A wealth of scholarly research and real-world case studies demonstrate the myriad ways in which open access and open data benefit researchers and society alike. A representative sample is found below. If you have a suggestion for this list, please contact us.
Bertagnolli, Monica M., et al. "Advantages of a Truly Open-access Data-sharing Model." The New England Journal of Medicine 376.12 (2017): 1178-1181. This article argues that a key way to honor and reward the altruism of patients who participate in clinical trials is to share the data gathered in these trials with other researchers in a responsible and meaningful way.
Jain, Anubhav, Kristin A. Persson, and Gerbrand Ceder. "Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases." APL Materials 4.5 (2016): 053102. This article examines data sharing in materials science. It observes that data sharing can drastically shorten the materials research cycle by reducing the burden of data collection for individual research groups, and by enabling more efficient development of scientific hypotheses and property prediction models. This, in turn, has the practical benefit of speeding the discovery and optimization of new materials.
Tennant JP, Waldner F, Jacques DC et al. “The academic, economic and societal impacts of Open Access: an evidence-based review [version 3; referees: 3 approved, 2 approved with reservations].” F1000Research 2016, 5:632. This review analyzes the scholarly literature on the impact of open access. It concludes that the overall evidence points to a favorable impact of open access on the scholarly literature through increased dissemination and reuse. It also finds that current levels of access in the developing world are insufficient and unstable, and only open access has the potential to foster the development of stable research ecosystems.
Yang S, Cline M, Zhang C, Paten B, Lincoln S e. “Data Sharing and Reproducible Clinical Genetic Testing: Successes and Challenges. Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing.” 2016;22:166-176. This conference proceeding examines the open sharing of clinical genetic data. It concludes that participation in the NIH ClinVar initiative has improved research reproducibility. This, in turn, positively impacts direct patient care in oncology, cardiology, neurology, pediatrics, obstetrics, and other clinical specialties.
Open Data: Enabling Fact-Based, Data-Driven Decisions. The Global Open Data for Agriculture and Nutrition (GODAN) initiative is a collaboration between the United States Department of Agriculture and more than 700 partners across the public and private sectors. GODAN promotes the proactive sharing of open data to make information about agriculture and nutrition available, accessible, and usable worldwide. In doing so, GODAN has helped farmers around the world make evidence-based decisions related to agriculture and nutrition.
Online Epidemic Tracking Tool Embraces Open Data and Collective Intelligence. Researchers from the Wellcome Trust Sanger Institute and Imperial College London developed Microreact, a free, real-time epidemic visualisation and tracking platform used to monitor outbreaks of Ebola, Zika and antibiotic-resistant microbes. The Microreact team collaborated with the Microbiology Society to openly share data and metadata sets, which can then be visualised and explored dynamically by any researcher around the world. This collaboration is explicitly designed to democratise genomic data and resulting insights about disease outbreaks.
From Ideas to Industries: Human Genome Project. With openness as a core tenet, the Human Genome Project generated $965 billion in economic output between 1988 and 2012, creating more than $293 billion in personal income through wages and benefits, and nearly four million jobs.
Battling Disease with Open: Open Source Malaria Consortium. The Open Source Malaria Consortium invites scientists from around the around to freely share their research on anti-malaria drugs through a transparent, online platform. The hope is to accelerate discovery of new drug candidates to be entered into pre-clinical development. The Consortium has attracted more than a hundred contributors who post their drug discovery and development findings, discuss their work, and build on each other’s ideas for potential cures. The project serves as a repository of projects so researchers can see what molecules have and have not proven promising. All information is machine discoverable so others can locate the work and reuse the data.
Using Open Data to Predict Adverse Treatment Effects. As part of the NCBI "Dream Challenge" program, a group of scientists developed a crowdsourced, open data model with which to predict early discontinuation of docetaxel, a metastatic prostate cancer treatment. A total of 34 international teams analyzed open data from clinical trials in order to formulate a hypothesis regarding which patients were most likely to stop docetaxel treatment due to adverse side effects. Seven of the 34 teams identified a common set of predictive factors. An additional positive outcome was a decision by these seven teams to further collaborate to refine their models.
Sluggish Data Sharing Hampers Reproducibility Effort. The Reproducibility Project: Cancer Biology is examining the replicability of 50 high-impact cancer biology studies, published between 2010 and 2012. The project coordinators have found that free, unfettered access to the experimental data has been a major hurdle to overcome. Without this access, understanding whether promising research in cancer biology can reproduced and verified is a significant challenge. This may slow follow-on research, or, in a more dire outcome, lead scientists to pursue experiments that are, in fact, a dead-end.
How Open Data Can Help the World Better Manage Coral Reefs. Scientific divers from the US National Oceanic and Atmospheric Administration (NOAA) spent seven years collecting physical, chemical, and biological data on fish and coral reefs in the west central Pacific. By sharing these datasets openly, the researchers enabled valuable meta-analysis and follow-on research. Among the tangible results - development of fishing benchmarks and an assessment mechanism for reef management efforts.