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.
4,014
datasets available to search
ShareScore release 0.9.0
Dataset results
4,014 results for “nutrition”
Infection-nutrition feedbacks: fat supports pathogen clearance but pathogens reduce fat in a wild mammal
<p>Though far less obvious than direct effects (clinical disease or mortality), the indirect influences of pathogens are difficult to estimate but may hold fitness consequences. Here, we disentangle the directional relationships between infection and energetic reserves, evaluating the hypotheses that energetic reserves influence infection status of the host and that infection elicits costs to energetic reserves. Using repeated measures of fat reserves and infection status in individual bighorn sheep (<em>Ovis canadensis</em>) in the Greater Yellowstone Ecosystem, we documented that fat influenced ability to clear pathogens (<em>Mycoplasma ovipneumoniae</em>) and infection with respiratory pathogens was costly to fat reserves. Costs of infection approached, and in some instances exceeded, costs of rearing offspring to independence in terms of reductions to fat reserves. Fat influenced probability of clearing pathogens, pregnancy, and over-winter survival; from an energetic perspective, an animal could survive for up to 23 days on the amount of fat that was lost to high levels of infection. Cost of pathogens may amplify tradeoffs between reproduction and survival. In the absence of an active outbreak, the influence of resident pathogens often is overlooked. Nevertheless, the energetic burden of pathogens likely has consequences for fitness and population dynamics, especially when food resources are insufficient.</p> <p><span> </span></p>
Figure 2. Tree heliotrope growing along a in Of turtles and trees: Nutritional analysis of tree heliotrope (Heliotropium foertherianum) leaves consumed by green turtles (Chelonia mydas) in Hawaiʻi
Figure 2. Tree heliotrope growing along a seawater canal at Nan Madol, Pohnpei, FSM. Photo by Gregory A. Koob, US FWS, 2016
Figure 1 in Of turtles and trees: Nutritional analysis of tree heliotrope (Heliotropium foertherianum) leaves consumed by green turtles (Chelonia mydas) in Hawaiʻi
Figure 1. Green turtle consuming floating, senescent tree heliotrope leaves in the Hilton Waikoloa Lagoon, Kona, Hawaiʻi. Photo by M. Rice
Fig. 4 in The use of morphological and histological features as nutritional condition indices of Pagrus pagrus larvae
Fig. 4. Histological sections of Pagrus pagrus larvae, indicators of long term nutritional condition. Sagital section of 4 µm thick stained with Harris's hematoxylin and eosin counterstain (H-E). (a-c) nervous system, (d-f) cartilage, (g-i) muscle, (j-l) notochord; left = fed larvae, middle = delayed fed larvae and right = starved. hsn: heavily stained nucleus, ics: inter-cellular space, nc: notochord contraction, pn: prominent nucleus. Scale bar indicates 10 µm.
Fig. 1 in The use of morphological and histological features as nutritional condition indices of Pagrus pagrus larvae
Fig. 1. Mean values of morphometrical variables and standard error against larval age expressed as days after hatching (DAH) of Pagrus pagrus larvae from the different feeding treatments. BDA: body depth at the anus, ED: eye diameter, HD: head depth, SL: standard length. T0: before the experiment begins, T1: without food, T2: delayed feeding and T3: fed. Arrows indicate the moment when food was supplied to larvae in T3 (black) and T2 (dark grey). Initial condition of larvae (T0, white squares) was also included in order to allow comparisons.
Fig. 7 in The use of morphological and histological features as nutritional condition indices of Pagrus pagrus larvae
Fig. 7. Mean histological condition index (HCI) and standard error for Pagrus pagrus larvae calculated employing short or long term tissues and a mean value employing all tissues against larval age expressed as days after hatching (DAH) from the feeding treatments, a: without food (T1); b: delayed feeding (T2); c: fed (T3).
Fig. 5 in The use of morphological and histological features as nutritional condition indices of Pagrus pagrus larvae
Fig. 5. Mean histological condition index (HCI) and standard error for Pagrus pagrus larvae from the feeding treatments against larval age expressed as days after hatching (DAH). Different lowercase letters indicate significant differences (p<0.05) among feeding treatments, after one way ANOVA followed by post-hoc Tukey's test. Different capital letters indicate significant differences (p<0.05) among feeding treatments, after Kruskal Wallis test followed by multiple comparisons. T0: before the experiment begins, T1: without food, T2: delayed feeding and T3: fed.
Fig. 6 in The use of morphological and histological features as nutritional condition indices of Pagrus pagrus larvae
Fig. 6. Mean histological condition index (HCI) and standard error for Pagrus pagrus larvae from the feeding treatments calculated employing short or long term tissues and a mean value employing all tissues. Different letters indicate significant differences (p<0.05) among feeding treatments, after one way ANOVA followed by post-hoc Tukey's test. T1: without food, T2: delayed feeding and T3: fed, S: short term HCI, M: mean HCI, L: long term HCI.
Fig. 2 in The use of morphological and histological features as nutritional condition indices of Pagrus pagrus larvae
Fig. 2. Scatterplot of PC2 on PC1 for Pagrus pagrus larvae from the feeding treatments. a: PCA based upon normalized morphometrical variables; b: PCA based upon normalized morphometrical variables and observations grouped by feeding treatments. Correlations between the variables also showed. BDA: body depth at the anus, ED: eye diameter, HD: head depth. T1: without food, T2: delayed feeding and T3: fed. –N indicates normalized morphometrical variables.
Figure 2 in Evaluation of some nutritional quality criteria of seventeen Moroccan dates varieties and clones, fruits of date palm (Phoenix dactylifera L.)
Figure 2. Representation of dates varieties and clones according to their quality characteristics. (a) Representation of variables according to PCA. (b) Segregation of 17 dates varieties and clones according to their quality attributes.
Figure 2 in Root deformation affects mineral nutrition but not leaf gas exchange and growth of Genipa americana seedlings during the recovery phase after soil flooding
Figure 2. Concentrations of P in leaves for G. americana seedlings without or with root deformation (RD) after 28 days of soil drainage (recovery). N = 3. Means followed by the same letter are not significantly different according to Tukey's test (p <0.05). Capital letters represent comparisons water effects within root conditions and lower case letters represent comparisons of roots effects within water conditions.
Figure 1 in Root deformation affects mineral nutrition but not leaf gas exchange and growth of Genipa americana seedlings during the recovery phase after soil flooding
Figure 1. Four months old seedlings of G. americana without (A) and with (B) root deformation (RD) caused by errors in the pricking out process, and a detail of the RD (C).
Fig. 1 in The nutritional ecology of Dectes texanus (Coleoptera: Cerambycidae): Does host choice affect the macronutrient levels in overwintering larvae?
Fig. 1. The mean (± SE) A) head capsule width, B) wet mass, C) levels of protein per unit mass, D) levels of carbohydrates per unit mass, and E) levels of lipids per unit mass in larvae from sunflower (Sun) and soybean (Soy) plant hosts. An asterisk indicates that the levels are significantly different.
Data Report: Educational Pathway on Food and Nutrition in Amyotrophic Lateral Sclerosis on the AVASUS platform
<p><strong>Dataset name:</strong><em> nutri_als_dataset.csv </em></p> <p><strong>Version: </strong>1.0 </p> <p><strong>Dataset period:</strong> 06/01/2021 - 06/05/2024</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>20967</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education<strong> </strong></p> <p><strong>Sources: </strong></p> <ul> <li>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2024a);</li> <li>Brazilian Occupational Classification (CBO) (Brasil, 2024b);</li> <li>National Registry of Health Establishments (CNES) (Brasil, 2024c); </li> <li>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2024d). </li> </ul> <p><strong>Description</strong>:<strong> </strong>The “nutri_als_dataset.csv” dataset (see Table 1) originates from participants of the educational pathway on Food and Nutrition in Amyotrophic Lateral Sclerosis. The educational pathway is available on the AVASUS (Brasil, 2024a). This dataset provides elementary data to analyze the scope of the educational pathway courses and the profile of their participants.</p> <p><br><strong>Note</strong>: The content of the dataset is provided in Brazilian Portuguese (pt-br), as it originates from native speakers.</p> <p><strong>Table 1: </strong>Description of AVASUS dataset features. </p> <div> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>Datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>user_id</strong></p> </td> <td> <p>Unique identifier for a person (anonymous).</p> </td> <td> <p>Categorical</p> </td> <td> <p>Person unique identifier.</p> </td> </tr> <tr> <td> <p><strong>course_enrollment</strong></p> </td> <td> <p>Course enrollment period.</p> </td> <td> <p>Datetime </p> </td> <td> <p>year-month-day.</p> </td> </tr> <tr> <td> <p><strong>course_name</strong></p> </td> <td> <p>Name in Portuguese referring to the course.</p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>Alimentação por sonda na ELA;</p> </li> <li> <p>Alimentação e Nutrição na ELA;</p> </li> <li> <p>Orientações nutricionais específicas na ELA; or</p> </li> <li> <p>Modificações Dietéticas na ELA.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>certificate</strong></p> </td> <td> <p>The period in which the course participant obtained the right to a certificate.</p> </td> <td> <p>Datetime</p> </td> <td> <p>year-month-day hours, minutes, and seconds.</p> </td> </tr> <tr> <td> <p><strong>gender </strong></p> </td> <td> <p>Gender of the course participant. </p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>Female;</p> </li> <li> <p>Male; or</p> </li> <li> <p>Not informed.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>region</strong></p> </td> <td> <p>Brazilian region in which the participant resides.</p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>North;</p> </li> <li> <p>Northeast;</p> </li> <li> <p>Central-West;</p> </li> <li> <p>Southeast;</p> </li> <li> <p>South;</p> </li> <li> <p>Abroad; or</p> </li> <li> <p>Not reported.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant. </p> </td> <td> <p>Numerical</p> </td> <td> <p>0, 1, 2, 3, 4, 5, or NaN.</p> </td> </tr> <tr> <td> <p><strong>evaluation_commentary</strong></p> </td> <td> <p>Comment made by the participant about the course.</p> </td> <td> <p>Categorical</p> </td> <td> <p>Free text or NaN.</p> </td> </tr> <tr> <td> <p><strong>CBO</strong></p> </td> <td> <p>Participant occupation.</p> </td> <td> <p>Categorical</p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem filiação formal.” (In English, “Individual without formal affiliation.”)</p> </td> </tr> </tbody> </table> </div> <p> </p> <p> </p> <p><strong>REFERENCES</strong></p> <p>Brasil (2024a). AVASUS - Virtual Learning Environment of the Brazilian Health System. Available from: <a href="https://avasus.ufrn.br/local/avasplugin/dashboard/transparencia.php">https://avasus.ufrn.br/local/avasplugin/dashboard/transparencia.php</a>. Accessed Jul 21, 2024.</p> <p>Brasil (2024b). CBO - classificação brasileira de ocupações. Available from: <a href="https://cbo.mte.gov.br/cbosite/pages/home.jsf">https://cbo.mte.gov.br/cbosite/pages/home.jsf</a>. Accessed Jul 21, 2024.</p> <p>Brasil (2024c). CNES - cadastro nacional de estabelecimentos de saúde. Available from: <a href="https://cnes.datasus.gov.br/">https://cnes.datasus.gov.br/</a>. Accessed Jul 21, 2024.</p> <p>Brasil (2024d). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: <a href="https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/39525-censo-2022-informacoes-de-populacao-e-domicilios-por-setores-censitarios-auxiliam-gestao-publica">https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/39525-censo-2022-informacoes-de-populacao-e-domicilios-por-setores-censitarios-auxiliam-gestao-publica</a>. Accessed Jul 21, 2024.</p> <p> </p> <p><strong>ARTICLE:</strong></p> <p>Data Report: Educational Pathway on Food and Nutrition in Amyotrophic Lateral Sclerosis on the AVASUS platform <br> </p> <p><strong>AUTHORS:</strong></p> <p>Karla M. D. Coutinho<sup>1,2</sup>, Felipe Fernandes<sup>2</sup>, Kelson C. Medeiros<sup>2,6</sup>, Karilany D. Coutinho<sup>2,4,5</sup>, Aline de Pinho Dias<sup>2,4</sup>, Ricardo A. M. Valentim<sup>2,4,5</sup>, Lúcia Leite-Lais<sup>3</sup>, Kenio Costa Lima1</p> <p> </p> <p><sup>1</sup>Postgraduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>2</sup>Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil </p> <p><sup>3</sup>Department of Nutrition, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>4</sup>Postgraduate Program in Management and Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>5</sup>Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>6</sup>Federal Institute of Education, Science and Technology of Rio Grande do Norte, Natal, Brazil</p> <p> </p> <p> </p>
Fig. 4 in Genome size of chrysophytes varies with cell size and nutritional mode
Fig. 4 Comparison of genome size within different taxonomic groups. Mixotrophic (blue) and heterotrophic (dark red) chrysophytes rank among the smallest eukaryotic genomes (values obtained from [1] Mohanta and Bae 2015; Egertová and Sochor 2017; [2] Gregory 2017; [3] Bennett 2012; [4] Courties et al. 1994)
Fig. 1 in Genome size of chrysophytes varies with cell size and nutritional mode
Fig. 1 Cell volumes [μm 3] of different chrysophytes. Different colors represent the different nutritional modes present. Phototrophic chrysophytes (light green) do have highest cell volumes compared to
Fig. 2 in Genome size of chrysophytes varies with cell size and nutritional mode
Fig. 2 Genome size [pg] of investigated chrysophytes. Different colors represent the different nutritional modes present. Heterotrophic chrysophytes (dark red) tend to have smaller genome sizes, compared to phototrophic chrysophytes (light green), while mixotrophic chrysophytes (blue) show intermediate genome sizes. *Dinobryon sociale var. americana cf. div. schauinslandii; HF = Heterotrophic flagellate
Fig. 5 in Genome size of chrysophytes varies with cell size and nutritional mode
Fig. 5 Model of evolution of genome size, cell volume, and nutritional mode of chrysophytes: nutrient limitations may have driven genome size reduction in the ancestors of mixotrophic (and heterotrophic) chrysophytes, as well as the evolution of phagotrophic mechanisms to attain additional nutrients. Cell size reduction is supposedly a more gradual process, coming into play in taxa which were already able to obtain nutrients by phagotrophy, which optimized food uptake by the optimization of the predator-prey size ratio. This may have triggered the evolution of obligate heterotrophs in many chrysophyte lineages independently
Code for 'Food-insecure women eat a less diverse diet in a more temporally variable way: Evidence from the US National Health and Nutrition Examination Survey, 2013-4'
<p>Code to reproduce the analyses in the study '<strong>Food-insecure women eat a less diverse diet in a more temporally variable way: Evidence from the US National Health and Nutrition Examination Survey, 2013-4'</strong></p> <p>The analysis requires two R scripts available here, plus original 2013-4 NHANES data files, downloadable from the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2013).</p> <p>The first R script, 'merging script.r' takes the original NHANES files, extracts the variables required for the study, merges them into a single data frame, and saves this in .csv format. The NHANES files it requires are:</p> <p># Demographics, food insecurity and BMI<br> DEMO_H.XPT<br> FSQ_H.XPT<br> BMX_H.XPT</p> <p># Summary files of food recalls<br> DR1TOT_H.XPT<br> DR2TOT_H.XPT</p> <p># Individual foods files from food recalls<br> DR1FF_H.XPT<br> DR2FF_H.XPT</p> <p>The second R script takes the .csv file output by the merging script, and reproduces the analyses and figures described in the paper.</p> <p>Initially uploaded by Daniel Nettle, April 23rd 2019. Slightly revised versions uploaded August 6th 2019 by Daniel Nettle.</p>
Figure 1 in About the nutrition of Cleroclytus semirufus Kraatz, 1884 (Coleoptera, Cerambycidae) with the exudate of the Fire blight of fruit crops
Figure 1.Cleroclytus semirufus: A - habitus, dorsal view; B - nutrition on the flowers of Spiraea; C, D - feeding on exudate of the bacterium Erwinia amylovora on an apple tree.
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.