Pharmaceutical firms frequently engage in preclinical research collaboration with universities. Many of these collaborative preclinical research projects are conducted by firms’ subsidiaries. In this setting, the challenge of transferring knowledge from university–industry collaborations (UICs) to internal research and development (R&D) increases, since knowledge has to be transferred twice: to the subsidiary and to the parent. To benefit from tacit and complex knowledge obtained through collaboration with academic partners, firms need to have a high absorptive capacity. However, existing research on absorptive capacity's effect on the efficacy of UIC has provided mixed results, and the knowledge transfer mechanisms remain unclear, particularly in the case of basic (or preclinical) research and of the complex knowledge transfer process from subsidiaries’ collaborative efforts. Building on the knowledge‐based view and following a multidimensional perspective of absorptive capacity, this study investigates the role a firm's capabilities play in successful university knowledge integration in a firm's internal R&D. Therefore, this paper analyzes both parent firms’ UICs and their subsidiaries’ UICs in basic (preclinical) research. The results of our longitudinal analysis of a unique panel data set of 56 global pharmaceutical firms indicate that firms do successfully exploit valuable knowledge from preclinical research UIC in internal R&D, measured by the number of performed clinical trials. This holds true for the UIC involving the parent firm and its subsidiaries. As transformative learning dimensions of absorptive capacity, high diversity in therapeutic activity and high R&D intensity levels strengthen the positive relationship between parents’ UIC and R&D performance. A high exploration intensity level of the firm and high diversity in therapeutic activity help to transfer the knowledge from subsidiaries’ preclinical research UIC to parents’ innovation projects.

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MeSH terms), where those are missing. After assembling the dataset, we conduct experiments on link prediction in the co-annotation graph to analyze to what extent the research directions, expressed as the co-occurrence of MeSH terms, can be known ahead of time. In practice, link prediction in co-annotation graphs could be used for recommending promising research directions to researchers. Such applications are only possible because of the expanded view of our newly assembled dataset.In summary, our contributions are:• We provide a new dataset of COVID-19 publication data.• The dataset contains COVID-19 research papers along with first-order cited work. • We use ConceptMapper [2] to generate MeSH annotations, whenever those annotations are not present. • We conduct experiments on link prediction between concepts from the newly created dataset. • We describe the procedure for assembling the dataset and provide the code for keeping the data collection up-to-date. II. RELATED DATA COLLECTIONS We describe existing collections of COVID-19 research articles that are relevant to the dataset introduced in this work. a) CORD-19: COVID-19 Open Research Dataset 3 [1]. CORD-19 is a free and open dataset of research articles on COVID-19. It is maintained by the Semantic Scholar team at the Allen Institute for AI in collaboration with leading research groups. As of Aug 9, 2021, The dataset covers more than 280,000 scholarly articles. b) CrossRef: CrossRef has released a 65GB data file 4 to support COVID-19 research. The file contains 112M metadata records. These records are, however, not limited to COVID-19 2 https://www.nlm.nih.gov/mesh/meshhome.html 3 https://www.semanticscholar.org/cord19 4 https://www.crossref.org/blog/free-public-data-file-of-112-million-crossref-records/ Abstract-COVID-19 research datasets are crucial for analyzing research dynamics. Most collections of COVID-19 research items do not to include cited works and do not have annotations from a controlled vocabulary. Starting with ZB MED KE data on COVID-19, which comprises CORD-19, we assemble a new dataset that includes cited work and MeSH annotations for all records.Furthermore, we conduct experiments on the analysis of research dynamics, in which we investigate predicting links in a co-annotation graph created on the basis of the new dataset. Surprisingly, we find that simple heuristic methods are better at predicting future links than more sophisticated approaches such as graph neural networks.

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