Sentences with phrase «data modeling efforts»

Not exact matches

Keeping up with or ahead of competitors is a necessity, with Australia facing the reality of being left behind if a concentrated effort is not put into pursuing information integration, and the business models that will underpin the exchange and application of this data.

Think - tank Activities SOAAN will progress in stepwise fashion in order to develop the outputs mentioned above: Step 1) Review existing science and actions: An overview or survey of the existing science on the topic, including a discussion of the limitations of scope, empirical data, and their reliability and applicability to the organic sector's understanding of best practice; Step 2) Actors engagements: Identification in both public and private sectors of the institutions and individuals active on the topic and their respective roles and networks, so that SOAAN and the organic sector then moves to engage them, along with think tanking activities; Step 3) Think tank: Development of the model, the strategy and promotional / advocacy tools through think tank efforts including piloting cases to gain experience and showcase practicability; Step 4) Synthesis: Publication of the results, tools and recommendations.

In our collaboration on diabetes, for example, data generated by experimental colleagues contribute to modeling and building a concerted mathematical effort at understanding the disease.

«When used in conjunction with forecasted data, the model predictions could be useful for focusing both surveillance efforts, and the pre-positioning of material and equipment in areas and periods of particularly high risk.

The researchers then analyzed the data using a statistical model to explore whether there were links between social engagement in the community, and disaster preparedness efforts including following disaster - related news closely, preparing a family emergency plan or a disaster supplies kit, or planning to migrate to a lower risk area.

«We were fortunate that a group of collaborators, including Fritjof Helmchen from the Brain Research Institute and David Jörg and Benjamin Simons from the University of Cambridge, joined efforts to bring together their expertise in deep brain imaging and theoretical modeling, which allowed us to obtain and understand our data.»

One postdoc presents data on her efforts to develop an organoid model for small - cell lung cancer; another reports progress on culturing hormone - secreting organoids from human gut tissue.

Muotri noted that the research represents one of the first efforts to use iPSCs and brain in - a-dish technology to generate novel insights about a disease process and not simply replicate data from other models.

It was a huge effort from the community to gather the necessary input data using Hubble, VLT - MUSE, and Keck and to construct the lens models,» explains Tommaso Treu, lead author of the modelling comparison paper, from the University of California at Los Angeles, USA.

From these data, we quantify the population genetic parameters of the intra-patient environment to aid modeling efforts such as the spatial - monotherapy work.

«The data presented at AACR provide important new models and mechanistic insights that inform our planned development efforts to evaluate the ability of microbiome therapy to augment immune checkpoint inhibitors,» said David Cook, Ph.D., Chief Scientific Officer and Executive Vice President of Research at Seres.

«After the neurobiological studies pointed out the important role of the GABAB receptor in nicotine reward, and we had positive data in a variety of animal models of nicotine dependence, it was time to focus our efforts on discovery of new molecules that could become therapeutics to assist people to quit smoking.»

-- 7) Forest models for Montana that account for changes in both climate and resulting vegetation distribution and patterns; 8) Models that account for interactions and feedbacks in climate - related impacts to forests (e.g., changes in mortality from both direct increases in warming and increased fire risk as a result of warming); 9) Systems thinking and modeling regarding climate effects on understory vegetation and interactions with forest trees; 10) Discussion of climate effects on urban forests and impacts to cityscapes and livability; 11) Monitoring and time - series data to inform adaptive management efforts (i.e., to determine outcome of a management action and, based on that outcome, chart future course of action); 12) Detailed decision support systems to provide guidance for managing for adaptmodels for Montana that account for changes in both climate and resulting vegetation distribution and patterns; 8) Models that account for interactions and feedbacks in climate - related impacts to forests (e.g., changes in mortality from both direct increases in warming and increased fire risk as a result of warming); 9) Systems thinking and modeling regarding climate effects on understory vegetation and interactions with forest trees; 10) Discussion of climate effects on urban forests and impacts to cityscapes and livability; 11) Monitoring and time - series data to inform adaptive management efforts (i.e., to determine outcome of a management action and, based on that outcome, chart future course of action); 12) Detailed decision support systems to provide guidance for managing for adaptModels that account for interactions and feedbacks in climate - related impacts to forests (e.g., changes in mortality from both direct increases in warming and increased fire risk as a result of warming); 9) Systems thinking and modeling regarding climate effects on understory vegetation and interactions with forest trees; 10) Discussion of climate effects on urban forests and impacts to cityscapes and livability; 11) Monitoring and time - series data to inform adaptive management efforts (i.e., to determine outcome of a management action and, based on that outcome, chart future course of action); 12) Detailed decision support systems to provide guidance for managing for adaptation.

It will compile all available and newly collected bathymetric data into a single high - quality, high - resolution digital model of the ocean floor and promote international collaborative efforts to collect new data.

One of the main hurdles was not just getting meaningful data to work with in the first place, but more so interpreting the data of these non-model species in a world where most wet - and - dry - lab efforts are focused on selected model species.

The Data Wise Improvement Process is a collaborative inquiry model that helps schools organize their daily efforts around the continuous improvement of teaching and learning.

The best way to reduce bullying is not with a one - time assembly or a poster campaign, but with homegrown, data - driven, sustained efforts by a caring, committed staff — a model I call the six R's, a blueprint for effective bullying prevention.

Since 2000, the foundation has invested $ 2 billion in innovative school models, improved systems for data and accountability, and school district efforts.

This is why, in our modeling efforts, we do massive multivariate, longitudinal analyses in order to exploit the covariance structure of student data over grades and subjects to dampen the errors of measurement in individual student test scores.

Presenters will use recently published state lists of evidence - based literacy practices to model effective, specific goal - setting for students and adults as well as model the use of effort and fidelity data to support educators in their professional learning in service to student outcomes.

Since joining the Association in June 2007, Eileen led the Association in notable efforts including: developing a model that measures «value - added» growth in achievement, which is used for A-F rankings of all Arizona schools; creating trainings that enable teachers and school leaders to collaboratively use data; launching joint purchasing programs; filing lawsuits for equitable funding for all K - 12 students; increasing positive public perceptions of charters; and, building a comprehensive program to support prospective charter school operators.

In accordance with this model, the research efforts for the first two years for each cohort focus on gathering baseline and formative data on the design and initial stages of PICCS implementation.

She also leads special education data collection and analysis efforts to help strengthen the position of charter schools as well as identify and replicate successful special education models for improving academic outcomes of students with special needs in charter schools throughout the state.

Efforts to model income mixing using national demographic data confirm these insights.

Reading and math scores for the nation's 12th graders have stagnated since 2009, according to new data published today, prompting U.S. Secretary of Education Arne Duncan to urge for an overhaul of the nation's high school model and amplified efforts to narrow the achievement gap for minority students.

In this role, he leads data collection and research efforts, as well as initiatives to increase the fidelity of implementation of the CIS Model and interventions implemented as part of the CIS Model.

The BETA report concludes that «the model selected to estimate growth scores for New York State represents a first effort to produce fair and accurate estimates of individual teacher and principal effectiveness based on a limited set of data» (p. 35).

I am ready to help your team with any of the following tasks: • Develop logic models and action plans (Leadership / Planning) • Refine governing practices and optimize board composition and norms (Leadership / Governance) • Refine improvement efforts and data review processes (Data Driven) • Conduct an organization - wide analysis to determine expansion readiness (Leadership / Planning) • Develop, launch and analyze surveys to inform practices or address issues (Staff / Stakeholder) • Conduct an organization - wide analysis to identify gaps and strengths and craft improvement plan (Leadership) • Refine mission / vision statements, develop values or norms, or improve organizational culture (Mission Vision Valdata review processes (Data Driven) • Conduct an organization - wide analysis to determine expansion readiness (Leadership / Planning) • Develop, launch and analyze surveys to inform practices or address issues (Staff / Stakeholder) • Conduct an organization - wide analysis to identify gaps and strengths and craft improvement plan (Leadership) • Refine mission / vision statements, develop values or norms, or improve organizational culture (Mission Vision ValData Driven) • Conduct an organization - wide analysis to determine expansion readiness (Leadership / Planning) • Develop, launch and analyze surveys to inform practices or address issues (Staff / Stakeholder) • Conduct an organization - wide analysis to identify gaps and strengths and craft improvement plan (Leadership) • Refine mission / vision statements, develop values or norms, or improve organizational culture (Mission Vision Values)

Oregon's efforts to support innovation and school improvement have focused on proficiency - based learning models and early steps toward creating multiple measures data dashboards.

E3 Alliance has provided ground - breaking data analysis and community facilitation recognized by the White House as a national model to help the Greater Austin Area My Brother's Keeper effort to eliminate equity gaps for our young men of color.

Effectively applying AI involves extensive manual effort to develop and tune many different types of machine learning and deep learning algorithms (e.g. automatic speech recognition, natural language understanding, image classification), collect and clean the training data, and train and tune the machine learning models.

The new model is a joint effort from FICO, whose credit score dominates the mortgage lending industry, and CoreLogic, a consumer data firm that collects information from public records and its own unique sources.

And data from communities and countries that sterilize community dogs show the same results: a decline in the number of dog bites, with «officials point [ing] to a variety of factors: the obvious effect of sterilization on dog behavior, including behaviors associated with mating, reduced numbers of dogs and reduced home range of individual dogs resulting in fewer chance encounters with humans, an increased respect and thus kinder treatment towards dogs due to the positive role model of rescuers, and the impact of community education by rescuers that often accompanies these efforts.

Central to this model is an «advisory council or task force representing a wide spectrum of community concerns and perspectives» whose members review available dog bite data, current laws, and «sources of ineffectiveness» and recommend realistic and enforceable policy, coupled with outreach to the media and educational efforts directed at those in regular contact with «dog owners and potential victims» (e.g., medical and veterinary professionals, animal control / shelters, teachers)(AVMA, 2001).

That understanding will be advanced by new and more extensive data collection efforts, improvements to methods used to synthesise that data, and more extensive and collaborative use of climate model simulations over this period — both to understand the forcing / response of the climate, but also to serve as testbed for the various reconstruction methodologies.

Moving on, many of the following quotes are relevant to various iterations of CMIP, which is an ongoing effort to coordinate modeling activities by creating some standards (both in terms of input data and experimental setups).

It's a first effort at digging info out of data, making corrections, and modeling what they think may be happening:

Enhance existing efforts to create a comprehensive observing system to document changes in these critical indicators and to provide data for calibration and validation of models.

I remember that some time ago Lindzen was complaining about the big efforts that were being dedicated to correct the observational data, just because it didn't fit the models» expectations.

There should be support for things that will better define climate response to forcing, like better quality aerosol data and better cloud data, but much less for duplicative modeling efforts, studies that use wildly uncertain models to make wildly uncertain predictions, and silly chicken - little scare - story studies of utter doom.

Our research activity is thus part of a national and international effort to provide the scientific community with new data sets usefull for ocean circulation modeling, climate studies, bio-optics and bio-chemistry of the ocean.

This includes novel combinations of in situ and remotely sensed data with modeling efforts.

So far, efforts to quantify, model, and manage these emissions have been limited by data availability and inconsistencies in methodological approach.

This is no problem at all, rule one of climate «science «states that «when models and reality differ in value, it is reality which always in error «Having spent all that time and effort in making the data give the «right results «how could it be otherwise.

Here, thickness data, which are sorely lacking but available in a few locations as the result of International Polar Year efforts and from satellite - derived estimates of ice age or type, constrain modeled thickness distributions.

Apart from efforts in generating reliable data products (for example, data assimilation), models need to be refined to incorporate key processes of drought such as land - atmosphere interaction, temperature, soil moisture, and human activities.

Similarly to weather forecasting, efforts can be made to setup a model to match initial conditions at a certain point in time but they are likely to break down pretty quickly because we lack the quantity and quality of data to be precise enough in the setup (and possibly because the chosen model does not accurately produce variability similar to that observed on Earth).

The efforts of myself and others have made far more climate model data available to far more people than ever before; to dismiss our efforts as «irrelevant» because of your issues with specific individuals is unfair.

We have also developed computer models that predict body temperatures to within several degrees using data from weather stations and satellites, and current efforts under way in David Wethey's lab will eventually allow us to predict patterns of temperature on a global basis.

One effort called Old Weather, for example, asks people with spare time to translate ships» logbooks and extract weather data, which researchers can then use to both understand historic weather patterns and model future ones.