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3,693 results for “native”
Allelopathy of Frangula Alnus to Native New England Wetland Vegetation at Harvard Forest 2008
Glossy buckthorn (Frangula alnus), an invasive shrub from Eurasia, colonizes both upland and mesic sites in New England, USA, reducing the growth and survival of native tree saplings and lowering species richness. Although a generalist, buckthorn thrives particularly well along river, pond, and wetland margins, which are traditional habitat for speckled alder (Alnus incana ssp. rugosa), a native nitrogen-fixing shrub. As buckthorn’s dense, monospecific growth is typical of allelopaths, we wondered whether it chemically suppresses the growth of alder and other indigenous shrubs. We thus propagated three native shrub species: Alnus incana ssp. rugosa, Viburnum dentatum and Spiraea latifolia, in invasive buckthorn and native dogwood (Cornus amomum) root and leaf mulch. After seven weeks, alder grown in buckthorn root demonstrated significantly smaller basal diameter than alder grown in buckthorn leaf or other mulches. Therefore, putative buckthorn allelopathy to alder likely occurs through root exudation instead of leaf litter effects. Meadowsweet grown in buckthorn mulch, in contrast, was taller, thicker, and had more numerous leaves than meadowsweet grown in native mulch. These species-specific effects point to allelopathy as a mechanism by which buckthorn changes the structure of native plant communities.
Survey of Native American Archaeological Sites in Massachusetts 12000-300 BP
The interpretation that pre-contact Native American land-use played an increasing role in landscape dynamics through the Holocene is prevalent in historical, scientific and popular literature. This exerts a strong influence on modern conservation practices especially the use of prescribed fire (Cronon 1984, Abrams 2002, Pyne 1984, Mann 2002) and yet there has never been a robust analysis of relevant archaeological and paleoecological data on the subject. This data is used in the archaeological component of a larger National Science Foundation (NSF)-funded research project intended to analyze the triggers and drivers of ecosystem dynamics. More specifically, the research aims to determine the role of human activity (fire, land clearance, horticulture) in shaping vegetation dynamics. Some of the alternative hypotheses examined in the archaeological analysis include: (1) do we see progressively intensive cultural development and increasingly intensive land use throughout the pre-Contact period?; (2) do we see cultural continuity with fairly passive responses to environmental change and minimal ecological impact of people?; or (3) is cultural adaptation environmental and/or cultural specific, with clear influence of human agency? Our collaborative ecological and social research (Duranleau 2009, Foster and Aber 2004, Chilton et al. 2010) position us to undertake such a regional synthesis as one critical element of the proposed study on ecological dynamics and regime shifts. This synthesis will allow us to consider basic ecological questions concerning interactions among climate, disturbance and human activity in ecosystem dynamics; provide a landscape and regional test of a hypothesis put forth by Munoz et al. (2010) concerning the link between environmental change and cultural development in northeastern North America; and position our archaeological community to apply new ecological perspectives to their research. The archaeological component is derived from intensive
Coastal Native American Archaeological Sites in New York and Southern New England 12000-400 BP
Information on coastal Native American archaeological sites in New York and southern New England was compiled from various state historical commissions. The data include the following characteristics: Project Area, Project Size, and Cultural Remains, which identifies the presence of either pre-Contact (PRE) or historic (HI) material, or the lack of cultural material (NCM). Site type Site Type delineates the type of pre-Contact site represented by the artifacts and features identified at a location, which ranges from a Find Spot (FS) (a tool or piece of chipping debris found on the surface or found alone), and a Short-term Site (ST) (presence of artifacts indicating temporary use of a location), to a Seasonal Site (SE) (a site containing evidence of repeated occupation over long periods of time and with features and evidence for a broad range of activities), and a Sedentary Site (SD) (evidence of year-round habitation in form of botanical, faunal, fish and shellfish remains; presence of hearths, storage pits and middens; large variety and great quantity of tool types). Time periods Time Periods include: the Paleoindian Period (P) (12,000-10,000 BP); Early Archaic (EA) (10,000-8000 BP), Middle Archaic (MA) (8000-6000 BP), Late Archaic (LA) (6000-3000 BP), Transitional Archaic (TA) (3500-2500 BP), Early Woodland (EW) (3000-2000 BP), Middle Woodland (MW) (2000-1000 BP), Late Woodland (LW) (1000-500 BP), Contact period (C) (A.D. 1500-1620), and sites of unknown temporal affiliation (U). Activities The activities represented by the artifacts recovered from the sites include: lithic tool repair (LTR) (chipping debris), lithic workshop (LTW) (chipping debris, hammerstones, cores), hunting (HU) (faunal material and/or projectile points), fishing (FI) (net weights, fish hooks, fish bone), shellfish gathering (SG) (presence of shell), plant gathering (PG) (presence of charred seeds and/or nuts), food processing (FP) (bifaces, scrapers, knives, blades, and other lithic tools, f
Native and invasive species abundance distributions in lakes at North Temperate Lakes LTER 1979-2010
These data were compiled from multiple sources. We collated data on the abundance or density of aquatic invasive and native species sampled in more than 20 sites using the same methods. To control for sampling methodology and allow comparisons among native and invasive species, we only included data where both invasive and native species from a taxonomic group were sampled using the same methods across multiple sites. Exceptions were made to include rusty crayfish (Orconectes rusticus) in its native range and zebra mussel (Dreissena polymorpha) data.
Occurrence cubes for non-native taxa in Belgium and Europe
<p>This package contains aggregated occurrence data ("occurrence cubes") for non-native taxa in Belgium and Europe. These occurrence cubes were generated by grouping species occurrence data from the <a href="https://www.gbif.org/">Global Biodiversity Information Facility (GBIF)</a> by year (year), 1x1km spatial <a href="https://www.eea.europa.eu/en/datahub/datahubitem-view/3c362237-daa4-45e2-8c16-aaadfb1a003b">EEA reference grid</a> cell (eea_cell_code) and taxon (taxonKey or classKey). For each grouping, the number of occurrences found in GBIF (n) and the minimum <a href="http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters">coordinateUncertaintyInMeters</a> (min_coord_uncertainty) are provided. The provided coordinateUncertaintyInMeters of an occurrence is taken into account when assigning it to a grid cell (see <a href="https://github.com/trias-project/occ-cube-alien/blob/20201201/src/europe/2_assign_grid.Rmd#L463-L481">this code</a>). The occurrence cubes have been used as input data for indicators and risk modelling/mapping for the <a href="http://trias-project.be/">Tracking Invasive Alien Species (TrIAS)</a> project and are now used for monitoring the effectiveness of the early detection and rapid eradication of emerging Invasive Alien Species (IAS) for the <a href="https://www.riparias.be/">LIFE RIPARIAS</a> project.</p> <p>The occurrence cubes are built on open science principles and intended to be completely reproducible:</p> <ul> <li>The input data are publicly available on GBIF, with the download DOIs listed in the related identifiers of this package.</li> <li>The code to process the data to cubes is publicly available on GitHub at <a href="https://github.com/trias-project/occ-cube-alien">https://github.com/trias-project/occ-cube-alien</a> (version <a href="https://github.com/trias-project/occ-cube-alien/releases/tag/20240118">20240118</a>).</li> </ul> <h2>Files</h2> <ul> <li><strong>be_alientaxa_cube.csv</strong>: occurrence cube of alien taxa listed by the Global Register of Introduced and Invasive Species - Belgium (Desmet et al. 2019) (GRIIS) and limited to occurrences in Belgium (country=BE).</li> <li><strong>be_alientaxa_info.csv</strong>: taxonomic information for taxa in be_alientaxa_cube.csv.</li> <li><strong>be_classes_cube.csv</strong>: occurrence cube of all <a href="http://rs.tdwg.org/dwc/terms/class">classes</a> found in Belgium (country=BE), used to assess sampling effort bias in be_alientaxa_cube.csv.</li> <li><strong>eu_modellingtaxa_cube.csv</strong>: occurrence cube of <a href="https://github.com/trias-project/occ-cube-alien/blob/2ada0ded33c034946380b02a28cb9a8d2884d54a/references/modelling_species.tsv">selected modelling species</a> in Europe (bounding box).</li> <li><strong>eu_modellingtaxa_info.csv</strong>: taxonomic information for taxa in eu_modellingtaxa_cube.csv.</li> </ul> <h2>Acknowledgements</h2> <p>This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS), the European Union's LIFE program (LIFE19 NAT/BE/000953 - LIFE RIPARIAS) and the European Union's Horizon Europe Research and Innovation Programme (ID No 101059592 - Biodiversity Building Blocks for Policy).</p>
Benchmarking on Microservices Configurations and the Impact on the Performance in Cloud Native Environments
<p><strong>The peer reviewed publication for this dataset has been published in LCN 2022, 47th Annual IEEE Conference on Local Computer Networks. Please cite this paper when referring to the dataset: https://www.eurecom.fr/publication/6971.</strong></p> <p>Cloud-native and containerization have changed the way to develop and deploy applications. Cloud-native rethinks the application architecture by embracing a microservice approach, where each microservice is packaged into containers to run in a centralized or an edge cloud. When deploying the container running the micro-service, the tenant has to specify the needed computing resources to run their workload in terms of the amount of CPU and memory limit. However, it is not straightforward for a tenant to know in advance the computing amount that allows running the microservice optimally. This will have an impact not only on the service performances but also on the infrastructure provider, particularly if the resource overprovisioning approach is used. To overcome this issue, we conduct an experimental study aiming to detect if a tenant's configuration allows running its service optimally. We run several experiments on a cloud-native platform, using different types of applications under different resource configurations. The obtained results are presented in the accepted IEEE LCN paper (https://www.eurecom.fr/publication/6971) and are shared in this dataset.</p> <p>The datasets are collected for 3 types of applications: Web servers written in python and Golang, RabbitMQ data broker and the OpenAirInterface 5G Core network function AMF (Access and Mobility Management Function).</p> <p><br> </p> <p><strong>Web Servers:</strong></p> <p><strong>files: </strong>golang-web-server-performance.csv, python-web-server-performance.csv</p> <p>We used Golang and Python-based web servers for the test. Each request to the web server returns a video of a size 43 MB. For testing we used ApacheBench, a command-line program used for benchmarking HTTP web servers. ApacheBench allows parallel requests from multiple clients. For each web server instance we send a number of requests ranging from 100 to 1000 and a concurrency level between 1 and 100, representing the number of parallel clients performing the requests.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of requests sent to the container.</p> <p><strong>c:</strong> the concurrency level in the requests.</p> <p><strong>lat50:</strong> the least response time for the best 50% requests in microseconds.</p> <p><strong>lat66:</strong> the least response time for the best 66% requests in microseconds.</p> <p><strong>lat75:</strong> the least response time for the best 75% requests in microseconds.</p> <p><strong>lat80:</strong> the least response time for the best 80% requests in microseconds.</p> <p><strong>lat90:</strong> the least response time for the best 90% requests in microseconds.</p> <p><strong>lat95:</strong> the least response time for the best 95% requests in microseconds.</p> <p><strong>lat98:</strong> the least response time for the best 98% requests in microseconds.</p> <p><strong>lat99:</strong> the least response time for the best 99% requests in microseconds.</p> <p><strong>lat100:</strong> the least response time in microseconds.</p> <p> </p> <p><strong>5G Core network’s AMF:</strong></p> <p><strong>file: </strong>amf-performance.csv</p> <p>For testing we use my5G-RANTester, a tool for emulating control and data planes of the UE and gNB (5G base station). The number of simultaneous registration requests that are sent to each instance of the AMF varies between 10 and 400.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of parallel registration requests sent to the AMF.</p> <p><strong>mean:</strong> the mean registration time for all the registration requests in microseconds.</p> <p><strong>lat50:</strong> the median registration time for registration requests in microseconds.</p> <p><strong>lat75: </strong>the least registration time for the best 75% registration requests in microseconds.</p> <p><strong>lat80:</strong> the least registration time for the best 80% registration requests in microseconds.</p> <p><strong>lat90:</strong> the least registration time for the best 90% registration requests in microseconds.</p> <p><strong>lat95:</strong> the least registration time for the best 95% registration requests in microseconds.</p> <p><strong>lat98:</strong> the least registration time for the best 98% registration requests in microseconds.</p> <p><strong>lat99:</strong> the least registration time for the best 99% registration requests in microseconds.</p> <p><strong>lat100:</strong> the least registration time in microseconds.</p> <p> </p> <p><strong>RabbitMQ data broker:</strong></p> <p><strong>file: </strong>rabbitmq-performance.csv</p> <p>For testing we used RabbitMQ PerfTest which is a throughput testing tool that simulates basic workloads and provides the throughput and the time that a message takes to be consumed by a consumer. For each deployed RabbitMQ server we used a number of producers and consumers that ranges from 50 to 500. Each producer sends messages to the broker with a rate of 100 messages per second for a period of time of 90 seconds.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of producers sending messages to the RabbitMQ server.</p> <p><strong>Min:</strong> the minimum consumption time for the producer messages.</p> <p><strong>lat50:</strong> the median consumption time for the producer messages.</p> <p><strong>lat75:</strong> the least consumption time for the best 75% messages in microseconds.</p> <p><strong>lat95:</strong> the least consumption time for the best 95% messages in microseconds.</p> <p><strong>lat99:</strong> the least consumption time for the best 99% messages in microseconds.</p>
International Non-native Insect Establishment Data
<p><span>These data list individual non-native insect species established in nine regions around the globe (New Zealand, South Korea, Japan, Okinawa, Ogasawara, Europe, Great Britain, North America (north of Mexico), Hawaii, Galapagos, Chile and South Africa). Taxonomy, attributes and occurrences for each taxa are included, as well as a source table which can be used to find more details on occurrences. </span></p> <p><span>This dataset was assembled from various sources by an interdisciplinary scientific working group funded by the National Socio-Environmental Synthesis Center. See the main source references in the References metadata section for this publication.</span></p> <p><span>Data have been cleaned of most typographic and taxonomic errors using the code in the R package insectcleanr: Initial release (DOI: 10.5281/zenodo.4555787), which is based on the Global Biodiversity Information Facility (GBIF) taxonomic backbone (GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2022-02-09, i.e. the </span><a href="https://doi.org/10.15468/43g7-9874"><span>https://doi.org/10.15468/43g7-9874</span></a><span> backbone).</span></p> <p><em>DISCLAIMER: This dataset is provisional. Although these data have been subjected to review and the dataset is substantially complete, the authors reserve the right to revise the data pursuant to further analysis and review. There may be remaining errors, and additions and removals of data in future updates may occur. Neither the University of Maryland, U.S. Government, Scion, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights.</em></p>
Vegetation survey (BACI and Paired-plots) from arid central Australia for impacts of buffel grass on resident native plant communities
<p>The data set accompanies the accepted paper in Ecosphere. The data set includes two experimental appraoches to assess the spread and impacts of buffel grass, Cenchrus cilairis, in the Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands of arid central Australia: a Before-After-Control-Impact (BACI) experiment over 25 years at 15 sites (surveyed in 1994-95 and 2018-19), and a spatially paired-plot (randomised-block) experiment at 18 sites (surveyed in 2018-19). Both experiments spanned two geographic regions (~ 300 km apart) and multiple vegetation communities amongst flat plains and rocky hills landforms. Each experimental design has a plant species data set, and a data set that includes site variables and summed relative cover of plant functional groups. Data collection methodology is described in the accompanying paper, and summarised here.</p> <p>Each site was one hectare in size. The ecological data was collected in accordance with standard biological survey methods in South Australia (Heard and Channon 1997), including recording of plant species and cover abundance, life form, height class and habitat variables including percent bare earth, litter, rock/strew and soil type (clay percent). Fire history for the previous 25 years was also available from fire scar mapping. Species cover-abundance was estimated in the field using a modified Braun-Blanquet scale and later converted to a raw continuous variable based on the mid-point of the cover class: 1% (1-10 plants, <5% cover); 2% (sparsely present, <5% cover; 3% (plentiful but <5% cover); 15% (5 to 25% cover class); 37% (25 to 50% cover class); 63% (50 to 75% cover class). Buffel grass was recorded on the same scale. Plant species were vouchered and identification checked post-field by the South Australian Hebarium. Plant taxonomy reflects current names (as of 2015) in the Biological Databases of South Australia and taxonomy was aligned between the 1990s and 2020s decades. Recently some species have been split into multiple species (e.g. <em>Acacia aneura</em>, Mulga) but this latest taxonomy was not adopted to retain taxonomic alignment within the dataset. The raw mid-point percent cover was converted to relative percent cover by dividing each species’ (or groups’) raw cover by the summed cover of all species at that site (including buffel grass + understorey + overstorey species). Classification of plants into functional groups was based on field assessed (1) height class + (2) life form, and literature-derived (3) life strategy (perennial or annual) + (4) Native status to South Australia. Height classes were grouped into overstorey (>1m in height) and understorey (≤1m). Summed relative cover for each functional group per site is included in the site and cover data sets to facilitate modelling of cover with site variables. The plant species data sets is the full list of species and cover abundance recorded at each site which can be used for analysis of community composition, diversity, turnover or individual species change. Sensitive species (one species in this dataset) has had the coordinates denatured by 10km due according to the requirements of the Biological Database of South Australia for sensitive species. All coordinates provided in MGA 52 Eastings and Northings (UTM, Australian National Grid). </p> <p>The authors wish to acknowledge Traditional Owners and Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands Organisation who gave permission for collaboration, data collection, photographs and reporting on and about their Traditional Lands. Data is jointly the Intellectual Property of Aṉangu as the Traditional Owners and the author team, and approval has been granted for research and publication use with appropriate acknowledgment of Aṉangu and the author team. The 1990s baseline data is also the Intellectual Property of the South Australian Government and is made publicly available under a licencing agreement with the Biological Databases of South Australia (licence number 2412). Many people assisted in the field during the 1990s and 2020s vegetation surveys and are wholly acknowledged. APY Land Management, Alinytjara Wilurara Landscape Board, Central Land Council, Ten Deserts Project, Charles Darwin University, South Australian Department for Environment and Water, State Herbarium of South Australia, Holsworth Wildlife Research Endowment, Jill Landsberg Trust and Ecological Society of Australia all provided either funding and/or in-kind support of the project. Study conducted with APY Executive Board approval, South Australian Scientific Permit Q26782 and Northern Territory Wildlife Permit 63104. </p> <p> </p> <p> </p>
The Sierra Lakes Inventory Project: Non-Native fish and community composition of lakes and ponds in the Sierra Nevada, California
The Sierra Lakes Inventory Project (SLIP) was a research endeavor that ran from 1995-2002 and has supported research and management of Sierra Nevada aquatic ecosystems and their terrestrial interfaces. We described the physical characteristics of and surveyed aquatic communities for > 8,000 lentic water bodies in the southern Sierra Nevada, including lakes, ponds, marshes, and meadows. We also created digital map layers for these water bodies when such layers did not exist. The original objective of SLIP was to describe impacts of non-native fish on lake communities, but SLIP data has subsequently enabled study of additional ecological issues, including regional amphibian declines and their impacts on communities, and impacts of non-native fish on terrestrial species. In addition, these data are being used to develop fish removal efforts to restore aquatic ecosystems and recover endangered amphibians. The SLIP data is stored in a relational database that collectively describes water bodies (e.g., depth, elevation, location), surveys (conditions, effort), and communities (including approximately 170 fish, amphibian, reptile, benthic macroinvertebrate, and zooplankton taxa).
Webis Generated Native Ads 2024
<p>Version of the <a href="https://zenodo.org/records/10802427">Webis Generated Native Ads 2024</a> dataset prepared for Sub-Task 2 of the <a href="https://touche.webis.de/clef25/touche25-web/advertisement-detection.html">Advertisement in Retrieval-Augmented Generation</a> task at Touché 2025.</p> <p>The dataset contains the same data but split into JSONL-files and with separate files for responses/sentence pairs and labels.</p> <h2>Citation</h2> <pre>@InProceedings{schmidt:2024,<br> author = {Sebastian Schmidt and Ines Zelch and Janek Bevendorff and Benno Stein and Matthias Hagen and Martin Potthast},<br> booktitle = {WWW '24: Proceedings of the ACM Web Conference 2024},<br> doi = {10.1145/3589335.3651489},<br> publisher = {ACM},<br> site = {Singapore, Singapore},<br> title = {{Detecting Generated Native Ads in Conversational Search}},<br> year = 2024<br>}</pre>
Supplementary Material for "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City"
<p>This data repository is for the publication "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City" and contains all R scripts and data files to reproduce results as well as all supplementary tables and figures.</p>
Data from 'Tracability of Forest Reproductive Material with the quality label 'Plant van Hier': A DNA database with genetic profiles of native autochthonous tree and shrub species of Flanders, Belgium'
<h2>Background</h2> <p>Indigenous trees and shrubs play an important role in multifunctional forest management. They form a significant part of the biodiversity in our forests. Forest reproductive material (FRM) of autochthonous Flemish origin is sold under the quality label ‘Plant van Hier’, a certification mark of the Agency for Nature and Forests. To ensure the provenance of the seedlings, we developed a DNA-database of genetic profiles of potential parent trees, using species-specific genetic markers. This database enables the traceability of FRM of the ‘Plant van Hier’ label throughout the entire production chain; from seed harvesting and cultivation to planting by the end user.</p> <p>This database contains the genetic profiles of almost all possible parent trees present within 27 Flemish autochthonous seed orchards of eight ecologically important tree and shrub species: <em>Carpinus betulus</em>, <em>Corylus avellana</em>, <em>Frangula alnus</em>, <em>Populus tremula</em>, <em>Sorbus aucuparia</em>, <em>Tilia cordata</em>, <em>Tilia platyphyllos,</em> and <em>Ulmus laevis</em>. The profiles were established using microsatellite markers (11 to 24 markers per species). New genetic markers were developed for <em>Carpinus betulus</em> and <em>Ulmus laevis</em>. PCR products were run on an ABI 3500 Genetic Analyser (Thermo Fisher Scientific).</p> <h2>Files</h2> <p>The files will be updated when new genotypes are added to the seed orchards. The current data files contain data from genotypes collected in the period 2018-2023. </p> <h3>Species_genotypes</h3> <p>These files contain the genetic fingerprints of the parent trees of autochthonous Flemish seed orchards. Missing data is indicated as ‘MD’. For <em>Carpinus betulus</em>, an octoploid species, the allelic phenotype is given instead of the genotype as the number of times that an allele occurs on a specific locus is not known.</p> <p>The next metadata is additionally given:<br>- Species: the Latin name of the species<br>- Seed_orchard: the name of the seed orchard in which the genotypes are located<br>- Code_seed_orchard: the code of the seed orchard in which the genotypes are located as given in the Register of Flemish Forest Reproductive Material (‘Register bosbouwkundig uitgangsmateriaal’; inbo.be)<br>- Genotype: the fieldname given to the genotype<br>- Origin: the location where the genotype was collected in Flanders, Belgium. Genotypes were collected from natural stands which are assumed to have an autochthonous origin. When the specific location is unknown, the location ‘Flanders’ is given. <br>- Year_sampled: the year in which the genotypes were sampled in the respective seed orchard for genetic analysis.</p> <h3>Species_binsets</h3> <p>These files contain the binsets and allele names that are used to score the alleles of the genotypes in the programme Geneious Prime 2019.3.2 (<a href="https://www.geneious.com">https://www.geneious.com</a>). For <em>Tilia platyphyllos </em>and <em>Tilia cordata</em>, the same binsets were used.</p>
Plant Atlas 2020 — Plant native statuses for Britain, Ireland and the Channel Islands
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data behind statements concerning species’ native statuses, for various geographical levels and areas, presented in the Plant Atlas 2020 website (<a href="http://www.plantatlas2020.org"><span>www.plantatlas2020.org</span></a><span>) and book (Stroh et al., 2023).</span></p>
Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population
<h1>Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population</h1> <h1> </h1> <p>These files contain data on bacteria present in the guts of wild great tit (Parus major) obtained from faecal samples and sequenced using Illumina MiSeq. These data resulted from an experiment which provided supplementary mealworms at the nest during the breeding season at number of woodland sites in Cork, Ireland. Approximately half of these nests were given mealworms covered in a freeze dried bacterial powder containing the bacteria Lactobacillus kimchicus, which had been isolated from great tit faeces from the previous season. This treatment aimed to disrupt the gut microbiota of the treatment birds in order to provide evidence for the gut microbiotas role in birds health and fitness. Included here are the 3 elements necessary to create a 'phyloseq object' containing the sample metadata, ASV (Amplicon Sequence Variant) count table and a taxonomy table. The metadata file includes the alpha diversity scores for each individual. The data include all negative control samples taken during sample collection and library preparation, which were removed before the main analyses. All analyses, except for the beta-diversity analyses, were conducted in R. All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p> <h2> </h2> <h2>## Description of the data and file structure </h2> <p>Taxonomy, ASV and metadata files required to create a phyloseq object in R. metadata.csv file contains data on individual birds (i.e. individual samples). The metadata includes descriptions of the bird itself and it's environment, namely:</p> <ul> <li>Rownames: unique sample ID for each sample, corresponds with asvTable.csv. </li> <li>Nest: unique identifier for the nest box associated with the bird being sampled. </li> <li>Sample.ID: unique identifier for the faecal sample or control sample.</li> <li>Bird.ID: Identity of the bird the sample came from, note some individuals sampled twice so some bird.ID's may reoccur in metadata with different Sample.ID.</li> <li>Date: Date the sample was taken dd/mm/yyyy.</li> <li>Day: Date the sample was taken, in days since 1st March.</li> <li>Ring.Mark: British Trust for Ornithology (BTO) metal ring ID where applicable. Birds only ringed at D15 so some young birds do not have IDRings.</li> <li>Site: ID of woodland site that bird was sampled at.</li> <li>Chick.LetterID: ID letter differentiates between different birds from the same nest. Either 'A'-'F' for nestlings, 'Fe' for females or 'M' for males.</li> <li>Age.code: BTO age code.</li> <li>Age.category: Age category that bird is in. D8 = 8 days post hatching, D15 = 15 days post hatching, adult = 1+ years post hatching.</li> <li>Sex: Bird's sex, only determined for adult birds. Fe = Female, M = Male.</li> <li>Wing_mm: Wing length in mm.</li> <li>Tarsus_mm: minimum tarsus length of bird in mm.</li> <li>Weight_g: bird's weight in grams.</li> <li>Faecal.Sample: bird's age at sampling.</li> <li>newRing: whether bird was fitted with a new BTO ring. Only relevant to adults.</li> <li>Treatment: the experimental treatment group that the bird was in. Either 'Treatment' when nest given L. kimchicus treated mealworms or 'Control' when nest given plain mealworms.</li> <li>Notes: field notes.</li> <li>Main.sample: indicates whether this sample was the main sample to be used for analysis, an alternative sample taken as a backup.</li> <li>Plate: the ID of the PCR plate which the sample was amplified on.</li> <li>Azenta_noPeriod: sample ID given to sequencing facility without special characters. Corresponds to fastq files and ASV table counts.</li> <li>Qubit_prePool: samples qubit score before pooling.</li> <li>Date_extracted: date the sample was extracted on dd/mm/yyyy.</li> <li>SampleType: whehther the sample was a 'main' sample intended for downstream analysis, a 'control' sample for detecting contamination during library preparation, a 'duplicate' for detecting PCR issues, a 'label_error' where sample was suspected of being mislabelled at some point, a 'repeat' sample intended to detect errors or issues, a 'contam' sample which was suspected of being contaminated, a 'common' sample used across different PCR plates to detect issues. Extraction_notes: notes regarding the DNA extraction of the sample. </li> <li>LibPrep_notes: notes regarding the library preparation of the sample.</li> <li>Ring.Mark.lab: the ring or sample ID written on the sample tube, recorded to help detect mislabelling.</li> <li>Post_lab_notes: notes regarding issues found post sequencing.</li> <li>NumberOfReads: number of sequence reads associated with the sample. </li> <li>DistanceToEdge: distance between nest and woodland edge in metres. </li> <li>BroodSize.D8: number of nestlings in the nest at day-8 post hatching. </li> <li>BroodSize.D15: number of nestlings in the nest at day-15 post hatching.</li> <li>firstEggLayDate: Date the first egg in the clutch was laid, in days since 1st March.</li> <li>lastEggLayDate: Date the last egg in the clutch was laid, in days since 1st March.</li> <li>Observed: number of unique ASV's (or taxa) detected in the sample.</li> <li>Chao1: Chao1 diversity of the sample.</li> <li>Shannon: Shannon diversity of the sample.</li> </ul> <p>The file 'taxonomy.csv' contains the taxonomic breakdown of each bacterial Amplicon Sequence Variant (ASV) found in the dataset from Phylum to Species. Obtained by using the Naive Bayes Classifier against the Silva (v138) taxonomic database.</p> <p>The file 'asvTable.csv' contains counts of each amplicon sequence variant's occurrence for each individual sample. Samples are rows and taxa are columns.</p> <p> </p> <h2>Sharing/Access information </h2> <p>All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p>
Supplementary Table S27.1: Animal species native to South Africa that have invasive populations elsewhere.
<p>Animal species native to South Africa that have invasive populations elsewhere. Sorted by expected chronological appearance in the first place they were recorded as alien species. Notes are made on whether the introduction is known to be (Y) or not (N) from South Africa (or unknown U). Pathways are according to the CBD pathway classification scheme (Harrower et al. 2017), along with an indication of whether the introduction was intentional or accidental. Species that have multi-continental distributions, and which may in addition have some introduced populations are shown at the end of the table.</p>
Data from: The thermal limits of native plant species in California Coastal Sage Scrub
<p>Field and laboratory data for Goldsmith et al. (<em>In Review</em>) entitled, "The thermal limits of native plant species in California Coastal Sage Scrub." Four data files are included: </p> <p><strong><em>Goldsmithetal_PlantFunctionalTraitMetaData-18July24.xlsx </em></strong>-Provides metadata (header, description, units, measurement type, and expample) for each column of the file entitled "<em>Goldsmithetal_PlantFunctionalTraitData-18July24.csv." </em></p> <p><em><strong>Goldsmithetal_PlantFunctionalTraitData-18July24.csv </strong>- </em>Provides raw data for field and lab observations of plant functional traits as described in the methods section of this data record. </p> <p><em><strong>Goldsmithetal_PlantFvFmLabData-29March24.csv </strong>- </em>Provides raw data for experimental lab observations of leaf fv/fm following experimental heat treatments as described in the methods section of this data record. <em><br></em></p> <p><em><strong>Goldsmithetal_PlantFvFmLabMetaData-2Aug23.xlsx</strong> - </em>Provides metadata (header, description, units, measurement type, and expample) for each column of the file entitled "Goldsmithetal_PlantFvFmLabData-29March24.csv." </p> <p> </p> <p>Contact Greg Goldsmith (goldsmith at chapman dot edu) for additional information. </p>
SInAS: A global dataset of native and alien distributions of alien species
<p>The SInAS dataset represents a collection of regional lists of alien (also called non-native or non-indigenous) species and includes information about their native ranges, alien ranges, invasion status for alien ranges, habitats and year of first record. This dataset has been generated by standardising and integrating large global databases of alien species occurrences using the SInAS workflow version 2.0. </p> <p>The SInAS dataset is described in more detail in the following scientific article, which need to be cited when using this dataset:</p> <p>Gómez-Suárez, M., Laeseke, P., and Seebens, H. (submitted) A global dataset of native and alien distributions of alien species </p> <p>The code to generate the dataset is stored on Github (https://github.com/hseebens/SInAS) with releases available on Zenodo (https://doi.org/10.5281/zenodo.3763221).</p>
Maximum height for the native vegetation in Minas Gerais State, Brazil
<p>Maximum height for native vegetation of Minas Gerais State (Brazil) based on GEDI measurements and environmental factors. The environmental layers included annual average temperature, annual average precipitation, terrain elevation, slope, number of cloud free days, number of months with precipitation below 100 mm. The GEDI height records were overlapped to the environmental layers, and filtered, considered the efficiency frontier.</p>
Invasive grass litter suppresses a native grass species and promotes disease
Plant litter can alter ecosystems and promote plant invasions by altering resource availability, depositing phytotoxins, and transmitting microorganisms to living plants. Transmission of microorganisms from invasive plant litter to live plants may gain importance as invasive plants, which often escape pathogens upon introduction to a new range, acquire new pathogens over time. It is unclear, however, if invasive plant litter affects native plant communities by promoting disease. Microstegium vimineum is an invasive grass that suppresses native populations, in part through litter production, and has acquired new fungal leaf spot diseases since its introduction to the United States. In a greenhouse experiment, we evaluated how M. vimineum litter and its pathogens mediated competition with the native grass Elymus virginicus. Microstegium vimineum litter promoted disease on E. virginicus and suppressed establishment and biomass of both species. Litter had stronger negative effects on E. virginicus than M. vimineum, increasing the relative biomass of M. vimineum. Live plant competition reduced biomass of both species and live M. vimineum increased disease incidence on E. virginicus. Altogether, invasive grass litter suppressed both species, ultimately favoring the invasive species in competition, and increased disease incidence on the native species.
Native tree growth and reproduction in response to reduction in the coconut palm (Cocos nucifera) canopy at Palmyra Atoll
These data describe competition for light (open solar path) between introduced coconut palm trees (Cocos nucifera) and native tree species between 2004 and 2008 at Palmyra Atoll, Northern Line Islands, Pacific Ocean. Data are contained in one table, including values from the start, end, and intermediate samples. The dataset measures the change in tree growth (DBH and height) and reproductive potential (flower and fruit production) in relation to time and open solar path value. Two treatments are considered: OSP values less than 50% created by C. nucifera removal, and OSP values greater than 50%.
ScienceDex guides
Understand access before you commit
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.