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.
808
datasets available to search
ShareScore release 0.9.0
Dataset results
808 results for “dogs”
Fig. 1 in The threat of free-ranging domestic dog to native wildlife: implication for conservation in Southeast Asia
Fig. 1. Bayesian networks modelling (a) perceived risk to carnivore mammalian species, (b) perceived risk to non-carnivore mammalian species, (c) perceived risk to Avian species and, (d) the spatial distribution of perceived risk from domestic dogs in mainland Southeast Asia.
Figure 1 in A 19th Century New Ireland Dog, Canis familiaris novaehiberniae Lesson, 1827 and the Status of Canis hallstromi Troughton, 1957
Figure 1. Map of New Guinea region showing colonial territories in the late 1800s and type localities of: (1) Canis familiaris novaehiberniae Lesson, 1827a, New Ireland; (2) C. f. papuensis Ramsay, 1879, unspecified, southeastern New Guinea; and (3) C. hallstromi Troughton, 1957, Lavani Valley, Southern Highlands District.
Air temperature and humidity effects on the performance of conservation detection dogs
<p>This is the dataset which underlies the submitted manuscript entitled " <strong>Air temperature and humidity effects on the performance of conservation detection dogs</strong>".</p> <p>The uploaded data contain an xlsx file,which includes the data, as well as a txt readme file, which explains the header information in the data file.</p>
Fig. 2 in First description of Bartonella koehlerae infection in a Spanish dog with infective endocarditis
Fig. 2 Endocardium. Mixed infhammatoru and fibrinous exudate associated to a bacteriah cohonu (white arrow). H&E staininc (×200). Scale-bar: 40 μm
Fig. 3 in First description of Bartonella koehlerae infection in a Spanish dog with infective endocarditis
Fig. 3 Endocardium. Fibrinous and neutrophihic exudation accompanied sith intense fibrovascuhar reactivitu (bottom richt). H&E staininc (×400). Scale-bar: 40 μm
Fig. 4 in First description of Bartonella koehlerae infection in a Spanish dog with infective endocarditis
Fig. 4 Evohutionaru rehationships of taxa. The evohutionaru historu sas inferred usinc the Neichbor-Joininc method. The optimah tree is drasn to scahe, sith branch hencths (next to the branches) in the same units as those of the evohutionaru distances used to infer the phuhocenetic tree. The evohutionaru distances sere computed usinc the Maximum Composite Likehihood method and are in the units of the number of base substitutions per site
Dogs do not use their own experience with novel barriers to infer others' visual access
<p>Despite extensive research into the Theory of Mind abilities in nonhuman animals, it remains controversial whether they can attribute mental states to other individuals or whether they merely predict future behaviour based on previous behavioural cues. In the present study, we tested pet dogs (in total, N=92) on adaptations of the "goggles test" previously used with human infants and great apes. In both a cooperative and a competitive task, dogs were given direct experience with the properties of novel screens (one opaque, the other transparent) inserted into identical, but differently coloured, tunnels. Dogs learned and remembered the properties of the screens even when, later on, these were no longer directly visible to them. Nevertheless, they were not more likely to follow the experimenter's gaze to a target object when the experimenter could see it through the transparent screen. Further, they did not prefer to steal a forbidden treat first in a location obstructed from the experimenter's view by the opaque screen. Therefore, dogs did not show perspective-taking abilities in this study in which the only available cue to infer others' visual access consisted of the subjects' own previous experience with novel visual barriers. We conclude that the behaviour of our dogs, unlike that of infants and apes in previous studies, does not show evidence of experience projection abilities.</p>
Fig. 2 in Strongyloidiasis in humans and dogs in Southern Italy: an observational study
Fig. 2 (MAP 2): Geographic distribution of positive dogs (n=6) and humans (n=9). Different colours as in map 1 are indicative of the different habits (red= kennels, blue= agricultural farms; violet= livestock farm). None of the kennels/farms positive for dogs were positive for humans and vice-versa
Fig. 1 in Strongyloidiasis in humans and dogs in Southern Italy: an observational study
Fig. 1 (MAP 1): Distribution of sampled farms and kennels differentiated by colours (red=kennels; violet=livestock farms; Blue=agricultural farms)
Data and code for: A standardised approach to quantifying activity in domestic dogs
<p>Objective assessment of activity via accelerometry can provide valuable insights into dog health and welfare. Common activity metrics involve using acceleration cut-points to group data into intensity categories and reporting the time spent in each category. Lack of consistency and transparency in cut-point derivation makes it difficult to compare findings between studies. We present an alternative metric for use in dogs: the acceleration threshold <em>(</em>as a fraction of standard gravity<em>,1g = 9.81m/s<sup>2</sup>)</em> above which the animal's X most active minutes are accumulated (MX<sub>ACC</sub>) over a 24-hour period. We report M2<sub>ACC,</sub> M30<sub>ACC</sub> and M60<sub>ACC</sub> data from a colony of healthy beagles (n=6) aged 3-13 months. To ensure that reference values are applicable across a wider dog population, we incorporated labelled data from beagles and volunteer pet dogs (n=16) of a variety of ages and breeds. The dogs' normal activity patterns were recorded at 200 Hz for 24-hours using collar-based Axivity-AX3 accelerometers. We calculated acceleration vector magnitude and MX<sub>ACC</sub> metrics. Using labelled data from both beagles and pet dogs, we characterise the range of acceleration outputs exhibited for a variety of behaviours, enabling meaningful interpretation of MX<sub>ACC</sub>. These metrics will help standardise measurement of canine activity, inform development of exercise guidelines and adherence monitoring, and serve as outcome measures for veterinary and translational research. This repository contains two files: 1) `LMM_input_data.csv`, a spreadsheet file containing the raw data used to explore the effect of modifying epoch length and sample frequency on aggregate activity metrics using linear mixed effects models as described in the manuscript. There is one row per dog per epoch length, sample frequency and age combination, and 2) `getMostActiveMinsThresh.m`, a MATLAB function used to compute the MX ACC outcome metrics that are explored in the manuscript.</p>
Dataset: Dogness (International) Corporation (DOGZ) 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.
Figure 1 in Newly registered tracks of Raccoon dogs (Nyctereutes procyonoides) indicate the presence of resident population in the region of Bolata dere (NE Bulgaria)
Figure 1. Schematic map of Bolata bay. The red dot indicates the position of the footprints of a Raccoon dog found on 16.04.2015. Abbreviations: N. p. tracks – Nyctereutes procyonoides tracks; r.n. - road north, r.s.- road south. Scale bar – 60 m.
Рис. 3. ПоΔразΔеΛение Обжоровского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги. Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны; 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 3. Obzhorovsky transect by zones and subzones of the lower reaches of the Volga delta. Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 3. ПоΔразΔеΛение Обжоровского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги. Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны; 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 3. Obzhorovsky transect by zones and subzones of the lower reaches of the Volga delta. Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
Рис. 2. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 2). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 2. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 2). 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 2. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 2). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 2. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 2). 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
Рис. 1. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 1). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 1. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 1). Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 1. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 1). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 1. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 1). Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
Fig. 7 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs
Fig. 7. Probabilities of flea occupancy (W) for black-tailed prairie dogs (Cynomys ludovicianus) in differing body condition during May–September 2011, at the Vermejo Park Ranch, New Mexico. The solid line depicts estimates of occupancy and dotted lines depict 95% confidence intervals.
Fig. 4 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs
Fig. 4. Model-averaged probabilities for detecting fleas (p) on a black-tailed prairie dog (Cynomys ludovicianus) during May–September 2011, at the Vermejo Park Ranch, New Mexico. Bars depict 95% confidence intervals.
Fig. 3 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs
Fig. 3. Indices for and estimates of flea prevalence on prairie dogs inside old colonies. The estimates are model-averaged values from occupancy models that accounted for imperfect detection of fleas. The naïve indices do not consider imperfect detection. Gains in precision (95% confidence interval) when estimating prevalence are depicted on the right. Confidence intervals for the estimates of prevalence during July–September are very small.
Fig. 2 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs
Fig. 2. The robust design for occupancy models of flea prevalence on black-tailed prairie dogs (Cynomys ludovicianus). Prairie dogs were sampled during primary occasions in different months of the year (May–September 2012). Each primary occasion comprised three secondary occasions (combings) during which fleas might be detected (p = probability of detection, given presence). A prairie dog was ''open'' to colonization by fleas between primary occasions. Once a prairie dog was colonized, it was occupied by fleas during all subsequent primary occasions (thus, the extinction probability, E, was fixed at zero, once a prairie dog was occupied by fleas). Closure was assumed during the secondary occasions, but we used behavioral covariates to account for removal of fleas from hosts during each secondary combing (REMOVAL1 and REMOVAL2, see text). In the example encounter history, a '1' indicates that at least one flea was detected during a combing event, and a '0' indicates that no fleas were detected.
Fig. 5 in Using occupancy models to investigate the prevalence of ectoparasitic vectors on hosts: An example with fleas on prairie dogs
Fig. 5. Model-averaged probabilities of flea occupancy (W) and flea colonization (γ) for black-tailed prairie dogs (Cynomys ludovicianus) in old and young colonies, and natural and translocation colonies during May–September 2011, at the Vermejo Park Ranch, New Mexico (see Fig. 1 and text for colony descriptions). Bars depict 95% confidence intervals. We do not report estimates of colonization for September, because few prairie dogs were sampled in that month.
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.