Throughout the data set, missing values are denoted as NA. All questionnaires were presented in German; the following data set has been translated to English. id ................................. Unique participant identification number. Numeric. duration ........................... Time spent completing the survey (in seconds). Values of -1 indicate that the participant left the survey page at some point (no constant page focus). Those participants were excluded from the analysis. We furthermore excluded participants who completed the study unplausibly fast, i.e. 1 SD faster than the mean duration of five trained readers who tested the survey. The readers took M = 477.625, SD = 196.244 seconds to complete the study, which results in a cut-off value of 281.38 seconds. student_check ...................... Whether participants were currently studying to become a teacher (1) or not (2). Those with a value of 2 were excluded from the analysis. seriousness ........................ Seriousness check. We asked participants whether they took part in the study seriously and whether we could safely use their data (1) or not (2). Those with a value of 2 were excluded from the analysis. condition .......................... Experimental condition. Participants either read a neutral ("neutral") or uncivil ("uncivil") debate scenario. gender ............................. Participant gender. "Female", "male" or "not specified". age ................................ Participant age in years. school_type ........................ German school type participants were planning to teach in after finishing their studies. Levels are: "primary school", "secondary school", "grammar school", "vocational school", or "other". school_type_other .................. If participants responded with "other" as their school type, they specified at which school type they were planning to teach at after finishing their studies. SCHOOL SUBJECTS The following columns each contain a school subject. For each subject, participants indicated whether they were studying the subject (1) or not (0). This refers to the column names: "structural_engineering", "biology", "chemistry", "chinese", "german", "electrical_engineering", "english", "housekeeping", "protestant_religion", "production_technology", "finance", "french", "geography", "history", "health_sciences", "greek", "computer_science", "information_technology", "islamic_religion", "italian", "japanese", "catholic_religion", "arts", "latin", "math_and_language" (German: "Lernbereich Mathematische Grundbildung und Lernbereich Sprachliche Grundbildung"), "science_teaching" (German: "Lernbereich Natur- und Gesellschaftswissenschaften (Sachunterricht)"), "mechanical_engineering", "math", "media_design", "music", "dutch", "philosophy", "physics", "production", "psychology", "education", "russian", "social_sciences", "spanish", "sports", "economics", "other" semesters .......................... How many semesters participants had studies (bachelor's and master's summed up). uni ................................ University participants studied at. Was only added to the survey at a later time point, so the first participants have missing values here. usage .............................. On a slider from 1 (definitely no) to 100 (definitely yes), participants indicated whether they thought the fictional vocabulary training programme PAVLOV should be used. confidence ......................... Confidence in the usage rating on a slider from 1 (not confident at all) to 100 (very confident). CONFLICT EXPLANATION The following 4 columns all refer to the question: "In your opinion, what are the reasons for the conflict that emerged in the panel discussion?" They were then presented with 4 statements and had to rate each on a scale from 1 (do not agree at all) to 7 (fully agree). conflict1 .......................... "The debaters referred to different research findings." conflict2 .......................... "There was a personal conflict between the debaters." conflict3 .......................... "The debaters referred to different effects of the programme." conflict4 .......................... "The debaters focussed on different goals of the programme." potency ............................ On a scale from 1 (do not agree at all) to 7 (fully agree), participants answered the question whether science is equipped to answer the question whether the fictional vocabulary programme PAVLOV should be used or not. METI The following 14 columns all refer to the Münster Epistemic Trust Inventory (Hendriks et al., 2015). Participants rated the debaters from the newspaper articles by choosing between 14 word pairs on a 7-point Likert-type scale, presented as semantic differentials (e.g. 1 = competent vs. 7 = incompetent). The data provided is already inverted in such a way that lower scores represent negative ratings (i.e. 1 = incompetent, 7 = competent). Items 01 - 06 belong to the subscale "expertise", items 07 - 10 belong to the subscale "integrity" and items 11 - 14 belong to the subscale "benevolence". The data columns represent the following semantic differentials: meti01 ............................. competent–incompetent meti02 ............................. intelligent–unintelligent meti03 ............................. well educated–poorly educated meti04 ............................. professional–unprofessional meti05 ............................. experienced–inexperienced meti06 ............................. qualified–unqualified meti07 ............................. sincere–insincere meti08 ............................. honest–dishonest meti09 ............................. just–unjust meti10 ............................. fair–unfair meti11 ............................. moral–immoral meti12 ............................. ethical–unethical meti13 ............................. responsible–irresponsible meti14 ............................. considerate–inconsiderate SCIENCE IN TEACHING PRACTICE The following 9 items (teaching01 - teaching09) refer to the subscale "Benefit of Science for Professional Practice" as implemented in the questionnaire about scientific thinking (of pre-service teachers) by Zeuch and Souvignier (2015). Participants indicated their agreement to 9 statements (1 = do not agree at all - 7 = fully agree). In the data set, all items have been reverse-scored if necessary, so that higher scores always indicate that participants consider educational science to be more useful for their teaching practice. teaching01 ......................... Understanding how knowledge is generated in educational science will help teachers for their teaching practice. teaching02 ......................... Theories from educational science can help to predict behaviour. teaching03 ......................... Evidence from educational science can help teachers to cope with a lot of challenges of their profession. teaching04 ......................... In the classroom, teachers best rely on their experience, not on evidence from educational science. (Already reverse coded in the data set.) teaching05 ......................... Evidence from educational science can help to implement individual support for students. teaching06 ......................... Educational science is out of touch with reality when it comes to teaching practice. (Already reverse coded in the data set.) teaching07 ......................... If teachers manage to implement evidence from educational science into their teaching practice, they - and their students will profit from it. teaching08 ......................... When planning individual support, teachers should always consider evidence from educational science. teaching09 ......................... It is unlikely that the knowledge about educational science that is taught to student teachers at university will be useful for teaching practice. (Already reverse coded in the data set.) NORMS AND COUNTER-NORMS In the following 8 items, participants indicated how much they thought statements about norms (Merton, 1942) and counter-norms (Mitroff, 1975) described actual scientific practice on a scale from 1 (not representable at all) to 5 (fully representable). Each (counter-)norm was represented with one item. Norms are: disinterestedness, (organized) skepticism, communality, universalism Counter-norms are: particularism, (organized) dogmatism, self_interestedness, solitariness disinterestedness .................. Scientists are generally motivated by the desire for knowledge and discovery, and not by the possibility of personal gain. skepticism ......................... (Organized Skepticism). Scientists make an attempt to consider all new evidence, hypotheses, theories, and innovations, even those that challenge or contradict their own work. particularism ...................... Scientists generally assess new knowledge and its applications based on the reputation and past productivity of the individual or research group. communality ........................ Scientists openly share new findings with all colleagues. dogmatism .......................... (Organized Dogmatism) Scientists generally invest their careers in promoting their own most important findings, theories, or innovations. self_interestedness ................ Scientists compete with others in the same field for funding and recognition of their achievements. universalism ....................... Scientists generally evaluate research only on its merit (i.e., according to accepted standards of the field). solitariness ....................... Scientists emphasize the protection of their newest findings to ensure priority in publishing, patenting, or applications. datetime ........................... Date and time at which participants completed the survey. exclusion .......................... Whether participants where excluded from data analysis based on the exclusion criteria ("yes") or not ("no").