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Virtually all State Early Childhood Comprehensive System Initiatives have adopted or identified indicators for monitoring program performance and child outcomes related to early childhood systems. These are primarily based on nationally recommended indicators or on state initiatives. However, although a functional set of indicators is needed to monitor progress of ECCS initiatives across the states, there is no one overarching set of indicators consistently being used. The challenge for states' ECCS leadership is to select an indicator set that is both comprehensive enough to monitor system developments and specific and limited enough to be useful and manageable. This Short Take reviews the characteristics of good indicators and proposes 36 indicators, based on a review of the literature, an analysis of key national indicator sets, and a comparative review of indicators set out in State ECCS reports and plans.

In light of the globally increasing prevalence of diet-related chronic diseases, new scalable and non-invasive dietary monitoring techniques are urgently needed. Automatically collected digital receipts from loyalty cards hereby promise to serve as an objective and automatically traceable digital marker for individual food choice behavior and do not require users to manually log individual meal items. With the introduction of the General Data Privacy Regulation in the European Union, millions of consumers gained the right to access their shopping data in a machine-readable form, representing a historic chance to leverage shopping data for scalable monitoring of food choices. Multiple quantitative indicators for evaluating the nutritional quality of food shopping have been suggested, but so far, no comparison has validated the potential of these alternative indicators within a comparative setting. This manuscript thus represents the first study to compare the calibration capacity and to validate the discrimination potential of previously suggested food shopping quality indicators for the nutritional quality of shopped groceries, including the Food Standards Agency Nutrient Profiling System Dietary Index (FSA-NPS DI), Grocery Purchase Quality Index-2016 (GPQI), Healthy Eating Index-2015 (HEI-2015), Healthy Trolley Index (HETI) and Healthy Purchase Index (HPI), checking if any of them performs differently from the others. The hypothesis is that some food shopping quality indicators outperform the others in calibrating and discriminating individual actual dietary intake. To assess the indicators' potentials, 89 eligible participants completed a validated food frequency questionnaire (FFQ) and donated their digital receipts from the loyalty card programs of the two leading Swiss grocery retailers, which represent 70% of the national grocery market. Compared to absolute food and nutrient intake, correlations between density-based relative food and nutrient intake and food shopping data are stronger. The FSA-NPS DI has ...

In light of the globally increasing prevalence of diet-related chronic diseases, new scalable and non-invasive dietary monitoring techniques are urgently needed. Automatically collected digital receipts from loyalty cards hereby promise to serve as an objective and automatically traceable digital marker for individual food choice behavior and do not require users to manually log individual meal items. With the introduction of the General Data Privacy Regulation in the European Union, millions of consumers gained the right to access their shopping data in a machine-readable form, representing a historic chance to leverage shopping data for scalable monitoring of food choices. Multiple quantitative indicators for evaluating the nutritional quality of food shopping have been suggested, but so far, no comparison has validated the potential of these alternative indicators within a comparative setting. This manuscript thus represents the first study to compare the calibration capacity and to validate the discrimination potential of previously suggested food shopping quality indicators for the nutritional quality of shopped groceries, including the Food Standards Agency Nutrient Profiling System Dietary Index (FSA-NPS DI), Grocery Purchase Quality Index-2016 (GPQI), Healthy Eating Index-2015 (HEI-2015), Healthy Trolley Index (HETI) and Healthy Purchase Index (HPI), checking if any of them performs differently from the others. The hypothesis is that some food shopping quality indicators outperform the others in calibrating and discriminating individual actual dietary intake. To assess the indicators' potentials, 89 eligible participants completed a validated food frequency questionnaire (FFQ) and donated their digital receipts from the loyalty card programs of the two leading Swiss grocery retailers, which represent 70% of the national grocery market. Compared to absolute food and nutrient intake, correlations between density based relative food and nutrient intake and food shopping data are stronger. The FSA-NPS DI has the best calibration and discrimination performance in classifying participants' consumption of nutrients and food groups, and seems to be a superior indicator to estimate nutritional quality of a user's diet based on digital receipts from grocery shopping in Switzerland.

This training book is designed to help professionals enhance their knowledge of community quality-of-life indicators, and to develop viable community projects. Chapter 1 describes the theoretical concepts that guide the formulation of community indicator projects. Chapter 2 creates a sample community indicator project as a template of the entire process. Chapter 3 describes the planning process: how to identify sponsors, secure funding, develop an organizational structure, select a quality-of-life model, select indicators, and so on. Chapter 4 focuses on data collection. Finally, Chapter 5 describes efforts related to dissemination and promotion of community indicators projects. Written by a stalwart in the field of quality-of-life research, this book provides the tools of sound community project planning for quality-of-life researchers, social workers, social marketers, community research organizations, and policy-makers.

The rising demand for policy-relevant impact indicators is a product of two relatively recent developments: the success of science indicators in monitoring the achievement of intrinsic (cognitive) goals of science, combined with the advent of political initiatives that seek to improve the performance of science in attaining extrinsic (social) goals of science. The search for policy relevant impact indicators, while it will continue to benefit from scholarly research in social studies of science, presupposes an appropriate accounting scheme that facilitates the organization of impact indicators. One such accounting scheme—the social knowledge system (see Holzner et al., this volume)—has demonstrated its practical utility by drawing us closer to a programmatic goal of the National Science Foundation: the establishment of a system of social impact of science (SIS) indicators (see Dunn et al., this volume) that improves our understanding of science and the way it affects the world around us.

The rational use and conservation of biodiversity requires programmes of inventorying and monitoring that allow understanding the past and present states of biodiversity and the causes of its change. Inventories establish a baseline distribution of biodiversity for a particular place at a particular time. Monitoring addresses the issue of change or lack of change of biodiversity through time at particular places. Ideally some sort of modelling should allow for predicting future states of biodiversity. Biodiversity is a dynamic property of an ecosystem . The goal of a monitoring program is to document natural patterns of change or lack of change in order to establish a baseline for understanding the impact of natural disturbance on species composition and abundance in communities and ecosystems. Once this baseline is established it can be used to detect changes in biodiversity that result from human disturbance. An inventory will establish the magnitude of biodiversity over relatively short time spans whereas monitoring will serve to connect these observations over time, to assist in hypothesis testing and to help establish an early warning system that may be part of a global biodiversity assessment. The total assessment of biodiversity at a given site, let alone for a country or a region, through enumeration of the genetic diversity, the species and habitats is an impossible task to fulfil. A number of indicators or proxies is therefore required that provide information that is as unbiased as possible. Besides indicators for biodiversity other indicators are used to measure environmental health. The choice of an appropriate indicator is very important in terms of information and cost. To increase the usefulness of an indicator several frameworks have been developed. One of the most widely used is the Drivers-Pressure-State-Impact-Response DPSIR framework, but simpler and perhaps more convenient schemes are in use as well. At the European level, loss of biodiversity is one of the about ten policy fields to which these schemes are applied. All these indicators require massive collection of data and the good use of these collections is still a major problem in applied and fundamental ecological research both on governmental and academic level. Dealing with the massive information that has already been gathered and the still more massive information that will be collected in the future is one of the major challenges of the scientific community and the end-users of scientific information in general. The networking efforts of marine biodiversity in Europe provide a good example of the problems and the ways towards their solution.

With personal income, consumption, and manufacturing and trade industry sales released, no downturn yet. If consensus holds, the momentum continues into August. Here are some key indicators followed by the NBER BCDC, along with monthly GDP and GDPNow. Figure 1: Nonfarm payroll employment, NFP (dark blue), civilian employment (orange), industrial production (red), personal income excluding […]

The European Union (EU) soil policy is described in general terms in the EU Soil Strategy (EC 2006a) and the legally binding elements of the policy are proposed in the draft Soil Framework Directive (SFD; EC 2006b). In these documents, eight main threats to soil were identified by the EU Commission. The EU FP6 project ENVASSO (Environmental Assessment of Soil for Monitoring) had the aim to design a single, integrated and operational set of EU-wide criteria and indicators to provide the basis for a harmonised comprehensive soil and land information system for monitoring in Europe. Here, a proposal is made for a set of suitable indicators for monitoring the decline in soil biodiversity (Bispo et al. 2007). These indicators were selected both from a literature review and an inventory of national monitoring programmes. Decline in soil biodiversity was defined as the reduction of forms of life living in soils (both in terms of quantity and variety) and of related functions, causing a deterioration of one or more soil functions or ecosystem services. Whereas literature review allows the identification of about 100 possible indicators, the inventory of existing monitoring networks shows that few indicators are actually measured. For monitoring application it was considered in ENVASSO that only three key indicators per soil stress were practical. For indicating biodiversity decline it was difficult to arrive at a small set of indicators due to the complexity of soil biota and functions. Therefore, three stringent criteria were applied: an indicator should 1) have a standardized sampling and/or measuring methodology; 2) be complementary to other indicators; and 3) be easy to interpret at both scientific and policy levels.

AbstractThe purpose of this paper is to comment on and give historical perspective to two methdologies for estimating parameters of beta distributions. Fielitz and Myers [3] [4] developed and advocated a methodology using the method of moments, while Romesburg [20] advocated a methodology usingthe method of maximum likelihood. However, what Fielitz and Myers presented as new research and suggested as an area needing further study is ground already trampled. The authors have prepared a graph to underline the superiority of the maximum likelihood method in fitting beta distributions.

This report aims to develop a set of indicators that measure sustainable development of Arctic seafood production. It builds on an environmental, a social, and an economic dimension, three dimensions of sustainable development. Our indicator system provides decision support information and monitoring tool for relevant decision makers. It aims to facilitate orientation in a complex multitude of social and ecological systems as well as their intersection. The indicator set is structured as a pyramid made up of the three dimensions of sustainable development, subdivided into policy categories, each described by one or few indicator target areas, supplemented by a number of more contextual indicators. We give a short presentation of each indicator and assess data availability. While this report provides decision support it cannot stand alone and is part of the synthesis of the European Union project Arctic Climate Change Economy and Society.