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Automated screening tools

Automated screening of scientific manuscripts can help authors to identify and fix common problems, such as failing to state whether experiments were blinding or randomized, using potentially misleading bar graphs to present continuous data, or failing to acknowledge study limitations. Tools can screen a manuscript and provide authors with customized feedback in seconds. This makes automated screening a valuable strategy for improving transparency and reproducibility on a large scale, across many fields.

At QUEST, we have developed several new screening tools and are founding members of an international working group that combines many differnt tools into a powerful screening pipeline (ScreenIT).

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Bridging the gap between researchers: transforming biomedical research through evidence synthesis.

A project funded by the anniversary initiative "Wirkung hoch 100" of the Stifterverband.

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COVID-evidence: a living database of trials on interventions for COVID-19

The COVID-19 pandemic is characterized by an unprecedented urgency to obtain reliable information on therapeutic options and their evaluation in clinical trials. This project aims at providing a freely available and daily updated online database of worldwide trial evidence on benefits and harms of interventions for COVID-19, including interventions for prevention, diagnosis, treatment and clinical management.

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Facility for the systematic review and meta-analysis of animal studies

In biomedical science, animal experiments are conducted to improve human health. However, efforts to translate results to clinical benefit in humans are often unsuccessful. Systematic review and meta-analysis are transparent, reproducible methods to objectively summarise published studies. In animal research, systematic reviews can help to identify reasons for translational failure, promising experimental treatments, and optimal 3Rs-related experimental designs.

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BIH supports the AllTrials Initiative. In this context we are dealing with the publication of clinical studies. With the IntoValue study we - together with the Hannover Medical School - are investigating the exact proportion of studies conducted at all 36 German university hospitals, for which no results have been published.

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AI roadmap for robustness and transparency studies on AI in healthcare

Major challenges impede the successful translation of AI technology to the clinical setting. In this work, we aim to present a roadmap how the translation of AI in healthcare can be improved.

Meta-Research Innovation Center Berlin (METRIC-Berlin)

Under the leadership of Professor John P. A. Ioannidis (Einstein BIH Visiting Fellow, funded by the Stiftung Charité) the so-called Meta-Research Innovation Center Berlin (METRIC-Berlin), the European "sister" of the Meta-Research Innovation Center at Stanford (METRICS), is being build up at the QUEST Center. The aim of the Center is to investigate the origin and the reliability, validity, accessibility and reproducibility of biomedical research. The Center is funded by the Stiftung Charité and the Einstein Foundation Berlin.

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The Meta Research reading list for peer reviewers

Looking for ways to improve your manuscripts and peer reviews? This “science of science” reading list can help! 

When reviewing papers or writing your own papers, it’s important to remember that the fact that something is standard practice for your field doesn’t mean that it’s the best way of doing things. Scientists, journals and funding agencies are increasingly recognizing the limitations of many existing practices and are implementing new policies to improve transparency, rigor and reproducibility. Here we present a list of meta-research articles for authors and peer reviewers. These “science of science” papers will help peer reviewers learn to identify and understand the problems with some very common practices.
This collection of articles also offer constructive solutions that make it easier for authors to improve transparency, rigor and reproducibility.

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Preclinical systematic review and meta-analysis

Preclinical systematic review and meta-analysis are meta-research tools that we use to investigate some of the challenges of translational biomedical research. They provide a summary of research findings in a field and allow judgement of both the range and quality of available evidence, including the likelihood that findings are at risk of bias. The results can help us identify areas for improvement in preclinical research and inform the development of strategies to address these challenges.

We focus primarily on the review of data from studies involving animal models of stroke and other neurological diseases and have a range of ongoing projects.

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Program evaluation at the QUEST Center

The program evaluation at the QUEST Center aims at evaluating our own performance of QUEST’s activities - namely ‘Education and Training’, ‘ELN Implementation’, ‘Open Data and Research Data Management’, ‘Patient and Stakeholder Engagement’, ‘Value of Open Science’ as well as ‘Incentives and Indicators’.

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Survey on the research climate

In cooperation with the German Centre for Higher Education and Science Studies, we conducted a survey on the research climate among all scientific employees at the Charité.

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QUEST Center

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