The dataset has been developed within the framework of the EU EIT-Climate Kic Flagship Project "Re-Industrialise" and it includes data of Carbon Emission Intensity (CEI) from industrial sources for the European Regions. CEI is considered as a proxy for analysing the Industrial Sustainability Transition pathways and is calculated as the ratio between CO2 equivalent emissions (CO2e) and Gross Domestic Product (GDP) of the industrial sector over a nine-year timespan, i.e. from 2008 to 2016. CO2e data at plant level have been retrieved from EU Emission Trading System (EU ETS) register and aggregated at different geographical scales, corresponding to the nested structure of NUTS (Nomenclature of Territorial Units for Statistics), proposed by EUROSTAT. Industrial GDP data have been selected from EUROSTAT database to match the industrial sectors covered by EU ETS.This article presents unique survey data focused on local democracy, political attitudes and political participation. The main aim of the research was to understand the relationships between political values, political participation, political knowledge and voting behavior at the local level. The survey was held at autumn 2018 after local elections in the Czech Republic. The data are unique in terms of their focus on local politics combined with variables that are standardly examined in the context of national politics. The dataset links research fields that are directly related to local politics (local electoral participation, local electoral behavior, local non-electoral participation, trust in local institutions and evaluation of local policy efficacy) with the fields usually connected with national politics (political interest, trust in institutions, political cynicism, populist attitudes and political knowledge). Research also looks at attitudes towards democracy as such. The provided data can be used by scholars in the field of local politics, local governance and electoral behavior. Data are comparable to other large-scale individual level surveys, or may serve as data source for meta-analysis.Stress is inevitably linked to life. It has many and complex facets. Notably, perception of stressful stimuli is an important factor when mounting stress responses and measuring its impact. Indeed, moved by the increasing number of stress-triggered pathologies, several groups drew on advanced neuroimaging techniques to explore stress effects on the brain. From that, several regions and circuits have been linked to stress, and a comprehensive integration of the distinct findings applied to common individuals is being pursued, but with conflicting results. Herein, we performed a volumetric regression analysis using participants' perceived stress as a variable of interest. Data shows that increased levels of perceived stress positively associate with the right amygdala and anterior hippocampal volumes.Osteoid osteoma is one of the osteoblastic benign bone tumors, which occurs frequently at the cortex of long bones, usually in the diaphysis or metadiaphysis. Although the tumor location in the bone varies, epiphyseal intramedullary osteoid osteoma has been rarely reported. Herein, we report a 14-year-old male patient with epiphyseal intramedullary osteoid osteoma, occurring at the distal radius, with magnetic resonance imaging findings.Traumatic injuries of the extensor tendons of the hand are common and are more frequent predisposed to tendon injuries due to the presence of chronic tendon damage. We present the case of a 61-year-old woman, tailor by profession, who showed acute rupture (80 %) with degenerative etiology of the extensor tendon of the V finger of the fifth level according to Kleinert and Verdan classification.Many research agencies are now requiring that data collected as part of funded projects be shared. read more However, the practice of data sharing in education sciences has lagged these funder requirements. We assert that this is likely because researchers' generally have not been made aware of these requirements and the benefits of data sharing. Furthermore, data sharing is usually not a part of formal training, so many researchers may be unaware how to properly share their data. Finally, the research culture in education science is often filled with concerns regarding the sharing of data. In this article, we address each of these areas, discussing the wide range of benefits of data sharing, the many ways data can be shared, provide a step by step guide to start sharing data, and responses to common concerns.As one of the most important estimators in classical statistics, the uniformly minimum variance unbiased estimator (UMVUE) has been adopted for point estimation in many statistical studies, especially for small sample problems. Moving beyond typical settings in the exponential distribution family, it is usually challenging to prove the existence and further construct such UMVUE in finite samples. For example in the ongoing Adaptive COVID-19 Treatment Trial (ACTT), it is hard to characterize the complete sufficient statistics of the underlying treatment effect due to pre-planned modifications to design aspects based on accumulated unblinded data. As an alternative solution, we propose a Deep Neural Networks (DNN) guided ensemble learning framework to construct an improved estimator from existing ones. We show that our estimator is consistent and asymptotically reaches the minimal variance within the class of linearly combined estimators. Simulation studies are further performed to demonstrate that our proposed estimator has considerable finite-sample efficiency gain. In the ACTT on COVID-19 as an important application, our method essentially contributes to a more ethical and efficient adaptive clinical trial with fewer patients enrolled.Under-representation of certain populations, based on gender, race/ethnicity, and age, in data collection for predictive modeling may yield less-accurate predictions for the under-represented groups. Recently, this issue of fairness in predictions has attracted significant attention, as data-driven models are increasingly utilized to perform crucial decision-making tasks. Methods to achieve fairness in the machine learning literature typically build a single prediction model subject to some fairness criteria in a manner that encourages fair prediction performances for all groups. These approaches have two major limitations i) fairness is often achieved by compromising accuracy for some groups; ii) the underlying relationship between dependent and independent variables may not be the same across groups. We propose a Joint Fairness Model (JFM) approach for binary outcomes that estimates group-specific classifiers using a joint modeling objective function that incorporates fairness criteria for prediction. We introduce an Accelerated Smoothing Proximal Gradient Algorithm to solve the convex objective function, and demonstrate the properties of the proposed JFM estimates.