data
<p>This file contains the supporting material for von Fromm et al (2024) Controls and relationships of soil organic carbon abundance and persistence vary across pedo-climatic regions published in Global Change Biology (https://doi.org/10.1111/gcb.17320). </p> <p>The data comes originally from the International Soil Radiocarbon Database (ISRaD; <a href="https://soilradiocarbon.org/">https://soilradiocarbon.org/</a>). We filtered the data and applied a mass-preserving spline function. For more details see the corresponding manuscript and the github repository (https://github.com/SophievF/Global_SOC_Abundance_Persistence).</p>
Figure 6 in Towards a global synthesis of Collembola knowledge - challenges and potential solutions
Figure 6. Example of a spatially-explicit soil sampling design, based on five nested triangles. Dots with numbers represent soil core samples, distributed between thee base poles (A, B, C). Distances between the samples follow a decimal logarithmic scale. The largest triangle edge length is 10 meters. Note that some samples are directly next to each other. A more extensive design based on triangles or rectangles can be found in Saraeva et al. (2015) or Widenfalk et al. (2015), respectively.
Figure 4 in Towards a global synthesis of Collembola knowledge - challenges and potential solutions
Figure 4. Ecosystem processes that can be affected by different functional groups of Collembola. Soil-dwelling Collembola such as Onychiuridae are associated with roots and involved in rhizosphere processes. Many litter-dwelling Isotomidae and Tomoceridae are associated with decomposition processes by grazing and transformation of litter. Most of Hypogastruridae and Neanuridae and litter-dwelling Entomobryidae are largely microbivores and impact directly microbial communities in the litter. Surface-dwelling Symphypleona, Entomobryidae and Paronellidae affect microbial biofilms aboveground. Collembola, especially large surface-dwelling species, support various groups of terrestrial invertebrate predators. Summarised from Potapov et al. (2020, 2016) and Rusek (1998).
Figure 1 in Towards a global synthesis of Collembola knowledge - challenges and potential solutions
Figure 1. Trends in Collembola taxonomy. New species description accumulated up till the 1960's but stays linear afterwards (A); Most of species were recorded from Asia, Europe and North America (B); While the rate of species description stays unchanged for the last 70 years, the number of described taxa per taxonomist (first authorship) declines (C); The number of species described is driven by a low number of productive taxonomists, with the first 23 highly productive taxonomists (5.6 % of all first authors of species descriptions) being responsible for ca. 50 % of total known Collembola species (D). All estimations for the panels A, B and C were calculated in 5-year intervals. For the panel D only persons listed in the authorship of the species were included in the analysis. The figure is produced based on the data from September, 2020 available from http://collembola.org (Bellinger et al. 1996-2020).
Figure 1 in sOilFauna - a global synthesis effort on the drivers of soil macrofauna communities and functioning
Figure 1. Location of the transects in the present version of the MACROFAUNA database (A) Map of the data (B) Location of the data in relation to the biomes.
Data for: The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis
<p><strong><em>Data description</em></strong></p> <p>This dataset includes detailed information about five different types of land use change reported in “The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis (Agriculture, Ecosystems and Environment; <a href="https://doi.org/10.1016/j.agee.2023.108402)">https://doi.org/10.1016/j.agee.2023.108402)</a>”. </p> <p> </p> <p>Lists of five different types of land use change</p> <p>1) conversion of primary forest to cropland</p> <p>2) conversion of primary forest to grassland</p> <p>3) conversion of cropland to forest</p> <p>4) conversion of grassland to forest</p> <p>5) conversion of grassland to cropland</p> <p> </p> <p>Lists of detailed information</p> <ul> <li>Land use change (pre-LUC, post-LUC)</li> <li>Country, Location, Geographic position (Longitude, Latitude) </li> <li>Altitude (m)</li> <li>Climate zone</li> <li>Weather [rainfall (mm yr<sup>-1</sup>) and temperature (°C)]</li> <li>Reported time of change (years)</li> <li>Vegetation type (pre-LUC, post-LUC)</li> <li>Fertilizer (pre-LUC, post-LUC: type, application; change)</li> <li>Soil sampling depth (cm)</li> <li>Soil type [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil pH, bulk density, CEC [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil organic carbon [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil total nitrogen [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil total phosphorus [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil C:N [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil C:P [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil N:P [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Reference</li> </ul> <p> </p> <p><em><strong>Data collection method</strong></em></p> <p>We analyzed five different types of LUC: 1) conversion of primary forest to cropland, 2) conversion of primary forest to grassland, 3) conversion of cropland to forest, 4) conversion of grassland to forest, and 5) conversion of grassland to cropland.</p> <p>We classified primary forest as forest that had not previously been cleared and used for other land uses. The conversion of cropland or grassland to forest includes naturally generated and intentionally planted forest. Cropland is land used for growing agricultural crops and may include short pasture phases, and grassland is land used continuously for grazing purposes, but may include occasional and repeated pasture-renewal phases.</p> <p>While we tried to make categorical distinctions between these land-use types, land uses are often more fluid in practice, which may not always have been stated in the publications underlying our data compilation.</p> <p>When a paper reported both contents and stocks, we used the stock-based measure. We used reported stocks if the original work had already been corrected to equivalent soil mass (Ellert and Bettany, 1995) or if corrected stocks had been reported in previous reviews or meta-analyses (Don et al., 2011; Poeplau et al., 2011; Guo and Gifford, 2002). Where bulk-density correction had not been applied, we tried to make those corrections to estimate changes to equivalent soil mass if studies provided sufficient information on soil bulk density and depth, using the method of Zhang et al. (2004). If that was not possible, we used the reported SOC, TN, or TP contents.</p> <p> </p> <p><em><strong>Acknowledgements</strong></em></p> <p>We thank scientists who measured, analyzed, and published the data compiled for this study. We are especially grateful to Drs. Axel Don, Christopher Poeplau, Lex Bouwman, and Gaihe Yang, who provided their global meta-data through personal communication. D.-G.K. acknowledges support from the IAEA CRP D15020. M.U.F.K and L.L.L. were supported by the Strategic Science Investment Fund (SSIF) of New Zealand’s Ministry of Business, Innovation and Employment.</p>
Data from: A global synthesis of how plants respond to climate warming from traits to fitness
Open the record for dataset details and reuse information.
A global synthesis on land-cover changes in watersheds shaping freshwater detrital food webs
Open the record for dataset details and reuse information.
Bacterial zoonoses impacts to conservation of wildlife populations: A global synthesis
<p>Emerging infectious diseases have significantly increased in recent years; approximately 60% of these emerging diseases are of zoonotic origin, and of those, around 70% were identified to start with wild animals. To better understand the impacts of zoonotic diseases on wildlife, there is a need to identify the distribution and impact of zoonotic pathogens in wildlife, particular those species with threatened populations. To investigate this question, we constructed a zoonotic disease database identifying wildlife hosts for each bacterial zoonotic disease and recorded clinical signs of disease for each host-pathogen relationship if found. Species of least concern were found to have a significantly higher prevalence and richness of zoonotic bacterial diseases (n= 0.0608, x<sup>2</sup>= 5.898, p=0.01516). The taxonomic level Order was found to be the best predictor for pathogen richness, and the Artiodactyla and Carnivora contain a significantly higher pathogen richness than other vertebrate Orders. Species with the greatest pathogen richness include the wild boar (<em>Sus scrofa</em>, n=66), Northern raccoon (<em>Procyon lotor</em>, n=16) and the red fox (<em>Vulpes vulpes</em>, n=41). Our results indicate bacterial zoonoses are not likely to be a driving factor of species population declines, and future zoonotic disease surveillance efforts should target species in the Artiodactyla and Carnivora families. The wild boar (<em>Sus scrofa</em>) in particular may be a good candidate for zoonotic disease monitoring due to its high pathogen richness, wide range, and large population. Understanding the impacts and distribution of bacterial zoonoses in wildlife populations can help in planning for future wildlife management efforts, particularly in species of conservation concern and wildlife disease monitoring.</p>
Distance decay 2.0 – a global synthesis of taxonomic and functional decay in ecological communities
<p>Datasets used in the analysis of the manuscript by Graco-Roza, C., Aarnio, S., Abrego, N., Acosta, A. T., Alahuhta, J., Altman, J., ... & Soininen, J. (2022). Distance decay 2.0–a global synthesis of taxonomic and functional turnover in ecological communities.<em> Global Ecology and Biogeography.</em></p> <p> </p> <p><strong>raw_data.zip - </strong>Includes the raw datasets used in the analysis. </p> <p><strong>processed_data.xlsx - </strong>Includes the results from the distance decay analysis, specifically: </p> <p>- Dataset : dataset code (same as in raw_data)</p> <p>- Beta_type: The component of beta diversity (i.e., total similarity, replacement, richness differences)</p> <p>- Level : Taxonomic (TAX) or functional (FUN)</p> <p>- Based: Occurrence (occ) or Abundance (abund)</p> <p>- Organism: Code used to describe organisms (see Appendix S1 of the paper)</p> <p>- Realm: Aquatic, Terrestrial, or Freshwaters</p> <p>- Body_size </p> <p>- Dispersal_mode: Seeds, Passive or Active</p> <p>- Latitude: Mean latitude of the dataset (average of all data points)</p> <p>- Latitude_range Distance in kilometres between the two vertically most distant points.</p> <p>- Longitude_range: Distance in kilometres between the two horizontally most distant points.</p> <p>- spa_min: minimum distance between sites (in kilometres) </p> <p>- spa_mean: average distance between sites (in kilometres) </p> <p>- spa_max: maximum distance between sites (in kilometres)</p> <p>- ext: area in kilometres covered by all sites in the dataset</p> <p>- n_sites: Number of sites in each dataset</p> <p>- n_var: Number of environmental variables in each dataset</p> <p>- gamma_spe: Number of species observed in each dataset</p> <p>- gamma_trait: Volume of the hypervolume constructed using the traits in each dataset</p> <p>- n_traits: Number of traits in each dataset</p> <p>- Intercept_spa: Intercept of GLM including community similarity and spatial distances</p> <p>- Slope_Spa: Slope of GLM including community similarity and spatial distances</p> <p>- R2_spa: R² of GLM including community similarity and spatial distances</p> <p>- Intercept_env: Intercept of GLM including community similarity and environmental distances</p> <p>- Slope_env: Slope of GLM including community similarity and environmental distances</p> <p>- R2_env: R² of GLM including community similarity and environmental distances</p> <p>- Mantel_spa: Mantel statistics of community similarity and spatial distances</p> <p>- spa_signif: Significance of Mantel statistics considering community similarity and spatial distances</p> <p>- Mantel_env: Mantel statistics considering community similarity and environmental distances</p> <p>- env_signif: Significance of Mantel statistics considering community similarity and environmental distances</p> <p><strong>Null_models.zip - </strong>Includes the results from the null models for each dataset.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
A synthesis of nitric oxide emissions across global fertilized croplands from crop-specific emission factors
<p><span>Nitrogen (N)-fertilizer application to agricultural soils results in substantial emissions of nitric oxide (NO), a key substance in tropospheric chemistry involved in climate forcing and air pollution. </span><span>However, estimates of global cropland NO emissions remain uncertain due to a lack of information on direct NO emission factors (EF<sub>d<sup>s</sup></sub>) of applied N for variours cropping systems at seasonal or annual scales. Here we quantified the crop-specific seasonal and annual-scale NO EF<sub>d<sup>s</sup></sub> through synthesizing </span><span>1094 measurements from 125 field-based studies worldwide. </span><span>The global mean crop-specific seasonal EF<sub>d</sub> was 0.53%, with the highest for vegetables (0.75%). Among cereal crops, the EF<sub>d</sub> of maize (0.45%) or wheat (0.47%) was about three-times higher than for rice (0.12%). At annual scale, the mean EF<sub>d</sub> across all cropping systems was</span><span> 0.58%, with tea plantations having the highest (1.54%). For other cropping systems, the annual-scale EF<sub>d<sup>s</sup></sub> ranged from 0.02% to 1.07%. Besides crop type, also soil organic carbon, total N and pH as well as N fertilizer type were the main factors explaining the variations of NO EF<sub>d<sup>s</sup></sub>. Based on obtained specific EF<sub>d<sup>s</sup></sub> for each crop type, we estimated that NO emissions due to the use of synthetic fertilizers from global croplands are about 0.42–0.62 Tg N yr<sup>−1</sup>. Our budgets are relatively lower if compared to estimates derived by the use of IPCC defaults for NO emissions (0.72–1.66 Tg N yr<sup>−1</sup>) or reported elsewhere (0.67–1.04 Tg N yr<sup>−1</sup>). In our estimates, cash crops (vegetable, tea and orchard), which cover only 9% of the world cropland area, contributed about 31% to total NO emissions from global fertilized croplands. Overall, our meta-analysis provides improved crop-specific NO EF<sub>d<sup>s</sup></sub> reflecting current stage of knowledge. The work also highlights the relative importance of cash crop production as sources for atmospheric NO, i.e., agricultural systems on which mitigation efforts may focus</span><span>.</span></p>
Dataset: A global synthesis of human impacts on the multifunctionality of streams and rivers
<p><span>Human impacts, particularly nutrient pollution and land-use change, have caused significant declines in the quality and quantity of freshwater resources. Most global assessments have concentrated on species diversity and composition, but effects on the multifunctionality of streams and rivers remain unclear. Here, we analyse the most comprehensive compilation of stream ecosystem functions to date to provide an overview of the responses of nutrient uptake, leaf litter decomposition, ecosystem productivity, and food web complexity to six globally pervasive human stressors. We show that human stressors inhibited ecosystem functioning for most stressor-function pairs. Nitrate uptake efficiency was most affected and was inhibited by 347% due to agriculture. However, concomitant negative and positive effects were common even within a given stressor-function pair. Some part of this variability in effect direction could be explained by the structural heterogeneity of the landscape and latitudinal position of the streams. Ranking human stressors by their absolute effects on ecosystem multifunctionality revealed significant effects for all studied stressors, with wastewater effluents (194%), agriculture (148%), and urban land use (137%) having the strongest effects. Our results demonstrate that we are at risk of losing the functional backbone of streams and rivers if human stressors persist in contemporary intensity, and that freshwaters are losing critical ecosystem services that humans rely on. We advocate for more studies on the effects of multiple stressors on ecosystem multifunctionality to improve the functional understanding of human impacts. Finally, freshwater management must shift its focus towards an ecological function-based approach and needs to develop strategies for maintaining or restoring ecosystem functioning of streams and rivers.</span></p>
Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>Synthesis of fuel load and fuel consumption field measurements accompanying the publication:</p><p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p><p>Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5</p><p>1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br>2Department of Earth System Science, University of California, Irvine, CA 92697, USA<br>3Woodwell Climate Research Center, Falmouth, MA 02540, USA<br>4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br>5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p><p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p><p> </p><p>Units are g C / m2</p>
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.