dataesr/scientific-paragraphs-categorization huggingface dataset:
4. 2). Dutch food composition testing data were fully described by English food names, food groups and subgroups, and LanguaL food descriptors. The German training set of food composition data was missing English names and food subgroups, and the LanguaL coded food items did not cover the variety of the extensive food consumption data. Although the German food composition dataset includes many food items (14,800), we could use compositional data and LanguaL codes for a limited subset of 1200 foods. Consequently, the German case study showed that querying by English names and groups/subgroups provided almost all matches at level 2 or higher. In many cases, however, when excluding food names and providing only the main food group without subgroups in the query, the results were much better (more level 0 and level 1 codes proposed).
This can be explained by the fact that the provided training dataset only covered the main original GloboDiet food groups since the manual food matching in Germany the food subgroups were not relevant. The Italian food consumption data collected by the INRAN-DIARIO 3. 1 software were described by FoodEx2, and their food composition data were described by LanguaL. Also, the UK testing dataset was described by FoodEx2, and their food composition data were described by LanguaL. Therefore, the matching of Italian and UK food consumption to composition data was performed by using the approach for matching FoodEx to LanguaL (as described in Section 2. 4. 2). In the UK case, only level 0 matches were obtained, since only matching on food names was conducted. In Italy, no or few level 2 suggested matches were offered by the algorithm.
, 2017 [44]. Matches proposed by the algorithm for each country were subjectively validated by national experts. Different expectations and methods of judgements between the experts might explain some of the differences between countries and should be considered in the interpretation of the results. A poor match might be caused by certain foods or recipes not being available in the food composition database, or because the matching algorithm could not create the match. The high percentage of good matches in the German case study probably refers to the large number of foods in the German food composition table. A sufficient match was usually a match with the same food with different descriptors, or between foods that were more generic foods in the food composition database and more specific in the consumption survey data or vice versa.
In the Dutch test, matches for fried or deep-fried foods were sufficient rather than good, because in the GloboDiet data these foods are the prepared foods, but excluding type of fat. Such types of foods are not available in the NEVO composition database (in contrast to the German BLS, which was adapted to the preparation methods of GloboDiet). Also, because information on brand names (available in the GloboDiet consumption data) could not be given as input facet for the matching, sufficient rather than good matches were often selected. The German experts found time consuming and difficult to judge matches because in many cases the number of options was too large (50 or in few cases even 150). It would be expected that good quality matches (as judged by the human expert) would be derived more often from the level 0 matches as suggested by the algorithm and that poor-quality matches would more often be suggested matches from a higher level.
There was, however, no consistent relationship between both types of quality levels. The quality level, such as indicated by the algorithm, was mainly driven by differences between countries. For Germany, most suggested (and, therefore, selected) matches were only level 2 or higher as indicated by the matching algorithm. The results indicate that a working knowledge of the UK composition dataset was important in increasing the accuracy of matches and returning the most relevant results. However, even for the inexperienced researcher, the 'correct' match (compared to the expected match) could still be achieved in most cases, although often more irrelevant matches were also returned. The exercise demonstrated the importance of knowledge of food descriptions when using the algorithm, particularly where only food names are used.
The UK testing involved using the description straight from the subject food diary, which is seldom described as per the composition dataset. If facet codes are available, the accuracy of the food description is perhaps less important as the algorithm has another component to match on. Furthermore, it was observed in the Dutch testing that it was sometimes necessary to alter the search terms in order to find good matches. Since the involved experts knew the food composition and food consumption database very well, this resulted in a high percentage of equal matches, which might not have been obtained by inexperienced experts. Similarly, in the Italian testing, it was concluded that knowledge of the database to search in is crucial to increase the probability of exact matching. In the German testing, the expert did not change the food names in the search terms to improve the results.
Conclusions The case study showed that the most relevant requirement for efficient computer-based food matching is high quality of food consumption and composition databases. The type of classification and coding system used is of less importance. The quality of food information refers to the documentation of the data (including food description, component identification and descriptions of data source, sampling, analytical methods and laboratory performance), and food description is the important determinant for food matching. EuroFIR has produced a range of tools to help data compilers, including procedures for documenting data values, and has supported the development and publication of a standard for food data  [13]. The main challenge in food matching is a diversity of datasets to be matched.
They differ in the number of food items, the classification and coding system, the way of food naming, the level of details provided by descriptors, etc. A food matching algorithm needs to overcome these problems, considering all the information that is available. In the approach presented in this paper, we combined food information from different food composition and consumption datasets. For instance, food matching was enabled between a given food consumption dataset and all available food composition datasets. In this way, not only the training (learning) was more efficient, but also different countries could get an insight into the food information from other countries. In the H2020 project RICHFIELDS [45], we are designing a new research platform aimed for collecting, harmonizing and linking food-, nutrition-and health-related data.
Acknowledgments: The study was performed within the  FP7 EuroDISH project , which received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration (Contract no  311788 ). The views expressed are those of the authors and not necessarily those of the European Union's  Seventh Framework Programme  for research, technological development and demonstration. The authors acknowledge the financial support from the  Slovenian Research Agency  (Research Core Funding Number  P2-0098 ). We would also like to show our gratitude to the dieticians involved in the Dutch case study:  Henny Brants  and  Maryse Niekerk.
Acknowledgments We are grateful to  Jean-Pierre Vilotte ,  Oleg A. Godin  and  Raphaël F. Garcia  for fruitful discussion. The Ph. D. grant of  Alexis Bottero  was awarded by  ENS Cachan, France. This work was granted access to the French HPC resources of TGCC under allocation # gen7165  and of CINES under allocation # A0020407165 , both made by  GENCI , and of the Aix- Marseille Supercomputing Mesocenter  under allocations # b025. We gratefully acknowledge the support of  NVIDIA Corporation  with the donation of hardware for this research through their  Hardware Grant Request program.
OSF Project page From the main OSF project page for this RRR ({ HYPERLINK "https://osf. io/k27hm/" }), readers can access the experimental protocol ({ HYPERLINK "https://osf. io/gxpm5/" }), all materials and experiment scripts ({ HYPERLINK "https://osf. io/6whj3/" }), data and analysis scripts along with additional analyses ({ HYPERLINK "https://osf. io/fyptm/" }), and a list of participating labs with links to their pre-registration information and descriptions of their testing setting ({ HYPERLINK "https://osf. io/e32su/" }). The project page also includes the draft of the manuscript compiled before data analysis began. This final manuscript included a few modifications from that pre-analysis draft, including the addition of an abstract and the discussion section as well as some minor editing for clarity (e.
Participants Each lab was instructed to test a minimum of 25 participants per cell in a 2 (prime: professor vs. hooligan) × 2 (gender: female vs. male) between participant design, with approximately the same proportion of men and women in each priming condition. Labs were encouraged to recruit at least 50 participants for each cell of the design. As in the original study, participants were recruited from undergraduate psychology participant pools or from an equivalent population (e. g. , behavioral marketing) in similar ways. Participants were required to be college or university students aged 18-24 years old, with an average age within each lab of approximately 18-20 years. Predictably, not every lab had access to large populations, so the total sample size collected in each lab varied. All sample-size targets were pre-registered and the lead researchers and editor remained blind to the outcomes of individual studies until all data collection was completed.
Main Study Session Laboratories that needed a study description for recruiting purposes described the study as: "Complete a series of writing tasks and general knowledge questions. " Prior to the study, the experimenter read the following to participants: "This study consists of a number of unrelated tasks that will provide pilot data and help us develop materials for a variety of future studies. We will let you know the purpose of each task before you complete it, and the computer will provide the instructions for each task. " The experimenter initiated the program and recorded the participant's sex and ID number for each session. The remainder of the task was administered through the PsychoPy program and required no input from the experimenter. First, participants were instructed to spend five minutes writing about themselves as if they were either a typical soccer hooligan or a typical university professor.
" Following the writing task, participants were told that the first task was concluded, and that a second task was for a cognitive psychology student who was developing a general knowledge scale. The experimental script further explained that the student required a pilot sample to test the differences between trivia questions of varying levels of difficulty. All participants were told that they had been assigned to the most difficult set of trivia questions, and then answered 30 general knowledge questions. The questions were presented in a fixed order, but the PsychoPy script randomized the order of the answer options for each participant. After completing the priming and trivia tasks, participants entered their age, gender, native language, major, and year of study in college. Finally, participants completed the funneldebriefing questionnaire.
Stopping rules and exclusions Each lab pre-registered its stopping rule to end data collection, and the editor approved those plans. The rules were designed to ensure that each lab would meet the minimum data collection requirements for the protocol and that the decision to end data collection would not be influenced by the results of the study. Data from participants were excluded from analyses for any of the following reasons: they were not college or university students, they were not in the required age range (18-24 years old), they failed to record their age, they did not follow instructions, they did not complete the priming and trivia tasks, they reported being aware of the other condition in the study, or the experimenter did not administer the instructions or tasks correctly. Excluded data from each lab are provided on their OSF project page, and additional details are reported in the appendix.
Given that many psychology participant pools have many more women than men, a number of labs experienced difficulty recruiting enough male participants during the initial data collection period. This problem was exacerbated somewhat by issues with the script crashing. Although 40 labs submitted data for the project, 17 labs were unable to meet the pre-registered inclusion criteria of providing data from 25 men and 25 women in each condition. Therefore, the pre-registered analyses in this RRR contain data from the 23 labs that met all inclusion criteria. However, as the 17 labs that did not meet the full inclusion criteria collected data from a large number of participants, the lead lab and editor made a data-blind decision to include these labs in a set of supplementary analyses that were otherwise identical in output to the primary analyses.
Coding and Analysis Scripts Each individual laboratory was provided with an R script to analyze their data in a way that is consistent with the pre-registered protocol. The output of the script reports the overall difference in trivia performance between participants who were assigned to the professor and hooligan primes (ignoring participant gender). It also reports an estimate of the moderation of that effect by providing separate analyses for the difference in trivia performance between the professor and hooligan primes for men and women. The individual labs were able to independently calculate means and standard deviations for trivia performance for each of the four cells of the study. Katherine Wood wrote the R scripts using simulated data, before any actual data were collected. These scripts required minor modifications after data collection to address differences in the order of output from translated scripts.
These modifications did not affect the analysis functions, and the script used for each lab's analysis is available on that lab's OSF page. A separate R script, also written before data collection, was used to conduct the metaanalysis across labs. It directly imports the raw data from all labs and uses similar analysis functions to compute descriptive statistics. Note that this script also required minor modifications to handle data importing across variations introduced during translation and due to differences in how PsychoPy outputs csv files across computer platforms. The meta-analysis script includes analyses of the overall effect of priming condition on trivia performance and of the moderation of that effect by gender. For each meta-analytic result, we provide a forest plot showing the overall difference between professor and hooligan primes for each laboratory.
At the top of each forest plot we show the original result from Dijksterhuis and van Knippenberg (1998), and below the forest plot we provide the results of a random-effects meta-analysis across laboratories for that measure (the meta-analysis does not include the original Dijksterhuis and van Knippenberg result). Tables with the details for each laboratory that went into each forest plot are provided on the OSF project page. Due to unforeseen inconsistencies in the operation of PsychoPy across languages and computer systems (especially with text entry), some labs experienced a large number of computer crashes during testing. In many cases, those crashes occurred after the priming and trivia tasks were complete. The experiment script was updated to address some of these issues during the testing process (without changing the procedures).
These updates also saved a text file backup of each participant's data as they moved through the program so that data from a participant could be included provided that the crash occurred after the primary tasks. Katherine Wood wrote a recovery script that converted those backup text files to the standard csv format for data analysis purposes. This recovery script also required minor modifications for labs testing in languages other than English. In a small number of cases, the csv output files included additional characters that prevented the analysis scripts from running properly. In those cases, labs provided the problematic files to Katherine Wood, and she corrected the improper formatting of those individual files. Labs retained the original and corrected files and both versions are available.
The 23 labs that met all of our inclusion criteria collected data from a total of 5,146 participants. Data from 653 subjects were excluded based on our preregistered exclusion criteria, leaving a total sample of 4,493 included in our preregistered analyses. Our meta-analysis showed an average difference of 0. 042 more questions answered correctly out of 30, or 0. 14% (95% confidence interval: -0. 71% to 1%) between the two priming conditions in the expected direction (see Figure  1 ). The difference in percentage correct between the professor and hooligan prime conditions ranged from -4. 99% to 4. 24% across the included labs. The variability in the effect size among the labs (i. e. , heterogeneity) was not significantly different from what would be expected by chance (τ = 0. 86, I 2 = 17.
We used the provided PsychoPy scripts after translating the contents into Polish. Our pre-registered target sample was 160 participants, however we were unable to recruit 25 male participants per condition, therefore, as preregistered, we kept recruiting participants until we had usable data from 25 male participants per condition. Frenk van Harreveld, University of Amsterdam Michiel van Elk, University of Amsterdam Bastiaan Rutjens, University of Amsterdam OSF Project: https://osf. io/zx928/ A total of 140 students (Professor n=70; Hooligan n=70) were recruited from the psychology subject pool at University of Amsterdam and received course credit or money (5 euros) for participating. Participants tested in a room with individual cubicles separating participants from each other. We used the provided PsychoPy scripts with after translating the contents into Dutch, in collaboration with the other Dutch research teams.
In all other respects, we followed the had included the usable data from participants where the script had crashed prior to the completion of the experiment. Ben R. Newell, University of New South Wales Aba Szollosi, University of New South Wales Thomas F. Denson, University of New South Wales OSF Project: https://osf. io/6dhx4/ A total of 142 students were recruited from the psychology subject pool at the University of New South Wales and either received a flat fee of 7. 50 AUD or course credit for participating. From these participants, 69 were assigned to the Professor condition and 61 to the Hooligan condition; the data of 4 participants were unrecoverable due to a crash in the experimental program, and the data of further 8 participants were deleted because they were under 18 years old.
Participants were tested individually in separate cubicles with a maximum of 4 participants tested simultaneously per session. We used the provided PsychoPy scripts in English. In all other respects, we followed the official protocol. Michael O'Donnell, Haas School of Business, University of California, Berkeley Leif D. Nelson, Haas School of Business, University of California, Berkeley OSF Project: { HYPERLINK "https://osf. io/vg7ss/" } A total of 218 students (Professor n =116; Hooligan n =102) were recruited from the Marketing subject pool at Haas and received course credit for participating. Participants were tested in individual cubicles. We used the provided PsychoPy scripts, and were data-blind until after preregistering the pre-data manuscript. In all other aspects, we followed the official protocol.
Asil Ali Özdoğru, Üsküdar University Nursena Balatekin, Üsküdar University OSF Project: https://osf. io/ctkup/ A total of 121 students (Professor n=52; Hooligan n=69) were recruited from the undergraduate programs at Üsküdar University and received course credit for participation. Participants were tested one at a time in a small room by the experimenter. We used the provided PsychoPy scripts after translating the contents into Turkish. After completing the computer tasks, participants responded to two brief paper-pencil self-report measures. In all other respects, we followed the official protocol. Michael C. Philipp, Massey University Matt N. Williams, Massey University Peter R. Cannon, Massey University Aaron Drummond, Massey University. OSF Project: https://osf. io/7tny4/ A total of 168 participants (Professor n = 86; Hooligan n = 82) were recruited via online advertisements, presentations in classes, and in-person recruitment on campus at Massey University.
Participants were provided with shopping vouchers as compensation for their time. Participants were tested at both the Palmerston North and Auckland campuses (in a room with dividers at Palmerston North, and in separate sound-proofed adjoining booths at Auckland). The whether the data would be recoverable, or not. Further, we refrained from promoting data collection especially for male participants after we had reached the desired number of female participants, although the pre-registered plan specified to do so, because this would have introduced a systematic difference between the male and female participants in our sample. This decision was made in consultation with the editor. We ended data collection with usable data from 96 participants in the professor condition and 95 participants in the hooligan condition.
Ivan Ropovik, University of Presov Gabriel Banik, University of Presov Peter Babincak, University of Presov OSF Project: https://osf. io/mj7yn/ A total of 210 students (Professor n=110; Hooligan n=100) were recruited from the social sciences subject pool at University of Presov and received course credit for participating. Participants were tested in a room with dividers separating participants from each other. We used the provided PsychoPy scripts after adapting the contents into Slovak. In all other respects, we followed the official protocol. Katey Sackett, Rochester Institute of Technology John E. Edlund, Rochester Institute of Technology OSF Project: https://osf. io/n2974/ A total of 104 students (Professor n=45, Hooligan n=53, Unknown data failure = 6) were recruited through the SONA subject pool at Rochester Institute of Technology and received course credit for participating.
Participants were tested in a room with dividers separating participants from each other. We used the provided PsychoPy scripts in the original format and did not differ from the original protocol in any way. Although our pre-registered plan specified that we would test 50 men and 50 women in each condition, we were unable to recruit enough participants after significant computer malfunctions to meet these quotas. We ended data collection of usable data at 49 males (professor n=22, hooligan n=27) and 38 females (professor n=14, hooligan n=24). Imbalances in condition are due to random assignment by software. Blair Saunders, University of Dundee Michael Inzlicht, University of Toronto OSF Project: https://osf. io/25p8z/ A total of 152 students (Professor n=67; Hooligan n=85) were recruited from the psychology subject pool at the University of Toronto Scarborough and received course credit for participating.