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.
1,813
datasets available to search
ShareScore release 0.9.0
Dataset results
1,813 results for “Longitudinal”
FIGURE 5. CMM-V-10108, a in A Miocene cetacean vertebra showing a partially healed longitudinal shear-compression fracture, possibly the result of domoic acid toxicity or failed predation
FIGURE 5. CMM-V-10108, a second Miocene pathological cetacean vertebra (CT-scans shown in Figure 6) associated with the one shown in Figures 2-4. A. Posterior view showing that the neural spine is incomplete and that the sides of the centrum are covered with periosteal reactive bone. B. ventral view to highlight the periosteal reactive bone. In B, the anterior end of the centrum is up.
FIGURE. 1 in A Miocene cetacean vertebra showing a partially healed longitudinal shear-compression fracture, possibly the result of domoic acid toxicity or failed predation
FIGURE. 1. The site along Calvert Cliffs where the two pathological cetacean vertebrae (CMM-V-10108) and associated Otodus megalodon tooth (CMM-V-8522) were found in situ in Shattuck-Zone 12. Looking north along the cliffs at Warrior's Rest. Photo by M. Ellwood.
FIGURE 4. CMM-V-10108, a in A Miocene cetacean vertebra showing a partially healed longitudinal shear-compression fracture, possibly the result of domoic acid toxicity or failed predation
FIGURE 4. CMM-V-10108, a single CT-scan image in the sagittal plane of a Miocene pathological cetacean vertebra in left lateral view showing the broken lower portion of the centrum, the displaced piece of bone, and the new bone growth (periosteal reactive bone ventrally).
Data from: Longitudinal gut microbiome dynamics in relation to age and senescence in a wild animal population
<p>In humans, gut microbiome (GM) differences are often correlated with, and sometimes causally implicated in, ageing. However, it is unclear how these findings translate in wild animal populations. Studies that investigate how GM dynamics change within individuals, and with declines in physiological condition, are needed to fully understand links between chronological age, senescence, and the GM, but have rarely been done. Here, we use longitudinal data collected from a closed population of Seychelles warblers (<em>Acrocephalus sechellensis</em>) to investigate how bacterial GM alpha diversity, composition, and stability are associated with host senescence. We hypothesised that GM diversity and composition will differ, and become more variable, in older adults, particularly in the terminal year prior to death, as the GM becomes increasingly dysregulated due to senescence. However, GM alpha diversity and composition remained largely invariable with respect to adult age and did not differ in an individual's terminal year. Furthermore, there was no evidence that the GM became more heterogenous in senescent age groups (individuals older than 6 years), or in the terminal year. Instead, environmental variables such as season, territory quality, and time of day, were the strongest predictors of GM variation in adult Seychelles warblers. These results contrast with studies on humans, captive animal populations, and some (but not all) studies on non-human primates, suggesting that GM deterioration may not be a universal hallmark of senescence in wild animal species. Further work is needed to disentangle the factors driving variation in GM-senescence relationships across different host taxa.</p>
Identification of age-related CpG sites from longitudinal avian methylomes
<p>Sex chromosomes are thought to play an important role in sex-dependent ageing, yet they are neglected in epigenetic aging research. We identified genome-wide age-related CpG (AR-CpG) sites in two avian species (zebra finch and jackdaw) and found AR-CpG sites to be overrepresented on the haploid, female-specific W chromosome in both species, and on the Z chromosome in the zebra finch. </p>
Fig. 7 in Longitudinal use of feeding resources and distribution of fish trophic guilds in a coastal Atlantic stream, southern Brazil
Fig. 7. Spearman correlations between the distribution (percentage frequency) of guilds and scores of the first axis of the principal components analysis (PCA1) applied to the correlation matrix of abiotic variables. White circle = headwaters; gray circle = middle stretch; and black circle = mouth of the Vermelho River, East Atlantic basin, Antonina, Paraná State, Brazil.
Fig. 5 in Longitudinal use of feeding resources and distribution of fish trophic guilds in a coastal Atlantic stream, southern Brazil
Fig. 5. Longitudinal niche breadth (B values) of each fish trophic guild in the Vermelho River, East Atlantic basin, Antonina, Paraná State, Brazil. Aq = aquatic; ter = terrestrial.
Fig. 4 in Longitudinal use of feeding resources and distribution of fish trophic guilds in a coastal Atlantic stream, southern Brazil
Fig. 4. Mean ± standard error of diet breadth (B) of fish trophic guilds in the Vermelho River, East Atlantic basin, Antonina, Paraná State, Brazil. Aq = aquatic; ter = terrestrial. Piscivores and omnivores are not included in the ANOVA.
Fig. 6 in Longitudinal use of feeding resources and distribution of fish trophic guilds in a coastal Atlantic stream, southern Brazil
Fig. 6. Density (a) and biomass (b) of trophic guilds along the longitudinal gradient of the Vermelho River, East Atlantic basin, Antonina, Paraná State, Brazil. Aq = aquatic; ter = terrestrial.
Fig. 3 in Longitudinal use of feeding resources and distribution of fish trophic guilds in a coastal Atlantic stream, southern Brazil
Fig. 3. Ordination of fish species in the respective trophic guilds (a, b, c) and food items (d, e, f) along the longitudinal gradient of the Vermelho River, East Atlantic basin, Antonina, Paraná State, Brazil. Circles represent species belonging to the same trophic guild. Codes according to Table 3.
Fig. 2 in Longitudinal use of feeding resources and distribution of fish trophic guilds in a coastal Atlantic stream, southern Brazil
Fig. 2. Ordination of sampling sections along the environmental gradient defined by the first two axes of the principal components analysis (PCA1 and PCA2) applied to the correlation matrix of abiotic variables of the Vermelho River, East Atlantic basin, Antonina, Paraná State, Brazil.
Fig. 1 in Longitudinal use of feeding resources and distribution of fish trophic guilds in a coastal Atlantic stream, southern Brazil
Fig. 1. Location of the Vermelho River, East Atlantic basin, Antonina, Paraná State, Brazil. Black circles indicate the sampling sections; arrow indicates the direction of flow.
Figure 13 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 13. – Indicator species for each cluster of the dendrogram resulting from the self-organizing map procedure (n = 10). IndVal values (in %) are shown in brackets. Shown indicator values (p <0.05) are only those greater than 25%.
Figure 2 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 2. – Variation (average and standard deviation) in species richness by sampling sites. Site order follows the upstream-downstream gradient.
Figure 12 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 12. – Correlation circle of the environmental variable, which discriminate clusters (n = 3) defined by the self-organizing map for stations in the F1 x F2 design for the factorial discriminant analysis. Dis, distance from source; FoBa, forest area; CaHi, canopy height; Vol, flow velocity; Cond, conductivity; Trans, water transparency; Alt, altitude.
Figure 7 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 7. – Hierarchical classification of the nodes of the Kohonen map based on the species richness of the sites (n = 10). A: Self-organizing map (SOM) (20 nodes); B: Hierarchical clustering of the SOM nodes with a Ward linkage method and a Euclidean distance: the numbers (i.e. ranging from 1 to 20) correspond to those assigned on each node of the SOM.
FIGURE 21. Cheiracanthus grandispinus scale histology. 1, NMS G.2019.9.3.9 from Gamrie, vertical longitudinal section through posterior protuberance. 2, 9, 12, NMS G.2019.9.8 in A redescription of the three longest-known species of the acanthodian Cheiracanthus from the Middle Devonian of Scotland
FIGURE 21. Cheiracanthus grandispinus scale histology. 1, NMS G.2019.9.3.9 from Gamrie, vertical longitudinal section through posterior protuberance. 2, 9, 12, NMS G.2019.9.8 from Jessie Port, Ross and Cromarty: 2, NMS G.2019.9.8.14, anterior crown vertical transverse section; 9, NMS G.2019.9.8.10, crown vertical transverse section; 12, NMS G.2019.9.8.13, crown horizontal section. 3, NMS G.2019.9.35.6, from Achanarras, midscale vertical transverse section. 4, 5, 10, 11, from Achanarras: 4, NMS G.2019.9.2.4, posterior half of scale, vertical transverse section; 5, NMS G.2019.9.2.2, vertical longitudinal section; 10, NMS G.1893.107.9.2, horizontal section through crown base; 11, NMS G.1893.107.9.2, horizontal section through midcrown. 6-8, NMS G.2019.9.10 from Cromarty: 6, NMS G.2019.9.10.3, off-centre vertical longitudinal section; 7, NMS G.2019.9.10.5, vertical transverse section; 8, NMS G.2019.9.10.9, oblique vertical section through side of scale. Scale bars equal 0.1 mm. Arrows indicate anterior.
Data from: Automatic Definition of Robust Microbiome Sub-states in Longitudinal Data
<p>Output files of the application of our R software (available at https://github.com/wilkinsonlab/robust-clustering-metagenomics) to different microbiome datasets already published.</p> <p>Prefixes:</p> <ul> <li>David2014_: original microbiome dataset published in [David et al.,2014] (http://genomebiology.com/2014/15/7/R89)</li> <li>Ballou2016_: original microbiome dataset published in [Ballou et al.,2016] (http://journal.frontiersin.org/article/10.3389/fvets.2016.00002/full)</li> <li>Gajer2012_: original microbiome dataset published in [Gajer et al.,2012] (http://stm.sciencemag.org/content/4/132/132ra52.long)</li> <li>LaRosa2014_: original microbiome dataset published in [LaRosa et al.,2014] (http://www.pnas.org/cgi/doi/10.1073/pnas.1409497111)</li> </ul> <p>Suffixes:</p> <ul> <li> <p>_All: all taxa</p> </li> <li> <p>_Dominant: only 1% most abundant taxa</p> </li> <li> <p>_NonDominant: remaining taxa after removing above dominant taxa</p> </li> <li> <p>_GenusAll: taxa aggregated at genus level</p> </li> <li> <p>_GenusDominant: taxa aggregated at genes level and then to select only 1% most abundant taxa</p> </li> <li> <p>_GenusNonDominant: taxa aggregated at genus level and then to remove 1% most abundant taxa</p> </li> </ul> <p>Each folder contains 3 output files related to the same input dataset:<br> - data.normAndDist_definitiveClustering_XXX.RData: R data file with a) a phyloseq object (including OTU table, meta-data and cluster assigned to each sample); and b) a distance matrix object.<br> - definitiveClusteringResults_XXX.txt: text file with assessment measures of the selected clustering.<br> - sampleId-cluster_pairs_XXX.txt: text file. Two columns, comma separated file: sampleID,clusterID</p> <p>Abstract of the associated paper:</p> <p>The analysis of microbiome dynamics would allow us to elucidate patterns within microbial community evolution; however, microbiome state-transition dynamics have been scarcely studied. This is in part because a necessary first-step in such analyses has not been well-defined: how to deterministically describe a microbiome's "state". Clustering in states have been widely studied, although no standard has been concluded yet. We propose a generic, domain-independent and automatic procedure to determine a reliable set of microbiome sub-states within a specific dataset, and with respect to the conditions of the study. The robustness of sub-state identification is established by the combination of diverse techniques for stable cluster verification. We reuse four distinct longitudinal microbiome datasets to demonstrate the broad applicability of our method, analysing results with different taxa subset allowing to adjust it depending on the application goal, and showing that the methodology provides a set of robust sub-states to examine in downstream studies about dynamics in microbiome.</p> <p> </p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - Mouse feeding behavior dataset
<p>Dataset contains feeding and drinking behavioral recordings of C57BL6/J male mice. Mice were distributed into 2 groups (9 control mice and 8 high-fat diet mice) and tracked individually on Phecomp cages for 9 weeks. During the first experimental week all animals were given <em>ad libitum</em> access to a standard chow (habituation phase). After this first week, control mice continued with the same diet regime while high-fat mice were exclusively given <em>ad libitum</em> access to a high-fat chow. Data was used originally in this publication <a href="http://onlinelibrary.wiley.com/doi/10.1111/adb.12595/abstract">10.1111/adb.12595.</a></p> <p>The data set consist in:</p> <p>- a "mouse_recordings" folder containing a CSV file containing mouse recordings and the files.</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "phases" folder containing a CSV file containing experimental phases.</p> <p>- a "chromHMM_files" folder containing a cellmarkfiletable table used by chromHMM to learn a HMM model</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - D. melanogaster behavior dataset obtained with JAABA
<p>Dataset contains <em>Drosophila melanogaster </em>behavioral annotations of chasing. The dataset is formed by 20 GAL4 line flies group from a TrpA activation screen with increased propensity to chasing and by 19 pBDPGAL4U line (control) flies group. Ctrax motor trajectories derived from the original video-recordings (1000 seconds) were downloaded from this <a href="https://sourceforge.net/projects/jaaba/files/Sample%20Data/sampledata_v0.1.zip/download">link</a> and used to obtain the chasing behavioral annotations using <a href="http://jaaba.sourceforge.net/">JAABA</a> <a href="https://www.nature.com/articles/nmeth.2281">10.1038/nmeth.2281</a>: </p> <p>The data set consist in:</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "perframe_TrpA" folder containing mat files with three Ctrax derived variables (dnose2ell, dtheta and velmag) from the motor trajectories of the GAL4 line.</p> <p>- a "perframe_pBDPGAL4" folder containing mat files with three Ctrax derived variables (dnose2ell, dtheta and velmag) from the motor trajectories of the control line.</p> <p>- a "scores" folder including the JAABA chasing annotations in two mat file one for each fly line.</p>
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.