A practical guide on how to build and publish your dataset and
data visualizations

W 2025

A practical guide on how to build and publish your dataset and data visualizations

VALLEJO, Aida; María PAZ PEIRANO; Skadi LOIST; Terézia PORUBČANSKÁ; Thomas SCHICK et al.

Základní údaje

Originální název

A practical guide on how to build and publish your dataset and data visualizations

Autoři

VALLEJO, Aida; María PAZ PEIRANO; Skadi LOIST; Terézia PORUBČANSKÁ a Thomas SCHICK

Vydání

2025

Další údaje

Jazyk

angličtina

Typ výsledku

Uspořádání workshopu

Obor

60405 Studies on Film, Radio and Television

Stát vydavatele

Portugalsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14210/25:00143161

Organizační jednotka

Filozofická fakulta

Klíčová slova anglicky

data management; visualisation; film studies

Štítky

rivok

Příznaky

Mezinárodní význam

Změněno: 18. 2. 2026 12:42, Mgr. Ester Gaja Pučálková, Ph.D.

Anotace

V originále

Dataset creation is part of the research process, involving the collection of different types of data. While the common practice in Film Studies has been to keep datasets as documents for “internal use” of the researcher, the publication of datasets opens up new possibilities of understanding data collection as part of a collective endeavour that is more typical in the hard sciences. In this way, we operate in an open data logic. We explain essentials in the field such as setting up a data management plan (DMP), adding metadata and following FAIR(ER) principles, making data findable, accessible, interoperable, reusable, and also ethical and revisable (Verhoeven 2018). We also discuss available repositories for publishing research data and mention DH tools that help clean data, analyze and visualize datasets. By presenting a practical approach to dataset publication, this workshop offers key tools to understand dataset creation as part of a publishing process with its own procedures and logics. In the first part, workshop leaders offer a general introduction to the steps, tools and methods of dataset creation, data visualization (like ArcGIS for spatial mapping and Gephi for network visualization) and data publishing. We will employ our various project backgrounds in cinema-going history, film festival research and film analysis as examples to mention practical tips, tools that work, websites (e.g. Programming Historian) and repositories to use etc. Through a sample dataset, we demonstrate the advantages of interactive displays of quantitative data while critically addressing the methodological challenges and limitations of this approach. In the second part, we invite all workshop participants to join and work practically with a real dataset to go through these steps. All participants are also invited to bring their own projects and data to discuss in the workshop.

Návaznosti

MUNI/A/1704/2024, interní kód MU

Název: Kinematografie jako kreativní průmysl

Investor: Masarykova univerzita, Kinematografie jako kreativní průmysl

Citovat

VALLEJO, Aida; María PAZ PEIRANO; Skadi LOIST; Terézia PORUBČANSKÁ a Thomas SCHICK. A practical guide on how to build and publish your dataset and data visualizations. 2025.

@misc{2544640,
   author = {Vallejo, Aida and Paz Peirano, María and Loist, Skadi and Porubčanská, Terézia and Schick, Thomas},
   keywords = {data management; visualisation; film studies},
   language = {eng},
   title = {A practical guide on how to build and publish your dataset and data visualizations},
   url = {https://cms.necs.org/sites/default/files/documents/conference/necs2025_finalprogram_2.pdf},
   year = {2025}
}

TY  - CONF
ID  - 2544640
AU  - Vallejo, Aida - Paz Peirano, María - Loist, Skadi - Porubčanská, Terézia - Schick, Thomas
PY  - 2025
TI  - A practical guide on how to build and publish your dataset and data visualizations
KW  - data management
KW  - visualisation
KW  - film studies
UR  - https://cms.necs.org/sites/default/files/documents/conference/necs2025_finalprogram_2.pdf
N2  - Dataset creation is part of the research process, involving the collection of different types of data. While the common practice in Film Studies has been to keep datasets as documents for “internal use” of the researcher, the publication of datasets opens up new possibilities of understanding data collection as part of a collective endeavour that is more typical in the hard sciences. In this way, we operate in an open data logic. We explain essentials in the field such as setting up a data management plan (DMP), adding metadata and following FAIR(ER) principles, making data findable, accessible, interoperable, reusable, and also ethical and revisable (Verhoeven 2018). We also discuss available repositories for publishing research data and mention DH tools that help clean data, analyze and visualize datasets. By presenting a practical approach to dataset publication, this workshop offers key tools to understand dataset creation as part of a publishing process with its own procedures and logics. In the first part, workshop leaders offer a general introduction to the steps, tools and methods of dataset creation, data visualization (like ArcGIS for spatial mapping and Gephi for network visualization) and data publishing. We will employ our various project backgrounds in cinema-going history, film festival research and film analysis as examples to mention practical tips, tools that work, websites (e.g. Programming Historian) and repositories to use etc. Through a sample dataset, we demonstrate the advantages of interactive displays of quantitative data while critically addressing the methodological challenges and limitations of this approach. In the second part, we invite all workshop participants to join and work practically with a real dataset to go through these steps. All participants are also invited to bring their own projects and data to discuss in the workshop.
ER  -

VALLEJO, Aida; María PAZ PEIRANO; Skadi LOIST; Terézia PORUBČANSKÁ a Thomas SCHICK. \textit{A practical guide on how to build and publish your dataset and data visualizations}. 2025.

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