Find research datasets worth reusing

opencc-zeroNov 2020View details →

Final spatial dataset for native Norwegian vascular plants

Occurrence data for the native Norwegian vascular plant species obtained from the Global Biodiversity Information Facility (GBIF). The dataset contains 3,597,865 occurrences.

Nectar chemistry is not only a plant's affair: floral visitors affect nectar sugar and amino acid composition

This dataset contains data used in the analyses performed in the article entitled "Nectar chemistry is not only a plant’s affair: floral visitors affect nectar sugar and amino acid composition". The Excel file contains three sheets. 'Raw data' contains concentration of sugars, amino acids, pollen grains and yeast cells measured in several flowers and plants of Gentiana lutea subsp. symphyandra, belonging to different experimental treatments. 'Amino acid diversity' contains the concentration of specific protein and non-protein amino acids found in a subset of the above mentioned flowers. 'Pollen suspension test' contains the concentration of the same amino acids found in nectar after suspension of pollen of G. lutea at different time intervals (0, 1, 4, and 24 hours).

Quantification of plant morphology and leaf thickness with optical coherence tomography

The uploaded scripts and data are used to obtain the figures 2, 4, 5, 6 and 7 in the publication.  The code has been run with Python 3.7 in Spyder (Anaconda). There are three scripts, each needing specific datasets to run the code. 1. The core is the segmentation of the leaf surface and this is subsequently used to calculate leaf thickness and obtain en-face images. <a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/3D_segmentation_thickness_enface.py">3D_segmentation_thickness_enface.py</a>: This file loads the 3D processed OCT data, does the leaf surface segmentation and calculates the en face images. It needs the files processed_3Ddata.npy and videoim.npy <a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/processed_3Ddata.npy">processed_3Ddata.npy</a>: This file contains the processed 3D OCT dataset (linear amplitude data), with respectively dimensions z,x,y. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script. <a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/videoim.npy">videoim.npy</a>: This file contains the RGB image of Fig. 6(a) as image matrix. 2. The non-infiltrated and infiltrated image (Figure 4) <a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/2D_fig4.py">2D_fig4.py</a>: This script produces Figure 4 of the paper and also shows the two RGB images that indicate the scan location on the leaf. It needs the files OCTdata_figure4.npy (containing OCT data) and videoimages_figure4.npy <a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/OCTdata_figure4.npy">OCTdata_figure4.npy</a>: This file contains the processed 2D OCT dataset (linear amplitude data), with respectively dimensions (a/b),z,x. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script. <a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/videoimages_figure4.npy">videoimages_figure4.npy</a> This file contains the two RGB images that show the scan area of the data in Figure 4. 3. The calculation of the refractive index and making Figure 5 <a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/refractiveindex_fig5.py">refractiveindex_fig5.py</a>: this script segments the cuvette wall and leaf surface on 2D images and calculates the refractive index by evaluating equation 1 of the publication. It needs the file images_refractiveindex.npy <a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/images_refractiveindex.npy">images_refractiveindex.npy</a>: This file contains the processed 2D OCT dataset (linear amplitude data), with respectively dimensions (leaf/empty),z,x. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.

Code and Data for: "Signs of local adaptation and phenotypic plastic response to elevation shifted between environmental backgrounds in Snapdragon plants"

Code and data for manuscript: "Signs of local adaptation and phenotypic plastic response to elevation shifted between environmental backgrounds in Snapdragon plants"

opencc-by-4.0Oct 2020View details →

Dataset of Invasion risks and social interest of non-native woody plants in urban parks of Spain

Full datasets for the research entitled "Invasion risks and social interest of non-native woody plants in urban parks of Spain"

Dataset on: Land slugs in plant nurseries, a potential cause of dispersal in Argentina

Commercial plant nurseries may serve as causes of dispersal of land snails and slugs (native and non-native) through the trade of plants and the related transport of eggs and small individuals that may pass unnoticed. Studies on the possible role of plant nurseries as a potential cause of dispersal of slugs in South America are lacking. To explore the role of garden centers, we collected and identified slugs in 12 commercial nurseries in two cities in the province of Buenos Aires, Argentina. Eight species of slugs were found. Based on our findings we validate the existence of  Deroceras laeve and Belocaulus angustipes for Argentina and confirm the existence of  Ambigolimax valentianus, which was recently cited for Argentina. We recommend that plant nurseries be regularly monitored given that snail and slug species are accidentally spread through trade in plants.

Foliar stoichiometry of woody plants worldwide

This data includes foliar N, P, K % in DW of mature leaves in woody plants worldwide. It also contains the georeferenced information and specie. It gathers data from 230 published articles, TRY database (<a href="http://www.try-db.org/TryWeb/dp.php),">http://www.try-db.org/TryWeb/dp.php),</a> ICP forest database (<a href="http://icp-forests.net/page/data-requests),">http://icp-forests.net/page/data-requests),</a> Tundra Trait Team and the Catalan Forest Inventory (Gracia et al., 2004).

Dataset from Pardini, E. A., Parsons, L. S., Ştefan, V., & Knight, T. M. (2018). GLMM BACI environmental impact analysis shows coastal dune restoration reduces seed predation on an endangered plant. Restoration Ecology, 26(6), 1190-1194.

Data and its metadata used in the analysis from the publication: Pardini, E. A., Parsons, L. S., Ştefan, V., & Knight, T. M. (2018). GLMM BACI environmental impact analysis shows coastal dune restoration reduces seed predation on an endangered plant. Restoration Ecology, 26(6), 1190-1194. <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/rec.12678">https://onlinelibrary.wiley.com/doi/full/10.1111/rec.12678</a>

UV-Vis spectral dataset of distillation wastewaters from the production of essential oils of lavender cultivars and other aromatic plant species

<pre>The present database provides a set of 16 ultraviolet-visible (UV-Vis) spectra characterizing the residual by-products (distillation wastewaters) of the production of essential oils from lavender (Lavandula angustifolia Mill.) and other aromatic plant species, including interspecific hybrids and cultivars.</pre>

opencc-zeroJan 2021View details →

Effects of plant hydraulic traits on the flammability of live fine canopy fuels in 62 Australian plant species

<ol> <li>Plant species vary in how they regulate moisture and this has implications for their flammability during wildfires. We explored how fuel moisture is shaped by variation within six hydraulic traits: saturated moisture content, cell wall rigidity, cell solute potential, symplastic water fraction and tissue capacitance.</li> <li>Using pressure-volume curves, we measured these hydraulic traits distal shoots (i.e. twigs + leaves) in 62 plant species across four wooded communities in south-eastern Australia. For a subset of 30 of those species, we also measured hydraulic traits of twigs using moisture-release curves. Moisture content of fine fuels was then estimated for circumstances typical of fire weather. These projections were made assuming that under the hot, dry, windy conditions typical of large wildfires, leaves and fine twigs would function at internal water pressures close to wilting point (i.e. turgor loss point, TLP). The effect of different moisture contents at TLP on ignition time was then modelled using a fully mechanistic, finite element model of biomass ignition based on standard principles of physical chemistry.</li> <li>We also measured predawn water potential, an indication of plant access to soil water that is influenced by root architecture. These data were used to model how root traits influence fuel moisture and ignition time.</li> </ol>

Robots for Microfarms (ROMI) - Plant Scanner Video - D5.3

The following video shows the functionalities and usage of the Plant Scanner developed within the Robots for Microfarms (ROMI) project funded by EU Grant 773875 Videos are available in: <ul> <li>hi-res (4K Apple ProRes)</li> <li>mid-red (4K H264)</li> <li>low-res (1080p H264)</li> </ul> You can also watch it on <a href="https://www.youtube.com/watch?v=LtcDBj2Y2uM">Youtube</a> Video script: The ROMI Plant Scanner is a plant phenotyping robot that generates high quality and high precision imaging data. It allows us to analyze the shoot architecture of a medium sized plant in indoor conditions in three dimensions. The image acquisition is non-destructive, allowing life-time analysis of the same plant, or the option to reuse a plant imaged in the Plant Scanner in multiple analyses. The robot creates a phenotype of a single plant in a few minutes and can make up to a hundred in a day. Also, automated analysis pipelines have been optimized to take raw data as input and directly deliver the final analysis in a format for biologist end-users. This phenotyping is dedicated to Research & Development teams in plant science. Precision phenotyping is becoming invaluable to current questions of modern biology, seeking a quantitative understanding of the mechanisms governing plant growth and development. In relation to their sessile lifestyle, plant shoot systems generally explore the above-ground space in three dimensions. To make some shoot traits accessible to routine or exploratory phenotyping Automation is crucial. Moving the camera rather than the plant ensures that the plant can remain still -  providing more precision. The Romi Plant Scanner could be used to automate the phenotyping of any trait of the shoot system of a single plant. The plant Scanner is a fixed phenotyping station. Its reasonable size can easily fit into 2 to 3 meters squared, ideally near to a facility where plants are individually cultured in moveable pots. The ROMI has developed a proof-of-concept scenario using a challenging task: measuring the in-flor-escence phyllo-taxis of the model plant Arabodopsis thaliana. The hardware design shares many components from the Romi Rover and it shares the same camera module with the Romi Cable Bot, ensuring reusability and maintainability across the full ROMI stack. The rover is available as an Open Source project. All of the source code and plans are freely available. This allows us to improve the design over time using input from farmers and engineers. That is also why we made the design modular using components that can be found “off-the-shelf” or that can be produced using 3D printers and laser cutters. People with development skills can also contribute. Our software is available online on Github. This makes the Romi Scanner a good platform to experiment with innovative tools for farming research.

opencc-zeroJan 2021View details →

Data from: ZmIBH1-1 regulates plant architecture in maize

Leaf angle (LA) is a critical agronomic trait which affects grain yield through planting density in maize. Much research has been conducted in recent years to investigate the genes responsible for LA variation and a few genes were identified through map-based cloning. Here we cloned the ZmIBH1-1 gene, which is a bHLH transcription factor with both a basic binding region and a Helix-Loop-Helix domain; and qRT-PCR results showed that ZmIBH1-1 is a negative regulator of LA in maize. Histological analysis showed that the change in LA was mainly caused by differential cell wall lignification and cell elongation in the ligular region. To reveal the regulatory framework of ZmIBH1-1, we conducted RNA-Seq and DAP-Seq analysis. Overlay of the RNA-Seq and DAP-Seq results revealed 59 ZmIBH1-1 modulated target genes with annotation, and they were mainly cell wall related, cell development or hormone related genes. We have built a new regulatory model of ZmIBH1-1 gene controlling plant architecture in maize.

opencc-by-4.0Feb 2020View details →

Protein elution profiles accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"

Key to files Experiment_Order.csv <ul> <li>Description: Meta details of each experiment.</li> <li>Format: experiment_name,ExperimentID_order,tissue,experiment_type,spec,ExperimentID</li> </ul> Fraction_Details.csv <ul> <li>Description: Meta details of each fraction</li> <li>Format:FractionID,frac_order,ExperimentID</li> </ul> plant_virNOG_orthology.csv.gz <ul> <li>Description: Conversion between orthogroup and protein IDs.</li> <li>Format:ID,ProteinID,spec</li> </ul> orthogroup_annotation.csv.gz <ul> <li>Description: Orthogroup annotations</li> <li>Format:ID,Annotation,arath_genenames,arath_Entries,arath_Entry_names,arath_Protein_names,disruptions,tair_disruptions,lloyd2012_LOFs,arath_functions,arath_misc,pathway,unipathway,BioCyc,Reactome,BRENDA,kegg_pws,ec,arath_masses,arath_protein_names,arath_GO,devstages,tissues,tair,araport,orysj_genenames,orysj_Entries,orysj_Entry_names,orysj_Protein_names,orysj_disruptions,orysj_functions,orysj_misc</li> </ul> panplant_tidy_elution_virNOG.csv.gz <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed orthogroups for all experiments. Includes parts per million in each fraction. </li> <li>Format: ExperimentID,FractionID,ID,Total_PeptideCount,spec,ExperimentID_order,FractionID_order,abundance_ppm</li> </ul> panplant_tidy_elution_protcount.csv.gz <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed proteins for all experiments. </li> <li>Format: ExperimentID,FractionID,ProteinID,ProteinCount,spec,ExperimentID_order,FractionID_order</li> </ul> panplant_wide_elution_virNOG.csv.gz <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed orthogroups</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> panplant_wide_elution_virNOG_annot.csv.gz <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed orthogroups, includes annotation columns.</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> panplant_wide_elution_expnorm.csv.gz <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed orthogroups</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> panplant_wide_elution_expnorm_annot.csv.gz <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed orthogroups, including columns with annotations</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> [experiment_name].virNOG.wide.gz <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) for all observed orthogroups in one experiment</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <ul> </ul> [experiment_name].protcount.wide.gz <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) counts for all observed proteins in one experiment</li> <li>Format: ProteinID,[Fractions]</li> </ul> [species]_specconcat.virNOG.wide.gz <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed orthogroups from a particular species. Only present for species with more than one experiment. </li> <li>Format: OrthogroupID,[Fractions]</li> </ul> [species]_specconcat.protcount.wide.gz <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed proteins from a particular species. Only present for species with more than one experiment. </li> <li>Format: ProteinID,[Fractions]</li> </ul>   <ul> </ul> Species codes |Code | Species | Common name   | Use | |---|---|---| | arath  | Arabidopsis Thaliana  | Arabidopsis  |  | braol  | Brassica oleracea  | Broccoli  | | cansa  | Cannabis sativa  | hemp  |  | cerri | Ceratopteris richardii | C-fern |  | chlre  | Chlamydomonas reinhardtii  | Chlamydomonas   | | chqui  | Chenopodium quinoa  | Quinoa  |  | orysj  |  Oryza sativa var. japonica | Rice  | | selml | Selaginella moellendorffii | Selaginella  |  | sollc  | Solanum  lycopersicum | Tomato  |  | wheat  | Triticum  Aestivum | Wheat  |  | soybn | Glycine max | Soybean |  | cocnu | Cocos nucifera | Coconut |  | maize | MAIZE | maize |

opencc-by-4.0Apr 2020View details →

FIGURE 2. Tiganophyton karasense. A. Plant habit and habitat. B in From the frying pan: an unusual dwarf shrub from Namibia turns out to be a new brassicalean family

FIGURE 2. Tiganophyton karasense. A. Plant habit and habitat. B. Part of an old long shoot showing short shoots with their rosettes of foliage leaves (mainly) and bracts. C. Young, actively elongating long shoots with short shoots not yet fully developed in leaf axils; arrows indicate where a long shoot emerges from the apex of a short shoot. D. Long shoot densely covered with short shoots, the latter bearing flowers. Photographs: W. Swanepoel.

Effects of wind on honeybee and bumblebee foraging behaviour on multiple plant species

Dataset of results used for two publications. It shows the foraging behaviours of honeybees and bumblebees on multiple plant species in different wind speeds,

opencc-zeroJan 2021View details →

Data from: Quantifying nectar production by flowering plants in urban and rural landscapes

Floral resources (nectar and pollen) provide food for insect pollinators but have declined in the countryside due to land use change. Given widespread pollinator loss, it is important that we quantify their food supply to help develop conservation actions. While nectar resources have been measured in rural landscapes, equivalent data are lacking for urban areas, an important knowledge gap as towns and cities often host diverse pollinator populations. We quantified the nectar supply of urban areas, farmland and nature reserves in the UK by combining floral abundance and nectar sugar production data for 536 flowering plant taxa, allowing us to compare landscape types and assess the spatial distribution of nectar sugar among land uses within cities. The magnitude of nectar sugar production did not differ significantly among the three landscapes. In urban areas the nectar supply was more diverse in origin and predominantly delivered by non-native flowering plants. Within cities, urban land uses varied greatly in nectar sugar production. Gardens provided the most nectar sugar per unit area and 85% of all nectar at a city scale, while gardens and allotments produced the most diverse supplies of nectar sugar. Floral abundance, commonly used as a proxy for pollinators' food supply, correlated strongly with nectar resources, but left a substantial proportion of the variation in nectar supply unexplained. Synthesis. We show that urban areas are hotspots of floral resource diversity rather than quantity and their nectar supply is underpinned by the contribution of residential gardens. Individual gardeners have an important role to play in pollinator conservation as ornamental plants, usually non-native in origin, are a key source of nectar in towns and cities.

opencc-zeroSep 2019View details →

Data from: Plant uptake offsets silica release from a large Arctic tundra wildfire

Rapid climate change at high latitudes is projected to increase wildfire extent in tundra ecosystems by up to five-fold by the end of the century. Tundra wildfire could alter terrestrial silica (SiO2) cycling by restructuring surface vegetation and by deepening the seasonally-thawed active layer. These changes could influence the availability of silica in terrestrial permafrost ecosystems and alter lateral exports to downstream marine waters, where silica is often a limiting nutrient. In this context, we investigated the long-term effects of the largest Arctic tundra fire in recent times on plant and peat amorphous silica content and dissolved silica concentration in streams. Ten-years after the fire, vegetation in burned areas had 73% more silica in aboveground biomass compared to adjacent, unburned areas. This increase in plant silica was attributable to significantly higher plant silica concentration in bryophytes and increased prevalence of silica-rich gramminoids in burned areas. Tundra fire redistributed peat silica, with burned areas containing significantly higher amorphous silica concentrations in the O-layer, but 29% less silica in peat overall due to shallower peat depth post burn. Despite these dramatic differences in terrestrial silica dynamics, dissolved silica concentration in tributaries draining burned catchments did not differ from unburned catchments, potentially due to the increased uptake by terrestrial vegetation. Together, these results suggest that tundra wildfire enhances terrestrial availability of silica via permafrost degradation and associated weathering, but that changes in lateral silica export may depend on vegetation uptake during the first decade of post-wildfire succession.

opencc-zeroDec 2012View details →

Data from: Genetic diversity in widespread species is not congruent with species richness in alpine plant communities

The Convention on Biological Diversity (CBD) aims at the conservation of all three levels of biodiversity, i.e. ecosystems, species and genes. Genetic diversity represents evolutionary potential and is important for ecosystem functioning. Unfortunately, genetic diversity in natural populations is hardly considered in conservation strategies because it is difficult to measure and has been hypothesized to co-vary with species richness. This means that species richness is taken as a surrogate of genetic diversity in conservation planning, though their relationship has not been properly evaluated. We tested whether the genetic and species levels of biodiversity co-vary, using a large-scale and multi-species approach. We chose the high-mountain flora of the Alps and the Carpathians as study systems and demonstrate that species richness and genetic diversity are not correlated. Species richness thus cannot act as a surrogate for genetic diversity. Our results have important consequences for implementing the CBD when designing conservation strategies.

opencc-zeroDec 2018View details →

Data from: Nitrogen acquisition of Central European herbaceous plants that differ in their global naturalization success

It is frequently assumed that species capable of fast nitrogen (N) acquisition under different N-availability conditions should have a higher establishment success after their introduction into new regions. However, few experimental studies have explicitly tested this. Our multispecies experiment tested whether global naturalization success of plant species native to Central Europe is related to a high N-acquisition ability. We selected 41 common herbaceous species native to Germany that have all become naturalized, and thus been introduced, elsewhere. Twenty-two of these species are widely naturalized and 19 are less widely naturalized. We grew the 41 grassland species, sampled in Germany, under low and high N conditions in a greenhouse experiment, and assessed their N-acquisition abilities. Although the widely naturalized species grew faster on average, they had a significantly lower N-uptake rate than the less widely naturalized ones. The widely naturalized species, however, had a marginally significantly higher root-mass fraction. Despite these differences, the total plant N-content did on average not differ between the two groups of species. However, N addition tended to increase the total plant N-content more for the widely naturalized species than for the less widely naturalized species. Nitrogen addition also increased biomass production and N-uptake rate, and decreased the root-mass fraction of plants, but these responses did not differ between widely and less widely naturalized species. We conclude that although fast-growing species tend to have a higher global naturalization success than slow-growing species, the naturalization success of plants is not necessarily related to a high N-acquisition ability.