In the present article, we introduce the Nencki Affective Word List (NAWL), created in order to provide researchers with a database of 2,902 Polish words, including nouns, verbs, and adjectives, with ratings of emotional valence, arousal, and imageability. Measures of several objective psycholinguistic features of the words (frequency, grammatical class, and number of letters) are also controlled. The database is a Polish adaptation of the Berlin Affective Word List–Reloaded (BAWL-R; Võ et al., Behavior Research Methods 41:534–538, 2009), commonly used to investigate the affective properties of German words. Affective normative ratings were collected from 266 Polish participants (136 women and 130 men). The emotional ratings and psycholinguistic indexes provided by NAWL can be used by researchers to better control the verbal materials they apply and to adjust them to specific experimental questions or issues of interest. The NAWL is freely accessible to the scientific community for noncommercial use as supplementary material to this article.Electronic supplementary materialThe online version of this article (doi:10.3758/s13428-014-0552-1) contains supplementary material, which is available to authorized users.

The Nencki Affective Picture System (NAPS; Marchewka, Żurawski, Jednoróg, & Grabowska, Behavior Research Methods, 2014) is a standardized set of 1,356 realistic, high-quality photographs divided into five categories (people, faces, animals, objects, and landscapes). NAPS has been primarily standardized along the affective dimensions of valence, arousal, and approach–avoidance, yet the characteristics of discrete emotions expressed by the images have not been investigated thus far. The aim of the present study was to collect normative ratings according to categorical models of emotions. A subset of 510 images from the original NAPS set was selected in order to proportionally cover the whole dimensional affective space. Among these, using three available classification methods, we identified images eliciting distinguishable discrete emotions. We introduce the basic-emotion normative ratings for the Nencki Affective Picture System (NAPS BE), which will allow researchers to control and manipulate stimulus properties specifically for their experimental questions of interest. The NAPS BE system is freely accessible to the scientific community for noncommercial use as supplementary materials to this article.Electronic supplementary materialThe online version of this article (doi:10.3758/s13428-015-0620-1) contains supplementary material, which is available to authorized users.

Research on the processing of sexual stimuli has proved that such material has high priority in human cognition. Yet, although sex differences in response to sexual stimuli were extensively discussed in the literature, sexual orientation was given relatively little consideration, and material suitable for relevant research is difficult to come by. With this in mind, we present a collection of 200 erotic images, accompanied by their self-report ratings of emotional valence and arousal by homo- and heterosexual males and females (n = 80, divided into four equal-sized subsamples). The collection complements the Nencki Affective Picture System (NAPS) and is intended to be used as stimulus material in experimental research. The erotic images are divided into five categories, depending on their content: opposite-sex couple (50), male couple (50), female couple (50), male (25) and female (25). Additional 100 control images from the NAPS depicting people in a non-erotic context were also used in the study. We showed that recipient sex and sexual orientation strongly influenced the evaluation of erotic content. Thus, comparisons of valence and arousal ratings in different subject groups will help researchers select stimuli set for the purpose of various experimental designs. To facilitate the use of the dataset, we provide an on-line tool, which allows the user to browse the images interactively and select proper stimuli on the basis of several parameters. The NAPS ERO image collection together with the data are available to the scientific community for non-commercial use at http://naps.nencki.gov.pl.

The Nencki Affective Word List (NAWL) has recently been introduced as a standardized database of Polish words suitable for studying various aspects of language and emotions. Though the NAWL was originally based on the most commonly used dimensional approach, it is not the only way of studying emotions. Another framework is based on discrete emotional categories. Since the two perspectives are recognized as complementary, the aim of the present study was to supplement the NAWL database by the addition of categories corresponding to basic emotions. Thus, 2902 Polish words from the NAWL were presented to 265 subjects, who were instructed to rate them according to the intensity of each of the five basic emotions: happiness, anger, sadness, fear and disgust. The general characteristics of the present word database, as well as the relationships between the studied variables are shown to be consistent with typical patterns found in previous studies using similar databases for different languages. Here we present the Basic Emotions in the Nencki Affective Word List (NAWL BE) as a database of verbal material suitable for highly controlled experimental research. To make the NAWL more convenient to use, we introduce a comprehensive method of classifying stimuli to basic emotion categories. We discuss the advantages of our method in comparison to other methods of classification. Additionally, we provide an interactive online tool (http://exp.lobi.nencki.gov.pl/nawl-analysis) to help researchers browse and interactively generate classes of stimuli to meet their specific requirements.

Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the framework’s performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).