| scholarly article | Q13442814 |
| P50 | author | Andrey Rzhetsky | Q41049700 |
| James A. Evans | Q57966731 | ||
| Valentin Danchev | Q91546169 | ||
| P2093 | author name string | Andrey Rzhetsky | |
| James A Evans | |||
| Valentin Danchev | |||
| P2860 | cites work | Initial sequencing and analysis of the human genome | Q21045365 |
| Why most published research findings are false | Q21092395 | ||
| Drug development: Raise standards for preclinical cancer research | Q22348097 | ||
| Systematic integration of biomedical knowledge prioritizes drugs for repurposing | Q42255083 | ||
| How scientists fool themselves - and how they can stop | Q47607778 | ||
| Network dynamics of social influence in the wisdom of crowds. | Q47826740 | ||
| P-curve: a key to the file-drawer. | Q51186445 | ||
| A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. | Q52763617 | ||
| Combining probability from independent tests: the weighted Z-method is superior to Fisher's approach | Q58034970 | ||
| 'Big science' spurs collaborative trend | Q74202326 | ||
| Does Science Advance One Funeral at a Time? | Q90961628 | ||
| False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant | Q24273231 | ||
| An open investigation of the reproducibility of cancer biology research | Q24273259 | ||
| Replication, Communication, and the Population Dynamics of Scientific Discovery | Q24288652 | ||
| The extent and consequences of p-hacking in science | Q24288794 | ||
| Team assembly mechanisms determine collaboration network structure and team performance | Q24684815 | ||
| The Matthew Effect in Science | Q28243000 | ||
| The Ontology for Biomedical Investigations | Q28551680 | ||
| Making sense of replications | Q28559711 | ||
| Contextual sensitivity in scientific reproducibility | Q28597738 | ||
| SCIENTIFIC STANDARDS. Promoting an open research culture | Q28608410 | ||
| The Comparative Toxicogenomics Database: update 2017 | Q28818104 | ||
| Believe it or not: how much can we rely on published data on potential drug targets? | Q29547529 | ||
| Drug-target network | Q29614447 | ||
| The diversity of experimental organisms in biomedical research may be influenced by biomedical funding | Q30400635 | ||
| Electronic publication and the narrowing of science and scholarship | Q33352658 | ||
| Metaknowledge | Q33817898 | ||
| SCIENTIFIC INTEGRITY. Self-correction in science at work | Q34482470 | ||
| Inconsistency in large pharmacogenomic studies | Q34541198 | ||
| The increasing dominance of teams in production of knowledge | Q34618686 | ||
| How social influence can undermine the wisdom of crowd effect | Q34629195 | ||
| The Eigenfactor metrics. | Q34873486 | ||
| The effects of diversity and network ties on innovations: The emergence of a new scientific field | Q36275560 | ||
| Understanding the assembly of interdisciplinary teams and its impact on performance | Q37512009 | ||
| Publication bias and the canonization of false facts | Q37520828 | ||
| Low statistical power in biomedical science: a review of three human research domains | Q37722079 | ||
| Common pitfalls in preclinical cancer target validation | Q38775271 | ||
| What does research reproducibility mean? | Q38851031 | ||
| P4510 | describes a project that uses | ggplot2 | Q326489 |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | computational biology | Q177005 |
| decentralization | Q188961 | ||
| centralisation | Q190632 | ||
| reproducibility | Q1425625 | ||
| scientific culture | Q24550172 | ||
| meta-research | Q25345032 | ||
| P577 | publication date | 2019-07-02 | |
| P1433 | published in | eLife | Q2000008 |
| P1476 | title | Centralized scientific communities are less likely to generate replicable results | |
| P478 | volume | 8 |
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