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.
4,669
datasets available to search
ShareScore release 0.9.0
Dataset results
4,669 results for “Environment;”
Effects of developmental and adult environments on ageing
<p>Developmental and adult environments can interact in complex ways to influence the fitness of individuals. Most studies investigating effects of the environment on fitness focus on environments experienced and traits expressed at a single point in an organism's life. However, environments vary with time, so the effects of the environments that organisms experience at different ages may interact to affect how traits change throughout life. Here we test whether thermal stress experienced during development leads individuals to cope better with thermal stress as adults. We manipulated temperature during both development and adulthood and measured a range of life-history traits, including senescence, in male and female seed beetles (Callosobruchus maculatus). We found that thermal stress during development reduced adult reproductive performance of females. In contrast, lifespan and age-dependent mortality were affected more by adult than developmental environments, with high adult temperatures decreasing longevity and increasing age-dependent mortality. Aside from an interaction between developmental and adult environments to affect age-dependent changes in male weight, we did not find any evidence of a beneficial acclimation response to developmental thermal stress. Overall, our results show that effects of developmental and adult environments can be both sex- and trait- specific, and that a full understanding of how environments interact to affect fitness and ageing requires the integrated study of conditions experienced during different stages of ontogeny.</p>
How to quantify factors degrading DNA in the environment and predict degradation for effective sampling design
<p>Extra-organismal DNA (eoDNA) from material left behind by organisms (non-invasive DNA: e.g., faeces, hair) or from environmental samples (eDNA: e.g., water, soil) is a valuable source of genetic information. However, the relatively low quality and quantity of eoDNA, which can be further degraded by environmental factors, results in reduced amplification and sequencing success. This is often compensated for through cost- and time-intensive replications of genotyping/sequencing procedures. Therefore, system- and site-specific quantifications of environmental degradation are needed to maximize sampling efficiency (e.g., fewer replicates, shorter sampling durations), and to improve species detection and abundance estimates. Using ten environmentally diverse bat roosts as a case study, we developed a robust modelling pipeline to quantify the environmental factors degrading eoDNA, predict eoDNA quality, and estimate sampling-site-specific ideal exposure duration. Maximum humidity was the strongest eoDNA-degrading factor, followed by exposure duration and then maximum temperature. We also found a positive effect when hottest days occurred later. The strength of this effect fell between the strength of the effects of exposure duration and maximum temperature. With those predictors and information on sampling period (before or after offspring were born), we reliably predicted mean eoDNA quality per sampling visit at new sites with a mean squared error of 0.0349. Site-specific simulations revealed that reducing exposure duration to 2-8 days could substantially improve eoDNA quality for future sampling. Our pipeline identified high humidity and temperature as strong drivers of eoDNA degradation even in the absence of rain and direct sunlight. Furthermore, we outline the pipeline's utility for other systems and study goals, such as estimating sample age, improving eDNA-based species detection, and increasing the accuracy of abundance estimates.</p>
Multi-scale temporal variation of marine femtoplankton and picoplankton: the role of size and environment
<p>Supplementary Material of the article "Multi-scale temporal variation of marine femtoplankton and picoplankton: the role of size and environment":</p> <p>- Supplementary Table 1: Raw environmental data: sea surface temperature (°C), salinity, dissolved oxygen (ml.l<sup>-1</sup>), pH, phosphate (mM), nitrite (mM), nitrate (mM), ammonia (mM), chl-a, b, c and pheopigments (mg/m<sup>3</sup>). </p> <p>- Supplementary Table 2: Cell abundance (cell.mL<sup>-1</sup>) of <em>Synechococcus</em> spp. (SYN) and picoeukaryotes (PEUK), and abundance of Virus-like particles for each sample and their respective anomalies, after normalization.</p>
Generation time in stage-structured populations under fluctuating environments (Simulations)
<p>The study is about the computation of the generation time when individuals are classified based on their stage and the environment stochastically fluctuates with time. This folder contains simulation code in R to plot Figure 2 of the manuscript.</p>
Space Weather Environment During the SpaceX Starlink Satellite Loss in February 2022
<p>All data used in support of the journal article of the same name, including:</p> <ul> <li>operational NCEP/SWPC Whole Atmosphere Model (WAM), NRLMSISE-00, NRLMSIS 2.0, and DTM2020 fixed-height output of neutral atmospheric density in NetCDF format at ten-minute cadence</li> <li>observed and forecasted solar wind and geomagnetic space weather drivers in XML format</li> <li>NOAA Space Weather Prediction Center text advisory of geomagnetic activity</li> <li>select post-launch tracks (minimum of lat/lon/alt in one-minute cadence) of Starlink satellites for three launches: Group 4-4, December 2021; Group 4-5, January 2021; and Group 4-7, February 2021</li> </ul>
FIG. 6 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 6. — Two views on the same rafter (sunggau muke with renak ngelandas; 28 March 2017). Credits: M. Rhomadona (A), N. Césard (B).
FIG. 5 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 5. — Rendap rabas, before (A) and after (B) being improved (November 2013). The arrows indicate the cuts in the vegetation. Credits: N. Césard.
FIG. 1 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 1. — Hemispherical photographs of three rendap (in pale blue): direct (A), indirect (or semi-open) (B) and well (C) access paths. Credits: N. Césard.
Figures of Smart Contract Design via Modeling Environment: A proposal based on DasContract DSL
<p>Figures of "Smart Contract Design via Modeling Environment: A proposal based on DasContract DSL".</p>
Fully Calculated Samples for Discrete Manufacturing Simulation Environment
<p>This dataset is a MySQL file and contains fully calculated samples for different baseline situations:</p> <p><br> </p> <table> <thead> <tr> <th>Env-Version</th> <th>ammountOfCarriers</th> <th>uncertainty</th> <th>onlyForTraining</th> <th>quantity</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>4</td> <td>1</td> <td>0</td> <td>10000</td> </tr> <tr> <td>1</td> <td>4</td> <td>1</td> <td>1</td> <td>90786</td> </tr> <tr> <td>1</td> <td>4</td> <td>3</td> <td>0</td> <td>10000</td> </tr> <tr> <td>1</td> <td>4</td> <td>3</td> <td>1</td> <td>92254</td> </tr> <tr> <td>1</td> <td>6</td> <td>1</td> <td>0</td> <td>20000</td> </tr> <tr> <td>1</td> <td>6</td> <td>1</td> <td>1</td> <td>210000</td> </tr> <tr> <td>1</td> <td>6</td> <td>3</td> <td>0</td> <td>20000</td> </tr> <tr> <td>1</td> <td>6</td> <td>3</td> <td>1</td> <td>214000</td> </tr> <tr> <td>1</td> <td>7</td> <td>3</td> <td>1</td> <td>30000</td> </tr> <tr> <td>1</td> <td>8</td> <td>3</td> <td>1</td> <td>34188</td> </tr> <tr> <td>1</td> <td>9</td> <td>3</td> <td>1</td> <td>30000</td> </tr> <tr> <td>1</td> <td>10</td> <td>3</td> <td>1</td> <td>34000</td> </tr> <tr> <td>1</td> <td>12</td> <td>3</td> <td>1</td> <td>3272</td> </tr> <tr> <td>2</td> <td>4</td> <td>1</td> <td>0</td> <td>10000</td> </tr> <tr> <td>2</td> <td>4</td> <td>1</td> <td>1</td> <td>94791</td> </tr> <tr> <td>2</td> <td>4</td> <td>3</td> <td>0</td> <td>10000</td> </tr> <tr> <td>2</td> <td>4</td> <td>3</td> <td>1</td> <td>100000</td> </tr> <tr> <td>2</td> <td>6</td> <td>1</td> <td>0</td> <td>10000</td> </tr> <tr> <td>2</td> <td>6</td> <td>1</td> <td>1</td> <td>90423</td> </tr> <tr> <td>2</td> <td>6</td> <td>3</td> <td>0</td> <td>10000</td> </tr> <tr> <td>2</td> <td>6</td> <td>3</td> <td>1</td> <td>249154</td> </tr> </tbody> </table> <p> </p> <p>How do you use the files?<br> You need a MySQL instance on your computer (e.g. via XAMPP). If you do not already have one, create a database there with the name: "rlskipping". The command for this is: </p> <blockquote> <p>CREATE DATABASE IF NOT EXISTS rlskipping;</p> </blockquote> <p>On Windows:</p> <p> 1) Open a new CMD-Shell.<br> 2) Navigate to the path where the sql file is located.<br> (e.g.: cd C:\Users\MrScience\Desktop\Work\myProject\Paper\SQL)<br> 3) Import the Files.<br> (e.g.: mysql -u root -p rlskipping < evaluierung.sql)<br> (e.g.: mysql -u root -p rlskipping < calculatedsamples.sql)</p>
"An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment"
<p>Dar, Arslan Salim, Guillem Armengol Barcos, and Fernando Porté-Agel. "An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment." <em>Renewable Energy</em> (2022).</p>
Supplementary data for "ENGINEERED ADAPTATION MECHANISMS BETWEEN MARINE AND FRESHWATER ENVIRONMENTS IN FISH AFTER THE FLOOD" for the ICC 2023 in Cedarville, Ohio
<p>Supplementary data for "ENGINEERED ADAPTATION MECHANISMS BETWEEN MARINE AND FRESHWATER ENVIRONMENTS IN FISH AFTER THE FLOOD" for the ICC 2023 in Cedarville, Ohio.</p> <p>These include FishBase annotation, mtDNA sequence similarity matrixes, clustering, and statistics for nine fish orders:</p> <p>1. Acipenseriformes</p> <p>2. Angulliformes</p> <p>3. Beloniformes</p> <p>4. Characiformes</p> <p>5. Clupeiformes</p> <p>6. Cyprinodontiformes</p> <p>7. Elasmobranchii</p> <p>8. Pleuronectiformes</p> <p>9. Salmoniformes</p>
Figure 5: Screen shot of the generated collaborative virtual environment
<p>The output of the design is an xml file. The technical user has to customize<br> the designed session in order to put in the virtual environment engine in order<br> to generate the session. The customization regards technical parameters (for<br> example spatial coordinate, intensity of the light and so on). A screen shot of<br> the generated session is in figure 5.</p>
Figure 1. Affordance in Relation with Environment and Agent
<p>In figure 1, relevance emerges in a third dimension, as a result of the interaction between<br> perception/activity (through affordance and agent /environment relationship; environment is full of<br> meaning potential; the agent has certain abilities; aptitude, effectiveness, fitness, or whatever<br> psychologists, biologists or anthropologists might call it; affordance fuels perception and activity,<br> and brings about meaning.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 14. Number of artificial creatures that could survive in the external environment
<p>Artificial creatures and the virtual environment are designed and implemented in a C++ platform in which simulations are performed. In the GA algorithm, at First step a population of 100 complex artificial creatures that each of them had 150 neurons were tested and evaluated by GA; each neuron connected to 15 post-synaptic neurons with different axonal conduction delays between every two neurons. In every generation fitness function has been calculated for all population. The initial energy level for each creature is considered as 50. Figure 14 the number of survived chromosome increases with<br> generation progressing.</p>
Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p> The value of the final product incorporates not only quantity issues but also<br> quality issues, which are difficult to be measured or even estimated. The environment of each agent<br> is defined by the same parameters that define the physical environment in addition to internal states<br> reported by each agent.</p>
Data from: Mutations in yeast are deleterious on average regardless of the degree of adaptation to the testing environment
<p>The role of spontaneous mutations in evolution depends on the distribution of their effects on fitness. Despite a general consensus that new mutations are deleterious on average, a handful of mutation accumulation experiments in diverse organisms instead suggest that of beneficial and deleterious mutations can have comparable fitness impacts, i.e., the product of their respective rates and effects can be roughly equal. We currently lack a general framework for predicting when such a pattern will occur. One idea is that beneficial mutations will be more evident in genotypes that are not well adapted to the testing environment. We tested this prediction experimentally in the laboratory yeast <em>Saccharomyces cerevisiae</em> by allowing nine replicate populations to adapt to novel environments with complex sets of stressors. After >1000 asexual generations interspersed with 41 rounds of sexual reproduction, we assessed the mean effect of induced mutations on yeast growth in both the environment to which they had been adapting and the alternative novel environment. The mutations were deleterious on average, with the severity depending on the testing environment. However, we find no evidence that the adaptive match between genotype and environment is predictive of mutational fitness effects.</p>
Survey questionnaire data on Perceptions, interactions, and responses to urban natural environments through ecological momentary assessment
<p>Data consist of a R file format and contain the cleaned survey quistionaire data where people were asked about their perceptions, interactions, and responses to urban natural environments. For more information please read the following report Published as part of the Regreen Horizon project. </p> <p>Panduro, T.E., Zandersen, M., Guell, C., Lovell, R., Garrett, J., Taylor, T., Fullam, J., Amegbor, P. (2024) Perceptions, interactions, and responses to urban natural environments through ecological momentary assessment (EMA). Deliverable D4.6. REGREEN - Fostering nature-based solutions for smart, green and healthy urban transitions in Europe and China. Horizon2020 Grant No. 821016. https://www.doi.org/10.5281/zenodo.10594764</p>
Fig. 8 in Priabonian, late Eocene chronostratigraphy, depositional environment, and paleosol-trace fossil associations, Pipestone Springs, southwest Montana, USA
Fig. 8. Correlation of Pipestone Springs and Flagstaff Rim upper Eocene lithostratigraphy, and stratigraphic position of tuffs and vertebrate assemblages. → A. Location of Flagstaff Rim (Wyoming) and Pipestone Springs (Montana). B. Pipestone Springs reference section and Flagstaff Rim section showing its lithostratigraphy, and stratigraphic position of tuffs and vertebrate assemblages. Sections are correlated based upon Pipestone Spring's 40Ar/39Ar age of 36.00±0.20 Ma tuff and Flagstaff Rim's Ash B, and on middle Chadronian vertebrate assemblages in the PSMP and between Flagstaff Rim's Ash B to Ash G. 40Ar/39Ar age controls for Pipestone Springs strata are from this study; Flagstaff Rim tuff ages are from Swisher and Prothero (1990), Obradovich et al. (1995), and Sahy et al. (2015) Abbreviations: An, anorthoclase; Bi, biotite; S, sanidine (denote minerals used for single crystal 40Ar/39Ar age analyses of tuffs).
Data from: Local environment and coral composition affect recovery and determine long-term coral responses to recurrent mass mortalities in the Lakshadweep Archipelago
<p>A quarter century after the first global coral bleaching event in 1998, reports differ on the relative importance of anthropogenic influences, local environment and bleaching recurrence in determining the resilience of coral reefs. While life history traits largely determine how corals respond to temperature anomalies, it is unclear if these traits also determine how corals fare over time. From 1998 to 2022, we tracked compositional changes in reefs across the Lakshadweep Archipelago to explore how global El Niño events, and local environment (wave climate and depth) influenced coral responses to repeated mass bleaching. From the 1998 to the 2016 bleaching event, the magnitude of coral mortality reduced overall, particularly at deeper reefs (shallow: -38% to -3%; deep: -18% to -0.45%). Post-bleaching recovery correlated positively with higher wave exposure, linked to the creation of stable structures for coral settlement and survival. Across bleaching phases, recovery was initially slow (6-7 years post-mortality), but, given time, showed a much steeper increase, led by space-occupying genera like <em>Acropora</em>. However, recurring mass bleaching maintained coral cover low (~15% across all sites). These broad trends mask dynamic compositional patterns. Genera such as <em>Porites</em>, <em>Pocillopora</em>, and <em>Favia</em> declined less through time compared to <em>Acanthastrea</em>, <em>Turbinaria</em>, <em>Psammocora</em> and <em>Plesiastrea </em>among others. We identified six community clusters that describe contrasting long-term responses to local and global factors, mediated by depth and wave exposure. Interestingly, genera with different functional traits cluster together indicating that bleaching susceptibility interacts with depth and exposure, creating a spatial mosaic of coral assemblages. These clusters serve as a predictive, site-specific framework to understand the dynamically shifting but declining assemblage of Lakshadweep reefs. While local management could help maintain this changing composition, urgent global action is needed to secure the long-term ecological integrity of tropical reefs.</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.