Correction: Exploring climate change on Twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters.

[This corrects the article DOI: 10.1371/journal.pone.

Time series and regression methods for univariate environmental forecasting: An empirical evaluation.

One of the most common and valuable applications of science to the environment is to forecast the future, as it affects human lives in many aspects. However, it is not yet clear which methods -conventional time series or regression- deliver the highest performance in univariate time series forecasting. This study attempts to answer that question with a large-scale comparative evaluation that includes 68 environmental variables over three frequencies (hourly, daily, monthly), forecasted in one to twelve steps into the future, and evaluated over six statistical time series and fourteen regression methods.

Exploring climate change on Twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters.

How do climate change deniers differ from believers? Is there any correlation between human sentiment and deviations from historic temperature? We answer nine such questions using 13 years of Twitter data and 15 million tweets. Seven aspects are explored, namely, user gender, climate change stance and sentiment, aggressiveness, deviations from historic temperature, topics discussed, and environmental disaster events. We found that: a) climate change deniers use the term global warming much often than believers and use aggressive language, while believers tweet more about taking actions to fight the phenomenon, b) deniers are more present in the American Region, South Africa, Japan, and Eastern China and less present in Europe, India, and Central Africa, c) people connect much more the warm temperatures with man-made climate change than cold temperatures, d) the same regions that had more climate change deniers also tweet with negative sentiment, e) a positive correlation is observed between human sentiment and deviations from historic temperature; when the deviation is between -1.

Seagrass and hydrographic data for the Mediterranean Sea.

The dataset includes 1,771 locations of major seagrass families (), which are further divided into the species they include, as well as 1,284 locations of seagrass absence (algorithmically produced), in the Mediterranean Sea. For each location, 217 biological, chemical, physics, and human related parameters are available, which were merged from other publicly available data sources. As the most comprehensive dataset for seagrass in the Mediterranean to date, it is suitable for data analysis and machine learning.