Get The Research

“There is research of all types on Get The Research. In this early release it specializes in research in biology and medicine (papers indexed by PubMed) — this will be widened further in the future….

How do I know what research I can trust?

 

This is a great question. Get The Research flags each article with its “level of evidence” when we know it — is the article just a report about a single patient (a “case study”) or a more trustworthy analysis combining the results of many studies (a “meta-analysis”)? Click on the tags above the article titles to learn more. We rank articles with higher levels of evidence higher in the search results to make these easier to find. Reading the news studies about a paper (linked to from the “Learn More” page when we’ve found news articles) is a great way to find out what others think about the results….”

 

Do Preprints Require More Rigorous Screening? | The Scientist Magazine®

“Two weeks ago, a tweet storm erupted over what scientists normally consider a noble effort: the posting of a preprint to bioRxiv. The article originally went online in March, but in July, a reader noticed something missing in the draft—the methods. “As such it is not possible to critically evaluate the manuscript,” the anonymous commenter Preprint Now noted on bioRxiv.

Shortly after, the tweets arrived. “Preprints without methods are ads not scientific manuscripts and should be treated as such,” Michael Eisen, a biologist at the University of California, Berkeley, tweeted….”

Open Source–It’s in the Genes | Linux Journal

Just as software code can be open source rather than proprietary, so there are publicly funded genomic sequencing initiatives that make their results available to all. One of the largest projects, the UK Biobank(UKB), involves 500,000 participants. Any researcher, anywhere in the world, can download complete, anonymized data sets, provided they are approved by the UKB board. One important restriction is that they must not try to re-identify any participant—something that would be relatively easy to do given the extremely detailed clinical history that was gathered from volunteers along with blood and urine samples. Investigators asked all 500,000 participants about their habits, and examined them for more than 2,000 different traits, including data on their social lives, cognitive state, lifestyle and physical health.

Given the large number of genomes that need to be sequenced, the first open DNA data sets from UKB are only partial, although the plan is to sequence all genomes more fully in due course. These smaller data sets allow what is called “genotyping”, which provides a rough map of a person’s DNA and its specific properties. Even this partial sequencing provides valuable information, especially when it is available for large numbers of people. As an article in Science points out, it is not just the size and richness of the open data sets that makes the UK Biobank unique, it is the thorough-going nature of the sharing that is required from researchers….

It’s the classic “given enough eyeballs, all bugs are shallow”. By open-sourcing the genomic code of 500,000 of its citizens, the UK is getting the top DNA hackers in the world to find the “bugs”—the variants that are associated with medical conditions—that will help our understanding of them and may well lead to the development of new treatments for them. The advantages are so obvious, it’s a wonder people use anything else. A bit like open source….”

Envisioning data sharing for the biocomputing community | Interface Focus

Abstract:  The scientific community is facing a revolution in several aspects of its modus operandi, ranging from the way science is done—data production, collection, analysis—to the way it is communicated and made available to the public, be that an academic audience or a general one. These changes have been largely determined by two key players: the big data revolution or, less triumphantly, the impressive increase in computational power and data storage capacity; and the accelerating paradigm switch in science publication, with people and policies increasingly pushing towards open access frameworks. All these factors prompt the undertaking of initiatives oriented to maximize the effectiveness of the computational efforts carried out worldwide. Taking the moves from these observations, we here propose a coordinated initiative, focusing on the computational biophysics and biochemistry community but general and flexible in its defining characteristics, which aims at addressing the growing necessity of collecting, rationalizing, sharing and exploiting the data produced in this scientific environment.

novel open access web portal for integrating mechanistic and toxicogenomic study results | Toxicological Sciences | Oxford Academic

Abstract:  Applying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as GSEA, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the two approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for “hands-on” computer programming experience, the selection of one or more analysis methods (e.g. pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogenomics (CTox), an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements “best-practice” methods in computational biology. New study results are compared to over 4,000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.

novel open access web portal for integrating mechanistic and toxicogenomic study results | Toxicological Sciences | Oxford Academic

Abstract:  Applying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as GSEA, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the two approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for “hands-on” computer programming experience, the selection of one or more analysis methods (e.g. pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogenomics (CTox), an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements “best-practice” methods in computational biology. New study results are compared to over 4,000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.

Electronic Lab Notebooks | Harvard Biomedical Data Management

Lab notebooks are good for writing down procedures, observations, conclusions and for drawing flow charts and diagrams by hand. However, in order to accommodate the increase of digital data collected, researchers have taped instrumentation and computer printouts onto the pages of their notebooks, or cross-referenced larger data sets by recording file names and locations in the notebook.

An ELN (electronic lab notebook) is a software tool that in its most basic form replicates an interface much like a page in a paper lab notebook. In this electronic notebook you can enter protocols, observations, notes, and other data using your computer or mobile device. This offers several advantages over the traditional paper notebook.

The number of available ELN tools is increasing and the functions of each are quickly changing. As a result, it may be confusing to evaluate all of the advantages and limitations of each when looking for the right solution for your project.

The Electronic Lab Notebook Matrix has been created to aid HMS researchers in the process of identifying a usable Electronic Lab Notebook solutions to meet their specific research needs. Through this resource, researchers can compare and contrast the numerous solutions available today, and also explore individual options in-depth….”

A Critical Assessment of Open Science – Sage Bionetworks

At Sage Bionetworks, we think a lot about open science. Our organization was founded explicitly to use open practices to promote integration of large-scale data analytics into the life sciences. We were guided by a very specific definition of open science: the idea that scientific “teams of teams” working together on a growing commons of open data can unleash substantial increases in scientific throughput and capacity.

We have learned a lot over the past 10 years regarding best practices for successful application of open science in this context. We are curious to understand how our observations may overlap with those from others in the field. Is there a common set of guidelines that can help support the effective use of openness in the life sciences? On the flip side, is there a set of common mistakes that keep getting repeated? We quickly realized in our work, for example, that each project has a window of time where “openness” is optimally effective. Early in a project, openness can sometimes hinder creativity as people hold back on sharing ideas that are still immature….”

Homepage – preLights

“Welcome to preLights, the preprint highlights service run by the biological community and supported by The Company of Biologists.

What is preLights?

As the number of preprints grows, it will become increasingly difficult to find and filter relevant/interesting preprints. preLights does some of that work for you. Our dedicated team of scientists from the community select, highlight and comment on preprints they feel are of particular interest to the biological community. You’ll find a summary of each preprint, the reasons it was selected and the selector’s thoughts on its significance. You might also see relevant comments from the preprints’ authors. And we’d really welcome your thoughts and comments too.

Our regular digest of preprints means:

  • some of the hard work of sifting through the growing volume of preprints is done for you
  • you’ll see a mix of preprints across the biological sciences – which can highlight new thinking or new techniques that may be applicable to your research
  • you’ll see the comments and opinions of other researchers
  • you can comment on those that interest you….”

ZooArchNet: Connecting zooarchaeological specimens to the biodiversity and archaeology data networks

Abstract:  Interdisciplinary collaborations and data sharing are essential to addressing the long history of human-environmental interactions underlying the modern biodiversity crisis. Such collaborations are increasingly facilitated by, and dependent upon, sharing open access data from a variety of disciplinary communities and data sources, including those within biology, paleontology, and archaeology. Significant advances in biodiversity open data sharing have focused on neontological and paleontological specimen records, making available over a billion records through the Global Biodiversity Information Facility. But to date, less effort has been placed on the integration of important archaeological sources of biodiversity, such as zooarchaeological specimens. Zooarchaeological specimens are rich with both biological and cultural heritage data documenting nearly all phases of human interaction with animals and the surrounding environment through time, filling a critical gap between paleontological and neontological sources of data within biodiversity networks. Here we describe technical advances for mobilizing zooarchaeological specimen-specific biological and cultural data. In particular, we demonstrate adaptations in the workflow used by biodiversity publisher VertNet to mobilize Darwin Core formatted zooarchaeological data to the GBIF network. We also show how a linked open data approach can be used to connect existing biodiversity publishing mechanisms with archaeoinformatics publishing mechanisms through collaboration with the Open Context platform. Examples of ZooArchNet published datasets are used to show the efficacy of creating this critically needed bridge between biological and archaeological sources of open access data. These technical advances and efforts to support data publication are placed in the larger context of ZooarchNet, a new project meant to build community around new approaches to interconnect zoorchaeological data and knowledge across disciplines.