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AbstractOpen innovation programmes related to data and artificial intelligence have interested European policy-makers as a means of supporting startups and small and medium-sized enterprises to succeed in the digital economy. We discuss the objectives behind the typical service offerings of such programmes and propose a case for exploring how they align with the motivations of individual companies who are targeted by these initiatives. Using a qualitative analysis of 50 startup applications from the Data Market Services Accelerator programme, we find that applicants wrote most frequently about fundraising, acceleration and data skills. A smaller number of startups expressed interest in services related to standardization or legal guidance on General Data Protection Regulation and intellectual property rights, which are some of the ongoing priority areas for the European Commission. We discuss how the value propositions of these less desired offerings can be amplified by appealing the existing business motivations of data-driven startups.

The force of infection, describing the rate at which a susceptible person acquires an infection, is a key parameter in models estimating the infectious disease burden, and the effectiveness and cost-effectiveness of infectious disease prevention. Since Muench formulated the first catalytic model to estimate the force of infection from current status data in 1934, exactly 75 years ago, several authors addressed the estimation of this parameter by more advanced statistical methods, while applying these to seroprevalence and reported incidence/case notification data. In this paper we present an historical overview, discussing the relevance of Muench's work, and we explain the wide array of newer methods with illustrations on pre-vaccination serological survey data of two airborne infections: rubella and parvovirus B19. We also provide guidance on deciding which method(s) to apply to estimate the force of infection, given a particular set of data. ; We thank the editor and both referees for their valuable suggestions that have led to an improved version of the manuscript. This work was supported by research project (MSM 0021620839), funded by 'SIMID', a strategic basic research project funded by the institute for the Promotion of Innovation by Science and Technology in Flanders (IWT) (project number 06008); by the Fund of Scientific Research (FWO, Research Grant G039304) in Flanders, Belgium; and by the TAP research network (no. P6/03) of the Belgian Government (Belgian Science Policy). The R-code used to analyse the datasets in this manuscript is available from the authors.

As the organizational environment becomes more volatile, uncertain, complex, and ambiguous, and the economy becomes increasingly knowledge-based, organizational knowledge management is key for companies' success. This is especially important as organizational ties are weaker and job-hopping becomes a more prevalent phenomenon. As human resource mobility increases, companies must ensure that knowledge remains within the company despite employee exit. In this context, the current study sought to understand how leaders' actions can facilitate employee knowledge sharing, focusing on paradoxical leadership. Besides examining the impact of paradoxical leadership on employees' propensity to adopt knowledge-sharing behaviors, this study also explored the effects of one potential intervening variable (i.e., promotive voice behavior) and one potential boundary condition (i.e., trust in the leader) on this relationship. A two-wave time-lagged correlational study was conducted with a sample of 154 workers from various sectors. The results of moderated mediation analysis suggest that paradoxical leaders indirectly promote greater knowledge-sharing among subordinates by fostering their promotive-voice behaviors, but only for those with high levels of trust in the leader. The implications of these findings for current organizational challenges regarding knowledge management are discussed.

Abstract
Large-scale student assessments are a cornerstone of proposed educational reforms to improve student achievement from very low levels in low- and middle-income countries. Yet, this promise relies on their presumed reliability. I use direct audit evidence from a large Indian state (Andhra Pradesh) to show that, as currently administered, official learning assessments substantially overstate proficiency and understate the 'learning crisis' of low student achievement. In an experiment covering over 2,400 schools, I evaluate whether digital tablet-based testing could reduce distortion. Although paper-based assessments proctored by teachers severely exaggerate achievement, tablet-based assessments closely match independent test data and are much less likely to be flagged for cheating. Furthermore, I use the direct audit-based retest to directly validate existing (indirect) statistical procedures for detecting cheating at scale and establish that it would be feasible to monitor data integrity cheaply and at scale with such methods. Overall, these results suggest that well-designed technology-aided interventions may improve data integrity at scale, without which these learning assessments are unlikely to serve as a catalyst for policy action.

The number of genetic factors associated with common human traits and disease is increasing rapidly, and the general public is utilizing affordable, direct-to-consumer genetic tests. The results of these tests are often in the public domain. A combination of factors has increased the potential for the indirect estimation of an individual's risk for a particular trait. Here we explain the basic principals underlying risk estimation which allowed us to test the ability to make an indirect risk estimation from genetic data by imputing Dr. James Watson's redacted apolipoprotein E gene <i>(APOE)</i> information. The principles underlying risk prediction from genetic data have been well known and applied for many decades, however, the recent increase in genomic knowledge, and advances in mathematical and statistical techniques and computational power, make it relatively easy to make an accurate but indirect estimation of risk. There is a current hazard for indirect risk estimation that is relevant not only to the subject but also to individuals related to the subject; this risk will likely increase as more detailed genomic data and better computational tools become available.

The paper gives a personal view on how empirical research and subsequent data analysis and reporting should ideally proceed. Although the introduction concedes that practical limitations and imperfect knowledge usually impose restrictions on our possibilities to approximate this ideal situation, it is argued that investigators and statistical consultants should make a strong effort to come close to it. At the stages of design, analysis, and reporting, this requires careful consideration of the selection of the units of analysis, the choice of variables to be included, their measurement process, and the extent to which relevant assumptions are fulfilled. In so far as exploratory data analysis techniques offer the suggestion that they can produce results without such careful consideration, their use should be discouraged unless they are followed by a confirmatory analysis in which the claims of generalizability are better substantiated. Some protest is raised against the idea that the data themselves have a clear structure lying ready to be unveiled by the investigator by means of some numerical or spatial representation, which would permit to draw general conclusions.

Part 1: Invited Talks ; International audience ; Governments around the world are opening their data vaults to public (and corporate) access and scrutiny. Notable examples including New York City's NYC Datamine, Philadelphia's OpenData Philly, Europe's Open Data Challenge, and Canada's Open Data Framework, which now spans several cities including Toronto, Vancouver, Edmonton, and Ottawa. Anyone can read government plans, budgets, contractor lists, and in many cases, documents relating to individual citizens. The intention behind these data transparency projects is laudable, but it behooves those interested in privacy to take a careful look at just what information our governments are sharing with the world. There have already been "Open Data Hackathons" which have discovered interesting and unforeseen vulnerabilities, often by combining multiple data sources. There are also commercial ventures using government released data in combination with other sources in ways that were never anticipated, such as genealogical research. We are breaking new ground here and we need to generate new principles to protect privacy in the face of data that is going from "public" to "super-public".

To contribute to the understanding of the relationship between job decentralization and central-city well-being, this study undertakes some statistical tests using intermetropolitan-level data for the United States. Unlike previous studies, the relationship is tested here with sectoral data. A strong negative association between job decentralization and economic well-being of central-city residents is observed in terms of almost all indicators of well-being. This association is stronger when decentralization is considered in the low-wage sectors, such as retailing. The research findings have serious policy implications in regard to location of activities and provision of transportation infrastructure and facilities.

Abstract
The progress in precision medicine has resulted in genomic technology spreading worldwide. This has raised concerns about the ethics of genetic data sharing and privacy. This article focuses on South Asia and first aims to identify, analyze, and understand the laws, regulations, and policies related to genetic data privacy and discrimination in that region. This is accomplished through a qualitative examination of existing laws and policies on privacy rights and a doctrinal analysis of legislation gathered from seven jurisdictions, viz. Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka. Following the presentation of the country-specific study, the article turns to discussing the study's results, which suggest that the area lacks both national norms and a regional policy specific to the protection of personal genetic data. The article then offers possible reasons for this outcome and suggests that the countries must formulate culturally sensitive regulations and universally applicable legal principles for genetic privacy notwithstanding the existing challenges of poverty, non-uniform demographics, and an absence of political will.

AbstractAs thousands of 2019 Corona virus disease (Covid-19) clinical trials are continuously getting added to various registries these days, good practices on data sharing and transparency have become one of the prime topics of discussion than ever before. Although trial registration is considered a crucial step, there is a lack of integration between registration and published literature. Trial outcomes are a matter of public interest, but sponsor compliances are not adequate with the recommended guidelines. Although the global recognition of data transparency increases day by day, there is still a long journey to travel. It is high time that scholarly publishing stakeholders should put in a collaborative effort to check author compliance. In this article, we aimed to comprehend and discuss the imperative roles of various scholarly publishing stakeholders in improving clinical trial transparency during this pandemic situation and highlight the changing paradigm towards the pressing need for reporting clinical trial data more effectively.

For the first time since a question on religion was first included in the Census of Population (in 1861), the recently published results of the 1991 Census show that less than half of the population of Northern Ireland declared themselves as belonging to a Protestant Christian denomination. Discussions surrounding the future political and constitutional arrangements for Northern Ireland frequently include evidence adduced about the relative size of the two 'tribes' widely identified by the labels 'Protestant' and 'Catholic'. The major source of that evidence comes from the question on religion contained in the Northern Ireland Census, and this paper is concerned with the nature and use of that data. The legitimacy of certain uses of the data is questioned; the reliability of the data is considered; and certain tentative findings are presented based on the data published in the (hard copy) reports. Ways are indicated by which the data may be of use in analysing structural inequality in Northern Ireland society.

[EN] We are witnessing the dramatic consequences of the COVID¿19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID¿19 pandemic to create a decision support system for policy¿makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14¿day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14¿day CI in scenarios with and without trend changes, reaching 0.93 R2, 4.16 RMSE and 1.08 MAE. ; This work has been partially supported by the Spanish Ministry of Science and Innovation, under Grants RYC2018-025580-I, RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, ...

Air quality within the European Union (EU) is controlled by the Member States' monitoring networks. In this study, measured data is compared with the EURAD (EURopean Air pollution Dispersion) model system diagnostic output Simulations for the German state North Rhine-Westphalia (NRW) with a horizontal grid resolution of 5 km x 5 km are analyzed. The comparison is performed for NO2, O-3, and PM10, for the 4-year time period from 2002 through 2005. Although the spatial representativity of data of the two systems differs, the analyzed temporal variability of the averages shows good agreement of modeled and observed concentrations for all three parameters. This confirms the applicability of the EURAD model to mesoscale air quality assessment. Discrepancies between the model and observed data occurred at low concentrations close to the detection limits of the analyzers, for high O-3 concentrations in summer, and for PM10 before 2004, when earlier versions of the MM5 meteorological module and of the emission inventory were used. Possible causes of O-3 overprediction and NO2 underprediction preferably showing up in summer are considered. It is found that modeled O-x = O-3 + NO2 leads to better representation of the observations than the individual species themselves. The model performance can probably be increased by further development of the emission inventories and more accurate land use data. These input data seem to be the main reason for deviations between observations and model results. (C) 2012 Elsevier B.V. All rights reserved.