(1) Overview
Repository location
The dataset is openly available on Zenodo (https://doi.org/10.5281/zenodo.19711128).
The broader public-facing digital archive associated with the dataset is available at the Techno-Optimism Archive website.1
Context
The dataset was created as part of the Techno-Optimism Archive, a public-facing digital resource documenting historical press examples of technological optimism in newspapers, magazines, books, advertisements, speeches, and related printed sources. The archive brings together dispersed and often difficult-to-locate examples from already digitized sources in a curated and reusable form. It focuses on moments when then-new technologies, such as the telegraph, telephone, or radio, were framed as solutions to social, political, economic, and moral problems.
Conceptually, the dataset sits at the intersection of research on techno-optimism (Danaher, 2022), technological solutionism (Morozov, 2013), sociology of expectations (Borup et al., 2006), hype studies (Galanos et al., 2026), media history (Marvin, 2020), and history of technology (Gitelman, 2008). It is designed as a research resource for tracing recurring historical patterns in solutionist and utopian understandings of technology. It is not a complete corpus of all optimistic discourse about technology, but a collection of cases centered on far-reaching predictions and unrealized promises.
The first public release, Techno-Optimism Archive Dataset (1.0), contains 70 curated records drawn from nineteenth- and twentieth-century press materials (Łukasik, 2026). The dataset is versioned and intended to remain expandable in future releases, with the longer-term aim of developing into a larger resource encompassing additional technologies, more clipping examples, and new national contexts. Fuller documentation of the dataset structure and fields is provided in the accompanying README and codebook.
(2) Method
Steps
The dataset was compiled through archival research in digitized historical sources accessed through public digital archives and related repositories, such as HathiTrust, the Library of Congress (Chronicling America), World Radio History, and the Internet Archive. These repositories were not selected in advance as a fixed corpus, but emerged through an iterative search process shaped by digital accessibility, searchability, and the availability of relevant materials. Their unequal representation in the current version of the dataset reflects those conditions of discovery rather than any attempt at balanced archival coverage.
The workflow followed five main stages. First, potentially relevant materials were identified through keyword-based searching, often by combining technology terms with promissory motifs, such as “telegraph” and “world peace” or by combining technology terms with problems, such as “telephone” and “loneliness”. Some search and discovery stages were supported by generative AI tools (OpenAI GPT-5.4 and Claude Opus 4.6), primarily through suggesting keyword combinations and identifying candidate examples that were subsequently checked manually in the source repositories. A smaller number of examples were also identified through secondary literature, online historical writing on particular technologies, and occasional crowdsourced suggestions. Second, candidate items were read and assessed for substantive fit with the project’s focus on techno-optimistic, solutionist, utopian, and strongly promissory claims about technology. Third, bibliographic and source metadata were verified, standardized, and entered into the dataset as far as possible. This included normalizing publication dates into separate year, month, and day fields where known, regularizing missing values, and harmonizing descriptive fields such as author, headline, publication title, and source archive according to project conventions. Fourth, a short project-authored summary was written for each selected record and one or more controlled promise_type tags and technology_type tags were assigned. Finally, a selected excerpt was added to the Techno-Optimism Archive website together with part of the corresponding metadata.
Sampling strategy
The dataset uses a purposeful sampling strategy (Babbie, 2013). Records were selected on the basis of their relevance to the archive’s central theme: historical claims presenting new technologies as likely to produce broad and often disproportionate benefits, including world peace, mutual understanding, social cohesion, educational revolution, or end of work. In practice, sampling was iterative. Early discoveries informed later searches by revealing recurring motifs and rhetorical patterns. For example, finding claims that the automobile would provide fresh air led to more targeted searches for related formulations.
The resulting dataset should therefore be understood as a curated and theoretically informed sample rather than a statistically representative corpus. It reflects the selectivity of working with digitized archives as well as interpretive decisions about relevance, description, and classification, all of which shape what becomes visible as a historical pattern (Coburn, 2021; Schwartz & Cook, 2002).
Quality control
To improve internal consistency and reuse value, all records were manually checked against the source material. Identifiers, permalinks, date fields, and missing values were standardized. Both promise_type tags and technology_type tags were assigned using a controlled vocabulary defined in separate files. Project-authored summaries were reviewed against the source, while key descriptive fields were harmonized according to conventions documented in the accompanying files. Some historical ambiguities remain unavoidable, as not all items preserve a named author, exact publication date, or distinct title.
(3) Dataset Description
Repository name
Zenodo.
Object name
Techno-Optimism Archive Dataset.
Format names and versions
CSV, XLSX, Markdown (.md), and TXT. The Zenodo deposit comprises the main dataset, supporting documentation, and controlled vocabularies in these formats.
Creation dates
Start date: 2026-03-01.
The dataset is versioned and will continue to be expanded in future releases.
Dataset creators
Krystian Łukasik (Harvard Kennedy School; University of Warsaw): Conceptualization; Data curation; Project administration; Methodology; Resources; Visualization; Software; Writing – original draft; Writing – review & editing.
Language
English is used for variable names, documentation, and project-authored summaries. Source materials in v1.0 are also in English.
License
CC BY 4.0.
Publication date
2026-04-23.
(4) Reuse Potential
The dataset has reuse potential both within and beyond academic research. In the history of technology, media history, hype studies, digital humanities, and science and technology studies, it can support qualitative work on recurring narratives and discourses attached to different technologies, including the ways in which newspapers and related printed sources framed technologies as solutions to war, poverty, isolation, educational inequality, and other social or political problems.
As it grows, the dataset may also support comparative, mixed-method, and quantitative research across technologies and historical periods by making recurring promissory patterns easier to identify, compare, and track over time. Its current structure may also support classification-oriented work and the discovery of comparable cases in other digitized collections, including forms of LLM-assisted exploration in which the existing taxonomy and curated examples serve as a scaffold for identifying similar materials. Future expansion may make it possible to examine how particular types of claim are distributed across technologies, periods, source types, and national contexts, while also extending the archive into additional domains such as nuclear energy, biotechnology, space technologies, medical technologies, industrial automation, and household technologies.
Beyond academic research, the dataset may be useful for teaching, public history, journalism, curatorial work, and critical commentary on contemporary technological hype. It offers a curated body of historical examples that can help contextualize present-day claims about artificial intelligence, blockchain, platforms, 3D printing, and other emerging technologies by showing that expansive technological promises have a much longer history. In particular, it can help place familiar contemporary claims – such as promises that AI will soon reduce working time – in relation to earlier examples in which similar promises were made about other technologies, such as computers in the 1960s. For that reason, it may be useful not only in scholarly analysis but also in classrooms, exhibitions, public writing, podcasts, and other interpretive or educational formats.
There are, however, clear barriers to reuse. First, the dataset is curated rather than exhaustive and is designed to capture strongly promissory, solutionist, and utopian cases, especially those that in retrospect appear exaggerated or historically unrealized; it therefore does not include optimistic expectations that were broadly fulfilled. Second, coverage is shaped by the uneven availability and searchability of digitized sources, which means that some technologies, periods, and publication types are easier to recover than others. In practice, version 1.0 is weighted toward nineteenth- and twentieth-century Anglophone materials, especially U.S.-based sources. This creates a double imbalance: earlier materials are underrepresented because the current archive is strongly shaped by U.S.-centered source availability, while many later examples are harder to identify because copyright-protected materials are often not accessible through public repositories. Third, bibliographic precision varies across records, especially in advertisements, reprinted speeches, and reproduced statements, where named authors, exact publication dates, or distinct titles are not always preserved. Fourth, some source_url links do not lead directly to an individual newspaper scan but instead to higher-level archival interfaces, especially in repositories that use collection-level links, such as Library of Congress Control Number (LCCN) permalinks. In such cases, the item can still be located using the accompanying metadata, though this may require additional searching in the archive interface. Finally, although the deposited dataset itself consists of project-authored metadata, summaries, and related descriptive fields, not all of the original source materials described in those records are in the public domain.
Note
[1] https://technooptimism.org (last accessed: 15 June 2026).
AI Declaration
Generative AI tools were used to support some search and discovery tasks and to assist in drafting provisional summaries during dataset compilation. All records and summaries were subsequently selected, read, checked, and manually revised by the author, and nothing was added to the dataset automatically. AI tools were also used for language correction and editorial refinement during manuscript drafting, with the final text reviewed and approved by the author.
Author Contributions
Krystian Łukasik: Conceptualization; Data curation; Project administration; Methodology; Resources; Visualization; Software; Writing – original draft; Writing – review & editing.