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4,153 results for “survival”
Soil Microbial Community Effects on Quercus Rubra Seedling Survival at Harvard Forest 2016-2017
Feedbacks between plants and their soil microbial communities often drive negative density dependence in tropical forests, but their importance for tree demographics in temperate forests remains unclear. Additionally, the relative contribution of intraspecific seedling competition and soil pathogens to density-dependent patterns has rarely been assessed. We assessed how the soil microbial community influenced Quercus rubra seedling survival by growing seedlings in a greenhouse inoculated with soil collected from beneath conspecific and heterospecific mature trees. We found that seedlings grown with soil from beneath conspecific adults had a higher mortality rate than seedlings grown with soil from beneath heterospecific adults; therefore adult plant-soil feedbacks decrease seedling survival in Q. rubra.
Laboratory study on microplastic fiber size and concentration effects on leopard frog (Lithobates pipiens) tadpole survival, development, behavior, and parasite susceptibility
This dataset contains comprehensive raw data from a completed laboratory experiment conducted from May 24 to June 30, 2021 (with additional analysis performed in 2025), investigating the effects of polyester microplastic (MP) fiber exposure on northern leopard frog (Lithobates pipiens) tadpoles and their interactions with echinostome trematodes (Echinostoma sp.). Tadpole egg masses were collected from a wetland in Indiana, USA, and ramshorn snails (Helisoma trivolvis), serving as trematode hosts, were collected from Tioga County, New York, USA. The experiment was conducted under controlled laboratory conditions using a static-renewal design, exposing tadpoles to short (~0.24 mm) or long (~1.50 mm) polyester MP fibers at concentrations of 0, 10, or 40 µg L⁻¹ for 32 days, followed by controlled exposure to echinostome cercariae. The dataset includes measurements of tadpole mortality, developmental traits (mass, snout-to-vent length, Gosner stage), behavioral activity (number of moving pre- and post-parasite exposure), MP fiber ingestion, and susceptibility to trematode infection (metacercarial cyst counts in kidneys). These data provide a resource for studying the ecological and toxicological impacts of microplastics on amphibian health, and host-parasite dynamics in freshwater ecosystems, making the dataset suitable for researchers in ecotoxicology, and disease ecology. The dataset is complete, with no ongoing data collection, and is designed to support analyses of microplastic-mediated effects on aquatic organisms.
Grass seedling survival and microhabitat vegetation cover among herbivore exclusion treatments across grassland-shrubland ecotones at 3 sites, 2022 and 2023
The aim of this study is to reveal how mammalian herbivores differentially affect the survival of grass seedlings depending on herbivore taxa (cattle, oryx, lagomorphs, rodents) and microhabitat vegetation structure surrounding grass seedlings. This dataset includes data tracking the survival of grass seedlings among herbivore exclusion treatments across grassland, ecotone, and shrubland habitats. Seedling survival trials were established at 3 spatial blocks associated with the Ecotone Study: JER Pastures 9 and 12, and CDRRC Pasture 3. Survival trials were conducted on Pasture 12 in 2022, and on Pastures 3, 9, and 12 in 2023. Each spatial block contained 3 sites (grassland, ecotone, shrubland) that were further subdivided into 5 replicate plots (n = 45 plots). Two trays (1 control open to all herbivores, 1 caged allowing only rodent access) of 25 seedlings each were buried at ground level at each plot and their condition (i.e., alive & undamaged, alive & herbivore damaged, senesced or absent via herbivory, senesced due to environmental stress, resprouted following herbivory, unknown fate, or herbivory following senescence) recorded every 3 days for a total of 15 days. Microhabitat vegetation cover surrounding the seedling trays was collected using ocular estimates of cover across plant functional types (e.g., perennial grasses, forbs, sub-shrubs, shrubs, etc.) within 1 square-meter PVC quadrats placed on both the east and west face of seedling trays established in the field. Maximum height of vegetative (non-reproductive) plant tissue of each functional type was additionally recorded to gauge the level of grass seedling concealment.
CBC02 Winter-spring survival and response of birds to variable climate using mist-net captures at Konza Prairie
This dataset includes captures of small-bodied landbirds captured via passive mist-netting efforts. The objectives are to (a) initiate a long-term survey of the non-breeding birds of the site, (b) understand the behavioral and physiological mechanisms that allow birds to cope with the unpredictable, variable, and often harsh conditions during winter months, and (c) provide a training platform for students. The collection of this dataset is fully integrated into the teaching of “Wild Bird Research” (an undergraduate hands-on research course in the Division of Biology) and less formal instruction in bird research methods for graduate students. Additionally, the banding efforts have benefited from the engagement of Konza Prairie docents and frequently hosts class visits and other visitors interested in witness bird banding operations.
Survival Data of Strengthened and Non-Strengthened Oysters on Two Restored Reefs in Georgia, USA
The eastern oyster, Crassostrea virginica, is known to respond to chemical cues from their predators by strengthening their shell in defense. The chemical cues homarine and trigonelline, found in the urine of blue crabs, Callinectes sapidus, are two metabolic waste products known to cause this inducible defense in juvenile oysters. We tested whether this shell strengthening defense is beneficial for juvenile oysters in a restored reef setting by inducing oyster spat with chemical cues, placing them onto a restored reef and measuring their survival for 50-100 days. The first reef location is a previously restored reef (approximately 10 years old) with limited physical exposure to wind waves and boat wake. Juvenile oysters were placed at this site in September of 2021 and survival was measured for 49 days. The second reef location is a newly restored oyster reef (less than 1 year old) that acts as a living shoreline, with significant exposure to boat wake and wind waves. Juvenile oysters were protected from predation using mesh wrapping with an opening of 1 square cm and placed at the reef site in April 2023, where survival was monitored for 103 days. Site locations: Reef 1 (dock site) - 31.988948°, -81.024001° Reef 2 (living shoreline reef) - 32.067957°,-80.985005°
Chamaecrista fasciculata Survival and Biomass in Response to Microbe Stress History and Contemporary Stress, 2018-2019
This dataset includes Chamaecrista fasciculata biomass and survival data collected as part of a greenhouse experiment that took place at Indiana University in 2018. Rhizosphere soil was collected from Chamaecrista fasciculata plants at the end of a field experiment in which plants were treated with four stress treatments: salt, herbicide, herbivory, and no stress. These field soils were used to inoculate a greenhouse experiment in which Chamaecrista fasciculata individuals from 50 manternal families were treated with these same four stress treatments in a full factorial design (4 microbe histories x 4 contemporary stress environments), plus a sterile microbial control treatment. We measured the days to first flower, noted when plants never flowered (i.e., did not survive to flower), and measured aboveground biomass.
Survival, growth and biomass estimates of two dominant palmetto species of south-central Florida from 1981 - 2022, ongoing at 5-year intervals
This data package is comprised of three datasets all pertaining to two dominant palmetto species, Serenoa repens and Sabal etonia, at Archbold Biological Station in south-central Florida. The first dataset, palmetto_data, contains survival and growth data across multiple years, habitats and experimental treatments. The second dataset, seedlings_data, follows the fate of marked putative palmetto seedlings in the field to assess survivorship and growth. The final dataset, harvested_palmetto_data, contains size data and estimated dry mass (biomass in grams) of 33 destructively harvested palmetto plants (17 S. repens and 16 S. etonia) of varying sizes and across habitats. Thirty-two of these were used to calculate estimated biomass, using regression equations, for palmettos sampled in the palmetto_data. Below we summarize experimental setup and data collected for each dataset. Palmetto data Demographic data were collected as three separate components. The first component compared growth among habitats. Starting in 1981, equal numbers of both palmetto species were marked across scrubby flatwoods (oak scrub) and flatwoods habitats (3 sites per habitat) for a total of 240 marked plants. These habitats had not burned within the last decade, but historically had experienced a natural fire return interval of 5 - 20 years prior to this studies initiation. The second component added an additional 400 palmettos (200 of each species), which were marked in sand pine scrub (n = 200) in 1985 and sandhill habitat (n = 200) in 1989 on Archbold's Red Hill. At the time of this project's initiation, all Red Hill management units were last burned in 1927 and were considered long unburned. Part of Archbold's management plan included restoring fire into some management units while leaving others long unburned to serve as reference units. Therefore, for our second component, we were able to create a 2x2 factorial design using habitat types on Red Hill and fire management as factors, with 100
Alaska 2004 Burns: Growth and survival of tree seedlings in post-fire experimental transplant study across 39 sites
This dataset contains measurements of tree seedlings growth for an experimental transplant study started in 2005 at sites that burned in 2004 in interior Alaska. Records are from a set of 39 intensive study sites that were formerly dominated by black spruce along the Steese, Taylor, and Dalton highways. Seedlings were monitored for 10 years, with detailed measurements in 2006, 2008, 2011, 2013, and 2015. Aboveground biomass was harvested in 2011.
Data from: "Little evidence of inbreeding depression for birth mass, survival and growth in Antarctic fur seal pups"
<p>This data repository contains:</p> <ul> <li><span>"msats_growth_individuals.xlsx" - Microsatellite data (39 loci) of Antarctic fur seals<br></span></li> <li><span>"pup_growth_2017-2020.xlsx" - Birth weight and tagging weight data for pups collected in 2017-2020.<br></span></li> <li><span>"Rebeccas_Samples_Mendel_OriginalPedigree" - SNP array data (75k SNPs) in PLINK format for a subset of individuals<br></span></li> <li><span>"GrowthRM_BI1820_Day60.new.csv" - Repeated weight measures for a subset of individuals</span></li> </ul> <p><strong><br>Manuscript abstract</strong></p> <p><span>Inbreeding depression, the loss of offspring fitness due to consanguineous mating, is generally detrimental for individual performance and population viability.<span> </span>We therefore investigated inbreeding effects in a declining population of Antarctic fur seals (<em>Arctocephalus gazella</em>) at Bird Island, South Georgia.<span> </span>Here, localised warming has reduced the availability of the seal’s staple diet, Antarctic krill, leading to a temporal increase in the strength of selection against inbred offspring, which are increasingly failing to recruit into the adult breeding population.<span> </span>However, it remains unclear whether selection operates before or after nutritional independence at weaning.<span> </span>We therefore used microsatellite data from 885 pups and their mothers, and SNP array data from 98 mother-offspring pairs, to quantify the effects of individual and maternal inbreeding on three important neonatal fitness traits: birth mass, survival and growth.<span> </span>We did not find any clear or consistent effects of offspring or maternal inbreeding on any of these traits.<span> </span>This suggests that selection filters inbred individuals out of the population as juveniles during the time window between weaning and recruitment.<span> </span>Our study brings into focus a poorly understood life-history stage and emphasises the importance of understanding the ecology and threats facing juvenile pinnipeds.</span></p> <p><strong><span>Funding</span></strong></p> <p><span>This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) priority programme “Antarctic Research with Comparative Investigations in Arctic Ice Areas” SPP 1158 (project number 424119118) and the SFB TRR 212 (NC³) (Project Numbers 316099922 & 396774617). This work contributes to the Ecosystems project of the British Antarctic Survey, Natural Environmental Research Council, and is part of the Polar Science for Planet Earth Programme.</span></p>
Supporting data for: Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines
<p><strong>SUMMARY OF THE STUDY</strong></p> <p>We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta-cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate <em>cis-</em>regulatory elements (cCREs) in beta-cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with <em>SOCS1</em>, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta-cells during inflammation that modulate T1D risk.</p> <p><strong>DESCRIPTION OF FILES:</strong></p> <ul> <li>Supplementary Data 1. List of islet cCREs annotated with cell type and cytokine response - also in GSE205853</li> <li>Supplementary Data 2. Coaccessible sites in untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 3. Coaccessible sites in cytokine-treated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 4. Coaccessible sites in cytokine treated and untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 5. Chromatin interactions in EndoC-BH1 cells - also in GSE205853</li> <li>Supplementary Data 6. Variants selected for SNP-SELEX assay </li> <li>Supplementary Data 7. Variants with TF binding and allelic binding results from SNP-SELEX</li> <li>Supplementary Data 8. snATAC-seq barcodes and metadata - also in GSE205853</li> <li>Supplementary Data 9. CRISPR-KO screen results - also in GSE205853</li> <li>Supplementary Data 10. Bulk ATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 11. Bulk RNA-seq count matrix - also in GSE205853</li> <li>Supplementary Data 12. Alpha cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 13. Acinar cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 14. Beta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 15. Stellate cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 16. Endothelial cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 17. Delta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 18. Luciferase assay rs10483809</li> <li>Supplementary Data 19. SOCS1 knockdown qPCR results</li> <li>Supplementary Data 20. SOCS1 knockdown Apotracker (flow-cytometry)results</li> </ul> <p><strong>Raw data deposited at GEO, accessions GSE205853 and GSE118725.</strong></p> <p><em>Please refer to publication and GEO for details on methods.</em></p>
Proline and β-alanine influence bumblebee nectar consumption without affecting survival
<p>These files (.txt) contain the dataset used for analyses of bumblebee aminoacid consumption and survival in the article "Proline and β-alanine influence bumblebee nectar consumption without affecting survival" by Bogo G. et al., accepted for publication in Apidologie (2024, xx:xxx-xxx, DOI: xxx).</p>
Data for predicting piglet survival until weaning using birth weight and within-litter birth weight variation as easily measured proxy predictors
<p>The data was used in the analysis presented in the manuscript: Predicting piglet survival until weaning using birth weight and within-litter birth weight variation as easily measured proxy predictors. The manuscript is published in <em>Animal</em> journal. The data is for piglet survival survival at different time-points from birth to weaning from two research farms.</p>
Survey of aspen herbivory and aspen leaf miner (Phyllocnistis populiella) survival and abundance from 2006 to 2022
Leaf-level measurements of herbivory damage on quaking aspen caused by the aspen leaf miner (ALM) and externally-feeding herbivores, and ALM abundance and survival, from four sites near Fairbanks, Alaska over time.
Data to support "Stochastic density effects on adult fish survival and implications for population fluctuations"
Data on stage-specific abundance of black surfperch (Embiotoca jacksoni), the amount of foraging habitat and the availability of surfperch prey (crustaceans) were collected at fixed sites on the north shore of Santa Cruz Island, California annually (autumn) from 1993-2009. Data are grouped into four regions. Counts of fish distinguished among young-of-year, juveniles (1 year old) and adults (>= 2 years old). These data have been presented in Okamoto, D. K., R. J. Schmitt and S. J. Holbrook. 2016. Sochastic density effects on adult fish survival and implications for population fluctuations. Ecology Letters, 19:153-162. doi: 10.1111/ele.12547.
From Boston to Eden - or how to get systems that are really autonomous and sufficiently intelligent to survive in their niche
<p><a href="https://www.researchgate.net/project/Theoretical-artificial-intelligence/update/5e5f931a3843b0499fec8f6f?_iepl%5BviewId%5D=FMNsczWoobvAHwiOMdftISgB&_iepl%5Bcontexts%5D%5B0%5D=projectUpdatesLog&_iepl%5BinteractionType%5D=projectUpdateDetailClickThrough">From Boston to Eden - or how to get systems that are really autonomous and sufficiently intelligent to survive in their niche</a></p> <p>[lecture for the Dept. of AI, University of Groningen, Tuesday, March 3rd, 2020]</p> <p>As impressive as the robots of the Boston Dynamics company are (no AI involved) and as impressive the many results of deep learning are (no AI involved, either), the goal of creating autonomous, intelligent machines is as far away as it ever was. <br> In this presentation, I will give a brief overview of several deep-learning projects in our group. As a next step I will try to indicate <br> what may be missing, as regards 'real' AI. We may need a closer look at biological systems, i.e., the brain of animals. There exists a wide gap between the control systems at the low level of reflexive movement and the equilibria that need to be maintained ('Boston') versus the higher levels of processing, up to the levels of cognition and reasoning, which are very much upstairs ('Eden'). The missing middleware layer should not be underestimated: It contains the brain stem, up to the thalamus in animals and humans. <br> It corresponds to the 300-million year period before the 200 million years period where the neocortex was present. <br> What is this middleware doing? The conclusion may be that there is no autonomy without self protection, possible due to the presence of a separate and specialized valuation network that determines probability times utility (p*U), similar to what brain stem, midbrain and amygdala are doing in animals.</p>
DWCox: A Density-Weighted Cox Model for Outlier-Robust Prediction of Prostate Cancer Survival
<p>This package, <strong>DWCox</strong>, implements a <strong>d</strong>ensity-<strong>w</strong>eighted <strong>Cox</strong> regression model that is more robust against outliers in the training data. DWCox gives more accurate predictions than the standard Cox regression on prostate cancer survival, especially in cases where the training data are expected to contain a lot of outliers. More details can be found in our paper (coming soon) and the README file inside this package.</p>
Survival, fecundity and reproductive tissue data from false killer whales (Pseudorca crassidens)
<p>These data come from specimens from false killer whales from combined strandings (South Africa, 1981) and harvest (Japan 1979-80). The South African material was collected from 65 false killer whales that stranded en masse on the west coast of the Western Cape Province. Scientists reached the site two days after the stranding event was reported, so the material was not fresh and fixation of tissue samples was suboptimal. Data are available from 41 (including 32 mature) females. The Japanese material (96 females, 57 mature) originated from 6 schools harvested during shore-drive fisheries operations at Iki Island. In each case, as many false killer whales as possible were randomly examined. Data presented here come from 91 females if which 89 were mature. The Japanese and South African data were combined to estimate survival, but fecundity information is available for each separately. These data are associated with the following publication: Theoni Photopoulou, Ines M. Ferreira, Peter B. Best, Toshio Kasuya and Helene Marsh. 2017. Evidence for a postreproductive phase in female false killer whales <em>Pseudorca crassidens. </em>Frontiers in Zoology. 14:30. DOI 10.1186/s12983-017-0208-y</p>
Advanced Non-Clear Cell Renal Cell Carcinoma Treatments and Survival: A Real-World Single-Centre Experience
<p>Dataset of the paper "Advanced Non-Clear Cell Renal Cell Carcinoma Treatments and Survival: A Real-World Single-Centre Experience"</p>
Ash (Fraxinus excelsior L.) in vitro survival data for the UKs Living Ash Project
<p><em>In-vitro</em> propagation and survival data sets (including nursery survival) of the ash plants generated i.e. <em>Fraxinus excelsior</em> L., plus the PCR primers used and conditions applied.</p> <p>Surveyed from a range of ash seed material taken from across the UK, and held at the UK ash collection hosted by the Earth Trust in Oxfordshire, UK.</p> <p>A more detailed analysis of this data is currently expected to be be published in the <em>Annals of Forest Science</em>, and which has already provisionally accepted this work for publication, subject to the underlying data being made available i.e. here</p> <p>The data deposited here represents the underlying data that will be presented in graphical form in the forthcoming paper by Fenning et al., plus the associated metadata and statistical analyses, along with the original .jpg of the photos used.</p>
Data from: Estimation in the multinomial reencounter model - Where do migrating animals go and how do they survive in their destination area?
<p><strong>Abstract</strong></p> <p>Spatial variation in survival has individual fitness consequences and influences population dynamics. Which space animals use during the annual cycle determines how they are affected by this spatial variability. Therefore, knowing spatial patterns of survival and space use is crucial to understand demography of migrating animals. Extracting information on survival and space use from observation data, in particular dead recovery data, requires explicitly identifying the observation process. We build a fully stochastic model for animals marked in populations of origin, which were found dead in spatially discrete destination areas. It acts on the population level and includes parameters for use of space, survival and recovery probability. The model is based on the division coefficient and the multinomial reencounter model. We use a likelihood-based approach, derive Restricted Maximum Likelihood-like estimates for all parameters and prove their existence and uniqueness. In a simulation study we demonstrate the performance of the model by using Bayesian estimators derived by the Markov chain Monte Carlo method. We obtain unbiased estimates for survival and recovery probability if the sample size is large enough. Moreover, we apply the model to real-world data of European robins <em>Erithacus rubecula</em> ringed at a stopover site. We obtain annual survival estimates for different spatially discrete non-breeding areas. Additionally, we can reproduce already known patterns of use of space for this species. We would like to thank the Greifswalder Oie Bird Observatory of the Verein Jordsand, Ahrensburg, and the Hiddensee Bird Ringing Centre, Güstrow, for providing the robin data.</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.