DOI: 10.6071/m3138f
title: Thesaurus for Sustainable Development Goals

description: This paper addresses the important question of how national research
 systems can support the implementation of the United Nations' 17
 sustainable development goals (SDGs) set out in the 2030 agenda. Much
 attention on this topic has so far coalesced around understanding and
 measuring possible synergies and trade-offs that emerge in the SDGs. We
 contribute to this discussion by arguing that it is necessary to move from
 a focus on system interaction towards system transformation. A conceptual
 approach is presented based on the notion that research that “builds
 bridges” between science and technology and the social and environmental
 pillars of sustainable development can more fully support simultaneous
 achievement of the SDGs and thus be transformational. This proposition is
 put to the test empirically through a study of the Mexican research system
 using methods from bibliometrics and social network analysis. Our results
 can help to provide a diagnostic of how research systems are approaching
 SDGs and where potential exists for transformative research.
description: Our thesaurus with 2,103
 search items was constructed by means of the following steps.
 1. We extracted key terms
 from the UN official list of Goals, Targets and Indicators (884
 terms). 2. In
 order to enrich our thesaurus, we searched a preliminary set with 884
 search items in Web of Science and SciELO so as to identify a first set of
 papers related to SDGs. It permitted us to identify synonyms and academic
 jargon related to SDGs in scientific publications. We only picked up very
 frequent keywords which were deeply related to SDGs. 3. We also identified that there
 were some items that are not considered to have a social or environmental
 sustainability perspective, for example, “economic growth” on its own. A
 more careful analysis of the words chosen for inclusion was therefore
 undertaken, particularly around SDG goals 8, 9 and 17. Here it was decided
 to include only words which have some normative association with
 sustainable futures whether social or environmental in the article search
 items. For instance, in SDG 8, we only included bibliometric sources
 related to economic issues that also contain sustainability and/or
 directionality topics. The result is that our dataset and results will be
 biased towards references that include social or sustainable objectives.
 This will result in some SDGs being underreported but is a more accurate
 reflection that addresses the SDGs.  4. In order to overcome partly
 this bias, we enriched our data set by selecting some of the keywords
 suggested by Duran-Silva et al., (2019) (SIRIS project) and the Colombian
 Green Book (2018). Their method was based on machine learning and
 literature review around SDGs. This dataset permitted us to enrich the
 synonyms term of our data set and therefore to extend the scope of the
 scientific publications labelled. However, we did not take all their
 keywords due to some drawbacks, including a large range of keywords for
 each SDG (SDG 3 has around 622 keywords compared to SDG 1 with 101) which
 can generate bias to compare the number of publications across
 SDGs. 5. Since we
 identified that the number of keywords may have a high correlation with
 the number of scientific publications labelled. Therefore, the number of
 search items in every SDG was limited to between 100 and 140
 6. Lastly, search items
 were categorized into two groups. A first group of 781 search items) that
 are always together, such as “climate change” or “clean energy” (type N1).
 A second group consisted of 1,322 topics (type N2) that mix two or more
 terms that are not always together such as “economic” and
 “sustainability” Note: 1. We used as training data
 scientific publications from WoS and SciELO Citation Index related to
 Colombia, Mexico and Brazil Research. This thesaurus was developed to
 study SDGs publication in Latin America. However, it could be extended to
 other regions. 2.
 Once identified those sources that have at least one tag from the
 thesaurus, we proceed to evaluate which proportion of them correspond to
 misidentifications by analysing a random sample of 1,000 sources in SciELO
 and 3,000 in WoS. Thus, we excluded research areas with a proportion of
 misidentifications larger than 70%. Acoustics, astronomy and astrophysics,
 crystallography, optics, history, literature, instrumentation, mineralogy,
 mining and mineral processing, sport sciences, metallurgy and
 metallurgical engineering, arts and humanities, mathematics and
 physics). 3. We
 used a stemming words technique to transform our search terms into a
 standard form of every word. It permits to increase the labelling of match
 scientific publications by reducing the mismatching due to plurals or the
 different parts of speech (Nouns, Verbs, Adjectives, and Adverbs).
 Therefore, in order to use this thesaurus, you must use the same technique
 to stem the text where you are searching these search terms.