impact.bib

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@webpage{gitstar-ranking:2018aa,
	Author = {Kokubun, Takashi},
	Date-Added = {2018-11-03 11:11:07 -0400},
	Date-Modified = {2018-11-03 11:13:34 -0400},
	Title = {GitHub star ranking for users, organizations and repositories},
	Url = {https://github.com/k0kubun/gitstar-ranking},
	Year = {2018},
	Bdsk-Url-1 = {https://www.researchgate.net/post/Is_there_too_many_bioinformatics_tools2}}

@article{seglen1997impact,
	Author = {Seglen, Per O},
	Date-Added = {2018-11-03 10:19:29 -0400},
	Date-Modified = {2018-11-03 10:19:29 -0400},
	Journal = {BMJ: British Medical Journal},
	Number = {7079},
	Pages = {498},
	Publisher = {BMJ Publishing Group},
	Title = {Why the impact factor of journals should not be used for evaluating research.},
	Volume = {314},
	Year = {1997}}

@article{da2017citescore,
	Author = {da Silva, Jaime A Teixeira and Memon, Aamir Raoof},
	Date-Added = {2018-11-03 10:16:00 -0400},
	Date-Modified = {2018-11-03 10:16:00 -0400},
	Journal = {Scientometrics},
	Number = {1},
	Pages = {553--556},
	Publisher = {Springer},
	Title = {CiteScore: A cite for sore eyes, or a valuable, transparent metric?},
	Volume = {111},
	Year = {2017}}

@misc{fersht2009most,
	Author = {Fersht, Alan},
	Date-Added = {2018-11-03 10:13:06 -0400},
	Date-Modified = {2018-11-03 10:13:06 -0400},
	Publisher = {National Acad Sciences},
	Title = {The most influential journals: Impact Factor and Eigenfactor},
	Year = {2009}}

@article{garfield2006history,
	Author = {Garfield, Eugene},
	Date-Added = {2018-11-03 09:56:05 -0400},
	Date-Modified = {2018-11-03 09:56:05 -0400},
	Journal = {Jama},
	Number = {1},
	Pages = {90--93},
	Publisher = {American Medical Association},
	Title = {The history and meaning of the journal impact factor},
	Volume = {295},
	Year = {2006}}

@article{Mangul452532,
	Abstract = {Developing new software tools for analysis of large-scale, biological data is a key component of advancing computational, data-enabled research. Scientific reproduction of published findings requires running computational tools on data generated by such studies, yet little attention is presently allocated to the usability and archival stability of computer code encapsulated as computational software tools. Scientific journals require data and code sharing, but none currently require authors to guarantee software usability and long-term archival stability of newly published tools. We developed an accurate estimation of the accessibility of computational biology software tools by performing an empirical analysis of usability and archival stability of 24,490 omics software resources published from 2000 to 2017. We found that 26\% of all omics software resources are currently not accessible through URLs published in the paper. Among the tools selected for our comprehensive and systematic usability test, 49\% were deemed "difficult to install," and 28\% of the tools failed to be installed due to problems in the implementation. Moreover, for papers introducing new software, we found that the number of citations significantly increased when authors provided an easy installation process for published software. We propose for incorporation into journal policy several practical solutions for increasing the widespread usability and archival stability of published bioinformatics software.},
	Author = {Mangul, Serghei and Mosqueiro, Thiago and Duong, Dat and Mitchell, Keith and Sarwal, Varuni and Hill, Brian and Brito, Jaqueline and Littman, Russell and Statz, Benjamin and Lam, Angela and Dayama, Gargi and Grieneisen, Laura and Martin, Lana and Flint, Jonathan and Eskin, Eleazar and Blekhman, Ran},
	Date-Added = {2018-11-03 09:42:53 -0400},
	Date-Modified = {2018-11-03 09:42:53 -0400},
	Doi = {10.1101/452532},
	Eprint = {https://www.biorxiv.org/content/early/2018/10/25/452532.full.pdf},
	Journal = {bioRxiv},
	Publisher = {Cold Spring Harbor Laboratory},
	Title = {A comprehensive analysis of the usability and archival stability of omics computational tools and resources},
	Url = {https://www.biorxiv.org/content/early/2018/10/25/452532},
	Year = {2018},
	Bdsk-Url-1 = {https://www.biorxiv.org/content/early/2018/10/25/452532},
	Bdsk-Url-2 = {https://doi.org/10.1101/452532}}

@webpage{bioinformatics-impact:2018aa,
	Author = {Dozmorov, Mikhail G.},
	Date-Added = {2018-10-10 09:00:10 -0400},
	Date-Modified = {2018-10-10 10:28:48 -0400},
	Title = {Methods for 'Impact of bioinformatics software' paper},
	Url = {https://github.com/mdozmorov/bioinformatics-impact},
	Year = {2018},
	Bdsk-Url-1 = {https://www.researchgate.net/post/Is_there_too_many_bioinformatics_tools2}}

@inproceedings{Marlow2013,
	Author = {Jennifer Marlow and Laura Dabbish and Jim Herbsleb},
	Booktitle = {Proceedings of the 2013 conference on Computer supported cooperative work - {CSCW} {\textquotesingle}13},
	Date-Added = {2018-10-10 08:48:24 -0400},
	Date-Modified = {2018-10-10 08:48:24 -0400},
	Doi = {10.1145/2441776.2441792},
	Publisher = {{ACM} Press},
	Title = {Impression formation in online peer production},
	Url = {https://doi.org/10.1145/2441776.2441792},
	Year = {2013},
	Bdsk-Url-1 = {https://doi.org/10.1145/2441776.2441792}}

@webpage{too_many_tools:2013aa,
	Author = {Cazier, Jean-Baptiste},
	Date-Added = {2018-10-10 08:48:08 -0400},
	Date-Modified = {2018-10-10 08:48:08 -0400},
	Title = {Is there too many bioinformatics tools?},
	Url = {https://www.researchgate.net/post/Is_there_too_many_bioinformatics_tools2},
	Year = {2013},
	Bdsk-Url-1 = {https://www.researchgate.net/post/Is_there_too_many_bioinformatics_tools2}}

@article{Russell2018,
	Author = {Pamela Russell and Rachel L Johnson and Shreyas Ananthan and Benjamin Harnke and Nichole E Carlson},
	Date-Added = {2018-10-10 08:47:58 -0400},
	Date-Modified = {2018-10-10 08:47:58 -0400},
	Doi = {10.1101/321919},
	Month = {may},
	Publisher = {Cold Spring Harbor Laboratory},
	Title = {A large-scale analysis of bioinformatics code on {GitHub}},
	Url = {https://doi.org/10.1101/321919},
	Year = {2018},
	Bdsk-Url-1 = {https://doi.org/10.1101/321919}}

@article{Hu2016,
	Author = {Yan Hu and Jun Zhang and Xiaomei Bai and Shuo Yu and Zhuo Yang},
	Date-Added = {2018-10-10 08:47:45 -0400},
	Date-Modified = {2018-10-10 08:47:45 -0400},
	Doi = {10.1186/s40064-016-2897-7},
	Journal = {{SpringerPlus}},
	Month = {aug},
	Number = {1},
	Publisher = {Springer Nature},
	Title = {Influence analysis of Github repositories},
	Url = {https://doi.org/10.1186/s40064-016-2897-7},
	Volume = {5},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1186/s40064-016-2897-7}}

@inproceedings{Sheoran2014,
	Author = {Jyoti Sheoran and Kelly Blincoe and Eirini Kalliamvakou and Daniela Damian and Jordan Ell},
	Booktitle = {Proceedings of the 11th Working Conference on Mining Software Repositories - {MSR} 2014},
	Date-Added = {2018-10-10 08:47:29 -0400},
	Date-Modified = {2018-10-10 08:47:29 -0400},
	Doi = {10.1145/2597073.2597114},
	Publisher = {{ACM} Press},
	Title = {Understanding "watchers" on {GitHub}},
	Url = {https://doi.org/10.1145/2597073.2597114},
	Year = {2014},
	Bdsk-Url-1 = {https://doi.org/10.1145/2597073.2597114}}

@inproceedings{Dabbish2012,
	Author = {Laura Dabbish and Colleen Stuart and Jason Tsay and Jim Herbsleb},
	Booktitle = {Proceedings of the {ACM} 2012 conference on Computer Supported Cooperative Work - {CSCW} {\textquotesingle}12},
	Date-Added = {2018-10-10 08:47:14 -0400},
	Date-Modified = {2018-10-10 08:47:14 -0400},
	Doi = {10.1145/2145204.2145396},
	Publisher = {{ACM} Press},
	Title = {Social coding in {GitHub}},
	Url = {https://doi.org/10.1145/2145204.2145396},
	Year = {2012},
	Bdsk-Url-1 = {https://doi.org/10.1145/2145204.2145396}}

@article{Lima:2014aa,
	Archiveprefix = {arXiv},
	Author = {Antonio Lima and Luca Rossi and Mirco Musolesi},
	Bibsource = {dblp computer science bibliography, https://dblp.org},
	Biburl = {https://dblp.org/rec/bib/journals/corr/LimaRM14},
	Date-Added = {2018-10-10 08:47:06 -0400},
	Date-Modified = {2018-10-10 08:47:06 -0400},
	Eprint = {1407.2535},
	Journal = {CoRR},
	Timestamp = {Wed, 07 Jun 2017 14:40:11 +0200},
	Title = {Coding Together at Scale: GitHub as a Collaborative Social Network},
	Url = {http://arxiv.org/abs/1407.2535},
	Volume = {abs/1407.2535},
	Year = {2014},
	Bdsk-Url-1 = {http://arxiv.org/abs/1407.2535}}

@article{Bryant:2017aa,
	Abstract = {
        Data analysis, statistical research, and teaching statistics have at least one thing in common: these activities all produce many files! There are data files, source code, figures, tables, prepared reports, and much more. Most of these files evolve over the course of a project and often need to be shared with others, for reading or edits, as a project unfolds. Without explicit and structured management, project organization can easily descend into chaos, taking time away from the primary work and reducing the quality of the final product. This unhappy result can be avoided by repurposing tools and workflows from the software development world, namely, distributed version control. This article describes the use of the version control system Git and and the hosting site GitHub for statistical and data scientific workflows. Special attention is given to projects that use the statistical language R and, optionally, R Markdown documents. Supplementary materials include an annotated set of links to step-by-step tutorials, real world examples, and other useful learning resources.
      },
	Author = {Bryan, Jennifer},
	Date-Added = {2018-10-10 08:46:56 -0400},
	Date-Modified = {2018-10-10 08:46:56 -0400},
	Doi = {10.7287/peerj.preprints.3159v2},
	Issn = {2167-9843},
	Journal = {PeerJ Preprints},
	Keywords = {Git, GitHub, workflow, data science, R Markdown, reproducibility, R language},
	Month = aug,
	Pages = {e3159v2},
	Title = {Excuse me, do you have a moment to talk about version control?},
	Url = {https://doi.org/10.7287/peerj.preprints.3159v2},
	Volume = 5,
	Year = 2017,
	Bdsk-Url-1 = {https://doi.org/10.7287/peerj.preprints.3159v2}}

@article{Blischak:2016aa,
	Author = {Blischak, John D and Davenport, Emily R and Wilson, Greg},
	Date-Added = {2018-10-10 08:46:49 -0400},
	Date-Modified = {2018-10-10 08:46:49 -0400},
	Doi = {10.1371/journal.pcbi.1004668},
	Journal = {PLoS Comput Biol},
	Journal-Full = {PLoS computational biology},
	Mesh = {Computational Biology; Humans; Programming Languages; Software},
	Month = {Jan},
	Number = {1},
	Pages = {e1004668},
	Pmc = {PMC4718703},
	Pmid = {26785377},
	Pst = {epublish},
	Title = {A Quick Introduction to Version Control with Git and GitHub},
	Volume = {12},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1371/journal.pcbi.1004668}}

@article{Wilson:2017aa,
	Abstract = {Computers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don't know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.},
	Author = {Wilson, Greg and Bryan, Jennifer and Cranston, Karen and Kitzes, Justin and Nederbragt, Lex and Teal, Tracy K},
	Date-Added = {2018-10-10 08:45:52 -0400},
	Date-Modified = {2018-10-10 08:45:52 -0400},
	Doi = {10.1371/journal.pcbi.1005510},
	Journal = {PLoS Comput Biol},
	Journal-Full = {PLoS computational biology},
	Mesh = {Computer Security; Computing Methodologies; Data Accuracy; Documentation; Guidelines as Topic; Research; Science; Software},
	Month = {Jun},
	Number = {6},
	Pages = {e1005510},
	Pmc = {PMC5480810},
	Pmid = {28640806},
	Pst = {epublish},
	Title = {Good enough practices in scientific computing},
	Volume = {13},
	Year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1371/journal.pcbi.1005510}}

@article{adie2013gaming,
	Author = {Adie, Euan},
	Date-Added = {2018-10-10 08:45:25 -0400},
	Date-Modified = {2018-10-10 08:45:25 -0400},
	Journal = {Altmetric blog, September},
	Title = {Gaming altmetrics},
	Volume = {18},
	Year = {2013}}

@article{Piwowar:2013aa,
	Author = {Piwowar, Heather},
	Date-Added = {2018-10-10 08:45:14 -0400},
	Date-Modified = {2018-10-10 08:45:14 -0400},
	Doi = {10.1038/493159a},
	Journal = {Nature},
	Journal-Full = {Nature},
	Mesh = {Animals; Bibliometrics; Blogging; Data Collection; Employee Performance Appraisal; Internet; Records as Topic; Research; Research Personnel; Research Support as Topic; Software},
	Month = {Jan},
	Number = {7431},
	Pages = {159},
	Pmid = {23302843},
	Pst = {ppublish},
	Title = {Altmetrics: Value all research products},
	Volume = {493},
	Year = {2013},
	Bdsk-Url-1 = {https://doi.org/10.1038/493159a}}

@article{shema2014blog,
	Author = {Shema, Hadas and Bar-Ilan, Judit and Thelwall, Mike},
	Date-Added = {2018-10-10 08:45:04 -0400},
	Date-Modified = {2018-10-10 08:45:04 -0400},
	Journal = {Journal of the Association for Information Science and Technology},
	Number = {5},
	Pages = {1018--1027},
	Publisher = {Wiley Online Library},
	Title = {Do blog citations correlate with a higher number of future citations? Research blogs as a potential source for alternative metrics},
	Volume = {65},
	Year = {2014}}

@article{priem2010altmetrics,
	Author = {Priem, Jason and Taraborelli, Dario and Groth, Paul and Neylon, Cameron},
	Date-Added = {2018-10-10 08:44:53 -0400},
	Date-Modified = {2018-10-10 08:44:53 -0400},
	Title = {Altmetrics: A manifesto},
	Year = {2010}}

@webpage{prepubmed:2018aa,
	Author = {Anaya, Jordan},
	Date-Added = {2018-10-10 08:44:39 -0400},
	Date-Modified = {2018-10-10 08:44:39 -0400},
	Lastchecked = {10-05-2018},
	Month = {October},
	Title = {PrePubMed Monthly Statistics for September 2018},
	Url = {http://www.prepubmed.org/monthly_stats/?Subject=Bioinformatics},
	Year = {2018},
	Bdsk-Url-1 = {http://www.prepubmed.org/monthly_stats/?Subject=Bioinformatics}}

@webpage{preprint_growth:2018aa,
	Author = {Lin, Jennifer},
	Date-Added = {2018-10-10 08:44:30 -0400},
	Date-Modified = {2018-10-10 08:44:30 -0400},
	Month = {May},
	Title = {Preprints growth rate ten times higher than journal articles},
	Url = {https://www.crossref.org/blog/preprints-growth-rate-ten-times-higher-than-journal-articles/},
	Year = {2018},
	Bdsk-Url-1 = {https://www.crossref.org/blog/preprints-growth-rate-ten-times-higher-than-journal-articles/}}

@article{Duck2016,
	Author = {Geraint Duck and Goran Nenadic and Michele Filannino and Andy Brass and David L. Robertson and Robert Stevens},
	Date-Added = {2018-10-10 08:44:15 -0400},
	Date-Modified = {2018-10-10 08:44:15 -0400},
	Doi = {10.1371/journal.pone.0157989},
	Editor = {Shoba Ranganathan},
	Journal = {{PLOS} {ONE}},
	Month = {jun},
	Number = {6},
	Pages = {e0157989},
	Publisher = {Public Library of Science ({PLoS})},
	Title = {A Survey of Bioinformatics Database and Software Usage through Mining the Literature},
	Url = {https://doi.org/10.1371/journal.pone.0157989},
	Volume = {11},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1371/journal.pone.0157989}}

@article{Wren:2008aa,
	Abstract = {UNLABELLED: As biomedical data accumulates, the need to store, share and organize it grows. Consequently, the number of Internet-accessible databases has been rapidly growing on an annual basis. Bioinformatics regularly publishes descriptions of biomedically relevant databases, Nucleic Acids Research has published an annual database issue since 1996 and now a new open-access journal,
DATABASE: The Journal of Biological DATABASEs and Curation, will soon be launched by Oxford University Press in 2009 (http://www.oxfordjournals.org/our_journals/databa/). Since databases can be made publicly available on the Internet without publication, it is worth considering what factors prioritize publication of database descriptions in a peer-reviewed journal. In general, publication of a database description in a journal advertises it as a valuable resource for scientific research. Implicitly, it is assumed that this resource is publicly available (most likely for free) and will be maintained. However, therein lies the problem: DATABASE papers are simply not of the same nature as regular research articles. Over time, some databases simply become inaccessible, some are created but not maintained or updated, and some databases are never used (Galperin, 2006). Thus, for database creators, reviewers and journal editors, there are several additional considerations to judge, prior to publication, how potentially valuable these new databases may be.},
	Author = {Wren, Jonathan D and Bateman, Alex},
	Date-Added = {2018-10-10 08:44:04 -0400},
	Date-Modified = {2018-10-10 08:44:04 -0400},
	Doi = {10.1093/bioinformatics/btn464},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Computational Biology; Database Management Systems; Databases, Genetic; Information Storage and Retrieval; Information Systems; Internet},
	Month = {Oct},
	Number = {19},
	Pages = {2127-8},
	Pmid = {18819940},
	Pst = {ppublish},
	Title = {Databases, data tombs and dust in the wind},
	Volume = {24},
	Year = {2008},
	Bdsk-Url-1 = {https://doi.org/10.1093/bioinformatics/btn464}}

@article{Wren:2016aa,
	Abstract = {MOTIVATION: To analyze the relative proportion of bioinformatics papers and their non-bioinformatics counterparts in the top 20 most cited papers annually for the past two decades.
RESULTS: When defining bioinformatics papers as encompassing both those that provide software for data analysis or methods underlying data analysis software, we find that over the past two decades, more than a third (34%) of the most cited papers in science were bioinformatics papers, which is approximately a 31-fold enrichment relative to the total number of bioinformatics papers published. More than half of the most cited papers during this span were bioinformatics papers. Yet, the average 5-year JIF of top 20 bioinformatics papers was 7.7, whereas the average JIF for top 20 non-bioinformatics papers was 25.8, significantly higher (P < 4.5 × 10(-29)). The 20-year trend in the average JIF between the two groups suggests the gap does not appear to be significantly narrowing. For a sampling of the journals producing top papers, bioinformatics journals tended to have higher Gini coefficients, suggesting that development of novel bioinformatics resources may be somewhat 'hit or miss'. That is, relative to other fields, bioinformatics produces some programs that are extremely widely adopted and cited, yet there are fewer of intermediate success.
CONTACT: jdwren@gmail.com
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
	Author = {Wren, Jonathan D},
	Date-Added = {2018-10-10 08:43:53 -0400},
	Date-Modified = {2018-10-10 08:43:53 -0400},
	Doi = {10.1093/bioinformatics/btw284},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Computational Biology; Humans; Periodicals as Topic; Publications; Publishing; Software},
	Month = {09},
	Number = {17},
	Pages = {2686-91},
	Pmid = {27153671},
	Pst = {ppublish},
	Title = {Bioinformatics programs are 31-fold over-represented among the highest impact scientific papers of the past two decades},
	Volume = {32},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1093/bioinformatics/btw284}}