Abstract
Predictors of macroeconomic indicators rely primarily on traditional data sourced from National Statistical Offices. However, new data sources made available from recent technological advancements, namely data from online activities, have the potential to bring about fresh perspectives on monitoring economic activities and enhance the accuracy of forecasting. This paper reviews the literature on predicting macroeconomic indicators, such as the gross domestic product, unemployment rate, consumer price index or private consumption, based on online activity data sourced from Google Trends, Twitter (rebranded to X) and mobile devices. Based on a systematic search of publications indexed on the Web of Science and Scopus databases, the analysis of a final set of 56 publications covers the publication history of the data sources, the methods used to model the data and the predictive accuracy of information from such data sources. The paper also discusses the limitations and challenges of using online activity data for macroeconomic predictions. The review concludes that online activity data can be a valuable source of information for predicting macroeconomic indicators. However, one must consider certain limitations and challenges to improve the models' accuracy and reliability. © 2024 - IOS Press. All rights reserved.

Abstract
EMOS (The European Master in Official Statistics) was set up to strengthen the collaboration within academia and producers of official statistics and help develop professionals able to work with European official data at different levels in the fast-changing production system of the 21st century. In this paper we address the need for training in Official Statistics, particularly in current times, where new skill sets and competencies are necessary. In particular, the needs for new data sources currently used by national statistical systems require the development of new methodologies. For that purpose, we do a matching between National Statistical Offices (NSO) needs and the offer from universities.

Abstract
Policymakers often have to make decisions based on incomplete economic data because of the usual delay in publishing official statistics. To circumvent this issue, researchers use data from Google Trends (GT) as an early indicator of economic performance. Such data have emerged in the literature as alternative and complementary predictors of macroeconomic outcomes, such as the unemployment rate, featuring readiness, public availability and no costs. This study deals with extensive daily GT data to develop a framework to nowcast monthly unemployment rates tailored to work with real-time data availability, resorting to Mixed Data Sampling (MIDAS) regressions. Portugal is chosen as a use case for the methodology since extracting GT data requires the selection of culturally dependent keywords. The nowcasting period spans 2019 to 2021, encompassing the time frame in which the coronavirus pandemic initiated. The findings indicate that using daily GT data with MIDAS provides timely and accurate insights into the unemployment rate, especially during the COVID-19 pandemic, showing accuracy gains even when compared to nowcasts obtained from typical monthly GT data via traditional ARMAX models.

Abstract
Seasonal and daily variations of gaseous emissions from naturally ventilated dairy cattle barns are important figures for the establishment of effective and specific mitigation plans. The present study aimed to measure methane (CH4) and ammonia (NH3) emissions in three naturally ventilated dairy cattle barns covering the four seasons for two consecutive years. In each barn, air samples from five indoor locations were drawn by a multipoint sampler to a photoacoustic infrared multigas monitor, along with temperature and relative humidity. Milk production data were also recorded. Results showed seasonal differences for CH4 and NH3 emissions in the three barns with no clear trends within years. Globally, diel CH4 emissions increased in the daytime with high intra-hour variability. The average hourly CH4 emissions (g h-1 livestock unit- 1 (LU)) varied from 8.1 to 11.2 and 6.2 to 20.3 in the dairy barn 1, from 10.1 to 31.4 and 10.9 to 22.8 in the dairy barn 2, and from 1.5 to 8.2 and 13.1 to 22.1 in the dairy barn 3, respectively, in years 1 and 2. Diel NH3 emissions highly varied within hours and increased in the daytime. The average hourly NH3 emissions (g h-1 LU-1) varied from 0.78 to 1.56 and 0.50 to 1.38 in the dairy barn 1, from 1.04 to 3.40 and 0.93 to 1.98 in the dairy barn 2, and from 0.66 to 1.32 and 1.67 to 1.73 in the dairy barn 3, respectively, in years 1 and 2. Moreover, the emission factors of CH4 and NH3 were 309.5 and 30.6 (g day- 1 LU-1), respectively, for naturally ventilated dairy cattle barns. Overall, this study provided a detailed characterization of seasonal and daily gaseous emissions variations highlighting the need for future longitudinal emission studies and identifying an opportunity to better adequate the existing mitigation strategies according to season and daytime.

Abstract
Palaeoclimate time series, reflecting the state of Earth's climate in the distant past, occasionally display very large and rapid shifts showing abrupt climate variability. The identification and characterisation of these abrupt transitions in palaeoclimate records is of particular interest as this allows for understanding of millennial climate variability and the identification of potential tipping points in the context of current climate change. Methods that are able to characterise these events in an objective and automatic way, in a single time series, or across two proxy records are therefore of particular interest. In our study the matrix profile approach is used to describe Dansgaard-Oeschger (DO) events, abrupt warmings detected in the Greenland ice core, and Northern Hemisphere marine and continental records. The results indicate that canonical events DO-19 and DO-20, occurring at around 72 and 76 ka, are the most similar events over the past 110 000 years. These transitions are characterised by matching transitions corresponding to events DO-1, DO-8, and DO-12. They are abrupt, resulting in a rapid shift to warmer conditions, followed by a gradual return to cold conditions. The joint analysis of the delta 18O and Ca2+ time series indicates that the transition corresponding to the DO-19 event is the most similar event across the two time series.