Excel file (*.xlsx) of counted permanent resident species in tidepools. Rows are species and columns are sample sites. Abbreviation of locations: AL – Albion; BB – Blue Bay; LH – Lighthouse at Pointe aux Caves; PE – Péreybère.

In the first few decades of European settlement, records of Australian vegetation ranged from descriptions by explorers and new settlers, artists? drawings, through to specimens sent to international herbaria and museums. In the late 19th century more systematic observations were published and form the basis of today?s quantitative approaches. In the 1980s a project was conducted in which data across the continent of Australia from 711 terrestrial and littoral vegetation surveys were collected and digitised to enable an objective assessment of the conservation status of Australian plant communities, the ?Conservation Atlas? project (Specht et al. 1995, Specht and Specht, 2013). The source files (reprints and reports) were retained as completely as possible, while the extracted data were retained as print outs and/or stored digitally using the technology current at the time.

The data delivered through the Atlas of Living Australia (and as a full data package through the Knowledge Network for Biocomplexity: http://doi.org/10.5063/F1QC01QK ; Specht et al., 2018a) is the result of an effort to retrieve the data from the Conservation Atlas project. Accessible paper and digital sources were obtained and re-entered where required, georeferences and sources were updated. This work resulted in a collection of sites x species observation records mapped to Darwin Core format. The data from 1390 communities incorporating records of 9450 taxa were retrieved from a total of 705 sources between 1879 to 1989. This was a considerable loss from the initial project, but substantial nevertheless. The project aimed to provide a sustainable open-access resource to the research community and others to enable better long-term comprehension of vegetation change, and to provide insight into the long-term challenges of effective data curation.

The details of the retrieval project can be obtained in Specht et al. (2018).

References Specht A. and Specht R.L. (2013) Australia: Biodiversity of Ecosystems. In, The Encyclopedia of Biodiversity Vol. 1 (ed. B. Levin, et al.) pp 291-306. Waltham, MA: Academic Press. Specht, R.L., Specht, A. Whelan, M.B. and Hegarty, E.E. (1995) Conservation Atlas of Plant Communities in Australia. Centre for Coastal Management in association with Southern Cross University Press. Specht A., Bolton M.P., Kingsford B., Specht R.L., Belbin L. (2018a) Data from the Conservation Atlas of Australian Plant communities 1879-1989 (1995). Knowledge Network for Biocomplexity. doi:10.5063/F1QC01QK. Specht A., Bolton M.P., Kingsford B., Specht R.L., Belbin L. (2018b) A story of data won, data lost and data re-found: the realities of ecological data preservation. Biodiversity Data Journal 6:e28073. doi: 10.3897/BDJ.6.e28073

Local ecological evidence is key to informing conservation. However, many global biodiversity indicators often neglect local ecological evidence published in languages other than English, potentially biassing our understanding of biodiversity trends in areas where English is not the dominant language. Brazil is a megadiverse country with a thriving national scientific publishing landscape. Here, using Brazil and a species abundance indicator as examples, we assess how well bilingual literature searches can both improve data coverage for a country where English is not the primary language and help tackle biases in biodiversity datasets. We conducted a comprehensive screening of articles containing abundance data for vertebrates published in 59 Brazilian journals (articles in Portuguese or English) and 79 international English-only journals. These were grouped into three datasets according to journal origin and article language (Brazilian-Portuguese, Brazilian-English and International). ..., Data collection We collected time-series of vertebrate population abundance suitable for entry into the LPD (livingplanetindex.org), which provides the repository for one of the indicators in the GBF, the Living Planet Index (LPI, Ledger et al., 2023). Despite the continuous addition of new data, LPI coverage remains incomplete for some regions (Living Planet Report 2024 – A System in Peril, 2024). We collected data from three sets of sources: a) Portuguese-language articles from Brazilian journals (hereafter “Brazilian-Portuguese†dataset), b) English-language articles from Brazilian journals (“Brazilian-English†dataset) and c) English-language articles from non-Brazilian journals (“International†dataset). For a) and b), we first compiled a list of Brazilian biodiversity-related journals using the list of non-English-language journals in ecology and conservation published by the translatE project (www.translatesciences.com) as a starting point. The International dataset was obtained ..., # Knowledge from non-English-language studies broadens contributions to conservation policy and helps to tackle bias in biodiversity data

Dataset DOI: 10.5061/dryad.ngf1vhj68

Description of the data and file structure

We collected time-series of vertebrate population abundance suitable for entry into the LPD (livingplanetindex.org), which provides the repository for one of the indicators in the GBF, the Living Planet Index (LPI, Ledger et al., 2023).

We collected data from three sets of sources: a) Portuguese-language articles from Brazilian journals (hereafter “Brazilian-Portuguese†dataset), b) English-language articles from Brazilian journals (“Brazilian-English†dataset) and c) English-language articles from non-Brazilian journals (“International†dataset). For a) and b), we first compiled a list of Brazilian biodiversity-related journals using the list of non-English-language journals in ecology and conservat...,

This is a dataset containing extensive information on agroforestry systems (AFSs) and reference vegetation areas where the diversity of taxonomic groups or growth forms was measured. AFSs and their reference areas are located in tropical ecoregions across de globe, and data were gathered through a systematic review including 92 papers and 294 data sampling sites. We considered each taxonomic group or growth form studied in a sampling site (AFS or reference vegetation area) as an observation, and extracted the following information to the file “Main_data.csv†: location, ecoregion, climate, soil, size, method of biodiversity sampling, and number of species, individuals, and/or biodiversity index found in each AFS and reference area; origin (whether the AFS originated from areas with alternative land use, such as agriculture, pastures, or degraded areas, or in areas with natural vegetation that are converted into productive systems), type (simple or biodiverse), age, crop type, management ..., For our systematic review on biodiversity in agroforestry systems (AFSs) implemented in tropical ecoregions, we searched for papers using the following keywords present in the title: field 1- agroforest*; field 2- diversity OR richness OR abundance; field 3- tropical. We conducted our search in the platforms Periódicos CAPES, Web of Science, Scielo, and Google Scholar. We did not restrict the oldest publication date and we finished our search in July 2023. For a paper to be included in the systematic review, it necessarily had to compare the abundance, richness, and/or diversity index of some taxonomic group or growth form between AFSs and reference vegetation areas located in tropical ecoregions, anywhere in the world. We considered each taxonomic group or growth form studied in an AFS or reference area as an observation and extracted the information provided in Main_data.csv., # Data from: Biodiversity in agroforestry systems implemented in tropical ecoregions: A systematic review

Dataset DOI: 10.5061/dryad.dr7sqvb8v

Description of the data and file structure

This dataset contains the data used in the article:

Ortolan, E.; Maciel, E.A. & Martins, V.F. Biodiversity in agroforestry systems implemented in tropical ecoregions: a systematic review. Journal of Environmental Management.

Authors:

Ezequiel Ortolan

Programa de Pós-Graduação em Agricultura e Ambiente, Universidade Federal de São Carlos (UFSCar), Araras, SP, Brazil.

E-mail: ziqueortolan@yahoo.com.br

Everton A. Maciel

Chair of Plant Ecology, University of Bayreuth, Universitätsstr. 30, 95447 Bayreuth, Germany.

Departamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil.

E-mail: Everton.Maciel@uni-bayreuth.de

Valéria Forni Mar...,

This data publication originated as part of developing a biodiversity-related knowledge hub on COVID-19 via COVID19-TAF - Communities Taking Action (https://cetaf.org/covid19-taf-communities-taking-action), a community-rooted initiative raised jointly by the Consortium of European Taxonomic Facilitaties (CETAF, https://cetaf.org) and Distributed Systems of Scientific Collections (DiSSCo, https://www.dissco.eu/).

This archive contains the biodiversity datasets of interest identified in period 14 April-21 May 2020 through COVID19-TAF activities and subsequently indexed by Global Biotic Interactions (GloBI, https://globalbioticinteractions.org). GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, virus-host, parasite-host) by combining existing open datasets using open source software.

These identified datasets (see references and reviews below) add to a growing collection of open species interaction datasets already indexed by GloBI. So, this data publication only includes a small subset of indexed datasets and include only datasets that were added as a direct consequence of COVID19-TAF activities of the biodiversity-related knowledge hub working group.

If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email.

Funding:
The creation of this archive was made possible in part by reporting software developed as part of the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926 . Also, this material is based upon work supported by the National Science Foundation under Grant No. DGE-1545433 .

References:
Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.

GloBI Data Review Report

Datasets under review:
- Marcus Guidoti, Tatiana Ruschel, Donat Agosti. 2020. Corona virus related biotic associations manually extracted from literature. Plazi. accessed via https://github.com/globalbioticinteractions/plazi-covid19/archive/326578b0d9f974760dcd2e962d86636a6487a6c0.zip on 2020-05-21T17:02:04.918Z
- De Rojas M, Doña J, Dimov I (2020) A comprehensive survey of Rhinonyssid mites (Mesostigmata: Rhinonyssidae) in Northwest Russia: New mite-host associations and prevalence data. Biodiversity Data Journal 8: e49535. https://doi.org/10.3897/BDJ.8.e49535 accessed via https://github.com/globalbioticinteractions/pensoft-table/archive/3488e0397ca4e083d5eca6949951e426a75713e3.zip on 2020-05-21T17:02:09.381Z
- Pensoft Darwin Core Archives with associateTaxa columns accessed via https://github.com/globalbioticinteractions/pensoft-dwca/archive/a3e075e4d7a03ba3605af144e3f2a4e55e4bdb03.zip on 2020-05-21T17:02:17.674Z
- Pensoft Darwin Core Archives available via Integrated Publication Toolkit accessed via https://github.com/globalbioticinteractions/pensoft-ipt/archive/4ad4b47978324681289e36f8c2b247b1bcc97b1a.zip on 2020-05-21T17:02:45.806Z
- Olival, K. J., Hosseini, P. R., Zambrana-Torrelio, C., Ross, N., Bogich, T. L., & Daszak, P. (2017). Host and viral traits predict zoonotic spillover from mammals. Nature, 546(7660), 646–650. doi:10.1038/nature22975 accessed via https://github.com/globalbioticinteractions/olival2017/archive/f61070a5339d0e6c6e76d7eb4e2102decb52317d.zip on 2020-05-21T17:02:48.553Z
- Chen L, Liu B, Yang J, Jin Q, 2014. DBatVir: the database of bat-associated viruses. Database (Oxford). 2014:bau021. doi:10.1093/database/bau021 accessed via https://github.com/globalbioticinteractions/dbatvir/archive/bcfe5b7ada567771aedf291474c896dc94550681.zip on 2020-05-21T17:02:52.034Z
- Geiselman, Cullen K. and Tuli I. Defex. 2015. Bat Eco-Interactions Database. www.batplant.org accessed via https://github.com/globalbioticinteractions/batplant/archive/1fe61d1e90335cf3716365d1322c79abde5a4ca7.zip on 2020-05-21T17:11:53.998Z
- Eneida L. Hatcher, Sergey A. Zhdanov, Yiming Bao, Olga Blinkova, Eric P. Nawrocki, Yuri Ostapchuck, Alejandro A. Schäffer, J. Rodney Brister, Virus Variation Resource – improved response to emergent viral outbreaks, Nucleic Acids Research, Volume 45, Issue D1, January 2017, Pages D482–D490, https://doi.org/10.1093/nar/gkw1065 . Data downloaded via https://www.ncbi.nlm.nih.gov/labs/virus/vssi on 2020-03-14 accessed via https://github.com/globalbioticinteractions/ncbi-virus/archive/60769efcda06b4719e358e3bae7bad93ccebabe6.zip on 2020-05-21T17:13:26.182Z
- Quentin J. Groom. 2020. Bat interation data manually extracted from literature. accessed via https://zenodo.org/record/3816676/files/qgroom/batinterations-v1.0.1.zip on 2020-05-22T02:13:51.699Z

Generated on:
2020-05-22

by:
GloBI's Elton 0.9.8
(see https://github.com/globalbioticinteractions/elton).

Note that all files ending with .tsv are files formatted
as UTF8 encoded tab-separated values files.

https://www.iana.org/assignments/media-types/text/tab-separated-values

Included in this review archive are:

README:
This file (lightly edited after initial automated generation).

review_summary.tsv:
Summary across all reviewed collections of total number of distinct review comments.

review_summary_by_collection.tsv:
Summary by reviewed collection of total number of distinct review comments.

indexed_interactions_by_collection.tsv:
Summary of number of indexed interaction records by institutionCode and collectionCode.

review_comments.tsv.gz:
All review comments by collection.

indexed_interactions_full.tsv.gz:
All indexed interactions for all reviewed collections.

indexed_interactions_simple.tsv.gz:
All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.

datasets_under_review.tsv:
Details on the datasets under review.

elton.jar:
Program used to update datasets and generate the review reports and associated indexed interactions.

datasets.zip:
source datasets collected by elton in process of executing the generate_report.sh script.

generate_report.sh:
program used to generate the report

generate_report.log:
log file generated as part of running the generate_report.sh script

The following dataset presents all of the specimen records included in the data paper titled: The stoneflies of Arkansas: a preliminary species checklist using museum specimen data. This paper is published in Biodiversity Data Journal. This dataset combines specimen record data from Illinois Natural History Survey Insect Collection, Canadian National Collection of Insects, Western Kentucky University, and several other institutional, personal, and literature records. The data includes specimens to their lowest possible taxonomic rank based on identification and determination. The data includes geo-references for all specimens if possible. Some records lacked precise locality information or were only designated to county level geographic area. This data file will be available for download from Global Biodiversity Information Facility (GBIF). This data file contains a total of 3561 records, structured in 77 columns of data in DwCA format. These data were gathered from April 2024-August 2024. Many specimens used in this work were previously digitized and geo-referenced for a project that was supported by the United States National Science Foundation: CSBR: Natural History: Securing Alcohol Types and Donated Alcohol Specimens at the INHS Insect Collection NSF DBI: CSBR 14-58285.

These are the data from the publication in Journal of Applied Ecology in 2011, See References for full reference and doi link. The dataset contains a readme file, a csv file with data and a shp file (collection of four separate files to be opened in a GIS program) showing the locations of sampling sites. Please consult the readme for information regarding data structure, and the journal article for sampling information and scientific context.

BIEN data validation and standardization tools.

Version 02, 23 Feb. 2024

42 specimens previously described under Syrphidae were identified at least to genus level.

Update to current taxonomy was done on genera Merodon, Hybobathus, Ocyptamus and Dioprosota, following recent revisions by Mengual at al. (2018) and Likov et al. (2019).

A few other minor corrections to the data set were also carried out.

The main paper is now published :

Version 01

Data on 1071 specimens of hoverflies collected or received by Jean Timon-David (1902-1968) and hosted at the Marseille Natural History Museum, France.

The data set follows the GBIF format (https://www.gbif.org/darwin-core). In the CSV dataset format used here, fields are separated by tabs, all encoding is UTF-8, which allowed for all diacritic signs to be retained. Apostrophes (') were used wherever appropriate in locality names. Uncertain readings from the labels are indicated by a question mark in the verbatimEventDate or verbatimLocality fields. If the locality name was uncertain, no coordinates were given.

This work is licensed under a Creative Commons Attribution (CC-BY) 4.0 Licence. All work derived from the present study should cite it appropriately, including the Museum where the material is held.

For details see

Fields used in the data set (Column label: Column description):

occurrenceID: Individual identification code, same as CatalogNumber

catalogNumber: MHNM individual identification: combination of Museum name, collection identification, box number and specimen number within each box

basisOfRecord:The specific nature of the data record (i.e. PreservedSpecimen)

eventDate: Event date in the format YYYY-MM-DD if the capture date is known to the date, or YYYY-MM if only the month and yaear are known, or YYY if only the year is known

year: Year of capture if known

month: Month of capture if known

day: Day of capture if known

verbatimEventDate: Date of capture, as mentioned on the label

scientificName: Lowest taxonomic rank possible, usually the species name. If unknown the genus or family names are given.

identificationQualifier: In case the identification could be given only to a species group 'cf.' is input.

typeStatus: One specimen is a paratype, which is indicated here as such

identificationRemarks: Any comment on the identification of the specimen

kingdom: Kingdom (i.e. Animalia)

phylum: Phylum (i.e. Arthropoda)

class: Class (i.e. Insecta)

order: Order (i.e. Diptera)

family: Family name (i.e. Syrphidae)

genus: Genus name

specificEpithet: Species epithet of the scientificName

sex: Male (M) or female (F)

taxonRank: Taxonomic rank of the most specific name in the scientificName

identifiedBy: Name of the entomologist who identified the specimen. The name is written within square brackets if it does not appear on the label, but can be inferred from other specimens with similar handwriting, locality and date.

dateIdentified: Year of identification

identificationVerificationStatus: Whether (coded 1) or not (coded 0) the identification was recently checked

previousIdentifications: Species name originally given on the specimen labels

minimumElevationInMeters: Lower limit of the range of altitudes indicated on the label or in the associated reference

maximumElevationInMeters: Higher limit of the range of altitude indicated on the label or in the associated reference

decimalLatitude: Geographic latitude (in decimal degrees) of the capture location

decimalLongitude: Geographic longitude (in decimal degrees) of the capture location

geodeticDatum: Coordinate system and set of reference points upon which the geographic coordinates are based (i.e. WGS 84)

coordinateUncertaintyInMeters: Uncertainty in coordinates, in meters

continent: Continent of capture

country: Country of capture

countryCode: Two letter country code of the specimen origin

stateProvince: French departmental administrative division. In the case of non-French data, any relevant country administrative subdivision

locality: Location of capture, usually the locality

verbatimLocality: Any geographical indication on the label

InstitutionCode: Museum where the specimen is held (i.e. MHNM)

occurrenceRemarks: Any ecological data or comment on the label

LocationRemarks: Any comment regarding the location

recordedBy: Name of collector (i.e. legit information)

associatedRereferences: Any reference citing the specimen

organismQuantity: Number of individuals bearing the same label (usually 1)

organismQuantityType: individuals

georeferencedBy: Identity of the person who added the Latitude and longitude data, i.e. Nève, Gabriel

georeferenceProtocol: How the georeference was computed, i.e. from label data (verbatimLocality)

georeferenceSources: Georeference code was inferred from geoportail.fr, French ING maps or googleEarthPro

georeferencedDate: Georeference work was performed in 2023

language: The data set is mainly written in French, apart from column headings, which are in English

collectionCode: Identifier of collection (i.e. MNHN.15441)

otherCatalogueNumbers: Any other catalog number the specimen may have

Funding

This scientific work is part of the natural heritage inventory (inpn.mnhn.fr). It received support from PatriNat (OFB-CNRS-MNHN) and from GDR Pollinéco (Dir. Bertrand Schatz, CEFE, CNRS, Monpellier).

The Sea Around Us is a research initiative at The University of British Columbia (located at the Institute for the Oceans and Fisheries, formerly Fisheries Centre) that assesses the impact of fisheries on the marine ecosystems of the world, and offers mitigating solutions to a range of stakeholders.

The Sea Around Us was initiated in collaboration with The Pew Charitable Trusts in 1999, and in 2014, the Sea Around Us also began a collaboration with The Paul G. Allen Family Foundation to provide African and Asian countries with more accurate and comprehensive fisheries data.

The Sea Around Us provides data and analyses through View Data, articles in peer-reviewed journals, and other media (News). The Sea Around Us regularly update products at the scale of countries’ Exclusive Economic Zones, Large Marine Ecosystems, the High Seas and other spatial scales, and as global maps and summaries.

The Sea Around Us emphasizes catch time series starting in 1950, and related series (e.g., landed value and catch by flag state, fishing sector and catch type), and fisheries-related information on every maritime country (e.g., government subsidies, marine biodiversity). Information is also offered on sub-projects, e.g., the historic expansion of fisheries, the performance of Regional Fisheries Management Organizations, or the likely impact of climate change on fisheries.

The information and data presented on their website is freely available to any user, granted that its source is acknowledged. The Sea Around Us is aware that this information may be incomplete. Please let them know about this via the feedback options available on this website.

If you cite or display any content from the Site, or reference the Sea Around Us, the Sea Around Us – Indian Ocean, the University of British Columbia or the University of Western Australia, in any format, written or otherwise, including print or web publications, presentations, grant applications, websites, other online applications such as blogs, or other works, you must provide appropriate acknowledgement using a citation consistent with the following standard:

When referring to various datasets downloaded from the website, and/or its concept or design, or to several datasets extracted from its underlying databases, cite its architects. Example: Pauly D., Zeller D., Palomares M.L.D. (Editors), 2020. Sea Around Us Concepts, Design and Data (seaaroundus.org).

When referring to a set of values extracted for a given country, EEZ or territory, cite the most recent catch reconstruction report or paper (available on the website) for that country, EEZ or territory. Example: For the Mexican Pacific EEZ, the citation should be “Cisneros-Montemayor AM, Cisneros-Mata MA, Harper S and Pauly D (2015) Unreported marine fisheries catch in Mexico, 1950-2010. Fisheries Centre Working Paper #2015-22, University of British Columbia, Vancouver. 9 p.”, which is accessible on the EEZ page for Mexico (Pacific) on seaaroundus.org.

To help us track the use of Sea Around Us data, we would appreciate you also citing Pauly, Zeller, and Palomares (2020) as the source of the information in an appropriate part of your text;

When using data from our website that are not part of a typical catch reconstruction (e.g., catches by LME or other spatial entity, subsidies given to fisheries, the estuaries in a given country, or the surface area of a given EEZ), cite both the website and the study that generated the underlying database. Many of these can be derived from the ’methods’ texts associated with data pages on seaaroundus.org. Example: Sumaila et al. (2010) for subsides, Alder (2003) for estuaries and Claus et al. (2014) for EEZ delineations, respectively.

The Sea Around Us data are (where not otherwise regulated) under a Creative Commons Attribution Non-Commercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/). Notices regarding copyrights (© The University of British Columbia), license and disclaimer can be found under http://www.seaaroundus.org/terms-and-conditions/. References:

Alder J (2003) Putting the coast in the Sea Around Us Project. The Sea Around Us Newsletter (15): 1-2.

Cisneros-Montemayor AM, Cisneros-Mata MA, Harper S and Pauly D (2015) Unreported marine fisheries catch in Mexico, 1950-2010. Fisheries Centre Working Paper #2015-22, University of British Columbia, Vancouver. 9 p.

Pauly D, Zeller D, and Palomares M.L.D. (Editors) (2020) Sea Around Us Concepts, Design and Data (www.seaaroundus.org)

Claus S, De Hauwere N, Vanhoorne B, Deckers P, Souza Dias F, Hernandez F and Mees J (2014) Marine Regions: Towards a global standard for georeferenced marine names and boundaries. Marine Geodesy 37(2): 99-125.

Sumaila UR, Khan A, Dyck A, Watson R, Munro R, Tydemers P and Pauly D (2010) A bottom-up re-estimation of global fisheries subsidies. Journal of Bioeconomics 12: 201-225.

Sequences in fasta format for the fragment of the COI mtDNA gene used as a standard DNA barcode in animals. Each sequence is identified by a chain of characters consisting of, in the following order and separated by pipes: processID, sampleID, species_name, DNA marker

This repository contains phylogenetic and functional trait data, raster files, and tables with sampling information and references used in the article "Historical and current environmental selection on functional traits of trees in the Atlantic Forest biodiversity hotspot" by Silva, J.L.A., Souza, A., and Vitória, A.P. Journal of Vegetation Science, https://doi.org/10.1111/jvs.13049 . Description of files: (1) "Species-level_Trait_Data_Silva_et_al._2021.csv": This file contains species-specific mean trait values and the plant growth form of the 2,122 studied species, whenever available. Trait values were compiled from public sources such as original papers, master and doctoral dissertations, and global trait databases. (2) "Phylogenetic_Tree_Silva_et_al._2021.txt": This file contains the phylogenetic tree of the 2,122 studied species. (3) "CWM_Trait_Maps.zip": This file contains seven rasters of spatially contiguous surfaces produced by Ordinary Kriging Interpolation using Community-Weighted Means (CWM) of each functional trait. (4) "References-abundance-data.csv": This file contains sampling details and the references used to compile species abundance data for each studied site. (5) "References-trait-data.csv": This file contains sampling details and the references used to compile functional trait data.

An ESRI Shapefile (EPSG: 27700) containing spatial polygons delimiting the sampled areas (500 x 500 m sub-cells; Fig. 1) of this study. Attribute table columns (title; contents) are: 1km_square (1 km2 cell reference according to the lettered format of the Britian National grid); Quadrant (compass-point quadrant, i.e. 500 x 500 m sub-cell sampled); Location (textual description of geographic location); Ref (reference to this data paper).

The intended use of this archive is to facilitate (meta-)analysis of Biological Associations captured in TaxonWorks [1]. TaxonWorks is an integrated web-based workbench for taxonomists and biodiversity scientists. It allows you to capture, organize, and enrich your data; share it with collaborators; and package it for analysis and publication.

This dataset provides versioned snapshots of the TaxonWorks network as tracked by Preston [2,3,4] between 2023-08-16 and 2023-08-16 using:

preston track -u https://sfg.taxonworks.org

. In addition, this dataset provides a processed version of the biological associations using the "preston tw-stream" command as generated by the following bash script:

#!/bin/bash
#
# Generates GloBI interaction JSON Lines from provided provenance log as generated by preston tw-stream.
#

/usr/local/bin/preston cat hash://sha256/ff5e709305e593c87711e897b6341b94e775e2f312aa6d4ae5ed6120babd6f5e\
 | /usr/local/bin/preston tw-stream

The script itself was executed using:

cat transform.sh | preston bash

The execution of this transform.sh script (with content id hash://sha256/ea6c131a7436c9080b9380c7712dd32654a564fbb6f0568ff97bd7a8f28337a4), as well as their results, is captured within this datasets also. A rdf/quads formatted machine readable version of the workflow execution description can be found via:

preston cat hash://sha256/ab7550368905e7c919e70a306efbb97719a1edbba2cfe4c4515f635ebc0be4bb 

And, the resulting JSON Lines file has content id (or signature) hash://sha256/93c748c9e88a8c221f4d9cf4de37f0506d8427a4383cf1ee2078983cdeb31a14 and is also included as interactions.json to facilitate access.

The first json record can be generated using:

preston cat hash://sha256/93c748c9e88a8c221f4d9cf4de37f0506d8427a4383cf1ee2078983cdeb31a14\
 | head -n1\
 | jq .

or, provided that the interactions.json has content id starting with hash://sha256/da34e70...

cat interactions.json\
 | head -n1\
 | jq .

This produces the following (formatted) json object:

{
 "http://www.w3.org/ns/prov#wasDerivedFrom": "hash://sha256/f1c123b38bdfab129d8a393b06842eddae823bf08a2c91b1100399940b582a9a",
 "http://www.w3.org/1999/02/22-rdf-syntax-ns#type": "application/vnd.taxonworks+json",
 "referenceId": "https://sfg.taxonworks.org/api/v1/sources/49013",
 "interactionId": "https://sfg.taxonworks.org/api/v1/biological_associations/84748",
 "taxonRootsResolved": 2,
 "referenceResolved": true,
 "referenceCitation": "@article{49013,
 author = {Abate, T.},
 booktitle = {Journal of Applied Entomology},
 journal = {Journal of Applied Entomology},
 month = {mar},
 day = {31},
 pages = {278-285},
 title = {The bean fly Ophiomyia phaseoli Tryon (Diptera: Agromyzidae) and its parasitoids in Ethiopia.},
 volume = {111(3)},
 year = {1991},
 stated_year = {1991},
 year_suffix = {a},
 issn = {0044-2240}
}
",
 "interactionTypeId": "gid://taxon-works/BiologicalRelationship/14",
 "interactionTypeName": "Primary host",
 "sourceTaxonName": "Eupelmus",
 "sourceTaxonId": "gid://taxon-works/TaxonName/456381",
 "sourceTaxonRank": "genus",
 "sourceTaxonAuthorship": "Dalman, 1820",
 "sourceTaxonPath": "Root | Animalia | Arthropoda | Insecta | Hymenoptera | Chalcidoidea | Eupelmidae | Eupelminae | Eupelmus",
 "sourceTaxonPathIds": "gid://taxon-works/TaxonName/455455 | gid://taxon-works/TaxonName/703648 | gid://taxon-works/TaxonName/703653 | gid://taxon-works/TaxonName/703658 | gid://taxon-works/TaxonName/455456 | gid://taxon-works/TaxonName/455458 | gid://taxon-works/TaxonName/455504 | gid://taxon-works/TaxonName/455506 | gid://taxon-works/TaxonName/456381",
 "sourceTaxonPathNames": "nomenclatural rank | kingdom | phylum | class | order | superfamily | family | subfamily | genus",
 "targetTaxonName": "Agromyzidae",
 "targetTaxonId": "gid://taxon-works/TaxonName/513569",
 "targetTaxonRank": "family",
 "targetTaxonAuthorship": "",
 "targetTaxonPath": "Root | Animalia | Arthropoda | Insecta | Diptera | Agromyzidae",
 "targetTaxonPathIds": "gid://taxon-works/TaxonName/455455 | gid://taxon-works/TaxonName/703648 | gid://taxon-works/TaxonName/703653 | gid://taxon-works/TaxonName/703658 | gid://taxon-works/TaxonName/513567 | gid://taxon-works/TaxonName/513569",
 "targetTaxonPathNames": "nomenclatural rank | kingdom | phylum | class | order | family"
}

In this example, a claim is made that, according to https://sfg.taxonworks.org/api/v1/sources/49013 [6] Eupelmus (a parasitic wasp) has a primary host in the family of Agromyzidae (leaf-miner flies).

In total, 138,246 such claims can be found in the generated resource with alias interactions.json and content id starting with hash://sha256/93c748c... .

In addition, the archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the TaxonWorks content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 .

To retrieve and verify the downloaded TaxonWorks biodiversity dataset graph, first concatenate all the downloaded preston-*.tar.gz files (e.g., cat preston-*.tar.gz > preston.tar.gz). Then, extract the archives into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using:

java -jar preston.jar clone --remote https://zenodo.org/record/8253729/files

After that, verify the index of the archive by reproducing the following provenance log history:

 java -jar preston.jar history --log tsv

to be:

hash://sha256/a4d651aac5220487835e6178511886e98b845b2d98cb7c5447fb2b042e0654d2  http://www.w3.org/ns/prov#wasDerivedFrom  hash://sha256/ab7550368905e7c919e70a306efbb97719a1edbba2cfe4c4515f635ebc0be4bb
hash://sha256/ab7550368905e7c919e70a306efbb97719a1edbba2cfe4c4515f635ebc0be4bb  http://www.w3.org/ns/prov#wasDerivedFrom  hash://sha256/ff5e709305e593c87711e897b6341b94e775e2f312aa6d4ae5ed6120babd6f5e  
urn:uuid:0659a54f-b713-4f86-a917-5be166a14110  http://purl.org/pav/hasVersion  hash://sha256/ff5e709305e593c87711e897b6341b94e775e2f312aa6d4ae5ed6120babd6f5e  

To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.

java -jar preston.jar verify

Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".

Files in this data publication:

--- start of file descriptions ---

-- description of archive and its contents (a rendition of this file) --
README

-- biological associations indexed from TaxonWorks expressed in a GloBI [5] compatible JSON Lines file --
interactions.json

-- executable java jar containing preston [2,3,4] v0.7.4-SNAPSHOT. --
preston.jar

-- preston archives containing TaxonWorks data files, associated provenance logs and a provenance index --
preston-[00-ff].tar.gz -- individual provenance index files --

2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a
f98d36a9dc7bd833c93b3b61130865628f7bc2f7bb0920e95afcd16fba3dc6a8
29306c5c144c3d7fd21be344d8b6b554b6f6efa3b8f8f5c0b27cdf0e88785652

--- end of file descriptions ---

References
[1] TaxonWorks is an integrated web-based workbench for taxonomists and biodiversity scientists. (TaxonWorks, https://sfg.taxonworks.org) accessed from 2023-08-16 to 2023-08-16 with provenance hash://sha256/a4d651aac5220487835e6178511886e98b845b2d98cb7c5447fb2b042e0654d2.
[2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .
[3] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132 hash://sha256/136c3c1808bcf463bb04b11622bb2e7b5fba28f5be1fc258c5ea55b3b84f482c
[4] MJ Elliott, JH Poelen, JAB Fortes (2023). Signing data citations enables data verification and citation persistence. Scientific Data. https://doi.org/10.1038/s41597-023-02230-y hash://sha256/f849c870565f608899f183ca261365dce9c9f1c5441b1c779e0db49df9c2a19d
[5] Poelen JH, Simons JD, Mungall CJ. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.
[6] Abate, T. (1991) The bean fly Ophiomyia phaseoli Tryon (Diptera: Agromyzidae) and its parasitoids in Ethiopia. Journal of Applied Entomology 111(3), 278–285."

This work is funded in part by grant NSF OAC 1839201, NSF DBI 1901932, NSF DBI 1901926, and NSF DBI 2102006 from the National Science Foundation.

These data are a harmonized collation of vegetation survey plot datasets from across Australia, representing 205,084 plots. Source data were obtained from the main custodians of vegetation plot information in Australia, being primarily state/territory and federal government agencies. The collated source data were harmonised to a common structured data format through customised scripts written in R. The data provided here are those for which the source data licenses enable adaptation and sharing, though data were also collated and harmonised from a number of sources that could not be included in this data release due to license restrictions. Important methodological attributes have been incorporated where possible, including the taxonomic scope of each survey and the size/shape of the survey plots used. This harmonised dataset may enable a wide variety of analyses that improve our understanding of Australian plant diversity and vegetation patterns. Lineage: Given the absence of an existing large-scale harmonised plant community survey plot dataset for Australia, we obtained and collated data primarily from state/territory and federal agencies, that are custodians of the largest survey datasets. In some cases we downloaded data directly from publicly accessible websites, while for others we required assistance and permission to obtain relevant data.

Given the different formats of the source data, we developed a simple, customised and structured data format to harmonise across sources, based broadly on the Veg-X schema (Wiser, et al. 2011), with existing standards for data fields used wherever possible (Veg-X, Darwin Core). Source data were harmonised to the common format using a customised script in R. Taxonomic nomenclature was standardised to the Australian Plant Census (CHAH 2022), using code adapted from Falster et al. (2021), with only vascular plant species retained.

Key methodological aspects of the component datasets were also incorporated, including the taxonomic scope of the vegetation survey (e.g. all vascular plants , dominant species only) and the size / configuration of the plot that was surveyed. Obtaining such information often involved identifying publications related to the data and cataloguing the methods described.

Data products The data were formatted and prepared into the following files, linked by common identifiers: •\tproject.csv – this file describes attributes of the projects undertaken to sample survey plots. Most projects are associated with surveying multiple plots over space and time, using a common methodology. •\tplot.csv – this file describes attributes of the plots that have been surveyed. A plot is a fixed area/location in space, that may be surveyed at one or more times, for one or more attributes (e.g. plant species, soil attributes). •\tplotObservation.csv – this file describes the attributes associated with the observations taken at a plot at a particular time (date). There may be multiple plot observations for a single plot. •\taggregateOrganismObservation.csv – this file describes the attributes of plant species observed in a specific plot observation, such as the species observed and any measure of abundance that was made. •\taggregateSoilObservation.csv – this file describes the attributes of the soil that were made in a specific plot observation. •\tspeciesAttributes.csv – this file describes the attributes associated with species names included in the dataset, where the scientific name for each plant species is that accepted by the Australian Plant Census (CHAH 2022).

The contents (data fields) of each file listed above are described in the file: HAVPlot_Data_Format.csv

The sources of the plot data provided here are shown in the file: HAVPlot_source_citations.docx . Use of the data provided here should comply with the data license conditions of the source data.

A coded example (using R) for combining and manipulating the component HAVPlot data files is provided in the file: HAVPlot_data_query_example_R_code.R

Summary The HAVPlot data comprise 213,101 observations across 205,084 plots. A summary of the full HAVPlot data are also available in Mokany et al. (2022).

References CHAH (2022). Australian Plant Census, Centre of Australian National Biodiversity Research. Council of Heads of Australasian Herbaria (CHAH). https://id.biodiversity.org.au/tree/51354547. Falster, D., et al. 2021. AusTraits, a curated plant trait database for the Australian flora. - Scientific Data 8: 254. Mokany, K., et al. 2022. Patterns and drivers of plant diversity across Australia. – Ecography e06426. https://doi.org/10.1111/ecog.06426 Wiser, S. K., et al. 2011. Veg-X - an exchange standard for plot-based vegetation data. - Journal of Vegetation Science 22: 598-609.

A selection of 9sec gridded National climate change variables for biodiversity modelling. This collection represents 30-year averages centred on each of 1990, 2050, 2070, 2090. Projected future climates were generated by applying within-model changes for two circulation model outputs: GFDL and ACCESS1.0; and for two representative concentration pathways (RCP 4.5, 8.5), calculated at the native general circulation model grid resolution to these current surfaces, using ANUCLIM 6.1 prior to radiative adjustment. That the maximum temperature variables have been adjusted for topographic slope/aspect and shading effects. A short methods summary is provided in the file 9sClimateMethodsSummary.pdf for further information, including a nomenclature for files. The selected climate variables provided in this collection are: TNM - mean annual minimum temperature TXM - mean annual maximum temperature TXX - mean maximum monthly maximum temperature TXI - mean minimum monthly maximum temperature TNI - mean minimum monthly minimum temperature TNX - mean maximum monthly minimum temperature PTA - Average total annual rainfall PTX - mean maximum monthly rainfall PTI - mean minimum monthly rainfall Other variables (evaporation and water balance, temperature range, and seasonality, etc) are available upon application. The data are provided in ESRI binary float grid format (*.hdr, *.flt), Projection is geographic GDA94. Lineage: Climate surfaces for the present were based on the ANUCLIM 6.1 (Xu and Hutchinson, 2011) 30 year average climate surfaces for Australia (1976-2005), with elevational lapse rate correction applied over the 9s GEODATA digital elevation model (Hutchinson et al , 2008). Radiative correction derived from the same DEM was applied to radiation and maximum temperature before calculation of evaporation, using the CSIRO TerraFormer software. Summary statistics for each variable were then calculated including variables described in Williams et al (2012: Which environmental variables should I use in my biodiversity model? International Journal of Geographic Information Sciences 26(11), 2009-2047. DOI: 10.1080/13658816.2012.698015.). Details are given in the short summary report by Tom Harwood, Noboru Ota, Justin Perry, Kristen Williams, Ian Harman, Simon Ferrier (2014) gridded continental climate variables for Australia: November 2014. CSIRO Land and Water, Canberra. Attached with the collection. Key published references: Reside AE, VanDerWal J, Phillips B, Shoo L, Rosauer D, Anderson BA, Welbergen J, Moritz C, Ferrier S, Harwood TD, Williams KJ, Mackey B, Hugh S and Williams SE (2013) Climate change refugia for terrestrial biodiversity: Defining areas that promote species persistence and ecosystem resilience in the face of global climate change. National Climate Change Adaptation Research Facility, Griffith University, Gold Coast, Qld. Xu T and Hutchinson MF (2013) New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package. Environmental Modelling & Software 40(0), 267-279. DOI: http://dx.doi.org/10.1016/j.envsoft.2012.10.003. ACCESS: Bi D, Dix M, Marsland SJ, O’Farrell S, Rashid HA, Uotila P, Hirst AC, Kowalczyk E, Golebiewski M, Sullivan A, Yan H, Hannah N, Franklin C, Sun Z, Vohralik P, Watterson I, Zhou X, Fiedler R, Collier M, Ma Y, Noonan J, Stevens L, Uhe P, Zhu H, Griffies SM, Hill R, Harris C and Puri K (2013) The ACCESS coupled model: description, control climate and evaluation. Australian Meteorological and Oceanographic Journal 63(1), 41-64. GFDL: Dunne JP, John JG, Shevliakova E, Stouffer RJ, Krasting JP, Malyshev SL, Milly PCD, Sentman LT, Adcroft AJ, Cooke W, Dunne KA, Griffies SM, Hallberg RW, Harrison MJ, Levy H, Wittenberg AT, Phillips PJ and Zadeh N (2013) GFDL’s ESM2 global coupled climate–carbon earth system models. Part II: Carbon system formulation and baseline simulation characteristics. Journal of Climate 26(7), 2247-2267. DOI: 10.1175/JCLI-D-12-00150.1.

We present a checklist of annelids from recent United Kingdom Seabed Resources (UKSR) expeditions (Abyssal Baseline - ABYSSLINE project) to the eastern abyssal Pacific Clarion-Clipperton Zone (CCZ) polymetallic nodule fields, based on DNA species delimitation, including imagery of voucher specimens, Darwin Core (DwC) data and links to vouchered specimen material and new GenBank sequence records. This paper includes genetic and imagery data for 129 species of annelids from 339 records and is restricted to material that is, in general, in too poor a condition to describe formally at this time, but likely contains many species new to science. We make these data available both to aid future taxonomic studies in the CCZ that will be able to link back to these genetic data and specimens and to better underpin ongoing ecological studies of potential deep-sea mining impacts using the principles of FAIR (Findable, Accessible, Interoperable, Reusuable) data and specimens that will be available for all.We include genetic, imagery and all associated metadata in Darwin Core format for 129 species of annelids from the Clarion-Clipperton Zone, eastern abyssal Pacific, with 339 records.

The Wilson and Henderson-Sellers Secondary Vegetation Classes and Class Reliability data sets are part of the "Wilson Henderson-Sellers land cover and soils for global circulation modeling project " and were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the US National Geophysical Data Center (NGDC). The data sets are part of the World Data Bank II. This data Bank is provided in a Database on diskette called ""The Global Change Data Base"". The Data Bank II is part of larger project called "Global Ecosystems Database Project". This is a cooperative effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the US Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The data sets are accompanied by an ASCII documentation file which contains information necessary for the use of the dataset in GIS or other software.

References: Wilson, M.F./ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models. Journal of Climatology, vol.5, pp.119-143.

Source : Digitized from available sources: FAO/UNESCO Soil Map of the World Publication Date : 1985 Projection : lat/lon Type : Raster Format : IDRISI

Ukrainian Plant Trait Database (UkrTrait v. 1.0) represents a collection of plant trait data from Ukraine. We compiled and digitized plant traits from local Ukrainian literature sources. Furthermore, we performed our own field and laboratory measurements of various plant traits that were not previously available in the literature. In the current version of the UkrTrait, we focus on vascular plant species that are absent from the other European trait databases, with emphasis on species that are representative for the steppe vegetation. Traits assembled from literature include life span (annuals, biennials, perennials), plant height, flowering period (flowering months), life form (by Raunkiaer), plant growth form, and others. Our own measured traits include seed mass, seed shape, leaf area, leaf nitrogen concentration, and leaf phosphorus concentration. The current version, i.e. UkrTrait v. 1.0, comprises digitized literature data of 287,948 records of 75 traits for 6,198 taxa and our own trait measurements of 2,390 records of 12 traits for 388 taxa.

More detailed information on content and methodology is available in:

Vynokurov D, Borovyk D, Chusova O, Davydova A, Davydov D, Danihelka J, Dembicz I, Iemelianova S, Kolomiiets G, Moysiyenko I, Shapoval V, Shynder O, Skobel N, Buzhdygan O, Kuzemko A (2024) Ukrainian Plant Trait Database: UkrTrait v. 1.0. Biodiversity Data Journal 12: e118128. https://doi.org/10.3897/BDJ.12.e118128

Taxonomical background. We used the Ukrainian Checklist (Mosyakin and Fedoronchuk 1999) as a primary taxonomical source to preserve the original taxa names and their corresponding trait values, which is especially meaningful for the traits collected from the existing literature sources. Additionally, we provided the crosswalks between the Ukrainian checklist and international sources: GBIF Backbone Taxonomy, World Checklist of Vascular Plants (World Checklist of Vascular Plants (World Checklist of Vascular Plants (WCVP), World Flora Online (WFO) and Euro+Med PlantBase. However, the provided nomenclature crosswalks should be used with caution, since online databases are constantly updated. Therefore, we recommend conducting an additional match of the original taxa names to obtain up-to-date nomenclature information.

The dataset includes four files in two formats (*.tsv and *.csv):

1. database-measured-traits is a dataset of the measured plant traits including generative plant height measured in the field (cm); vegetative plant height measured in the field (cm); average dry leaf mass (mg); average specific leaf area (SLA) (mm2 * mg-1); leaf nitrogen concentration (mg * g-1); leaf phosphorus concentration (mg * g-1); length of a seed (mm); width of a seed (mm); thickness of a seed (mm); variance of its three dimensions (unitless, raging from 0 to 1); average dry mass of a seed (mg).

2. database-literature-traits is a dataset including plant traits compiled from the literature sources. It includes the following traits: Average height of the whole plant; Plant flowering period; Plant life span (annuals, biennials or short-lived or perennials); Raunkiaer life form (phanerophytes, chamaephytes, hemicryptophytes, geophytes, hydrophytes, therophytes); Plant growth form (trees, shrubs, semishrubs, polycarpic and monocarpic herbs, epiphytes, woody and herbaceous lianas); Species geographic range; Leaf phenology; Rosette plants; Root system; Golubev life form (trees, shrubs, low shrubs, subshrubs, low subshrubs, polycarpic herbs, perenial monocarpic herbs, spring annuals, autumn annuals, epihydrophytes, idiohydrophytes, lianas, sparse cushion-shaped plants, spherical-shaped plants, creeping, succulents and fleshy plants, parasitic, semi-parasitic, saprophytic, carnivorous, rhizomatous, bulbosous); Biomorphological adaptations for vegetative renewal and reproduction; Listed in the Red Data Book of Ukraine (edition 2021); Conservation status according to the Red Data Book of Ukraine (edition 2021); Residence time status of alien species; Region of origin of the alien species; Naturalisation degree of the alien species; Cultivated plants; Escaped from cultivation plants.

3. traits-ontologies is a file linking trait terminology used in the UkrTrait to the Thesaurus of Plant Characteristics (TOP), the Plant Trait Ontology (TO) and the TRY Plant Trait Database.

4. taxonomy_UkrTrait is a file containing taxonomical crosswalks between the UkrTrait species list and other nomenclature sources (Mosyakin et Fedoronchuk 1999; Euro+Med PlantBase; GBIF Backbone Taxonomy; World Checklist of Vascular Plants (WCVP); World Flora Online (WFO).

Recommended citation for this database:

Vynokurov D, Borovyk D, Chusova O, Davydova A, Davydov D, Danihelka J, Dembicz I, Iemelianova S, Kolomiiets G, Moysiyenko I, Shapoval V, Shynder O, Skobel N, Buzhdygan O, Kuzemko A (2024) Ukrainian Plant Trait Database: UkrTrait v. 1.0. Biodiversity Data Journal 12: e118128. https://doi.org/10.3897/BDJ.12.e118128

Popular and scientific name of the species present in the sample.