DOI: 10.5061/dryad.8gtht771c
title: Assembling ensembling: An adventure in approaches across disciplines

description: When we think of model ensembling or ensemble modeling, there are many
 possibilities that come to mind in different disciplines. For example, one
 might think of a set of descriptions of a phenomenon in the world, perhaps
 a time series or a snapshot of multivariate space, and perhaps that set is
 comprised of data-independent descriptions, or perhaps it is quite
 intentionally fit to data, or even a suite of data sets with a common
 theme or intention. The very meaning of ‘ensemble’ - a collection together
 - conjures different ideas across and even within disciplines approaching
 phenomena. In this paper, we present a typology of the scope of these
 potential perspectives. It is not our goal to present a review of terms
 and concepts, nor is it to convince all disciplines to adopt a common
 suite of terms, which we view as futile. Rather, our goal is to
 disambiguate terms, concepts, and processes associated with ‘ensembles’
 and ‘ensembling’ in order to facilitate communication, awareness, and
 possible adoption of tools across disciplines.
description: # Assembling ensembling: An adventure in approaches across disciplines
 Dataset DOI: [10.5061/dryad.8gtht771c](10.5061/dryad.8gtht771c) ##
 Description of the data and file structure ## About When we think of model
 ensembling or ensemble modeling, there are many possibilities that come to
 mind in different disciplines. For example, one might think of a set of
 descriptions of a phenomenon in the world, perhaps a time series or a
 snapshot of multivariate space, and perhaps that set is comprised of
 data-independent descriptions, or perhaps it is quite intentionally fit to
 data, or even a suite of data sets with a common theme or intention. The
 very meaning of ‘ensemble’ - a collection together - conjures different
 ideas across and even within disciplines approaching phenomena. In this
 paper, we present a typology of the scope of these potential perspectives.
 It is not our goal to present a review of terms and concepts, nor is it to
 convince all disciplines to adopt a common suite of terms, which we view
 as futile. Rather, our goal is to disambiguate terms, concepts, and
 processes associated with ‘ensembles’ and ‘ensembling’ in order to
 facilitate communication, awareness, and possible adoption of tools across
 disciplines. To help achieve this goal, we present a living compendium in
 dashboard form. The dashboard provides both a visual of the
 interconnectivity of ensembles across academic disciplines and a “living”
 table to facilitate the expansion of the static table found in the
 original paper. A link to the preprint of the paper can be found at:
 [https://arxiv.org/abs/2405.02599](https://arxiv.org/abs/2405.02599) ##
 The Dashboard The dashboard includes two sections: (1) “The Big Picture”
 and (2) “Examples”. “The Big Picture” contains an interactive
 visualization of the main topics in the manuscript. Users can move,
 collapse, and expand the included nodes. The “Examples” page includes the
 living compendium, allowing users to add their own examples to the table.
 As can be seen in the sidebar of the page, there are five options that
 must be filled in to add an entry to the table: (1) Theme, (2) Discipline,
 (3) Ensemble Approach, (4) Manuscript Title, (5) Source. When providing
 entries to the table, please include only one title and source per entry.
 However, if the manuscript falls under more than one theme and such is
 indicated upon submission, multiple rows will be created (i.e., one per
 theme). Please use the’; ’ to separate entries to indicate more than one
 discipline or ensemble approach for a given manuscript or source. Once all
 fields have been completed and the “Update Table” button has been clicked,
 the entry will be submitted for approval by the site moderator. Once
 approved, the entry will be added to the table. A link to the full
 interactive dashboard can be found at:
 [https://collabdashboard.shinyapps.io/Shiny-Dashboard/#section-about](https://collabdashboard.shinyapps.io/Shiny-Dashboard/#section-about) ### Files and variables #### File: BaseTable.csv **Description:** The base file used to create the dashboard shown in the R Shiny App.  ##### Variables * Theme: Underlying topic included within the manuscript of interest.  * Discipline: The discipline within which the manuscript of interest falls.  * Ensemble-Approach: The type of ensemble approach used within the manuscript of interest.  * Title: The title of the manuscript.  * Source: The URL or DOI of the manuscript of interest.  #### File: Shiny-Dashboard.Rmd **Description:** The dashboard provides both a visual of the interconnectivity of ensembles across academic disciplines and a “living” table to facilitate the expansion of the static table found in the original paper. It requires both R and RStudio. A online version of the dashboard can be found at: https://collabdashboard.shinyapps.io/Shiny-Dashboard/#section-about.  ## Code/software The dashboard requires both R and RStudio.  ## Access information Other publicly accessible locations of the data: * https://collabdashboard.shinyapps.io/Shiny-Dashboard/#section-about * https://github.com/bleicham/Assembling-Ensembles