Modeling interregional research collaborations in German biotechnology using industry directory data.
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ABSTRACT:
SUBMITTER: Mitze T
PROVIDER: S-EPMC6301979 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC5856329 | biostudies-literature
Project description:BackgroundMedical-product companies often outsource research and manufacturing needs to contracting or partnering organizations but then must manage a challenging patchwork of regulatory activities. A standalone regulatory agreement could clarify the relationships and responsibilities between companies working jointly on a single regulated product. This study explored the need for and current use of standalone regulatory agreements.MethodsA survey instrument was developed using an implementation framework and disseminated to mid- to senior-level employees and consultants for sponsor and vendor companies in the medical products sector.ResultsOf 294 respondents, about half, primarily from companies with more than 200 employees, were familiar with standalone regulatory agreements, and half of this subgroup had moved forward to implement them. Such agreements were considered beneficial to clarify regulatory roles and responsibilities, standardize regulatory expectations between the companies, and stimulate earlier discussion about joint regulatory strategies. However, the development of regulatory agreements appears challenged by the fact that such agreements are not required by regulatory agencies overseeing medical products and have no standardized templates, agency or industry guidance. Respondents whose organizations do not now use regulatory agreements either had not considered or did not see a need for a standalone agreement.ConclusionsStandalone regulatory agreements are becoming more common but are not yet implemented fully by most companies. Their usefulness and content appeared to depend upon the type of partner, the complexity of the relationship and the availability of internal expertise and support.
| S-EPMC11335966 | biostudies-literature
Project description:We have developed a cooperative-learning, case studies project model that has teams of students working with biotechnology professionals on company-specific problems. These semester-long, team-based projects can be used effectively to provide students with valuable skills in an industry environment and experience addressing real issues faced by biotechnology companies. Using peer-evaluations, we have seen improvement in students' professional skills such as time-management, quality of work, and level of contribution over multiple semesters. This model of team-based, industry-sponsored projects could be implemented in other college and university courses/programs to promote professional skills and expose students to an industry setting.
| S-EPMC3867760 | biostudies-other
Project description:Science is changing: the volume and complexity of data are increasing, the number of studies is growing and the goal of achieving reproducible results requires new solutions for scientific data management. In the field of neuroscience, the German National Research Data Infrastructure (NFDI-Neuro) initiative aims to develop sustainable solutions for research data management (RDM). To obtain an understanding of the present RDM situation in the neuroscience community, NFDI-Neuro conducted a comprehensive survey among the neuroscience community. Here, we report and analyze the results of the survey. We focused the survey and our analysis on current needs, challenges, and opinions about RDM. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, (3) lack of secure and privacy preserving research infrastructure for sensitive data, (4) lack of RDM literacy, and (5) lack of resources (time, personnel, money) for proper RDM. However, an overwhelming majority of community members (91%) indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Taking advantage of this willingness and overcoming the existing barriers requires the systematic development of standards, tools, and infrastructure, the provision of training, education, and support, as well as additional resources for RDM to the research community and a constant dialogue with relevant stakeholders including policy makers to leverage of a culture change through adapted incentivization and regulation.
| S-EPMC9933933 | biostudies-literature
Project description:The Bioinformatics Links Directory, http://bioinformatics.ca/links_directory, is an actively maintained compilation of servers published in this and previous issues of Nucleic Acids Research issues together with many other useful tools, databases and resources for life sciences research. The 2007 update includes the 130 websites highlighted in the July 2007 Web Server issue of Nucleic Acids Research and brings the total number of servers listed in the Bioinformatics Links Directory to just under 1200 links. In addition to the updated content, the 2007 update of the Bioinformatics Links Directory includes new features for improved navigation, accessibility and open data exchange. A complete listing of all links listed in this Nucleic Acids Research 2007 Web Server issue can be accessed online at, http://bioinformatics.ca/links_directory/narweb2007. The 2007 update of the Bioinformatics Links Directory, which includes the Web Server list and summaries is also available online, at the Nucleic Acids Research web site, http://nar.oupjournals.org.
| S-EPMC1933129 | biostudies-literature
Project description:Whole-genome array comparative genomic hybridization (aCGH) of human ependymoma tumors. DOP-PCR products were spotted in triplicate onto NexterionTM Slide E epoxysilane-coated slides (PEQLAB, Erlangen, Germany) using a spotting robot (VersArray ChipWriterTM Pro system,BioRad, Munich, Germany) at 20C and 40% humidity. After spotting, slides were cross-linked,baked for 1 hr at 80C, and cross-linked again. Fresh frozen tumor material was collected during tumor resection. Copy number aberrations represent the status at diagnosis.
2011-08-14 | E-GEOD-27286 | biostudies-arrayexpress
Project description:BackgroundInternational collaboration (IC) is essential for the advance of stem cell research, a field characterized by marked asymmetries in knowledge and capacity between nations. China is emerging as a global leader in the stem cell field. However, knowledge on the extent and characteristics of IC in stem cell science, particularly China's collaboration with developed economies, is lacking.Methods and findingsWe provide a scientometric analysis of the China-Canada collaboration in stem cell research, placing this in the context of other leading producers in the field. We analyze stem cell research published from 2006 to 2010 from the Scopus database, using co-authored papers as a proxy for collaboration. We examine IC levels, collaboration preferences, scientific impact, the collaborating institutions in China and Canada, areas of mutual interest, and funding sources. Our analysis shows rapid global expansion of the field with 48% increase in papers from 2006 to 2010. China now ranks second globally after the United States. China has the lowest IC rate of countries examined, while Canada has one of the highest. China-Canada collaboration is rising steadily, more than doubling during 2006-2010. China-Canada collaboration enhances impact compared to papers authored solely by China-based researchers This difference remained significant even when comparing only papers published in English.ConclusionsWhile China is increasingly courted in IC by developed countries as a partner in stem cell research, it is clear that it has reached its status in the field largely through domestic publications. Nevertheless, IC enhances the impact of stem cell research in China, and in the field in general. This study establishes an objective baseline for comparison with future studies, setting the stage for in-depth exploration of the dynamics and genesis of IC in stem cell research.
| S-EPMC3585304 | biostudies-literature
Project description:BackgroundDigital innovations in medicine are disruptive technologies that can change the way diagnostic procedures and treatments are delivered. Such innovations are typically designed in teams with different disciplinary backgrounds. This paper concentrates on 2 interdisciplinary research teams with 20 members from the medicine and engineering sciences working jointly on digital health solutions.ObjectiveThe aim of this paper was to identify factors on the individual, team, and organizational levels that influence the implementation of interdisciplinary research projects elaborating on digital applications for medicine and, based on the results, to draw conclusions for the proactive design of the interdisciplinary research process to make these projects successful.MethodsTo achieve this aim, 2 interdisciplinary research teams were observed, and a small case study (response rate: 15/20, 75%) was conducted using a web-based questionnaire containing both closed and open self-report questions. The Spearman rank correlation coefficient was calculated to analyze the quantitative data. The answers to the open-ended questions were subjected to qualitative content analysis.ResultsWith regard to the interdisciplinary research projects investigated, the influencing factors of the three levels presented (individual, team, and organization) have proven to be relevant for interdisciplinary research cooperation.ConclusionsWith regard to recommendations for the future design of interdisciplinary cooperation, management aspects are addressed, that is, the installation of a coordinator, systematic definition of goals, required resources, and necessary efforts on the part of the involved interdisciplinary research partners. As only small groups were investigated, further research in this field is necessary to derive more general recommendations for interdisciplinary research teams.Trial registrationGerman Clinical Trials Register, DRKS00023909, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00023909 ; German Clinical Trials Register, DRKS00025077, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00025077.
| S-EPMC9118027 | biostudies-literature
Project description:BackgroundModern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis.ResultsWe have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de.ConclusionsDIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use.
| S-EPMC3424966 | biostudies-literature
Project description:High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications. In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences. Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.
| S-EPMC8939798 | biostudies-literature