TropiRoot 1.0 is a new tropical root database with root characteristics across environment gradients. It has data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 includes root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology and root chemistry. This initiative represents an approximately 30% increase in the currently available data for tropical roots in the Fine Root Ecology Database (FRED). TropiRoot 1.0, contains root characteristics from 25 different countries where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data was available, including soil data, these data was either extracted and included in the database or their availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match the ones reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions, and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models.

Rooting depths were estimated from a global database of root profiles that was assembled from the primary literature to study relationships of abiotic and biotic factors associated with belowground vegetation structure. Variables used to characterize belowground vegetation structure include the depths above which 50% of all roots and 95% of all roots are located in the profile. For each root profile, information recorded includes latitude and longitude, elevation, soil texture, depth of organic horizons, type of roots measured (e.g., fine or total, live or dead), sampling methods, units of measurements (root mass, length, number, surface area), and sampling depth.

The root shoot database is a collection of literature data gathered over the space of a year covering studies up until the end of 2022. The primary driver for the development of the root shoot database was to provide root and shoot estimates for key European crops. The specific criteria for a study to be included in the data base are the following: Overall criteria for a study to qualify: 1) Contain root and shoot estimates and/or RS ratios 2) Originate from annual and perennial crops grown in Europe (according to list of countries defined by EJP Soil) 3) Include a minimum of necessary meta-data 4) Originates from field studies (i.e. pot studies were excluded because of artificial effects on roots) During the initial literature survey, a range of existing databases were assessed to identify suitable articles for inclusion in this analysis. This included existing data collections, data from partners and literature review: • Data from Martin Bolinder’s (SLU) collection • Data from several project partners (From France, Denmark, Portugal, Switzerland) • GrooT and TRY database screening In addition to gathering data from published articles, where appropriate, contact was made with corresponding authors to resolve uncertainties and fill gaps in the associated meta data. This resulted in a library of assessments for each country and entries in the RS database. In total we identified 23 studies resulting in 451 individual measurements across 11 countries in Europe (including the UK).

To address the need for a centralized root trait database, we compiled the Fine-Root Ecology Database (FRED) from published and unpublished data sources. We have continued to add to the FRED database since the release of FRED 2.0 in 2018, and a new version of FRED is now available. FRED 3.0 has more than 150,000 observations of more than 330 root traits, with data collected from more than 1400 data sources. FRED 3.0 has 45% more root trait observations than FRED 2.0, particularly in the categories of root anatomy, morphology, and microbial associations; ancillary data on associated site, vegetation, edaphic, and climatic conditions from across the globe have also increased concurrently. FRED is focused on fine roots (traditionally defined as roots less than 2 mm in diameter), as coarse roots are studied using different methodology, often at very different scales, and have different traits and trait interpretations. However, FRED accepts data collected from roots of all sizes, and already contains several observations of coarse roots. Data collection will continue for the foreseeable future.

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37609 Global export shipment records of Root with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Estimates of root turnover rates were calculated from measurements of live root standing crop and belowground net primary production (BNPP) compiled from the primary literature. Vegetation characteristics, soil properties, and climate conditions were associated with turnover rates to examine patterns and controls for biomes worldwide. Building on prior analyses (Jackson et al. 1996, 1997), data were compiled from approximately 190 papers from additional journals, book chapters, technical reports, and unpublished manuscripts that included information on live root standing crop and belowground BNPP. The papers described research on every continent except Antarctica, although the majority were from North America. In the database, the plant functional type and biome coverage were most abundant for grasslands and temperate zones.

A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics, and source references associated with two papers (Jackson et al. 1996 and 1997).Understanding and predicting ecosystem functioning (e.g., carbon and water fluxes) and the role of soils in carbon storage requires an accurate assessment of plant rooting distributions.

Data Set Root

## Overview

Data Set Root is a dataset for instance segmentation tasks - it contains Root annotations for 556 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

A database of vertical root profiles for global terrestrial ecosystems was assembled from the primary literature in order to characterize the belowground structure of global vegetation types and to study relationships of belowground vegetation structure with climate, soil characteristics, and aboveground vegetation structure.

Variables used to characterize belowground vegetation structure include the depth above which 50 percent of all roots are located and the depth above which 95 percent of all roots are located in the profile. For each root profile, information recorded includes latitude and longitude, elevation, soil texture, depth of organic horizons, type of roots measured (e.g., fine or total, live or dead), sampling methods, units of measurements (root mass, length, number, surface area), and sampling depth. Some profiles lack information on one or more of these variables. Also recorded are presence and dominance of plant life forms (including succulents, forbs, grasses, semi-shrubs, shrubs, and four categories of trees: needle-leaved vs. broadleaved, evergreen vs. deciduous) and whether the vegetation was relatively natural or altered by humans (e.g., forest plantations and pastures). The database also includes data on mean annual precipitation and the seasonal distribution of precipitation.

Data sets that are related to this root profile data set include root nutrient concentrations (for approximately 372 site-pit-depths from 56 papers in Gordon and Jackson 2000) and root turnover rates (data for approximately 188 sites from 152 papers that were used to estimate root turnover rates for 341 site-vegetation combinations in Gill and Jackson 2000). The three recent papers include most of the data contained in the initial root data set; however, some observations may have been excluded because of more stringent selection criteria. Many of the source papers provided data for all three of the rooting data sets and users are encouraged to review all three data sets.

The Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) for Biogeochemistry Dynamics organized and formatted these data for long-term archive. The archived data are contained in two files: (1) The ecosystem root profiles file, containing estimated 50 percent rooting depths (D50) and 95 percent rooting depths (D95) plus information on sampling methods, vegetation, climate, and soil, and (2) a file containing the references to file (1). These files were obtained from H. Jochen Schenk, Department of Biological Science, California State University Fullerton, California, in February 2003. The data were placed into a spreadsheet format and stored as an ASCII comma-separated (.csv) file. Missing values are represented by -999.

The FIFE Root Biomass data were collected from 16 locations within the FIFE study area during the 1987 growing season. They provide a measure of the below-ground biomass for the study area. Biomass reported as grams per square m assumes that the depth of the core samples is sufficient to include all root biomass under the surface to an infinite depth. Prairie vegetation does possess roots deeper than the 20 cm coring, however, the fraction of total root biomass below 20 cm is minuscule and safely ignored in a study of biomass.

Standing stock of fine and coarse root biomass plus root carbon (C) and nitrogen (N) concentrations and stable isotopes from soil Megapits. Samples were collected in 10cm increments for the first 1m depth and 20cm increments thereafter to 2m depth. Ground, oven-dried root tissues from each depth increment are available for request from the NEON Biorepository. Data were collected once, at the establishment of each site. See the soil Megapit data product (DP1.00096.001) for physical and chemical data from Megapit soils.

Although rhizosphere fungi are essential for plant survival and ecosystem functioning, little is known about the processes that structure plant–fungal association networks. In this study, we constructed association networks between 43 plant species and two groups of root-associated fungi (mycorrhizal and pathogenic fungi; MF and PF, respectively) in a diverse subtropical forest. We then evaluated the modularity of plant–MF and plant–PF networks and linked them to the functional traits and phylogenies of both plants and fungi. We observed strong modularity in both plant–MF and plant–PF networks. Phylogenetically related fungi tended to emerge in the same modules. MF from distinct modules associated with plants with different specific root length and specific root area in plant–MF networks. PF from distinct modules associated with plants with different dark respiration rate and light compensation point in plant–PF networks. Plant affiliation to modules was explained by both plant traits and phylogeny (22% for plant–MF and 37% for plant–PF networks). In contrast, fungal affiliation to modules was explained by fungal phylogeny (16% for plant–MF and 29% for plant–PF networks). Our results elucidate the link between modularity in plant–root fungal networks and the functional traits and phylogeny of the plants and fungi. Our study highlights the importance of traits and phylogeny in governing root fungal community assembly from network perspective. Methods This study was conducted in a 50-ha plot in a subtropical forest in Heishiding Nature Reserve, Southern China (23°25′–23°29′ N，111°49′–111°55′ E). The mean annual temperature of this area is 19.7 °C, and the annual precipitation is 1,750 mm. The total area of this nature reserve is 4,200 ha, including a 2,202 ha core area and a 1,660 ha experimental area. We established the 50-ha forest plot in 2012, and identified all trees with a diameter at breast height (DBH) >1 cm. In total, this plot included approximately 2,69,000 stems of 213 woody plant species (Wang et al. 2019). We compiled the dataset of fungal communities from 512 root samples of the 43 plant species (no less than 5 sampled individuals for each plant species) in the Heishiding plot (Wang et al., 2019). These plant species were selected based on their taxonomic placement and abundance. For each plant species, we randomly selected 5–15 individuals for fine root sampling. At least three root fragments (each approximately 2 cm in length) around an individual tree were traced from different directions and then pooled to create a single sample. The fine root samples were immediately cooled on ice in the field and stored at -20 °C in a refrigerator until processing. More sampling details can be found in a previously published paper (Wang et al. 2019). Of 100 fine-root samples (randomly selected from all root samples), the tree species of 97 root samples traced in the field were correctly confirmed by rbcLa sequences obtained from a Sanger sequencing platform. Thus, we considered the tracing method as an accurate strategy to capture the taxonomic (species level) information of sampled fine roots. Root-associated fungi were identified by the internal transcribed spacer (ITS) region of fungal rDNA. Root-associated fungi were identified by the internal transcribed spacer (ITS) region of fungal rDNA. After removing chimeric sequences, we obtained 11,000,000 high-quality reads of the ITS region of fungal rDNA. The operational taxonomic units (OTUs) of root fungi were discriminated using a threshold of 97% sequence identity. Each sequence was assigned to a taxonomic label based on the UNITE database using the Ribosomal Database Project (RDP) classifier (Wang, Garrity, Tiedje, & Cole, 2007). Each fungal genus was then assigned into functional categories. We identified EM fungi by blasting our fungal genera against the fungal genera in a database of EM taxa and lineages (Tedersoo and Smith 2013). We assigned all OTUs in Glomeromycota to AM fungi (Schüßler 2002). Because we could only identify 21 OTUs of AM, EM and AM were pooled to represent the MF guild. Identifying fungal plant pathogens is challenging, because identification can only take place after the plants are diseased. Therefore, pathogenic genera were initially identified using the FUNGuild database (Nguyen et al. 2016). We then consulted the literature and retained only potential pathogens (OTUs) that had been identified to the species level and are known to be pathogenic to woody plants. To evaluate network modularity, we constructed a plant–PF association network including 113 fungal plant pathogens and 43 plant species, as well as a plant–MF association network including 883 mycorrhizal fungi (862 EM and 21 AM) and 43 plant species. To account for the sampling inequality, each cell in each network matrix was filled with the mean abundance (sequenced reads) of each fungal OTU (species) on each sampled tree, and the numbers were rounded to the nearest integer. Abundance of fungal OTUs on each sampled tree was calculated after subsampling each sample to 3000 sequence reads to eliminate the effects of sample size (Wang et al. 2019).

This database provides reference data on controlled cell image experiments. The database contains cell images of A-10 rat smooth muscle and NIH-3T3 mouse fibroblasts. A novel rule and root based method is used to create experimental metadata as described in About Us page.

2095 Global import shipment records of China Root with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Rooting depths were estimated from a global database of root profiles assembled from the primary literature to study relationships of abiotic and biotic factors associated with belowground vegetation structure. For each root profile, information recorded includes latitude and longitude, elevation, soil texture, depth of organic horizons, type of roots measured (e.g., fine or total, live or dead), sampling methods, units of measurements (root mass, length, number, surface area), and sampling depth.

This report documents 3 data files with names of root_production.dat,root_mortality.dat, and root_crop.dat. These data were recorded biweekly during the growing season on five FACE plots from 1998 through 2006. Beginning in 2004, data also were recorded monthly during the winter.The calculation of root-length production, mortality, and standingcrop is described in Hendrick, R.L. and K.S.Pregitzer (1993) The dynamics of fine root length, biomass, and nitrogen content in two northern hardwood ecosystems. Can. J. For. Res. 23:2507-2520. Methods for collecting these data are documented in Norby, R.J., J. Ledford, C.D. Reilly, N.E. Miller, and E.G. O'Neill (2004) Fine-root productiondominates response of a deciduous forest to atmospheric CO2 enrichment. Proc. Natl. Acad. Sci. 101:9689-9693. Also, the conversion factors needed to convert the fine-root data from mm/tube to g/m**2 are discussed inthis paper.

This dataset contains root measurements collected in the MaNiP experiment at the experimental site Majadas de Tietar (El-Madany et al., 2018) between November 2016 and May 2018.We collected direct measurements of root biomass, ingrowth cores (always initiated in December of the previous year to sampling date) and minirhizotron measurements from approximately every other month during the growing season.

These data are reported in Nair et al., (2018) along with commentary on the methods of sampling. In this dataset we include data as shown in the paper; this includes the top 13 cm of fine root biomass (the majority of fine roots at most times of the year).

The experimental site is a nutrient manipulation experiment (MaNiP: Large Scale Manipulation Nitrogen and Phosphorus) experiment in a typical ‘Iberic Dehesa’ in western Spain. The site has has about 20 % tree cover (98% Quercus ilex) interspaced with a diverse herbaceous pasture. We use a site-level fertilization design with nutrient (+N and +NP) fertilizers applied beneath 3 eddy covariance towers each with a footprint of ca. 20 ha (El-Madany et al., 2018). For root measurements we use sampling scheme described Nair et al., (2018) aiming to characterize differences between footprints and between cover types across 36 ‘locations’ where minirhizotron and direct root measurements are closely paired. These are arranged in a stratified design with the tower footprints, in sets of four minirhizotrons around individual trees.

Variables and units are described in the readme.txt file.

The dataset consists of two files:

MANIP_MrPARTS_minirhizotron.csv containing minirhizotron data on a per-image level. , we present these a metric of root cover in the individual images which we demonstrate in Nair 2019 is closely analogous to RLD (Root Length Density) at the site.

MANIP_MrPARTS_roots_direct.csv containing root biomass and ingrowth core measurements.

Some of these data were collected and all processed as part of the EC project 748893 'MrPARTS' awarded to Richard Nair. We also thank the von Humbold Stiftung for financial support of the MaNiP project through the Max Planck research prize 2013 to Markus Reichstein

The second version of the Fine-Root Ecology Database is available for download! Download the full FRED 2.0 data set, user guidance document, map, and list of data sources here. Prior to downloading the data, please read and follow the Data Use Guidelines, and it's worth checking out some tips for using FRED before you begin your analyses. Also, see here for an updating list of corrections to FRED 2.0.

The FungalRoot database accumulates information about plant mycorrhizal status and root colonization intensity, The database was assembled based on previously published reviews, local databases and a large number of yet neglected case studies and recent studies published in nine globally most important languages. The database enables to distinguish between reports of a presence of a particular mycorrhizal type, and reports where the plants were checked for all existing mycorrhizal types. In addition, our database provides information about the locality, ecosystem type, soil chemical data, and the method of mycorrhizal assessment that enable users to build more specific, local reference databases. The database contains 36,303 species by site observations for 14,870 plant species.