Bip4covid19
Releasing impact measures for articles relevant to Covid-19
Bibliographic Data
| ID | 21255846 |
|---|---|
| Authors | Thanasis Vergoulis (0000-0003-0555-4128, Athena Research and Innovation Center In Information Communication & Knowledge Technologies), Ilias Kanellos (0000-0003-2146-3795, Athena Research and Innovation Center In Information Communication & Knowledge Technologies), Serafeim Chatzopoulos (0000-0003-1714-5225, University of Peloponnese), Danae Pla Karidi (0000-0002-3154-6212, Athena Research and Innovation Center In Information Communication & Knowledge Technologies), Theodore Dalamagas (0000-0002-5002-7901, Athena Research and Innovation Center In Information Communication & Knowledge Technologies) |
| Year | 2021 |
| Volume | 2 |
| Issue | 4 |
| Pages | 1447-1465 |
| Publication date | 2021-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Quantitative Science Studies (JOURNAL) |
| Journal identifiers | ISSN: 2641-3337 • E-ISSN: 2641-3337 |
| Publisher | MIT Press (PUBLISHER • US) |
| DOI | 10.1162/qss_a_00169 |
| OpenAlex | W3213099165 |
| Language | EN |
| Citations received | 2 |
| References cited | 25 |
Since the beginning of the coronavirus pandemic, a large number of relevant articles have been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies or even government actions to limit the spread of the disease, and directing treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate, making the exploration of the relevant literature and the identification of useful knowledge challenging. In this work, we describe BIP4COVID19, an open data set that offers a variety of impact measures for coronavirus-related scientific articles. These measures can be exploited for the creation or extension of added-value services aiming to facilitate the exploration of the respective literature, alleviating the aforementioned issue. In the same context, as a use case, we provide a publicly accessible keyword-based search interface for COVID-19-related articles, which leverages our data to rank search results according to the calculated impact indicators
Data science · Disease · Geography · Information retrieval · Preprint · Scientific literature · World Wide Web · Academic Publishing and Open Access · Computer Science · Medicine · Misinformation and Its Impacts · scientometrics and bibliometrics research · Artificial Intelligence
| Citations per year | 0.4 |
|---|---|
| Citation span | 2021 - 2024 (4) |
| Highly cited | No |