This package contains data on five text analysis types (term extraction, contract analysis, topic modeling, network mapping), based on the survey data where researchers selected research output that are related to the 17 Sustainable Development Goals (SDGs). This is used as input to improve the current SDG classification model v4.0 to v5.0

Sustainable Development Goals are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This Aurora SDG dashboard is the initial outcome as proof of practice.)

The initiative started from the Aurora Universities Network in 2017, in the working group "Societal Impact and Relevance of Research", to investigate and to make visible 1. what research is done that are relevant to topics or challenges that live in society (for the proof of practice this has been scoped down to the SDGs), and 2. what the effect or impact is of implementing those research outcomes to those societal challenges (this also have been scoped down to research output being cited in policy documents from national and local governments an NGO's).

Context of this dataset | classification model improvement workflow

The classification model we have used are 17 different search queries on the Scopus database.

SDG search queries version 4.0 (SQv4) have been created, Published here:

Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v4.0 by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817443

A survey has been distributed to senior researchers to test the robustness of SQv4. Published here:

Survey data of "Mapping Research output to the Sustainable Development Goals SDGs" by Aurora Universities Network (AUR) doi:10.5281/zenodo.3798385

This text analysis has been made as one of the inputs to improve the classification model. Published here:

Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals SDGs" by Aurora Universities Network (AUR) doi:10.5281/zenodo.3832090

Improved SDG search queries version 5.0 (SQv5) have been created, Published here:

Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v5.0 by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817445

Methods used to do the text analysis

Term Extraction: after text normalisation (stemming, etc) we extracted 2 terms in bigrams and trigrams that co-occurred the most per document, in the title, abstract and keyword

Contrast analysis: the co-occurring terms in publications (title, abstract, keywords), of the papers that respondents have indicated relate to this SDG (y-axis: True), and that have been rejected (x-axis: False). In the top left you'll see term co-occurrences that a clearly relate to this SDG. The bottom-right are terms that are appear in papers that have been rejected for this SDG. The top-right terms appear frequently in both and cannot be used to discriminate between the two groups.

Network map: This diagram shows the cluster-network of terms co-occurring in the publications related to this SDG, selected by the respondents (accepted publications only).

Topic model: This diagram shows the topics, and the related terms that make up that topic. The number of topics is related to the number of of targets of this SDG.

Contingency matrix: This diagram shows the top 10 of co-occurring terms that correlate the most.

Software used to do the text analyses

CorTexT: The CorTexT Platform is the digital platform of LISIS Unit and a project launched and sustained by IFRIS and INRAE. This platform aims at empowering open research and studies in humanities about the dynamic of science, technology, innovation and knowledge production.

Resource with interactive visualisations

Based on the text analysis data we have created a website that puts all the SDG interactive diagrams together. For you to scrall through. https://sites.google.com/vu.nl/sdg-survey-analysis-results/

Data set content

In the dataset root you'll find the following folders and files:

/sdg01-17/

This contains the text analysis for all the individual SDG surveys.

/methods/

This contains the step-by-step explanations of the text analysis methods using Cortext.

/images/

images of the results used in this README.md.

LICENSE.md

terms and conditions for reusing this data.

README.md

description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.

Inside an /sdg01-17/-folder you'll find the following:

This contains the step-by-step explanations of the text analysis methods using Cortext.

/sdg01-17/sdg04-sdg-survey-selected-publications-combined.db

his contains the title, abstract, keywords, fo the publications in the survey, including the and accept or rejection status and the number of respondents

/sdg01-17/sdg04-sdg-survey-selected-publications-combined-accepted-accepted-custom-filtered.db

same as above, but only the accepted papers

/sdg01-17/extracted-terms-list-top1000.csv

the aggregated list of co-occuring terms (bigrams and trigrams) extracted per paper.

/sdg01-17/contrast-analysis/

This contains the data and visualisation of the terms appearing in papers that have been accepted (true) and rejected (false) to be relating to this SDG.

/sdg01-17/topic-modelling/

This contains the data and visualisation of the terms clustered in the same number of topics as there are 'targets' within that SDG.

/sdg01-17/network-mapping/

This contains the data and visualisation of the terms clustered in co-occuring proximation of appearance in papers

/sdg01-17/contingency-matrix/

This contains the data and visualisation of the top 10 terms co-occuring

note: the .csv files are actually tab-separated.

Contribute and improve the SDG Search Queries

We welcome you to join the Github community and to fork, branch, improve and make a pull request to add your improvements to the new version of the SDG queries. https://github.com/Aurora-Network-Global/sdg-queries

Credit report of Prime Aurora Green Development contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

The HD Map Autonomous Vehicle Market is projected to expand at a 19.38% CAGR during the forecast period, reaching a value of $4.06 billion by 2033. The market growth is attributed to the increasing adoption of autonomous vehicles, which require high-definition maps for accurate navigation and path planning. Moreover, government regulations mandating the use of HD maps in autonomous vehicles are further driving the market growth. Key trends shaping the market include the integration of sensor fusion technology for real-time map updates, the adoption of cloud-based mapping for efficient data management, and the development of in-vehicle mapping for enhanced personalization. Major players in the market include Renovo, Mapbox, Waymo, Intel, Atlanter, Qualcomm, Ground Truth, Tesla, Aurora, Mobileye, ZF Friedrichshafen, NVIDIA, Bosch, TomTom, and HERE Technologies. The market is segmented by application (navigation, obstacle detection, traffic management, fleet management), end use (personal vehicles, commercial fleets, public transport), technology (sensor fusion, cloud-based mapping, in-vehicle mapping, real-time updating), and vehicle type (passenger cars, light commercial vehicles, heavy commercial vehicles, buses). Recent developments include: , Recent developments in the HD Map Autonomous Vehicle Market indicate significant advances and investments from key players. Waymo is enhancing its mapping technology to improve safety and efficiency, while Intel is focusing on integration with its mobility solutions. Mapbox recently launched new mapping tools aimed at optimizing the development of autonomous vehicle applications. Qualcomm's innovations in automotive chipsets have also accelerated mapping processes. Tesla continues to refine its self-driving technology, pushing the boundaries of HD mapping in real-time navigation., Merger and acquisition activities have seen companies like NVIDIA exploring partnerships to bolster data processing capabilities, while Bosch has been expected to expand its mapping business through collaborations. Current market dynamics reflect a growing interest in precision mapping, necessitating companies to invest in advanced technologies and partnerships. The market’s valuation has witnessed an upward trend as demand for more accurate and reliable mapping systems intensifies, with players such as Aurora and Mobileye positioning themselves for future growth. As a result, market players are increasingly collaborating, forming alliances, and acquiring relevant companies to strengthen their offerings in this rapidly evolving sector., HD Map Autonomous Vehicle Market Segmentation Insights. Key drivers for this market are: Increased demand for autonomous vehicles, Growth in smart city initiatives; Expansion of V2X communication technology; Rising investment in AI integration; Enhanced safety regulations and standards. Potential restraints include: Technological advancements in mapping, Increasing demand for automation; Rising investment in autonomous vehicles; Regulatory support for self-driving cars; Growing partnerships in mapping ecosystem.

This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs).

Sustainable Development Goals are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This Aurora SDG dashboard is the initial outcome as proof of practice.)

In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to capture expert knowledge from senior researchers in their research domain related to the SDG. The survey was open to the world, but mainly distributed to researchers from the Aurora Universities Network. The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America.

17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey. The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs.

The survey contains the following questions (see inside dataset for exact wording):

• Are you familiar with this SDG?
  • Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.
• Suggest research papers that are relevant for this SDG (upload list)
  • This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)
• Select research papers that are relevant for this SDG (radio buttons: accept, reject)
  • A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.
• Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions)
  • The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.
• Suggest SDG related glossaries with relevant keywords (text fields: url)
  • Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.
• Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions)
  • The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.
• Suggest improvements for the current queries (text field: suggestions per target)
  • We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.

In the dataset root you'll find the following folders and files:

• /00-survey-input/
  • This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.
• /01-raw-data/
  • This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.
• /02-aggregated-data/
  • This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.
• /03-scripts/
  • This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.
• /04-processed-data/
  • This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.
• /images/
  • images of the results used in this README.md.
• LICENSE.md
  • terms and conditions for reusing this data.
• README.md
  • description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.

In the /04-processed-data/ you'll find in each SDG sub-folder the following files.:

• SDG-survey-questions.pdf
  • This file contains the survey questions

  </li>
  <li><strong>SDG-survey-questions.doc</strong>
  <ul>
    <li>This file contains the survey questions</li>
  </ul>
  </li>
  <li><strong>SDG-survey-respondents-per-sdg.csv</strong>
  <ul>
    <li>Basic information about the survey and responses</li>
  </ul>
  </li>
  <li><strong>SDG-survey-city-heatmap.csv</strong>
  <ul>
    <li>Origin of the respondents per SDG survey</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-publications.txt</strong>
  <ul>
    <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-publications-with-eid-match.csv</strong>
  <ul>
    <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-publications-accepted.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-publications-rejected.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-keywords.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-unselected-keywords.csv</strong>
  <ul>
    <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-keywords.csv</strong>
  <ul>
    <li>List of keywords researchers suggest to use to find papers related to this SDG</li>
  </ul>
  </li>
  <li><strong>SDG-survey-glossaries.csv</strong>
  <ul>
    <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-journals.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-unselected-journals.csv</strong>
  <ul>
    <li>As "selected-journals", this is the list of journals