Data Quality Objectives Sample Clauses

Data Quality Objectives. The data of primary interest in this verification are the reductions in emissions of the FTP primary pollutants: NOx, hydrocarbons (HC), PM, and carbon monoxide (CO). The DQOs of this GVP are the requirements of the test methods specified in 40 CFR Part 86 (highway diesel engines) or 89 (nonroad diesel engines) when conducting the number and type of tests called for by the approved test/QA plan for the SCR. ETV tests that do not meet the FTP and SET QA requirements are invalid. The number of and type of FTP tests (cold- or hot-start) required for ETV is determined from the following criteria: First, a minimum of three tests is required to provide the basic ETV result of a mean emission reduction and the 95 percent confidence interval on that mean based on measured variability for each of the measured emissions and test parameters. For highway engines, this minimum is satisfied with one cold start test and three hot start tests. For nonroad engines, three replicates of the appropriate test sequence (i.e., three 8-mode tests or three 6-mode tests) are required. A three­ test minimum is currently the same as is required by the State of California for its program. Second, additional tests may be required to meet the ETV requirement that the test/QA plan provide a 90 percent probability of detecting the expected emissions reductions when computed using the expected experimental errors for the various measurements. These criteria become controlling for low emissions reductions and/or high test variability. This is a planning requirement for the test/QA plan. Third, additional tests may be desired by the applicant to reduce the width of the 95 percent confidence interval on the mean emission reduction. This third criterion is a consequence of applying standard statistical procedures to the ETV test design and data analysis. At a fixed measurement variability, normal statistical procedures lead to a small number of tests giving a broader 95 percent confidence interval than would a larger number of tests. To any regulator or potential technology user, an emission reduction of 40 ± 5 percent is better than 40 ± 20 percent and will be given more credence. Noncritical measurements, including ammonia slip, will also be made as described in later sections. These are not considered critical, and the methods and DQOs for them will be stated in the test/QA plan. The FTP tests referenced above are conducted following test cycles specified in 40 CFR. As discussed in Section ...
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Data Quality Objectives. Provide data quality objectives that identify what data are needed and the intended use of the data.
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Data Quality Objectives. The U.S. EPA has developed the Data Quality Objective (DQO) Process as the agency’s recommended planning process when environmental data are used to select between two alternatives or derive an estimate of contamination. The DQO Process is used to develop performance and acceptance criteria (or DQOs) that clarify study objectives, define the appropriate type of data, and specify tolerable levels of potential decision errors that will be used as the basis for establishing the quality and quantity of data needed to support decisions. Under this contract, the contractor shall implement the DQO process to ensure data of adequate quality are collected to support project decisions. Laboratories may be subject to on-site government audits of their Quality Assurance/Quality Control (QA/QC) protocols and procedures (not subject to the expense of the contractor). All laboratories shall meet DQOs specified in installation sampling and analysis requirements, and all laboratories shall perform QA/QC requirements as specified in the project-specific SAP.
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Data Quality Objectives. The DQO process is the application of systematic pla nning to generate performance and acceptance criteria for collecting environmental data. The out put of the DQO process is a set of qualitative and quantitative statements that describe s a data collection activity. Adherence to the DQO process ensures that data of known and appropriate quality support project decisions . The DQO planning process is the formalization of the normal process of planning, designing, and implementing environmental data collection activities. The output of the DQO process is a detailed sampling and analysis strategy. The relationship between the DQO process and the normal project li fecycle is illustrated in Table 2-1. (All tables appear at the end of this section.) The DQO process consists of determining what information is needed , why it is needed , how it will be used , and who will use it . The DQO process:  Evaluates different sampling approaches based on cost and resource constraints .  Selects the most cost -effective monitoring approach that will meet the needs of the ultimate data us er.  Determines specific sampling and laboratory methodology requirements. The DQO process will facilitate data collection activities and will yield data meeting the needs of the user as defined in Guidance on Systematic Planning Using the Data Quality Obje ctives Process , EPA QA/ G -4, EPA/ 240/ B -06/ 001 (USEPA, 2006a). As defined in the above reference, the DQO process includes the following step s:  Define Problem Statement .  Identify the Goal of the Study .  Identify Information Inputs .  Define the Boundaries of th e Study .  Develop Analytic Approach .  Specify Performance or Acceptance Criteria .  Develop the Detailed Plan for Obtaining Data . Additional guidance that may be helpful in developing project specific DQOs includes Systematic Planning: A Case Study for Hazardous Waste Site Investigations , EPA/ 240/ B -06/ 004 (USEPA, 2006b). Development of project DQOs is an iterative proc ess and should reflect a common -sense approach to environmental data collection and analysis. RWQCB anticipates that the general types of activit ies or steps that will be conducted using this QAPP will include, but will not be limited to:  Initial site investigation.  Site characterization.  Remedial actions and site cleanup.  Site closure. Figure 2-6 illustrates the outputs of the DQO process as it relates to site cleanup within RWQCB jurisdiction. Table 2-2 presents considerati...
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Data Quality Objectives. ‌ The project objectives are to collect data in a manner that complies with WQCD guidance for surface-water quality monitoring programs, to support decisions related to TMAL development, stream standards modifications, permit decisions, water quality assessments and CRP ROD compliance. The following paragraphs define the measurement performance criteria necessary to support the project objectives.
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Data Quality Objectives. ‌ The overall project data quality objective is to provide valid data of known and documented quality to characterize sources, determine location of contaminants at levels equal to or above screening or natural background quality levels, and screen for threats the site may pose to human health and/or the environment. Data gathered during the site investigation will provide the basis for decisions relating to future investigation requirements, human health and ecological risk screening or assessment, and remedial measures. The data quality assurance objectives for this project are to develop and implement procedures to collect representative samples and to provide chemical data of known quality. In order to meet these objectives, all field activities will be conducted according to the methods described in this SAP. Of particular importance will be to obtain data of sufficient quantity and quality, with appropriately low method detection limits, to support appropriate risk screening.
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Data Quality Objectives. Provide data quality objectives that identify what data are needed and the intended use of the data following the U.S. Environmental Protection Agency procedures in Guidance For The Data Quality Objectives Process, EPA QA/G-4, September 1994 or the most recent edition.
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Data Quality Objectives. The last sentence on page 1-14 is cut off and the missing text is found beneath Figure 1.5. but not visible to the reader. Please fix this issue.
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Data Quality Objectives 
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Related to Data Quality Objectives

  • Program Objectives Implement a rigorous constructability program following The University of Texas System, Office of Facilities Planning and Construction Constructability Manual. Identify and document project cost and schedule savings (targeted costs are 5% of construction costs). Clarification of project goals, objectives.

  • Benchmarks for Measuring Accessibility For the purposes of this Agreement, the accessibility of online content and functionality will be measured according to the W3C’s Web Content Accessibility Guidelines (WCAG) 2.0 Level AA and the Web Accessibility Initiative Accessible Rich Internet Applications Suite (WAI-ARIA) 1.0 for web content, which are incorporated by reference. Adherence to these accessible technology standards is one way to ensure compliance with the College’s underlying legal obligations to ensure that people with disabilities are able to acquire the same information, engage in the same interactions, and enjoy the same benefits and services within the same timeframe as their nondisabled peers, with substantially equivalent ease of use; that they are not excluded from participation in, denied the benefits of, or otherwise subjected to discrimination in any College programs, services, and activities delivered online, as required by Section 504 and the ADA and their implementing regulations; and that they receive effective communication of the College’s programs, services, and activities delivered online.

  • Quality of Services (a) The Consultant shall be responsible for the professional quality, technical accuracy, and the coordination of all designs, drawings, specifications, and other services furnished pursuant to this Agreement.

  • Goals & Objectives 1. The goal of this Agreement is (INSERT GOAL(S) OF AGREEMENT).

  • Targets and Milestones Comparing the relative performance of different groups to the over or under- representation within the institution and taking into account our current performance in our Access Agreement milestones, areas for particular focus include: Low Participating Neighbourhoods; Low income groups; Target groups to include gender, disability and care leavers; Black and minority ethnic (BME) group attainment; Completion rates. As a result of the analysis of our performances, our access, success and progression interventions will concentrate on the following: Continuation of involvement in collaborative outreach activity via the KMPF and the Kent and Medway Collaborative Network (KMCNet) as part of the National Network for Collaborative Outreach (NNCO); Recognition of the importance of carefully targeted activity; The use of serial rather than one-off interventions; The importance of long-term outreach to include the whole student lifecycle; The helpfulness of Higher Education Access Tracker (HEAT) for evaluating the impact of interventions; The importance of a whole institution approach; The importance of student attendance monitoring; Ease of access to information and student welfare support; An increasing emphasis on evaluation of activities across the student lifecycle; Accessibility of employability advice and support. Given our relatively strong record to date for widening access and student success, most of the targets seek to maintain, and where possible improve, this performance within a more challenging financial environment. Such targets may be especially challenging and stretching in relation to the access of those from Low Participating Neighbourhoods (LPNs), given the demographic decline in the number of young people (aged 18-21) in the population and the University’s already high recruitment levels from these groups. We have removed the University’s NS-SEC target in response to the UK Performance Indicator Steering Group announcement that HESA will no longer be publishing the NS-SEC indicator after 2016. As we already have LPN and Household Income targets in place we shall not be replacing this target with an alternative. We have reviewed our success targets and added new progression targets for 2017. There was a concern in the institution that our internal reporting did not allow for national and regionally adjusted benchmark comparison. We have therefore made the following adjustments to our success targets: Non-continuation two years following year of entry: part-time first degree entrants – all entrants: Replacing the OFFA agreement target with the similar data from HESA allows for national benchmarking to be undertaken in order to ensure that the University is maintaining its commitment to these students. We aim to keep our non-continuation rate in this area below our HESA benchmark rate. Non-continuation following year of entry: UK domiciled full-time first degree entrants – mature entrants: Changing the target to clearly focus on mature full-time first degree students (to match the national HESA data) ensures that we focus our efforts on this section of the student population and for the outcomes to be compared with HESA benchmarks rather than internally produced data. We aim to ensure that this student population’s non-continuation rate is at or below the HESA benchmark rate by 2020/21. Non-continuation following year of entry: UK domiciled full-time first degree entrants – all entrants: In order to ensure that young students are not disadvantaged by the focus on mature entrants, the University will also commit to maintaining the overall non-continuation rate for all students at or below the HESA benchmark. BME: the University will replace the current phrasing of the target around BME success with a more explicit aim of reducing the success gap experienced by BME students. Progression: the University has added a progression target that aims to keep us around or above the sector benchmark for the Employment Indicator from the DLHE survey. Combined targets from the collaborative KMPF project (agreed by all partners) are to raise applications and subsequent conversions to higher education from within the target schools and colleges in LPNs. These targets will need to be reviewed in the coming years to reflect changes to GCSE grading in schools. Our institutional and collaborative targets are included in tables 7a and 7b respectively.

  • Performance Targets Threshold, target and maximum performance levels for each performance measure of the performance period are contained in Appendix B.

  • Project Objectives The Program consists of the projects described in Annex I (each a “Project” and collectively, the “Projects”). The objective of each of the Projects (each a “Project Objective” and collectively, the “Project Objectives”) is to:

  • Development Milestones In addition to its obligations under Paragraph 7.1, LICENSEE specifically commits to achieving the following development milestones in its diligence activities under this AGREEMENT: (a) (b).

  • Technical Objections to Grievances It is the intent of both parties to this agreement that no grievance shall be defeated merely because of a technical error other than time limitations in processing the grievance through the grievance procedure. To this end an arbitration board shall have the power to allow all necessary amendments to the grievance and the power to waive formal procedural irregularities in the processing of a grievance in order to determine the real matter in dispute and to render a decision according to equitable principles and the justice of the case.

  • Commercial Milestones In partial consideration of the rights granted by AstraZeneca to Licensee hereunder, Licensee shall pay to AstraZeneca the following payments, which shall be non-refundable, non-creditable and fully earned upon the first achievement of the applicable milestone event:

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