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.
11,506
datasets available to search
ShareScore release 0.9.0
Dataset results
11,506 results for “Dynamics”
Data for "effects of fault contact heterogeneity on laboratory earthquake initiation and dynamic rupture"
<p>The second column in the files named by "Time_and_dLP", "Time_and_Mu0", "Time_and_Sigma0", and "Time_and_Tau0" indicate the along-fault loading point displacement (dLP), macroscopic friction coefficient (Mu0), macroscopic normal stress (Sigma0), and macroscopic shear stress (Tau0), respectively, measured in the loading apparatus. The first column in these files are time.</p> <p>Local fault displacement data are named by the form of, for example, "Event101_FaultDisplacement_L1(x=270mm)", which means that the fault displacement measured by Sensor L1 located at x=270 mm during stick-slip Event 101. Their corresponding time is save in the file named by "Event101_FaultDisplacement_Time".</p> <p>Local shear stress data are named by the form of, for example, "Event101_ShearStress_S1(x=-323.95mm)", which means that the shear stress measured by Sensor S1 located at x=-323.95 mm during stick-slip Event 101. Their corresponding time is save in the file named by "Event101_ShearStress_Time".</p>
Figure 4 in Dynamics of meiobenthic fauna in a tropical sandy coast (Chennai, Bay of Bengal) with reference to the physicochemical parameters
Figure 4. CCA analysis between meiobenthic groups and physicochemical parameters.
Figure 2 in Dynamics of meiobenthic fauna in a tropical sandy coast (Chennai, Bay of Bengal) with reference to the physicochemical parameters
Figure 2. Physicochemical parameters of the Chennai coast (a- Phosphate; b- Nitrate).
Figure 1 in Dynamics of meiobenthic fauna in a tropical sandy coast (Chennai, Bay of Bengal) with reference to the physicochemical parameters
Figure 1. Sampling locations.
Water table dynamics and surface soil moisture from an experimental peatland restoration area (Forsinard, Scotland, UK), 2017-2022/2023
<p>This dataset is from an experiment aimed to understand the changes in water level and soil moisture dynamics after rewetting of formerly afforested blanket bog areas. Specifically, it was aiming to test whether the water table and soil moisture dynamics in these areas return to those of control areas that had never been drained or afforested. The data span the period of summer 2017- summer 2022/2023, with gaps in individual time series marked with -9999 entries.</p>
Data from: Competition between mixo- and heterotrophic ciliates under dynamic resource supply
<p>The outcome of species competition strongly depends on the traits of the competitors and associated trade-offs, as well as on environmental variability. Here we investigate the relevance of consumer trait variation for species coexistence in a ciliate consumer – microalgal prey system under fluctuating regimes of resource supply. We focus on consumer competition and feeding traits, and specifically on the consumer's ability to overcome periods of resource limitation by mixotrophy, i. e. the ability of photosynthetic carbon fixation via algal symbionts in addition to phagotrophy. In a 48-day chemostat experiment, we investigated competitive interactions of different heterotrophic and mixotrophic ciliates of the genera <em>Euplotes</em> and <em>Coleps</em> under different resource regimes, providing prey either continuously or in pulses under constant or fluctuating light, entailing periods of resource depletion in fluctuating environments, but overall providing the same amount of prey and light. Although ultimate competition results remained unaffected, population dynamics of mixotrophic and heterotrophic ciliates were significantly altered by resource supply mode. However, the effects differed among species combinations and changed over time. Whether mixotrophs or heterotrophs dominated in competition strongly depended on the genera of the competing species and thus species-specific differences in the minimum resource requirements that are associated with feeding on shared prey, nutrient uptake, light harvesting and access to additional resources such as bacteria. Potential differences in the curvature of the species' resource-dependent growth functions may have further mediated the species-specific responses to the different resource supply modes. Overall, our study demonstrates that genus- or species-specific traits other than related to nutritional mode may override the relevance of acquired phototrophy by heterotrophs in competitive interactions, and that the potential advantage of photosynthetic carbon fixation of symbiont-bearing mixotrophs in competition with pure heterotrophs may differ greatly among different mixotrophs, playing out under different environmental conditions and depending on the specific requirements of the species. Complex trophic interactions determine the outcome of competition, which can only be understood by taking on a multidimensional trait perspective.</p>
Molecular dynamics trajectories of pYEEI:SH2 recognition, unbiased, at all-atom resolution.
<div> </div> <p>Set of 772 all-atom trajectories simulated from an unbound (apo) configuration of the human p56 -lck tyrosine kinase SH2 domain with its high-specificity phosphopeptide recognition substrate pYEEI (initial structure based on PDB:<a href="https://www.rcsb.org/structure/1LKK">1LKK</a> ). Approximately 24 trajectories spontaneously reach a bound state with ligand RMSD < 2 Â from the crystal. System building and run details are described in [1].</p> <p>A preliminary version of this dataset have been analyzed and discussed in [1] (approx 200 ns per trajectory were available and used in [1]). </p> <p>The trajectories provided here are extended to ~800 ns each, for a total of ~640 μs sampled time. The full dataset is analyzed in [2] with a SOM-based technique.</p> <div> <h2>Notes</h2> </div> <div> <ul> <li>These are all-atom simulations (with TIP3P water). Water molecules have been stripped off from these files (filtered).</li> <li>Not all trajectories have the same length. Some are cut short due to the distributed computing setup.</li> <li>Frame-to-frame interval is 1 ns.</li> </ul> </div> <h2>Acknowledgments</h2> <p>We thank the volunteers of the GPUGRID.net project for donating computing time.</p> <p> </p> <h2>References</h2> <p>[1] T. Giorgino, I. Buch, and G. De Fabritiis. <a href="https://pubs.acs.org/doi/10.1021/ct300003f">Visualizing the Induced Binding of SH2-Phosphopeptide</a>, J. Chem. Theory Comput. 2012, 8, 4, 1171-1175. doi:10.1021/ct300003f</p> <p>[2] Lara Callea, Camilla Caprai, Laura Bonati, Toni Giorgino, Stefano Motta. Self-Organizing Maps of Unbiased Ligand-Target Binding Pathways and Kinetics. J. Chem. Phys, 2024. https://doi.org/10.1063/5.0225183 </p> <p> </p> <div> </div> <div> <p> </p> </div>
Development of human pancreatic cancer avatars as a model for dynamic immune landscape profiling and personalised therapy
<div> <div> <div> <p>Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, a disease with dismal overall survival. Advances in treatment are hindered by a lack of preclinical models. Here we show how a personalised organotypic 'avatar' created from resected tissue, allows spatial and temporal reporting on a complete in situ tumour microenvironment, and mirrors clinical responses. Our perfusion culture method extends tumour slice viability, maintaining stable tumour content, metabolism, stromal composition, and immune cell populations for 12 days. Using multiplexed immunofluorescence and spatial transcriptomics, we identify immune neighbourhoods and potential for immunotherapy. We employed avatars to assess the impact of a pre-clinically validated metabolic therapy and show recovery of stromal and immune phenotypes and tumour re-differentiation. To determine clinical relevance, we monitored avatar response to gemcitabine treatment and identified a patient avatar-predicable response from clinical follow-up. Thus, avatars provide valuable information for the syngeneic testing of novel therapeutics and a truly personalised therapeutic assessment platform for patients.</p> </div> </div> </div>
Fig. 8 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 8: Biomass of gillnet landings.
Fig. 17 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 17: Total value and selling price per unit weight of gillnet landings.
Fig. 12 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 12: Trammel net landing biomass per vessel for the main target species.
Fig. 4 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 4: Number of active vessels using traps.
Fig. 3 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 3: Number of active vessels using trammel nets.
Fig. 1 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 1: Study area: the Adriatic Sea subdivided into GFCM Geographical Sub-Area (GSA) 17 and 18.
Fig. 2 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 2: Number of active vessels using gillnets.
Fig. 11 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 11: Gillnet landing biomass per vessel for the main target species.
Fig. 19 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 19: Total value and selling price per unit weight of traps landings.
Fig. 10 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 10: Biomass of traps landings.
Fig. 13 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 13: Trap landing biomass per vessel for S. officinalis.
Fig. 9 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 9: Biomass of trammel net landings.
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.