Dataset Information

Principles of scientific research team formation and evolution.

ABSTRACT: Research teams are the fundamental social unit of science, and yet there is currently no model that describes their basic property: size. In most fields, teams have grown significantly in recent decades. We show that this is partly due to the change in the character of team size distribution. We explain these changes with a comprehensive yet straightforward model of how teams of different sizes emerge and grow. This model accurately reproduces the evolution of empirical team size distribution over the period of 50 y. The modeling reveals that there are two modes of knowledge production. The first and more fundamental mode employs relatively small, "core" teams. Core teams form by a Poisson process and produce a Poisson distribution of team sizes in which larger teams are exceedingly rare. The second mode employs "extended" teams, which started as core teams, but subsequently accumulated new members proportional to the past productivity of their members. Given time, this mode gives rise to a power-law tail of large teams (10-1,000 members), which features in many fields today. Based on this model, we construct an analytical functional form that allows the contribution of different modes of authorship to be determined directly from the data and is applicable to any field. The model also offers a solid foundation for studying other social aspects of science, such as productivity and collaboration.

SUBMITTER: Milojevic S

PROVIDER: S-EPMC3964124 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Principles of scientific research team formation and evolution.

Milojević Staša S

Proceedings of the National Academy of Sciences of the United States of America 20140303 11

Research teams are the fundamental social unit of science, and yet there is currently no model that describes their basic property: size. In most fields, teams have grown significantly in recent decades. We show that this is partly due to the change in the character of team size distribution. We explain these changes with a comprehensive yet straightforward model of how teams of different sizes emerge and grow. This model accurately reproduces the evolution of empirical team size distribution ov ...[more]

PMID: 24591626

Similar Datasets

Project description:The efficacy of an antiretroviral (ARV) treatment regimen depends on the activity of the regimen's individual ARV drugs and the number of HIV-1 mutations required for the development of resistance to each ARV - the genetic barrier to resistance. ARV resistance impairs the response to therapy in patients with transmitted resistance, unsuccessful initial ARV therapy and multiple virological failures. Genotypic resistance testing is used to identify transmitted drug resistance, provide insight into the reasons for virological failure in treated patients, and help guide second-line and salvage therapies. In patients with transmitted drug resistance, the virological response to a regimen selected on the basis of standard genotypic testing approaches the responses observed in patients with wild-type viruses. However, because such patients are at a higher risk of harbouring minority drug-resistant variants, initial ARV therapy in this population should contain a boosted protease inhibitor (PI) - the drug class with the highest genetic barrier to resistance. In patients receiving an initial ARV regimen with a high genetic barrier to resistance, the most common reasons for virological failure are nonadherence and, potentially, pharmacokinetic factors or minority transmitted drug-resistant variants. Among patients in whom first-line ARVs have failed, the patterns of drug-resistance mutations and cross-resistance are often predictable. However, the extent of drug resistance correlates with the duration of uncontrolled virological replication. Second-line therapy should include the continued use of a dual nucleoside/nucleotide reverse transcriptase inhibitor (NRTI)-containing backbone, together with a change in the non-NRTI component, most often to an ARV belonging to a new drug class. The number of available fully active ARVs is often diminished with each successive treatment failure. Therefore, a salvage regimen is likely to be more complicated in that it may require multiple ARVs with partial residual activity and compromised genetic barriers of resistance to attain complete virological suppression. A thorough examination of the patient's ARV history and prior resistance tests should be performed because genotypic and/or phenotypic susceptibility testing is often not sufficient to identify drug-resistant variants that emerged during past therapies and may still pose a threat to a new regimen. Phenotypic testing is also often helpful in this subset of patients. ARVs used for salvage therapy can be placed into the following hierarchy: (i) ARVs belonging to a previously unused drug class; (ii) ARVs belonging to a previously used drug class that maintain significant residual antiviral activity; (iii) NRTI combinations, as these often appear to retain in vivo virological activity, even in the presence of reduced in vitro NRTI susceptibility; and rarely (iv) ARVs associated with previous virological failure and drug resistance that appear to have possibly regained their activity as a result of viral reversion to wild type. Understanding the basic principles of HIV drug resistance is helpful in guiding individual clinical decisions and the development of ARV treatment guidelines.

Project description:PurposeThis retrospective, about a single "mobile" laboratory in six locations on two continents, is intended as a case study in discovery for trainees and junior faculty in the medical sciences. Your knowledge of your topic is necessary to expect the unexpected.Historical methodIn 1972, there was no tamoxifen, only ICI 46, 474, a non-steroidal anti-estrogen with little chance of clinical development. No one would ever be foolish enough to predict that the medicine, 20 years later, would achieve legendary status as the first targeted treatment for breast cancer, and millions of women would benefit from long-term adjuvant tamoxifen therapy. The secret of tamoxifen's success was a translational research strategy proposed in the mid 1970's. This strategy was to treat only patients with estrogen receptor (ER)-positive breast cancer and deploy 5 or more years of adjuvant tamoxifen therapy to prevent recurrence. Additionally, tamoxifen prevented mammary cancer in animals. Could the medicine prevent breast cancer in women?ResultsTamoxifen and the failed breast cancer drug raloxifene became the first selective estrogen receptor modulators (SERMs): a new drug group, discovered at the University of Wisconsin, Comprehensive Cancer Center. Serendipity can play a fundamental role in discovery, but there must be a rigorous preparation for the investigator to appreciate the possibility of a pending discovery. This article follows the unanticipated discoveries when PhD students "get the wrong answer." The secret of success of my six Tamoxifen Teams was their technical excellence to create models, to decipher mechanisms, that drove the development of new medicines. Discoveries are listed that either changed women's health or allowed an understanding of originally opaque mechanisms of action of potential therapies. These advances in women's health were supported entirely by government-sponsored peer-reviewed funding and major philanthropy from the Lynn Sage Breast Cancer Foundation, the Avon Foundation, and the Susan G. Komen Breast Cancer Foundation. The resulting lives saved or extended, families aided in a time of crisis and the injection of billions of dollars into national economies by drug development, is proof of the value of Federal or philanthropic investment into unencumbered research aimed at saving millions of lives.

Dataset Information

Principles of scientific research team formation and evolution.

Publications

Principles of scientific research team formation and evolution.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure