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22,710 results for “Plants for planting”
Figure 7 in Colotis lais (Butler, 1876) and Colotis euippe omphale (Godart, [1819]) use Cadaba aphylla (Thunb.) Wild LC as a host-plant at Tswalu Kalahari, Northern Cape Province, South Africa)
Figure 7 – Depiction of GPS-marked Cadaba aphylla host plants (white markers) selected for ecological monitoring, north and north-west of the Dedeben Research Centre at Tswalu Kalahari.
Figure 5 in Colotis lais (Butler, 1876) and Colotis euippe omphale (Godart, [1819]) use Cadaba aphylla (Thunb.) Wild LC as a host-plant at Tswalu Kalahari, Northern Cape Province, South Africa)
Figure 5 – Colotis euippe omphale male puddling (imbibing water and nutrients from wet soil) at a damp area near the Dedeben Research Centre, Tswalu Kalahari, South Africa, 22 December 2021. Photo: R.F. Terblanche.
Figure 3 in Colotis lais (Butler, 1876) and Colotis euippe omphale (Godart, [1819]) use Cadaba aphylla (Thunb.) Wild LC as a host-plant at Tswalu Kalahari, Northern Cape Province, South Africa)
Figure 3 – Colotis lais female resting on bare ground with open wings in typical fashion, north-west of the Dedeben Research Centre, Tswalu Kalahari, South Africa, 22 December 2021. Photo: R.F. Terblanche.
Figure 4 – Freshly emerged Colotis euippe omphale reared from a in Colotis lais (Butler, 1876) and Colotis euippe omphale (Godart, [1819]) use Cadaba aphylla (Thunb.) Wild LC as a host-plant at Tswalu Kalahari, Northern Cape Province, South Africa)
Figure 4 – Freshly emerged Colotis euippe omphale reared from a larva collected on 19 December 2021 on a Cadaba aphylla shrub, north of the Dedeben Research Centre, Tswalu Kalahari, South Africa. The CRG reference number for this rearing is RFT21H79. Photo: R.F. Terblanche.
Figure 2 in Colotis lais (Butler, 1876) and Colotis euippe omphale (Godart, [1819]) use Cadaba aphylla (Thunb.) Wild LC as a host-plant at Tswalu Kalahari, Northern Cape Province, South Africa)
Figure 2 – Colotis lais male, puddling (imbibing water and nutrients from wet soil) at a damp area near the Dedeben Research Centre, Tswalu Kalahari, South Africa, 24 December 2021. Photo: R.F. Terblanche.
Figure 1 in Colotis lais (Butler, 1876) and Colotis euippe omphale (Godart, [1819]) use Cadaba aphylla (Thunb.) Wild LC as a host-plant at Tswalu Kalahari, Northern Cape Province, South Africa)
Figure 1 – Freshly emerged adult male of Colotis lais reared from a larva collected on 19 December 2021 on a Cadaba aphylla shrub, north of the Dedeben Research Centre, Tswalu Kalahari, South Africa. The CRG reference number for this rearing is RFT21H78. Photo: R.F. Terblanche.
Plant traits shape global spatiotemporal variations in photosynthetic efficiency
<p>Dataset to reproduce key results in the following work: Plant traits shape global spatiotemporal variations in photosynthetic efficiency</p>
Assessment of whole-site methane emissions from anaerobic digestion plants: towards establishing emission factors for various plant configurations
<p>This dataset supplements the publication "Assessment of whole-site methane emissions from anaerobic digestion plants: towards establishing emission factors for various plant configurations" by Wechselberger et al. (2025).</p> <p>The dataset contains primary and secondary data underlying the statistical analysis and reported methane emission factors. Emission factors were calculated as described in section 2.3 of the paper. </p> <p>Available files (UTF-8 encoded):</p> <ul> <li>Data.csv (dataset)</li> <li>Glossary.csv (column/variable descriptions of dataset)</li> </ul> <p>The dataset includes plant characteristics and whole-site methane losses of 135 anaerobic digestion plants, covering normal and various other-than-normal operating conditions (155 rows). For statistical analysis, only periods during normal operation and plants with information on the analyzed emission factors and plant characteristics were considered (cf. supplementary information C of the paper). Consequently, the final dataset contained 109 anaerobic digestion plants for statistical analysis on the methane emission factor (% of methane produced) and 28 plants when analyzing the wastewater-specific emission factor (kg methane per population equivalent and year). All but one facility continuously processed feedstock without any post-rotting stages. Plant DE-MH_WP5_1 of the secondary data implemented garage digesters.</p> <p>Data from three plants were collected only after completion of statistical analyses. These data were used to compare methane losses during normal and other-than-normal operating conditions. The respective rows are marked accordingly in the dataset (column “data_collected_after_statistical_analyses”).</p> <p>Version v2 contains the final reference to the publication Wechselberger et al. (2025). The data are the same as in version v1.</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>
Supporting files for Turečková et al. 2024 "A New Abscisic Acid Conjugate, ABA‑L‑Glutamate, Determined in Different Plant Species by Combined Immunoaffinity Chromatography‑Tandem Mass Spectrometry"
Open the record for dataset details and reuse information.
Interactive and unimodal relationships between plant biomass, abiotic factors, and plant diversity in global grasslands
<p>The R file CodeGrasslandBiomass contains all R code necessary to reproduce all results of the manuscript “Interactive and unimodal relationships between plant biomass, abiotic factors, and plant diversity in global grasslands” based on the data in the csv file DataGB.</p> <p> </p>
Shapefiles with the outline of maximum water spread resulting from the catastrophic release of the Kakhovka Reservoir after the destruction of the Kakhovka Hydroelectric Power Plant by Russian occupying forces
<p>The map is based on remote sensing data from Sentinel-2A (Processing Level L2A), dated June 8, June 13, and June 18, 2023, and Landsat-9 (Collection 2 Level-1), dated June 9, 2023. </p> <p>The following Sentinel-2 remote sensing data granules were used:<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TUS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TVS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TWS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TVT_20230608T132103.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TUS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TVS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TWS_20230613T102806.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TUS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TVS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TWS_20230618T151602.SAFE</p> <p>The following remote sensing data scenes from Landsat-9 were used:<br>LC09_L1TP_179028_20230609_20230610_02_T1<br>LC09_L1TP_179027_20230609_20230610_02_T1</p> <p>The contour of the maximum water spread was constructed using a method of manual visual interpretation of remote sensing data, relying on knowledge of the local terrain. We consciously chose not to use automated methods with water indices such as the Normalized Difference Water Index (NDWI) or the Modified Normalized Difference Water Index (MNDWI), as these do not effectively distinguish water surfaces in areas covered with forest or dense reed thickets. Similarly, we did not use the SRTM digital elevation model due to significant artifacts in the study area, where the model shows the height of the forest canopy instead of the ground surface in forested areas.</p> <p>For visual interpretation of Sentinel-2A remote sensing data, we used combinations of spectral bands NIR-Red-Green (8-4-3) and SWIR2-NIR-Green (12-8-3). For the visual interpretation of Landsat-9 remote sensing data, we used combinations of bands SWIR1-NIR-Red (6-5-4) and NIR-Red-Green (5-4-3). To better align the resolution of Sentinel-2A remote sensing data (10 m/pixel) with that of Landsat-9 (30 m/pixel), the latter's data was enhanced using the panchromatic channel (Band 8) through IHS-based pansharpening to 15 m/pixel. The pansharpening was performed using a custom bash script, utilizing command-line tools and utilities such as ImageMagick (<a href="https://imagemagick.org" rel="nofollow">https://imagemagick.org</a>), listgeo, and geotifcp (<a href="https://github.com/OSGeo/libgeotiff">https://github.com/OSGeo/libgeotiff</a>). To expedite the pansharpening process, both Landsat scenes were cropped to the study region and merged by bands using the gdal_translate and gdal_merge.py utilities from the GDAL library (<a href="https://gdal.org/" rel="nofollow">https://gdal.org/</a>). For convenience, the Sentinel-2A data tiles T36TUS, T36TVS, and T36TWS were also cropped and merged by bands using custom scripts available at <a href="https://doi.org/10.5281/zenodo.13205058" rel="nofollow">https://doi.org/10.5281/zenodo.13205058</a>.</p> <p>During visual interpretation, the above-mentioned remote sensing data were compared with satellite images acquired before the destruction of the Kakhovka Hydroelectric Power Plant. In particular, Landsat-9 remote sensing data were compared with Landsat-8 data from June 1, 2023, and Sentinel-2A data were compared with Sentinel-2B data from June 3, 2023.</p> <p>Repository files:<br>floodMax_UTM36N.zip — contains the shapefile in UTM36N projection (EPSG:32636);<br>floodMax_WGS84.zip — contains the shapefile in geographic coordinates in WGS84 (EPSG:4326);<br>floodMax_WGS84.geojson.zip — contains a GeoJSON file in WGS84 coordinates (EPSG:4326).</p> <div> <h1>Web version of the map</h1> </div> <p>The web version of the maximum water spread map is available at:<br><a href="https://yumoskalenko.github.io/floodmap_Kakhovka2023/" rel="nofollow">https://yumoskalenko.github.io/floodmap_Kakhovka2023/</a></p> <p> </p> <p>Embed code for the map on a webpage:</p> <div> <pre><code><iframe style="border: 1px solid black" src="https://yumoskalenko.github.io/floodmap_Kakhovka2023/index.html" marginwidth="0" marginheight="0" scrolling="no" width="100%" height="360" frameborder="0"></iframe> </code></pre> <div> </div> </div> <p><em><strong>This scientific and technical product was created by the scientists of the Black Sea Biosphere Reserve of the National Academy of Sciences of Ukraine during the implementation of research on the topic "Monitoring the condition of natural complexes of the Black Sea Biosphere Reserve (‘Chronicle of Nature’)" (state registration number 0121U109174).</strong></em></p>
LegacyVegetation: Northern Hemisphere reconstruction of past plant cover and total tree cover from pollen archives of the last 14 ka
Open the record for dataset details and reuse information.
Floristic survey of vascular plants of the Pantanal Park Road in Mato Grosso do Sul, Brazil
<p>The Pantanal is one of the largest floodplains and the most extensive tropical wetland in the world, covering approximately 139,000 km². It is located in central South America, covering areas in Bolivia, Paraguay, and the Brazilian states of Mato Grosso and Mato Grosso do Sul. Flora inventories of the Pantanal have shown a varied species richness, ranging from 1,872 to 2,567 species. The Pantanal Park Road (PPR) is an area of special tourist interest located within the Pantanal of Mato Grosso do Sul and covers 6,700 km², passing through the municipalities of Corumbá and Ladário. This data paper plays a crucial role in organizing and disseminating essential information on local flora, recognizing the value of floristic surveys for understanding regional biodiversity and informing conservation efforts. It aimed to provide information to guide public policies for preserving local biodiversity. The floristic survey was generated by combining data from field expeditions with botanical databases, such as JABOT and SpeciesLink. Additional information included life form, substrate type, conservation status, and species distribution across Brazilian biomes and states, obtained from Flora e Funga do Brasil using the flora package. Data from Flora e Funga do Brasil were also used to identify new occurrences among the listed species. The vascular plant list of the PPR included 755 species. Most species throughout the PPR were classified as herbs, followed by shrubs or subshrubs, climbers, and trees. Thirty species with a known response to fire were identified, of which eight are fire-sensitive, two are fire-tolerant, and 21 are fire-stimulated. Of the total species recorded, 710 are native to Brazil, with eight species endemics to Mato Grosso do Sul, ten to the Pantanal, and four specifically to the Pantanal in Mato Grosso do Sul. According to the Red List Category from the National Center for Plant Conservation in Brazil, the PPR contains two species classified as Data Deficient (DD), 71 as Least Concern (LC), three as Near Threatened (NT), four as Vulnerable (VU), four as Endangered (EN), and one as Critically Endangered (CR).</p>
Model Inputs and Results - The role of coal plant retrofitting strategies in decarbonizing India's power system
<p>These files are the model inputs and results for the submission based on GenX version v0.3.6 - The role of coal plant retrofitting strategies in decarbonizing India’s power system</p>
scmcclelland/joint-mediation-study: Data, Analysis, and Figure Scripts for "Soil organic carbon sequestration jointly-mediated by plants and microbes after compost application"
<p>This repository contains data, analysis, and figure scripts to create findings from the manuscript "Soil organic carbon sequestration jointly-mediated by plants and microbes after compost application" currently under minor revisions.</p> <p>This release includes updated code, primarily improvements to figures, and a new script for a supplementary map figure.</p>
Supplementary dataset for "Phenylacetic acid metabolism in land plants: novel pathways and metabolites"
<p>Supplementary dataset with measured data for publication "Phenylacetic acid metabolism in land plants: novel pathways and metabolites"</p>
Timing of short-term drought structures plant-herbivore dynamics
<p>Drought is an important stressor that affects plant growth, survival and physiology and, through plant responses, alters plant-herbivore interactions and herbivore population dynamics. Short-term drought can occur at different times during a growing season, affecting herbivore populations and plants at various stages of development and growth. As phenology influences drought response, drought timing could strongly structure plant-herbivore interactions. We grew common milkweed plants with or without its dominant aphid herbivore, allowing both plants and herbivore populations to develop over time as in a typical growing season. To determine how drought timing affects plants and aphid populations, we applied short-term (1 week) drought at different times. Plants and aphids were sensitive to drought timing, with a few weeks difference shifting the effects of drought from minimal to causing massive declines in plant growth, plant survival and herbivore population density. Aphid herbivory added to or overrode drought consequences in plants, with different plant fitness components responding distinctly to herbivory. Drought timing also affected whether plant and herbivore responses were parallel or divergent, suggesting that shifts in the seasonal timing of drought have the potential to disrupt plant-herbivore interactions and affect the larger ecological community. Our results show drought timing has important and disparate consequences for herbivores and their host plants and indicates the necessity of considering phenology and timing in assessing drought response.</p>
Fig. 3 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 3. – Origin of introduced plant species in Burkina Faso. The majority of introduced species originates in the Americas.
Fig. 6 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 6. – Province species richness in relation to province characteristics. Species richness per province is shown dependent on 4 factors.
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.