Resources by Stefan

Instagram Posts from Football Players

Instagram Posts from Football Players

Stefan
Open Dataset
Creators: Klostermann, Jan
Publication Date: 2023
Creators: Klostermann, Jan

This dataset includes information on 334,071 Instagram posts from 1,435 male professional football players that were under contract at any of the 56 clubs in the English Premier League, the Spanish La Liga, and the German Bundesliga. The data was colleced December 31th, 2019 and includes the whole history of Instagram posts up to that point in time.

The information provided in the dataset are the following:

  • Player information: Information on each of the football player in the dataset is collected from http://www.transfermarkt.de and includes club, position, market value (at the time of collecting the data), highest market value, and the year in which highest market value was observed. Further, the Instagram account name is provided.
  • Instagram post information: Information on the Instagram posts including the shortcode (which can be used to open the post on instagram.com), date, caption text, number of likes, number of comments, post type (image, sidecar, video).
  • Instagram post images: For each post, we analyzed the content of the image (first image for sidecar posts, first frame for video posts) using Google Vision and extract the number of persons, their age, and their gender. Further, we extract all tags that are included in the image, such as “soccer” or “car”.
  • Additional information: Additional information such as the images of the posts can be requested from the authors.

The dataset has been used in the following paper:

Klostermann, J., Meißner, M., Max, A., & Decker, R. (2023). Presentation of celebrities’ private life through visual social media. Journal of Business Research, 156, 113524.

Please cite the paper when using the dataset for your own research. It is recommended to read the paper for further information on the dataset.

The Economist Historical Advertisements - Master Dataset

The Economist Historical Advertisements - Master Dataset

Stefan
Open Dataset
Creators: Kluge, Stefan; Gehrmann, Leonie; Stahl, Florian
Publication Date: 2023
Creators: Kluge, Stefan; Gehrmann, Leonie; Stahl, Florian

This dataset contains metadata of 512.599 historical advertisements from all 8,840 issues of The Economist magazine, years 1843 to 2014. It is part of a series of datasets related to The Economist Historical Archive (https://www.gale.com/intl/c/the-economist-historical-archive). You will need this Master Dataset, if you want to work with any of the related datasets. Each advertisement entry includes various metadata fields such as publication date, issue number, page number, and advertisement dimensions. This structured information enables detailed analyses of trends and patterns within the advertising practices over time. In total, the dataset has a size of 195,4 MB.

MLW Zettelmaterial

MLW Zettelmaterial

Stefan
Open Dataset
Creators: Bayerische Akademie der Wissenschaften (BAdW)
Publication Date: 2023
Creators: Bayerische Akademie der Wissenschaften (BAdW)

General information:

The data set comprises a total of 114,653 images (18,9 GB), corresponding to 3,507 distinct lemmas.
All images are in RGB, but not uniform in size, i.e. height, and width differ from image to image.
Additionally, the information on the corresponding lemma is available for each image in a separate json file.

Structure:

Most record cards follow the same structure being composed of three main parts.

  • The first one (1), and the one deemed most challenging, is the lemma, which is always located in the upper left corner of the record card.
  • The second part (2) is the index of the text where the lemma is found.
  • The third part (3) contains a text extract in which the word (corresponding to the lemma) occurs in context.

Character inventory:

There is a total of 17 different first letters, eight of which are each upper- and lowercase, as well as one special character.
The capitalization of a word plays a crucial role since a word’s meaning changes depending on capitalization.
Since the majority of our data stems from the S-series of the dictionary, most lemmas start with the letter “s”.
Likewise, a larger number of lemmas also starts with “m”, “v”, “t”, “u”, “l”, and “n”.

Occurrence frequencies:

  • A total of 2,420 lemmas (69%) were found to appear on ten record cards or less
  • 854 lemmas (24.4%) are present on between 10 and 100 record cards
  • 233 lemmas (6.6%)can be found on more than 100 record cards
  • 1,123 lemmas (approximately 36.7%) had only one record card

Lengths:

  • Lemma lengths range from one character up to a maximum of 19 characters.
  • The average length of the lemmas lies between five and six characters.

Availability:

Research activity:

  • Koch, P., Nuñez, G. V., Arias, E. G., Heumann, C., Schöffel, M., Häberlin, A., & Aßenmacher, M. (2023). A tailored Handwritten-Text-Recognition System for Medieval Latin. arXiv preprint arXiv:2308.09368.
The Economist Historical Advertisements - Faces Dataset

The Economist Historical Advertisements - Faces Dataset

Stefan
Open Dataset
Creators: Kluge, Stefan
Publication Date: 2023
Creators: Kluge, Stefan

This dataset contains 116.746 identified faces (bounding box location on image, predicted age and gender) for all historical advertisements from all 8,840 issues of The Economist magazine, years 1843 to 2014. Faces have been detected using the following library:  https://pythonrepo.com/repo/timesler-facenet-pytorch-python-deep-learning. You will need the The Economist Historical Advertisements – Master Dataset as well, to work with the data. In total, the dataset has a size of 20,2 MB and is organized as follows:

  • Filename: A unique identifier corresponding to each advertisement where a face has been detected. This identifier links directly to the specific advertisement within The Economist archives. ​

  • Bounding Box Coordinates:

    • Bounding Box relative X1 and Y1: These values represent the top-left corner coordinates of the bounding box encapsulating the detected face, expressed as proportions relative to the image dimensions.​
    • Bounding Box relative X2 and Y2: These values denote the bottom-right corner coordinates of the bounding box, also as relative proportions. To determine the absolute pixel coordinates, multiply these relative values by the image’s width and height, respectively. ​

  • Segmentation Confidence Score: A numerical value indicating the confidence level of the neural network algorithm that the identified bounding box indeed contains a face. Higher scores reflect greater confidence in accurate face detection. ​

  • Size Relative: A metric indicating the proportion of the advertisement occupied by the detected face. For example, a value of 1 signifies that the face covers the entire advertisement, while 0.5 indicates it covers half. ​

  • Predicted Age: An estimated age of the individual based on facial analysis performed by the detection algorithm. ​

  • Gender Probability: A probability score representing the likelihood of the detected face being female. A value of 0 indicates male, 1 indicates female, and intermediate values (e.g., 0.4) suggest a 40% likelihood of the individual being female

The Economist Historical Advertisements - Objects Dataset

The Economist Historical Advertisements - Objects Dataset

Stefan
Open Dataset
Creators: Kluge, Stefan
Publication Date: 2023
Creators: Kluge, Stefan

This dataset contains 191.994 identified object locations and classes for all historical advertisements from all 8,840 issues of The Economist magazine, years 1843 to 2014. We used a state of the art classifier to detect the objects: https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1. You will need the The Economist Historical Advertisements – Master Dataset, as well, to work with the data. The dataset has a size of 29,8 MB.

The Economist Historical Advertisements - Industry Subset: Banking

The Economist Historical Advertisements - Industry Subset: Banking

Stefan
Open Dataset
Creators: Kluge, Stefan
Publication Date: 2023
Creators: Kluge, Stefan
This dataset is a specialized collection of metadata from advertisements related to the banking industry, extracted from The Economist magazine issues spanning 1843 to 2014. It contains metadata of 92,592 historical advertisements from the banking industry, from all 8,840 issues of The Economist magazine, years 1843 to 2014. It is part of a series of  datasets related to The Economist Historical Archive (https://www.gale.com/intl/c/the-economist-historical-archive). In total, the dataset has a size of 136,0 MB. Each advertisement entry includes various metadata fields such as publication date, issue number, page number, and advertisement dimensions. This structured information enables detailed analyses of trends and patterns within the banking industry’s advertising practices.
Raw Bay Area Craigslist Rental Housing Posts

Raw Bay Area Craigslist Rental Housing Posts

Stefan
Open Dataset
Creators: Pennigton, Kate
Publication Date: 2018
Creators: Pennigton, Kate

Like many cities, San Francisco doesn’t track rents. ​The Bay Area Craigslist Rental Housing Posts dataset comprises rental housing listings from the San Francisco Bay Area, spanning from 2000 to 2018. Each entry includes various attributes such as posting date, neighborhood, price, number of bedrooms and bathrooms, square footage, and geographic coordinates, facilitating in-depth analysis of housing trends. There are 200,796 individual rental listings documented in the dataset with a total size of 16,3 kB. The dataset is organized into three main components:​

  1. Raw Data (2000-2012): This subset includes 167,090 entries with fields for posting date, title, and neighborhood.​

  2. Raw Data (2013-2018): Comprising 58,551 entries, this subset offers more detailed information, including post ID, date, neighborhood, price, square footage, number of bedrooms, address, latitude, longitude, description, title, and details.​

  3. Cleaned Data (2000-2018): This consolidated and processed dataset contains 200,796 entries with variables such as post ID, date, year, neighborhood, city, county, price, number of bedrooms, number of bathrooms, square footage, room type indicator, address, latitude, longitude, title, description, and details.

​

"LaCour!" - a multilingual dataset of hearing transcripts with 2 million+ tokens

"LaCour!" - a multilingual dataset of hearing transcripts with 2 million+ tokens

Stefan
Open Dataset
Creators: Habernal, Ivan
Publication Date: 2023
Creators: Habernal, Ivan

This dataset contains transcribed court hearings sourced from official hearings of the European Court of Human Rights (https://www.echr.coe.int/webcasts-of-hearings). The hearings are 154 selected webcasts (videos) from 2012-2022 in their original language (no interpretation). With manual annotation for language labels and automatic processing of the extracted audio with pyannote and whisper-large-v2, the resulting dataset contains 4000 speaker turns and 88920 individual lines. The dataset has a size of 1,9 MB and contains two subsets, the transcripts and the metadata with linked documents. The transcripts are additionally available as .txt or .xml.
Languages

The largest amounts in the transcripts are: English, French

A smaller portion also contains the following languages:

Russian, Spanish, Croatian, Italian, Portuguese, Turkish, Polish, Lithuanian, German, Ukrainian, Hungarian, Dutch, Albanian, Romanian, Serbian

The collected metadata is: English
Dataset Structure
Data Instances

Each instance in transcripts represents an entire segment of a transcript, similar to a conversation turn in a dialog.

{ ‘id’: 0, ‘webcast_id’: ‘1021112_29112017’, ‘segment_id’: 0, ‘speaker_name’: ‘UNK’, ‘speaker_role’: ‘Announcer’, ‘data’: { ‘begin’: [12.479999542236328], ‘end’: [13.359999656677246], ‘language’: [‘fr’], ‘text’: [‘La Cour!’] } }

Each instance in documents represents a information on a document in hudoc associated with a hearing and the metadata associated with a hearing. The actual document is linked and can also be found in hudocwith the case_id. Note: hearing_type states the type of the hearing, type states the type of the document. If the hearing is a “Grand Chamber hearing”, the “CHAMBER” document refers to a different hearing.

{

‘id’: 16,

‘webcast_id’: ‘1232311_02102012’,

‘hearing_title’: ‘Michaud v. France (nos. 12323/11)’,

‘hearing_date’: ‘2012-10-02 00:00:00’,

‘hearing_type’: ‘Chamber hearing’,

‘application_number’: [‘12323/11’],

‘case_id’: ‘001-115377’,

‘case_name’: ‘CASE OF MICHAUD v. FRANCE’,

‘case_url’: ‘https://hudoc.echr.coe.int/eng?i=001-115377’,

‘ecli’: ‘ECLI:CE:ECHR:2012:1206JUD001232311’,

‘type’: ‘CHAMBER’,

‘document_date’: ‘2012-12-06 00:00:00’,

‘importance’: 1,

‘articles’: [‘8’, ‘8-1’, ‘8-2′, ’34’, ’35’],

‘respondent_government’: [‘FRA’],

‘issue’: ‘Decision of the National Bar Council of 12 July 2007 “adopting regulations on internal procedures for implementing the obligation to combat money laundering and terrorist financing, and an internal supervisory mechanism to guarantee compliance with those procedures” ; Article 21-1 of the Law of 31 December 1971 ; Law no. 2004-130 of 11 February 2004 ; Monetary and Financial Code’,

‘strasbourg_caselaw’: ‘André and Other v. France, no 18603/03, 24 July 2008;Bosphorus Hava Yollari Turizm ve Ticaret Anonim Sirketi v. Ireland [GC], no 45036/98, ECHR 2005-VI;[…]’,

‘external_sources’: ‘Directive 91/308/EEC, 10 June 1991;Article 6 of the Treaty on European Union;Charter of Fundamental Rights of the European Union;Articles 169, 170, 173, 175, 177, 184 and 189 of the Treaty establishing the European Community;Recommendations 12 and 16 of the financial action task force (“FATF”) on money laundering;Council of Europe Convention on Laundering, Search, Seizure and Confiscation of the Proceeds from Crime and on the Financing of Terrorism (16 May 2005)’,

‘conclusion’: ‘Remainder inadmissible;No violation of Article 8 – Right to respect for private and family life (Article 8-1 – Respect for correspondence;Respect for private life)’,

‘separate_opinion’: True

}

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