READ ME File For ' Vaccinating the Nexus'

Dataset DOI: 10.5258/SOTON/D2048

Date that the file was created: 12, 2021

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GENERAL INFORMATION
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ReadMe Author: Paul Kemp, University of Southampton [ORCID iD: 0000-0003-4470-0589]

Date of data collection: June 2018

Information about geographic location of data collection: N/A Quantitative Literature review 

Related projects:
EPSRC Vaccinating the Nexus

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SHARING/ACCESS INFORMATION
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Licenses/restrictions placed on the data, or limitations of reuse: None

Recommended citation for the data: Vaccinating the Nexus quantitative review

This dataset supports the publication:

AUTHORS: Paul S. Kemp, Michele Acuto, Shaun Larcom, Darren Lumbroso and Markus R. Owen
TITLE:Exorcising Malthusian ghosts: Vaccinating the Nexus to advance integrated water,energy and food resource resilience
JOURNAL: Current Research in Environmental Sustainability
PAPER DOI IF KNOWN: https://doi.org/10.1016/j.crsust.2021.100108

Links to other publicly accessible locations of the data:N/A

Links/relationships to ancillary or related data sets: N/A


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DATA & FILE OVERVIEW
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This dataset contains:

A single spreadsheet file entitled Nexus Meta-analysis.xlsx


Relationship between files, if important for context:  N/A

Additional related data collected that was not included in the current data package: N/A

If data was derived from another source, list source: N/A

If there are there multiple versions of the dataset, list the file updated, when and why update was made: N/A



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METHODOLOGICAL INFORMATION
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Description of methods used for collection/generation of data: <Include links or references to publications or other documentation containing experimental design or protocols used in data collection>

Methods for processing the data: <describe how the submitted data were generated from the raw or collected data>

For methods: To develop insight on potential biases and gaps in understanding in the published literature on the Water-Energy-Food (WEF) Nexus, an assessment of available literature was conducted using a “rapid” vote-counting systematic review (e.g. Kemp et al., 2012) in June 2018. Available literature was collected using the bibliographic search engine “Google Scholar” by employing the search string “Water-Energy-Food-Nexus”. Articles were interrogated and key information collated in relation to ranked order of relevance (the first article on the first page retrieved was ranked 1), authors, the source (e.g. journal, book, or report), title, and year of publication. The information returned was entered into a database. A simple vote-counting methodology was used to categorise the focal topic of the article / book chapter (e.g. terrestrial, marine or freshwater ecosystem services or domains) and subject area of the source (Table 1). We were particularly interested in understanding whether there were biases in food systems (e.g. agriculture versus aquaculture or fisheries), geographic region of focus, and discipline (e.g. social sciences, engineering etc.). In-line with our aim to create a “rapid” and easy to use coarse-resolution method that others with limited resources might easily follow, we set an arbitrary cut-off at 80 returns (first 8 pages of Google scholar) based on judgements that this was sufficient to provide a general holistic impression of the biases and gaps. For other subjects, with greater depth of information, a larger number may be required. 

Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: N/A

Standards and calibration information, if appropriate: N/A

Environmental/experimental conditions: N/A

Describe any quality-assurance procedures performed on the data:N/A

People involved with sample collection, processing, analysis and/or submission: First author only.


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DATA-SPECIFIC INFORMATION <Create sections for each datafile or set, as appropriate>
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Number of variables: 10 Columns in an excel spreadsheet: 

Number of cases/rows: 80 cases (rows)

Variable list, defining any abbreviations, units of measure, codes or symbols used:Qualitative variables for data review = Position	Authors	Title	Source	1 = book or book chapter, 2 = Journal, 	Year	Main focus	Primary focus (if there is one) water = red, energy = blue, food = yello	Fisheries mentioned in abstract	fisheries mentioned elsewhere in article if not abstract

   
Missing data codes: N/A

Specialized formats or other abbreviations used: N/A