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This dataset provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dataset contains the OpenAIRE COVID-19 Gateway records, identified via full-text mining and inference techniques applied to the OpenAIRE Graph. The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dataset consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.8238913.

As of December 2020, COVID-19 has spread all over the world with more than 81 million cases and more than 1.8 million deaths. The rapidly increasing number of patients mandates the consideration of potential treatments for patients under severe and critical conditions. Convalescent plasma (CP) treatment refers to the approach of infusing patients with plasma from recently recovered patients. CP appears to be a possible therapeutic option to manage patients suffering from severe or even lethal infectious disorders, in which “traditional therapies” have failed to obtain any result. In the present study, we develop a mathematical model on the treatment-donation-stockpile dynamics for an optimal implementation of CP therapy to examine potential benefits and complications in the logistic realization of this therapy in a large-scale population. We parameterize the model with COVID-19 epidemics in Italy, and conduct scenario analyses to estimate outcomes of population-wide CP therapy and to examine the maximum number of CP donation processions per day. Under the assumption that the efficacy of CP is 90%, we show that by the end of year 2020, initiating the population-wide CP therapy from April 2020 can save as many as 19,215 lives (ranging from 5000 - 28,000 depending on donor availability), while the demand for apheresis use is manageable in all scenarios: the maximum daily demand is 156 (ranging from 27 - 519 depending on donor availability) for the first outbreak wave and 1,434 (ranging from 224 - 4,817 depending on donor availability) for the second wave. Given that Italy has 61 centers with apheresis this maximum demand level corresponds to a daily average of 2.5 and 23.5 processions of CP donation being performed by each center with respect to each outbreak wave. Our analyses show that population-wide CP therapy can contribute to curbing COVID-19 related deaths, and the logistic implementation is feasible for developed countries. The reduction of deaths can be very significant if the CP therapy is started earlier at the outbreak, and remains significant even if it is implemented during the outbreak peak time.

Background: Acute diarrhea is one of the most serious problems in global public health that causes considerable morbidity and mortality worldwide. Human caliciviruses (HuCV) including norovirus (NoV, genogroup GI and GII) and sapovirus (SaV), is a leading cause of acute sporadic diarrhea in individuals across all age groups. However, few studies had been conducted clarifying the characteristics of HuCV in diarrhea cases across all age groups in China. Our study was aimed at assessing the HuCV-related diarrhea burden and NoV genotypes distribution in southwest China. Methods: The study was conducted in four hospitals in Kunming city, Yunnan province, from June 2014 to July 2015. Stool specimens were collected from 1,121 diarrhea cases and 319 healthy controls in outpatient departments. Reverse transcription polymerase chain reaction (RT-PCR) was used to detect NoV (GI, GII) and SaV. Sequencing was applied to confirm the three viral infections and phylogenetic analysis was performed to determine their genotypes. A structured questionnaire was used to record the demographic information and clinical symptoms of subjects. Results: HuCV was detected at an 11.0 % infection rate in 1,121 diarrhea cases and at 3.4 % rate in 319 non-diarrhea subjects (p < 0.0001, OR = 3.5, 95 % CI 1.8–6.5). The prevalence of the NoV genogroup GII and genotype GII.4 in diarrhea cases was significantly higher than that found in healthy controls (p < 0.0001, p = 0.018, respectively). NoV GII (n = 118, 10.5 %) was the most common HuCV subtype in diarrhea cases, followed by SaV (n = 3, 0.3 %) and NoV GI (n = 2, 0.2 %). Of 118 NoV GII strains isolated from diarrhea patients. GII.4 (n = 55, 46.6 %) was the predominant strain, followed by GII.3 (n = 28, 23.7 %), GII.12 (n = 25, 21.2 %), GII.17 (n = 8, 6.8 %), and GII.5 (n = 2, 1.7 %). Of the 55 GII.4 strains, the GII.4 Sydney 2012 variant had absolutely predominant prevalence (n = 52, 94.5 %), followed by the NoV GII.4-2006b variant (n = 3, 5.5 %). The GII.4 Orleans 2009 variant was not found in diarrhea cases of the study. Conclusions: NoV GII was the major genogroup and GII.4 was the most predominant strain detected in diarrhea patients. The GII.17 is an emergent variant in sporadic diarrhea and might become the predominant strain in diarrhea cases in the near future. Rapid, accurate detection kits need to be developed to help us find and treat NoV-associated diarrhea in clinical settings in a timely manner. Figure 1Phylogenetic trees of SaV and NoV(GI, GII) based on dependent RNA polymerase ●: diarrhea cases over five years; ○: controls over five years; ▲: diarrhea cases under five years; △: controls under five years; The contents in brackets is that the number of same genotype The molecular analysis of SaV and NoV (GI, GII) among diarrhea cases and healthy controls in this study, NoV GII was the major pathogen and GII.4 was the most predominant strain detected in diarrhea patients, novel GII.17 variant was emergence in diarrhea cases.Phylogenetic trees of SaV and NoV(GI, GII)The representative gene sequences

Database COVID

In the course of our PubMed searches and preprints from MedRxiv, we identified a number of protocols for RCTs on preventive measures and treatments for Covid-19. This file is updated regularly.

In recent years, an increasing number of Open Science initiatives have emerged that are accessible to anyone, but mainly target researchers. We believe that students are overlooked within the Open Science movement and do not receive adequate education on many of the Open Science practices. For example, many students do not know about the replication crisis or that some research is questionable. In turn, this may have consequences on how they will conduct their own research in the future. Our initiative feels the urge to spread Open Science within the student community. To realize this goal, we started a student organization that promotes Open Science and emphasizes its applicability directly to students. We are Data Sharing. We are Open Access. We are Reproducibility. We are Open Science, from students for students. We aim to promote Open Science by organizing lectures, debates, and workshops (e.g., how to pre-register a bachelor/master thesis). As such, we inform students how Open Science might be able to solve issues within research and how they can engage. Additionally, we educate students on why the current way to do science is not the way forward (e.g. panel discussions). We want to strongly emphasize the students' perspective on the applicability of Open Science to their life as a student. We believe that by creating awareness at an early stage of their career, we will be able to improve research practices in science in the long run. Besides, Open Science might not only be valuable within their studies (e.g. thesis) - and when pursuing a career in academia - but also when being confronted with research after their studies (e.g, when interpreting other’s work). Our initiative is unique and innovative by solely focusing on the engagement of students. Continuous support and increased interest from students even outside our university has motivated us to encourage students to start a SIOS at their own university. To help motivated students get started, we wrote a Step-by-Step guide that describes how our organization was established and gives advice on how to structure and manage a student initiative around the topic of Open Science. Within the manual, we also included a COVID- 19 crisis management section where we give tips on how to keep an initiative alive in times of online meetings and closed faculties. For the Step-by-Step guide, we have just received a grant by Berkeley University that will fund distributing the project to different universities. In the spirit of evaluating our own effectiveness, we further aim to design studies measuring the impact of our Step-by-Step guide and initiative. This will help guide our decisions in how to further promote Open Science practices among students by better understanding what students actually need. Besides our annual introductory lectures to Open Science, our plans for the following year include the following projects: Taking part in the Open Science festival, inviting lecturers on the topic of power analysis, writing a blogpost on the experiences with supervisors with poor academic integrity, organizing an introductory workshop on Bayesian statistics, and many more.

The crystallographic data processing is investigated with the goal of extracting more structural information from serial crysallographic experiments. Systematic errors resulting from uncontrollable aspects in the experimental setup were reduced in several places, but most significantly by modelling the expected intensity of partially observed intensities using Gaussian basis functions to improve merging. These improvements are tied in with several applications, like phasing of continuous diffraction and drug screening for combating SARS-CoV-2. Dissertation, University of Hamburg, 2023; 143 pp. (2023). = Dissertation, University of Hamburg, 2023