Find research datasets worth reusing

opencc-by-4.0May 2024View details →

GLOBAL SNAPSHOT Physician Distribution and Density of Physicians per 1000 population - Worldwide 2021

The chart presents the most up-to-date data (2021) available for 49 of the worldâ€™s 195 countries, focusing on the total number of physicians and the number of physicians per 1000 population(1). The countries are categorized into four income groups based on World Bank classifications, which are updated annually on July 1st each year(2). Only 25% of the countries present current data. This information is critical for decision-making for healthcare planning and policy development. Equally crucial, is for researchers to have comparable data to propose initiatives, to establish benchmarks and  for crafting holistic strategies to gauge and advance progress in healthcare systems globally. Data sources: UnData <a href="https://data.un.org/">https://data.un.org/</a> Visualization tools used: RAWGraphs <a href="https://www.rawgraphs.io/">https://www.rawgraphs.io/</a>, MS PowerPoint and Microsoft Excel Intended Audience: Academics and Researchers; Students and Educators; Healthcare Administrators and Policy Makers; Non-Governmental Organizations The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The NNLM Data Visualization Challenge happens through work funded by the National Institutes of Health's National Library of Medicine, grant number U24LM013751   References: 1. United Nations, Department of Economic and Social Affairs. 10 Health Personnel. In: Statistical Yearbook. 66th issue (2023). New York: United Nations; 2023. (ST/ESA/STAT/SER.S/42). [Dataset available at UnData] <a href="https://data.un.org/_Docs/SYB/CSV/SYB66_154_202310_Health%20Personnel.csv">https://data.un.org/_Docs/SYB/CSV/SYB66_154_202310_Health%20Personnel.csv</a> 2 World Bank. World Bank Country and Lending Groups. World Bank Data Help Desk [Internet]. [cited 2024 Apr 5]. Available from:<a href="https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups"> https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</a>

opencc-by-4.0Oct 2023View details →

SInAS: A global dataset of native and alien distributions of alien species

The SInAS dataset represents a collection of regional lists of alien (also called non-native or non-indigenous) species and includes information about their native ranges, alien ranges, invasion status for alien ranges, habitats and year of first record. This dataset has been generated by standardising and integrating large global databases of alien species occurrences using the SInAS workflow version 2.0.  The SInAS dataset is described in more detail in the following scientific article, which need to be cited when using this dataset: Gómez-Suárez, M., Laeseke, P., and Seebens, H. (submitted) A global dataset of native and alien distributions of alien species  The code to generate the dataset is stored on Github (https://github.com/hseebens/SInAS) with releases available on Zenodo (https://doi.org/10.5281/zenodo.3763221).

opencc-by-4.0Jan 2023View details →

Global taxonomic occurrence grids using GBIF data for species distribution models.

To achieve large geographic coverage, species occurrence databases that are composed of ad hoc species data collections such as that provided by the Global Biodiversity Information Facility (GBIF) are often used. A drawback to using these data is their geographic sampling bias, in which some regions are more intensively sampled than others, while other areas have very little to none reported sampling effort. Uneven sampling effort can mislead conclusions about biodiversity patterns and species distributions (Gotelli & Colwell, 2001; Lobo, 2008). Here we provide taxonomic occurrence grids to help mitigate the effects of sampling bias in species distribution modeling. These grids can be used to exclude areas of (a custom-defined) low sampling effort from the background when sampling for pseudo-absences’ (Phillips et al., 2009; Barbet-Massin et al.,2012). The occurrence grids have a 1 degree spatial resolution using WGS 84 as the geographic coordinate system. Each 1 degree grid cell contains the number of records present in GBIF corresponding to a specific taxonomic group: plants, mammals, reptiles, amphibians, birds and molluscs. To construct the occurrence grids, we used the 1- by 1-degree world latitude and longitude vector grid provided by ESRI (Redlands, California). It has a custom license which permits it reuse as long as ESRI is cited. It was downloaded from : <a href="https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7">https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7</a> To map spatial sampling effort, the number of georeferenced occurrences corresponding to each taxonomic group contained by each 1- by 1-degree grid cell were counted. The grids were then converted to GeoTIFFs. The raster values correspond to the number of occurrences reported for the grid cells. For the purposes of the <a href="https://osf.io/7dpgr/">TrIAS project</a>, grid cells with fewer than 5 occurrences were removed. The TrIAS taxonomic occurrence grids are used as inputs to the TrIAS risk modelling and mapping workflow: https://github.com/trias-project/risk-modelling-and-mapping. Full (with all grid cells containing at least one occurrence) taxonomic occurrence grids are also provided. GBIF data for each taxonomic group were downloaded using the following criteria: “Basis of Record”: Observation, Machine Observation, Human Observation, Specimen, Material sample, Literature Occurrence, Unknown evidence., "HasCoordinate is true", "HasGeospatialIssue is false", "TaxonKey is Amphibia", "Year 1975-2005". Raster Attributes <table> <tbody> <tr> <td> Attribute </td> <td> Description </td> </tr> <tr> <td> OID </td> <td> numeric row ID </td> </tr> <tr> <td> Value </td> <td> the number of records contained in the grid cell </td> </tr> <tr> <td> Count </td> <td> the number of times the value appears in the raster </td> </tr> </tbody> </table>     The extent of each taxonomic occurrence grid: <ul> <li> longitude -180.0; latitude -90.0 (southwest corner) </li> <li> longitude 180.0; latitude 90.0 (northeast corner) </li> </ul>   Files: TrIAS taxonomic occurrence grids amphib_1deg_min5.tif birds_1deg_min5.tif mammals_1deg_min5.tif molluscs_1deg_min5.tif reptiles_1deg_min5.tif   Raw taxonomic occurrence grids amphib_1deg_grid.tif birds_1deg_grid.tif mammals_1deg_grid.tif molluscs_1deg_grid.tif reptiles_1deg_grid.tif

opencc-by-4.0Dec 2022View details →

Current and future global distribution of potential biomes under climate change scenarios

Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023)   conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080. Files are named according to the following naming convention, e.g.: <ul> <li>biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410</li> </ul> with the following fields: <ul> <li>generic theme: biomes,</li> <li>variable name: graminoid.and.forb.tundra.rcp85,</li> <li>variable type, e.g. probability ("p"), hard class ("c"), model deviation ("md")</li> <li>spatial resolution: 1km,</li> <li>depth reference, e.g. below ("b"), above ("a") ground or at surface ("s"),</li> <li>begin time (YYYYMMDD): 20610101,</li> <li>end time: 20801231,</li> <li>bounding box, e.g. global land without Antarctica ("go"),</li> <li>EPSG code: epsg.4326,</li> <li>version code, e.g. creation date: v20230410.</li> </ul> We provide probability and hard class layers using a revised classification system of the <a href="https://www.jstor.org/stable/2846196">BIOME 6000 project</a> explained in the work of <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a>. The 20 classes from this classification system have then been aggregated in 6 biome classes following the <a href="https://global-ecosystems.org/page/typology">IUCN Global Ecosystem Typology</a> classification system. For probability layers, the uncertainty (model deviation: md) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel. For hard class layers the uncertainty is calculated using the margin of victory (<a href="https://doi.org/10.1016/j.rse.2020.112148">Calderón-Loor et al., 2021</a>) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the md layers to properly interpret the results of the map. Styling files are provided in both .SLD and .QML format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics. The R scripts and a tutorial will be uploaded to the <a href="https://github.com/Envirometrix/PNVmaps">PNVmaps Github repository</a>, where previous versions of the biomes maps from <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a> is currently hosted. To cite the maps and the methodology, it is possible to refer to the scientific publication: Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 <a href="https://doi.org/10.7717/peerj.15593">https://doi.org/10.7717/peerj.15593</a>

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Linking temporal changes in species composition and biomass in a globally distributed grassland experiment: The Nutrient Network

Global change drivers, such as anthropogenic nutrient inputs, are increasing globally. Nutrient deposition simultaneously alters plant biodiversity, species composition, and ecosystem processes like aboveground biomass production. These changes are underpinned by species extinction, colonization, and shifting relative abundance. Here, we use the Price equation to quantify and link the contributions of species that are lost, gained, or that persist to change in aboveground biomass in 59 experimental grassland sites. Under ambient (control) conditions, compositional and biomass turnover was high, and losses (i.e., local extinctions) were balanced by gains (i.e. colonization). Under fertilization, the decline in species richness resulted from increased species loss and from decreases in species gained. Biomass increase under fertilization resulted mostly from species that persist, and to a lesser extent from species gained. Drivers of ecological change can interact relatively independently with diversity, composition, and ecosystem processes and functions such as aboveground biomass due to the individual contributions of species lost, gained, or persisting.

openCC0Sep 2022View details →

opencc-by-4.0Jan 2024View details →

Mapping the global distribution of C4 vegetation using observations and optimality theory

This dataset includes annual C4 vegetation distribution and its uncertainty from 2001 to 2019. We also provide the distribution of C4 natural grasses and C4 crops during the same period, as well as the code and interim dataset to generate the main figures. Please refer to manuscript for more details: Luo, X., Zhou, H., Satriawan, T.W., Tian, J., Zhao, R., Keenan, T.F., Griffith, D. M., Sitch, S. Smith, N.G. & Still, C.J. (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory. Nature Communications. https://doi.org/10.1038/s41467-024-45606-3. Update (Nov 2023): we have updated the observational constraint from a linear model to a non-linear model - logistic curve, to better depict how C4 photosynthetic advantage translates into C4 grass coverage changes (C4_distribution_NUS_v2.2.nc). Update (August 2023): we corrected the issue caused by a bias in the remote sensing grassland base map, and released the version 2 of the C4 vmap (C4_distribution_NUS_v2.nc). Update (June 2023): we noticed there is a critical issue in the version 1 of our C4 map, due to the quality of remote sensing grassland base map used. We are now working on providing a new version (V2) in the next few months (Jun 2023).

opencc-by-4.0Mar 2024View details →

Global patterns of soil organic carbon distribution in the 20–100 cm soil profile for different ecosystems: A global meta-analysis

The file named “Rawdata.xlsx” contains data sourced from the literature. The file name is “GE_β.tif”, GE represents global ecosystems, which including cropland (CL), grassland (GL), and forestland (FL). “FL_β.tif” represents the spatial distribution of β for forestland at 20-100 cm depth. The file name is “GE_d_SOCD.tif”, where SOCD represents soil organic carbon density, d represents soil depth, for example, “FL_20-100_SOCD.tif” represents the spatial distribution of SOCD for forestland at 20-100 cm depth.

opencc-by-4.0Feb 2022View details →

Supplementary data for publication Global distribution of mcr gene variants in 214K metagenomic samples

# Supplementary data for the manuscript "Global distribution of mcr gene variants in 214,095 metagenomic samples" SD1_mapped_runids.csv : tab-separated file with columns of run_accessions downloaded from ENA and whether the metagenome were positive for at least one of the mcr genes. SD2_mcr_df.csv : compositional table of mcr-positive metagenomes with associated metadata (collection_year, country, and host) for each run_accession, as well as mapping results. SD3_mcr_contigs.fa : FASTA file with contigs carrying mcr genes. The header contains the run_accession ID. SD4_aldex2_results.csv: CSV file containing ALDEx2 results. The columns are as follows: * group: metadata category (year, country or host). If the column contains more than one label, e.g., "Denmark - 2020 - Pigs", significance is tested within Danish pig samples from 2020. * rab.all:  median clr value for all samples in the feature * rab.win.conditionA:  median clr value for the condition A of samples * rab.win.conditionB: median clr value for the condition B of samples * diff.btw: median difference in clr values between A and B conditions * diff.win: median of the largest difference in clr values within A and B conditions * effect : median effect size: diff.btw / max(diff.win) for all instances * overlap : proportion of effect size that overlaps 0 (i.e. no effect) * we.ep: Expected P value of Welch’s t test * we.eBH: Expected Benjamini-Hochberg corrected P value of Welch’s t test * wi.ep: Expected P value of Wilcoxon rank test * wi.eBH: Expected Benjamini-Hochberg corrected P value of Wilcoxon test * parts: gene name * conditionA: label of condition A that is compared against condition B * conditionB: label of condition B that is compared against condition A * conditions.A.vs.B: label to explain condition A compared against condition B NOTE: see for more explanation of the output of ALDEx2 https://www.bioconductor.org/packages/release/bioc/vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html#5_ALDEx2_outputs SD5: Multi-VCF file containing SNP information on mcr alleles. Can be used to construct consensus sequences. SD6: FASTA file containing all unique consensus sequences reported in the manuscript. SD7: CSV file with an overview of which metagenome contains which unique consensus sequence.

opencc-by-4.0Dec 2021View details →

Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective

Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. Functional Ecology, 2022. We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.

opencc-by-4.0Nov 2018View details →

Estimating the Global Distribution of Field Size using Crowdsourcing

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used but both have limitations, e.g. limited geographical coverage by remote sensing or coarse spatial resolution when using census data. This paper demonstrates another approach to quantifying and mapping field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced an improved global field size map (over the previous version) and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy no more than 40% of agricultural areas, which means that, potentially, there are much more smallholder farms in comparison with the current global estimate of 12%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts and contribute to SDG 2, among many others.   The dataset (global field sizes.zip) contains: - map of dominant field sizes (dominant_field_size_categories.tif) and description of legend items (legend_items.txt) - table with all submissions by the participant (those who completed more than 10 classifications) and table description - table with quality score of all the participants and table description - table with estimated dominant field sizes at each location and table description

opencc-by-4.0Feb 2023View details →

Global distribution map of Rhenish stoneware during the 16th to 18th century

The dataset provides a distribution map of Rhenish stonewares between the 16th and 18th century. The data was collected from published archaeological data (print and online) available to the author. According the published information the pottery was classified to different wares (Cologne, Frechen, Siegburg, Raeren, Westerwald). Values are given for individual sherd numbers. If no information was given in the publication, the value is set to "1". Bibligraphic reference is given by author - date. Full bibliographic reference can be found in the pdf-file. The csv-file contains next to location name, bibliographic reference and pottery counts values for longitude and latitude. The coordinate reference is WGS 84 - EPSG:4326.

opencc-by-4.0Sep 2023View details →

Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations)

This is the outcome data from our study titled "<a href="http://dx.doi.org/10.1016/j.scitotenv.2024.170481" target="_blank" rel="noopener">Prediction of global wheat cultivation distribution under climate change and socioeconomic development</a>" which was published in The Science of The Total Environment.  The present study represents a significant extension of our previous research on "<a href="http://dx.doi.org/10.1016/j.scitotenv.2019.06.153" target="_blank" rel="noopener">The Potential Distribution and Dynamics of Global Wheat under Multiple Climate Change Scenarios"</a>. Socioeconomic and climate change are both critical factors influencing the global distribution of crop cultivation. However, there has been limited exploration of the role of socioeconomic factors in predicting future crop cultivation distribution under climate change. We have proposed the MaxEnt-SPAM approach under the assumption that environmental conditions are the primary determinants of land suitability for cultivating wheat, while socioeconomic factors play a crucial role in influencing farmers' crop choices. In essence, the distribution of wheat cultivation is contingent upon maximizing potential revenue and ensuring suitability for wheat planting. The proposed MaxEnt-SPAM approach was utilized to estimate the distribution of wheat cultivation in three combined Representative Concentration Pathway (RCP) - Shared Socioeconomic Pathway (SSP) scenarios, namely RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3. The methodology involved estimating wheat planting suitability under future RCP scenarios using the MaxEnt model, predicting farmers' crop choices under future SSP scenarios through Time series-Backpropagation (TS-BP) models, and ultimately estimating global wheat cultivation distribution based on the SPAM model. Validation of this approach against major known datasets on the distribution of wheat cultivation demonstrated satisfactory accuracy, with a predictive accuracy exceeding 85% and a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets. Based on the aforementioned concept and methodology, a global wheat cultivation distribution grid (0.5 degree × 0.5 degree) was projected under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The findings suggest that RCP8.5-SSP3 may offer the most favorable conditions for wheat cultivation. Additionally, socioeconomic development significantly constrains the potential distribution of wheat cultivation, with estimated areas accounting for an average of 77% of the potential distribution determined by climatic factors under the selected RCP-SSP scenarios. Socioeconomic development appears to have a positive impact on wheat cultivation in Africa. Our results illustrate the influence of socioeconomic factors on crop distribution within a market economy framework, underscoring the importance of integrating socioeconomic factors and climate change for accurate predictions of crop cultivation distribution. We contend that the global wheat cultivation distribution datasets under future climatic and socio-economic conditions (RCP-SSP combinations) are a valuable addition to existing products. This prediction data is among the few products to consider both climate change and socio-economic development, providing a more comprehensive understanding of crop cultivation distribution dynamics. The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) is expected to enhance our comprehension of the dynamics and distribution of global wheat cultivation under different climate change and socio-economic development paths in the future, potentially supporting research in earth system simulation and agricultural sciences. The dataset for the Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) and the Maxent-SPAM approach code is stored in a zip package named SPAM_MaxEnt.zip, which contains two folders (code and data). code: This sub-folder provides the main program and example data for the MaxEnt-SPAM approach. Codes are written in Matlab language by Puying Zhang. There are also 'read me.txt' files under the code folder to provide the necessary information. The exampleData contains 1. h_pri.tif: prior data 2. h_res.tif: global C3 crop cultivation proportion Run the main programme: cross_entroy.m data: This sub-folder contains global wheat cultivation distribution stored in GeoTIFF file format. 1 Global distribution of the long-term wheat-cultivation area fraction: This sub-folder contains the data for the global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The value of each data ranges from 0 to 1, indicating the long-term wheat-cultivation area fraction in each grid, and the higher the value, the more wheat cultivated. r2s1f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1 scenario r4s2f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP4.5-SSP2 scenario r8s3f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP8.5-SSP3 scenario 2 Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution: This sub-folder contains the data for Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution in multi-scenarios. The value of each data contains three values:{1, 2, 3}, 1 wheat cultivation existed but was predicted to be unsuitable to plant wheat; 2 presented a reduction in the wheat cultivation area compared to the land's suitability; 3 presented the region that wheat cultivation existed and was predicted to be suitable to plant wheat. com_suit_fra126.tif: the spatial overlap between the long-term period land suitability for wheat planting and wheat cultivation distribution in (a) RCP2.6-SSP1 scenario and RCP2.6 com_suit_fra245.tif: the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (b) RCP4.5-SSP2 scenario and RCP4.5 com_suit_fra385.tif: the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (c) RCP8.5-SSP3 scenario and RCP8.5 3 Differences in the proportion of long-term wheat cultivation: This sub-folder contains the data for the difference in the proportion of long-term wheat cultivation under the RCP-SSP scenarios and the distribution of long-term wheat planting suitability under the same RCP scenarios. The value of each data ranges from -1 to 1, This data is obtained by using the wheat-cultivation area fraction minus planting suitability grid to grid. the negative value indicates that the proportion of wheat cultivation is lower than the wheat planting suitability, while this positive value indicates that the proportion of wheat cultivation is higher than the wheat planting suitability. r2s1_f.tif: Difference in the proportion of long-term wheat cultivation under the RCP2.6-SSP1 scenario and the distribution of long-term wheat planting suitability under the RCP2.6 scenario r4s2_f.tif: Differences between the proportion of long-term wheat cultivation in RCP4.5-SSP2 and the suitability of long-term wheat planting under the RCP4.5 scenario r8s3_f.tif: Differences between the proportion of long-term wheat cultivation in RCP8.5-SSP3 and the suitability of long-term wheat planting under the RCP8.5 scenario  References: Yaojie Yue, Puying Zhang, Yanrui Shang. The Potential Distribution and Dynamic of Global Wheat under Multiple Climate Change Scenarios. Science of the Total Environment, 2019, 688: 1308-1318. Xi Guo, Puying Zhang, Yaojie Yue. Prediction of global wheat cultivation distribution under climate change and socio-economic development. Science of the Total Environment, 2024, 919: 170481. For more details on the MaxEnt (Maximum entropy) model, please refer to (Phillips et al., 2006; Elith et al., 2011). SPAM (spatial production allocation model) refers to (You et al., 2009; You et al., 2014). Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of maxent for ecologists. Divers Distrib 17 (1), 43-57. https://coi.org/10.1111/j.1472-4642.2010.00725.x. Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190 (3-4), 231-259. https://coi.org/10.1016/j.ecolmodel.2005.03.026. You, L.Z., Wood, S., Wood-Sichra, U., 2009. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agr Syst 99 (2-3), 126-140. https://coi.org/10.1016/j.agsy.2008.11.003. You, L.Z., Wood, S., Wood-Sichra, U., Wu, W.B., 2014. Generating global crop distribution maps: from census to grid. Agr Syst 127, 53-60. https://coi.org/10.1016/j.agsy.2014.01.002

edi44/100

High-frequency water temperature, chlorophyll fluorescence, wind speed, and photosynthetically active radiation data for 18 globally-distributed lakes 2008 - 2013

Abstract: This dataset was used in the analysis described in the manuscript by Rusak, J. A.J. Tanentzap, J.L. Klug, K. Rose, L.A. Winslow R. Smyth, E. Jennings, D. Pierson, S. Hendricks, A. Laas, E. Ryder, D. White, R. Adrian, L. Arvola, E. de Eyto, H. Feuchtmayr, M. Honti, V. Istanovics, I. Jones, C. McBride, S. Schmidt, G. Zhu. Wind and trophic status explain the temporal and spatial variability of chlorophyll in lakes. In review: Limnology and Oceanography Letters. The variation in chlorophyll fluorescence from 18 globally distributed lakes, was tested at monthly, daily and hourly scales in related to high-frequency measurements of wind, water temperature and radiation within lakes as well as lake productivity and morphometry among lakes. Overall, monthly variation in algal biomass was greater than that expressed at either daily or hourly scales but, combined, these latter time scales were equivalent to seasonal variation. Among lakes, algal biomass variation increased with trophic status while, within-lake variation increased with increasing wind speed variation. Together, our results suggest that predicted changes associated with a changing climate, as well as widespread ongoing cultural eutrophication, have the potential to substantially alter the variability of algal biomass and thus the predictability of the services it provides. This dataset includes the data used in the analysis described above.

openCC (other)Dec 2017View details →

opencc-by-4.0May 2020View details →

The age distribution of global soil carbon inferred from radiocarbon measurements

We use 789 radiocarbon (∆14C) profiles, along with other geospatial information, to create globally-gridded datasets of mineral soil ∆14C and mean age. The spatial resolution is 0.5 degree by 0.5 degree and the vertical resolution is at each 1 cm increment to a soil depth of 1 meter.

opencc-by-3.0Dec 2017View details →

Fig. 1 in Global checklist of species of Grania (Clitellata: Enchytraeidae) with remarks on their geographic distribution

Fig. 1. Specimen of Grania chilensis Prantoni, De Wit & Erséus, 2016. Photograph by Pierre De Wit.

opencc-by-3.0Dec 2017View details →

Fig. 2 in Global checklist of species of Grania (Clitellata: Enchytraeidae) with remarks on their geographic distribution

Fig. 2. World map showing the description localities, and the three evolutionary lineages (color-marked) of Grania spp. genetically analyzed by Prantoni et al. (2016: clades A, B and C). Green numbers = Atlantic species (clade A); red numbers = South Pacific and Atlantic species (clade B); blue numbers = Australian and Asian species (clade C); black numbers = species presently without molecular data, i.e., not yet allocated to any particular lineage.