A soil seed bank is the collective name for viable seeds that are stored naturally in the soil. This database is the result of a comprehensive literature search, including all seed bank studies from the Web of Science from which data could be extracted, as well as an additional search of the Russian language literature. The database contains information on the species richness, seed density and/or seed abundance in 3096 records from at least 1778 locations across the world’s seven continents, extracted from 1442 studies published between 1940 and 2020. Records are grouped into five broad habitat categories (aquatic, arable, forest, grassland and wetland), including information relating to habitat degradation from, or restoration to other habitats (total 14 combinations). Sampling protocols were also extracted for each record, and the database was extensively checked for errors. The location of each record was then used to extract summary climate data and biome classification from external published databases (Karger et al. 2017, 2018 and Olson et al. 2001, respectively).

A full data descriptor for this dataset is published as a data paper in the journal Ecology. As such, the data are described according to the journal's specifications in the file MetadataS1.pdf, with additional information in data_entry_intructions.pdf. The initial version of the dataset is also published as supporting information to the data paper. The file DataS1.zip described in MetadataS1.zip contains the files gsb_db.csv, gsb_code.R and data_entry_intructions.pdf.

References: Karger, D. N., O. Conrad, J. Böhner, T. Kawohl, H. Kreft, R. W. Soria-Auza, N. E. Zimmermann, H. P. Linder, and M. Kessler. 2017. Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4:170122. https://doi.org/10.1038/sdata.2017.122

Karger, D. N., O. Conrad, J. Böhner, T. Kawohl, H. Kreft, R. W. Soria-Auza, N. E. Zimmermann, H. P. Linder, and M. Kessler. 2018. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4

Olson, D. M., E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, J. A. D’amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks, T. F. Allnutt, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W. W. Wettengel, P. Hedao, and K. R. Kassem. 2001. Terrestrial Ecoregions of the World: A New Map of Life on Earth A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51:933–938. Link to data: https://files.worldwildlife.org/wwfcmsprod/files/Publication/file/6kcchn7e3u_official_teow.zip

Biochar is being evaluated as an amendment to improve soil characteristics to increase crop yields, revitalize degraded soils and facilitate the establishment of plant cover. Unfortunately, there are few rapid tests to determine potential effects of biochar on soil and associated plant responses. Seed germination (emergence of hypocotyl) is a critical parameter for plant establishment and may be a rapid indicator of biochar effects. We adapted Oregon State University Seed Laboratory procedures to develop a “rapid-test” to screen for effects of biochar on seed germination and soil characteristics. Soils were amended with 1% biochar by weight and placed in 11.0 cm square x 3.5 cm deep containers fitted with premoistened blotter paper. Seeds were placed in a uniform 5 x 5 pattern and covered with 15 g of the soil-biochar mixtures. Two South Carolina Coastal Plain soils, the Norfolk (Fine-loamy, kaolinitic, thermic Typic Kandiudults) and Coxville (Fine, kaolinitic, thermic Typic Paleaquults), were used. Eighteen biochars were evaluated produced from 6 feedstocks [pine chips (PC), poultry litter (PL), swine solids (SS), switchgrass (SG); and two blends of PC and PL, 50% PC/50% PL (55), and 80% PC/20% PL (82). For each feedstock biochars were made by pyrolysis at 350, 500 and 700°C for 1-2 hours. Percent germination and shoot dry weight were evaluated for cabbage, carrot, cucumber, lettuce, oat, onion, perennial ryegrass and tomato. Soil pH, electrical conductivity (EC) and extractable phosphorus (EP), factors which can affect seed germination and early seedling growth, were determined after plant harvests. Germination primarily was affected by soil type with few biochar effects. Shoot dry weight was increased for carrot, lettuce, oat and tomato; primarily with biochars containing PL. Soil pH and EC increased with PL, SS, 55 and most 82 treatments across soil types and plant species. Soil EP increased substantially with SS and PL and to a lesser extent with 55 and 82 for both soils across species, and with SG pyrolyzed at 550 and 750°C soil for the Norfolk soil across species. Thus, this rapid-test method can be an early indicator of the effects of biochar on seed germination and important soil health characteristics which can be affected by biochar and effect seed germination. This dataset is associated with the following publication: Olszyk, D.M., T. Shiroyama, J.M. Novak, and M.G. Johnson. A Rapid-Test for Screening Biochar Effects on Seed Germination. Communications in Soil Science and Plant Analysis. Taylor & Francis Group, London, UK, 49(16): 2025-2041, (2018).

This is an identification key to genera for seeds and fruits of the legume family. The coverage is world wide, and for each genus there are descriptions of the seeds and fruits, distribution data, and images. The interactive software system INTKEY is used for accessing the data and images. The key can be used for identifying to genus unknown legume samples or for querying the data and images for legume genera, and is designed for seed analysts, technicians, port inspectors, weed scientists, ecologists, botanists, and researchers who need to identify isolated legume fruits and seeds. Procedures relating to preparation, collection, and authentication of data are provided in the 'Procedures' resource file. In order to utilize the identification key the entire folder needs to be downloaded and extracted with all internal structure unmodified. Resources in this dataset:Resource Title: Legume (Fabaceae) Fruits and Seeds Version 2. File Name: Fabaceae.zipResource Description: This electronic database contains the following: 685 accepted legume genera with accepted scientific name and author(s) for each genus. No synonyms are given; for synonyms, refer to Polhill and Raven (1981), Gunn et al. (1992), and Mabberley (1997). The classification follows Polhill (1994a, 1994b), and has been modified according to recently published findings. The following is recorded for each genus: phylogenetic number, according to Polhill (1994b); subfamily; tribe; subtribe, when used; group, when used; number of species; and number of species examined to collect data for this database. 157 fruit characters and 128 seed characters for each genus. Unless indicated, these are original observations. 205 character and 1,379 generic images. When adequate materials were available, fruit and seed photographs and/or drawings, testa SEMs at 50 and 1,000 magnifications, and embryo and cotyledon drawings are given. The character images, whenever possible, were prepared from the generic images. For some characters, schematic drawings are presented. Native distribution of each genus Pertinent notes for each genus and tribe concerning their classification and fruits and seeds. Complete bibliography. Resource Title: Accepted legume genera information files. File Name: info.zipResource Description: 685 accepted legume genera with accepted scientific name and author(s) for each genus. No synonyms are given; for synonyms, refer to Polhill and Raven (1981), Gunn et al. (1992), and Mabberley (1997). This information is also contained in the Legume (Fabaceae) Fruits and Seeds Version 2 database files resource Text documents contain scientific name, subfamily, phylogenetic number, tribe, species in genus / species studied, fruit description, seed description, distribution, generic notes, and tribal notes. Resource Title: Character and generic images. File Name: images.zipResource Description: 205 character and 1,379 generic images. When adequate materials were available, fruit and seed photographs and/or drawings, testa SEMs at 50 and 1,000 magnifications, and embryo and cotyledon drawings are given. The character images, whenever possible, were prepared from the generic images. For some characters, schematic drawings are presented. This information is also contained in the Legume (Fabaceae) Fruits and Seeds Version 2 database files resource GIF images Resource Title: Procedures - Legume (Fabaceae) Fruits and Seeds v2. File Name: procs.rtfResource Description: Procedures relating to preparation, collection, and authentication of data This information is also contained in the Legume (Fabaceae) Fruits and Seeds Version 2 database files resourceResource Title: Version History - Legume (Fabaceae) Fruits and Seeds. File Name: verhist.rtfResource Description: This information is also contained in the Legume (Fabaceae) Fruits and Seeds Version 2 database files resource

A comprehensive dataset of 40K+ road sign images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

This dataset compiles all the data of the Seed Microbiota Database associated to the publication Simonin et al. 2021 (BioRxiv) Seed microbiota revealed by a large-scale meta-analysis including 50 plant species. This database includes metabarcoding data from 63 seed microbiota studies on 50 plant species ( total of 3190 seed samples) based on 5 different molecular markers (16S rRNA gene - V4 region, 16S rRNA gene - V5-V6 region, gyrB gene, ITS1 region, ITS2 region). All the studies were re-processed from the fastq files (raw data) using DADA2 and Qiime2 and merged in 5 different datasets depending on the molecular marker targeted. The README file presents the structure of the database (Subsets) and files available. This database can be queried online without downloading it on the Askomics instance : https://askomics-192-168-100-151.vm.openstack.genouest.org/ For a full access to the results, you can log to the Askomics instance with the following credentials: Username: consult Password: OcOU83D5

Most terrestrial plants disperse by seeds, yet the relationship between seed mass, seed dispersal traits, and plant dispersion is poorly understood. We quantified seed traits for 48 species of native and introduced plants from grasslands of western Montana, USA, to investigate the relationships between seed traits and plant dispersion patterns. Additionally, because the linkage between dispersal traits and dispersion patterns might be stronger for actively dispersing species, we compared these patterns between native and introduced plants. Finally, we evaluated the efficacy of a global trait database, the TRY plant traits database, versus locally collected data for examining these questions. This archive contains species-level data used in analyses, including species metadata (origin, growth form), mean values of measured seed traits (size metrics and type of dispersal structures), two metrics of dispersion (local and broad scales, respectively) derived from grassland surveys in the study region, and information on the seed mass accessed from the TRY traits database. Note that the latter seed mass data could not be included in the archive, but can be acquired directly from the TRY plant traits database (https://www.try-db.org/TryWeb/Home.php). Methods Our study took place in semi-arid grasslands of the Intermountain Region in western Montana, U.S.A. The native system is dominated primarily by bluebunch wheatgrass (Pseudoroegneria spicata) with other grasses and a great variety of forbs diversifying the system, but it is heavily invaded by exotics. We identified our study species, comprised of 23 native and 25 exotic species, to reflect a range of dispersion patterns by using data from 620 1-m2 vegetation plots from 31 grassland sites spread over 20,000 km2 of western Montana. Plant dispersion patterns were defined at a local-scale by the proportion of plots occupied within a site and at a broad-scale by the proportion of sites occupied per species. For each species, we collected at least 50 seeds from each of 10 plants at each of 3 locations in Missoula and Lake County, Montana in either 2020 or 2021. Collection locations were chosen opportunistically based on species presence and hence differed by species. Although these locations did not align with sites surveyed for species dispersion per se, they were generally drawn from the central portion of the study area. Seeds were stored in a laboratory under ambient conditions until measurements were taken, at which point they were cleaned by hand and sorted based primarily on visual characteristics to remove potentially non-viable seeds. To determine the mean seed mass per species, we weighed a fixed number of samples (three or four) from each of the three locations. The number of seeds weighed per sample was set per species to ensure a total mass >1.5 mg, the minimum reading needed for an accuracy of 2% per the specifications of the balance. For 32 of our 48 species, only 10 seeds were needed to reach this minimum. For remaining species, we increased the number of seeds included per sample in increments of 10 (range 20–150 seeds/sample) until the minimum mass was reached. Seed mass included the entire diaspore (e.g., endosperm, seed coat, awns, and dispersal appendages) to ensure that all species could be treated in the same way (e.g., dispersal appendages such as wings would have been very difficult to remove from small-seeded species). Though the inclusion of dispersal appendages potentially biases seed mass estimates for this subset of species, we note that this bias should be small relative to the large variation in seed mass across species. Indeed, estimates for three exotic species (Lactuca serriola, Taraxacum officinale, and Tragopogon dubius) with pappuses showed that these structures increased seed mass measures by <12%. For the remaining measurements, we used a ProgRes C10 camera (Jenoptik, CCD/CMOS) to create images of 20 seeds per species drawn from the 3 sampling locations (n=6 from two locations and n=8 from the third, chosen randomly). We used the images to obtain the following measurements for each seed via ImageJ software (Rasband 1997-2018): seed length (maximum), seed width (maximum), and seed surface area. These seed measurements excluded dispersal structures. Mean values per species for all seed size measurements are included in the species-level dataset archived here. Finally, we inspected seeds to determine whether seeds of each species possessed dispersal structures including pappuses, awns, wings, or plumes. For smaller-seeded species, we accomplished this using the seed images and also checked the literature to assure that dispersal structures were not missed. To enable comparison of empirical seed measures to those available in online trait databases, we used the TRY plant trait database (accessed 22 September – 7 October 2022), a global database integrating ~700 datasets including other major collective databases. This database included seed mass data for 44 of our 48 species but contained insufficient data to evaluate the other seed traits (i.e., length, width, and surface area) we measured (i.e., for only 2-40% of our study species). Importantly, 63% of n=831 seed mass records obtained from the TRY database could not be used in analyses. This is because these contained duplicate data that resulted from the consolidation of many datasets with common sources. See the publication for a full description of our process for identifying duplicate values. Remaining seed mass values from the TRY database were averaged to generate the mean estimate used in analyses. See the archive for sample size information per species.

A comprehensive dataset of 500K+ macro insect images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification, and segmentation.

There is some evidence that seed traits can affect the long-term persistence of seeds in the soil. However, findings on this topic have differed between systems. Here, we brought together a worldwide database of seed persistence data for 1474 species to test the generality of seed mass-shape-persistence relationships. We found a significant trend for low seed persistence to be associated with larger and less spherical seeds. However, the relationship varied across different clades, growth forms and species ecological preferences. Specifically, relationships of seed mass-shape-persistence were more pronounced in Poales than in other order clades. Herbaceous species that tend to be found in sites with low soil sand content and precipitation have stronger relationships between seed shape and persistence than in sites with higher soil sand content and precipitation. For the woody plants, the relationship between persistence and seed morphology was stronger in sites with high soil sand content and low precipitation than in sites with low soil sand content and higher precipitation. Improving ability to predict the soil seed bank formation process, including burial and persistence, could benefit the utilization of seed morphology-persistence relationships in management strategies for vegetation restoration and controlling species invasion across diverse vegetation types and environments. Methods (a) Worldwide data compilation We searched the ISI Web of Science and Google Scholar for papers in English containing the words “seed shape”, “seed bank” and “seed mass or seed size”, in the title or abstract (Figure S1). Our selection criteria for research sites required that plant data come primarily from English literature and local natural ecosystems, from which single-species studies and agricultural systems were excluded (Table S1, Figure S1). This search yielded data from 23 geographical locations that contained information on both seed shape, mass, and seed bank type. The search yielded integrated seed morphology and seed persistence information for 1772 records of 1474 species from 650 genera and 112 families. Nomenclature to the species, genus, and family levels was revised using http://www.catalogueoflife.org and ‘Taxonstand’ R package. Seed shape was calculated using the three-dimensional variance equation from Thompson et al. [1] and Bekker et al. [53]: Seed shape
where x is the seed trait value normalized by the seed length, such as x is seed length/seed length, seed width/seed length, and seed height/seed length, is average of the three. The value of the perfectly spherical seed would be zero and the maximal value of non-spherical (elongated or disc-shaped seed) is 0.333. Inconsistent seed shape algorithms among extracted study sites were transformed. The diaspore including seed and indehiscent single-seeded fruit was broadly defined as a ‘seed’. Seed mass was defined as the average dry weight of the seeds with seed coat for each species. The final dataset included a wide range of seed mass (represents the seed weight), spanning seven orders of magnitude, from 0.001 mg (Pyrola minor) to 1081 mg (Beilschmiedia tawa). Seed shape ranged from 0.0001 (Brassica tournefortii) to 0.33 (Stipa pennata). To unify the seed persistence classifications of the original studies, we grouped species into two classes [1,2]: transient vs persistent. Transient seeds existed in the soil for less than one or two years, while persistent seeds existed in the soil for at least one or two years (details in Table S1). Twenty study sites measured seed persistence using soil seedling emergence experiment, two sites used seed burial experiment, and one site mixed both soil seedling emergenceand seed burial experiment (see Table S1 for details). Soil seedling emergence experiments included two types: the seed abundance of different soil depths distribution relative to aboveground species (seventeen study sites); presence in the seed bank through time after controlling for seed rain (three study sites; Table S1). The seed burial experiments process involved mixing seeds with soil and then burying them in a natural environment, with the seeds' persistence assessed based on their survival rate after one year. (b) Phylogenetic tree and growth type The phylogenetic tree was constructed with branch lengths for the 1,474 species from the megatree based on Zanne et al. [54] and Smith & Brown [55], using the ‘phylo.maker’ function in the V.PhyloMaker R package [56]. Growth type (herbaceous vs woody) of species in the soil persistence dataset was collected from the native floristic databases of the study areas (http://rian.inta.gov.ar/, https://www.nzpcn.org.nz/, http://www.floralibrary.com/, https://www.infoflora.ch/de/, https://plantnet.rbgsyd.nsw.gov.au/, https://vicflora.rbg.vic.gov.au/, https://eol.org, https://gobotany.nativeplanttrust.org/, www.iplant.cn/frps and https://plants.usda.gov. Species of shrubs and trees were classified as woody. (c) Climate and soil data To explore the factors affecting the prediction of seed bank persistence in different research sites, we used the soil sand content and mean annual precipitation to represent soil and climate factors. The values of MAP were first obtained from original literature sources, and the vacant values were extracted from the WorldClim using the R package ‘raster’ v.3.3-13 [57]. Soil sand content was extracted from the Harmonized World Soil Database v 1.2 (http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/) using the ArcGIS (v.10.8, Esri) software. When climate and soil data were extracted, if a study site had specific latitude and longitude coordinates, the data was extracted accordingly, collecting average point data for that site. If the study site was a range area rather than a specific site, five points would be extracted evenly within the study area. The data from these five points were averaged to represent the climate and soil characteristics of that particular location. Data analysis for R All the analyses were conducted in the R software v. 4.3.0. To reflect the ability of the seed traits to predict seed bank persistence at the unique species level, we calculated generalized linear models (family = ‘binomial’), with seed bank persistence (transient vs. persistent) being the response variable, and seed mass and shape as the predictors. In this analysis, the species was classified as belonging to the persistent seed bank if a species had multiple records and the species had at least one record belonging to the persistent seed bank (following Gioria et al.). Average values of seed mass and shape were used in the generalized linear model, and seed mass values were log10 transformed to meet a normal distribution. We obtained the optimal probability node of the seed traits prediction of persistent seed banks at the species level using the ‘pROC’ package [59]. Trait values less than the optimal probability node implied that species with low trait values are more likely to form persistent seed banks. Similarly, the analysis of seed traits to predict the seed bank persistence was tested in different growth types (herbaceous and woody), and phylogenetic order, respectively. The accuracy probabilities of using seed traits to predict seed bank persistence were tested across multiple species and sites by the ‘pROC’ package. Analysis of variance between herbaceous and woody species for seed mass and seed shape using ‘aov’ function of the ‘stats’ package. The inclusion of species phylogenies in the model analysis depends on whether the traits are phylogenetically conserved. The phylogenetic signal in the two continuous variables (seed mass and shape) for 1,474 unique species were estimated by Pagel's λ for 10,000 randomized simulations tests with the ‘phylosig’ function in the R package ‘phytools’ v.0.6-99. The phylogenetic signal for the binary categorical variable (seed bank persistence) was estimated using the D statistic for 10,000 randomized simulation tests with ‘phylo.d’ in the R package “caper”. We presented the distribution of traits and seed bank persistence for the 1,474 species, and relationships between seed traits and seed bank persistence in phylogenetic order clades using the R package ‘ggtree’ and ‘ggtreeExtra’. Multiple comparisons for seed mass and seed shape among phylogenetic clades were analyzed using the “LSD.test” function in the R package “agricolae”. Phylogenetic signals (Pagel's λ) of continuous factors (seed mass and shape) were estimated for 10000 randomization simulations tests with the ‘phylosig’ function of the R package ‘phytools’ v.0.6-99. Phylogenetic signal for bivariate factors (persistence) were estimated using the ‘phylo.d’ function of the R package ‘caper’. When λ = 0, related taxa are no more similar than expected by chance, while when λ = 1, the trait is evolving following a constant variance random walk or Brownian motion model; intermediate values of λ indicate a phylogenetic correlation in trait evolution that does not fully follow a Brownian motion model. To test the effect of seed mass and shape on seed bank persistence while taking into account the shared phylogeny, we fitted a threshold model for the 1,772 multi-record species using generalized mixed-effect models with Bayesian estimation (Markov Chain Monte Carlo generalized linear mixed models, MCMCglmms) as implemented in the R package MCMCglmm. Seed bank persistence (transient as 1 vs. persistent as 0) was the response variable. Species persistence maintains the original record even when persistence and transients exist simultaneously in different study sites. Fixed effects included (i) seed mass; (ii) seed shape; (iii) the interaction between seed mass and shape; (iv) growth type (herbaceous and woody) and its interaction with seed

Small mammal seed predation x seed size datasets.

All variables included in the two datasets are detailed on the README tab associated with each file.

Please note that the seed removal dataset was updated to rebut a technical comment by Chen et al. (2021). The updated seed removal dataset is associated with a separate Dryad archive (https://doi.org/10.5061/dryad.hqbzkh1fp).

Seeds from 600 common plants including weeds, crop plants, herbs and resources plants were collected in the southwestern part of the Korean Peninsula. Seeds collected were carefully stored in refrgerator until use, investigated morphologically, and photographed. The seeds in database were described with color photographs, their taxonomical position, and morphological characteristics. Korean-English bilingual description of the species included Korean name, family, scientific name, English and Japanese common names, habitat, biotechnological importance, distrubution, propagation and characters in eco-physiology and keys of correct identification of each plant part such as leaves, stems, roots, fruits and seeds. In describing plant species, difficulties also arise from the variation that occurs within species, depending on where or when the plant grows under natural or agricultual coditions. Database was converted from MySQL and constructed using a PHP (http://ruby.kisti.re.kr/~seeds)

Soil seed banks can strongly affect survival and expansion of plant populations by spreading mortality risks and distributing genetic diversity through time. Knowledge of the main factors regulating the ability of seeds to persist in the soil beyond the first growing season is however limited. While morphological and physiological seed traits, and the degree of environmental uncertainty are considered important in shaping the seed-banking strategies of plants, global assessments that explicitly account for phylogenetic relatedness are lacking.

Using a global seed bank database comprising data for 2350 angiosperms, we examined the extent to which two seed bank properties, i.e., seed bank type (transient vs. persistent) and density of viable seed banks, are determined by phylogenetic relatedness. We then tested phylogenetic correlations between these properties with seed mass and seed dormancy (dormant vs. non-dormant), and the contribution of phylogenetic relatedness relative to that of climatic and habitat-related variables in shaping seed bank properties.

We found significant phylogenetic signal in seed bank type and density, providing evidence that the ability to form persistent seed banks is not randomly distributed across the phylogeny. While the ability to persist in the soil was phylogenetically correlated to the production of dormant and smaller seeds, seed mass and seed dormancy per se were poor predictors of seed persistence. Interestingly, habitat-related variables (mainly disturbance and canopy openness) but not climate significantly affected the ability of seed plants to form persistent seed banks.

Synthesis: Our study is the first to show that phylogenetic relatedness plays an important role in explaining seed bank properties in angiosperms and how these properties relate to early life-history traits, climate and habitat-related variables. These findings represent a starting point to assess the generality of persistent seed banks as a bet-hedging strategy in unpredictable environments and provides important insights into how seed plants might respond to global environmental changes.

Methods This dataset contains soil seed bank data, i.e., seed bank type (transient vs. persistent, sensu Thompson et al., 1997), and seed bank density (mean number of seedlings per square meter), for 2350 angiosperm taxa obtained from 195 studies, in 11,893 records. Relevant literature was identified by searching the Web of Science (ISI) and Google Scholar, using the keyword ‘seed’ or ‘diaspore’ in combination with ‘bank’, ‘below-ground’, ‘buried’, ‘community’, ‘flora’, ‘reservoir’, ‘soil’, and ‘stored’. Additional studies were searched by screening the reference lists provided in the resulting papers as well as papers citing the papers originally retrieved. For papers that contained potentially relevant data that could not be extracted directly, we contacted the authors for additional information. A full description of the methodology used to compile an earlier version of this dataset (updated until April 2018) is described in Gioria et al. (2019), including the criteria used to identify and select the sources from which seed bank data were extracted. The last search for relevant literature in the present study was conducted in February 2020.

This dataset includes seed bank data collected from the native distribution range and estimated using the seedling emergence approach (Thompson et al., 1997), thus estimating only viable soil seed banks. We included only studies providing mean density values at the study sites, coming from multiple samples (and not values from single samples at each site), maximum densities per site, total numbers of seeds/seedlings per site, or frequency values. This was done to minimise any potential confounding effect associated with the large spatial and temporal variation that characterises soil seed banks.

For each species, we included information on seed mass (mg), obtained from the Royal Botanic Gardens Kew Seed Information Database (2020), seed dormancy (dormant vs. non-dormant), based on the classification provided in the Baskin Dormancy Database (Baskin & Baskin, 2014), and life form (annual, perennial, woody).

For each record, we provided the geographic coordinates and information on 11 climatic variables (2.5 minutes) extracted from WorldClim (Hijmans et al., 2005). We also provided information on three habitat variables summarising local environmental conditions, i.e., disturbance (disturbed vs. nonditurbed), soil moisture (dry, moist, wet), and openness (open vs. close canopies).

References

Baskin, C. C. & Baskin, J. M. (2014). Seeds: ecology, biogeography, and evolution of dormancy and germination (2nd ed.). San Diego, CA: Academic/Elsevier.

Gioria, M., Le Roux, J. J., Hirsch, H., Moravcová, L. & Pyšek, P. (2019). Characteristics of the soil seed bank of invasive and non-invasive plants in their native and alien distribution range. Biological Invasions, 21, 2313–2332.

Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. (2005). Very high- resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965–1978.

Royal Botanic Gardens Kew. (2020). Seed Information Database (SID). Version 7.1. http://data.kew.org/sid/

Thompson, K., Bakker, J. P. & Bekker, R. M. (1997). Soil seed banks of NW Europe: methodology, density and longevity. Cambridge, UK: Cambridge University Press.

No description is available. Visit https://dataone.org/datasets/farshid25.38.1 for complete metadata about this dataset.

SEED-Data-Edit

SEED-Data-Edit is a hybrid dataset for instruction-guided image editing with a total of 3.7 image editing pairs, which comprises three distinct types of data: Part-1: Large-scale high-quality editing data produced by automated pipelines (3.5M editing pairs). Part-2: Real-world scenario data collected from the internet (52K editing pairs). Part-3: High-precision multi-turn editing data annotated by humans (95K editing pairs, 21K multi-turn rounds with a maximum… See the full description on the dataset page: https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit-Part1-Unsplash.

3526 Global import shipment records of Anise Seeds with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The dataset contains measured thousand-seed weight data (g) for taxa of the Central European flora.

A comprehensive dataset of 750K+ car images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

The dataset represents the results of direct measurements of the geometric dimensions (length, width and thickness in mm) and the mass in grams of each individual seed, as well as the calculated values of the projection area and the volume of the described ellipsoid based on these parameters. The dataset allows for correlation and regression analyses between geometric parameters and individual seed weight, and can also be linked to other datasets in the FLR-Library for the formation of summary queries.

Seed is not only the main reproductive organ of most of herbal plants but also an important part of Traditional Chinese Medicine (TCM). Seed TCMs possess important medicinal properties and have been widely used as components of pharmaceutical products. In parallel with the increasing popularity and accessibility of seeds as medicinal products in recent years, numerous substitutes and adulterants have also appeared on the market. Due to the small volume and similar appearances of many seed TCMs, they are very difficult to accurately identify the constituent plant species through organoleptic methods. Usage of the wrong herb may be ineffective or may worsen the condition and even cause death. Correct identification of seed herbal medicines is therefore essential for their safe use. Here, we acquired 177 ITS2 sequences and 15 psbA-trnH sequences from 51 kinds of seed TCMs belonging to 64 species that have been described in the Chinese Pharmacopoeia. Tree-building analysis showed that the ITS2 sequences of 48 seed TCMs can be differentiated from each other, and they formed distinct, non-overlapping groups in the maximum-likelihood tree. Furthermore, all of the sequences acquired in this study have been submitted to the public DNA barcoding system for herbal medicine, and this integrated database was used to identify 400 seed TCM samples purchased from medicinal markets, drug stores, and the Internet, enabling the identification of 7.5% of the samples as containing non-declared species. This study provides a brief operating procedure for the identification of seed TCMs found in herbal medicine. In the future, researchers and traditional herbal medicine enterprises can use this system to test their herbal materials.

No description was included in this Dataset collected from the OSF

The Forest Response to Stress and Damage (frequently referred to as FORSTAD) and long term forest monitoring project began in 1992 to study how mixed-oak forests respond to multiple forms of environmental change. The research took place at Cary Institute of Ecosystem Studies in the Hudson Valley of New York. FORSTAD included several sub-projects including (1) air pollution and nutrient cycling, (2) spongy moth population dynamics, (3) small mammal dynamics and (4) vegetation dynamics. This dataset is a contribution to the Cary Institute of Ecosystem Studies, and is part of the Long term monitoring of forest ecosystems: Cary vegetation dynamics archive.Vegetation dynamics: To understand the impacts of anthropogenic stressors on forest trees, measurements were made of aspects of trees that had to do with the condition and survival of trees. The measurements included the size and species of trees in designated plots in the forest, the size and species of saplings in smaller subplots, the quantity of seeds produced by trees using seed traps and the predation of seeds and survival of seedlings in subplots. Canopy defoliation levels were recorded via fisheye canopy photography on the 20 vegetation monitoring plots, and on other plots used specifically for spongy moth or small mammal monitoring. The vegetation data were used for direct analysis of change in forest structure and composition, and also to parameterize a computer model of vegetation dynamics, which was used as a research and management tool. Datasets include:• Canopy census – species composition, age & size structure of trees, reproductive status & condition of canopy• Canopy condition & leaf area index via fisheye camera photos• Seed production• Seed predation• Seedling survival and growth• Mapping of all plots, trees, seed & seedling data collection plots• Deer & small mammal exclosure census• Soil moistureThe data included here are data from seed traps in Cary Forest Plots and Yellow and Red Control Grids. Note that data collection continued after the end of the FORSTAD project first under the research program of Gary Lovett, then under the research program of Rick Ostfeld. Data collection continues to be collected annually as of 2024. Note also that the archived seeds were discarded around 2017 but newly collected seeds have been archived at Cary Institute.File list:FORSTAD_Seed_Production_Metadata_1992_2005.pdf -contains complete project metadata, personnel, methodology, and definitions for data variables in all data files.FORSTAD_Seed_Production_Cary_Forest_Plots_1992_2000.csvFORSTAD_Seed_Production_Grid_Sites_1992_2005.csvFORSTAD_Protocol_Seed_Rain_1997.pdfFORSTAD_Protocol_Seed_Rain_1999.pdfSee Related Materials for more data from the FORSTAD vegetation sub-project.