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46 results for “Plant health”
Compost Amendments up to One Inch Restore Dry Rangeland Soil Health and Plant Productivity in New Mexico, 2020-2022
Dry rangelands are important systems for coproducing food and other ecosystem services, but degradation of productivity, diversity, and water holding capacity may require active intervention to restore. Use of compost amendments on grasslands has been shown to improve many outcomes related to carbon, water, and nutrients unless excessive amounts are used, but practitioners lack guidance of optimal and cost-effective use to meet their management goals. We compared compost additions from 0-2.54 cm at two ranches in New Mexico and measured plant composition and biomass, soil characteristics such as bulk density, infiltration rate, aggregate stability, and total carbon content under baseline conditions and one- and two years after addition.
Needs of plant health laboratories and applicability of the horizontal proficiency testing approach based on the questionnaire answers
<p>Data collected in the framework of work package 5 of the Valitest project. They correspond to the needs and expectation concerning proficiency assessment expressed by plant health laboratories during a survey conducted online in 2019.</p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
Flash Poll June 2022 - Plant Health
<p>This survey by EFSA provides insights in terms of:</p> <p>• Europeans’ perception and knowledge of plant health, perceived benefits of healthy plants and perceived problems of risks with plant health; perceived concerns regarding the effects of plant pests and diseases on different areas;</p> <p>• Europeans’ awareness; acceptance; concerns re non-compliance; and knowledge of phytosanitary requirements for passengers carrying plants;</p> <p>• Europeans’ interest in plant health, concern about environmental matters, among others.</p> <p>The survey was implemented by the Teleperformance in 24 member states (i.e. all EU27 countries except Cyprus, Luxembourg, and Malta) between 20th and 24th of June 2022. A total of 8,600 respondents from different social and demographic groups completed the survey online in their mother tongue, with 300 to 500 respondents per country. These sample sizes provide robust results and ensure that responses are representative in each of the countries to be surveyed.</p> <p> </p> <p>The sample was nationally representative with respect to age and gender. Other demographic information collected included education, among others.</p>
CoUDlabs_TA_OTHU-SuDS-NBS-RECON: Correlating plant health with soil moisture in NBS
<p>This repository contains the dataset ‘CoUDlabs_TA_OTHU-SuDS-NBS-RECON: Correlating plant health with soil moisture in NBS’ which is a result from the Transnational Access, within Co-UDlabs project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008626. </p> <p>These experiments introduce a new approach which uses low-cost multi-spectral camera (Plant-o-Meter) to estimate plant health (through various parameters) to link with soil moisture of the bioretention systems (biofilters and green roof) as a common element of nature-based solutions (NBS). Soil moisture is used as a proxy for polluted water retention and pollutant removal within the system. The aim of this work is to provide a proof of concept for indirect measurement of urban polluted water treatment through different types of NBS. The experiments have been done on real stormwater biofilters, under controlled conditions (artificial irrigation). Data from the experimental campaign presented here will help to understand fundamental correlations between plant health parameters and the change in soil moisture, giving us a tool for quick assessment of the need for maintenance of various NBS as a crucial parts of UD systems.</p> <p>The authors acknowledge financial support from the European Union under the Horizon 2020 program within a contract for Integrating Activities for Starting Communities (Ref. 101008626)</p> <p>>>>For detailed description of the dataset, please see <a href="https://zenodo.org/api/records/14191602/draft/files/README_NBS-RECON_dataset.txt/content" target="_blank" rel="noopener noreferrer">README_NBS-RECON_dataset.txt</a> and <a href="https://zenodo.org/api/records/14191602/draft/files/CoUDlabsDataStorageReport_NBS-RECON.docx/content" target="_blank" rel="noopener noreferrer">CoUDlabsDataStorageReport_NBS-RECON.docx</a></p>
Plant health data from Belgian Plant Sentinel Network
<p>The enclosed files contain the questions (in Dutch, English & French) and the responses from tree surveys conducted under the Belgian Plant Sentinel Network.</p> <p>Background to the project</p> <p>Botanic gardens and arboreta possess outstanding scientific collections of a wide diversity of plants, frequently growing outside their natural geographical range. These can be used as sentinels for both early detection of emerging pests and for potentially invasive pests. This is why the International Plant Sentinel Network was launched by EUPHRESCO (EUropean PHytosanitary RESearch COordination), an international network of organisations funding research projects and coordinating national research programmes in the phytosanitary area. International Network developed a transnational network consisting of gardens, diagnostic laboratories and National Plant Protection Organisations, working together in order to provide an early warning system for new and emerging plant pests and diseases. It focused on developing tools and took place in a limited number of countries.</p> <p>Belgium did not participate in the first phase of International Plant Sentinel Network, even though there are many botanic gardens and arboreta in the country. The largest of them is Meise Botanic Garden, which is one of the largest botanic gardens in the world (92 ha). Besides Meise there are several gardens with diverse and unique collections spread all over the country. Among these gardens many have staff members with good knowledge of plant protection, which assure the control of pests and diseases in the collections of the gardens. On the other hand, up to now there have been only limited interactions between the gardens and the National Reference Laboratories for plant pests and diseases. It would be interesting to strengthen these interactions, both for the gardens and arboreta, for a better management and protection of their living collections, and for the Reference Laboratories, in order to gather more data on the presence of pests and diseases in the country.</p> <p>That is why we have created a Belgian network similar to international network, aiming at supporting national plant health policy by early warning of new pest threats, and meanwhile operating in the new transnational initiative of the international network. Together they can form a dense Belgian network to gather data and expand the surveillance of emerging pests over the entire national territory. They also hold most of the plant diversity present in the whole country.</p> <p>Standardized methods and tools for making the plant health surveys have been developed by the International Plant Sentinel Network during its first phase from 2013 to 2016. One of these tools is the Plant Health Checker, a form for making standardized surveys of trees. Two versions are available, one for surveying deciduous trees and one for conifers, as the symptoms to watch for differ between these two groups in some cases. Each form is available in English as a paper form with two sides. Moreover, a reference guide with instructions on how to use the checker had also been made with it, as well as a guide to taking photographs for diagnostic purposes.</p> <p>For the Belgian project we adapt these forms in order to facilitate their use. We translated them to French and Dutch, the two main languages of the country, so that all gardeners could use them. A second goal was to adapt them to the test cases selected for the project. Indeed, some symptoms that are important for these organisms are not included in the original plant heath checker, mainly concerning the symptoms on roots for the root-knot nematode and <em>Phytoplasma</em> case. A third aim was to investigate whether the forms could be simplified for making the surveys in the field. For the latter, however, it was decided to await the user experience of the first year, so as to make an evaluation and suggest eventual amendments.</p> <p>It is also planned to develop an electronic version of the checker form, so as to be able to input the survey data directly in digital format and thus skipping a second and tedious step of entering the data from paper forms into the computer. Moreover, it is preferable to have a system which can easily make the data available to a central data system. That is why we chose to use Google Forms within Google Drive, because it can be utilized as a central data system and flexible, and has many functionalities such as sharing the data. This system also allows the direct input of data with an internet connection.</p>
Linked collectors and determiners for: ABC_Useful plants of Malawi; medicinal plants used in maternal and child health care.
Natural history specimen data linked to collectors and determiners held within, "ABC_Useful plants of Malawi; medicinal plants used in maternal and child health care". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/789e1248-c737-4d6a-bcec-e4b92775becc">https://bionomia.net/dataset/789e1248-c737-4d6a-bcec-e4b92775becc</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/789e1248-c737-4d6a-bcec-e4b92775becc">https://gbif.org/dataset/789e1248-c737-4d6a-bcec-e4b92775becc</a>. Formatted as a Frictionless Data package.
Data from: Colony personality and plant health in the Azteca-Cecropia mutualism
For interspecific mutualisms, the behavior of one partner can influence the fitness of the other, especially in the case of symbiotic mutualisms where partners live in close physical association for much of their lives. Behavioral effects on fitness may be particularly important if either species in these long-term relationships displays personality. We conducted a field study on collective personality in Azteca constructor colonies that live in Cecropia trees, one of the most successful and prominent mutualisms of the neotropics. These pioneer plants provide hollow internodes for nesting and nutrient-rich food bodies; in return, the ants provide protection from herbivores and encroaching vines. We tested the consistency and correlation of five colony-level behavioral traits, censused colonies, and measured the amount of leaf damage for each plant. Four of five traits were both consistent within colonies and correlated among colonies. This reveals a behavioral syndrome along a docile-aggressive axis, with higher-scoring colonies showing greater activity, aggression, and responsiveness. Scores varied substantially between colonies and were independent of colony size and age. Host plants of more active, aggressive colonies had less leaf damage, suggesting a link between a colony's personality and effective defense of its host, though the directionality of this link remains uncertain. Our field study shows that colony personality is an ecologically relevant phenomenon and sheds light on the importance of behavioral differences within mutualism dynamics.
Data on plant health stakeholder priorities for tests and general prioritisation framework
<p>Data collected in the framework of work package 4 of the Valitest project. They correspond to a qualitative assessment of plant health stakeholder requirements, have been collected using online surveys supplemented by desk-based research, as well as impact assessments</p>
Results from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>This repository contains results from the paper, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied Statistics</em>. The storage of these results helps facilitate access to and replication of the analysis in the paper. The results include output from the:</p> <ol> <li>Sulfate analysis <ul> <li>tx-2016-100k-int-mx.RDS</li> </ul> </li> <li>Asthma analysis <ul> <li>asthma-pois-cut.RDS</li> <li>asthma-pois-plugin.RDS</li> <li>asthma-bart-cut.RDS</li> <li>asthma-bart-plugin.RDS</li> </ul> </li> <li>Medicare analysis <ul> <li>medicare-pois-cut.RDS</li> <li>medicare-pois-plugin.RDS</li> <li>medicare-bart-cut.RDS</li> <li>medicare-bart-plugin.RDS</li> </ul> </li> <li>Simulation study <ul> <li>simstudy-cm1-lm.RDS</li> <li>simstudy-cm1-bart.RDS</li> <li>simstudy-cm2-lm.RDS</li> <li>simstudy-cm2-bart.RDS</li> <li>simstudy-cm3-lm.RDS</li> <li>simstudy-cm3-bart.RDS</li> <li>simstudy-pm1-pois.RDS</li> <li>simstudy-pm1-bart.RDS</li> <li>simstudy-pm2-pois.RDS</li> <li>simstudy-pm2-bart.RDS</li> <li>simstudy-pm3-pois.RDS</li> <li>simstudy-pm3-bart.RDS</li> </ul> </li> <li>Log-linear BART sensitivity analysis <ul> <li>sensitivity-m100.RDS</li> <li>sensitivity-m200.RDS</li> <li>sensitivity-m300.RDS</li> <li>sensitivity-m400.RDS</li> <li>sensitivity-power05.RDS</li> <li>sensitivity-power1.RDS</li> <li>sensitivity-power15.RDS</li> <li>sensitivity-power2.RDS</li> <li>sensitivity-power25.RDS</li> <li>sensitivity-power3.RDS</li> <li>sensitivity-power4.RDS</li> <li>sensitivity-power5.RDS</li> </ul> </li> </ol> <p>A description of these results can be found at <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a>, along with the R code used to generate these (and other results, such as figures) found in the manuscript and supplementary material.</p>
Plant Health Status
<p>These are the results of the predictions from the xylella classifier on the validation dataset, which contains 1200 health statuses, with 400 for each health status condition. As we can see from the confusion matrix, the model almost perfectly recognizes Asymptomatic health status, distinguishing it clearly from Mild and Evident symptomatic statuses.</p>
Animal versus plant protein and adult bone health: a systematic review and meta-analysis from the National Osteoporosis Foundation- S1 File
<p>All calculations and meta-analyses for the systematic review "Animal versus plant protein and adult bone health: a systematic review and meta-analysis from the National Osteoporosis Foundation" were conducted in Stata SE 13 (Stata Corp) using this analytical dataset <strong>(S1 File).</strong></p>
Results of questionnaire on live plant health nematode collections
<p>In the framework of the Euphresco project 2016-F-186 'Inventory of living collections of cyst and root knot nematodes in Europe and their maintenance techniques (Cyst and Melo Collect)', a survey was organised during the Autumn 2017 with the aim to collect information on the live nematode collections in different countries. 32 (reference) collections in Austria, Belgium, Canada, Czech Republic, France, Germany, Italy, Latvia, Mexico, Netherlands, Portugal, Spain, United Kingdom, and United States of America participated in the survey.</p> <p>Of the 32 mentioned collections the preferred nematode populations are clearly different between the USA and Europe: the tropical <em>Meloidogyne</em> species are the most represented in North America while <em>Globodera</em> species are in Europe.</p> <p>The comparison of the protocols for live nematode storage for short and long periods of time showed high variability, although in general, <em>Globodera</em> spp. can be stored at 4 °C for longer time (>20 years) than <em>Meloidogyne</em> spp., whose optimal storage time is 8 months at 14 °C, when kept in soil.</p> <p>The need to verify the population for its trueness, with other words, the frequency of identification differs; various possibilities are mentioned between never and almost monthly to frequently, each time the nematodes are extracted from the soil (90 days for PCN, 16 weeks for <em>Meloidogyne</em>) to yearly or only upon arrival. The frequency depends on the amount of time people can afford to put into this work and the risk assessment for getting cross contamination.</p> <p>The conditions for rearing and maintaining were inventoried as well. For <em>Meloidogyne</em> spp., host differences for rearing and maintaining them are rarely observed (often <em>Solanum lycopersicum</em> -tomato- is used for both), although <em>Ficus carina</em> for long maintenance of <em>Meloidogyne</em> spp. and <em>Solanum dulcamara</em> for <em>M. fallax</em> have been used in France (pers. comm. Fabrice Ollivier, ANSES). For cyst nematode species their specific host is used for both rearing and maintenance purposes: <em>Solanum tuberosum</em> (potato), <em>Nicotiana tabacum</em> (tobacco), or <em>Glycine max</em> (soybean) are used respectively for the potato cyst nematodes (<em>Globodera rostochiensis</em> and <em>G. pallida</em>), <em>G. tabacum</em> and <em>G. glycines</em>.</p> <p>The project report is also available on Zenodo https://zenodo.org/record/1442881#.W7PaxmgzbIU</p>
Phytophthora in horticultural nursery green waste - a risk to plant health
<p>This dataset on Zenodo accompanies the manuscript Schiffer-Forsyth <em>et al.</em> (2023), Phytophthora in horticultural nursery green waste – a risk to plant health.</p> <p>There are two files:</p> <ul> <li>metadata.tsv - plain text table as tab-separated variables</li> <li>raw_data.tar.gz - compressed archive of 81 paired raw FASTQ files</li> </ul> <p>This represents a complete Illumina MiSeq run, with the names of the unrelated samples redacted.</p> <p>To repeat the analysis described in the paper, first install THAPBI PICT. See <a href="https://github.com/peterjc/thapbi-pict/">https://github.com/peterjc/thapbi-pict/ </a>for instructions. At the time of the paper, v0.14.1 was the current release (with a near-identical v1.0.0 expected to be released shortly).</p> <p>Next, decompress the raw data into a folder of paired gzipped FASTQ files. There is no need to decompress those:</p> <pre><code class="language-bash"> $ tar -zxvf raw_data.tar.gz $ ls -1 raw_data/</code></pre> <p>If you wish, verify the checksums to confirm the data integrity:</p> <pre><code class="language-bash"> $ cd raw_data/ $ md5sum -c MD5SUM.txt $ cd ..</code></pre> <p>Setup output directories:</p> <pre><code class="language-bash"> $ mkdir -p intermediate/ summary/</code></pre> <p>You can run the analysis in one step. This should take under five minutes: </p> <pre><code class="language-bash">$ thapbi_pict pipeline -i raw_data/ \ -n raw_data/SynCtrl_*.fastq.gz \ -y raw_data/SynCtrl_*.fastq.gz \ -s intermediate/ -o summary/ \ -t metadata.tsv -x 3 -c 1,2,4,5</code></pre> <p>The options here are as follows:</p> <ul> <li>-i raw_data - input directory of paired raw FASTQ files.</li> <li>-n raw_data/SynCtrl_*.fastq.gz - negative controls used to increase the absolute abundance threshold</li> <li>-y raw_data/SynCtrl_*.fastq.gz - synthetic controls used to increase the fractional abundance threshold</li> <li>-s intermediate/ - optional location to store intermediate files</li> <li>-o summary/ - output location for reports</li> <li>-t metadata.tsv -x 3 -c 1,2,4,5 - show and sort on metadata columns 1, 2, 4 and 5 from metadata.tsv using column 3 to cross-reference the FASTQ filename stems (semi-colon separated lists for replicates).</li> </ul> <p>This assumes the following key default settings:</p> <ul> <li>-a 100 -f 0.001 - default absolute and fractional abundance thresholds</li> <li>-d - - default to the provided ITS1 database</li> </ul> <p>With these settings, only synthetic sequences were found in the controls, and therefore the thresholds were not automatically increased any further.</p> <p>Note some of these options could change in future releases of the software, and in particular there would likely be additional Phytophthora species or sequences in future updates to the default database.</p> <p>Output file summary/ITS1.samples.onebp.xlsx (and .tsv) is equivalent to Table 2 (after pooling replicates, and applying human judgement to resolve ambiguous ITS1 markers shared by multiple species).</p> <p>Note <em>P. austrocedri</em> was identified in three samples, N2-Water_S2 and N2-Water_S22 described in this work, and a third sample REDACTED_S28 from another location.</p>
Data for: Detection of oomycete pathogens in UK peat-free growing media and implications for plant health
<p>This dataset on Zenodo accompanies the manuscript Frederickson-Matika <em>et al.</em> (2024), Detection of oomycete pathogens in UK peat-free growing media and implications for plant health.</p> <p>There are two files:</p> <ul> <li>metadata.tsv - plain text table as tab-separated variables</li> <li>raw_data.tar.gz - compressed archive of 43 paired raw FASTQ files</li> </ul> <p>This represents a subset of two complete Illumina MiSeq plates (in two dated folderes) run at the James Hutton Institute containing other environmental samples using the same protocol. Only the synthetic controls and peat-free samples are provided here.<br><br>To repeat the analysis described in the paper, first install THAPBI PICT. See <a href="https://github.com/peterjc/thapbi-pict/">https://github.com/peterjc/thapbi-pict/ </a>for instructions. At the time of the paper, v1.0.14 was the current release.</p> <p>Next, decompress the raw data into a folder of paired gzipped FASTQ files. There is no need to decompress those:</p> <pre><code> $ tar -zxvf raw_data.tar.gz<br> $ ls -1 plate_20220505/ plate_20230608/</code></pre> <p>If you wish, verify the checksums to confirm the data integrity:</p> <pre><code> $ cd plate_20220505/ $ md5sum -c MD5SUM.txt<br> $ cd ../plate_20230608/ $ md5sum -c MD5SUM.txt<br> $ cd ..</code></pre> <p>Setup output directories:</p> <pre><code><code> $ mkdir -p intermediate/ summary/</code></code></pre> <pre>Run the THAPBI PICT pipeline:</pre> <pre><code> $ thapbi_pict pipeline -m 1s3g \<br> -i plate_*/ -o summary/peat-free \<br> -y plate_*/GBL*.fastq.gz \<br> -n plate_*/GBL*.fastq.gz \<br> -s intermediate/ \<br> -t metadata.tsv -u \<br> -x 9 -c 1,2,3,4,5,6,7,8</code><br><br></pre> <p>The options here are as follows:</p> <ul> <li>-i - two input directories of paired raw FASTQ files.</li> <li>-n - negative controls used to increase the absolute abundance threshold</li> <li>-y - synthetic controls used to increase the fractional abundance threshold</li> <li>-s - optional location to store intermediate files</li> <li>-o - output stem for reports</li> <li>-t - filename for tab-separated-variable metadata</li> <li>-u - show unsequenced samples defined in the metadata</li> <li>-x - which metadata column contains Illumina FASTQ filename stems</li> <li>-c - which metadata columns to include in the report.</li> </ul> <p>This assumes the following key default settings:</p> <ul> <li>-a 100 (default absolite abundance threshold)</li> <li>-f 0.001 (default fractional abundance threshold)</li> <li>-d -(default provided ITS1 database).</li> </ul> <p>With these settings, only synthetic sequences were found in the controls, and therefore the thresholds were not automatically increased any further.</p> <p>Opening the output file summary/peat-free.ITS1.samples.1s3g.xlsx in Excel or similar should show you a table resembling Table 1 in the paper, but one row per sequencing sample, and additional columns with per-sample per-species read counts etc.</p>
Data from: Colony personality and plant health in the Azteca-Cecropia mutualism
Open the record for dataset details and reuse information.
Dataset - Global site-specific health impacts of fossil energy, steel mills, oil refineries and cement plants
<div> <div> <p>Climate change and particulate matter air pollution present major threats to human well-being by causing impacts on human health. Both are connected to key air pollutants such as carbon dioxide (CO<span><span><span>2</span></span></span>), primary fine particulate matter (PM<span><span><span>2.5</span></span></span>), sulfur dioxide (SO<span><span><span>2</span></span></span>), nitrogen oxides (NO<span><span><span>x</span></span></span>) and ammonia (NH<span><span><span>3</span></span></span>), which are primarily emitted from energy-intensive industrial sectors. We present the first study to consistently link a broad range of emission measurements for these substances with site-specific technical data, emission models, and atmospheric fate and effect models to quantify health impacts caused by nearly all global fossil power plants, steel mills, oil refineries and cement plants. The resulting health impact patterns differ substantially from far less detailed earlier studies due to the high resolution of included data, highlighting in particular the key role of emission abatement at individual coal-consuming industrial sites in densely populated areas of Asia (Northern and North-Eastern India, Java in Indonesia, Eastern China), Western Europe (Germany, Belgium, Netherlands) as well as in the US. Of greatest health concern are the high SO<span><span><span>2</span></span></span> emissions in India, which stand out due to missing flue gas treatment and cause a particularly high share of local health impacts despite a limited number of emission sites. At the same time, the massive infrastructure and export capacity build-up in China in recent years is taking a substantial toll on regional and global health and requires more stringent regulation than in the rest of the world due to unfavorable environmental conditions and high population densities. The current phase-out of highly emitting industries in Europe is found not to have started with sites having the greatest health impacts. Our detailed site-specific emission and impact inventory is able to highlight more effective alternatives and to track future progress.</p> </div> </div>
Fig. 1 in Plant-cyanobacteria interactions: Beneficial and harmful effects of cyanobacterial bioactive compounds on soil-plant systems and subsequent risk to animal and human health
Fig. 1. Cyanobacterial active compounds induce negative, (A) ROS and enzyme activities such as superoxide dismutase (SOD), glutathione peroxidase (GPx), peroxidase (POD); and positive effects (B) expression of stress responsive genes (Ssglc and slr1562) that can have a positive effect on increasing plants' stress tolerance.
Impact of a Plant-Based Diet on Indices of Cardiovascular Health in African Americans
ClinicalTrials.gov study NCT05344287. IPD Sharing: YES. Countries: 1. Publications: 1.
Food Based Intervention Rich in Plant Components to Improve Metabolic Health in Prediabetics (FBIP) Study
ClinicalTrials.gov study NCT04745702. IPD Sharing: NO. Countries: 1. Publications: 1.
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.