Filmwissenschaft

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Film Circulation dataset
Loist, Skadi; Samoilova, Evgenia (Zhenya) (2023)
#### 1 Film Dataset Festival Programs The Film Dataset consists a data scheme image file, a codebook and two dataset tables in csv format. The codebook (csv file “1_codebook_film-dataset_festival-program”) offers a detailed description of all variables within the Film Dataset. Along with the definition of variables it lists explanations for the units of measurement, data sources, coding and information on missing data. The csv file “1_film-dataset_festival-program_long” comprises a dataset of all films and the festivals, festival sections, and the year of the festival edition that they were sampled from. The dataset is structured in the long format, i.e. the same film can appear in several rows when it appeared in more than one sample festival. However, films are identifiable via their unique ID. The csv file “1_film-dataset_festival-program_wide” consists of the dataset listing only unique films (n=9,348). The dataset is in the wide format, i.e. each row corresponds to a unique film, identifiable via its unique ID. For easy analysis, and since the overlap is only six percent, in this dataset the variable sample festival (fest) corresponds to the first sample festival where the film appeared. For instance, if a film was first shown at Berlinale (in February) and then at Frameline (in June of the same year), the sample festival will list “Berlinale”. This file includes information on unique and IMDb IDs, the film title, production year, length, categorization in length, production countries, regional attribution, director names, genre attribution, the festival, festival section and festival edition the film was sampled from, and information whether there is festival run information available through the IMDb data. #### 2 Survey Dataset The Survey Dataset consists of a data scheme image file, a codebook and two dataset tables in csv format. The codebook “2_codebook_survey-dataset” includes coding information for both survey datasets. It lists the definition of the variables or survey questions (corresponding to Samoilova/Loist 2019), units of measurement, data source, variable type, range and coding, and information on missing data. The csv file “2_survey-dataset_long-festivals_shared-consent” consists of a subset (n=161) of the original survey dataset (n=454), where respondents provided festival run data for films (n=206) and gave consent to share their data for research purposes. This dataset consists of the festival data in a long format, so that each row corresponds to the festival appearance of a film. The csv file “2_survey-dataset_wide-no-festivals_shared-consent” consists of a subset (n=372) of the original dataset (n=454) of survey responses corresponding to sample films. It includes data only for those films for which respondents provided consent to share their data for research purposes. This dataset is shown in wide format of the survey data, i.e. information for each response corresponding to a film is listed in one row. This includes data on film IDs, film title, survey questions regarding completeness and availability of provided information, information on number of festival screenings, screening fees, budgets, marketing costs, market screenings, and distribution. As the file name suggests, no data on festival screenings is included in the wide format dataset. #### 3 IMDb & Scripts The IMDb dataset consists of a data scheme image file, one codebook and eight datasets, all in csv format. It also includes the R scripts that we used for scraping and matching. The codebook “3_codebook_imdb-dataset” includes information for all IMDb datasets. This includes ID information and their data source, coding and value ranges, and information on missing data. The csv file “3_imdb-dataset_aka-titles_long” contains film title data in different languages scraped from IMDb in a long format, i.e. each row corresponds to a title in a given language. The csv file “3_imdb-dataset_awards_long” contains film award data in a long format, i.e. each row corresponds to an award of a given film. The csv file “3_imdb-dataset_companies_long” contains data on production and distribution companies of films. The dataset is in a long format, so that each row corresponds to a particular company of a particular film. The csv file “3_imdb-dataset_crew_long” contains data on names and roles of crew members in a long format, i.e. each row corresponds to each crew member. The file also contains binary gender assigned to directors based on their first names using the GenderizeR application. The csv file “3_imdb-dataset_festival-runs_long” contains festival run data scraped from IMDb in a long format, i.e. each row corresponds to the festival appearance of a given film. The dataset does not include each film screening, but the first screening of a film at a festival within a given year. The data includes festival runs up to 2019. The csv file “3_imdb-dataset_general-info_wide” contains general information about films such as genre as defined by IMDb, languages in which a film was shown, ratings, and budget. The dataset is in wide format, so that each row corresponds to a unique film. The csv file “3_imdb-dataset_release-info_long” contains data about non-festival release (e.g., theatrical, digital, tv, dvd/blueray). The dataset is in a long format, so that each row corresponds to a particular release of a particular film. The csv file “3_imdb-dataset_websites_long” contains data on available websites (official websites, miscellaneous, photos, video clips). The dataset is in a long format, so that each row corresponds to a website of a particular film. The dataset includes 8 text files containing the script for webscraping. They were written using the R-3.6.3 version for Windows. The R script “r_1_unite_data” demonstrates the structure of the dataset, that we use in the following steps to identify, scrape, and match the film data. The R script “r_2_scrape_matches” reads in the dataset with the film characteristics described in the “r_1_unite_data” and uses various R packages to create a search URL for each film from the core dataset on the IMDb website. The script attempts to match each film from the core dataset to IMDb records by first conducting an advanced search based on the movie title and year, and then potentially using an alternative title and a basic search if no matches are found in the advanced search. The script scrapes the title, release year, directors, running time, genre, and IMDb film URL from the first page of the suggested records from the IMDb website. The script then defines a loop that matches (including matching scores) each film in the core dataset with suggested films on the IMDb search page. Matching was done using data on directors, production year (+/- one year), and title, a fuzzy matching approach with two methods: “cosine” and “osa.” where the cosine similarity is used to match titles with a high degree of similarity, and the OSA algorithm is used to match titles that may have typos or minor variations. The script “r_3_matching” creates a dataset with the matches for a manual check. Each pair of films (original film from the core dataset and the suggested match from the IMDb website was categorized in the following five categories: a) 100% match: perfect match on title, year, and director; b) likely good match; c) maybe match; d) unlikely match; and e) no match). The script also checks for possible doubles in the dataset and identifies them for a manual check. The script “r_4_scraping_functions” creates a function for scraping the data from the identified matches (based on the scripts described above and manually checked). These functions are used for scraping the data in the next script. The script “r_5a_extracting_info_sample” uses the function defined in the “r_4_scraping_functions”, in order to scrape the IMDb data for the identified matches. This script does that for the first 100 films, to check, if everything works. Scraping for the entire dataset took a few hours. Therefore, a test with a subsample of 100 films is advisable. The script “r_5b_extracting_info_all” extracts the data for the entire dataset of the identified matches. The script “r_5c_extracting_info_skipped” checks the films with missing data (where data was not scraped) and tried to extract data one more time to make sure that the errors were not caused by disruptions in the internet connection or other technical issues. The script “r_check_logs” is used for troubleshooting and tracking the progress of all of the R scripts used. It gives information on the amount of missing values and errors. #### 4 Festival Library Dataset The Festival Library Dataset consists of a data scheme image file, one codebook and one dataset, all in csv format. The codebook (csv file “4_codebook_festival-library_dataset”) offers a detailed description of all variables within the Library Dataset. It lists the definition of variables, such as location and festival name, and festival categories, units of measurement, data sources and coding and missing data. The csv file “4_festival-library_dataset_imdb-and-survey” contains data on all unique festivals collected from both IMDb and survey sources. This dataset appears in wide format, all information for each festival is listed in one row. This includes data on festival ID, sources, festival name and alternatives, location data for country, region and city, data on award recognition, founding year, whether a festival has a specialization and if so the ascribed festival categories.
Crossing Borders Archives – CROBORA
Shen, Shiming (2024)
The ANR CROBORA project investigates the influence of stock shots in fostering a shared European cultural imaginary through television and online media, focusing particularly on the selective deployment of visual elements. This data paper centers on an extensive dataset of French television content preserved by the Institut national de l'audiovisuel (INA), covering the period from 2001 to 2021. Enhanced by meticulous manual annotation, the dataset provides a rich basis for examining the semantic nuances of these visual elements. The project aims to uncover patterns in the usage of these images, tracking their evolution and distribution across time and institutional boundaries. Additionally, the CROBORA project plans to offer this dataset on an innovative visual platform equipped with advanced functionalities to support exploratory data analysis, enabling researchers to integrate various keywords and delve into the data more effectively.
Film Charts of the Nazi Era
Garncarz, Joseph; Muschol, Christina (2024)
The dataset contains the film charts of the Nazi era for the seasons 1933/34 to 1944/45. They were calculated on the basis of the POPSTAT method developed by John Sedgwick (2000: 70-73) and modified by Joseph Garncarz (2021: 69-80). The dataset is based on a representative sample of cinemas in Germany during the Nazi era, which has been validated by selective audience figures that have been handed down. The information on the sample is contained in the file "Cinemas.csv". The "Screenings" files contain information on all films screened in the sampled cinemas, as well as other data relevant for the calculation of the POPSTAT indices (such as the weighting of each cinema in terms of their potential to attract viewers, the index of average cinema attendance per weekday and month). The POPSTAT indices have been converted into admission figures. The "Charts" files contain the hit lists of films, sorted in descending order according to the POPSTAT indices or the number of admissions. The files labelled "Version1" are those used by Garncarz (2021). Minor errors were discovered and corrected in the data for some seasons. These files are labelled "Version2". Garncarz (2021: 45-100) discusses the data collection methodology used here in terms of concepts, survey and calculation methods, and sample construction. Garncarz, Joseph (2021): Begeisterte Zuschauer: Die Macht des Kinopublikums in der NS-Diktatur. Köln: Herbert von Halem. [Enthusiastic Viewers: The Power of the Cinema Audience During the Nazi Dictatorship] Sedgwick, John (2000): Popular Filmgoing in 1930s Britain: A Choice of Pleasures. Exeter: University of Exeter Press.
Series Fatigue. A Qualitative Data Set
Einwächter, Sophie G.; Jensen, Thessa (2024)
This data set is the result of a 2023-2024 international collaborative study, conducted by Sophie G. Einwächter (Philipps-Universität Marburg, Germany) and Thessa Jensen (Aalborg Universiteit, Denmark). The qualitative study aimed to explore ‘series fatigue,’ a phenomenon seemingly arising from the abundant availability of serial media content online. The set complements all publications resulting from this study. The data set contains: questionnaire (1), preliminary coding chart (2), final codebook with code descriptions and examples (3), survey answers and interview transcripts (4), consent form (template) (5). The set may be of particular use to: (I) Scholars interested in transformations of viewing patterns and consumer expectations in the digital era, (II) Cultural, Fan, and Audience Studies researchers dealing with questions of non-fandom, anti-fandom, and viewer disenchantment, (III) Scholars interested in the phenomenon of decision fatigue, (IV) Early career researchers aiming to gain insight into an exploratory, qualitative research process, (V) Anyone seeking insight into contemporary viewing behavior or how people reflect on different types of platform-based entertainment media (series, films, livestream)

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