International Research Networks in Pharmaceuticals: Structure and Dynamics

Knowledge production and scientific research have become increasingly more collaborative and international, particularly in pharmaceuticals. We analyze international research networks on the country level in different disease groups. Our empirical analysis is based on a unique dataset of scientific publications related to pharmaceutical research. Using social network analysis, we find that both the number of countries and their connectivity increase in almost all disease groups. The cores of the networks consist of high income OECD countries and remain rather stable over time. We use network regression techniques in order to analyze the dynamics of the networks. Our results indicate that an accumulative advantage based on preferential attachment and point connectivity as a proxy for multi-connectivity are positively related to changes in the countries' collaboration intensity.International Cooperation, Pharmaceuticals, Research Networks, Network Dynamics, MRQAP

The interdependence between biodiversity and socioeconomic variables on a local level: evidence for german counties

This paper explores possible interdependence of biodiversity and several socioeconomic and political factors at the county level. It is aimed at the empirical identification of direct and indirect effects between biodiversity (loss) and their theoretical major impact factors. To date, research shows that in addition to geography, agriculture is one major determinant of biodiversity status. However, the impact of regional socioeconomic structures on biodiversity should not be underestimated. Specifically, in regard to biodiversity loss, the socioeconomic structure counteracts political measures instituted to protect biodiversity and change agricultural practice.biodiversity, socioeconomic interdependence, Bavaria, Thuringia

Collaboration in pharmaceutical research: Exploration of country-level determinants.

In this paper we focus on proximity as one of the main determinants of international collaboration in pharmaceutical research. We use various count data specifications of the gravity model to estimate the intensity of collaboration between pairs of countries as explained by the geographical, cognitive, institutional, social, and cultural dimensions of proximity. Our results suggest that geographical distance has a significant negative relation to the collaboration intensity between countries. The amount of previous collaborations, as a proxy for social proximity, is positively related to the number of cross-country collaborations. We do not find robust significant associations between cognitive proximity or institutional proximity with the intensity of international research collaboration. Moreover, there is no robust and significant relation between the interaction terms of geographical distance with social, cognitive, or institutional proximity, and international research collaboration. Our findings for cultural proximity do not allow of unambiguous conclusions concerning their influence on the collaboration intensity between countries. Linguistic ties among countries are associated with a higher amount of cross-country research collaboration but we find no clear association for historical and colonial linkages.International Cooperation, Pharmaceuticals, Proximity

Entrepreneurship and Cultural Creativity

We investigate the relationship between cultural creativity and entrepreneurship in two respects: first, cultural and personal creativity as a characteristic of self-employed individuals; second, self-employment in professions that can be classified as belonging to the 'Creative Class' as compared to the non-creative class. The analysis is based on micro-data for individuals of the German Socio Economic Panel (SOEP). We find, indeed, some significant links between entrepreneurship and cultural creativity that deserve further investigation.Entrepreneurship, new business formation, cultural creativity, creative class

Agri-Environmental Schemes and Grassland Biodiversity: Another Side of the Coin

In this paper part of the existing Agri-Environmental Schemes (AES) of the European Union are evaluated by using data on county level instead of applying field studies. The attempt is made to disentangle the effects of AES on land management practice as well as land use on biodiversity. It is argued that subsidies as AES should promote environmental-friendly land use which, in turn, should lead to biodiversity conservation. First results show that AES promotes ecological land use rather than extensive agricultural practice. Furthermore, AES is predominantly allocated in biodiversity rich counties and not in counties with low biodiversity which should be enhanced. Furthermore, no clear evidence is so far found, that land use practice is improving the biodiversity status.AES effectiveness, biodiversity, policy evaluation

Reproducible Domain-Specific Knowledge Graphs in the Life Sciences: a Systematic Literature Review

Knowledge graphs (KGs) are widely used for representing and organizing structured knowledge in diverse domains. However, the creation and upkeep of KGs pose substantial challenges. Developing a KG demands extensive expertise in data modeling, ontology design, and data curation. Furthermore, KGs are dynamic, requiring continuous updates and quality control to ensure accuracy and relevance. These intricacies contribute to the considerable effort required for their development and maintenance. One critical dimension of KGs that warrants attention is reproducibility. The ability to replicate and validate KGs is fundamental for ensuring the trustworthiness and sustainability of the knowledge they represent. Reproducible KGs not only support open science by allowing others to build upon existing knowledge but also enhance transparency and reliability in disseminating information. Despite the growing number of domain-specific KGs, a comprehensive analysis concerning their reproducibility has been lacking. This paper addresses this gap by offering a general overview of domain-specific KGs and comparing them based on various reproducibility criteria. Our study over 19 different domains shows only eight out of 250 domain-specific KGs (3.2%) provide publicly available source code. Among these, only one system could successfully pass our reproducibility assessment (14.3%). These findings highlight the challenges and gaps in achieving reproducibility across domain-specific KGs. Our finding that only 0.4% of published domain-specific KGs are reproducible shows a clear need for further research and a shift in cultural practices

GI Systems for public health with an ontology based approach

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Health is an indispensable attribute of human life. In modern age, utilizing technologies for health is one of the emergent concepts in several applied fields. Computer science, (geographic) information systems are some of the interdisciplinary fields which motivates this thesis. Inspiring idea of the study is originated from a rhetorical disease DbHd: Database Hugging Disorder, defined by Hans Rosling at World Bank Open Data speech in May 2010. The cure of this disease can be offered as linked open data, which contains ontologies for health science, diseases, genes, drugs, GEO species etc. LOD-Linked Open Data provides the systematic application of information by publishing and connecting structured data on the Web. In the context of this study we aimed to reduce boundaries between semantic web and geo web. For this reason a use case data is studied from Valencia CSISP- Research Center of Public Health in which the mortality rates for particular diseases are represented spatio-temporally. Use case data is divided into three conceptual domains (health, spatial, statistical), enhanced with semantic relations and descriptions by following Linked Data Principles. Finally in order to convey complex health-related information, we offer an infrastructure integrating geo web and semantic web. Based on the established outcome, user access methods are introduced and future researches/studies are outlined

A Two-Level Information Modelling Translation Methodology and Framework to Achieve Semantic Interoperability in Constrained GeoObservational Sensor Systems

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation. Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data models underpinning these information systems are typically too rigid in their data representation to allow for the ever-changing and evolving nature of geo-spatial domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in an interoperable and computable way. The health domain experiences similar challenges with representing complex and evolving domain information concepts. Within any complex domain (such as Earth system science or health) two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves, and new discoveries are made. Health informaticians have developed a sophisticated two-level modelling systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how data, information, and knowledge interoperability among heterogenous systems can be achieved. This research investigates whether two-level modelling can be translated from the health domain to the geo-spatial domain and applied to observing scenarios to achieve semantic interoperability within and between spatial data infrastructures, beyond what is possible with current state-of-the-art approaches. A detailed review of state-of-the-art SDIs, geo-spatial standards and the two-level modelling methodology was performed. A cross-domain translation methodology was developed, and a proof-of-concept geo-spatial two-level modelling framework was defined and implemented. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard was re-profiled to aid investigation of the two-level information modelling approach. An evaluation of the method was undertaken using II specific use-case scenarios. Information modelling was performed using the two-level modelling method to show how existing historical ocean observing datasets can be expressed semantically and harmonized using two-level modelling. Also, the flexibility of the approach was investigated by applying the method to an air quality monitoring scenario using a technologically constrained monitoring sensor system. This work has demonstrated that two-level modelling can be translated to the geospatial domain and then further developed to be used within a constrained technological sensor system; using traditional wireless sensor networks, semantic web technologies and Internet of Things based technologies. Domain specific evaluation results show that twolevel modelling presents a viable approach to achieve semantic interoperability between constrained geo-observational sensor systems and spatial data infrastructures for ocean observing and city based air quality observing scenarios. This has been demonstrated through the re-purposing of selected, existing geospatial data models and standards. However, it was found that re-using existing standards requires careful ontological analysis per domain concept and so caution is recommended in assuming the wider applicability of the approach. While the benefits of adopting a two-level information modelling approach to geospatial information modelling are potentially great, it was found that translation to a new domain is complex. The complexity of the approach was found to be a barrier to adoption, especially in commercial based projects where standards implementation is low on implementation road maps and the perceived benefits of standards adherence are low. Arising from this work, a novel set of base software components, methods and fundamental geo-archetypes have been developed. However, during this work it was not possible to form the required rich community of supporters to fully validate geoarchetypes. Therefore, the findings of this work are not exhaustive, and the archetype models produced are only indicative. The findings of this work can be used as the basis to encourage further investigation and uptake of two-level modelling within the Earth system science and geo-spatial domain. Ultimately, the outcomes of this work are to recommend further development and evaluation of the approach, building on the positive results thus far, and the base software artefacts developed to support the approach

The Extension of Clusters: Difference-in-Differences Evidence from the Bavarian State-Wide Cluster Policy

If one cluster increases local competitiveness, can politicians, by interlinking clusters, achieve an even better effect at the state level? To answer this question, the paper analyzes the “Cluster Initiative” introduced in 1999 by the Bavarian State Government. The purpose of the initiative was to create a Bavarian-wide innovation network in support of state-wide knowledge flows. Using a difference-in-differences approach, we find that introducing the Bavarian-wide cluster policy increased the likelihood of innovation by a firm in the targeted industry by 4 to 7 percentage points. However, this effect is mainly driven by large firms’ increased likelihood to innovate.difference-in-differences, cluster policy, regional policy

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives