Analytical code sharing practices in biomedical research.

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten articles organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability. Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses. In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.

Analytical code sharing practices in biomedical research.

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten papers organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability (p=2.71×10-9). Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses (p=1.15*10-07). In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.

A Decade of GigaScience: Milestones in Open Science.

Open Science has gained momentum over the past decade, and embracing that, GigaScience, from its launch a decade ago has aimed at pushing scientific publishing beyond just making articles open access toward making the entire research process open and available as an embedded part of the publishing process. Before the journal's launch in July 2012, the editors aimed to make publishing more than a narrative presentation of work already done into a fully open process. Major milestones include creating our own data repository, embracing FAIR principles, promoting and integrating preprints, and working with other platforms to contribute to a 21st century publishing infrastructure. Almost 10 years after GigaScience's launch, UNESCO published its Open Science Recommendations. With these in mind, looking back, we are happy to have contributed in various ways to UNESCO's aim to "foster a culture of Open Science and aligning incentives for Open Science" from the very beginning, and, more, to use those recommendations to guide our path into the future: to truly embrace the full spectrum of information, tools, and access to Open Science for all participants in scientific endeavours.

Recommendations to enhance rigor and reproducibility in biomedical research.

Biomedical research depends increasingly on computational tools, but mechanisms ensuring open data, open software, and reproducibility are variably enforced by academic institutions, funders, and publishers. Publications may present software for which source code or documentation are or become unavailable; this compromises the role of peer review in evaluating technical strength and scientific contribution. Incomplete ancillary information for an academic software package may bias or limit subsequent work. We provide 8 recommendations to improve reproducibility, transparency, and rigor in computational biology-precisely the values that should be emphasized in life science curricula. Our recommendations for improving software availability, usability, and archival stability aim to foster a sustainable data science ecosystem in life science research.

Editorial: We need to talk about authorship.

In our day-to-day editorial work at GigaScience, time and again we see issues cropping up that make us worry whether everyone understands good scientific practice when it comes to listing author names on the title page. There are many issues that underlie inappropriate authorship designations, but there are also guidelines to help potential authors determine when and how a researcher should be included with a manuscript. Here, we help clarify this and also provide a clear statement of our expectations around how authors are assigned to manuscripts submitted to GigaScience.

Looking back: forward looking.

GigaScience is now 5 years old, having been launched at the 2012 Intelligent Systems for Molecular Biology conference. Anyone who has attended what is the largest computational biology conference since then has had the opportunity to join us for each birthday celebration-and receive 1 of our fun T-shirts as a party prize. Since launching, we have pushed our agenda of openness, transparency, reproducibility, and reusability. Here, we look back at our first 5 years and what we have done to forward our open science goals in scientific publishing. Our mainstay has been to create a process that allows the availability and publication of as many "research objects" as possible to create a more complete way of communicating how the research process is done.

Zinc finger motif-1 antagonizes PDGF-BB-induced growth and dedifferentiation of vascular smooth muscle cells.

Zinc finger motif-1 (ZFM1) represses proinflammatory gene expression in vascular smooth muscle cells (SMCs) at a global level and thus may also be involved in the attenuation of growth factor-induced phenotype changes in these cells. Using human primary cultured thymus vein SMCs, we have investigated the molecular mechanism by which a potent SMC mitogen, platelet-derived growth factor-BB (PDGF-BB), causes a rapid decrease in ZFM1 expression in a concentration-dependent manner and consequences thereof. Reporter gene analyses and chromatin immunoprecipitation showed that PDGF-BB-induced ZFM1 repression occurs at the level of transcription through replacement of the activating transcription factor Sp1 by Egr-1. The subsequent drop in ZFM1 abundance disinhibits SMC proliferation, migration, and synthetic gene expression in a concerted manner. Stabilizing ZFM1 levels in a PDGF-BB-independent way with a GFP-ZFM1 expression construct or by using Egr-1-specific decoy oligonucleotides abrogates all PDGF-BB effects. Conversely, siRNA-mediated knockdown of ZFM1 alone not only increases the sensitivity of SMCs for PDGF-BB, but even mimics PDGF-BB-induced proliferation and gene expression. Our findings suggest that ZFM1 is an important factor for the stabilization of a contractile SMC phenotype under basal or mildly activating conditions and that, as a prerequisite for efficient action, PDGF-BB must repress ZFM1 expression to alter the SMC phenotype.