Policies that mandate public data archiving (PDA) successfully increase accessibility to data underlying scientific publications. However, is the data quality sufficient to allow reuse and reanalysis? We surveyed 100 datasets associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy. Out of these datasets, 56% were incomplete, and 64% were archived in a way that partially or entirely prevented reuse. We suggest that cultural shifts facilitating clearer benefits to authors are necessary to achieve high-quality PDA and highlight key guidelines to help authors increase their data’s reuse potential and compliance with journal data policies.
Citation: Roche DG, Kruuk LEB, Lanfear R, Binning SA (2015) Public Data Archiving in Ecology and Evolution: How Well Are We Doing? PLoS Biol 13(11): e1002295. doi:10.1371/journal.pbio.1002295
Published: November 10, 2015
Copyright: © 2015 Roche et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Funding: DGR and SAB were supported by grants from the Australian National University (http://biology.anu.edu.au), the Natural Science and Engineering Research Council of Canada (http://www.nserc-crsng.gc.ca/index_eng.asp) and the Fonds de Recherche du Québec Nature et Technologies (http://www.frqnt.gouv.qc.ca/accueil). LEBK and RL were supported by Australian Research Council Future Fellowships (http://www.arc.gov.au/ncgp/futurefel/future_default.htm). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: CC0, Creative Commons Zero licence; DORA, Declaration on Research Assessment; E&E, ecology and evolution; GBIF, Global Biodiversity Information Facility; JDAP, Joint Data Archiving Policy; KNB, Knowledge Network for Biocomplexity; PDA, public data archiving
Mandated public data archiving (PDA) is becoming the norm for leading journals in many fields, including ecology and evolution. Funding agencies, researchers, and publishers increasingly recognize that research articles are not the only product of scientific investigation, and greater value is being placed on the underlying data. PDA has numerous benefits for the scientific and wider community (sensu [1,2–5]), namely by allowing research results to be reproduced and data to be reused [6–8], which maintains both scientific rigor and public confidence in science [5,9,10]. Similarly, sharing data accelerates scientific discoveries and saves taxpayers’ money by avoiding unnecessary duplication of data collection [3,7,11–13].
Despite the obvious benefits of PDA for science, many researchers remain reluctant to share their data publicly [1,3,12,14–19]. This reluctance probably stems from concerns about competition for publications based on shared data, the time necessary to prepare files for archiving, a lack of recognition for PDA, and concerns about data misinterpretation [1,19, 20]. As such, perceived costs to individual researchers or research projects might offset potential group benefits for the scientific community [1,21]. To increase archiving rates, many journals have therefore resorted to strong policies including mandatory PDA. These policies work. For example, a recent review of studies in population genetics showed that implementing a PDA policy requiring a data availability statement in the published manuscript increases PDA nearly 1,000-fold , and an evaluation of phylogenetic studies found that data are more likely to be deposited in online archives if the journal has a strong PDA policy .
Making data publicly available is, however, only one requirement of PDA policies, the core aim of which is to allow reproduction of the results in the paper [24–26]. Despite growing evidence that PDA policies ensure that something is archived, assessments of the reproducibility of scientific results are rare and, to date, restricted to genetic data. Amongst these, one recent survey of 18 microarray studies found that only two were fully reproducible using the archived data . Another study of 19 papers in population genetics found that 30% of analyses could not be reproduced from the archived data and that 35% of datasets were incorrectly or insufficiently described . These findings are notable given that PDA is arguably most widely accepted in areas of biology that produce genetic data [12,28]. There are many factors that can hinder reproducibility, including failure to adequately describe methods  or failure to archive the computer code used to clean or analyse the data [30,31]. Here, we focus on the completeness and reusability of the archived datasets themselves.
How well do (nonmolecular) experimental and observational studies in ecology and evolution (E&E) fare in comparison to molecular studies? The question is of particular interest given that (1) mandatory PDA is much more recent in these fields [12,32], (2) many E&E journals currently lacking a PDA policy are likely to implement one in the near future (e.g., ), and (3) some of the concerns about PDA, in particular data misinterpretation, are perceived to be particularly widespread in E&E [1,19].
To answer this question, we examined data from 100 nonmolecular evolutionary and/or ecological publications that were archived in the popular data repository Dryad (http://datadryad.org/) between 2012 and 2013, from seven leading journals that regularly publish E&E research (Table 1). These journals all have strong data archiving policies: either by implementing their own policy (i.e., close to mandatory [22,34]) or by adopting the Joint Data Archiving Policy (JDAP), which requires that “data supporting the results in the paper be archived in an appropriate public archive” [35,36]. We evaluated the quality of archived data on two counts (Fig 1, S1 Text). First, are all the data supporting a study’s findings publicly available (“completeness”), thereby complying with the journals’ archiving policies? Second, although JDAP does not explicitly require that data be archived in a way that facilitates reuse, how readily can the archived data be accessed and reused by third parties (“reusability”)? We assigned each study separate completeness and reusability scores between 1 (low) and 5 (high) (see Table 2 and S1 Text for the scoring system and S2 Text for an assessment of score agreement across different raters, which was high for both scores).
The expectation of PDA that exists in genetics and molecular biology is rapidly permeating throughout ecology and evolution. With the advent of data archiving policies and integrated data repositories, journals and funders now have effective means of mandating PDA. However, the quality of publicly archived data associated with experimental and observational (nonmolecular) studies in ecology and evolution is highly variable. Illustration by Ainsley Seago.
At the time of data deposition in the repository, journals had either a “strong” PDA policy or adhered to the Joint Data Archiving Policy (JDAP), both of which require that data necessary to replicate a study’s results be archived in a public repository. Datasets were examined to assess completeness and reusability.
Scoring system and criteria used to assess data completeness and reusability of 100 studies with data archived in the public repository Dryad.
How Well Are We Doing?
We found considerable variation in the quality of publicly archived data from the 100 studies surveyed, even though all were published either in JDAP journals or journals with a strong PDA policy. In most studies (56%), the archived datasets were incomplete, either because of missing data or insufficient metadata, resulting in a completeness score of 3 or less (Figs 1 and 2A). Therefore, these studies do not comply with the PDA policy of the journal in which they were published (Fig 2A), as strong policies (JDAP or other) require all the data supporting a paper’s results to be available in a public repository. Secondly, datasets for 64% of studies were archived in a way that either partially or fully prevented reuse (Fig 2B), either because they lacked essential metadata, because the data were presented in processed rather than raw form, or because inadequate file formats were used (e.g., non-machine-readable file formats, such as pdf, that require specialized software to read) (Fig 2B). Thus, even if these datasets could in theory be used to reproduce a study’s results, their value is questionable. Finally, there was a strong correlation between the completeness and reusability scores (Fig 3; R = 0.59 ± 0.07 SE, p < 0.001; see S3 Text for further details). In 22% of studies, some or all of the archived data were presented as electronic supplementary material. This is not ideal since, unlike files archived on Dryad, there are no standards for organizing supplementary data both within and across journals , and such data are often not readily discoverable or openly accessible (to those without a relevant journal subscription, for example) .
Frequency distribution of public data archiving (PDA) scores for (A) completeness and (B) reusability across 100 studies in 2012 (light blue bars) and 2013 (dark blue bars). A score of 5 indicates exemplary archiving, and a score of 1 indicates poor archiving (see Table 2). Studies with completeness scores of 3 or lower (left of the red dashed line in panel A) do not comply with their journal's PDA policy. Studies to the left of the red dashed line in panel B have a reusability score between “average” (score of 3) and very poor (score of 1).
Empty circles are individual data points (offset to avoid overlap).
These findings are concerning given that (1) the studies were published in journals that enforce PDA, (2) our completeness score likely underestimates the number of irreproducible results since we did not attempt to replicate each study’s statistical analyses (see ), and (3) one key objective of PDA beyond increasing reproducibility is to accelerate scientific progress by facilitating data reuse [2,5,7]. Recent enforcement of PDA policies has had a positive effect on data deposition rates [22,23]. However, most journals do not verify the quality of archived data beyond basic checks such as ensuring that a data availability statement and a valid DOI are provided in the manuscript [38–40]. Therefore, datasets can contain involuntary errors and omissions ; we ourselves acknowledge errors made and possible improvements to past archived datasets.
Almost 40% of the 56 non-JDAP or non-journal policy compliant studies lacked only small amounts of data (completeness score of 3; Fig 2A). This suggests that many of these omissions are unintended and can be avoided with some slight improvements to data archiving practices. It is important to note, however, that authors concerned about potential individual costs of PDA (see [1,41–44]) can deliberately archive data to make them difficult or impossible for a third party to reuse (e.g., by archiving incomplete data or data in unusable formats) [12,17,41,45–47]. Notable examples have recently been pointed out on Twitter and other social media [41,48–50].
Many authors willingly participate in PDA because they believe in sharing data from publicly funded research, they wish to contribute to science beyond their own publications, and/or because they see individual benefits in doing so (e.g., increased citation rate , opportunities for coauthorship and new collaborations [1,2,7,20]). Despite these motivations, we uncovered a suite of problems that made understanding and assessing data difficult: omission of data necessary to reproduce results, nonexistent or insufficient data descriptors (e.g., no unit specifications or explanations of abbreviations and column headings in tables), inflexible file formats (e.g., “.sav” files that required the proprietary software SPSS Statistics to open), nonstandard data formats (e.g., colour coding of cells in tables, unspecified column headings), poor data organization (e.g., unclear tab labels for Excel documents with multiple spreadsheets, mismatches between column headings and variable labels in the associated paper, variable labels in a language other than English), and inclusion of poorly identified data unrelated to the paper (e.g., unspecified subsets of the data used for the analyses). The most common pitfalls that affected data reusability were inadequate metadata, the use of proprietary and non-machine-readable file formats (e.g., data tables archived as PDF and word documents; S1 Table, S2 Table), and failure to archive raw data (S3 Table).
Ecologists and evolutionary biologists receive little or no training in data management and may be unfamiliar with the best practices for proper data archiving (Table 3) [12,30,52]. The fact that a dataset’s completeness score was generally higher than its reusability score suggests that authors understand their obligation to share data but struggle to do this effectively (Fig 3, S3 Text). Small, simple improvements can dramatically increase the reusability of archived data with minimal time or monetary investments (e.g., [53,54]). We summarise key recommendations in Table 3. Based on our assessment of articles, we found that the datasets that had the highest completeness and reusability scores were often those in which the authors explicitly linked the archived data to figures and analyses in the paper. This simple practice greatly enhances the organization and interpretability of the data, enabling both authors and third parties to verify that all data points are present.
Which Way Forward?
Participation in PDA is on the rise, but its benefits require that authors archive complete and reusable datasets. Suggestions to improve acceptance of PDA policies are diverse and include treating data associated with journal articles as formal publications (i.e., publish data papers) [6,20,40,61,62], providing incentives for best practices so that authors voluntarily archive high-quality, reusable data [2,7,28,53], and allowing reasonable embargoes for researchers who have planned further uses for their data [1,19,21,36]. Obviously, increased policing of publicly archived datasets by journals and/or archive curators (i.e., reviewing archived data) should also increase the quality of archived data [22,24,38,45,63]. All of these recommendations have merit, but it is unlikely that there is one ideal solution.
From a practical point of view, enforcing PDA on unwilling authors is largely ineffective because cheating is easy—trying to reproduce the results of every submitted manuscript is virtually impossible. Publishing data papers is a valid solution for large, important datasets with a high reuse potential [40,64], but there are good reasons to think that this model is both impractical and unlikely to succeed for data that underlie most publications , namely because many datasets are limited in their size, scope, and/or novelty, which might not warrant publication in a data journal [40,61]. Reviewers and editors are also already overloaded with article peer reviews, almost always without compensation from publishers. Therefore, additional requests to police data associated with traditional papers could be perceived as unreasonable . Finally, data repositories currently lack the funding to perform thorough technical reviews to verify that datasets and metadata are complete and concordant with the information in a paper [6,36]. For example, Dryad is currently forced to charge archiving fees to operate  but only has enough curators to perform basic checks on data submissions such as verifying that files can be opened and are free of viruses .
Rather than punishing researchers who do not share their data, there are strong arguments for rewarding those who do [1,66,67]. This idea is in line with recent calls for a culture shift towards more collaboration in science [68,69], in which the value and importance of PDA is emphasized and greater benefits given to active participants [1,12,31,33,63]. These benefits can take many forms, including credit from hiring or promotion committees and funding agencies , as well as prizes from departments, societies, and publishers for most reusable or reused dataset, best data paper, or most reproducible results . An important move in this direction was the 2013 San Francisco Declaration on Research Assessment (DORA), which recommends considering datasets and other types of scientific contributions (e.g., software, training) when scientists’ research outputs are evaluated .
Importantly, sociological studies (both experimental and theoretical) point to the fact that both “sticks” and “carrots” are necessary to improve cooperation [71,72]. A recent theoretical study of a public good game, a standard framework for cooperation in groups, showed that the policy “first carrot, then stick” is highly successful at promoting cooperation because it combines the effectiveness of rewarding to establish cooperation with the effectiveness of punishing to maintain it . Those who comply must first be rewarded, and, once compliance has become the norm, it can become mandatory and enforced by a penalty for noncompliance . This strategy has major advantages for PDA in that offering “carrots” can shift the culture to the point at which authors publicly archive their data even when they are not required to do so .
Our results suggest that at least some parts of public data archives are being used to maintain datasets in E&E that are of little use for reproducing existing studies or carrying out new ones. These findings, combined with those of the few other studies that have also explored this issue [9,27], suggest that the problem is ubiquitous, touching both molecular and nonmolecular fields of biology. Clearly, improvements to current PDA practices are necessary. Solutions might not be straightforward, but they may have to include strategies combining enforcement, reward, and flexibility . Importantly, PDA is quite new for ecologists and evolutionary biologists, and our results indicate that substantial improvements to its value can be made with relatively little effort.
The data and code for this study are available on the repository figshare: http://dx.doi.org/10.6084/m9.figshare.1393269.
The list of publications with associated data archived in Dryad from inception to 20 Sep 2013 was kindly compiled and publicly archived by Vision et al. .
S1 Table. Terminology used to describe data file formats.
S2 Table. Characteristics (nonproprietary, human readable, machine readable) of archived file formats encountered in this study.
0 = no, 1 = yes. A greater row total indicates a higher reuse potential (NA was treated as a 1).
S3 Table. Summary of information contained in the public dataset associated with this study.
Number of datasets (out of 100) that (1) have a useful readme file, (2) are archived in nonproprietary machine- and human-readable file formats, (3) were analysed with a statistical program that allows scripting/coding, (4) have associated analysis code publicly archived, (5) were analysed with an statistical program that is not specified in the publication. Mean completeness and reusability scores across the 100 datasets were examined.
S1 Text. Materials and methods.
S2 Text. Interrater agreement analysis.
S3 Text. Results: Relationship between reusability and completeness.
This study arose from many informative discussions with colleagues. In particular, we thank the Evolutionary Ecology Reading group at the Australian National University, Tim Vines, Todd Vision, Naomi Langmore, and Redouan Bshary. We thank Alyson Pavitt and Amy Asher who helped with the data collection for the interrater agreement analysis. Michael Jennions, Timothée Poisot and Carly Strasser provided helpful comments on the manuscript.
- 1. Roche DG, Lanfear R, Binning SA, Haff TM, Schwanz LE, Cain KE, et al. Troubleshooting public data archiving: suggestions to increase participation. PLoS Biol. 2014, 12: e1001779. doi: 10.1371/journal.pbio.1001779. pmid:24492920
- 2. Whitlock MC. Data archiving in ecology and evolution: best practices. Trends Ecol Evol. 2011, 26: 61–65. doi: 10.1016/j.tree.2010.11.006. pmid:21159406
- 3. Tenopir C, Allard S, Douglass K, Aydinoglu AU, Wu L, Read E, et al. Data sharing by scientists: practices and perceptions. PLoS ONE. 2011, 6: e21101. doi: 10.1371/journal.pone.0021101. pmid:21738610
- 4. Pitt MA, Tang Y. What should be the data sharing policy of cognitive science? Top Cogn Sci. 2013, 5: 214–221. doi: 10.1111/tops.12006. pmid:23335581
- 5. Duke CS, Porter JH. The ethics of data sharing and reuse in biology. BioScience. 2013, 63: 483–489. doi: 10.1525/bio.2013.63.6.10
- 6. Kratz J, Strasser C. Data publication consensus and controversies [v3; ref status: indexed, http://f1000r.es/4ja]. F1000Research. 2014, 3: 94. doi: 10.12688/f1000research.3979.3. pmid:25075301
- 7. Poisot TE, Mounce R, Gravel D. Moving toward a sustainable ecological science: don't let data go to waste! Ideas Ecol Evol. 2013, 6: 11–19. doi: 10.4033/iee.2013.6b.14.f
- 8. Fecher B, Friesike S, Hebing M. What Drives Academic Data Sharing? PLoS ONE. 2015, 10: e0118053. doi: 10.1371/journal.pone.0118053. pmid:25714752
- 9. Gilbert KJ, Andrew RL, Bock DG, Franklin MT, Kane NC, Moore JS, et al. Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering using the program structure. Mol Ecol. 2012, 21: 4925–4930. doi: 10.1111/j.1365-294X.2012.05754.x. pmid:22998190
- 10. Price M. To replicate or not to replicate 2011. http://sciencecareers.sciencemag.org/career_magazine/previous_issues/articles/2011_12_02/caredit.a1100133.
- 11. Piwowar HA, Vision TJ, Whitlock MC. Data archiving is a good investment. Nature. 2011, 473: 285. doi: 10.1038/473285a
- 12. Hampton SE, Strasser CA, Tewksbury JJ, Gram WK, Budden AE, Batcheller AL, et al. Big data and the future of ecology. Front Ecol Environ. 2013, 11: 156–162. doi: 10.1890/120103
- 13. Heidorn PB. Shedding light on the dark data in the long tail of science. Libr Trends. 2008, 57: 280–299. doi: 10.1353/lib.0.0036
- 14. Wicherts JM, Bakker M, Molenaar D. Willingness to share research data is related to the strength of the evidence and the quality of reporting of statistical results. PLoS ONE. 2011, 6: e26828. doi: 10.1371/journal.pone.0026828. pmid:22073203
- 15. Huang X, Hawkins BA, Lei F, Miller GL, Favret C, Zhang R, et al. Willing or unwilling to share primary biodiversity data: results and implications of an international survey. Conserv Lett. 2012, 5: 399–406. doi: 10.1111/j.1755-263x.2012.00259.x
- 16. Milia N, Congiu A, Anagnostou P, Montinaro F, Capocasa M, Sanna E, et al. Mine, yours, ours? Sharing data on human genetic variation. PLoS ONE. 2012, 7: e37552. doi: 10.1371/journal.pone.0037552. pmid:22679483
- 17. Savage C, Vickers A. Empirical study of data sharing by authors publishing in PLoS journals. PLoS ONE. 2009, 4: e7078. doi: 10.1371/journal.pone.0007078. pmid:19763261
- 18. Kolb TL, Blukacz-Richards EA, Muir AM, Claramunt RM, Koops MA, Taylor WW, et al. How to manage data to enhance their potential for synthesis, preservation, sharing, and reuse—A great lakes case study. Fisheries. 2013, 38: 52–64. doi: 10.1080/03632415.2013.757975
- 19. Mills JA, Teplitsky C, Arroyo B, Charmantier A, Becker PH, et al. (2015) Archiving primary data: solutions for long-term studies. Trends in Ecology & Evolution 30: 581–589. doi: 10.1016/j.tree.2015.07.006
- 20. Costello MJ. Motivating online publication of data. BioScience. 2009, 59: 418–427. doi: 10.1525/bio.2009.59.5.9
- 21. Portugal SJ, Pierce SE. Who's Looking at Your Data? 2014. http://sciencecareers.sciencemag.org/career_magazine/previous_issues/articles/2014_02_25/caredit.a1400052.
- 22. Vines TH, Andrew RL, Bock DG, Franklin MT, Gilbert KJ, Kane NC, et al. Mandated data archiving greatly improves access to research data. FASEB J. 2013, 27: 1304–1308. doi: 10.1096/fj.12-218164. pmid:23288929
- 23. Magee AF, May MR, Moore BR. The dawn of open access to phylogenetic data. PLoS ONE. 2014, 9: e110268. doi: 10.1371/journal.pone.0110268. pmid:25343725
- 24. Bloom T, Ganley E, Winker M. Data access for the open access literature: PLOS's data policy. PLoS Biol. 2014, 12: e1001797. doi: 10.1371/journal.pbio.1001797
- 25. Whitlock MC, McPeek MA, Rausher MD, Rieseberg L, Moore AJ. Data archiving. Am Nat. 2010, 175: 145–146. doi: 10.1086/650340. pmid:20073990
- 26. Moore AJ, McPeek MA, Rausher MD, Rieseberg L, Whitlock MC. The need for archiving data in evolutionary biology. J Evol Biol. 2010, 23: 659–660. doi: 10.1111/j.1420-9101.2010.01937.x. pmid:20149022
- 27. Ioannidis JP, Allison DB, Ball CA, Coulibaly I, Cui X, Culhane AC, et al. Repeatability of published microarray gene expression analyses. Nat Genet. 2008, 41: 149–155. doi: 10.1038/ng.295. pmid:19174838
- 28. Reichman O, Jones MB, Schildhauer MP. Challenges and opportunities of open data in ecology. Science. 2011, 331: 703–705. doi: 10.1126/science.1197962. pmid:21311007
- 29. Vasilevsky NA, Brush MH, Paddock H, Ponting L, Tripathy SJ, LaRocca GM, et al. On the reproducibility of science: unique identification of research resources in the biomedical literature. PeerJ. 2013, 1: e148. doi: 10.7717/peerj.148. pmid:24032093
- 30. Strasser CA, Hampton SE. The fractured lab notebook: undergraduates and ecological data management training in the United States. Ecosphere. 2012, 3: art116. doi: 10.1890/es12-00139.1
- 31. Nosek B, Alter G, Banks G, Borsboom D, Bowman S, Breckler S, et al. Promoting an open research culture. Science. 2015, 348: 1422–1425. doi: 10.1126/science.aab2374. pmid:26113702
- 32. Parr CS, Cummings MP. Data sharing in ecology and evolution. Trends Ecol Evol. 2005, 20: 362–363. pmid:16701396 doi: 10.1016/j.tree.2005.04.023
- 33. Caetano DS, Aisenberg A. Forgotten treasures: the fate of data in animal behaviour studies. Anim Behav. 2014, 98: 1–5. doi: 10.1016/j.anbehav.2014.09.025
- 34. Piwowar HA, Chapman WW. Public sharing of research datasets: a pilot study of associations. J Informetr. 2010, 4: 148–156. pmid:21339841 doi: 10.1016/j.joi.2009.11.010
- 35. Dryad. Joint Data Archiving Policy (JDAP) 2014. http://datadryad.org/pages/jdap.
- 36. Vision TJ. Open data and the social contract of scientific publishing. BioScience. 2010, 60: 330–331. doi: 10.1525/bio.2010.60.5.2
- 37. Santos C, Blake J, States DJ. Supplementary data need to be kept in public repositories. Nature. 2005, 438: 738. doi: 10.1038/438738a
- 38. Noor M, Zimmerman K, Teeter K. Data sharing: how much doesn't get submitted to GenBank? PLoS Biol. 2006, 4: e228. pmid:16822095 doi: 10.1371/journal.pbio.0040228
- 39. Roberts R. Dude, Where’s My Data? 2013. http://blogs.plos.org/biologue/2013/09/04/dude-wheres-my-data/.
- 40. Costello MJ, Michener WK, Gahegan M, Zhang Z-Q, Bourne PE. Biodiversity data should be published, cited, and peer reviewed. Trends Ecol Evol. 2013, 28: 454–461. doi: 10.1016/j.tree.2013.05.002. pmid:23756105
- 41. McKiernan EC. My concerns about PLOS' new open data policy 2014. http://emckiernan.wordpress.com/2014/02/26/my-concerns-about-ploss-new-open-data-policy.
- 42. Waldenström J. To share, or not to share your data–some thoughts on the new data policy for the PLOS journals 2014. https://zoonoticecology.wordpress.com/2014/03/04/to-share-or-not-to-share-your-data-some-thoughts-on-the-new-data-policy-for-the-plos-journals/.
- 43. McGlynn T. I own my data, until I don’t 2014. http://smallpondscience.com/2014/03/03/i-own-my-data-until-i-dont/.
- 44. DrugMonkey. PLoS is letting the inmates run the asylum and this will kill them 2014. https://drugmonkey.wordpress.com/2014/02/25/plos-is-letting-the-inmates-run-the-asylum-and-this-will-kill-them/.
- 45. Drew BT, Gazis R, Cabezas P, Swithers KS, Deng J, Rodriguez R, et al. Lost branches on the tree of life. PLoS Biol. 2013, 11: e1001636. doi: 10.1371/journal.pbio.1001636. pmid:24019756
- 46. Alsheikh-Ali A, Qureshi W, Al-Mallah M, Ioannidis J. Public availability of published research data in high-impact journals. PLoS ONE. 2011, 6: e24357. doi: 10.1371/journal.pone.0024357. pmid:21915316
- 47. Rivers C. "Send me your data—pdf is fine," said no one ever (how to share your data effectively) 2013. http://www.caitlinrivers.com/1/post/2013/04/send-me-your-data-pdf-is-fine-said-no-one-ever-how-to-share-your-data-effectively.html#comments.
- 48. Mounce R. [@rmounce] Journals/Publishers could easily contribute much towards aiding reproducibility. i.e. stop PDF'ing code & data #ievobio [Tweet]. Jun 24 2014. https://twitter.com/rmounce/status/481496394499624960.
- 49. Greshake B. [@gedankenstuecke] About those quilt plots: They uploaded their R code to create heatmaps to @figshare. In a *.doc file… This must be another OA sting, right? [Tweet] Jan 16 2014. https://twitter.com/gedankenstuecke/status/423939373873504256.
- 50. N.O'Donnell M. [@Mega_NO] A chart is NOT DATA. It's a representation of data. Can't say it enough times. https://t.co/jPnR2WXmcz [Tweet] May 05 2015. https://twitter.com/Mega_NO/status/595613359829049345.
- 51. Piwowar H, Vision TJ. Data reuse and the open data citation advantage. PeerJ. 2013, 1: e175. doi: 10.7717/peerj.175. pmid:24109559
- 52. Strasser C, Kunze J, Abrams S, Cruse P. DataUp: A tool to help researchers describe and share tabular data [v2; ref status: indexed]. F1000Research. 2014, 3:6. doi: 10.12688/f1000research.3-6.v2. pmid:25653834
- 53. White EP, Baldridge E, Brym ZT, Locey KJ, McGlinn DJ, Supp SR. Nine simple ways to make it easier to (re) use your data. Ideas Ecol Evol. 2013, 6: 1–10. doi: 10.4033/iee.2013.6b.6.f
- 54. Sandve GK, Nekrutenko A, Taylor J, Hovig E. Ten simple rules for reproducible computational research. PLoS Comput Biol. 2013, 9: e1003285. doi: 10.1371/journal.pcbi.1003285. pmid:24204232
- 55. Van Noorden R. Data-sharing: Everything on display. Nature. 2013, 500: 243–245. pmid:23930278 doi: 10.1038/nj7461-243a
- 56. Strasser C, Cook R, Michener W, Budden A. Primer on Data Management: What you always wanted to know. A DataONE publication Available via the California Digital Library. 2012.
- 57. Kratz J. Make data rain 2015. http://datapub.cdlib.org/2015/01/08/make-data-rain/.
- 58. Borer ET, Seabloom EW, Jones MB, Schildhauer M. Some simple guidelines for effective data management. Bull Ecol Soc Am. 2009, 90: 205–214. doi: 10.1890/0012-9623-90.2.205
- 59. Rüegg J, Gries C, Bond-Lamberty B, Bowen GJ, Felzer BS, McIntyre NE, et al. Completing the data life cycle: using information management in macrosystems ecology research. Front Ecol Environ. 2014, 12: 24–30. doi: 10.1890/120375
- 60. Roche DG, Jennions MD, Binning SA. Data deposition: fees could damage public data archives. Nature. 2013, 502: 171. doi: 10.1038/502171a. pmid:24108044
- 61. Lawrence B, Jones C, Matthews B, Pepler S, Callaghan S. Citation and peer review of data: Moving towards formal data publication. Int J Digit Cur. 2011, 6: 4–37. doi: 10.2218/ijdc.v6i2.205
- 62. Parsons M, Fox P. Is data publication the right metaphor? Data Science J. 2013, 12: WDS32–WDS46. doi: 10.2481/dsj.wds-042
- 63. Lin J, Strasser C. Recommendations for the role of publishers in access to data. PLoS Biol. 2014, 12: e1001975. doi: 10.1371/journal.pbio.1001975. pmid:25350642
- 64. Figshare. The rise of the ‘Data Journal’ 2015. http://tandf.figshare.com/blog/The_rise_of_the_Data_Journal_/149.
- 65. Dryad. Frequently asked questions 2015. https://datadryad.org/pages/faq.
- 66. Teunis T, Nota SP, Schwab JH. Do corresponding authors take responsibility for their work? A covert survey. Clin Orthop Relat Res. 2015, 473: 729–735. doi: 10.1007/s11999-014-3868-3. pmid:25123243
- 67. Page RDM. "Lost Branches on the Tree of Life"—why must the answer be enforcing behaviour? 2013. http://iphylo.blogspot.co.uk/2013/09/branches-on-tree-of-life-why-must.html.
- 68. Bolukbasi B, Berente N, Cutcher-Gershenfeld J, Dechurch L, Flint C, Haberman M, et al. Open data: crediting a culture of cooperation. Science. 2013, 342: 1041–4042. doi: 10.1126/science.342.6162.1041-b. pmid:24288316
- 69. Soranno PA, Cheruvelil KS, Elliott KC, Montgomery GM. It's good to share: why environmental scientists’ ethics are out of date. BioScience. 2015, 65: 69–73. doi: 10.1093/biosci/biu169
- 70. San Francisco Declaration on Research Assessment (DORA). 2013. http://am.ascb.org/dora/.
- 71. Andreoni J, Harbaugh W, Vesterlund L. The carrot or the stick: Rewards, punishments, and cooperation. Am Econ Rev. 2003: 893–902. doi: 10.1257/000282803322157142
- 72. Chen X, Sasaki T, Brännström Å, Dieckmann U. First carrot, then stick: how the adaptive hybridization of incentives promotes cooperation. J Royal Soc Interface. 2015, 12: 20140935. doi: 10.1098/rsif.2014.0935
- 73. Vision TJ, Scherle R, Mannheimer S. Data for: Embargo selections of Dryad data authors. figshare. 2013. http://dx.doi.org/10.6084/m9.figshare.805946.