Daniel López‐Padilla

23 papers receiving 160 citations

Peers

Daniel López‐Padilla
Comparison fields: 5 of 45
  • Endocrine and Autonomic Systems 16
  • Physiology 44
  • Pulmonary and Respiratory Medicine 47
  • Anesthesiology and Pain Medicine 7
  • Neurology 14
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Eun Kyeong Kang South Korea
T Barchfeld Germany
Netanel Eisenbach Israel
Pınar Ergenekon Türkiye
Anthony Davis United Kingdom
Stefan Frent Romania
Giuseppe Porciello Italy
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Citations per field
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Citations per year

Countries citing papers authored by Daniel López‐Padilla

Since Specialization
Citations

This map shows the geographic impact of Daniel López‐Padilla's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel López‐Padilla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel López‐Padilla more than expected).

Fields of papers citing papers by Daniel López‐Padilla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel López‐Padilla. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel López‐Padilla. The network helps show where Daniel López‐Padilla may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel López‐Padilla, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel López‐Padilla Line = papers co-authored together Daniel López‐Padilla links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202034
2 201532
3 201619
4 202118
5 201711
6 20158
7 20235
8 20194
9 20163
10 20223
11 20203
12 20203
13 20163
14 20163
15 20222
16 20162
17 20162
18 20201
19 20231
20 20171

About Daniel López‐Padilla

Daniel López‐Padilla is a scholar working on Pulmonary and Respiratory Medicine, Critical Care and Intensive Care Medicine, Physiology, Surgery and Health, having authored 29 papers that have together received 161 indexed citations. Recurring topics across this work include Respiratory Support and Mechanisms (6 papers), Intensive Care Unit Cognitive Disorders (6 papers), Chronic Obstructive Pulmonary Disease (COPD) Research (4 papers), Obstructive Sleep Apnea Research (3 papers), Transplantation: Methods and Outcomes (3 papers), Chronic Disease Management Strategies (2 papers), Telemedicine and Telehealth Implementation (2 papers) and Mobile Health and mHealth Applications (2 papers). The work is most often cited by research in Endocrine and Autonomic Systems (16 citations), Physiology (44 citations), Pulmonary and Respiratory Medicine (47 citations), Anesthesiology and Pain Medicine (7 citations) and Neurology (14 citations). Daniel López‐Padilla has collaborated with scholars based in Spain, France and Switzerland. Frequent co-authors include José Ignacio de Granda‐Orive, Adolfo Alonso‐Arroyo, Miguel Ángel Martínez‐García, Rafael Aleixandre‐Benavent, Gonzalo Segrelles‐Calvo, Carlos A. Jiménez-Ruíz, M.J. Díaz de Atauri, Andrés López‐Roldán, Antonio Vidal‐Infer and Beatriz Tarazona‐Álvarez. Their work appears in journals such as ERJ Open Research, Archivos de Bronconeumología, Life, Sleep Medicine and Respiratory Care.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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