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.
6,019
datasets available to search
ShareScore release 0.7.1
Dataset results
6,019 results for “Diet”
Figure 2 in Diet composition of an invasive population of Lithobates catesbeianus (American Bullfrog) from Argentina
Figure 2. Index of relative importance (IRI) of prey consumed by adult Lithobates catesbeianus; % Number (%N) represents the numerical percentage and % volume (%vol) the volumetric percentage, and the horizontal axis represents the frequency of occurrence of each item.
Figure 3 in Diet composition of an invasive population of Lithobates catesbeianus (American Bullfrog) from Argentina
Figure 3. Index of relative importance (IRI) of prey consumed by juvenile Lithobates catesbeianus; % Number (%N) represents the numerical percentage and % Volume (%Vol) the volumetric percentage, and the horizontal axis represents the frequency of occurrence of each item.
Figure 1 in Diet composition of an invasive population of Lithobates catesbeianus (American Bullfrog) from Argentina
Figure 1. (A) Location of studied invasive population of Lithobates catesbeianus from San Juan (black dot); (B) typical environment where the bullfrogs were captured for this study, around the Castaño Viejo River.
Investigating the Effect of Positional Variation on Mid-Lactation Mammary Gland Transcriptomics in Mice Fed Either a Low-Fat or High-Fat Diet
<p>Investigating the Effect of Positional Variation on Mid-Lactation Mammary Gland Transcriptomics in Mice Fed Either a Low-Fat or High-Fat Diet.</p>
Dataset of BPA concentration levels in food from the second French total diet study
<p><strong>Information about the BPA data set</strong></p> <p> </p> <p><em>1. Description of the total diet study 2 (TDS2) and the BPA study</em></p> <p>The French TDS2 consisted in collecting food products representative of the French population's consumption. This kind of study has been recommended by the World Health Organization (WHO) and the European Food Safety Authority (EFSA) and are implemented with standardized methods. The collected foods were prepared “as consumed” to take account of the population’s common practices, they were combined as composite/pooled samples and were analyzed for chemicals of public health interest. The aims of the TDS2 were to assess the population's exposure to the analyzed substances and the risk for the substances for which reference values exist. To achieve these objectives nearly 20,000 food products were purchased in about 30 cities throughout France (metropolitan territory) and prepared to constitute 1,319 samples corresponding to 212 food items (Sirot <em>et al</em>, 2009). Each sample was composed of 15 sub-samples of the same food and mass, considering different brands and consumer food preferences. All samples, except a few, were collected twice during the TDS to reflect the potential seasonal variability in composition or contamination. Some foods were also purchased in different regions of France because of the potential regional differences in contamination.</p> <p>Analysis of BPA contamination levels in food was not initially planned in the TDS2, and was conducted in a second wave of analyses. The laboratory (LABERCA) in charge of analysis verified that sample containers did not contain BPA. Some TDS samples had not a sufficient weight to be analyzed so only 207 food items were considered.</p> <p> </p> <p><em>2. Description of the data set</em></p> <p>The dataset presents the BPA contamination levels at the food item level, i.e. seasonal sample values were averaged at the regional level and then regional sample values were averaged to represent the national level. The original regional dataset is available in French at the following URL <a href="https://www.data.gouv.fr/fr/datasets/r/4b911e97-368b-4b0e-8891-cecd81aa39bb">https://www.data.gouv.fr/fr/datasets/r/4b911e97-368b-4b0e-8891-cecd81aa39bb</a>.</p> <p>The analytical methods and limits (limit of detection and quantification) are described in (Bemrah <em>et al</em> 2014).</p> <p> </p> <p>2.1. Pre-processing of concentration data</p> <p>Left censored data (concentration value below the analytical limits) were managed under 3 hypotheses:</p> <ul> <li>The lowerbound (LB) hypothesis: Concentrations below the LOD (undetected substances) were replaced by 0, and concentrations below the LOQ but above the LOD (known as "traces") were replaced by the LOD value;</li> <li>The middlebound (MB) hypothesis: Concentrations below the LOD were replaced by ½ LOD, and concentrations below the LOQ but above the LOD were replaced by ½ (LOD+LOQ) ;</li> <li>Upperbound (UB) hypothesis: Concentrations below the LOD were replaced by the LOD value, and concentrations below the LOQ but above the LOD were replaced by the LOQ.</li> </ul> <p> </p> <p>2.2. Thesaurus</p> <p>The variables of the dataset and their definitions were presented in the list below:</p> <ul> <li>family: name of the chemical group of the substance;</li> <li>subst: abbreviated name of the substance;</li> <li>subst_cn: complete name of the substance;</li> <li>unit: unit of the mean concentration value of the substance;</li> <li>mean_CONTA_LB: mean of the concentration values of sub-samples of the analyzed food calculated under the lower bound hypothesis;</li> <li>mean_CONTA_MB: mean of the concentration values of sub-samples of the analyzed food calculated under the middle bound hypothesis;</li> <li>mean_CONTA_UB: mean of the concentration values of sub-samples of the analyzed food calculated under the upper bound hypothesis;</li> <li>Food_code: identification number of the analyzed food;</li> <li>Food_group: food category of the analyzed food;</li> <li>Type_foods: analyzed food.</li> </ul>
Short-term movements of Boiga nigriceps (Squamata: Colubridae) with notes on its diet
<p>Supplementary files for the <em>Herpetology Notes </em>article titled "Short-term movements of <em>Boiga nigriceps </em>(Squamata: Colubridae) with notes on its diet".</p>
Exploring variability in the diet of depredating sperm whales in the Gulf of Alaska through stable isotope analysis
Sperm whales interact with commercially important groundfish fisheries offshore in the Gulf of Alaska (GOA). This study aims to use stable isotope analysis to better understand the trophic variability of sperm whales and their potential prey, and to use dietary mixing models to estimate the importance of prey species to sperm whale diets. We analyzed tissue samples from sperm whales and seven potential prey (five groundfish and two squid species). Samples were analyzed for stable carbon and nitrogen isotope ratios, and diet composition was estimated using Bayesian isotopic mixing models. Mixing model results suggest that an isotopically combined sablefish/dogfish group, skates, and rockfish make up the largest proportion of sperm whale diets (35%, 28% and 12%) in the GOA. The top prey items of whales that interact more frequently with fishing vessels consisted of skates (49%) and the sablefish/dogfish group (24%). This is the first known study to provide an isotopic baseline of adult male sperm whales and these adult groundfish and offshore squid species, and to assign contributions of prey to whale diets in the GOA. This study provides information to commercial fishermen and fisheries managers to better understand trophic connections of important commercial species.
Data on the taxon and morpho-specific year-round diet and endozoochorous seed dispersal of the world's largest grouse, the Capercaillie Tetrao urogallus
<p><span>Here we present the quantitative data from our original high-resolution taxon- and morpho-specific dietary study based on cuticle microhistological analyses of food remains from the feces of Western Capercaillies <em>Tetrao urogallus</em>. By providing integrative quantitative dietary data based on the functional classification of different plant parts representing 49 kinds of plant food items from four major food categories (</span><span>leaves, buds, inflorescences, and fruits</span><span>), and intact seeds, arthropods, and mineral particles (grit), our dataset has potential applications in dietary studies, dispersal capabilities, and the reintroduction biology of gallinaceous birds. </span><span><span> </span></span></p>
Fig. 2 in Diet And Feeding In The Sea Star Astropecten Indicus (Döderlein, 1888)
Fig. 2. Examples of 16 prey types found in the stomachs of Astropecten indicus (n = 69) collected in Singapore. The white bar at the bottom right of each item = 1 mm. *Cerithium sp. was dead before ingestion.
Fig. 5 in Diet And Feeding In The Sea Star Astropecten Indicus (Döderlein, 1888)
Fig. 5. Scatter plot showing the relationship between number of prey items ingested and Astropecten indicus (n=20) arm length after 24 h.
Fig. 3. a in Diet And Feeding In The Sea Star Astropecten Indicus (Döderlein, 1888)
Fig. 3. a) Number of Astropecten indicus that chose with-shell and without-shell Umbonium vestiarium and Musculista senhousia prey (n=30). b) Mean number + S.E. of prey ingested at 2 h and at 24 h. Differences between light and dark bars are significant for both a) and b).
Body size modulates the extent of seasonal diet switching by large mammalian herbivores in Yellowstone National Park
<div> <p><span>Large mammalian herbivores vary their diets markedly with changes in resource availability yet the ways that seasonal changes in individual foraging behaviors scale up to reconfigure complex trophic networks are poorly understood. Two years of dietary DNA data enabled us to quantify fine-grained dietary variation within and among populations of five large herbivore species at Yellowstone National Park, revealing remarkably strong and significant correlations between body size and five key indicators of diet seasonality (R<sup>2</sup> = 0.71–0.80). Data from GPS collars implicated seasonal changes in each species' movement- and habitat-use patterns as potential determinants of foraging constraints and specializations that give rise to the strong allometry in diet composition. Bison and elk showed relatively muted seasonal changes compared to smaller species that exhibited stronger switches. Whereas the taxonomic breadth of individual diets contracted for all species in winter, larger species generally consumed a greater functional diversity of plants and thus maintained more unique dietary niches under resource limitations.</span></p> </div>
From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector
<p>This work was supported by the European Union's Horizon 2020 project Nutri2Cycle (Grant agreement No. 773682) </p><p>These datasets are supplementary information to the manuscript titled "<a href="https://onlinelibrary.wiley.com/doi/10.1111/1477-9552.12530">From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector</a>"</p>
Diet of Curruca melanocephala in north Portugal
<p>Metadata of foraging interaction between Sardinian Warblers and animal and plant prey items in north Portugal. Data was obtained through DNA metabarcoding analysis using the primers <span>FwhF2-R2n</span> and <span>UniPlantF-R. Contains the diet of 234 individuals sampled across 12 months and four sites. It includes the code to replicate the methods described in the article "</span><em><span>DNA metabarcoding, diversity partitioning and null models reveal mechanisms of seasonal trophic specialisation in a Mediterranean warbler</span></em><span>" currently in the process of publishing.</span><span><br></span></p>
Stable isotope analysis reveals shifts in diet of a breeding montane bird
<p><span>Insectivorous breeding birds require access to high quality prey to produce a successful nest. A lack of suitable prey (e.g., low nutritional quality or low invertebrate availability) that fulfill energetic demands can negatively affect nestling growth and survival. In high elevation ecosystems (>900 m), cooler and wetter climates can have negative influences on invertebrate availability which in turn can affect bird diets. Yet we lack studies of how diet composition changes over elevation gradients. Here, we assessed the diet of Swainson's Thrush (<em>Catharus ustulaus</em>) within the White Mountains, New Hampshire using stable isotope analysis and DNA metabarcoding. We found that the proportion of detritivore arthropods in thrush diets increased with elevation, while the proportion of predatory arthropods and overall niche-width declined. Further, we show that high-elevation thrushes had diets that were different in composition, but similar in diversity to thrushes at low-elevation sites. Lepidoptera, araneae, and coleoptera were important diet items across all elevations, but increases in woodlice and ghost spiders contributed to familial-level differences in diet composition at high-elevation sites. This research suggests montane breeding birds may be consuming low quality prey (i.e., millipedes) at high elevation sites, due to either availability or preference. With considerations due to climate change, environmental contamination, and residual impacts on the diet and nutrient availability for breeding montane birds, understanding diet composition changes along environmental gradients can provide information on nutrient availability for species that breed in harsh climatic conditions. Future work on invertebrate availability and nutritional composition, daily energy expenditure, and dietary niche would help contribute to important context to conserving montane birds within these sensitive, high elevation systems. </span></p>
Linking diet switching to reproductive performance across populations of two Critically Endangered mammalian herbivores
<p>Data associated with Harvey Sky, N., Britnell, J., Antwis, R. <em>et al.</em> Linking diet switching to reproductive performance across populations of two critically endangered mammalian herbivores. <em>Commun Biol</em> <strong>7</strong>, 333 (2024). https://doi.org/10.1038/s42003-024-05983-3</p> <p>The data deposited here includes raw metabarcoding output fasta files and some processed metabarcoding and sample data in xslx files. We include a more detailed description of each file below.</p> <p>Data regarding Kenyan black rhino and Grevy’s zebra are treated as sensitive and confidential. There are therefore restrictions on the data that we can make available. Due to these confidentiality considerations, the sample data stored here does not include locations of sample collection within each reserve for either species, or the identity or breeding data for black rhino. It also only includes the final processed values for NDVI and rainfall. The remote sensing data is available from the repositories cited in the methods, but we cannot provide the shapefiles or other data used to calculate the final values for each sample. </p> <p><em><strong>Raw fasta files_plants.zip</strong></em></p> <p>A zipped folder containing the raw fasta files which were the output from the MiSeq sequencing of dietary plants in the faecal samples for both black rhino and Grevy's zebra. Within the zipped folder, the first part of the title of each fasta.gz file is the sample code (S1, S2, S3 etc), which allows you to cross reference these files with the sample data and processed sequencing data in the xslx files. Files with R1 in the title are foward reads, and R2 are reverse reads. </p> <p><em><strong>Raw fasta files_bacteria.zip</strong></em></p> <p>A zipped folder containing the raw fasta files which were the output from the MiSeq sequencing of microbiome bateria in the faecal samples for both black rhino and Grevy's zebra. Within the zipped folder, the first part of the title of each fasta.gz file is the sample code (S1, S2, S3 etc), which allows you to cross reference these files with the sample data and processed sequencing data in the xslx files. Files with R1 in the title are foward reads, and R2 are reverse reads. </p> <p><em><strong>Sample data and processed metabarcoding data_Black rhino.xlsx</strong></em></p> <p><em>Sample data tab</em></p> <p>The data that we are able to share that is associated with each black rhino sample.</p> <p>SampleID - The code used to identiy each sample which allows it be cross-referenced with other tabs and the fasta files. </p> <p>IndividualID - We are not able to share rhino names or other identifiers, but we have given each individual a unique number so that it can be seen which samples came from the same individuals. </p> <p>NDVI - Mean NDVI of each individual's area of utilisation in the 10-day period within which the sample was collected. The method used to calculate this is described in the methods of the article. </p> <p>Rainfall - Cumulative rainfall over the 30 days previous to sample collection for the 0.05 degree pixel under the sample. The method used to calculate this is described in the methods of the article. </p> <p>Season - Post is the post-rain sampling season June-July 2018. Pre is the pre-rain sampling season January-March 2019. </p> <p>Reserve - The reserve that the sample was collected on. </p> <p>Date - The date of sample collection. </p> <p>Dietary breadth - Shannon-Wiener index of dietary alpha diversity. The method used to calculate this is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p>Poaceae, Fabaceae, Ebenaceae - The relative abundance of each of these three dietary plant families that were the focus of our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p><em>Bacteria numbers of reads</em></p> <p>The number of reads assigned to each bacterial ASV found in each sample. </p> <p><em>Bacteria sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the microbiome metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p><em>Plant numbers of reads</em></p> <p>The number of reads assigned to each dietary plant ASV found in each sample. </p> <p><em>Plant sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the dietary plant metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p> </p> <p><em><strong>Sample data and processed metabarcoding data_Grevy's zebra.xlsx</strong></em></p> <p><em>Sample data tab</em></p> <p>The data that we are able to share that is associated with each Grevy's zebra sample.</p> <p>Sample ID - The code used to identiy each sample which allows it be cross-referenced with other tabs and the fasta files. </p> <p>NDVI - Mean NDVI of each individual's area of utilisation in the 10-day period within which the sample was collected. The method used to calculate this is described in the methods of the article. </p> <p>Rainfall - Cumulative rainfall over the 30 days previous to sample collection for the 0.05 degree pixel under the sample. The method used to calculate this is described in the methods of the article. </p> <p>Reserve - The reserve that the sample was collected on. </p> <p>Season - Post is the post-rain sampling season July-August 2018. Pre is the pre-rain sampling season January-February 2019. </p> <p>Date - The date of sample collection. </p> <p>Dietary breadth - Shannon-Wiener index of dietary alpha diversity. The method used to calculate this is described in the methods of the article. </p> <p>Poaceae, Fabaceae - The relative abundance of each of these two dietary plant families that were the focus of our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p>Indigofera - The relative abundance of each of this Fabaceae genus was included in our analyses. The method used to calculate these is described in the methods of the article. NA signifies that the number of reads returned for that sample was under the threshold that signified sequencing failure for the dietary plant metabarcoding.</p> <p><em>Bacteria numbers of reads</em></p> <p>The number of reads assigned to each bacterial ASV found in each sample. </p> <p><em>Bacteria sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the microbiome metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p><em>Plant numbers of reads</em></p> <p>The number of reads assigned to each dietary plant ASV found in each sample. </p> <p><em>Plant sequences and reads</em></p> <p>The sequence of each ASV, and the taxa assigned to each sequence in the dietary plant metabarcoding. The method for taxonomic assignment is described in the methods of the article. </p> <p> </p>
Data from: Seasonal diet partition among top predators of a small island, Iriomotejima island in the Ryukyu Archipelago, Japan
<p>In general, small islands lack predators because species at higher trophic levels often cannot survive. However, two predators—the Iriomote cat <em>Prionailurus bengalensis iriomotensis</em>, and the Crested Serpent Eagle<em> Spilornis cheela perplexus</em>—live on Iriomotejima Island in the Ryukyu Archipelago, which covers an area of approximately 284 square kilometers. To understand how these two top predators coexist on such a small island with limited resources, we focused on their seasonal feeding habits which are considered crucial for survival in such an island ecosystem. To compare the diets of the Iriomote cat and Crested Serpent Eagle, we used DNA metabarcoding analysis of their fecal samples. In the summer, we identified 16 prey items from Iriomote cat fecal samples, and 15 Crested Serpent Eagle fecal samples. In the winter, we identified 37 and 14 prey items, respectively. Using a non-metric multidimensional scaling (NMDS) and a permutational multivariate analysis of variance (PERMANOVA), our study reveals significant differences in the diet composition at the order level between the predators during both seasons. Furthermore, although some prey items at the species-to-order level overlapped between the two predators, the frequency of occurrence of most prey items differed between them in both seasons. These results suggest that this difference in diets was one of the reasons why the Iriomote cat and the Crested Serpent Eagle coexisted on such a small island.</p>
Figure 3 in Diet of the European eel Anguilla anguilla (Linnaeus, 1758) in two transitional waters of Southwestern Mediterranean
Figure 3. – Comparison of the IRI of major items ingested by Anguilla anguilla from Mellah lagoon (in black) and Wadi El Kebir (in gray).
Figure 1 in Diet of the European eel Anguilla anguilla (Linnaeus, 1758) in two transitional waters of Southwestern Mediterranean
Figure 1. – Geographical location of sampling sites (*) of Anguilla anguilla in Mellah lagoon (ML) and Wadi El Kebir (WEK).
Figure 2. Macrobrachium tenellum adult male who underwent a second spermatophore extraction using the electrostimulation technique. A in Sperm viability in wild-caught males of Macrobrachium tenellum (Smith, 1871) (Decapoda: Caridea: Palaemonidae) fed with different diets
Figure 2. Macrobrachium tenellum adult male who underwent a second spermatophore extraction using the electrostimulation technique. A= The dark brown, melanized spermatophore is different from that observed in healthy males.
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.