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.
421
datasets available to search
ShareScore release 0.9.0
Dataset results
421 results for “Ecology and Conservation”
Fig. 8 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 8. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about reproductive (spawning) season of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 5 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 5. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about migratory behavior of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 1 in Conserving biodiversity through ecological restoration: the potential contributions of botanical gardens and arboreta
Fig. 1. − Restoration in action. A. Remnant native forest being restored by protection, enrichment planting of native species, and the elimination of fire through using fire breaks; B. Firebreaks and controlled burning protect native forest from annual fires that burn throughout much of Madagascar's central plateau; C. Thicket vegetation with a monoculture of common buckthorn (Rhamnus cathartica L.), one of the region's worst invasive species; D. View of a section of McDonald Woods that has been restored by removing buckthorn, planting seedlings of native species, and returning the prescribed fire regime.
Fig. 1 in Conserving biodiversity through ecological restoration: the potential contributions of botanical gardens and arboreta
Fig. 1. − Restoration in action. A. Remnant native forest being restored by protection, enrichment planting of native species, and the elimination of fire through using fire breaks; B. Firebreaks and controlled burning protect native forest from annual fires that burn throughout much of Madagascar's central plateau; C. Thicket vegetation with a monoculture of common buckthorn (Rhamnus cathartica L.), one of the region's worst invasive species; D. View of a section of McDonald Woods that has been restored by removing buckthorn, planting seedlings of native species, and returning the prescribed fire regime. [A-B: Ankafobe forest, Madagascar, a restoration site run by the Missouri Botanical Garden; C-D:McDonald Woods, Chicago Botanical Garden, U.S.A.] [Photos: A: J. Leighton Reid; B: C. Birkinshaw; C-D: J. Steffen]
Fig. 4 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Reptiles
Fig. 4. Areas (polygons) in Western Podillya (Ukraine), where there is a predicted probability for the accommodation 9, 8 or 7 reptile species (gradient from dark gray — 9 species to light — 7 species). Districts numbered as in fig. 3.
Fig. 3 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Reptiles
Fig. 3. Areas (downward diagonal filled polygons) in Western Podillya (Ukraine), where the average predicted habitat suitability for reptile species exceeds 0.5 (Districts: 1 — Terebovlianskyi, 2 —Husiatynskyi, 3 — Buchatskyi, 4 — Chortkivskyi, 5 — Chemerovetskyi, 6 — Horodenkivskyi, 7 — Zalishchytskyi, 8 — Borshchivskyi, 9 — Kamianets-Podilskyi, 10 — Zastavnivskyi, 11 — Khotynskyi).
Fig. 4 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 4. Two upper categories ("Moderate" and "High") collapsed to identify areas of predicted presence (dark gray shading) for B. variegata in the study area.
Fig. 1 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 1. Response of Bombina variegata to Bio 11: x-axis — mean temperature of coldest quarter (°C x 10); y- axis— logistic output (probability of presence).
Fig. 3 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 3. Response of Triturus cristatus to the Human Footprint: x-axis — Human Footprint; y-axis — logistic output (probability of presence).
Fig. 2 in Using Ecological Niche Modeling For Biodiversity Conservation Guidance In The Western Podillya (Ukraine): Amphibians
Fig. 2. Response of Pelobates fuscus to Bio 3: x-axis — isothermality; y-axis — logistic output (probability of presence).
Replication Data and Analyses for: J. Monsimet, S. Sjögersten, N.J. Sanders, M. Jonsson, J. Olofsson & M. Siewert, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation.
<p>This dataset corresponds to the article: <strong>"Jérémy Monsimet*¹, Sofie Sjögersten², Nathan J. Sanders³, Micael Jonsson¹, Johan Olofsson¹, Matthias Siewert¹, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, <em>Remote Sensing in Ecology and Conservation</em>"</strong></p> <p>DOI: <a href="https://doi.org/10.1002/rse2.400" target="_blank" rel="nofollow noreferrer noopener">10.1002/rse2.400</a></p> <p>1 Department of Ecology and Environmental Science, Umeå University, Sweden<br>2 School of Biosciences, University of Nottingham, Loughborough, UK<br>3 Department of Ecology and Evolutionary Biology, University of Michigan, US</p> <p>The gitlab repository of this dataset is available at: <a href="https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/">https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/</a></p> <p>In this repository, you will find the analyses and results presented in the paper. In each folder, there is a html file that can be read after downloading locally the whole folder. You can either run the .qmd file used to produce the html file or walk through the html files (see the readme.md for more information).</p> <p>Paper abstract:</p> <p>High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the<em> Betula nana</em> vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.</p>
Fig. 2 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 2. The Rarity and Ecological Diversity (RED)-index of the different aquatic habitats (aquatic habitats with the same letter are not significantly different at p = 0.05 by non-parametric Tukey-test)
Fig. 1 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 1. The map of Hungary with the position of the sampling sites (filled squares show light traps)
Fig. 3 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 3. The diversity (A) and RAR-index (B) of the different aquatic habitats (aquatic habitats with the same letter are not significantly different at p = 0.05 by non-parametric Tukey-test)
Fig. 4 in A social beauty: distribution, ecology and conservation of Iris oratoria in the Central Mediterranean Region (Insecta: Mantodea)
Fig. 4 – Comparison of the Extent Of Occurrence (EOO) of the Mediterranen populations of Iris oratoria calculated on scientific records (red polygon) and on records from citizen science (green polygon). Base map: OpenStreetMap.
Fig. 2 in A social beauty: distribution, ecology and conservation of Iris oratoria in the Central Mediterranean Region (Insecta: Mantodea)
Fig. 2 – The strip transect at the Dune di Giovino (southern Italy, Calabria) in a retrodunal area (left) and a sub-adult male of Iris oratoria on Artemisia vulgaris.
Fig. 1 in A social beauty: distribution, ecology and conservation of Iris oratoria in the Central Mediterranean Region (Insecta: Mantodea)
Fig. 1 – Presence records of Iris oratoria in the Central Mediterranean region, from the original records here presented (orange dots), collecting records from preserved specimens in museum collections and literature (red dots), from occasional records not related to confirmed populations (blue dots) and from citizen-science observations (green dots). Base map: OpenStreetMap.
Raw data for: Spatial and temporal variation in farmland bird nesting ecology: Implications for effective Corn Bunting Emberiza calandra conservation
<p>These are raw data accompanying the study "<span>Spatial and temporal variation in farmland bird nesting ecology: Implications for effective Corn Bunting Emberiza calandra conservation</span>". All information on data origin, data analysis, and derived implications will be available with the original publiation.</p>
Рис. 2. Основные места концентрации фуражирующих особей Bombus distinguendus в АрхангеΛьской обΛасти: 1 — Разнотравно-зΛаковый Λуг с Trifolium pratense и Trifolium repens в окрестностях гороΑа Мезень; 2 — Разнотравно-зΛаковый Λуг по обочине Αороги с Centaurea scabiosa в окрестностях сеΛа ХоΛмогоры; 3 – Агроценоз со Stachys palustris в ΑеΛьте реки Северная Δвина; 4 — РуΑераΛьное сообщество с Chamaenerion angustifolium в ΑеΛьте реки Северная Δвина Fig. 2. Typical foraging habitats of Bombus distinguendus in Arkhangelsk Oblast: 1 — Meadow with Trifolium pratense and Trifolium repens near the town of Mezen; 2 — Roadside meadow with Centaurea scabiosa near the village of Kholmogory; 3 — Agricultural habitat with Stachys palustris in the delta of the Northern Dvina River; 4 — Ruderal community with Chamaenerion angustifolium in the delta of the Northern Dvina River in Bombus distinguendus Morawitz, 1869 (Hymenoptera: Apidae) in Arkhangelsk Oblast, Russia: Distribution, ecology and conservation
Рис. 2. Основные места концентрации фуражирующих особей Bombus distinguendus в АрхангеΛьской обΛасти: 1 — Разнотравно-зΛаковый Λуг с Trifolium pratense и Trifolium repens в окрестностях гороΑа Мезень; 2 — Разнотравно-зΛаковый Λуг по обочине Αороги с Centaurea scabiosa в окрестностях сеΛа ХоΛмогоры; 3 – Агроценоз со Stachys palustris в ΑеΛьте реки Северная Δвина; 4 — РуΑераΛьное сообщество с Chamaenerion angustifolium в ΑеΛьте реки Северная Δвина Fig. 2. Typical foraging habitats of Bombus distinguendus in Arkhangelsk Oblast: 1 — Meadow with Trifolium pratense and Trifolium repens near the town of Mezen; 2 — Roadside meadow with Centaurea scabiosa near the village of Kholmogory; 3 — Agricultural habitat with Stachys palustris in the delta of the Northern Dvina River; 4 — Ruderal community with Chamaenerion angustifolium in the delta of the Northern Dvina River
Рис. 1. Карта АрхангеΛьской обΛасти с точками нахоΑок Bombus distinguendus Fig. 1. Map of Arkhangelsk Oblast with records of Bombus distinguendus in Bombus distinguendus Morawitz, 1869 (Hymenoptera: Apidae) in Arkhangelsk Oblast, Russia: Distribution, ecology and conservation
Рис. 1. Карта АрхангеΛьской обΛасти с точками нахоΑок Bombus distinguendus Fig. 1. Map of Arkhangelsk Oblast with records of Bombus distinguendus
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.