Reproducing Ecological Niche Models with Whole Tale
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1) The document describes a workshop on research synthesis and reproducibility. 2) It discusses challenges with reproducibility in science and proposes provenance and conceptual tools like PRIMAD to help address these challenges. 3) The document presents a case study where an intern was able to reproduce results from a 2006 ecological niche modeling paper using the Whole Tale environment and MaxEnt software, demonstrating computational reproducibility.
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Reproducing Ecological Niche Models with Whole Tale
- 2. All-in-One (Teaser)• Reproducibility Crisis in Science • A conceptual tool: Provenance • Transparency? Explanation? Provenance ! • … why-, how-, where-, why-not-, data-, workflow- ... provenance ... • Terminological Chaos Reigns – … replicability … reproducibility … repeatability … • A modest proposal and (evolving) conceptual tool: PRIMAD – What’s fixed? What varies? (X à X’, Y à Y’ , … ) – What is the information gain when succeeding, failing to reproduce? • Tool Tools (cf. audio-book, e-book, book-book) – Computational Reproducibility? Whole-Tale (vms++) ! – Modeling (Dataflow) Dependencies? YesWorkflow ! – Terminological Confusion? EulerX ! (“Semantics”) • A Case Study – Whole Tale Summer Internship (Santiago Núñez-Corrales): – Reproducibility in Ecological Niche Models: the case of Phillips et al (2006) Ludäscher & Núñez-Corrales Whole Tale
- 6. Computational Provenance … • Origin, processing history of artifacts – data products, figures, ... – also: underlying workflow è understand methods, dataflow, and dependencies è role of computational provenance in HoH !? Ludäscher & Núñez-Corrales Climate Change Impacts in the United States U.S. National Climate Assessment U.S. Global Change Research Program
- 7. A conceptual tool: Provenance … • Grand Canyon’s rock layers are a record of the early geologic history of North America. The ancestral puebloan granaries at Nankoweap Creek tell archaeologists about more recent human history. (By Drenaline, licensed under CC BY-SA 3.0) • Not shown: computational archaeologists reconstructing past climate from multiple tree- ring databases è computational provenance is key for transparency & reproducibility Ludäscher & Núñez-Corrales
- 8. ... provenance is: Understanding what happened! Zrzavý, Jan, David Storch, and Stanislav Mihulka. Evolution: Ein Lese-Lehrbuch. Springer-Verlag, 2009. Author: Jkwchui (Based on drawing by Truth-seeker2004) Ludäscher & Núñez-Corrales
- 9. : Provenance in DataONE A DataONE search (here: “grass”) yields different packages with Data Provenance (not covered: Semantic Search) Ludäscher & Núñez-Corrales
- 10. Exploring Provenance in DataONE • Let’s go there è Mark Carls. 2017. Analysis of hydrocarbons following the Exxon Valdez oil spill, Gulf of Alaska, 1989 - 2014. Gulf of Alaska Data Portal. urn:uuid:3249ada0-afe3-4dd6-875e-0f7928a4c171. Ludäscher & Núñez-Corrales
- 13. Adding YesWorkflow to DataONE Yaxing’s script with inputs & output products Christopher’s YesWorkflow model Christopher using Yaxing’s outputs as inputs for his script Christopher’s results can be traced back all the way to Yaxing’s input Ludäscher & Núñez-Corrales
- 17. To succeed or to fail? What do we gain? • Successful reproducibility study: – increases trust in prior study J – … but no surprises L • Failed reproducibility study : – decreases trust (or falsifies) prior study L – … but surprising failure yields new info/knowledge J • Learning from failures! – not really a totally new idea.. – What does a positive vs negative result mean anyways? – When developing s/w, tools: fail early, fail often ... Ludäscher & Núñez-Corrales
- 18. PRIMAD: What have you “primed”? What do we gain? Ludäscher & Núñez-Corrales Dagstuhl Seminar #16041 Report Outputs = Exec(M,I,P,D) | RO, A - M = MaxEnt/.. - I = package XYZ - P = MacOS , Windows, .. - D = (Params, Files)
- 21. • SKOPE: system and tools to discover, access, analyze, visualize paleoenvironmental data – unprecedented ability to explore provenance (detailed, comprehensible record of computational derivation of results) – for researchers, tinkerers, and modelers • Whole Tale: – leverage & contribute to existing CI to support the whole tale (“living paper”), from workflow run to scholarly publication – integrate tools & CI (DataONE, Globus, iRODS, NDS, ...) to simplify use and promote best practices. – driven by science WGs (Archaeology/SKOPE, materials science, astro, bio ..) But first: Some Tools (“Cyberinfrastructure”) Ludäscher: Provenance Back & Forth 21
- 22. Provenance Support for Reproducible Science in SKOPE: Paleoclimate Reconstruction Science paper (OA) uses: • open source code: – R, PaleoCAR, … • Is that all we need? • What was the “workflow”? • Is there prospective and/or retrospective provenance? Ludäscher & Núñez-Corrales
- 23. Whole Tale: The next step in the evolution of the scholarly article: The “Living” Paper • 1st Generation: – narrative (prose) • 2nd Generation: plus … – name .. identify .. include (access to) data • 3rd Generation: plus … – name .. reference .. include code (software) .. – and provenance … and exec environment (containers, vms) Ludäscher & Núñez-Corrales Whole Tale Whole Tale Dashboard
- 25. Project Goals (… Reproducibility in Ecological Niche Models … ) ● Try to reproduce one set of results reported in the literature using maximum entropy methods (MaxEnt) within The Whole Tale environment ○ Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological modelling, 190(3-4), 231-259. ● Determine whether existing software tools focus more on the scientific modeling problem instead of on software usage while covering reproducibility concerns ○ Not with existing tools, either incomplete or desktop-based, not comparable ● Build scientific software for ecological niche modeling that helps users diversify and trace their stories ○ Introspection-based model
- 26. intros-MaxEnt: view in PRIMAD++ Actions Parameter Raw data Platform / Stack Implem. Method Research Objective Actor Gain Re-code (x) x Run MaxEnt models in the Whole Tale Validate (x) (x) (x) (x) x Determine MaxEnt robustness factors Re-use x Increase the user base for MaxEnt methods Independent x x Collectively verify MaxEnt experiments Introspect (x) (x) x Explore and adjust model contents Diff (x) (x) (x) x Test hypotheses dependent on state-change Trace (log) (x) (x) (x) (x) x Capture time-dep decision modeling pathways Package (x) (x) (x) (x) x Provide a zero cold-start entry for experiments Freire, J., Fuhr, N., & Rauber, A. (2016). Reproducibility of data-oriented experiments in e-Science (Dagstuhl Seminar 16041). In Dagstuhl Reports (Vol. 6, No. 1). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
- 27. Ecological niche models .. 1. Positive observations (i.e. presence-only data) suffice to compute a distribution of a species 2. The likelihood of the presence of an individual depends on biologically relevant environmental factors 3. Interactions between species can be abstracted as environmental factors, hence not modeled explicitly 4. The distribution is stated in terms of the probability of finding a member of the species at the locations of interest 5. An exact fit is not a good fit, but rather an overfit
- 28. Maximum Entropy ● Given input data, the best estimate for the probability distribution P it approximates is given by the distribution P’ that maximizes entropy ○ Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical Review, 106(4), 620. Shoaib, M., Siddiqui, I., Rehman, S., ur Rehman, S., & Khan, S. (2017). Speed distribution analysis based on maximum entropy principle and Weibull distribution function. Environmental Progress & Sustainable Energy, 36(5), 1480-1489.
- 29. Bradypus variegatus: reported vs reproduced (WT-SVM) Phillips et al. (2006), AUC = 0.873 Our rendition, AUC = 0.868
- 30. Microryzomys minutus: reported vs reproduced (WT-SVM) Phillips et al. (2006), AUC = 0.986 Our rendition, AUC = 0.994
- 38. Summary of Outcomes 1. Able to execute a version of MaxEnt with original data from Phillips et al (2006) within The Whole Tale a. Stated in terms of a regularized support vector machine (complex code!) b. Discovered problems with reproducibility and how to evaluate it 2. A tool for batch georeferencing DarwinCore based on minimal location data was implemented a. Helpful to assign geolocation data after taxonomy alignment b. Discovered data is much less clean than expected 3. A new “introspective” software version of MaxEnt a. Available in PyPI b. Based on a state machine
- 39. ... now what? • PRIMAD ++ – PRIMAD is built on the idea of – … keeping some things the same – ... and “wiggling” some things – We can start from the “execution stack”: • Hardware … Operating System … Libraries ... PLs ... IDEs .. – Then going into the domain: • … varying datasets, parameters, assumptions ... – Experimental Design ++ ! • PRIMAD ++ HoH (v2?) • Tools to support – “higher order” {data, parameter, method, …} sweeps – Automate these (workflow tools!) Ludäscher & Núñez-Corrales
- 43. Taxonomic concept alignment, Andropogon glomeratus-virginicus complex, spanning across 11 classifications authored 1889-2015 • 36 unique taxonomic names • 88 taxonomic concept labels Þ name sec. author strings • Alignment by A.S. Weakley Þ row position = congruence • 1/36 names with unique 1 : 1 name : meaning cardinality across all classifications • Andropogon virginicus • Source: Franz et al. 20161 1 Franz et al. 2016. Names are not good enough: reasoning over taxonomic change in the Andropogon complex. Semantic Web Journal (IOS). doi:10.3233/SW-160220 Ludäscher & Núñez-Corrales
- 44. Leaving taxon and species headaches … • To illustrate Euler think of a simpler use case: • Agreeing to disagree! • … when there are multiple, legitimate perspectives • Sorting things out! – Euler as a taxon concept (& name) “microscope” ... – .. or “time machine” ? Ludäscher & Núñez-Corrales
- 45. Half-Smokes in DC: Typical for the Northeast? … or the South !? (A tale of two taxonomies: NDC vs CEN) “…in the face of incompatible information or data structures among users or among those specifying the system, attempts to create unitary knowledge categories are futile. Rather, parallel or multiple representational forms are required” [Bowker & Star, 2000, p.159] West Southwest Southeast Midwest North- east West South Midwest North- east National Diversity Council map (NDC) US Census Buero map (CEN) Source: Yi-Yun (Jessica) Cheng (PhD student, iSchool @ Illinois) Ludäscher & Núñez-Corrales
- 46. EulerX tool: Sorting things out … Ludäscher & Núñez-Corrales CEN.Midwest CEN.USA CEN.South CEN.West CEN.Northeast NDC.Northeast NDC.USA NDC.Southeast NDC.Midwest NDC.Southwest NDC.West Nodes CEN 5 NDC 6 Edges is_a (CEN) 4 is_a (NDC) 5 CEN.South NDC.Northeast o NDC.Southwest o NDC.Southeast> CEN.Midwest NDC.Midwest= CEN.USA CEN.West CEN.Northeast NDC.USA = ! o NDC.West > < 5 6 4 5 9 CEN.Midwest CEN.USA CEN.South CEN.West CEN.Northeast NDC.Northeast NDC.USA NDC.Southeast NDC.Midwest NDC.Southwest NDC.West Nodes CEN 5 NDC 6 Edges is_a (CEN) 4 is_a (NDC) 5 • Given: – taxonomies T1, T2 – and relations T1 ~ T2 (articulations, alignment) • Find: – merged taxonomy T3 • Such that: – T1, T2 are preserved – all pairwise relations are explicit T1 T2
- 47. Merged taxonomy T3 (= T1 “+” T2) CEN.South NDC.Northeast NDC.Southwest CEN.USA NDC.USA CEN.West CEN.Northeast NDC.Southeast NDC.West CEN.Midwest NDC.Midwest N CE ND cong Ed is_a ( overlap CEN.Midwest CEN.USA CEN.South CEN.West CEN.Northeast NDC.Northeast NDC.USA NDC.Southeast NDC.Midwest NDC.Southwest NDC.West Nodes CEN 5 NDC 6 Edges is_a (CEN) 4 is_a (NDC) 5 CEN.Midwest CEN.USA CEN.South CEN.West CEN.Northeast NDC.Northeast NDC.USA NDC.Southeast NDC.Midwest NDC.Southwest NDC.West Nodes CEN 5 NDC 6 Edges is_a (CEN) 4 is_a (NDC) 5 CEN.South NDC.Northeast >< NDC.Southwest >< NDC.Southeast> CEN.Midwest NDC.Midwest== CEN.USA CEN.West CEN.Northeast NDC.USA == ! >< NDC.West > < des N 5 C 6 ges EN) 4 DC) 5 tions 9 T1 T2 T1 ~ T2 T3 Ludäscher & Núñez-Corrales overlap!
- 48. Ludäscher & Núñez-Corrales CEN.West NDC.Southwest CEN.USA NDC.USA CEN.Northeast NDC.Northeast CEN.South NDC.Southeast NDC.West CEN.DC NDC.DC CEN.NM NDC.NM CEN.ND NDC.ND CEN.Midwest NDC.Midwest CEN.AZ NDC.AZ CEN.CA NDC.CA CEN.MT NDC.MT CEN.MA NDC.MA CEN.IN NDC.IN CEN.NV NDC.NV CEN.MD NDC.MD CEN.CT NDC.CT CEN.NH NDC.NH CEN.KY NDC.KY CEN.PA NDC.PA CEN.CO NDC.CO CEN.WA NDC.WA CEN.MI NDC.MI CEN.VA NDC.VA CEN.WI NDC.WI CEN.NE NDC.NE CEN.SD NDC.SD CEN.MN NDC.MN CEN.MS NDC.MS CEN.ID NDC.ID CEN.WV NDC.WV CEN.NY NDC.NY CEN.NJ NDC.NJ CEN.UT NDC.UT CEN.ME NDC.ME CEN.IL NDC.IL CEN.TN NDC.TN CEN.VT NDC.VT CEN.GA NDC.GA CEN.DE NDC.DE CEN.NC NDC.NC CEN.OK NDC.OK CEN.MO NDC.MO CEN.SC NDC.SC CEN.AR NDC.AR CEN.TX NDC.TX CEN.LA NDC.LA CEN.OH NDC.OH CEN.IA NDC.IA CEN.KS NDC.KS CEN.RI NDC.RI CEN.WY NDC.WY CEN.FL NDC.FL CEN.OR NDC.OR CEN.AL NDC.AL Nodes CEN 3 NDC 4 comb 51 Edges input 61 inferred 3 overlapsinferred 3 CEN.West NDC.Southwest CEN.USA NDC.USA NDC.Northeast CEN.South NDC.Southeast CEN.DC NDC.DC CEN.NM NDC.NM CEN.AZ NDC.AZ CEN.MA NDC.MA CEN.MD NDC.MD CEN.CT NDC.CT CEN.KY NDC.KY CEN.VA NDC.VA CEN.MS NDC.MS CEN.WV NDC.WV CEN.TN NDC.TN CEN.GA NDC.GA CEN.DE NDC.DE CEN.NC NDC.NC CEN.OK NDC.OK CEN.SC NDC.SC CEN.AR NDC.AR CEN.TX NDC.TX CEN.LA NDC.LA CEN.FL NDC.FL NDC.OR CEN.AL NDC.AL DC is in both the South and the Northeast Adding the state-level makes the overlap explicit!
- 49. Conclusion: Be explicit! • Transparency, understanding, … => through provenance! • Reproducibility studies => PRIMAD++ model => understand what you gain! • Tool Tools: – Whole-Tale (“living paper”) – YesWorkflow (workflow modeling) – EulerX (reasoning about taxonomies) Ludäscher & Núñez-Corrales