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.
6,861
datasets available to search
ShareScore release 0.9.0
Dataset results
6,861 results for “Animals”
Animal metacommunities of temporary ponds in a flat grassland landscape of Uruguay
<p>The database comprises a survey of an animal metacommunity of temporary ponds carried out in October 2008, in a grassland landscape of Uruguay. The animal groups involve macroinvertebrates, fishes, and amphibians. Individual occurrence, taxonomic affiliation, and traits associated with diet and body size are provided. The metacommunity is located in a flat landscape surrounded by hills, where a maximum of 61 ponds, every year, are filled with water in winter and dry out in summer in the same spatial locations. Information on species and morphospecies abundances at the sampling unit level was recorded in 18 ponds, the number of ponds that were active (i.e., with water) at the sampling date. These ponds are part of the metacommunity for which plants have been sampled every year since 2005 in all ponds and for which environmental information was also compiled—data available in this same Dryad entry. Further surveys are being processed and will be available in the future. Data are provided for promoting the advancement of metacommunity theory, corroborating published studies, and generating novel ones. We are incorporating all the available information about the studied metacommunity that now comprises 20 years of continuous monitoring. The database includes animal species and morphospecies abundances at the sampling unit level for 18 temporary ponds sampled in October 2008. The species traits related to body sizes (measured for each individual) and trophic guild (obtained from literature) are presented together with the individual taxonomy. Note that species Order or family can also be used as a proxy of many conserved attributes like life history, resistance strategy, dispersal mode, or vulnerability to predators.</p>
FIGURE 1. Living animals. A in Two New Species of Nudibranch Mollusks from the Tropical Eastern Pacific of Mexico
FIGURE 1. Living animals. A. Rostanga ghiselini sp. nov., specimen from Punta Gringa, Bahía de los Angeles, 14 March 1992, photo by Hans Bertsch. B. Rostanga ghiselini sp. nov., holotype specimen from Punta Gringa, Bahia de los Angeles, 25 March 2017, photo by Craig Hoover. C. Tenellia ivetteae sp. nov., holotype, CASIZ 220143 (upper animal) and paratype, CASIZ (lower animal), from Punta Gringa, Bahia de los Angeles, 30 June 1992, photo by Hans Bertsch.
FIGURE 1. Living animals A–B in Three New Species of Aglajid Cephalaspidean Mollusks from the Tropical Indo-Pacific of the Verde Island Passage
FIGURE 1. Living animals A–B. Philinopsis aliciae sp. nov., holotype, PNM 41061, Mabini, Philippines. C. Philinopsis buntot sp. nov., holotype, PNM 41060, Mabini, Philippines, ventral view showing markings on foot. D. Philinopsis buntotparatype, CASIZ 185942, Tingloy, Philippines. E. Chelidonura alexisi sp. nov., holotype, PNM 41062, Mabini, Philippines.
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
Figure 2 in Genetic structure of Trypanosoma congolense "forest type" circulating in domestic animals and tsetse flies in the South-West region of Cameroon
Figure 2. NJ Tree based on Cavalli-Sforza and Edwards chord distance matrix of T. congolense "forest type" circulating in tsetse flies and domestic animals of Fontem.
From optimality to prestige: Investigating human-animal interactions at Late Mesolithic Hoge Vaart-A27 (Almere, the Netherlands) using a Prey Choice Model
<h2><strong>This dataset is from my Research Master's Thesis from Groningen University. </strong></h2> <p><strong>The dataset includes;</strong></p> <ul> <li>The CSV data necessary to recreate all models and graphs from the thesis</li> <li>The R.Script with all codes</li> </ul> <p><strong>Used packages and programming language: </strong></p> <ul> <li>Kassambara, A. (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots (R package version 0.6.0) [R; Rstudio]. R Foundation for Statistical Computing.<a href="https://doi.org/%3Chttps://CRAN.R-project.org/package=ggpubr%3E."> <https://CRAN.R-project.org/package=ggpubr>.</a></li> <li>Mei, W., Yu, G., & Greenwell, B. M. (2022). ggtrendline: Add Trendline and Confidence Interval to ‘ggplot’ (R package version 1.0.3) [R; Rstudio]. R Foundation for Statistical Computing.<a href="https://cran.r-project.org/package=ggtrendline"> https://CRAN.R-project.org/package=ggtrendline</a></li> <li>Pedersen, T. L. (2024). patchwork: The Composer of Plots (R package version 1.2.0) [R; RStudio]. R Foundation for Statistical Computing.<a href="https://cran.r-project.org/package=patchwork"> https://CRAN.R-project.org/package=patchwork</a></li> <li>R Core Team. (2022). R: A Language and Environment for Statistical Computing (R version 4.2.1) [R; RStudio]. R Foundation for Statistical Computing.<a href="https://www.r-project.org/"> https://www.R-project.org/</a></li> <li>Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC.</li> <li>Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.<a href="https://ggplot2.tidyverse.org"> https://ggplot2.tidyverse.org</a></li> <li>Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686.<a href="https://doi.org/10.21105/joss.01686"> https://doi.org/10.21105/joss.01686</a></li> </ul> <p><strong><span>Abstract</span></strong></p> <p><em><span>The research of the faunal assemblage from Hoge Vaart-A27 (Almere, the Netherlands) provides a new perspective on investigating the connections between foraging strategies and socio-cultural dynamics of the Late Mesolithic and Early Swifterbant communities in Northwest Europe. The significance of this topic lies in its potential to help elucidate the broader socio-cultural aspects of foraging and human-animal interactions during this period. Despite extensive research, there remains a gap in comprehending how prestige influenced prey selection alongside optimal foraging strategies. The study aims to address this gap by employing zooarchaeological methods combined with prey choice modelling to investigate prey selection complexities in Flevoland’s transitioning cultural and physical landscape. The methods include analyses of species abundance and detailed examination of red deer, horse, aurochs and wild boar. The key findings reveal that while (optimal) foraging strategies were practised at Hoge Vaart-A27, they were significantly influenced by motivations such as prestige. The results contribute significantly to research by offering a glimpse into how Northwest European foragers could have perceived and interacted with big game beyond subsistence. </span></em></p> <p><strong><span>Keywords</span></strong><span> </span><span>human-animal relationships, prestige, profitability, prey choice, Swifterbant Culture, optimal foraging theory, costly signalling theory</span></p>
Dataset: Phibro Animal Health Corporation (PAHC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 1 in A Most Unusual Animal Distribution Pattern: A New Siphonocryptid Millipede From Taiwan (Diplopoda, Siphonocryptida)
Fig. 1. Body shape, expressed as the relation between number of terga and body width of Hirudicryptus taiwanensis sp. n.
Figs 7–12 in A Most Unusual Animal Distribution Pattern: A New Siphonocryptid Millipede From Taiwan (Diplopoda, Siphonocryptida)
Figs 7–12. Scanning electron micrographs of Hirudicryptus spp.: 7–8 = caudal edge of midbody terga with pattern of tubercles: 7 = H. canariensis (LOKSA, 1967), 8 = H. taiwanensis sp. n.; 9–10 = left lateral edge of midbody segments with peritreme: 9 = H. canariensis (LOKSA, 1967) with ozopores (o), 10 = H. taiwanensis sp. n.; 11–12: caudal margin of last tergum: 11 = H. canariensis
Figs 2–6 in A Most Unusual Animal Distribution Pattern: A New Siphonocryptid Millipede From Taiwan (Diplopoda, Siphonocryptida)
Figs 2–6. Hirudicryptus taiwanensis sp. n.: 2 = holotype body pattern, dorsal view, 3 = holotype head, frontal view, 4 = paratype from Shiji, head and collum in frontal view (right antenna broken), scanning electron micrograph, 5 = head, right fronto-lateral view, scanning electron micrograph, 6 = holotype,
Fig.4 in A Review Of Latvian Blue (Lz) Cows From The List Of Animal Genetic Resources In Latvia
Fig.4. Association analysis between genotypes of A and B alleles of alpha – lactalbumin gene and protein content (%) at the first three lactations. Bar with a straight edge points to signs of an average group size with a standard deviation; * - refers to the association with the performance at a particular lactation; F – index of ANOVA; p F – statistical signification; η – index of correlation analyse.
Рис. 1. Вероятность обнаружения меченых животных (среΑнее ± ошибка) при пяти- и Αесятиметровых интерваΛах межΑу прикормочными станциями в Αвух экспериментах. По второму эксперименту расчеты сΑеΛаны ΑΛя резуΛьтатов отΛова в течение первых трех и поΛных Αесяти Αней. Значение «p» отражает уровень статистической значимости разΛичий межΑу ΑоΛями животных с меткой при Αвух интерваΛах Fig. 1. Probability of finding marked animals (average±standard error) between feeding stations placed at intervals of five and ten meters in the two experiments. In the second experiment, calculations were made for the results of trapping during the first three days and during the whole period of ten days. The p value reflects the statistical significance of differences between the fractions of animals with a mark for two types of intervals in Verification of the bottle-based method for estimating abundance of small mammals using biomarkers
Рис. 1. Вероятность обнаружения меченых животных (среΑнее ± ошибка) при пяти- и Αесятиметровых интерваΛах межΑу прикормочными станциями в Αвух экспериментах. По второму эксперименту расчеты сΑеΛаны ΑΛя резуΛьтатов отΛова в течение первых трех и поΛных Αесяти Αней. Значение «p» отражает уровень статистической значимости разΛичий межΑу ΑоΛями животных с меткой при Αвух интерваΛах Fig. 1. Probability of finding marked animals (average±standard error) between feeding stations placed at intervals of five and ten meters in the two experiments. In the second experiment, calculations were made for the results of trapping during the first three days and during the whole period of ten days. The p value reflects the statistical significance of differences between the fractions of animals with a mark for two types of intervals
Fig. 1 in Animal remains of the medieval settlement near Brankovtsi village (Vidin Region, NW Bulgaria)
Fig. 1. Some animal remains from the medieval settlement near Brankovtsi village: Capreolus capreolus, proximal right antler (a, b), Gallus gallus domestica, tibiotarsus sin. (c). Photo: Z. Boev
Fig. 2 in A report of 12 unrecorded prokaryotic species isolated from gastrointestinal tracts and feces of various endangered animals in Korea
Fig. 2. Phylogenetic tree based on 16S rRNA gene sequence comparisons, showing the relationship between the isolated strains in this study and the notable species from phylum Firmicutes (a) order Lactobacillales (In particular Enterococcus, Lactobacillus and Vagococcus), phylum Actinobacteria (c) and phylum Proteobacteria (d) and. The trees were mainly reconstructed using the neighbor-joining algorithm (NJ), Maximum parsimony (MP) and maximum likelihood (ML) algorithms were applied for additional comparison. Filled diamonds indicate branches present in the phylogenetic trees generated using the three different methods. Numbers on the nodes (>70%) represent bootstrap values as percentages of 1000 replicates (NJ/MP/ML). Clostridium butyricum DSM 10702T (AQQF01000149), Bifidobacterium bifidum ATCC 29521T (KE993182) and Spirochaeta aurantia subsp. aurantia DSM 1902T (FR749896) were used as outgroups, respectively. Bar, 0.02 (a, c, d) and 0.01 (b) accumulated changes per nucleotide.
Fig. 1 in A report of 12 unrecorded prokaryotic species isolated from gastrointestinal tracts and feces of various endangered animals in Korea
Fig. 1. Transmission electron micrographs of the isolated strains. Strain-1, AE4-1; strain-2, B3; strain-3, M3; strain-4, VM3408; strain-5, VT2418; strain-6, VM2501; strain-7, VT2414; strain-8, VT2504.
Fig. 1. Pyllidia ocellata Cuvier, 1804. A. rhinophores. B. anus. C. dorsal view. D. ventral view. E. oral tentacles. A-C. living animal. D, E in A report on species of phyllidiid and polycerid nudibranch including two species new to Korea
Fig. 1. Pyllidia ocellata Cuvier, 1804. A. rhinophores. B. anus. C. dorsal view. D. ventral view. E. oral tentacles. A-C. living animal. D, E. preserved specimen. An arrow indicates anus. Scales=5 mm.
FIG. 6 in Animal remains in mortuary contexts in Southern Patagonia: a case study in lake Salitroso (Santa Cruz, Argentina)
FIG. 6. — Cases with faunal associations. Circle mark faunal remains: A, SAC 1-7, Individual 1 with guanaco (Lama guanicoe Müller, 1776) bone on his chest and guanaco and lesser rhea (Rhea pennata d'Orbigny, 1834) bones by his knees;B, SAC 1-8, Individual 1 with associated Diplodon chilensis (J.E. Gray, 1828) valves; C, SAC 1-1, Individual 5 with huemul (Hippocamelus bisulcus Molina, 1782) bones in his mouth.Photos credits: The authors.
FIG. 5 in Animal remains in mortuary contexts in Southern Patagonia: a case study in lake Salitroso (Santa Cruz, Argentina)
FIG. 5. — Correspondence analysis of taphonomic and processing mark variables in chenques and domestic open-air sites in northwest Santa Cruz. References: CHENQUES, this paper;CP2OA, ILB, Rindel 2009; GSLN, LPA1, LPA2, LS2, LS3C1, LS5, MS1M4, MS3S3, Bourlot 2009. Abbreviations:CP2OA, Cerro Pampa 2 Ojo de Agua; cut, cutmarks;GSLN, Grippa Sí Litto No; ILB, Istmo Lago Belgrano; LPA1, La Primera Argentina 1; LPA2, La Primera Argentina 2; LS2, La Siberia 2; LS3C1, La Siberia 3 concentración 1; LS5, La Siberia 5;MS1M3, Médanos Sur 1 sondeo 3;MS1M4, Médanos Sur 1 muestreo 4;MS3S3, Médanos Sur 3 sondeo 3;NTAXA, number of species; pe, percussion; W0-5, wheathering stages.
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.