Google Dataset Search: Building a search engine for datasets in an open Web ecosystem

Brickley, Dan; Burgess, Matthew; Noy, Natalya F.

doi:10.1145/3308558.3313685

Cited by 184 publications

(135 citation statements)

References 19 publications

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“…20 Sénat, Commission des Finances Audition TGIR du 17 juillet 2019, Exposé de Sophie Moati, présidente de la troisième chambre, http://www.senat.fr/rap/r18-675/r18-675_mono.html. on the Web is recognized as possible (Brickley, Burgess, & Noy, 2019). Furthermore, it is essential for the progress of routes and maps that information search behaviors are modeled and that the semantics of the documentary choices made are stabilized by a solid "topic modeling," based on a "topic analysis-based approach" built through innovative and exhaustive methods (Tsatsaronis, 2020).…”

Section: Discussionmentioning

confidence: 99%

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Fabre

Egret

Schöpfel

et al. 2021

Quantitative Science Studies

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Research infrastructures (RI) offer researchers a multitude of research opportunities and services and play a key role in the performance, innovative strength and international competitiveness of science. As an important part of the generation and use of new knowledge and technologies, they are essential for research policies. Because of their strategic importance and their need for significant funding, there is a growing demand for the assessment of their scientific output and impact. Current research information systems (CRIS) contribute for many years now to the evaluation of universities and research organizations. Based on studies on the application of CRIS to infrastructures and on a recent French report on the scientometric assessment of RI, this paper analyses the potential of CRIS and their data models and standards (in particular the international CERIF format and the German RDC model) for the monitoring and evaluation of RI. The interaction between functional specificities of RI and standards for their assessment is outlined, with reference to their own potential to stimulate and share innovation in the networks located inside and outside RI. This societal challenge, more than an academic issue, is on the way to further harmonization and consolidation of shared and common RI metrics.

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Section: Discussionmentioning

confidence: 99%

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Fabre

Egret

Schöpfel

et al. 2021

Quantitative Science Studies

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“…Implementing these standards to a sufficient quality level to enable data reuse is often difficult. [40][41][42] Measuring ''FAIRness'' is not yet an established practice, 43 although some initial work exists, thanks to the FAIR metrics group (http://fairmetrics.org). This means, among other things, that someone making the effort into publishing their data according to (their interpretation of) FAIR, has limited ways to gauge how meaningful their work is in practice.…”

Section: Existing Guidance and Principles And Their Limitationsmentioning

confidence: 99%

“…Implementing these standards to a sufficient quality level to enable data reuse is often difficult. 40 , 41 , 42…”

Section: Dataset Reuse: the View Of The Literaturementioning

confidence: 99%

Dataset Reuse: Toward Translating Principles to Practice

et al. 2020

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“…Google recopila para su buscador datasets que cumplen el estándar de metadatos Schema.org (Brickley;Burgess;Noy, 2019), más concretamente el referido a datasets, según se explica en su enfoque sobre el descubrimiento de conjuntos de datos (Google, 2020). Esto ha permitido crear un instrumento de recopilación masiva que sin duda facilita la investigación científica.…”

Section: Introductionunclassified

Virus de ácido ribonucleico (ARN) y coronavirus en Google Dataset Search: alcance y correlación epidemiológica

Ochando

Prieto-Gutierrez

2020

EPI

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This paper presents an analysis of the publication of datasets collected via Google Dataset Search, specialized in families of RNA viruses, whose terminology was obtained from the National Cancer Institute (NCI) thesaurus developed by the US Department of Health and Human Services. The objective is to determine the scope and reuse capacity of the available data, determine the number of datasets and their free access, the proportion in reusable download formats, the main providers, their publication chronology, and to verify their scientific provenance. On the other hand, we also define possible relationships between the publication of datasets and the main pandemics that have occurred during the last 10 years. The results obtained highlight that only 52% of the datasets are related to scientific research, while an even smaller fraction (15%) are reusable. There is also an upward trend in the publication of datasets, especially related to the impact of the main epidemics, as clearly confirmed for the Ebola virus, Zika, SARS-CoV, H1N1, H1N5, and especially the SARS-CoV-2 coronavirus. Finally, it is observed that the search engine has not yet implemented adequate methods for filtering and monitoring the datasets. These results reveal some of the difficulties facing open science in the dataset field. Resumen Se presenta un análisis sobre la publicación de conjuntos de datos recogidos en el buscador Google Dataset Search, especializados en familias de virus de ARN, cuya terminología fue obtenida en el tesauro del National Cancer Institute (NCI), elaborado por el Department of Health and Human Services de los Estados Unidos. Se busca evaluar el alcance y capacidad de reutilización de los datos disponibles, determinando el número de datasets, su libre acceso, proporción en formatos de descarga reutilizables, principales proveedores, cronología de publicación y verificación de su procedencia científica. Por otra parte, definir posibles vínculos entre la publicación de datasets y las principales pandemias ocurridas en los últimos 10 años. Entre los resultados obtenidos se destaca que sólo el 52% de los datasets tienen correspondencia con investigaciones científicas y, en menor medida, un 15% son reaprovechables. También se observa una evolución al alza en la publicación de datasets, especialmente vinculada a la afectación de las principales epidemias. Esto es confirmado de manera evidente con los virus del Ébola, Zika, SARS-CoV, H1N1, H1N5 y, particularmente con el coronavirus SARS-CoV-2. Finalmente, se observa que el buscador aún no ha implementado métodos adecuados para el filtrado y supervisión de los datasets. Estos resultados muestran algunas de las dificultades que aún presenta la ciencia abierta en el campo de los datasets.

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Google Dataset Search: Building a search engine for datasets in an open Web ecosystem

Cited by 184 publications

References 19 publications

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Dataset Reuse: Toward Translating Principles to Practice

Virus de ácido ribonucleico (ARN) y coronavirus en Google Dataset Search: alcance y correlación epidemiológica

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