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.
143
datasets available to search
ShareScore release 0.7.1
Dataset results
143 results for “Species trend”
Figs 142–153 in Leafhoppers of the genus Macropsis Lewis, 1836 (Homoptera: Auchenorrhyncha: Cicadellidae: Macropsinae) on Sakhalin - different evolutionary trends in different species
Figs 142–153. Oscillograms of male calling signals: 142–147 — Macropsis costalis; 148–149 — M. remota; 150–153 — M. flavida; 142–144, 148–149 — males from Sakhalin; 145–147, 150–153 — males from the mainland. Faster oscillograms of the parts of signals indicated as "143–144", "146–147", "149", "151", and "153" are given under the same numbers. Рис. 142–153. Oscillograms of male calling signals: 142–147 — Macropsis costalis; 148–149 — M. remota; 150–153 — M. flavida; 142–144, 148–149 — самцы с Сахалина; 145–147 и 150–153 — самцы с материка. Фрагменты сигналов, обоЗначенные цифрами "143–144", "146–147", "149", "151" и "153" представлены на осциллограммах под такими же номерами.
Figs 29–53 in Leafhoppers of the genus Macropsis Lewis, 1836 (Homoptera: Auchenorrhyncha: Cicadellidae: Macropsinae) on Sakhalin - different evolutionary trends in different species
Figs 29–53. Macropsis spp: 29–31 — M. remota; 32–38 — M. flavida; 39–47 — M. hinganensis (39–44 — specimens from Sakhalin, 45–47 — specimens from the mainland); 48–53 — M. fuscula (48 — specimen from Sakhalin, 49–50 — specimens from Kunashir, 51 — specimen from Khabarovsk Krai, 52 — specimen from Southern Kazakhstan, 53 — specimen from Moscow Oblast). Рис. 29–53. Macropsis spp: 29–31 — M. remota; 32–38 — M. flavida; 39–47 — M. hinganensis (39–44 — ЭкЗемплЯры с Сахалина, 45–47 — ЭкЗемплЯры с материка); 48–53 — M. fuscula (48 — ЭкЗемплЯр с Сахалина, 49–50 — ЭкЗемплЯры с КунаШира, 51 — ЭкЗемплЯр иЗ Хабаровского краЯ, 52 — ЭкЗемплЯр иЗ Южного КаЗахстана, 53 — ЭкЗемплЯр иЗ Московской обл.).
Figure 2 in Bathymetric trends in distribution and size of demersal fish species in the north Aegean Sea
Figure 2. Bathymetric distribution of the dominant fish species within the depth range studied (30–500 m). Black circles represent the centre of gravity (COG); thick lines correspond to the habitat width (HW). Black arrows indicate a displacement in real terms of the COG beyond the depth range sampled. Grey arrows indicate a small displacement in real terms of COG, but included in the depth range considered. Numbers 1–13 on the top axis correspond to the 13 sectors into which the sampled depth interval was divided.
Figure 5 in Bathymetric trends in distribution and size of demersal fish species in the north Aegean Sea
Figure 5. Relationship between regression coefficients (slopes) describing the relationship between fish size and depth and maximum average length of the 15 species with positive sizedepth relationships.
Figure 4. Regression lines between average length and sampling depth for the 19 in Bathymetric trends in distribution and size of demersal fish species in the north Aegean Sea
Figure 4. Regression lines between average length and sampling depth for the 19 species with significant size-depth relationships.
Figure 3 in Bathymetric trends in distribution and size of demersal fish species in the north Aegean Sea
Figure 3. Frequency distribution of the Pearson r correlation coefficient between fish size and sampling depth.
Invasive alien species in Campos Sulinos: current status and future trends
<p>This file belongs to the Electronic supplemental material "Table S1. Researchers who contributed to records of occurrences of species from SISBIO data.".</p>
Supplementary material 4 from: Arianoutsou M, Adamopoulou C, Andriopoulos P, Bazos I, Christopoulou A, Galanidis A, Kalogianni E, Karachle PK, Kokkoris Y, Martinou AF, Zenetos A, Zikos A (2023) HELLAS-ALIENS. The invasive alien species of Greece: time trends, origin and pathways. NeoBiota 86: 45-79. https://doi.org/10.3897/neobiota.86.101778
CBD principal introduction pathways for marine invasive alien species of Greece per different categories
Supplementary material 2 from: Arianoutsou M, Adamopoulou C, Andriopoulos P, Bazos I, Christopoulou A, Galanidis A, Kalogianni E, Karachle PK, Kokkoris Y, Martinou AF, Zenetos A, Zikos A (2023) HELLAS-ALIENS. The invasive alien species of Greece: time trends, origin and pathways. NeoBiota 86: 45-79. https://doi.org/10.3897/neobiota.86.101778
CBD principal introduction pathways for terrestrial invasive alien species of Greece per different categories
Supplementary material 3 from: Arianoutsou M, Adamopoulou C, Andriopoulos P, Bazos I, Christopoulou A, Galanidis A, Kalogianni E, Karachle PK, Kokkoris Y, Martinou AF, Zenetos A, Zikos A (2023) HELLAS-ALIENS. The invasive alien species of Greece: time trends, origin and pathways. NeoBiota 86: 45-79. https://doi.org/10.3897/neobiota.86.101778
CBD principal introduction pathways for freshwater invasive alien species of Greece per different categories
Data for: Multi-scale relationships in thermal limits within and between two cold-water frog species uncover different trends in physiological vulnerability
<ol> <li>Critical thermal limits represent an important component of an organism's capacity to cope with future temperature changes. Understanding the drivers of variation in these traits may uncover patterns in physiological vulnerability to climate change. Local temperature extremes have emerged as a major driver of thermal limits, although their effects can be mediated by the exploitation of fine-scale spatial variation in temperature through behavioral thermoregulation.</li> <li>Here, we investigated thermal limits along elevation gradients within and between two cold-water frog species (<em>Ascaphus</em> spp.), one with a coastal distribution (<em>A. truei</em>) and the other with a continental range (<em>A. montanus</em>). We quantified thermal limits for over 700 tadpoles, representing multiple populations from each species. We combined local temporal and fine-scale spatial temperature data to quantify local thermal landscapes (i.e., thermalscapes), including the opportunity for behavioral thermoregulation.</li> <li>Lower thermal limits for either species could not be reached experimentally reached without the water freezing, suggesting that cold tolerance is <0.3℃. In contrast, upper thermal limits varied among populations, but this variation only reflected local temperature extremes in <em>A. montanus</em>, perhaps due to greater variation in stream temperatures across its range. Lastly, we found minimal fine-scale spatial variability in temperature, suggesting limited opportunity for behavioral thermoregulation and thus increased vulnerability to warming for all populations.</li> <li>By quantifying local thermalscapes, we uncovered different trends in the relative vulnerability of populations across elevation for each species. In <em>A. truei</em>, physiological vulnerability decreased with elevation, whereas in <em>A. montanus</em>, all populations were equally physiologically vulnerable. These results highlight how similar environments can differentially shape physiological tolerance and patterns of vulnerability of species, and in turn, impact their vulnerability to future warming. </li> </ol>
FIGURE 2. A in A review and analysis of the use of epithets in infrageneric, species, and infraspecific names in Kalanchoe (Crassulaceae subfam. Kalanchooideae): trends in the plant naming game
FIGURE 2. A selection of epithets used in Kalanchoe nomenclature. A. Kalanchoe alticola Compton (1975: 47) was named for a habitat feature. B. Kalanchoe beharensis Drake del Castillo (1903:), a toponym, was named for Behara, Madagascar. C. Kalanchoe blossfeldiana Von Poellnitz (1934: 159) [red- and yellow-flowered material], an eponym, was named for Robert Blossfeld (1882–1945). D. Kalanchoe daigremontiana, an eponym, was named for Mr and Mrs Daigremont, who were active around 1914. E. Kalanchoe fedtschenkoi RaymondHamet & Perrier de la Bâthie (1915: 75), an eponym, was named for Boris Alexjewitsch Fedtschenko (1872–1947). The leaf-variegated selection of the species is shown here. F. Kalanchoe gastonis-bonnieri, an eponym, is one of two names in which Gaston Eugène Marie Bonnier (1853–1922) is commemorated. G. Kalanchoe luciae, an eponym, was named for Lucy Dufour (?–?). H. Kalanchoe ×sogae Smith & Figueiredo (2022e: 99), an eponym, was named for Jotello Festiri Soga (1865–1906). All photographs taken by Gideon F. Smith, except D, which was taken by Estrela Figueiredo.
FIGURE 1. A in A review and analysis of the use of epithets in infrageneric, species, and infraspecific names in Kalanchoe (Crassulaceae subfam. Kalanchooideae): trends in the plant naming game
FIGURE 1. A selection of persons commemorated in eponymic epithets in Kalanchoe: A. Alwin Berger (1871–1931) in K. bergeri Raymond-Hamet & Perrier de la Bâthie (1914: 199). B. Bernard Marie Descoings (1931–2018) in K. ×descoingsii Shtein, Gideon F.Sm. & Ikeda in Shtein et al. (2021b: 249). C. Helena Madelain Lamond Forbes (1900–1959) in K. ×forbesiae Smith & Hankey (2021: 295). D. Mary Davidson Gunn (1899–1989) in K. ×gunniae Smith & Figueiredo in Smith et al. (2019b: 147). E. Jean Henri Humbert (1887– 1967) in K. humbertii Guillaumin (1939: 337) and in K. humbertii Mannoni & Boiteau (1947: 150), nom. illeg. (Turland et al. 2018: Art. 53.1). F. Julien Marnier-Lapostolle (1902–1976) (right) in K. marnieriana. Marcel Kroenlein (1928–1994) (left) was the Director of the Jardin Exotique de Monaco from 1969 to 1993. G. The family of Raymond-Hamet (1890–1972), pictured here, in K. hametorum. H. Philippe Édouard Léon van Tieghem (1839–1914) in K. tieghemii and in K. vantieghemii. I. Inez Clare Verdoorn (1896–1989) in K. ×verdoorniae Smith (2022h: 76). Photograph acknowledgements: A, photographer unknown; © and reproduced with the permission, through Lutz Schmalfuss, of the Alwin-Berger-Archiv, B̧rgerverein M̂schlitz e.V., Germany. B, photographer unknown; likely G.W. Reynolds; Descoings assisted Gilbert W. Reynolds in the field in Madagascar in the 1950s. Descoings here stands next to a large specimen of Aloestrela suzannae (Decary 1921: 26) Molteno & Gideon F.Sm. in Smith & Molteno (2019: 5). The original of the black-and-white image is apparently no longer extant. C, D, and I, photographers unknown; photographs © of the South African National Biodiversity Institute, and reproduced with permission. E, photographer unknown; photograph supplied by and © Dr Laurence J. Dorr, U.S. National Herbarium, Smithsonian Institution, U.S.A. F, photograph probably taken by Prof. Dr Werner Rauh in ca. 1974; Foto Heritage W. Rauh— © W. Barthlott; reproduced with permission. G, photographer unknown; image created and enhanced by the late Roy Mottram†, United Kingdom. H, photographer Eugène Pirou, Muséum national d'Histoire naturelle, Paris; image in the public domain because of its age (https://upload.wikimedia.org/wikipedia/commons/0/0c/Philippe_Van_Tieghem_%281839-1914%29.jpg).
Multiple long-term, landscape-scale datasets reveal intraspecific spatial variation in temporal trends for bird species
Open the record for dataset details and reuse information.
Standardised site-level trends, mean productivity, and survival for migrant (arid and humid-zone) and resident species breeding across Europe
Open the record for dataset details and reuse information.
Data from: Tracking ice phenology by migratory waterbirds: settling phenology and breeding success of species with divergent population trends
Open the record for dataset details and reuse information.
Data from: Species’ traits explain differences in Red list status and long-term population trends in longhorn beetles
Open the record for dataset details and reuse information.
Data for: Multi-scale relationships in thermal limits within and between two cold-water frog species uncover different trends in physiological vulnerability
Open the record for dataset details and reuse information.
Data from: Comparative impacts of long-term trends in snowmelt and species interactions on plant population dynamics
Open the record for dataset details and reuse information.
Data from: Assessing species richness trends: declines of bees and bumblebees in the Netherlands since 1945.
Estimating and predicting temporal trends in species richness is of general importance, but notably difficult because detection probabilities of species are imperfect and many datasets were collected in an opportunistic manner. We need to improve our capabilities to assess richness trends using datasets collected in unstandardized procedures with potential collection bias. Two methods are proposed and applied to estimate richness change, which both incorporate models for sampling effects and detection probability: (1) non-linear species accumulation curves with an error variance model and (2) Pradel capture-recapture models. The methods are used to assess nationwide temporal trends (1945-2018) in the species richness of wild bees in the Netherlands. Previously, a decelerating decline in wild bee species richness was inferred for part of this dataset. Among the species accumulation curves, those with non-constant changes in species richness are preferred. However, when analysing data subsets, constant changes became selected for non-Bombus bees (for samples in collections) and bumblebees (for spatial grid cells sampled in three periods). Smaller richness declines are predicted for non-Bombus bees than bumblebees. However, when relative losses are calculated from confidence intervals limits, they overlap and touch zero loss. Capture-recapture analysis applied to species encounter histories infers a constant colonization rate per year and constant local species survival for bumblebees and other bees. This approach predicts a 6% reduction in non-Bombus species richness from 1945 to 2018 and a significant 19% reduction for bumblebees. Statistical modelling to detect species richness time trends should be systematically complemented with model checking and simulations to interpret the results. Data inspection, assessing model selection bias and comparisons of trends in data subsets were essential model checking strategies in this analysis. Opportunistic data will not satisfy the assumptions of most models and this should be kept in mind throughout.
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.