Banks-Leite, C.

Barlow, J.

Ewers, R.M.

Rahbek, C.

English

Lancaster E-Prints

1  Recognising the value of the scientific resources generated by data 1 collectors and code developers 2 Robert. M. Ewers1,*, Jos Barlow2, Cristina Banks-Leite1 and Carsten Rahbek1,3 3 1 Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, United Kingdom 4 2 Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom 5 3 Center for Macroecology, Evolution and Climate, Natural History Museum of Denmark, University of 6 Copenhagen, 2100 Copenhagen, Denmark. 7 * Email: r.ewers@imperial.ac.uk 8  9 The current, authorship-based system for recognising individual contributions to science only 10 patchily recognises the contributions of the primary data collection that underpins, and code 11 development that supports, the entire discipline. While data collectors and code developers – 12 scientific resource generators – are progressively being forced to donate the grant income, time and 13 effort of generating, curating and documenting data and code to the discipline as a whole [1-3]. 14 Resource users – those that re-use previously published data and codes to generate new knowledge 15 and publications – benefit from that time and effort but are not required to recognise it in any 16 standardised manner. We need a new way to quantify and value what is currently anonymous; the 17 fundamental contribution to scientific progress that generating scientific resources provides. 18 Many scientists agree that authorship is the ultimate reward for collecting data or developing code. 19 However, the Vancouver Protocol tellingly states that “Participation solely in the … collection of data 20 does not justify authorship.” Citations are routinely raised as the obvious approach to solving this 21 dilemma [4, 5], but it is not enough. Citations carry less value to a scientist than authorship. 22 Moreover, citations to scientific resources are agnostic to the impact of the papers that used those 23 2  resources, resource citations are commonly buried in supplementary material where they do not get 24 picked up by citation tracking software, and published resources not associated with a published 25 manuscript do not contribute to a scientists’ citation indices.  26 We suggest one solution is to divorce authorship of a manuscript from authorship of the resources 27 used in the manuscript, which can be achieved by creating separate categories of authorship: 28 manuscript and resource authors. Here, a published paper would come with two separate author 29 lists. Manuscript authors are those who developed the question, analysed and interpreted the data, 30 and wrote the paper; “authorship for authors” [6]. Resource authors are those who contributed 31 some or all of the data that was analysed or code that was used. Membership of the two author lists 32 need not be mutually exclusive, as a single person could reasonably contribute resources and 33 contribute to the manuscript. In this system, a resource generator can still receive credit for 34 contributing to the paper, without implying they agree with, understand, or have even seen, the 35 analysis and the conclusions the manuscript authors have presented.  36 Resource authorship provides a path to quantify the value of a scientist’s provision of resources to 37 the wider community, and could be implemented within the framework of the existing, citation-38 based recognition system. Resource contributions could reasonably be tracked through the use of 39 exactly the same citation indices already in widespread use, but applied to resource rather than 40 manuscript authorship. This would ensures scientists contributing data or code that are frequently 41 re-used in highly cited, influential papers will have higher resource citation metrics than those 42 contributing resources that are infrequently used and published in low impact papers.  43 Separating the impact of generating scientific resources from the impact of using those resources 44 provides a way out of the resource generator-resource user tension. The two are complementary 45 aspects of a shared scientific enterprise. Data and reproducible codes represent empirical truth; 46 quantitative, repeatable measurements of the world around us against which we test our 47 understanding. The papers we write are our qualitative interpretation of what those data and codes 48 3  tell us; they are ephemeral position statements that implicitly embed the sum of our experiences, 49 knowledge and biases to date. Both are important contributions to the advancement of science, and 50 both need to be represented when quantifying the contribution that individuals make to that 51 advance. 52  53 References 54 1. Whitlock, M.C. (2011) Data archiving in ecology and evolution: best practices. Trends in Ecology & 55 Evolution 26 (2), 61-65. 56 2. Mislan, K.A.S. et al. (2016) Elevating the status of code in ecology. Trends in Ecology & Evolution 57 31 (1), 4-7. 58 3. Peng, R.D. (2011) Reproducible research in computational science. Science 334 (6060), 1226-1227. 59 4. Amann, R.I. et al. (2019) Toward unrestricted use of public genomic data. Science 363 (6425), 350-60 352. 61 5. Pierce, H.H. et al. (2019) Credit data generators for data reuse. Nature 570, 30-32. 62 6. Baskin, T.I. (2018) Keep authorship for writers. Nature 562, 494. 63  64 

Separate authorship categories to recognize data collectors and code developers

https://eprints.lancs.ac.uk/id/eprint/139111/1/ewers_et_al_resource_authors_190927.pdf

Separate authorship categories to recognize data collectors and code developers

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