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1,855 results for “Framework”
Supplementary material 4 from: Kumschick S, Wilson JRU, Foxcroft LC (2020) A framework to support alien species regulation: the Risk Analysis for Alien Taxa (RAAT). In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 213-239. https://doi.org/10.3897/neobiota.62.51031
List of contributors to the risk analysis process in South Africa 2018, 2019 (Appendix S4)
Supplementary material 2 from: Wilson JRU, Datta A, Hirsch H, Keet J-H, Mbobo T, Nkuna KV, Nsikani MM, Pyšek P, Richardson DM, Zengeya TA, Kumschick S (2020) Is invasion science moving towards agreed standards? The influence of selected frameworks. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 569-590. https://doi.org/10.3897/neobiota.62.53243
Supplementary material to a citation analysis of frameworks in invasion science
Using a Backcasting Framework to Develop Future School Models: What if compulsory schooling was a 21st century invention?
<p>The research results provide (a) a full list of the Focus School Elements with the consensus statements from Round Three of our modified Delphi process, and (b) a description of the five researcher developed scenarios developed from the 17 participant developed scenarios from Round Four of our modified Delphi process.</p>
A multidimensional framework for characterizing the citation impact of scientific publications
<p>This data set pertains to the following research article: Bu, Y., Waltman, L., & Huang, Y. (2020). <em>A multidimensional framework for characterizing the citation impact of scientific publications</em>. arXiv:1901.09663.</p>
Figure 2 from: Haak L, Greene S, Ratan K (2020) A New Research Economy: Socio-technical framework to open up lines of credit in the academic community. Research Ideas and Outcomes 6: e60477. https://doi.org/10.3897/rio.6.e60477
Figure 2 Collaborative Workflow of an Incremental Dataset in a Consortium Setting (click to view enlarged slide in Present mode). The workflow of an investigator's incremental dataset is shown as it is incorporated into the Facilitated Living Review (FLR), moving along a continuum from closed to open review. (1) After ideation and hypothesis formation by the team, early experimentation creates an incremental dataset. (2) The dataset is shared and discussed with the Project X research consortium, then iterated. (3) The v.3 dataset is shared with the consortium for further analysis, positioning, and citation in the context of the latest published evidence in the FLR. (4) The FLR with its cited, organically peer reviewed dataset is posted on a preprint server under the authorship of the FLR consortium. (5) Feedback from the broader community may lead to further iteration of the dataset at the discretion of the project team in the next round of the FLR revisions.
Hierarchical and Iterative Learning Control framework
<p>A diagram depicting a framework for Hierarchical and Iterative Learning Control of distributed HVAC systems.</p>
Synthesis, characterization and CO2 adsorption studies of DABCO based pillared Zn-BDC and Co-BDC metal organic frameworks
<p><span>This study focuses on pre-synthetic functionalized MOF material normally known as pillared layer MOFs. An additional component DABCO (1,4-diazabicyclo[2.2.2] octane) is added to the MOFs which works as a pillar to produce 3d structured MOFs. Zn-BDC-DABCO and Co-BDC-DABCO were studied for their performance in CO<sub>2</sub> capture application. The addition of DABCO turns the 2d-layered metal-BDC lattice to a 3d structure with enhance performance for CO<sub>2</sub> capture. The MOFs were characterized using XRD, SEM, TGA, FTIR and BET and the CO<sub>2</sub> capture capacity was tested at 25</span><span>°C and 0-25 bar. Zn-BDC-DABCO and Co-BDC-DABCO showed a maximum adsorption capacity of 6.3 and 4.4 mol/kg CO<sub>2</sub>.</span></p>
Data from: A penalized likelihood framework for high- dimensional phylogenetic comparative methods and an application to new-world monkeys brain evolution
Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.
Data from: Modelling social care provision in an agent-based framework with kinship networks
Current demographic trends in the UK include a fast-growing elderly population and dropping birth rates, and demand for social care amongst the aged is rising. The UK depends on informal social care -- family members or friends providing care -- for some 50% of care provision. However, lower birth rates and a graying population mean that care availability is becoming a significant problem, causing concern amongst policy-makers that substantial public investment in formal care will be required in decades to come. In this paper we present an agent-based simulation of care provision in the UK, in which individual agents can decide to provide informal care, or pay for private care, for their loved ones. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need, and geographical location. Results demonstrate that the model can produce similar patterns of care need and availability as is observed in the real world, despite the model containing minimal empirical data. We propose that our model better captures the complexities of social care provision than other methods, due to the socioeconomic details present and the use of kinship networks to distribute care amongst family members.
Data from: How to calculate the non-synonymous to synonymous rate ratio of protein-coding genes under the Fisher-Wright mutation-selection framework
First principles of population genetics are used to obtain formulae relating the non-synonymous to synonymous substitution rate ratio to the selection coefficients acting at codon sites in protein-coding genes. Two theoretical cases are discussed and two examples from real data (a chloroplast gene and a virus polymerase) are given. The formulae give much insight into the dynamics of non-synonymous substitutions and may inform the development of methods to detect adaptive evolution.
Data from: A framework for elucidating the temperature dependence of fitness
Climate warming is predicted to cause large-scale extinctions of ectothermic species. A striking difference between tropical and temperate ectotherms is that tropical species exhibit an upper temperature limit for survival (T_max) that is closer to the temperature at which fitness is maximized (T_opt). Thus, even a small increase in environmental temperature could put tropical ectotherms at high risk of extinction, whereas temperate ectotherms have a wider temperature cushion. Although this pattern is widely observed, the mechanisms that produce it are not well-understood. We develop a mathematical framework to partition the temperature response of fitness into its components (fecundity, mortality and development) and test model predictions with data for insects. We find that fitness declines at high temperatures because the temperature responses of fecundity and mortality act in opposite ways: fecundity decreases with temperature once temperatures exceed the optimal range, whereas mortality continues to increase. The proximity of T_opt to T_max depends on how the temperature response of development mediates the interaction between fecundity and mortality. When development is highly temperature-sensitive, mortality exceeds reproduction only after fecundity has started to decline with temperature, which causes fitness to decline rapidly to zero once temperatures exceed T_opt. The model correctly predicts empirically observed fitness-temperature relationships in insects from different latitudes. It potentially explains the widely-reported phenological shifts and latitudinal differences in fitness responses.
Data from: Challenging the conceptual framework of maintenance hosts for influenza A viruses in wild birds
1.The conceptual framework considering Anseriformes and Charadriiformes as the main maintenance hosts for influenza A viruses (IAV) in wild birds has shaped IAV research and surveillance over the last decades. 2.We challenge this framework by reviewing the worldwide surveillance data on non-Anseriformes and non-Charadriiformes (NANC) species, generally considered as playing little role in IAV maintenance, available in literature and online data bases (close to 200 sources). 3.Globally, we found an IAV infection rate of 1.51% (95% CI, 1.44%–1.59%) for c.101 000 birds tested from NANC species. If Anseriformes have, as expected, a higher infection rate than any other bird orders, eight bird orders have an infection rate higher or close to the Charadriiformes infection rate, challenging the status of Charadriiformes. 4.We interpret the attention paid in favour of Charadriiformes by an extrapolation bias from data collected in hotspots of IAV infection in Charadriiformes (e.g. Delaware Bay, USA). 5.The growing data on IAV in wild birds worldwide, summarised here, supports two non-exclusive hypotheses: (i) the quality of the diagnostic tools and techniques used explain the patterns observed; (ii) IAV maintenance is determined by complex multi-host systems composed of multiple bird species, dependent on the ecosystem and its bird diversity and composition. 6.Synthesis and applications. Our results have two main implications. First, new research and surveillance should be designed in order to understand influenza A viruses ecology in wild birds across the world, along with appropriate diagnostic tools and new hypotheses and dedicated protocols. This should be done in line with our new conceptual framework that conveys less a priori than its predecessor. Secondly, our results call for more bridging between biological and epidemiological sciences in order to tackle disease ecology in multi-host systems.
Data from: A morphometric and genetic framework for the genus Gazella de Blainville, 1816 (Ruminantia: Bovidae) with special focus on Arabian and Levantine mountain gazelles
Gazella is one of the most species-rich genera within horned ruminants. Despite overall similarity in body size and morphology, gazelles show variability in coloration and horn morphology. Unfortunately, however, species differentiation based on these characters, or on discrete skull characters, is very difficult due to high intraspecific variability. Furthermore, most species have fragmented and allopatric distributions, so that species boundaries were hard to define in the past. Mitochondrial DNA sequences have proven useful for investigating gazelle taxonomy in recent years, but especially for old museum material, i.e. type specimens, destructive sampling is often impossible. We provide a comprehensive morphometric framework for the genus Gazella based on linear skull measurements reconciled with results from molecular phylogenetic analysis based on the largest dataset available so far. In particular for males, the skull morphology shows interspecific differences concurrent with DNA data and provides a reliable tool for species identification. Based on morphometric data we synonymize G. karamii with G. marica, and confirm the identification of the G. arabica and G. a. rueppelli type skulls from analyses of mitochondrial DNA sequences.
Data from: A new framework for investigating biotic homogenization and exploring future trajectories: oceanic island plant and bird assemblages as a case study
Studies of biotic homogenization have focused primarily on characterizing changes that have occurred between some past baseline and the present day. In order to understand how homogenization may change in the future, it is important to contextualize the processes driving these changes. Here, we examine empirical patterns of change in taxonomic similarity among oceanic island plant and bird assemblages. We use these empirical cases to unpack dynamic properties of biotic homogenization, thereby elucidating two important factors that have received little attention: 1) initial similarity and 2) the influence of six classes of introduction and extinction events. We use Jaccard's Index to explore the interplay among these factors in determining the changes in similarity that have occurred between human settlement and the present. Specifically, we develop general formulas for changes in similarity resulting from each of the six types of introductions and extinctions, so that the effect of each event type is formulated in terms of initial similarity and species richness. We then apply these insights to project how similarity levels would change in the future if the present patterns of introductions and extinctions continue. We show that the six event types, along with initial similarity, can show dramatically different behavior in different systems, leading to widely variable influences on similarity. Plant and bird biotas have homogenized only slightly to date, but their trajectories of change are highly divergent. Although existing patterns of colonization and extinction might not continue unchanged, if they were to do so then plant assemblages would show little additional change, whereas bird assemblages would become much more strongly homogenized. Our results suggest that moderate changes in similarity observed to date mask the potential for more dramatic changes in the future, and that the interaction among initial similarity and differential introduction and extinction regimes drives these dynamics.
Data from: Information use and resource competition: an integrative framework
Organisms may reduce uncertainty regarding how best to exploit their environment by collecting information about resource distribution. We develop a model to demonstrate how competition can facilitate or constrain an individual's ability to use information when acquiring resources. Since resource distribution underpins both selection on information use and the strength and nature of competition between individuals, we demonstrate interdependencies between the two that should be common in nature. Individuals in our model can search for resources either personally or by using social information. We explore selection on social information use across a comprehensive range of ecological conditions, generalising the producer-scrounger framework to a wide diversity of taxa and resources. We show that resource ecology – defined by scarcity, depletion rate, and monopolisability – determines patterns of individual differences in social information use. These differences suggest co-evolutionary processes linking dominance systems and social information use, with implications for the evolutionary demography of populations.
Data from: Evolutionary drivers of group foraging: a new framework for investigating variance in food intake and reproduction
A proposed fundamental driver of group living is more reliable, predictable foraging and reproduction, i.e. reduced variance in food intake and reproductive output. However, existing theories on variance reduction in group foraging are simplistic, refer to variance at the level of individuals and groups without linking the two, and do not spell out crucial underlying assumptions. We provide a new, widely applicable framework for identifying when variance reduction conveys fitness benefits of group foraging in a wide range of organisms. We discuss critical limitations of established theories, the Central Limit Theorem and Risk-Sensitive Foraging Theory applied to group foraging, and incorporate them into our framework while addressing the confusion over the levels of variance and identifying previously unaddressed assumptions. Through a field study on colonial spiders, Cyrtophora citricola, we demonstrate the importance of evaluating the level of food sharing as a critical first step, previously overlooked in the literature. We conclude that variance reduction provides selective advantages only under narrow conditions and does not provide a universal benefit to group foraging as previously proposed. Our framework provides an important tool for identifying evolutionary drivers of group foraging and understanding the role of fitness variance in the evolution of group living.
Data from: A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula
We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article.
Data from: Integrated metabolic strategy: a framework for predicting the evolution of carbon-water tradeoffs within plant clades
1. The fundamental tradeoff between carbon gain and water loss has long been predicted as an evolutionary driver of plant strategies across environments. Nonetheless, challenges in measuring carbon gain and water loss in ways that integrate over leaf lifetime have limited our understanding of the variation in and mechanistic bases of this tradeoff. Furthermore, the microevolution of plant traits within species versus the macroevolution of strategies among closely related species may not be same, and accordingly, the latter must be addressed using comparative phylogenetic analyses. 2. Here we introduce the concept of 'integrated metabolic strategy' (IMS) to describe the ratio between carbon isotope composition (δ13C) and oxygen isotope composition above source water (Δ18O) of leaf cellulose. IMS is a measure of a leaf-level conditions that integrate several mechanisms contributing to carbon gain (δ13C) and water loss (Δ18O) over leaf lifespan, with larger values reflecting higher metabolic efficiency and hence less of a trade-off. We tested how IMS evolves among closely related yet ecologically diverse milkweed species, and subsequently addressed phenotypic plasticity in response to water availability in species with divergent IMS. 3. IMS varied strongly among 20 Asclepias species when grown under controlled conditions, and phylogenetic analyses demonstrate species-specific tradeoffs between carbon gain and water loss. Larger IMS values were associated with species from dry habitats, with larger carboxylation capacity, smaller stomatal conductance and smaller leaves; smaller IMS was associated with wet habitats, smaller carboxylation capacity, larger stomatal conductance and larger leaves. The evolution of IMS was dominated by changes in species' demand for carbon (δ13C) more so than water conservation (Δ18O). Although some individual physiological traits showed phylogenetic signal, IMS did not. 4. In response to experimental decreases in soil moisture, three species maintained similar IMS across levels of water availability because of proportional increases in δ13C and Δ18O (or little change in either), while one species increased IMS due to disproportional changes in δ13C relative to Δ18O. 5. Synthesis: IMS is a broadly applicable mechanistic tool; IMS variation among and within species may shed light on unresolved questions relating to evolution and ecology of plant ecophysiological strategies.
Data from: Modelling competition and dispersal in a statistical phylogeographic framework
Competition between organisms influences the processes governing the colonization of new habitats. As a consequence, species or populations arriving first at a suitable location may prevent secondary colonization. While adaptation to environmental variables (e.g., temperature, altitude, etc.) is essential, the presence or absence of certain species at a particular location often depends on whether or not competing species co-occur. For example, competition is thought to play an important role in structuring mammalian communities assembly. It can also explain spatial patterns of low genetic diversity following rapid colonization events or the "progression rule" displayed by phylogenies of species found on archipelagos. Despite the potential of competition to maintain populations in isolation, past quantitative analyses have largely ignored it because of the difficulty in designing adequate methods for assessing its impact. We present here a new model that integrates competition and dispersal into a Bayesian phylogeographic framework. Extensive simulations and analysis of real data show that our approach clearly outperforms the traditional Mantel test for detecting correlation between genetic and geographic distances. But most importantly, we demonstrate that competition can be detected with high sensitivity and specificity from the phylogenetic analysis of genetic variation in space.
Data from: A student-centered framework for teaching undergraduate parasitology
Many biology subdisciplines are re-evaluating their undergraduate curriculum amid changing student attitudes towards education. However, a modern framework for undergraduate parasitology has yet to be formally outlined. We present a student-centered approach to teaching parasitology, which diminishes the power of the lectern and emphasizes the use of active learning techniques.
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.