| 1. |
- Beck, S., et al.
(författare)
-
Experimenting with Open Innovation in Science (OIS) practices: A novel approach to co-developing research proposals
- 2021
-
Ingår i: CERN IdeaSquare Journal of Experimental Innovation. - 2413-9505. ; 5:2, s. 28-49
-
Tidskriftsartikel (refereegranskat)abstract
- Co-producing scientific research with those who are affected by it is an emerging phenomenon in contemporary science. This article summarizes and reflects on both the process and outcome of a novel experiment to co-develop scientific research proposals in the field of Open Innovation in Science (OIS), wherein scholars engaged in the study of open and collaborative practices collaborated with the “users” of their research, i.e., scientists who apply such practices in their own research. The resulting co-developed research proposals focus on scientific collaboration, open data, and knowledge sharing and are available as an appendix to this article.
|
|
| 2. |
- Bergamaschi, Peter, et al.
(författare)
-
European Obspack compilation of atmospheric carbon dioxide data from ICOS and non-ICOS European stations for the period 1972-2023; : obspack_co2_466_GLOBALVIEWplus_v8.0_2023-04-26
- 2023
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This data package contains high accuracy CO2 dry air mole fractions from 58 ICOS and non-ICOS European observatories at in total 132 observation levels, collected by the ICOS Atmosphere Thematic Centre (ATC) and provided by the station contributors. The package is part of the Globalviewplus v8.0 data product, released in 2022 and is intended for use in carbon cycle inverse modeling, model evaluation, and satellite validation studies. Please report errors and send comments regarding this product to the ObsPack originators. Please read carefully the ObsPack Fair Use statement and cite appropriately. This is the sixth release of the GLOBALVIEWplus (GV+) cooperative data product. Please review the release notes for this product at www.esrl.noaa.gov/gmd/ccgg/obspack/release_notes.html. Metadata for this product are available at https://commons.datacite.org/doi.org/10.18160/CEC4-CAGK. Please visit http://www.gml.noaa.gov/ccgg/obspack/ for more information.
|
|
| 3. |
- Demichev, Vadim, et al.
(författare)
-
A time-resolved proteomic and prognostic map of COVID-19
- 2021
-
Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 12:8, s. 780-794.e7
-
Tidskriftsartikel (refereegranskat)abstract
- COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.
|
|
