Details on the processing of plant functional trait data provided by TRY Plant Trait Database are presented in Chapter 2 and Appendix 7 in Andrii Kramarenko's MSc thesis document.

The global spectrum of plant form and function dataset (Diaz et al. 2022; Diaz et al. 2016; TRY 2022, accessed 15-May-2025) provides mean trait values for (i) plant height; (ii) stem specific density; (iii) leaf area; (iv) leaf mass per area; (v) leaf nitrogen content per dry mass; and (vi) diaspore (seed or spore) mass for 46,047 taxa.

Here I provide a dataset where the taxa covered by that database were standardized to World Flora Online (Borsch et al. 2020; taxonomic backbone version 2023.12) by matching names with those in the Agroforestry Species Switchboard (Kindt et al. 2025; version 4). Taxa for which no matches could be found were standardized with the WorldFlora package (Kindt 2020), using similar R scripts and the same taxonomic backbone data as those used to standardize species names for the Switchboard. Where still no matches could be found, taxa were matched with those matched previously with a harmonized data set for TRY 6.0 (Kindt 2024).

References

• Díaz, S., Kattge, J., Cornelissen, J.H.C. et al. The global spectrum of plant form and function: enhanced species-level trait dataset. Sci Data 9, 755 (2022). https://doi.org/10.1038/s41597-022-01774-9
• Díaz, S., Kattge, J., Cornelissen, J. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016). https://doi.org/10.1038
• TRY. 2022. The global spectrum of plant form and function dataset. https://www.try-db.org/TryWeb/Data.php#81
• Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. https://doi.org/10.1002/tax.12373
• Roeland Kindt, Ilyas Siddique, Ian Dawson, Innocent John, Fabio Pedercini, Jens-Peter B. Lillesø, Lars Graudal. 2025. The Agroforestry Species Switchboard, a global resource to explore information for 107,269 plant species. bioRxiv 2025.03.09.642182; doi: https://doi.org/10.1101/2025.03.09.642182
• Kindt, R. 2020. WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data. Applications in Plant Sciences 8(9): e11388. https://doi.org/10.1002/aps3.11388
• Kindt, R. (2024). TRY 6.0 - Species List from Taxonomic Harmonization – Matches with World Flora Online version 2023.12 (2024.10b) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13906338

Funding

The development of this dataset was supported by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, and by the Bezos Earth Fund to the Quality Tree Seed for Africa in Kenya and Rwanda project.

Standardized names for TRY 6.0 are available from the TRY File Archive (TFA; see here: https://www.try-db.org/TryWeb/Data.php#100); these were created for the following publication:

Schellenberger Costa, D., Boehnisch, G., Freiberg, M., Govaerts, R., Grenié, M., Hassler, M., Kattge, J., Muellner-Riehl, A.N., Rojas Andrés, B.M., Winter, M., Watson, M., Zizka, A. and Wirth, C. (2023), The big four of plant taxonomy – a comparison of global checklists of vascular plant names. New Phytol, 240: 1687-1702. https://doi.org/10.1111/nph.18961

Here matched records are provided with the taxonomic backbone of World Flora Online (WFO) version 2023.01, obtained from https://zenodo.org/records/10425161.

Matches with WFO are given in the Zenodo archive in the fields for SID (=taxonID in WFO), scientificName (as in WFO) and scientificNameAuthorship (as in WFO). Fields of TRY_SpeciesID, TRY_AccSpeciesNameScientific and RecommendedScientificName were directly obtained from the TFA.

Matching was done via following steps:

1. Fungi matched in the TFA via the http://indexfungorum.org were excluded (4,099 records).

2. The matching record in WFO was obtained by matching the TFA field of TPL_ID with the WFO field of tplID. Successful matches via this method are indicated in this Zenodo archive by the field of MATCH being set to TPL ID.

3. Where matches in WFO included a non-empty acceptedNameUsageID, the currently accepted name of the taxon was obtained via this ID. Successful matches via this method are indicated in this Zenodo archive by the field of MATCH being set to TPL ID and fields of synonymID, scientificName.synonym and scientificNameAuthorship.synonym showing details for the match with the synonym.

4. Taxa that were not matched in previous steps were matched for the TFA field of MatchedName. Matching was done via the WorldFlora package version 1.14-5 (Kindt 2020). Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of MATCH being set to manual. Taxa at specific and infraspecific levels from the TFA where matches had only been achieved at generic levels for the MatchedName were excluded in this step.

5. Taxa that were not matched in previous steps were matched for the TFA field of TRY_AccSpeciesName. Matching was done via the WorldFlora package version 1.14-5 (Kindt 2020). Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of MATCH being set to manual.

6. Taxa that were not matched in previous steps were matched for the TFA field of AlternativeName. Matching was done via the WorldFlora package version 1.14-5 (Kindt 2020). Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of MATCH being set to manual.

7. Taxa that were not matched in previous steps were matched for the TFA field of MatchedName against version 11 of the World Checklist of Vascular Plants. Matching was also done via the WorldFlora package version 1.14-5 (Kindt 2020), with similar scripts as shown in this Rpub. Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of MATCH being set to manual and by the field of SID (and possibly synonymID) containing records where the WCVP ID is preceded by WCVP-.

8. 836 records where neither the TFA nor the previous steps managed to establish a match were removed.

9. Taxa that could not be matched in previous steps are flagged by the field of MATCH being set to NONE - no species MatchedName (where TFA also did not achieve a match at the required specific or infraspecific level) or NONE (other records where no match was achieved).

The Zenodo archive contains 406,208 records with matches via the TPL, 96,040 records with manual matches, 1,822 records where the TFA also not achieve matches at required (infra-)specific levels and 361 records where no match was achieved (among this latter category were 202 records flagged in TFA as ‘not found in WFO’ and 140 records flagged in TFA as ‘found only here’).

Version 2024.10b was created with added fields from TFA of TRY_SpeciesName and BackboneDatabase. This was done especially to explain different matches for records with the same RecommendedScientificName but with different matches to WFO, as for example for Acacia adunca A.Cunn. & G.Don and Acacia aestivalis E.Pritz.

References

• Kattge J. 2023. TRY 6.0 - Species List from Taxonomic Harmonization. https://www.try-db.org/TryWeb/Data.php#100
• Schellenberger Costa, D., Boehnisch, G., Freiberg, M., Govaerts, R., Grenié, M., Hassler, M., Kattge, J., Muellner-Riehl, A.N., Rojas Andrés, B.M., Winter, M., Watson, M., Zizka, A. and Wirth, C. (2023), The big four of plant taxonomy – a comparison of global checklists of vascular plant names. New Phytol, 240: 1687-1702. https://doi.org/10.1111/nph.18961
• The World Flora Online Consortium, Alan Elliott, Roger Hyam, William Ulate, Mark Watson, Gregory Anderson, Giovani carlos Andrella, et al. “World Flora Online Plant List December 2023”. Zenodo, December 22, 2023. https://doi.org/10.5281/zenodo.10425161.
• Kindt R (2020). “WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data.” Applications in Plant Sciences, 8(9), e11388. https://doi.org/10.1002/aps3.11388
• Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. https://doi.org/10.1002/tax.12373
• Govaerts, R., Nic Lughadha, E., Black, N. et al. The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. Sci Data 8, 215 (2021). https://doi.org/10.1038/s41597-021-00997-6

The development of this archive supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project and through the Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso, by the Bezos Earth Fund to the Bezos Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.

Distilled from

Kattge, J., S. Díaz, S. Lavorel, I. C. Prentice, P. Leadley, G. Bönisch, E. Garnier, M. Westoby, P. B. Reich, I. J. Wright, J. H. C. Cornelissen, C. Violle, S. P. Harrison, P. M. Van Bodegom, M. Reichstein, B. J. Enquist, N. A. Soudzilovskaia, D. D. Ackerly, M. Anand, O. Atkin, M. Bahn, T. R. Baker, D. Baldocchi, R. Bekker, C. C. Blanco, B. Blonder, W. J. Bond, R. Bradstock, D. E. Bunker, F. Casanoves, J. Cavender-Bares, J. Q. Chambers, F. S. Chapin, J. Chave, D. Coomes, W. K. Cornwell, J. M. Craine, B. H. Dobrin, L. Duarte, W. Durka, J. Elser, G. Esser, M. Estiarte, W. F. Fagan, J. Fang, F. Fernández-Méndez, A. Fidelis, B. Finegan, O. Flores, H. Ford, D. Frank, G. T. Freschet, N. M. Fyllas, R. V Gallagher, W. A. Green, A. G. Gutierrez, T. Hickler, S. Higgins, J. G. Hodgson, A. Jalili, S. Jansen, C. Joly, A. J. Kerkhoff, D. Kirkup, K. Kitajima, M. Kleyer, S. Klotz, J. M. H. Knops, K. Kramer, I. Kühn, H. Kurokawa, D. Laughlin, T. D. Lee, M. Leishman, F. Lens, T. Lenz, S. L. Lewis, J. Lloyd, J. Llusià, F. Louault, S. Ma, M. D. Mahecha, P. Manning, T. Massad, B. Medlyn, J. Messier, A. T. Moles, S. C. Müller, K. Nadrowski, S. Naeem, Ü. Niinemets, S. Nöllert, A. Nüske, R. Ogaya, J. Oleksyn, V. G. Onipchenko, Y. Onoda, J. Ordoñez, G. Overbeck, W. A. Ozinga, S. Patiño, S. Paula, J. G. Pausas, J. Peñuelas, O. L. Phillips, V. Pillar, H. Poorter, L. Poorter, P. Poschlod, A. Prinzing, R. Proulx, A. Rammig, S. Reinsch, B. Reu, L. Sack, B. Salgado-Negret, J. Sardans, S. Shiodera, B. Shipley, A. Siefert, E. Sosinski, J.-F. Soussana, E. Swaine, N. Swenson, K. Thompson, P. Thornton, M. Waldram, E. Weiher, M. White, S. White, S. J. Wright, B. Yguel, S. Zaehle, A. E. Zanne, and C. Wirth. 2011. TRY - a global database of plant traits. Global Change Biology 17:2905–2935

Kattge, J., S. Díaz, S. Lavorel, I. C. Prentice, P. Leadley, G. Bönisch, E. Garnier, M. Westoby, P. B. Reich, I. J. Wright, J. H. C. Cornelissen, C. Violle, S. P. Harrison, P. M. Van Bodegom, M. Reichstein, B. J. Enquist, N. A. Soudzilovskaia, D. D. Ackerly, M. Anand, O. Atkin, M. Bahn, T. R. Baker, D. Baldocchi, R. Bekker, C. C. Blanco, B. Blonder, W. J. Bond, R. Bradstock, D. E. Bunker, F. Casanoves, J. Cavender-Bares, J. Q. Chambers, F. S. Chapin, J. Chave, D. Coomes, W. K. Cornwell, J. M. Craine, B. H. Dobrin, L. Duarte, W. Durka, J. Elser, G. Esser, M. Estiarte, W. F. Fagan, J. Fang, F. Fernández-Méndez, A. Fidelis, B. Finegan, O. Flores, H. Ford, D. Frank, G. T. Freschet, N. M. Fyllas, R. V Gallagher, W. A. Green, A. G. Gutierrez, T. Hickler, S. Higgins, J. G. Hodgson, A. Jalili, S. Jansen, C. Joly, A. J. Kerkhoff, D. Kirkup, K. Kitajima, M. Kleyer, S. Klotz, J. M. H. Knops, K. Kramer, I. Kühn, H. Kurokawa, D. Laughlin, T. D. Lee, M. Leishman, F. Lens, T. Lenz, S. L. Lewis, J. Lloyd, J. Llusià, F. Louault, S. Ma, M. D. Mahecha, P. Manning, T. Massad, B. Medlyn, J. Messier, A. T. Moles, S. C. Müller, K. Nadrowski, S. Naeem, Ü. Niinemets, S. Nöllert, A. Nüske, R. Ogaya, J. Oleksyn, V. G. Onipchenko, Y. Onoda, J. Ordoñez, G. Overbeck, W. A. Ozinga, S. Patiño, S. Paula, J. G. Pausas, J. Peñuelas, O. L. Phillips, V. Pillar, H. Poorter, L. Poorter, P. Poschlod, A. Prinzing, R. Proulx, A. Rammig, S. Reinsch, B. Reu, L. Sack, B. Salgado-Negret, J. Sardans, S. Shiodera, B. Shipley, A. Siefert, E. Sosinski, J.-F. Soussana, E. Swaine, N. Swenson, K. Thompson, P. Thornton, M. Waldram, E. Weiher, M. White, S. White, S. J. Wright, B. Yguel, S. Zaehle, A. E. Zanne, and C. Wirth. 2011. TRY - a global database of plant traits. Global Change Biology 17:2905–2935

Classify Try

## Overview

Classify Try is a dataset for object detection tasks - it contains Items ICRP annotations for 637 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Data collection

Plant hydraulic traits and height data were obtained from three sources: (1) field measurements of plant hydraulics for 210 forest species in China; (2) the TRY Plant Traits Database (https://www.try-db.org/TryWeb/Home.php; Kattge et al., 2020); and (3) published literature. For the latter we conducted searches on Web of Science, Google Scholar, and China National Knowledge Infrastructure (http://www.cnki.net) using keywords such as “hydraulic traits,” “xylem hydraulic conductivity,” “xylem vulnerability,” “water potential at 50% loss of hydraulic conductivity,” “xylem embolism resistance,” and “plant water conductivity.” A substantial portion of data in our study were obtained from published literature (Choat et al., 2012; Gleason et al., 2016) and the Xylem Functional Traits Database (XFT; https://xylemfunctionaltraits.org).

To minimize ontogenetic and methodological variation, we only included data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources; and (e) data values > 3 SD (standard deviation) were removed to reduce the effect of outliers (Carmona et al., 2021); (f) height data were reported at the same site where plant hydraulic traits were measured.

Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2; Fick & Hijmans, 2017; Table 1) if the original data were not available. The following variables measured at ~1 km² scale were extracted from WorldClim: mean annual wind speed (μ), mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, wind seasonality (μS; coefficient of variation across monthly measurements × 100), precipitation of driest month, and minimum temperature of coldest month. The VPD data were extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html; Abatzoglou et al., 2018). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data; Zomer et al., 2008). Moisture index (MI), which is the ratio of precipitation to PET.

Data analysis

Trait and environment data were log₁₀-transformed to achieve approximate normality, except for P₅₀ and temperature data. We first calculated correlations among all climatic variables and for subsequent analyses retained only those variables with correlation coefficients lower than |0.7| (Dormann et al., 2013). We then ran independent multiple linear models for each trait of interest using the retained climatic variables. Model selection based on a corrected Akaike information criterion and using the R package glmulti (Calcagno & de Mazancourt, 2010), identified the best linear model for each trait. The R package ‘visreg’ (Breheny & Burchett, 2017) was used to visualize the partial relationships between wind speed and hydraulic traits. Two-dimensional contour plots were then used to explore and visualise how plant hydraulic traits varied simultaneously with wind speed and moisture index.

To quantify the strength of wind effects on plant hydraulics, models with wind parameters μ and μS included were compared to those without these wind parameters.

To test for differences in the relationship between hydraulic traits and wind speed among species grouped into different climatic regions (i.e., dry vs. wet sites, and tropical vs. temperate regions), we used standardized major axis (SMA) analyses using the R package ‘smatr’ (Warton et al., 2012). A grouping factor was added in each SMA to test whether species groups share a common slope, with p > 0.05 indicating species groups share a common slope.

Variance partitioning analysis was performed using the ‘rdacca. hp’ R package to quantify the degree to which the effect of wind speed was independent from other climatic variables (Lai et al., 2022). The individual contribution of each predictor was estimated in this analysis. This analysis also helped to illustrate the significant values of climatic variables on plant hydraulics.

A Random Forest machine-learning algorithm (implemented using the R package ‘randomForest’) was utilized to further assess the relative importance of environmental variables for each plant hydraulic trait (Breiman, 2001). To avoid multicollinearity, this analysis only included variables with correlation coefficients lower than |0.7|. A higher value of the mean decrease in accuracy (%IncMSE) indicates the increased importance of a variable (e.g., a %IncMSE value of 50 indicates that the overall mean square error would increase by 50% if that variable were to be excluded from the analysis). This provides a measure of a variable's importance in estimating the value of the target variable across the trees in the forest.

Yolo Try

## Overview

Yolo Try is a dataset for object detection tasks - it contains Images annotations for 9,621 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This data tables contain the mean trait values for leaf area, growth height, specific leaf area, leaf nitrogen concentration, seed mass and stem specific density derived from TRY database (TRY_final.txt). Additionally it contains the download links for the plant images from iNaturalist for the publication 'Deep Learning and Citizen Science Enable Automated Plant Trait Predictions from Photographs' (2021) (species_links.txt). The data can be processed as shown on: https://github.com/ChrSchiller/cnn_traits/blob/main/code/03_join_GBIF_TRY.R

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.

Here are a few use cases for this project:

1. License Plate Recognition: Use lpcr-try to identify and decode license plates by detecting alphanumeric characters, ensuring accurate vehicle tracking and traffic management.

2. Optical Character Recognition (OCR): Apply lpcr-try to extract numbers and letters from scanned documents, images, or receipts, converting them into digital formats for easier data processing and analysis.

3. Security Systems: Integrate lpcr-try into visual security systems like entry gate monitors to detect authorized personnel access codes or vehicle registrations, enhancing building security.

4. Manufacturing Quality Control: Implement lpcr-try in production lines to recognize and verify serial numbers, batch numbers, or product codes, ensuring accurate tracking and quality checks on manufactured goods.

5. Education and e-Learning: Utilize lpcr-try to develop educational tools that can identify handwritten or printed alphanumeric inputs from students, assisting teachers with grading and providing a more interactive learning experience.

BDBOT LAST Try

## Overview

BDBOT LAST Try is a dataset for object detection tasks - it contains Explosives annotations for 743 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Final Try

## Overview

Final Try is a dataset for object detection tasks - it contains School Essentials annotations for 328 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Try Try

## Overview

Try Try is a dataset for object detection tasks - it contains Objects 0GLp annotations for 7,227 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

New Try

## Overview

New Try is a dataset for object detection tasks - it contains Incar Objects annotations for 3,196 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).