Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
634
datasets available to search
ShareScore release 0.7.1
Dataset results
634 results for “Plant invasions”
Supplementary material 1 from: Kendig AE, Canavan S, Anderson PJ, Flory SL, Gettys LA, Gordon DR, Iannone III BV, Kunzer JM, Petri T, Pfingsten IA, Lieurance D (2022) Scanning the horizon for invasive plant threats using a data-driven approach. NeoBiota 74: 129-154. https://doi.org/10.3897/neobiota.74.83312
Methods S1
Supplementary material 1 from: Bitani N, Shivambu TC, Shivambu N, Downs CT (2022) An impact assessment of alien invasive plants in South Africa generally dispersed by native avian species. NeoBiota 74: 189-207. https://doi.org/10.3897/neobiota.74.83342
Table S1
Supplementary material 2 from: Bitani N, Shivambu TC, Shivambu N, Downs CT (2022) An impact assessment of alien invasive plants in South Africa generally dispersed by native avian species. NeoBiota 74: 189-207. https://doi.org/10.3897/neobiota.74.83342
Table S2
Supplementary material 3 from: Sirbu C, Miu IV, Gavrilidis AA, Gradinaru SR, Niculae IM, Preda C, Oprea A, Urziceanu M, Camen-Comanescu P, Nagoda E, Sirbu IM, Memedemin D, Anastasiu P (2022) Distribution and pathways of introduction of invasive alien plant species in Romania. NeoBiota 75: 1-21. https://doi.org/10.3897/neobiota.75.84684
Appendix S3. Publications used to compile distribution of alien plant species in Romania.
Supplementary material 1 from: Sirbu C, Miu IV, Gavrilidis AA, Gradinaru SR, Niculae IM, Preda C, Oprea A, Urziceanu M, Camen-Comanescu P, Nagoda E, Sirbu IM, Memedemin D, Anastasiu P (2022) Distribution and pathways of introduction of invasive alien plant species in Romania. NeoBiota 75: 1-21. https://doi.org/10.3897/neobiota.75.84684
Appendix S1. List of invasive and potentially invasive alien plant species in Romania
Supplementary material 1 from: Piria M, Radočaj T, Vilizzi L, Britvec M (2022) Climate change may exacerbate the risk of invasiveness of non-native aquatic plants: the case of the Pannonian and Mediterranean regions of Croatia. In: Giannetto D, Piria M, Tarkan AS, Zięba G (Eds) Recent advancements in the risk screening of freshwater and terrestrial non-native species. NeoBiota 76: 25-52. https://doi.org/10.3897/neobiota.76.83320
Table S1
Supplementary material 1 from: Yazlık A, Ambarlı D (2022) Do non-native and dominant native species carry a similar risk of invasiveness? A case study for plants in Turkey. In: Giannetto D, Piria M, Tarkan AS, Zięba G (Eds) Recent advancements in the risk screening of freshwater and terrestrial non-native species. NeoBiota 76: 53-72. https://doi.org/10.3897/neobiota.76.85973
Tables S1–S3, Figure S1
Supplementary material 1 from: Connolly BM, Powers J, Mack RN (2017) Biotic constraints on the establishment and performance of native, naturalized, and invasive plants in Pacific Northwest (USA) steppe and forest. NeoBiota 34: 21-40. https://doi.org/10.3897/neobiota.34.10820
Table S1 : Explanation note: Site sites with UTM coordinates and elevation a.s.l.
Supplementary material 2 from: Lindemann-Matthies P (2016) Beasts or beauties? Laypersons' perception of invasive alien plant species in Switzerland and attitudes towards their management. NeoBiota 29: 15-33. https://doi.org/10.3897/neobiota.29.5786
English translation of the questionnaire :
Supplementary material 1 from: Lindemann-Matthies P (2016) Beasts or beauties? Laypersons' perception of invasive alien plant species in Switzerland and attitudes towards their management. NeoBiota 29: 15-33. https://doi.org/10.3897/neobiota.29.5786
Short description of the eight invasive alien plant species :
Figure 3 from: Chadin I, Dalke I, Zakhozhiy I, Malyshev R, Madi E, Kuzivanova O, Kirillov D, Elsakov V (2017) Distribution of the invasive plant species Heracleum sosnowskyi Manden. in the Komi Republic (Russia). PhytoKeys 77: 71-80. https://doi.org/10.3897/phytokeys.77.11186
Figure 3 - The prediction map of Heracleum sosnowskyi habitats prepared with the species distribution model based on bioclaimatic predictors. The borders of Plot 2 within which the model prediction was made. The colour scale shows the probability Heracleum sosnowskyi presence.
Figure 2 from: Chadin I, Dalke I, Zakhozhiy I, Malyshev R, Madi E, Kuzivanova O, Kirillov D, Elsakov V (2017) Distribution of the invasive plant species Heracleum sosnowskyi Manden. in the Komi Republic (Russia). PhytoKeys 77: 71-80. https://doi.org/10.3897/phytokeys.77.11186
Figure 2 - The prediction map of Heracleum sosnowskyi habitats prepared with the species distribution model based on vegetation cover map, nearest road proximity map, proximity map to the borders of agricultural areas. The colour scale shows the probability Heracleum sosnowskyi presence.
Figure 1 from: Chadin I, Dalke I, Zakhozhiy I, Malyshev R, Madi E, Kuzivanova O, Kirillov D, Elsakov V (2017) Distribution of the invasive plant species Heracleum sosnowskyi Manden. in the Komi Republic (Russia). PhytoKeys 77: 71-80. https://doi.org/10.3897/phytokeys.77.11186
Figure 1 - Study area. Red points indicate occurrences of Heracleum sosnowskyi described in the data paper.
Figure 1 in Towards a framework for invasive aquatic plant survey design in Great Lakes coastal areas
Figure 1. Great Lakes basin showing the five sites where aquatic vegetation sampling occurred. Data credits: Lakes: Great Lakes Aquatic Habitat Framework (GLAHF) Great Lakes shoreline v 1.1, 2014. Basin: Institute for Fisheries Research Great Lakes GIS basin outline GLB_basin_outline_noSLS_IFR, 2004. States/Provinces: ArcGIS Content Team U.S. States and Canada Provinces, 2010.
Figure 4 in Towards a framework for invasive aquatic plant survey design in Great Lakes coastal areas
Figure 4. The predicted richness surface (from forest classification model) showing, (A) predicted plant richness for each grid (sample unit), (B) the grids sampled with rakes (highlighted), and (C) the cells intersected with boat track during the 2019 Milwaukee survey (highlighted).
Figure 3 in Modelling hot spot areas for the invasive alien plant Elodea nuttallii in the EU
Figure 3. The model performance (regularized training gain) of the MaxEnt model generated using subset 3 of the occurrence data when the variable in question is omitted or used in isolation, compared to the performance of the model when all variables are used.
Figure 1 in Modelling hot spot areas for the invasive alien plant Elodea nuttallii in the EU
Figure 1. The model performance (regularized training gain) of the MaxEnt model generated using subset 1 of the occurrence data when the variable in question is omitted or used in isolation, compared to the performance of the model when all variables are used.
Spatial patterns and effects of invasive plants on soil microbial activity and diversity along river corridors - raw data
<p>environmental data, plant community data, CLPP profiles, microbial activity data</p>
Supplementary material 6 from: Tewes LJ, Mueller C (2018) Syndromes in suites of correlated traits suggest multiple mechanisms facilitating invasion in a plant range-expander. NeoBiota 37: 1-22. https://doi.org/10.3897/neobiota.37.21470
Figure S5. Pairwise correlations between individual traits of Bunias orientalis plants :
Supplementary material 5 from: Tewes LJ, Mueller C (2018) Syndromes in suites of correlated traits suggest multiple mechanisms facilitating invasion in a plant range-expander. NeoBiota 37: 1-22. https://doi.org/10.3897/neobiota.37.21470
Figure S4. Reproduction traits of Bunias orientalis plants :
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.