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5,828 results for “trait”
Effect of Warming on Thermal Adaptation of Soil Microbial Growth Traits at Harvard Forest 2013-2023
Adaptation of soil microbes due to warming from climate change has been observed, but it remains unknown what microbial growth traits are adaptive to warming. We studied bacterial isolates from the Harvard Forest Long-Term Ecological Research site, where field soils have been experimentally heated to 5ºC above ambient temperature with unheated controls for thirty years. We hypothesized that Alphaproteobacteria from warmed plots have (1) less temperature sensitive growth rates; (2) higher optimum growth temperatures; and (3) higher maximum growth temperatures compared to isolates from control plots. We made high-throughput measurements of bacterial growth in liquid cultures over time and across temperatures from 22-37ºC in 2-3ºC increments. We estimated growth rates by fitting Gompertz models to the growth data. Temperature sensitivity of growth rate, optimum growth temperature, and maximum growth temperature were estimated by the Ratkowsky 1983 model and a modified Macromolecular Rate Theory (MMRT) model. To determine evidence of adaptation, we ran phylogenetic generalized least squares tests on isolates from warmed and control soils. Our results showed evidence of adaptation of higher optimum growth temperature of bacterial isolates from heated soils. However, we observed no evidence of adaptation of temperature sensitivity of growth and maximum growth temperature. Our project begins to capture the shape of the temperature response curves, but illustrates that the relationship between growth and temperature is complex and cannot be limited to a single point in the biokinetic range.
Populist attitudes and other socio-political views and psychological traits of the UK population
<p>This dataset is the result of an original survey designed by an interdisciplinary team of researchers from National Universtity of Distance Education (UNED), University of Zaragoza, University of Córdoba and University of Valencia.</p> <p>The goal of the survey was to better understand the relationship between populist attitudes and relevant socio-political and psychology items and indexes. The UK was selected as case study given the lack of similar studies in this country and the relevance of the data to better understand the political context that had been heavily impacted by the Brexit referendum and proces of separation from the European Union.</p> <p>The survey was theoretically informed and included among others:</p> <ul> <li>Populism, Elitism and Pluralism items by Akkerman et al. (2014) (14 items)</li> <li>New items design for a new Multidimensional Scale of Populist Attitudes (37 items) (Olivas Osuna 2021; Olivas Osuna et al. 2024; Olivas Osuna et al. forthcoming)</li> <li>Conspiracy Beliefs items (8 items) (Bruder et al. 2013; Brotherton et al. 2013)</li> <li>Social alienation index (6 items) (Bélanger et al. 2019)</li> <li>Justification of violence index (6 items) (Bélanger et al. 2019)</li> <li>Radicalised network (3 items)(Moyano 2011)</li> <li>Meaning in life (presence and search)(4 items)(Steger et al. 2006)</li> <li>Bordering attitudes (6 items)(Olivas Osuna et al. forthcoming)</li> <li>Endorsement for political parties</li> <li>Items reflecting level of agreement with the main slogans and arguments used by British Eurosceptics (11 items)</li> <li>Items on satisfaction with democracy and importance of democracy and with illiberal views (ESS)</li> <li>Left-right ideological self-placement</li> <li>Socio-demographic variables (age, religion, education, etc.)</li> </ul> <p>Fieldwork was conducted between 17 November and 4 December 2020. Participants were recruited following socio-demographic representativity criteria via the online platform Prolific. Survey were collected via Google Forms (Survey title: <em>Political and social views in the UK</em>).</p> <p>Files uploaded include:</p> <ul> <li>Total responses received (N=849) (.xlsx file) </li> <li>Responses analysed once participants failing attention checks were eliminated from the sample (N=748) (.csv file)</li> <li>Survery questionnair (.pdf file)</li> </ul>
Leaf Traits of Darlingtonia Californica in Oregon and California 2001
Scaling relationships among photosynthetic rate, foliar nutrient concentration, and leaf mass per unit area (LMA) have been observed for a broad range of plants. Leaf traits of the carnivorous pitcher plant Darlingtonia californica, endemic to southern Oregon and northern California, USA, differ substantially from the predictions of these general scaling relationships; net photosynthetic rates of Darlingtonia are much lower than predicted by general scaling relationships given observed foliar nitrogen (N) and phosphorus (P) concentrations and LMA. At five sites in the center of its range, leaf traits of Darlingtonia were strongly correlated with elevation and differed with soil calcium availability and bedrock type. The mean foliar N : P of 25.2 6 15.4 of Darlingtonia suggested that these plants were P-limited, although N concentration in the substrate also was extremely low and prey capture was uncommon. Foliar N : P stoichiometry and the observed deviation of Darlingtonia leaf traits from predictions of general scaling relationships permit an initial assessment of the "cost of carnivory" in this species. Carnivory in plants is thought to have evolved in response to N limitation, but for Darlingtonia, carnivory is an evolutionary last resort when both N and P are severely limiting and photosynthesis is greatly reduced.
Effects of Long-Term Soil Warming on Microbial Yield, Acquisition, and Stress Traits at Harvard Forest 2014
Soil microbial traits drive ecosystem functions. This relationship can explain why microbial functional diversity is typically positively correlated with ecosystem function. However, microbial adaptation to climate change related warming stress can shift microbial traits with direct implications for carbon cycling in the soil. Here, we investigated how long-term warming affects the relationship between microbial trait diversity and ecosystem function. Soils were sampled after 24 years of +5\degree C warming alongside unheated control soils from the Harvard Forest Long-Term Ecological Research site. Ecosystem function was estimated from six different enzyme activities and microbial biomass. This data was coupled with metatranscriptomics sequencing, where reads were assigned to yield, acquisition, or stress trait categories. We found that in organic horizon soils, warming decreased the richness of acquisition-related traits. In the mineral soils, we observed that heated soils exhibited a negative relationship with the richness of acquisition related traits. These results suggest that the microbial communities exposed to long-term warming is shifting away from a resource acquisition life history strategy.
Functional Traits of Selected Tree Species in Harvard Forest, New Hampshire, and Southern Quebec 2015
Increasing evidence suggests that species' phenological responses may predict their performance with warming, but this work has generally ignored whether phenology is correlated with other traits known to drive plant performance. This is perhaps surprising given that interest in functional traits has also increased in recent decades, yet within the functional traits literature there has been an equally limited consideration of phenology, perhaps because robustly estimating it is time-intensive, and simple field estimates will show extreme variation across sites of different latitudes and climate regimes. Here we collected a suite of trait data on the same species for which we collected phenological data (see related dataset HF314, Leaf and Flower Phenology of Woody Plant Species at Harvard Forest and Southern Quebec 2015) to help address this gap. We focused on populations of trees in temperate forests in the Northeast face, which face different environmental conditions across their ranges. This project measured functional traits of trees at two to four sites, to provide a foundation for studies on the relationship between range shift, phenology, and functional traits.
Zooplankton community composition and trait data for Green Lakes Valley, 2009 - ongoing.
Starting in 2012 zooplankton sampling at Green Lake 4 was included in the long term monitoring data set at Niwot Ridge. Immediately after the ice has completely melted from the lakes, zooplankton samples are taken once a week for six consecutive weeks at the deepest portion of the lake from an inflatable raft. Zooplankton were sampled at the deepest location of each lake by pulling a conical net (Wisconsin net) vertically through the water column (i.e., vertical tow sample). For each zooplankton sample obtained, adult organisms were identified to species, or lowest taxonomic level (Chydoridae sp. and Bosminidae sp.). Larvae of cladocerans were counted together as neonates; calanoid and cyclopoid copepodites were counted together as nauplii. Individual body lengths of the first 50 -100 (when possible) individuals of each taxon were recorded using a calibrated eyepiece micrometer and means reported.
AusTraits: a curated plant trait database for the Australian flora
<p>AusTraits is a transformative database, containing measurements on the traits of Australia's plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 300 distinct sources, describing > 500 plant traits and > 34,000 taxa.</p> <p>To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project's GitHub repository, <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Further information on the project is available at the project website <a href="https://austraits.org">austraits.org</a> and in the associated publication (see below).</p> <p><strong>CONTRIBUTORS</strong></p> <p>The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University.</p> <p>We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment Land Water and Planning Victoria and the Royal Botanic Gardens Victoria.</p> <p>AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their "Transformative data collections" (https://doi.org/10.47486/TD044) and "Data Partnerships" (https://doi.org/10.47486/DP720, https://doi.org/10.47486/DP720A) programs; and grants from the Australian Research Council (FT160100113, DE170100208, FT100100910) and Macquarie University, The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS).</p> <p><strong>ACCESSING AND USE OF DATA</strong></p> <p>The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community.</p> <p>A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors:</p> <blockquote> <p>Falster, Gallagher et al (2021) <em>AusTraits, a curated plant trait database for the Australian flora</em>. Scientific Data 8: 254, <a href="https://doi.org/10.1038/s41597-021-01006-6">https://doi.org/10.1038/s41597-021-01006-6</a></p> </blockquote> <p>In addition, we encourage users you to cite the original data sources, wherever possible.</p> <p>Note that under the license data may be redistributed, provided the attribution is maintained.</p> <p>The downloads below provide the data in two formats:</p> <ul> <li>austraits-X.X.X.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python.</li> <li>austraits-X.X.X.rds: data as compressed R object. Suitable for users of R (see below).</li> <li> <div>austraits-X.X.X-flattened.rds: contains a flattened version of the dataset for direct loading in R; all data tables are joined into a wider format</div> </li> <li> <div>austraits-X.X.X-flattened.parquet: contains a flattened version of the dataset in parquet format; all data tables are joined into a wider format </div> </li> </ul> <p>For R users, access and manipulation of data is assisted with the <a href="http://github.com/traitecoevo/austraits">austraits R package</a>. The package can both download data and provides examples and functions for running queries.<br><br><strong>STRUCTURE OF AUSTRAITS</strong></p> <p>The compiled AusTraits database contains a series of relational tables and files. These elements include all the data, contextual information submitted with each contributed datasets, database schema, and trait definitions. The file dictionary.html provides the same information in textual format. Similar information is available at <a href="https://traitecoevo.github.io/traits.build-book/">https://traitecoevo.github.io/traits.build-book/</a>.</p> <p><strong>CONTRIBUTING</strong></p> <p>We envision AusTraits as an on-going collaborative community resource that:</p> <ol> <li>Increases our collective understanding the Australian flora;</li> <li>Facilitates accumulation and sharing of trait data;</li> <li>Builds a sense of community among contributors and users; and</li> <li>Aspires to fully transparent and reproducible research of the highest standard.</li> </ol> <p>As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Here are some of the ways you can contribute:</p> <p><strong>Reporting Errors</strong>: If you notice a possible error in AusTraits, please <a href="https://github.com/traitecoevo/austraits.build/issues">post an issue on GitHub</a>.</p> <p><strong>Refining documentation:</strong> We welcome additions and edits that make using the existing data or adding new data easier for the community.</p> <p><strong>Contributing new data</strong>: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p>
Fruit, seed dispersal, and life history traits of tropical rainforest trees of the Anamalai Hills, Western Ghats, India
<p>This dataset contains compiled Fruit, seed dispersal, and life history traits of tropical rainforest trees of the Anamalai Hills, Western Ghats, India. The list of species included are mainly from the following two related publications:<br>- Muthuramkumar, S., Ayyappan, N., Parthasarathy, N., Mudappa, D., Raman, T.R.S., Selwyn, M.A. and Pragasan, L.A. (2006), <a href="https://doi.org/10.1111/j.1744-7429.2006.00118.x">Plant Community Structure in Tropical Rain Forest Fragments of the Western Ghats, India</a>. <em>Biotropica</em>, 38: 143-160. https://doi.org/10.1111/j.1744-7429.2006.00118.x<br>- Osuri, A., Chakravarthy, D., Mudappa, D., Raman, T., Ayyappan, N., Muthuramkumar, S., & Parthasarathy, N. (2017). <a href="http://httpd//doi.org/10.1017/S0266467417000219">Successional status, seed dispersal mode and overstorey species influence tree regeneration in tropical rain-forest fragments in Western Ghats, India</a>. <em>Journal of Tropical Ecology</em>, 33(4), 270-284. doi:10.1017/S0266467417000219<br>The present dataset is an expanded and updated version of the related dataset available at <a href="https://doi.org/10.5061/dryad.vd0nn">https://doi.org/10.5061/dryad.vd0nn</a><br> <br>Species traits information was collated from <a href="http://www.biotik.org/">BIOTIK (http://www.biotik.org/</a>), <a href="http://www.flowersofindia.net/">Flowers of India (http://www.flowersofindia.net/)</a>, India Biodiversity Portal (http://indiabiodiversity.org/), <a href="https://doi.org/10.5061/dryad.234/1">Global wood density database (https://doi.org/10.5061/dryad.234/1)</a> and <a href="https://doi.org/10.1017/S0266467417000219">Osuri et al. (2014): https://doi.org/10.1017/S0266467417000219</a>. We also referred to the following previous studies that provided information on the successional status of rain-forest species in the Western Ghats (Chetana 2013, Pascal 1988, Raman et al. 2009, Sreejith 2005).</p> <p><strong>References:</strong><br>CHETANA, H. C. 2013. Assessing the ecological processes in abandoned tea plantations and its implication for ecological restoration in the Western Ghats, India. PhD thesis, Manipal University.<br>OSURI, A. M., KUMAR, V. S. & SANKARAN, M. 2014. Altered stand structure and tree allometry reduce carbon storage in evergreen forest fragments in India’s Western Ghats. <em>Forest Ecology and Management </em>329: 375–383.<br>PASCAL, J. P. 1988. <em>Wet evergreen forests of the Western Ghats of India: Ecology, structure, floristic composition and succession</em>. Institut Français de Pondichéry, Pondicherry.<br>RAMAN, T. R. S., MUDAPPA, D. & KAPOOR, V. 2009. Restoring rainforest fragments: survival of mixed-native species seedlings under contrasting site conditions in the Western Ghats, India. <em>Restoration Ecology</em> 17:137–147.<br>SREEJITH, K. A. 2005. Ecological and ecophysiological studies on the successional status of tree seedlings in tropical wet evergreen and semi-evergreen forests of Kerala. PhD thesis, Forest Research Institute, Dehradun.</p> <p><strong>Geographic Coverage:</strong><br>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India<br>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><strong>Temporal Coverage:</strong><br>1. Begins: 2003-03-01 (Year, Month, Day)<br>2. Ends: 2024-02-10 (Year, Month, Day)</p> <p>Besides the <strong>README.txt</strong> file, the dataset includes the following comma-delimited text (csv) file with the data in columns as explained below:</p> <p><strong>Anamalai_tree_traits_2024.csv</strong></p> <p><strong>spec_name_ORIG:</strong> Scientific name of the species used during the data collection<br><strong>genus:</strong> Genus of the taxon<br><strong>specificEpithet:</strong> Specific epithet of the taxon in the Latin binomial name<br><strong>Accept_name_WFO:</strong> Updated scientific name of the species as in Plants of the World Online (POWO, https://powo.science.kew.org/)<br><strong>Habit:</strong> life form of the species(tree/shrub/cane/palm)<br><strong>Distribution:</strong> Distribution of the species in the study area (Native/Endemic/Introduced)<br><strong>IUCN_status:</strong> IUCN status of the species (CR-Critically Endangered,DD-Data deficient,EN-Endangered,LC-Least Concern,NT-Near Threatened,VU-Vulnerable,NA-Unknown)<br><strong>Wden_final:</strong> Wood density value assigned for the species (g cm^-3); NA - not available; sourced from Global wood density database (https://doi.org/10.5061/dryad.234/1)<br><strong>wd_level:</strong> Level in which the wood density value belongs (Species - wood density value is from species level; genus - wood density value assigned is the genus level average value)<br><strong>fruit_type:</strong> Morphological type of fruit<br><strong>fleshy_dry:</strong> Whether fruit is a dry fruit or fleshy, with aril or other parts <br><strong>seed_size:</strong> Species seed size: L = Large (>3 cm); M = Medium (1-3 cm); S = Small (<1 cm)<br><strong>disperser:</strong> Categories indicating seed dispersal mode: Bird, mammal, bird and mammal (Mammal_bird), gravity, wind, or unknown<br><strong>habitat:</strong> Habitat affinity category: EG_edg - evergreen forest edge; EG_for - evergreen forest; Dec_for - deciduous forest; Int – Introduced species; Unknown – Unknown<br><strong>habt_new:</strong> Habitat affinity new category: Mature – mature forest; Secondary – secondary forest, NA - unknown/Introduced species<br><strong>ad_ht:</strong> Species maximum adult height (m)</p>
FuTRES (Functional Trait Resource for Environmental Studies) data store archival copy - 5/21/2022
<p> </p> <p>The Functional Trait Resource for Environmental Studies (FuTRES) project is a collaborative project among four universities (University of Oregon, University of Arizona, University of Florida, and Howard University). The key deliverables of FuTRES are a workflow for assembling functional trait data measured at the specimen level, a database to serve that data, and scientific publications demonstrating the utility of the assembled data. This dataset represents the FuTRES datastore as of 5/21/2022, providing an archive that is timestamped and providing all data that is not currently embargoed by providers. The column headers for FuTRES data are: basisOfRecord,catalogNumber,class,collectionCode,country,decimalLatitude,decimalLongitude,diagnosticID,eventID,family,genus,individualID,institutionCode,lifeStage,locality,mapped_project,materialSampleID,maximumChronometricAge,maximumChronometricAgeReferenceSystem,maximumElevationInMeters,measurementMethod,measurementSide,measurementType,measurementUnit,measurementValue,minimumChronometricAge,minimumChronometricAgeReferenceSystem,minimumElevationInMeters,observationID,occurrenceID,occurrenceRemarks,order,reproductiveCondition,samplingProtocol,scientificName,sex,specificEpithet,stateProvince,verbatimElevation,verbatimEventDate,verbatimLatitude,verbatimLocality,verbatimLongitude,verbatimMeasurementUnit,yearCollected,projectID,inferred_traits. The traits available and number of records for each trait: </p> <ul> <li><a href="https://futres-data-interface.netlify.app/">length (1,790,883)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tail length (520,281)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length (456,165)</a></li> <li><a href="https://futres-data-interface.netlify.app/">pes length (413,668)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ear length to notch (397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">external ear length (397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body mass (373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">weight (373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">width (7,429)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus width (1,705)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 proximal articular breadth (783)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface width (782)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 breadth (716)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 depth (706)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus length (649)</a></li> <li><a href="https://futres-data-interface.netlify.app/">long bone length (637)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface width (605)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus trochlea breadth (596)</a></li> <li><a href="https://futres-data-interface.netlify.app/">epiphysis width (581)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus breadth (560)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus medial depth (549)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tooth row length (498)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower tooth row length (425)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal width (413)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface length (402)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 4 occlusal surface width (361)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface length (343)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur width (301)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length (297)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus width (293)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface width (261)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface length (260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis width (260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface length (242)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia length (228)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal breadth (208)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal depth (201)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface width (197)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface length (187)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface width (185)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary canine tooth to premolar tooth 3 length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface length (180)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface length (177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface width (177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface length (176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface width (176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal width (162)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 3 occlusal surface length (159)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface length (155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface width (155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis breadth (145)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface length (120)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface width (119)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis depth (111)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface width (106)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper premolar tooth 1 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface width (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal breadth (85)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface length (81)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface length (79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface width (79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface length (78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface width (78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlea breadth (76)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus medial trochlear height (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear height at sagittal crest (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear sulcus height (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal depth (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1-2 length (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper tooth row length (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">anterior tibial tuberosity length (70)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus distal depth (69)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length (67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna width (67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia medial length (63)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis breadth (57)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis depth (54)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 3 occlusal surface length (51)</a></li> <li><a href="https://futres-data-interface.netlify.app/">trochlea tali length (49)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis breadth (45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">forelimb zeugopod bone length (45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal breadth (44)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna length (42)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal breadth (40)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to caput (38)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus length (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur caput depth (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to ventral tubercle (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus proximal breadth (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis depth (36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal depth (36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur trochlea breadth (32)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal depth (31)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface width (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus lateral length (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface length (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface width (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to lateral condyle (28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from greater trochanter to medial condyle (28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length with tail (25)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 1 occlusal surface length (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 2 occlusal surface length (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna depth across the process anaconaeus (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna proximal articular breadth (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1 occlusal surface length (22)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon depth (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon length (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 2 occlusal surface length (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">breadth of calcaneal body (18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus width (18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia lateral length (14)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to medial condyle (5)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius distal width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius length (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal articular width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body height (1)</a></li> <li><a href="https://futres-data-interface.netlify.app/">height (1)</a></li> </ul>
Plasma circulating microRNA-expression quantitative trait loci (eQTLs) data in the Rotterdam Study
<p>The dataset contains GWAS summary statistics for 2,083 plasma circulating microRNAs, obtained from nearly 2,178 participants of the Rotterdam Study. The dataset includes three files, as outlined below:</p> <p><strong>File1: SNP_reference_file_maf0.01_Rsq0.7.txt</strong></p> <p>A reference file for SNPs with good imputation quality (Rsq > 0.7) and minor allele frequency > 0.01 among participants included in our GWAS in the Rotterdam Study (N=2,178). The headers are:</p> <p>SNP: rsID</p> <p>chr: chromosome number according to GRCh37</p> <p>bp: basepair position according to GRCh37</p> <p>effect_allele: effect allele</p> <p>other_allele: other allele</p> <p>eaf: effect allele frequency</p> <p><strong>File2: miReQTLs_1e-5_maf0.01_Rsq0.7.txt</strong></p> <p>Summary statistics for all SNPs significantly associated with 2083 miRNAs (p-value < 1e-5), filtered by minor allele frequency > 0.01 and Rsq > 0.7. The headers are:</p> <p>SNP: rsID</p> <p>beta: effect estimate</p> <p>se: standard error</p> <p>pval: p-value</p> <p>miRNA: miRNA ID</p> <p><strong>File3: miReQTLs_nominal_sig.csv.gz</strong></p> <p>Summary statistics for all SNPs nominally associated with 2083 miRNAs (p-value < 0.05). The headers are:</p> <p>RSID: SNP ID</p> <p>p-value: p-value</p> <p>phenotype: miRNA</p> <p>SE: standard error</p> <p>BETA: effect estimate</p> <p> </p> <p>The SNP allelic information and frequency can be found in the reference file (<strong>File1</strong>). </p> <p><br>For more information, please contact: m.ghanbari@erasmusmc.nl</p>
Life History Traits of Resprouting Puerto Rican Tropical Dry Forest Trees, Guánica Forest, 1981-2018
This dataset provides trait and demographic data for 44 tropical dry forest tree species from the Guánica State Forest in southwest Puerto Rico. The study area spans 4,500 ha of semi-deciduous TDF, where the sampled species represent over 90% of all individuals with a diameter at breast height (dbh) ≥2.5 cm. The dataset integrates ten functional traits, combining newly collected measurements (2017–2018) with previously published data (Vargas et al. 2021b). Previously published data includes xylem-specific hydraulic conductivity (ks), Huber value (hv), and hydraulic safety margin (HSM), with species-level data availability ranging from 19 to 44 species, except for HSM, which was measured for six species. Trait measurements were primarily collected during the wet season (August–November), except stomatal behaviour traits (psimax, psidv, and gsmax), which were assessed during the winter dry season before leaf fall. Demographic data encompass species-specific growth rates and annual survival rates for adult trees, derived from four permanent census plots (625 m² to 10,000 m²) distributed across the forest. These plots, established in mature upland TDF on limestone substrates with mollisol soils, were monitored between 1992 and 2019. Growth rate estimates are based on diameter increments recorded at regular censuses over 20.4–26.4 years. Survival rates were calculated over a 21-year period (1998–2019), mitigating the influence of extreme drought events. Standardised measurement protocols ensured data consistency, including repeated diameter assessments at multiple stem locations and the exclusion of wet-season measurements to prevent water-related swelling artifacts. Growth rates were derived from the regression slope of dbh against time, incorporating a minimum of two dbh measurements per individual (following Poorter et al. 2010). Annual survival rate was calculated over a 21-year timespan (1998–2019) to avoid bias introduced by an intense drought in 1997. The followin
Crown Traits of Broadleaf Deciduous Trees at NEON Forest Sites (2018-2022)
Using NEON Airborne Observation Platform (AOP) measurements collected in 2018-2022 from nine broadleaf deciduous NEON forest sites, we quantified a broad suite of structural metrics and spectral reflectance indices for 305 tree crowns that were delineated in the field by NEON and met our data quality criteria. For each tree crown, we used 1-m^3 voxelated AOP LiDAR data to compute structural metrics, including plant area index (PAI), leaf area index (LAI), top rugosity, maximum canopy height (MAXCH), mean outer canopy height (MOCH), rumple, accumulative plant area density and accumulative LiDAR intensity at multiple tree heights. We used AOP imaging spectrometer to compute several spectral indices, including NDVI, NIRv, EVI, NDWI and chlorophyll index of red edge/green. The data are suitable for ecophysiological studies at tree crown and/or species level. The broad spatial extent allows for the exploration of variability in structure and function of common north American tree species across wide environmental gradients.
The Biomass and Plant Functional Traits of Leymus chinensis Affected by Genotypic Diversity and Soil Nitrogen Addition through a Two-year Experiment, Tianjin, China, 2021-2023
In order to investigate the effects of soil nitrogen addition on the genotypic diversity of Leymus chinensis, 12 genotypes of Leymus chinensis were used as plant material and a two-factor experimental design was carried out in this study. Factor one was genotypic diversity of L. chinensis, including three levels: mono-genotype (G1), three genotypes (G3), and six genotypes (G6). Factor two was the soil nitrogen addition level, which included four levels: no nitrogen addition (N0), 2.5 g N/(m²·a) nitrogen application (N2.5), 5 g N/(m²·a) nitrogen application (N5), and 10 g N/(m²·a) nitrogen application (N10). Each treatment had 12 combinations as replicates, and 12 genotypes of L. chinensis were used. The frequency of each genotype was standardized across all treatment levels of genotypic diversity × soil nitrogen addition. The experiment commenced in September 2021 and soil nitrogen was applied every 2 months. Plants were cultivated in the experimental field at Nankai University, but were moved to a greenhouse for overwintering from November to February each year. During the experiment, there were no stresses or disturbances such as shading, drought, or insect feeding; weeds were regularly removed.
AOP01 Correspondence between plant traits and NEON Airborne Observatory Platform (AOP) data at Konza Prairie (2017)
Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, allowing high-resolution trait mapping. However, the reliability of these data depend on establishing rigorous links with in-situ field measurements. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The weakest relationships were between AOP Canopy Nitrogen and ground-based measures of Nitrogen, as well as the CHM and ground-based canopy height measurements. We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had an R2 of more than 0.25. For all vegetation traits, R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products are unreliable, at least at this grassland site, and should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although additional assessment of varying spatial scales of field and AOP data, as well as corrections and data pre-processing to improve data quality, are recommended. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area,
PGT01 Konza prairie grass species trait
Evolutionary history plays a key role driving patterns of trait variation across plant species. For scaling and modeling purposes, grass species are typically organized into C3 versus C4 plant functional types (PFTs). PFT groupings may obscure important functional differences among species. Rather, grouping grasses by evolutionary lineage may better represent grass functional diversity. We measured 11 structural and physiological traits in situ from 75 grass species within the North American tallgrass prairie. We tested whether traits differed significantly among photosynthetic pathways or lineages (tribe) in annual and perennial grass species. We hypothesized that tribe would be the best predictor of traits, more so than photosynthetic pathway. We further hypothesized that there would be substantial variation of traits in species among the seven C4 lineages represented at our site.
Functional Trait Measurements of Macroalgal Communities in the Santa Barbara Channel
This dataset contains trait and elemental composition data for macroalgal samples collected across depth gradients at multiple sites in the Santa Barbara Channel, California. Each sample represents an individual specimen characterized by morphological measurements (e.g., blade thickness, stipe diameter, total height), biomass of anatomical parts (blade, stipe, holdfast, reproductive tissue), and anchoring strength. In addition, biochemical traits—including carbon (C), nitrogen (N), and hydrogen (H) content—were measured from tissue samples analyzed in the analytical laboratory. These data support a trait-based modeling approach to macroalgal community structure and distribution, contributing to our understanding of functional diversity and ecosystem dynamics in temperate marine systems. Accompanying metadata include collection site, date, time, depth, location coordinates, and substrate type, providing context for environmental variation across samples.
Floral traits of animal-pollinated Sevilleta plant species
Concern about pollinator populations is widespread, with bees documented to be in decline due to factors including habitat loss, disease, and pesticides. In addition, climate change may be an important cause of bee population losses, but few studies have examined bee abundance relationships with climate variables. Importantly, bees may respond directly to climate or may exhibit indirect responses to climate via changes in plant phenology or community composition. This study collected floral trait data to complement the Sevilleta LTER pollinator monitoring, plant phenology, and plant biomass datasets, with the aim of examining whether floral resource availability mediates bee responses to climate. For 71 common, animal-pollinated flowering plant species, we measured floral traits relevant to pollination in June–October 2018 and April–August 2019 within sites representing four ecosystem types at the Sevilleta National Wildlife Refuge: Plains grassland, Chihuahuan Desert grassland, Chihuahuan Desert shrubland, and piñon-juniper woodland. On a minimum of 5 individuals per plant species, we recorded the total number of open flowers and the corolla width of flowers, along with plant height and vegetative cover. These data may be used in combination with the Sevilleta LTER pollinator monitoring, phenology, and biomass datasets to examine how bee and floral resource abundance, diversity, and phenology vary across years and whether these changes correspond with one another, as well as to consider relationships among climate, floral resource abundance/diversity, and bee abundance/diversity.
Consumer Front Plant Trait Sampling in Two Virginia Coast Salt Marshes, 2018
A consumer front forms when dense aggregations of herbivores form at the edge of a resource. The front then propagates through the ecosystem in search of additional resources. In U.S. Atlantic salt marshes, the purple marsh crab, Sesarma reticulatum, creates consumer fronts as it grazes the smooth cordgrass, Spartina alterniflora. Sesarma fronts typically form at the heads of tidal creeks and create distinct zonation between the low marsh, tall-form Spartina zones and the high marsh, short-form Spartina zones, with a denuded band of mudflat in between. Over time, Sesarma consumer fronts are moving directionally inland towards the short-form zone and away from the tall-form zone. This movement inland allows for tall-form Spartina to revegetate, preventing further marsh loss. However, it remains unknown why these consumer fronts are moving inland. To test the hypothesis that plant traits (i.e., nutritional quality, palatability) are driving the Sesarma consumer front inland, we collected Spartina from consumer fronts at 8 unique creekheads across two marsh systems on the Eastern Shore of Virginia (4 consumer fronts at Upper Phillips Creek and 4 at Upshur Creek). Spartina was collected from 15 replicate quadrats (0.0625m^2) from the tall-form low marsh zones (TSA) and from the short-form low marsh zones (SSA) at each creekhead. The short-form zone was delineated into two additional zones, an interior (SSA-I) and an exterior (SSA-E), to assess if there were any differences in plant traits between Spartina being actively grazed (SSA-E, adjacent to consumer front) and those that have not been grazed (SSA-I, 2 meters from consumer front). Collected Spartina plants were then processed for a series of plant traits that can influence herbivore preference.
Variant, Metabolite and Source Data for: Population genomics uncover loci for trait improvement in the indigenous African cereal tef (Eragrostis tef)
<p>These files contain the variant and metabolome for a collection of 220 tef (<em>Eragrsotis tef)</em> accessions from an ethiopian diversity panel. The accessions were assembled and managed by the Ethiopian Institute of Agricultural Research (EIAR, Ethiopia). The variant data was produced at the John Innes Centre (UK). The metabolome data was produced at Aberystwyth University (UK). These dataset are described in Jones et al. (2024), <em>bioRxiv</em>, https://doi.org/10.1101/2024.09.30.615331. The source data for main figures in the publication are also included.</p> <p>The submission contains</p> <ol> <li>EIAR_filtered.vcf.gz: This is the variant data obtained from alignment of Illumina reads from all 220 teff accessions to the reference assembly of tef (Dabbi). Low quality variants were filtered out. This variant data was used for constructing the phylogenetic relationship between the accessions. The samples names corresponds to the DNA code in Supplementary Table S10 (Jones et al, 2024).</li> <li>pooled_EIAR_filtered.vcf.gz: After the phylogentic analysis described above, reads from accessions that were found to be genetically redundant were pooled before variant calling. This file was used for the SNP GWAS analysis. The samples names corresponds to the DNA code in Supplementary Table S10 (Jones et al, 2024).</li> <li> Metabolite_Profile.xlxs (source data for Figure 5): This file contains m/z feature intensities from untargeted metabolite fingerprinting using Flow Infusion Electrospray High-resolution Mass Spectrometry (FIE-HRMS). The sample names contains a combination of Location code and Plot number in Supplementary Table S10 e.g AT plot 1, CD plot 1, DZ plot 1, where AT, CD and DZ represent Alem Tena, Chefe Donsa and Debre Zeit, respectively. The data was used for the partial least squares discriminant analysis and differentially accumulated metabolites analysis presented in Figure 5.</li> <li>Source data: Numerical source data for graphs and charts in Figures 3 - 7.</li> <li>Tsedey TT2 Sequence from Improved Assembly: The 4A and 4B sequences around the TT2 orthologue in tef from the improved PacBio-based chromosome-scale assembly of tef. These sequences were used for plotting the LTR Copia alignments presented in Supplementary Figure 9. We thank Corteva for pre-publication access to this improved Tsedey genome assembly.</li> </ol>
Improving anxiety research novel approach to reveal trait anxiety through summary measures of multiple states - raw count data set - RNAseq
<p>Raw count data of the RNAseq analysis of a project and manuscript under the title "Improving anxiety research novel approach to reveal trait anxiety through summary measures of multiple states". The header of the table includes the subject identifiers except the first column "genes". The latter column includes all assessed gene identifiers.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.