100+ datasets found
  1. Spatially explicit database of tree related microhabitats (TreMs)

    • gbif.org
    Updated Aug 2, 2022
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    Daniel Kraus; Andreas Schuck; Peter Bebi; Markus Blaschke; Rita Bütler; Martin Flade; Wilfried Heintz; Frank Krumm; Thibault Lachat; Laurent Larrieu; Lenka Lehnerova; Martin Levin; Ulrich Mergner; Maciej Pach; Yoan Paillet; Patrick Pyttel; Tomas Rydkvist; Giovanni Santopuoli; Kristina Sever; Knut Sturm; Kris Vandekerkhove; Susanne Winter; Manfred Witz; Martin Winnock; Matteo Marcandella; Isabelle Roth; Armin Jakob; Reiner Dickele; Gerhard Hofmann; Dirk Ruis-Eckhardt; Stephan Boschen; Uwe Schölmerich; Bertram Leder; Martin Guericke; Hubert Merkel; Dagmar Löffler; Stephan Schusser; Monika Runkel; Alexandra Steinmetz; Karl Heinz Marx; Andre Mongelluzzi; Florian Wilshusen; Jean-Jacques Boutteaux; Loic Duchamp; Nicolas Dericbourg; Emmanuel Rouyer; Valeria Csikos; Ken Sweeny; Daniel Steichen; Michel Leytem; Stefan Konczal; Krzysztof Stereńczak; Marko Kazimirovic; Vladimír Šebeň; Teresa Baiges Zapater; Marieke van der Maaten-Theunissen; Andreas Pommer; Thomas Nord-Larsen; Marc Fuhr; Luc-Olivier Delebeque; Lidón Martínez Navarro; David Lasala; S.M. Waez-Mousavi; Kiomars Sefidi; Begoña Abellanas; Daniel Kraus; Andreas Schuck; Peter Bebi; Markus Blaschke; Rita Bütler; Martin Flade; Wilfried Heintz; Frank Krumm; Thibault Lachat; Laurent Larrieu; Lenka Lehnerova; Martin Levin; Ulrich Mergner; Maciej Pach; Yoan Paillet; Patrick Pyttel; Tomas Rydkvist; Giovanni Santopuoli; Kristina Sever; Knut Sturm; Kris Vandekerkhove; Susanne Winter; Manfred Witz; Martin Winnock; Matteo Marcandella; Isabelle Roth; Armin Jakob; Reiner Dickele; Gerhard Hofmann; Dirk Ruis-Eckhardt; Stephan Boschen; Uwe Schölmerich; Bertram Leder; Martin Guericke; Hubert Merkel; Dagmar Löffler; Stephan Schusser; Monika Runkel; Alexandra Steinmetz; Karl Heinz Marx; Andre Mongelluzzi; Florian Wilshusen; Jean-Jacques Boutteaux; Loic Duchamp; Nicolas Dericbourg; Emmanuel Rouyer; Valeria Csikos; Ken Sweeny; Daniel Steichen; Michel Leytem; Stefan Konczal; Krzysztof Stereńczak; Marko Kazimirovic; Vladimír Šebeň; Teresa Baiges Zapater; Marieke van der Maaten-Theunissen; Andreas Pommer; Thomas Nord-Larsen; Marc Fuhr; Luc-Olivier Delebeque; Lidón Martínez Navarro; David Lasala; S.M. Waez-Mousavi; Kiomars Sefidi; Begoña Abellanas (2022). Spatially explicit database of tree related microhabitats (TreMs) [Dataset]. http://doi.org/10.15468/ocof3v
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    Dataset updated
    Aug 2, 2022
    Dataset provided by
    Global Biodiversity Information Facilityhttps://www.gbif.org/
    Institut national de recherche pour l’agriculture, l’alimentation et l’environnement (INRAE)
    Authors
    Daniel Kraus; Andreas Schuck; Peter Bebi; Markus Blaschke; Rita Bütler; Martin Flade; Wilfried Heintz; Frank Krumm; Thibault Lachat; Laurent Larrieu; Lenka Lehnerova; Martin Levin; Ulrich Mergner; Maciej Pach; Yoan Paillet; Patrick Pyttel; Tomas Rydkvist; Giovanni Santopuoli; Kristina Sever; Knut Sturm; Kris Vandekerkhove; Susanne Winter; Manfred Witz; Martin Winnock; Matteo Marcandella; Isabelle Roth; Armin Jakob; Reiner Dickele; Gerhard Hofmann; Dirk Ruis-Eckhardt; Stephan Boschen; Uwe Schölmerich; Bertram Leder; Martin Guericke; Hubert Merkel; Dagmar Löffler; Stephan Schusser; Monika Runkel; Alexandra Steinmetz; Karl Heinz Marx; Andre Mongelluzzi; Florian Wilshusen; Jean-Jacques Boutteaux; Loic Duchamp; Nicolas Dericbourg; Emmanuel Rouyer; Valeria Csikos; Ken Sweeny; Daniel Steichen; Michel Leytem; Stefan Konczal; Krzysztof Stereńczak; Marko Kazimirovic; Vladimír Šebeň; Teresa Baiges Zapater; Marieke van der Maaten-Theunissen; Andreas Pommer; Thomas Nord-Larsen; Marc Fuhr; Luc-Olivier Delebeque; Lidón Martínez Navarro; David Lasala; S.M. Waez-Mousavi; Kiomars Sefidi; Begoña Abellanas; Daniel Kraus; Andreas Schuck; Peter Bebi; Markus Blaschke; Rita Bütler; Martin Flade; Wilfried Heintz; Frank Krumm; Thibault Lachat; Laurent Larrieu; Lenka Lehnerova; Martin Levin; Ulrich Mergner; Maciej Pach; Yoan Paillet; Patrick Pyttel; Tomas Rydkvist; Giovanni Santopuoli; Kristina Sever; Knut Sturm; Kris Vandekerkhove; Susanne Winter; Manfred Witz; Martin Winnock; Matteo Marcandella; Isabelle Roth; Armin Jakob; Reiner Dickele; Gerhard Hofmann; Dirk Ruis-Eckhardt; Stephan Boschen; Uwe Schölmerich; Bertram Leder; Martin Guericke; Hubert Merkel; Dagmar Löffler; Stephan Schusser; Monika Runkel; Alexandra Steinmetz; Karl Heinz Marx; Andre Mongelluzzi; Florian Wilshusen; Jean-Jacques Boutteaux; Loic Duchamp; Nicolas Dericbourg; Emmanuel Rouyer; Valeria Csikos; Ken Sweeny; Daniel Steichen; Michel Leytem; Stefan Konczal; Krzysztof Stereńczak; Marko Kazimirovic; Vladimír Šebeň; Teresa Baiges Zapater; Marieke van der Maaten-Theunissen; Andreas Pommer; Thomas Nord-Larsen; Marc Fuhr; Luc-Olivier Delebeque; Lidón Martínez Navarro; David Lasala; S.M. Waez-Mousavi; Kiomars Sefidi; Begoña Abellanas
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 1, 2014 - Dec 1, 2020
    Area covered
    Description

    ‘Tree – tree’ interactions are important structuring mechanisms for forest community dynamics. Forest management takes advantage of competition effects on tree growth by removing or retaining trees to achieve management goals. Both competition and silviculture have thus a strong effect on density and distribution of tree related microhabitats (TreMs) which are key features for forest taxa at the stand scale (e.g. Bouget et al. 2013, 2014). In particular, spatially explicit data to understand patterns and mechanisms of TreM formation in forest stands are rare. To train and eventually improve decision making capacities related to the integration of biodiversity aspects into forest management to date more than 100 usually 1 ha (100 m x 100m) permanent plots were established in different forest communities of Europe. Due to their demonstration character the selection of plots was non-systematic. They do, however, cover a broad range of forest types (e.g. beech-oak, beech-fir (-spruce), oak-hornbeam, pine-spruce, etc.), altitudinal gradients (from 25 m – 1850 m) and site conditions (e.g. oligotrophic Luzulo-Fagetum or Vaccinio-Pinetum to mesotrophic Galio-Fagetum or Milio-Fagetum). For each plot the following data is collected: (1) tree location as polar coordinates (stem base map), (2) tree species, (3) forest mensuration data (dbh in [cm], tree height in [m]), (4) tree related microhabitats (TreMs) and (5) tree status (living or standing dead). In addition to the spatial dendrometric data we provide information on plot establishment, forest type, plot location (state, region, country), elevation, means for annual precipitation and temperature, and the natural forest community (Kraus et al., 2018).

  2. u

    Urban tree database

    • agdatacommons.nal.usda.gov
    bin
    Updated Jan 22, 2025
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    E. Gregory McPherson; Natalie S. van Doorn; Paula J. Peper (2025). Urban tree database [Dataset]. http://doi.org/10.2737/RDS-2016-0005
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    binAvailable download formats
    Dataset updated
    Jan 22, 2025
    Dataset provided by
    Forest Service Research Data Archive
    Authors
    E. Gregory McPherson; Natalie S. van Doorn; Paula J. Peper
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This data publication contains urban tree growth data collected over a period of 14 years (1998-2012) in 17 cities from 13 states across the United States: Arizona, California, Colorado, Florida, Hawaii, Idaho, Indiana, Minnesota, New Mexico, New York, North Carolina, Oregon, and South Carolina.

    Measurements were taken on over 14,000 urban street and park trees. Key information collected for each tree species includes bole and crown size, location, and age. Based on these measurements, 365 sets of allometric equations were developed for tree species from around the U.S. Each “set” consists of eight equations for each of the approximately 20 most abundant species in each of 16 climate regions. Tree age is used to predict a species diameter at breast height (dbh), and dbh is used to predict tree height, crown diameter, crown height, and leaf area. Dbh is also used to predict age. For applications with remote sensing, average crown diameter is used to predict dbh. There are 171 distinct species represented within this database. Some species grow in more than one region. The Urban Tree Database (UTD) contains foliar biomass data (raw data and summarized results from the foliar sampling for each species and region) that are fundamental to calculating leaf area, as well as tree biomass equations (compiled from literature) for carbon storage estimates. An expanded list of dry weight biomass density factors for common urban species is made available to assist users in using volumetric equations.Information on urban tree growth underpins models used to calculate effects of trees on the environment and human well-being. Maximum tree size and other growth data are used by urban forest managers, landscape architects and planners to select trees most suitable to the amount of growing space, thereby reducing costly future conflicts between trees and infrastructure. Growth data are used to develop correlations between growth and influencing factors such as site conditions and stewardship practices. Despite the importance of tree growth data to the science and practice of urban forestry, our knowledge is scant. Over a period of 14 years scientists with the U.S. Forest Service recorded data from a consistent set of measurements on over 14,000 trees in 17 U.S. cities.These data were originally published on 03/02/2016. The metadata was updated on 10/06/2016 to include reference to a new publication. Minor metadata updates were made on 12/15/2016. On 01/07/2020 this data publication was updated to correct a few species' names and systematic errors in the data that were found. A complete list of these changes is included (\Supplements\Errata_Jan2020_RDS-2016-0005.pdf). In addition, we have included a list of changes for the General Technical Report associated with these data (\Supplements\Errata_Jan2020_PNW-GTR-253.pdf).

  3. f

    Data from: ToTE: A global database on trees of the treeline ecotone

    • figshare.com
    bin
    Updated Jan 19, 2024
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    Firdous Ahmad Dar; Anzar Ahmad Khuroo (2024). ToTE: A global database on trees of the treeline ecotone [Dataset]. http://doi.org/10.6084/m9.figshare.21922302.v5
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    binAvailable download formats
    Dataset updated
    Jan 19, 2024
    Dataset provided by
    figshare
    Authors
    Firdous Ahmad Dar; Anzar Ahmad Khuroo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The database deals with the trees of the treeline ecotone worldwide. The ToTE (Trees of Treeline Ecotone) includes data on 208 tree species distributed across 34 mountain regions of the world. The database on the global tree species richness of the treeline is based on systematic review of 1202 studies published from 1962-2022. The database follows the scientific nomenclature as per Plants of the World Online (2022), the mountain classification as per Global Mountain Biodiversity Assessment (GMBA) Mountain Inventory v_2.0 (2022) and the biome classification as per Koppen and provides the current conservation status of the taxa as per IUCN (2022) ver. 2

  4. ToI, Ver.-I : Trees of India, Version-I

    • figshare.com
    xlsx
    Updated Nov 27, 2023
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    Anzar Ahmad Khuroo; Muzamil Ahmad Mugal; Sajad Ahmad Wani (2023). ToI, Ver.-I : Trees of India, Version-I [Dataset]. http://doi.org/10.6084/m9.figshare.23226281.v1
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    xlsxAvailable download formats
    Dataset updated
    Nov 27, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Anzar Ahmad Khuroo; Muzamil Ahmad Mugal; Sajad Ahmad Wani
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    India
    Description

    The database deals with the trees of India. The ToI, Ver.-I (Trees of India, Version-I) includes data on 3708 tree species distributed across 35 states/union territories of India. The database is based on systematic review of 313 literature sources published from 1872-2022. The database follows the scientific nomenclature as per Plants of the World Online (2022). The database also includes 609 species endemic to India, and 347 species currently threatened as per IUCN (2022).

  5. d

    Data from: Raw urban street tree inventory data for 49 California cities

    • catalog.data.gov
    • agdatacommons.nal.usda.gov
    • +5more
    Updated Apr 21, 2025
    + more versions
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    U.S. Forest Service (2025). Raw urban street tree inventory data for 49 California cities [Dataset]. https://catalog.data.gov/dataset/raw-urban-street-tree-inventory-data-for-49-california-cities-47b23
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    Dataset updated
    Apr 21, 2025
    Dataset provided by
    U.S. Forest Service
    Area covered
    California
    Description

    This data publication contains urban tree inventory data for 929,823 street trees that were collected from 2006 to 2013 in 49 California cities. Fifty six urban tree inventories were obtained from various sources for California cities across five climate zones. The five climate zones were based largely on aggregation of Sunset National Garden Book's 45 climate zones. Forty-nine of the inventories fit the required criteria of (1) included all publicly managed trees, (2) contained data for each tree on species and diameter at breast height (dbh) and (3) was conducted after 2005. Tree data were prepared for entry into i-Tree Streets by deleting unnecessary data, matching species to those in the i-Tree database, and establishing dbh size classes. Data included in this publication include tree location (city, street name and number), diameter at breast height, species name and/or species code, and tree type.

  6. u

    Fire and tree mortality database (FTM): 2nd edition

    • agdatacommons.nal.usda.gov
    bin
    Updated Jan 22, 2025
    + more versions
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    C. Alina Cansler; Sharon M. Hood; J. Morgan Varner; Phillip J. van Mantgem; Michelle C. Agne; Robert A. Andrus; Matthew P. Ayres; Bruce D. Ayres; Jonathan D. Bakker; Michael A. Battaglia; Barbara J. Bentz; Carolyn R. Breece; James K. Brown; Daniel R. Cluck; Tom W. Coleman; R. Gregory Corace; W. Wallace Covington; Douglas S. Cram; James B. Cronan; Joseph E. Crouse; Adrian J. Das; Ryan S. Davis; Darci M. Dickinson; Stephen A. Fitzgerald; Peter Z. Fulé; Lisa M. Ganio; Lindsay M. Grayson; Charles B. Halpern; Jim L. Hanula; Brian J. Harvey; J. Kevin Hiers; David W. Huffman; MaryBeth Keifer; Tara L. Keyser; Leda N. Kobziar; Thomas E. Kolb; Crystal A. Kolden; Karen E. Kopper; Jason R. Kreitler; Jesse K. Kreye; Andrew M. Latimer; Andrew P. Lerch; Maria J. Lombardero; Virginia L. McDaniel; Charles W. McHugh; Joel D. McMillin; Jason J. Moghaddas; Joseph J. O’Brien; Daniel D.B. Perrakis; David W. Peterson; Susan J. Prichard; Robert A. Progar; Kenneth F. Raffa; Elizabeth D. Reinhardt; Joseph C. Restaino; John P. Roccaforte; Brendan M. Rogers; Kevin C. Ryan; Hugh D. Safford; Alyson E. Santoro; Timothy M. Shearman; Alice M. Shumate; Carolyn H. Sieg; Sheri L. Smith; Rebecca J. Smith; Nathan L. Stephenson; Mary Steuver; Jens T. Stevens; Michael T. Stoddard; Walter G. Thies; Nicole M. Vaillant; Shelby A. Weiss; Douglas J. Westlind; Travis J. Woolley; Micah Wright (2025). Fire and tree mortality database (FTM): 2nd edition [Dataset]. http://doi.org/10.2737/RDS-2020-0001-2
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    binAvailable download formats
    Dataset updated
    Jan 22, 2025
    Dataset provided by
    Forest Service Research Data Archive
    Authors
    C. Alina Cansler; Sharon M. Hood; J. Morgan Varner; Phillip J. van Mantgem; Michelle C. Agne; Robert A. Andrus; Matthew P. Ayres; Bruce D. Ayres; Jonathan D. Bakker; Michael A. Battaglia; Barbara J. Bentz; Carolyn R. Breece; James K. Brown; Daniel R. Cluck; Tom W. Coleman; R. Gregory Corace; W. Wallace Covington; Douglas S. Cram; James B. Cronan; Joseph E. Crouse; Adrian J. Das; Ryan S. Davis; Darci M. Dickinson; Stephen A. Fitzgerald; Peter Z. Fulé; Lisa M. Ganio; Lindsay M. Grayson; Charles B. Halpern; Jim L. Hanula; Brian J. Harvey; J. Kevin Hiers; David W. Huffman; MaryBeth Keifer; Tara L. Keyser; Leda N. Kobziar; Thomas E. Kolb; Crystal A. Kolden; Karen E. Kopper; Jason R. Kreitler; Jesse K. Kreye; Andrew M. Latimer; Andrew P. Lerch; Maria J. Lombardero; Virginia L. McDaniel; Charles W. McHugh; Joel D. McMillin; Jason J. Moghaddas; Joseph J. O’Brien; Daniel D.B. Perrakis; David W. Peterson; Susan J. Prichard; Robert A. Progar; Kenneth F. Raffa; Elizabeth D. Reinhardt; Joseph C. Restaino; John P. Roccaforte; Brendan M. Rogers; Kevin C. Ryan; Hugh D. Safford; Alyson E. Santoro; Timothy M. Shearman; Alice M. Shumate; Carolyn H. Sieg; Sheri L. Smith; Rebecca J. Smith; Nathan L. Stephenson; Mary Steuver; Jens T. Stevens; Michael T. Stoddard; Walter G. Thies; Nicole M. Vaillant; Shelby A. Weiss; Douglas J. Westlind; Travis J. Woolley; Micah Wright
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The Fire and Tree Mortality (FTM) database includes standardized observations of fire injury and survival or mortality for 171,177 individual tree-level observations, representing 142 tree species across the United States. Of these, 7,191 trees have burned twice. These trees were burned in 420 prescribed fires and wildfires occurring in 35 years, from 1981 to 2016. The database was developed using 41 contributed datasets from researchers, managers, and archived data products. At a minimum, datasets had to contain measurements of individual trees, size, fire injury, and post-fire survival, but some datasets include additional data such as bark beetle attack. Only trees that were alive before the fire were included in the database. We included any trees where post-fire status was measured within 10 years of the fire. If a tree re-burned in a subsequent fire, and post-fire injury and status information were available after that fire, then a new record (row) was made for that tree after the second fire.The purpose of the FTM database is to provide access to data on individual tree mortality or top-kill from wildland and prescribed fire. By pooling as many individual datasets as possible and creating consistent variables, data analysis with a much larger sample size is feasible. The FTM database also allows easy identification of data gaps to direct future data collection efforts. The FTM database also allows evaluation of post-fire mortality models, such as the First Order Fire Effects Model (FOFEM), BehavePlus, and the Fire and Fuels Extension to the Forest Vegetation Simulator (FFE-FVS).This data publication is a 2nd edition of the FTM database. The first edition (https://doi.org/10.2737/RDS-2020-0001) was originally published on 05/14/2020. This 2nd edition includes data updates such as a change to the format of live / dead status for years where data were not collected in the field, updates to the format of unobserved (NA) values, and additional data corrections. A thorough description of these updates is described in the process steps below.

  7. Data from: Tallo database

    • zenodo.org
    • data.niaid.nih.gov
    csv
    Updated Jun 15, 2022
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    Tommaso Jucker; Tommaso Jucker; Fabian Fischer; Jérôme Chave; David Coomes; John Caspersen; Arshad Ali; Grace Jopaul Loubota Panzou; Ted Feldpausch; Daniel Falster; Vladimir Usoltsev; Stephen Adu-Bredu; Luciana Alves; Mohammad Aminpour; Ilondea Angoboy; Niels Anten; Cécile Antin; Yousef Askari; Rodrigo Muñoz Avilés; Narayanan Ayyappan; Patricia Balvanera; Lindsay Banin; Nicolas Barbier; John Battles; Hans Beeckman; Yannick Bocko; Ben Bond-Lamberty; Frans Bongers; Samuel Bowers; Thomas Brade; Michiel van Breugel; Arthur Chantrain; Rajeev Chaudhary; Jingyu Dai; Michele Dalponte; Kangbéni Dimobe; Jean-Christophe Domec; Jean-Louis Doucet; Remko Duursma; Moisés Enríquez; Karin van Ewijk; William Farfán-Rios; Adeline Fayolle; Eric Forni; David Forrester; Hammad Gilani; John Godlee; Sylvie Gourlet-Fleury; Matthias Haeni; Jefferson Hall; Jie-Kun He; Andreas Hemp; José Hernández-Stefanoni; Steven Higgins; Robert Holdaway; Kiramat Hussain; Lindsay Hutley; Tomoaki Ichie; Yoshiko Iida; Hai-sheng Jiang; Puspa Raj Joshi; Hasan Kaboli; Maryam Kazempour-Larsary; Tanaka Kenzo; Brian Kloeppel; Takashi Kohyama; Suwash Kunwar; Shem Kuyah; Jakub Kvasnica; Siliang Lin; Emily Lines; Hongyan Liu; Craig Lorimer; Jean-Joël Loumeto; Yadvinder Malhi; Peter Marshall; Eskil Mattsson; Radim Matula; Jorge Meave; Sylvanus Mensah; Xiangcheng Mi; Stéphane Momo; Glenn Moncrieff; Francisco Mora; Sarath Nissanka; Kevin O'Hara; Steven Pearce; Raphaël Pelissier; Pablo Peri; Pierre Ploton; Lourens Poorter; Mohsen Javanmiri Pour; Hassan Pourbabaei; Juan Manuel Dupuy Rada; Sabina Ribeiro; Casey Ryan; Anvar Sanaei; Jennifer Sanger; Michael Schlund; Giacomo Sellan; Alexander Shenkin; Sonké, BonaventurSonké, Bonaventuree; Frank Sterck; Martin Svátek; Kentaro Takagi; Anna Trugman; Farman Ullah; Matthew Vadeboncoeur; Ahmad Valipour; Mark Vanderwel; Alejandra Vovides; Weiwei Wang; Li-Qiu Wang; Christian Wirth; Murray Woods; Wenhua Xiang; Fabiano de Aquino Ximenes; Yaozhan Xu; Toshihiro Yamada; Miguel Zavala; Fabian Fischer; Jérôme Chave; David Coomes; John Caspersen; Arshad Ali; Grace Jopaul Loubota Panzou; Ted Feldpausch; Daniel Falster; Vladimir Usoltsev; Stephen Adu-Bredu; Luciana Alves; Mohammad Aminpour; Ilondea Angoboy; Niels Anten; Cécile Antin; Yousef Askari; Rodrigo Muñoz Avilés; Narayanan Ayyappan; Patricia Balvanera; Lindsay Banin; Nicolas Barbier; John Battles; Hans Beeckman; Yannick Bocko; Ben Bond-Lamberty; Frans Bongers; Samuel Bowers; Thomas Brade; Michiel van Breugel; Arthur Chantrain; Rajeev Chaudhary; Jingyu Dai; Michele Dalponte; Kangbéni Dimobe; Jean-Christophe Domec; Jean-Louis Doucet; Remko Duursma; Moisés Enríquez; Karin van Ewijk; William Farfán-Rios; Adeline Fayolle; Eric Forni; David Forrester; Hammad Gilani; John Godlee; Sylvie Gourlet-Fleury; Matthias Haeni; Jefferson Hall; Jie-Kun He; Andreas Hemp; José Hernández-Stefanoni; Steven Higgins; Robert Holdaway; Kiramat Hussain; Lindsay Hutley; Tomoaki Ichie; Yoshiko Iida; Hai-sheng Jiang; Puspa Raj Joshi; Hasan Kaboli; Maryam Kazempour-Larsary; Tanaka Kenzo; Brian Kloeppel; Takashi Kohyama; Suwash Kunwar; Shem Kuyah; Jakub Kvasnica; Siliang Lin; Emily Lines; Hongyan Liu; Craig Lorimer; Jean-Joël Loumeto; Yadvinder Malhi; Peter Marshall; Eskil Mattsson; Radim Matula; Jorge Meave; Sylvanus Mensah; Xiangcheng Mi; Stéphane Momo; Glenn Moncrieff; Francisco Mora; Sarath Nissanka; Kevin O'Hara; Steven Pearce; Raphaël Pelissier; Pablo Peri; Pierre Ploton; Lourens Poorter; Mohsen Javanmiri Pour; Hassan Pourbabaei; Juan Manuel Dupuy Rada; Sabina Ribeiro; Casey Ryan; Anvar Sanaei; Jennifer Sanger; Michael Schlund; Giacomo Sellan; Alexander Shenkin; Sonké, BonaventurSonké, Bonaventuree; Frank Sterck; Martin Svátek; Kentaro Takagi; Anna Trugman; Farman Ullah; Matthew Vadeboncoeur; Ahmad Valipour; Mark Vanderwel; Alejandra Vovides; Weiwei Wang; Li-Qiu Wang; Christian Wirth; Murray Woods; Wenhua Xiang; Fabiano de Aquino Ximenes; Yaozhan Xu; Toshihiro Yamada; Miguel Zavala (2022). Tallo database [Dataset]. http://doi.org/10.5281/zenodo.6637599
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    csvAvailable download formats
    Dataset updated
    Jun 15, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Tommaso Jucker; Tommaso Jucker; Fabian Fischer; Jérôme Chave; David Coomes; John Caspersen; Arshad Ali; Grace Jopaul Loubota Panzou; Ted Feldpausch; Daniel Falster; Vladimir Usoltsev; Stephen Adu-Bredu; Luciana Alves; Mohammad Aminpour; Ilondea Angoboy; Niels Anten; Cécile Antin; Yousef Askari; Rodrigo Muñoz Avilés; Narayanan Ayyappan; Patricia Balvanera; Lindsay Banin; Nicolas Barbier; John Battles; Hans Beeckman; Yannick Bocko; Ben Bond-Lamberty; Frans Bongers; Samuel Bowers; Thomas Brade; Michiel van Breugel; Arthur Chantrain; Rajeev Chaudhary; Jingyu Dai; Michele Dalponte; Kangbéni Dimobe; Jean-Christophe Domec; Jean-Louis Doucet; Remko Duursma; Moisés Enríquez; Karin van Ewijk; William Farfán-Rios; Adeline Fayolle; Eric Forni; David Forrester; Hammad Gilani; John Godlee; Sylvie Gourlet-Fleury; Matthias Haeni; Jefferson Hall; Jie-Kun He; Andreas Hemp; José Hernández-Stefanoni; Steven Higgins; Robert Holdaway; Kiramat Hussain; Lindsay Hutley; Tomoaki Ichie; Yoshiko Iida; Hai-sheng Jiang; Puspa Raj Joshi; Hasan Kaboli; Maryam Kazempour-Larsary; Tanaka Kenzo; Brian Kloeppel; Takashi Kohyama; Suwash Kunwar; Shem Kuyah; Jakub Kvasnica; Siliang Lin; Emily Lines; Hongyan Liu; Craig Lorimer; Jean-Joël Loumeto; Yadvinder Malhi; Peter Marshall; Eskil Mattsson; Radim Matula; Jorge Meave; Sylvanus Mensah; Xiangcheng Mi; Stéphane Momo; Glenn Moncrieff; Francisco Mora; Sarath Nissanka; Kevin O'Hara; Steven Pearce; Raphaël Pelissier; Pablo Peri; Pierre Ploton; Lourens Poorter; Mohsen Javanmiri Pour; Hassan Pourbabaei; Juan Manuel Dupuy Rada; Sabina Ribeiro; Casey Ryan; Anvar Sanaei; Jennifer Sanger; Michael Schlund; Giacomo Sellan; Alexander Shenkin; Sonké, BonaventurSonké, Bonaventuree; Frank Sterck; Martin Svátek; Kentaro Takagi; Anna Trugman; Farman Ullah; Matthew Vadeboncoeur; Ahmad Valipour; Mark Vanderwel; Alejandra Vovides; Weiwei Wang; Li-Qiu Wang; Christian Wirth; Murray Woods; Wenhua Xiang; Fabiano de Aquino Ximenes; Yaozhan Xu; Toshihiro Yamada; Miguel Zavala; Fabian Fischer; Jérôme Chave; David Coomes; John Caspersen; Arshad Ali; Grace Jopaul Loubota Panzou; Ted Feldpausch; Daniel Falster; Vladimir Usoltsev; Stephen Adu-Bredu; Luciana Alves; Mohammad Aminpour; Ilondea Angoboy; Niels Anten; Cécile Antin; Yousef Askari; Rodrigo Muñoz Avilés; Narayanan Ayyappan; Patricia Balvanera; Lindsay Banin; Nicolas Barbier; John Battles; Hans Beeckman; Yannick Bocko; Ben Bond-Lamberty; Frans Bongers; Samuel Bowers; Thomas Brade; Michiel van Breugel; Arthur Chantrain; Rajeev Chaudhary; Jingyu Dai; Michele Dalponte; Kangbéni Dimobe; Jean-Christophe Domec; Jean-Louis Doucet; Remko Duursma; Moisés Enríquez; Karin van Ewijk; William Farfán-Rios; Adeline Fayolle; Eric Forni; David Forrester; Hammad Gilani; John Godlee; Sylvie Gourlet-Fleury; Matthias Haeni; Jefferson Hall; Jie-Kun He; Andreas Hemp; José Hernández-Stefanoni; Steven Higgins; Robert Holdaway; Kiramat Hussain; Lindsay Hutley; Tomoaki Ichie; Yoshiko Iida; Hai-sheng Jiang; Puspa Raj Joshi; Hasan Kaboli; Maryam Kazempour-Larsary; Tanaka Kenzo; Brian Kloeppel; Takashi Kohyama; Suwash Kunwar; Shem Kuyah; Jakub Kvasnica; Siliang Lin; Emily Lines; Hongyan Liu; Craig Lorimer; Jean-Joël Loumeto; Yadvinder Malhi; Peter Marshall; Eskil Mattsson; Radim Matula; Jorge Meave; Sylvanus Mensah; Xiangcheng Mi; Stéphane Momo; Glenn Moncrieff; Francisco Mora; Sarath Nissanka; Kevin O'Hara; Steven Pearce; Raphaël Pelissier; Pablo Peri; Pierre Ploton; Lourens Poorter; Mohsen Javanmiri Pour; Hassan Pourbabaei; Juan Manuel Dupuy Rada; Sabina Ribeiro; Casey Ryan; Anvar Sanaei; Jennifer Sanger; Michael Schlund; Giacomo Sellan; Alexander Shenkin; Sonké, BonaventurSonké, Bonaventuree; Frank Sterck; Martin Svátek; Kentaro Takagi; Anna Trugman; Farman Ullah; Matthew Vadeboncoeur; Ahmad Valipour; Mark Vanderwel; Alejandra Vovides; Weiwei Wang; Li-Qiu Wang; Christian Wirth; Murray Woods; Wenhua Xiang; Fabiano de Aquino Ximenes; Yaozhan Xu; Toshihiro Yamada; Miguel Zavala
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The Tallo database (v1.0.0) is a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. Data were compiled from 61,856 globally distributed sites and include measurements for 5,163 tree species.

    For a full description of the database, see: Jucker et al. (2022) Tallo a global tree allometry and crown architecture database. Global Change Biology, https://doi.org/10.1111/gcb.16302. If using the Tallo database in your work please cite the original publication listed above, as well as this repository using the corresponding DOI (10.5281/zenodo.6637599).

  8. Tree Trait Task Force (3TF) - Tree Functional Trait Database

    • figshare.com
    xlsx
    Updated Jul 18, 2023
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    Michael Belluau; Élise Bouchard; Mégane Déziel; Orane Mordacq; Christian Messier; Alain Paquette (2023). Tree Trait Task Force (3TF) - Tree Functional Trait Database [Dataset]. http://doi.org/10.6084/m9.figshare.14039504.v4
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jul 18, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Michael Belluau; Élise Bouchard; Mégane Déziel; Orane Mordacq; Christian Messier; Alain Paquette
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tree Trait Task Force (3TF) - Tree Functional Trait Database

    Corresponding author : belluaumichael@gmail.com paquette.alain@uqam.ca messier.christian@uqam.ca

    Our dataset was originally created to offer scientists and the public a “ready-to-use” functional trait database for trees. This dataset is the first step within a larger project to create a standardized tree functional trait database for applied projects. We aim to offer a clean and uniform database of traits with clear selection criteria for its retained values : that the trait value is original and can be traced back to a single publication (duplicates are removed); that the trait was measured on trees growing in their natural environment (not experimental); that measurement units are correct; that all remaining possible outliers are verified manually by going back to the original publication for confirmation.

    Full dataset description will follow soon

  9. T

    Providence Tree Inventory

    • data.providenceri.gov
    • tylertech.com
    • +3more
    application/rdfxml +5
    Updated Mar 9, 2016
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    Forestry Division (2016). Providence Tree Inventory [Dataset]. https://data.providenceri.gov/Neighborhoods/Providence-Tree-Inventory/uv9w-h8i4
    Explore at:
    application/rssxml, csv, json, application/rdfxml, xml, tsvAvailable download formats
    Dataset updated
    Mar 9, 2016
    Dataset authored and provided by
    Forestry Division
    Description

    Last updated 3/9/2016. In 2006, a complete inventory of all the City’s street trees, including trees located within sidewalks, between sidewalks and curbs, or within 6 feet of the street if no sidewalk existed was conducted. One hundred volunteers were trained to record address, location, tree species, tree diameter, condition, and other related information. Trees located in parks and other public property were not included. Approximately 25,000 street trees were counted and the data was loaded into a tree database that the Forestry Division uses daily to manage the trees, track tree work, and record constituent concerns.

  10. d

    Providence Tree Dataset

    • catalog.data.gov
    • data.providenceri.gov
    • +1more
    Updated Jun 21, 2025
    + more versions
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    data.providenceri.gov (2025). Providence Tree Dataset [Dataset]. https://catalog.data.gov/dataset/providence-tree-dataset
    Explore at:
    Dataset updated
    Jun 21, 2025
    Dataset provided by
    data.providenceri.gov
    Description

    In 2006, a complete inventory of all the City’s street trees, including trees located within sidewalks, between sidewalks and curbs, or within 6 feet of the street if no sidewalk existed was conducted. One hundred volunteers were trained to record address, location, tree species, tree diameter, condition, and other related information. Trees located in parks and other public property were not included. Approximately 25,000 street trees were counted and the data was loaded into a tree database that the Forestry Division uses daily to manage the trees, track tree work, and record constituent concerns.

  11. v

    Tree Species (Parks trees database)

    • opendata.victoria.ca
    Updated Feb 5, 2019
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    City of Victoria (2019). Tree Species (Parks trees database) [Dataset]. https://opendata.victoria.ca/datasets/36e90771770542baaa89afddce69195a
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    Dataset updated
    Feb 5, 2019
    Dataset authored and provided by
    City of Victoria
    License

    http://opendata.victoria.ca/pages/open-data-licencehttp://opendata.victoria.ca/pages/open-data-licence

    Area covered
    Description

    Tree Species information from the Parks Department. Data are updated by city staff as needed, and copied to VicMap and the Open Data Portal on a weekly basis. Parks Department tree species data are collected by GPS location. For surveyed trees maintained by the Engineering Department, please see the Surveyed Trees layer.Diameter at Breast Height (DBH) is in centimetres. Tree Canopy Height and Width are in metres.The "Last Updated" date shown on our Open Data Portal refers to the last time the data schema was modified in the portal, or any changes were made to this description. We update our data through weekly scripts which does not trigger the "last updated" date to change.Note: Attributes represent each field in a dataset, and some fields will contain information such as ID numbers. As a result some visualizations on the tabs on our Open Data page will not be relevant.

  12. TreeGOER: Tree Globally Observed Environmental Ranges

    • zenodo.org
    bin, txt
    Updated Aug 21, 2023
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    Roeland Kindt; Roeland Kindt (2023). TreeGOER: Tree Globally Observed Environmental Ranges [Dataset]. http://doi.org/10.5281/zenodo.8052331
    Explore at:
    txt, binAvailable download formats
    Dataset updated
    Aug 21, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Roeland Kindt; Roeland Kindt
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    TreeGOER (Tree Globally Observed Environmental Ranges) is a database that documents the environmental ranges (minimum, maximum, median, mean and 5%, 25%, 75% and 95% quantiles) for 48,129 tree species and for 51 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. These ranges were calculated after cleaning occurrence records and standardizing species names with the WorldFlora R package to World Flora Online or the World Checklist of Vascular Plants for a global GBIF occurrence download of 44,267,164 occurrences (GBIF.org 2021 GBIF Occurrence Download https://doi.org/10.15468/dl.77gcvq). The 5% and 95% quantiles were calculated separately for two methods of outlier detection and for the full data set. The process of compilation of TreeGOER with 30 arc-seconds global grid layers, two examples of BIOCLIM applications that investigated the effects of climate change on global tree diversity patterns and R scripts to repeat these analyses have been described by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.

    TreeGOER can be used in combination with the CitiesGOER database (https://doi.org/10.5281/zenodo.8175429) that documents the conditions for the same environmental variables (except elevation) for 52,602 cities with a human population ≥ 5000. TreeGOER could also be used with the TreeGOER Global Zones atlas that can be obtained from https://doi.org/10.5281/zenodo.8252756. This high resolution atlas includes sheets with global zones for the Climatic Moisture Index (CMI) and the number of months with average temperature > 10 degrees C (Tmo10); these are zones for which presence of the 48,129 species was documented by TreeGOER.

    Changes between different versions of the databases are documented in a specific sheet in the metadata file.

    The development of TreeGOER was 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, and by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project. When using TreeGOER in your work, cite the publication (Kindt 2023) as well as this repository using the DOI (https://doi.org/10.5281/zenodo.7922927).

  13. TreeAI Global Initiative - Advancing tree species identification from aerial...

    • zenodo.org
    Updated Mar 8, 2025
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    Mirela Beloiu Schwenke; Mirela Beloiu Schwenke; Zhongyu Xia; Arthur Gessler; Arthur Gessler; Teja Kattenborn; Teja Kattenborn; Clemens Mosig; Clemens Mosig; Stefano Puliti; Stefano Puliti; Lars Waser; Lars Waser; Nataliia Rehush; Nataliia Rehush; Yan Cheng; Yan Cheng; Liang Xinliang; Verena C. Griess; Verena C. Griess; Martin Mokroš; Martin Mokroš; Zhongyu Xia; Liang Xinliang (2025). TreeAI Global Initiative - Advancing tree species identification from aerial images with deep learning [Dataset]. http://doi.org/10.5281/zenodo.14888706
    Explore at:
    Dataset updated
    Mar 8, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Mirela Beloiu Schwenke; Mirela Beloiu Schwenke; Zhongyu Xia; Arthur Gessler; Arthur Gessler; Teja Kattenborn; Teja Kattenborn; Clemens Mosig; Clemens Mosig; Stefano Puliti; Stefano Puliti; Lars Waser; Lars Waser; Nataliia Rehush; Nataliia Rehush; Yan Cheng; Yan Cheng; Liang Xinliang; Verena C. Griess; Verena C. Griess; Martin Mokroš; Martin Mokroš; Zhongyu Xia; Liang Xinliang
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    TreeAI - Advancing Tree Species Identification from Aerial Images with Deep Learning

    Data Structure for the TreeAI Database Used in the TreeAI4Species Competition

    The data are in the COCO format, each folder contains training and validation subfolders with images and labels with the tree species ID.
    Training: Images (.png) and Labels (.txt)
    Validation: Images (.png) and Labels (.txt)
    Images: RGB bands, 8-bit, chip size 640 x 640 pixels = 32 x 32 m, 5 cm pixel spatial resolution.
    Labels: labels are prepared for object detection tasks, the number of classes varies per dataset, e.g. dataset 12_RGB_all_L has 53 classes, and the Latin name of the species is given for each class ID in the file named classDatasetName.xlsx.
    Species class: classDatasetName.xlsx contains 3 columns Species_ID, Labels (number of labels), and Species_Class (Latin name of the species).
    Masked images: The data set with partial labels was masked, i.e. a buffer of 30 pixels was created around a label, and the image was masked based on these buffers, e.g. 34_RGB_all_L_PascalVoc_640Mask.
    Additional filters to clean up the data:
    Labels at the edge: only images with labels at the edge were removed.
    Valid labels: images with labels that were completely within an image have been retained.
    Table 1. Description of the datasets included in the TreeAI database.

    a) Fully labeled images (i.e. the image has all the trees delineated and each polygon has species information)

    b) Partially labeled images (i.e. the image has only some trees delineated, and each polygon has species information)

    No.Dataset nameTraining imagesValidation imagesFully labeledPartially labeled
    112_RGB5cm_FullyLabeled1066304x
    2ObjectDetection_TreeSpecies42284x
    334_RGB_all_L_PascalVoc_640Mask951272 x
    434_RGB_PartiallyLabeled640917262 x
    Steps to access the dataset and participate in the TreeAI4Species competition:
    • Register: Access to the data will be granted upon registering for the competition, see the registration form: https://form.ethz.ch/research/tree-ai-global-database/treeai-competition.html
    • Request the dataset: Download the competition record after registration by requesting it. Enter your full name, purpose e.g. accept the TreeAI4Species data license, affiliation, and the country of affiliation in the request. This allows us to check whether you are already registered.
    • Test dataset: Only the participants registered for the competition will receive the test dataset.
    • Submit your DL models for evaluation by June 2025.
    • Award: The best models win a prize.
    • Publication: All participants in the competition who submit the required files for evaluation will be included in the subsequent publication.

    License

    == CC BY-NC-ND (Attribution-NonCommercial-NoDerivatives) ==
    Dear user,
    DATA ANALYSIS AND PUBLICATION
    The TreeAI database is released under a variant of the CC BY-NC-ND license. This database is confidential and can be used only for the TreeAI4Species data science competition. It is not permitted to pass on the data or the characteristics directly derived from it to third parties. Written consent from the data supplier is required for use for any other purpose.
    LIABILITY
    The data are based on the current state of existing scientific knowledge. However, there is no liability for the completeness. This is the first version of the database, and we plan to improve the tree annotations and include new tree species. Therefore, another version will be released in the future.
    The data can only be used for the purpose described by the user when requesting the data.
    ------------------------------------------------------
    ETH Zürich
    Dr. Mirela Beloiu Schwenke
    Institute of Terrestrial Ecosystems
    Department of Environmental Systems Science, CHN K75
    Universitätstrasse 16, 8092 Zürich, Schweiz
    mirela.beloiu@usys.ethz.ch

  14. d

    Tree families database

    • dknet.org
    • scicrunch.org
    • +2more
    Updated Oct 16, 2019
    + more versions
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    (2019). Tree families database [Dataset]. http://identifiers.org/RRID:SCR_013401
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    Dataset updated
    Oct 16, 2019
    Description

    A database of phylogenetic trees of animal genes. It aims at developing a curated resource that gives reliable information about ortholog and paralog assignments, and evolutionary history of various gene families. TreeFam defines a gene family as a group of genes that evolved after the speciation of single-metazoan animals. It also tries to include outgroup genes like yeast (S. cerevisiae and S. pombe) and plant (A. thaliana) to reveal these distant members.TreeFam is also an ortholog database. Unlike other pairwise alignment based ones, TreeFam infers orthologs by means of gene trees. It fits a gene tree into the universal species tree and finds historical duplications, speciations and losses events. TreeFam uses this information to evaluate tree building, guide manual curation, and infer complex ortholog and paralog relations.The basic elements of TreeFam are gene families that can be divided into two parts: TreeFam-A and TreeFam-B families. TreeFam-B families are automatically created. They might contain errors given complex phylogenies. TreeFam-A families are manually curated from TreeFam-B ones. Family names and node names are assigned at the same time. The ultimate goal of TreeFam is to present a curated resource for all the families. phylogenetic tree, animal, vertebrate, invertebrate, gene, ortholog, paralog, evolutionary history, gene families, single-metazoan animals, outgroup genes like yeast (S. cerevisiae and S. pombe), plant (A. thaliana), historical duplications, speciations, losses, Human, Genome, comparative genomics

  15. Data from: Plot and Trees Database of TROPIS

    • data.cifor.org
    Updated Sep 29, 2020
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    Center for International Forestry Research (CIFOR) (2020). Plot and Trees Database of TROPIS [Dataset]. http://doi.org/10.17528/CIFOR/DATA.00246
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    doc(0), application/msaccess(0)Available download formats
    Dataset updated
    Sep 29, 2020
    Dataset provided by
    Center for International Forestry Researchhttp://www.cifor.org/
    License
    Dataset funded by
    -
    Description

    TROPIS, the Tree Growth and Permanent Plot Information System, contains five elements: (1) a network of people willing to share permanent plot data and tree growth information, serviced by newsletters and information sources hosted at http://www.cgnet.org/cifor/research/tropis.html (or available from CIFOR), (2) an index of people and institutions holding permanent plot data, (3) a database management system to assist more efficient data management, (4) a system to facilitate site -matching by identifying comparable sites and allowing foreign data to be used when no local growth information exists, and (5) an inference system to allow growth estimates to be made in the absence of empirical data. The index or metadatabase contains references to 12,000 plots with 3,000 species provided by 100 contributors. Searches of the database are welcomed, and may be directed to the author.

  16. d

    A dataset of 5 million city trees from 63 US cities: species, location,...

    • datadryad.org
    • data.niaid.nih.gov
    • +1more
    zip
    Updated Jun 24, 2022
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    Dakota McCoy; Benjamin Goulet-Scott; Weilin Meng; Bulent Atahan; Hana Kiros; Misako Nishino; John Kartesz (2022). A dataset of 5 million city trees from 63 US cities: species, location, nativity status, health, and more. [Dataset]. http://doi.org/10.5061/dryad.2jm63xsrf
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    zipAvailable download formats
    Dataset updated
    Jun 24, 2022
    Dataset provided by
    Dryad
    Authors
    Dakota McCoy; Benjamin Goulet-Scott; Weilin Meng; Bulent Atahan; Hana Kiros; Misako Nishino; John Kartesz
    Time period covered
    2022
    Description

    See eLife manuscript for full details. Below, we provide a summary of how the dataset was collected and processed.

    Data Acquisition We limited our search to the 150 largest cities in the USA (by census population). To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transform...

  17. GlobalUsefulNativeTrees: useful tree species

    • zenodo.org
    bin, txt
    Updated May 18, 2024
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    Roeland Kindt; Roeland Kindt (2024). GlobalUsefulNativeTrees: useful tree species [Dataset]. http://doi.org/10.5281/zenodo.7994433
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    bin, txtAvailable download formats
    Dataset updated
    May 18, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Roeland Kindt; Roeland Kindt
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The GlobalUsefulNativeTrees database (GlobUNT; https://worldagroforestry.org/output/globalusefulnativetrees) was developed after a first step of combining native distribution data across 242 countries and territories from GlobalTreeSearch (accessed 8th May 2022; Beech et al. 2017; BGCI 2022) with information on ten categories of human usage documented in the World Checklist of Useful Plant Species (WCUPS; Diazgranados et al. 2020). GlobUNT was described in more detail in the following publication: Kindt et al. (2023) GlobalUsefulNativeTrees, a database of 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in restoration. Sci Rep 13, 12640. https://doi.org/10.1038/s41598-023-39552-1. Version v.2023.01 of the database includes 14,014 useful tree species, representing roughly a quarter of the known tree species (as listed by GlobalTreeSearch) and a third of the plant species from WCUPS. The data set included here provides the taxonomic names for all tree species included in GlobUNT together with details on the process of standardization via the WorldFlora package (Kindt 2020). This taxonomic standardization process was completed during the preparation of the third major release of the Agroforestry Species Switchboard (as a consequence, all species listed in GlobUNT are included among the 230,000+ species from the Switchboard).

    The development of GlobUNT was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration and 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. When using the GlobUNT species list in your work, please cite the publication (Kindt et al. (2023) provided above) as well as this repository using the DOI (https://zenodo.org/record/7994433).

  18. d

    Sidewalk Management Database - All Tree Damage (ATD)

    • catalog.data.gov
    • data.cityofnewyork.us
    Updated Jun 7, 2025
    + more versions
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    data.cityofnewyork.us (2025). Sidewalk Management Database - All Tree Damage (ATD) [Dataset]. https://catalog.data.gov/dataset/sidewalk-management-database-all-tree-damage-atd
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    Dataset updated
    Jun 7, 2025
    Dataset provided by
    data.cityofnewyork.us
    Description

    Sidewalk Management System is used to track and organize inspections, volitions and the status of New York City sidewalks. Tracks properties where all sidewalk defects on the taxlot are due to trees/tree roots (All Tree Defects - ATD) For more information please visit NYC DOT website: www.nyc.gov/sidewalks

  19. g

    Providence Tree Dataset | gimi9.com

    • gimi9.com
    + more versions
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    Providence Tree Dataset | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_providence-tree-dataset/
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    License

    Open Data Commons Attribution License (ODC-By) v1.0https://www.opendatacommons.org/licenses/by/1.0/
    License information was derived automatically

    Description

    🇺🇸 미국 English In 2006, a complete inventory of all the City’s street trees, including trees located within sidewalks, between sidewalks and curbs, or within 6 feet of the street if no sidewalk existed was conducted. One hundred volunteers were trained to record address, location, tree species, tree diameter, condition, and other related information. Trees located in parks and other public property were not included. Approximately 25,000 street trees were counted and the data was loaded into a tree database that the Forestry Division uses daily to manage the trees, track tree work, and record constituent concerns.

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    2015 Street Tree Census - Tree Data

    • data.cityofnewyork.us
    • bronx.lehman.cuny.edu
    • +4more
    application/rdfxml +5
    Updated Jun 3, 2016
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    Department of Parks and Recreation (DPR) (2016). 2015 Street Tree Census - Tree Data [Dataset]. https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/uvpi-gqnh
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    tsv, application/rssxml, csv, json, xml, application/rdfxmlAvailable download formats
    Dataset updated
    Jun 3, 2016
    Dataset authored and provided by
    Department of Parks and Recreation (DPR)
    Description

    Street tree data from the TreesCount! 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. Tree data collected includes tree species, diameter and perception of health. Accompanying blockface data is available indicating status of data collection and data release citywide.

    The 2015 tree census was the third decadal street tree census and largest citizen science initiative in NYC Parks’ history. Data collection ran from May 2015 to October 2016 and the results of the census show that there are 666,134 trees planted along NYC's streets. The data collected as part of the census represents a snapshot in time of trees under NYC Parks' jurisdiction.

    The census data formed the basis of our operational database, the Forestry Management System (ForMS) which is used daily by our foresters and other staff for inventory and asset management: https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93&Data-Collection_Data-Collection=Forestry+Management+System+%28ForMS%29

    To learn more about the data collected and managed in ForMS, please refer to this user guide: https://docs.google.com/document/d/1PVPWFi-WExkG3rvnagQDoBbqfsGzxCKNmR6n678nUeU/edit. For information on the city's current tree population, use the ForMS datasets.

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Daniel Kraus; Andreas Schuck; Peter Bebi; Markus Blaschke; Rita Bütler; Martin Flade; Wilfried Heintz; Frank Krumm; Thibault Lachat; Laurent Larrieu; Lenka Lehnerova; Martin Levin; Ulrich Mergner; Maciej Pach; Yoan Paillet; Patrick Pyttel; Tomas Rydkvist; Giovanni Santopuoli; Kristina Sever; Knut Sturm; Kris Vandekerkhove; Susanne Winter; Manfred Witz; Martin Winnock; Matteo Marcandella; Isabelle Roth; Armin Jakob; Reiner Dickele; Gerhard Hofmann; Dirk Ruis-Eckhardt; Stephan Boschen; Uwe Schölmerich; Bertram Leder; Martin Guericke; Hubert Merkel; Dagmar Löffler; Stephan Schusser; Monika Runkel; Alexandra Steinmetz; Karl Heinz Marx; Andre Mongelluzzi; Florian Wilshusen; Jean-Jacques Boutteaux; Loic Duchamp; Nicolas Dericbourg; Emmanuel Rouyer; Valeria Csikos; Ken Sweeny; Daniel Steichen; Michel Leytem; Stefan Konczal; Krzysztof Stereńczak; Marko Kazimirovic; Vladimír Šebeň; Teresa Baiges Zapater; Marieke van der Maaten-Theunissen; Andreas Pommer; Thomas Nord-Larsen; Marc Fuhr; Luc-Olivier Delebeque; Lidón Martínez Navarro; David Lasala; S.M. Waez-Mousavi; Kiomars Sefidi; Begoña Abellanas; Daniel Kraus; Andreas Schuck; Peter Bebi; Markus Blaschke; Rita Bütler; Martin Flade; Wilfried Heintz; Frank Krumm; Thibault Lachat; Laurent Larrieu; Lenka Lehnerova; Martin Levin; Ulrich Mergner; Maciej Pach; Yoan Paillet; Patrick Pyttel; Tomas Rydkvist; Giovanni Santopuoli; Kristina Sever; Knut Sturm; Kris Vandekerkhove; Susanne Winter; Manfred Witz; Martin Winnock; Matteo Marcandella; Isabelle Roth; Armin Jakob; Reiner Dickele; Gerhard Hofmann; Dirk Ruis-Eckhardt; Stephan Boschen; Uwe Schölmerich; Bertram Leder; Martin Guericke; Hubert Merkel; Dagmar Löffler; Stephan Schusser; Monika Runkel; Alexandra Steinmetz; Karl Heinz Marx; Andre Mongelluzzi; Florian Wilshusen; Jean-Jacques Boutteaux; Loic Duchamp; Nicolas Dericbourg; Emmanuel Rouyer; Valeria Csikos; Ken Sweeny; Daniel Steichen; Michel Leytem; Stefan Konczal; Krzysztof Stereńczak; Marko Kazimirovic; Vladimír Šebeň; Teresa Baiges Zapater; Marieke van der Maaten-Theunissen; Andreas Pommer; Thomas Nord-Larsen; Marc Fuhr; Luc-Olivier Delebeque; Lidón Martínez Navarro; David Lasala; S.M. Waez-Mousavi; Kiomars Sefidi; Begoña Abellanas (2022). Spatially explicit database of tree related microhabitats (TreMs) [Dataset]. http://doi.org/10.15468/ocof3v
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Spatially explicit database of tree related microhabitats (TreMs)

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3 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Aug 2, 2022
Dataset provided by
Global Biodiversity Information Facilityhttps://www.gbif.org/
Institut national de recherche pour l’agriculture, l’alimentation et l’environnement (INRAE)
Authors
Daniel Kraus; Andreas Schuck; Peter Bebi; Markus Blaschke; Rita Bütler; Martin Flade; Wilfried Heintz; Frank Krumm; Thibault Lachat; Laurent Larrieu; Lenka Lehnerova; Martin Levin; Ulrich Mergner; Maciej Pach; Yoan Paillet; Patrick Pyttel; Tomas Rydkvist; Giovanni Santopuoli; Kristina Sever; Knut Sturm; Kris Vandekerkhove; Susanne Winter; Manfred Witz; Martin Winnock; Matteo Marcandella; Isabelle Roth; Armin Jakob; Reiner Dickele; Gerhard Hofmann; Dirk Ruis-Eckhardt; Stephan Boschen; Uwe Schölmerich; Bertram Leder; Martin Guericke; Hubert Merkel; Dagmar Löffler; Stephan Schusser; Monika Runkel; Alexandra Steinmetz; Karl Heinz Marx; Andre Mongelluzzi; Florian Wilshusen; Jean-Jacques Boutteaux; Loic Duchamp; Nicolas Dericbourg; Emmanuel Rouyer; Valeria Csikos; Ken Sweeny; Daniel Steichen; Michel Leytem; Stefan Konczal; Krzysztof Stereńczak; Marko Kazimirovic; Vladimír Šebeň; Teresa Baiges Zapater; Marieke van der Maaten-Theunissen; Andreas Pommer; Thomas Nord-Larsen; Marc Fuhr; Luc-Olivier Delebeque; Lidón Martínez Navarro; David Lasala; S.M. Waez-Mousavi; Kiomars Sefidi; Begoña Abellanas; Daniel Kraus; Andreas Schuck; Peter Bebi; Markus Blaschke; Rita Bütler; Martin Flade; Wilfried Heintz; Frank Krumm; Thibault Lachat; Laurent Larrieu; Lenka Lehnerova; Martin Levin; Ulrich Mergner; Maciej Pach; Yoan Paillet; Patrick Pyttel; Tomas Rydkvist; Giovanni Santopuoli; Kristina Sever; Knut Sturm; Kris Vandekerkhove; Susanne Winter; Manfred Witz; Martin Winnock; Matteo Marcandella; Isabelle Roth; Armin Jakob; Reiner Dickele; Gerhard Hofmann; Dirk Ruis-Eckhardt; Stephan Boschen; Uwe Schölmerich; Bertram Leder; Martin Guericke; Hubert Merkel; Dagmar Löffler; Stephan Schusser; Monika Runkel; Alexandra Steinmetz; Karl Heinz Marx; Andre Mongelluzzi; Florian Wilshusen; Jean-Jacques Boutteaux; Loic Duchamp; Nicolas Dericbourg; Emmanuel Rouyer; Valeria Csikos; Ken Sweeny; Daniel Steichen; Michel Leytem; Stefan Konczal; Krzysztof Stereńczak; Marko Kazimirovic; Vladimír Šebeň; Teresa Baiges Zapater; Marieke van der Maaten-Theunissen; Andreas Pommer; Thomas Nord-Larsen; Marc Fuhr; Luc-Olivier Delebeque; Lidón Martínez Navarro; David Lasala; S.M. Waez-Mousavi; Kiomars Sefidi; Begoña Abellanas
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Mar 1, 2014 - Dec 1, 2020
Area covered
Description

‘Tree – tree’ interactions are important structuring mechanisms for forest community dynamics. Forest management takes advantage of competition effects on tree growth by removing or retaining trees to achieve management goals. Both competition and silviculture have thus a strong effect on density and distribution of tree related microhabitats (TreMs) which are key features for forest taxa at the stand scale (e.g. Bouget et al. 2013, 2014). In particular, spatially explicit data to understand patterns and mechanisms of TreM formation in forest stands are rare. To train and eventually improve decision making capacities related to the integration of biodiversity aspects into forest management to date more than 100 usually 1 ha (100 m x 100m) permanent plots were established in different forest communities of Europe. Due to their demonstration character the selection of plots was non-systematic. They do, however, cover a broad range of forest types (e.g. beech-oak, beech-fir (-spruce), oak-hornbeam, pine-spruce, etc.), altitudinal gradients (from 25 m – 1850 m) and site conditions (e.g. oligotrophic Luzulo-Fagetum or Vaccinio-Pinetum to mesotrophic Galio-Fagetum or Milio-Fagetum). For each plot the following data is collected: (1) tree location as polar coordinates (stem base map), (2) tree species, (3) forest mensuration data (dbh in [cm], tree height in [m]), (4) tree related microhabitats (TreMs) and (5) tree status (living or standing dead). In addition to the spatial dendrometric data we provide information on plot establishment, forest type, plot location (state, region, country), elevation, means for annual precipitation and temperature, and the natural forest community (Kraus et al., 2018).

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