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24 results for “IPM”
EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database
EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.
Vine Growers adoption of IPM CVP PDO
<p> Data from an online survey was conducted with 275 winegrowers from the Conegliano Valdobbiadene Prosecco Protected Designation of Origin area. Data were analysed using Structural Equation Modelling and a Multinomial Probit Model, revealing four out of five dimensions as components of winegrowers’ EA. The results demonstrated that increased EA significantly and positively affects the likelihood of adopting organic and Integrated Pest Management protocols.</p>
Text-fig. 6. Enamel ultrastructure of M2, Equus gmelini (Hirzhevo). a: type I and III, scale bar = 20 Μm; b: IPM and PE first type prisms, scale bar = 3 Μm; c, d: wavy/decussated enamel of TZ, scale bar = 20 and 10 Μm respectively; e: type II near OES border, scale bar = 2 Μm; f: type III, scale bar = 2 Μm. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 6. Enamel ultrastructure of M2, Equus gmelini (Hirzhevo). a: type I and III, scale bar = 20 Μm; b: IPM and PE first type prisms, scale bar = 3 Μm; c, d: wavy/decussated enamel of TZ, scale bar = 20 and 10 Μm respectively; e: type II near OES border, scale bar = 2 Μm; f: type III, scale bar = 2 Μm.
Text-fig. 2. Measurements (Μm) of the width of IPM and PE prisms of various types of enamel in representatives of Equidae from the "tarpan" group. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 2. Measurements (Μm) of the width of IPM and PE prisms of various types of enamel in representatives of Equidae from the "tarpan" group.
Text-fig. 3. Tendency of changes in IPM and PE width indicators in different types of enamel of the Equidae species of the "tarpan" group. I–III – types of enamel. a: IPM; b: PE. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 3. Tendency of changes in IPM and PE width indicators in different types of enamel of the Equidae species of the "tarpan" group. I–III – types of enamel. a: IPM; b: PE.
Figure 2 in Predatory mites, a green pesticide, and an entomopathogenic compound: A proposed IPM tactic based on pest species diversity indices and population dynamics
Figure 2. Schematic diagram of the experiment's plantation and IPM methodology, C.n: Cydnoseius negevi, A.s: Amblyseius swirskii, and P.p Phytoseiulus persimilis. (Photo credits: Dr. Zidan has created this diagram on www.biorender.com).
Figure 1 in Predatory mites, a green pesticide, and an entomopathogenic compound: A proposed IPM tactic based on pest species diversity indices and population dynamics
Figure 1. Google Earth map photography of the experimental locations (pointed with pin) – i) Om Sabir, Kom Hamada, El Beheira Governorate (30° 29' 50.6" N, 30° 46' 18.8" E), and ii) Kom Oshim, Fayoum Governorate (29° 34' 40.9" N, 30° 55' 38.3" E).
Oyster IPM Data
<p>Data used in:</p> <p>Moore, J. L., Lipcius, R. N., Puckett, B. and Schreiber, S. J. (2016), The demographic consequences of growing older and bigger in oyster populations. Ecol Appl. Accepted Author Manuscript. doi:10.1002/eap.1374</p>
Oyster IPM w/Positive Feedbacks - Metadata
<p>Description of data and code used in: </p> <p>Moore, J.L., Puckett, B., and Schreiber, S.J. Restoration of Eastern oyster populations with positive density dependence. <em>Submitted to Ecological Applications December 2017.</em></p>
Multispecies IPM for South polar skua and Adelie penguin
<p>Code and data for a multispecies IPM (integrated population model) on South polar skua and Adélie penguin.</p> <p>Please contact me for any question<br>Mail: <a href="mailto:lise.viollat@protonmail.com">lise.viollat@protonmail.com</a><br>Twitter: @LiseViollat</p> <p> </p> <p><strong>Code files : </strong></p> <p>- multispeIPM_skua_adelie_CODE.txt: Code of the multispecies IPM (Bugs language)</p> <p>- Parameter_init.txt : initialisation of the parameters used for the model</p> <p>Bayesian posterior distributions of the multispecies IPM were approximated with Markov chain Monte Carlo (MCMC) algorithms. Two independent chains MCMC of 30 000 iterations were used, with a burn-in period of 10 000 iterations. Gelman-Rubin convergence diagnostics (Brooks and Gelman, 1998) were below 1.1 for each parameter and the mixing of the chains was satisfactory. The analyses were performed using JAGS (Plummer, 2003; version 4.3.0) and program R (R version 4.0.5).<br><br></p> <p><strong>Data files : </strong></p> <p>From 1988 to 2018</p> <p>- Count_adelie.csv: Number of breeding pairs of Adelie Penguins and Chicks by year</p> <p>- Count_skua.csv : Number of occupied sites, breeder, non breeder and chicks of South polar Skua by year</p> <p>- Skua_matrix.txt: CMR data on South polar skua</p> <p> CMR code: 0 = non-observed</p> <p> 1 = seen as non-breeder</p> <p> 2 = seen as failed breeder</p> <p> 3 = seen as successful breeder with one fledged chick</p> <p> 4 = seen as successful breeder with two chicks fledged chicks</p> <p> 5 = seen in a uncertain state</p> <p> The last column is if the individual as been seen dead (-1) or not (1)</p> <p>- Climat_cova.txt: climatic covariables (Air temperature (AT), sea ice concentration (SIC), sea surface temperature anomalies (ssta), and number of emperor penguin dead chicks</p> <p>- N_immigrant: number of new individuals seen for the first time at the colony by year</p> <p> </p>
Deliverable D1.2 - Supplementary material : Datasets on substance prioritization for subsequent development of target analytical methods for relevant iPM(T) substances in waters in case studies CS#1, CS#2, CS#3 & CS#7.
<p>Additional information on deliverable D1.2. Data sets for substance prioritization and subsequent development of target analytical methods for relevant iPM(T) substances in waters in case studies CS#1, CS#2, CS#3 and CS#7.</p>
Data from: Quantifying demographic uncertainty: Bayesian methods for integral projection models (IPMs)
Integral projection models (IPMs) are a powerful and popular approach to modeling population dynamics. Generalized linear models form the statistical backbone of an IPM. These models are typically fit using a frequentist approach. We suggest that hierarchical Bayesian statistical approaches offer important advantages over frequentist methods for building and interpreting IPMs, especially given the hierarchical nature of most demographic studies. Using a stochastic IPM for a desert cactus based on a 10-year study as a worked example, we highlight the application of a Bayesian approach for translating uncertainty in the vital rates (e.g., growth, survival, fertility) to uncertainty in population-level quantities derived from them (e.g., population growth rate). The best-fit demographic model, which would have been difficult to fit under a frequentist framework, allowed for spatial and temporal variation in vital rates and correlated responses to temporal variation across vital rates. The corresponding posterior probability distribution for the stochastic population growth rate (λS) indicated that, if current vital rates continue, the study population will decline with nearly 100% probability. Interestingly, less-supported candidate models that did not include spatial variance and vital rate correlations gave similar estimates of λS. This occurred because the best-fitting model did a much better job of fitting vital rates to which the population growth rate was weakly sensitive. The cactus case study highlights several advantages of Bayesian approaches to IPM modeling, including that they: (1) provide a natural fit to demographic data, which are often collected in a hierarchical fashion (e.g., with random variance corresponding to temporal and spatial heterogeneity); (2) seamlessly combine multiple data sets or experiments; (3) readily incorporate covariance between vital rates; and, (4) easily integrate prior information, which may be particularly important for species of conservation concern where data availability may be limited. However, constructing a Bayesian IPM will often require the custom development of a statistical model tailored to the peculiarities of the sampling design and species considered; there may be circumstances under which simpler methods are adequate. Overall, Bayesian approaches provide a statistically sound way to get more information out of hard-won data, the goal of most demographic research endeavors.
IPM Decisions_D5.2_Appendix 8_Dataset from the second round of workshops
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IPM Decisions_D5.2_Appendix 5_Dataset from the first round of workshops
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Oyster IPM w/Positive Feedbacks - Data
<p>Data used in: </p> <p>Moore, J.L., Puckett, B., and Schreiber, S.J. Restoration of Eastern oyster populations with positive density dependence. <em>Submitted to Ecological Applications December 2017.</em></p>
Oyster IPM w/Positive Feedbacks - Code
<p>Code used in: </p> <p>Moore, J.L., Puckett, B., and Schreiber, S.J. Restoration of Eastern oyster populations with positive density dependence. <em>Submitted to Ecological Applications December 2017.</em></p>
Golden-cheeked warbler Integrated Population Model (IPM) data in Austin, TX (2011–2019)
<p>These data and code are associated with the publication in Ecosphere entitled "Urban land cover and El Nino events negatively impact population viability of an endangered North American songbird." We performed an integrated population model to evaluate the effect of climate patterns and urban land cover on the viability of an endangered wood-warbler breeding in central Texas. We used territory monitroing data from 2011–2019 to predict viability of the population 25 years into the future.</p>
Management of Rust in Wheat Using IPM Principles and Alternative Products
<p>During the project Rustwatch 19 field trials in winter wheat was carried out in 9 European countries during 2 seasons collecting disease data and yield data from the use of different cultivars and fungicide treatments. </p>
Data from: Quantifying demographic uncertainty: Bayesian methods for integral projection models (IPMs)
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Golden-cheeked warbler Integrated Population Model (IPM) data in Austin, TX (2011–2019)
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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.