D-Net

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        <h2 class="uk-text-center">About</h2>

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            <p>

              Open Science is gradually becoming the modus operandi in research practices, affecting the way researchers

              collaborate and publish, discover, and access scientific knowledge.

              Scientists are increasingly publishing research results beyond the article, to share all scientific

              products (metadata and files) generated during an experiment, such as datasets, software, experiments.

              They publish in scholarly communication data sources (e.g. institutional repositories, data archives,

              software repositories), rely where possible on persistent identifiers (e.g. DOI, ORCID, Grid.ac, PDBs),

              specify semantic links to other research products (e.g. supplementedBy, citedBy, versionOf), and possibly

              to projects and/or relative funders.

              By following such practices, scientists are implicitly constructing the Global Open Science Graph, where

              by "graph" we mean a collection of objects interlinked by semantic relationships.

              <br><br>

              The OpenAIRE Research Graph includes metadata and links between scientific products (e.g. literature,

              datasets, software, and "other research products"), organizations, funders, funding streams, projects,

              communities, and (provenance) data sources - the details of the <a

                href="https://zenodo.org/record/2643199#.XOqdstMzZ24" target="_blank">graph data model</a> can be found

              in Zenodo.org.

              <br><br>

              The Graph is available and obtained as an aggregation of the metadata and links collected from ~70.000

              trusted sources, further enriched with metadata and links provided by:</p>

            <ul class="portal-circle">

              <li class="uk-margin-bottom">OpenAIRE end-users, e.g. researchers, project administrators, data curators

                providing links from scientific products to projects, funders, communities, or other products;

              </li>

              <li class="uk-margin-bottom">OpenAIRE Full-text mining algorithms over around ~10Mi Open Access article

                full-texts;

              </li>

              <li>Research infrastructure scholarly services, bridged to the graph via OpenAIRE, exposing metadata of

                products such as research workflows, experiments, research objects, software, etc..

              </li>

            </ul>

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      <h2 class="uk-text-center">Architecture</h2>

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          <h3 class="uk-margin-medium-top portal-color">How we build it</h3>

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            <p>

              OpenAIRE collects metadata records from more than 70K scholarly communication sources from all over the

              world, including Open Access institutional repositories, data archives, journals.

              All the metadata records (i.e. descriptions of research products) are put together in a data lake,

              together

              with records from Crossref, Unpaywall, ORCID, Grid.ac, and information about projects provided by national

              and international funders.

              Dedicated inference algorithms applied to metadata and to the full-texts of Open Access publications

              enrich

              the content of the data lake with links between research results and projects, author affiliations,

              subject

              classification, links to entries from domain-specific databases.

              Duplicated organisations and results are identified and merged together to obtain an open, trusted, public

              resource enabling explorations of the scholarly communication landscape like never before.

            </p>

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          <ul #tabs uk-tab class="uk-tab">

            <li><a>Aggregation</a></li>

            <li><a>Deduplication</a></li>

            <li><a>Enrichment</a></li>

            <li><a>Post-Cleaning</a></li>

            <li><a>Indexing</a></li>

            <li><a>Stats Analysis</a></li>

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                    OpenAIRE collects metadata records from a variety of content providers as described in

                    <a href="https://www.openaire.eu/aggregation-and-content-provision-workflows" target="_blank">https://www.openaire.eu/aggregation-and-content-provision-workflows</a>.

                    <br><br>

                    OpenAIRE aggregates metadata records describing objects of the research life-cycle from content

                    providers compliant to the

                    <a href="https://guidelines.openaire.eu" target="_blank">OpenAIRE guidelines</a>

                    and from entity registries (i.e. data sources offering authoritative lists of entities, like OpenDOAR,

                    re3data, DOAJ, and funder databases).

                    After collection, metadata are transformed according to the OpenAIRE internal metadata model, which is

                    used to generate the final OpenAIRE Research Graph that you can access from the OpenAIRE portal and

                    the

                    APIs.

                    <br><br>

                    The transformation process includes the application of cleaning functions whose goal is to ensure that

                    values are harmonised according to a common format (e.g. dates as YYYY-MM-dd) and, whenever

                    applicable,

                    to a common controlled vocabulary.

                    The controlled vocabularies used for cleansing are accessible at

                    <a href="http://api.openaire.eu/vocabularies" target="_blank">http://api.openaire.eu/vocabularies</a>.

                    Each vocabulary features a set of controlled terms, each with one code, one label, and a set of

                    synonyms.

                    If a synonym is found as field value, the value is updated with the corresponding term.

                    Also, the OpenAIRE Research Graph is extended with other relevant scholarly communication sources that

                    are too big to be integrated via the “normal” aggregation mechanism: DOIBoost (which merges Crossref,

                    ORCID, Microsoft Academic Graph, and Unpaywall), and ScholeXplorer, one of the Scholix hubs offering a

                    large set of links between research literature and data.

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                    <li><a>Clustering</a></li>

                    <li><a>Matching & Election</a></li>

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                            Clustering is a common heuristics used to overcome the N x N complexity required to match all

                            pairs of objects to identify the equivalent ones.

                            The challenge is to identify a clustering function that maximizes the chance of comparing only

                            records that may lead to a match, while minimizing the number of records that will not be

                            matched while being equivalent.

                            Since the equivalence function is to some level tolerant to minimal errors (e.g. switching of

                            characters in the title, or minimal difference in letters), we need this function to be not

                            too

                            precise (e.g. a hash of the title), but also not too flexible (e.g. random ngrams of the

                            title).

                            On the other hand, reality tells us that in some cases equality of two records can only be

                            determined by their PIDs (e.g. DOI) as the metadata properties are very different across

                            different versions and no clustering function will ever bring them into the same cluster.

                            To match these requirements OpenAIRE clustering for products works with two functions:

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                              <div>DOI: the function generates the DOI when this is provided as part of the record

                                properties;

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                                Title-based function: the function generates a key that depends on

                                (i) number of significant words in the title (normalized, stemming, etc.),

                                (ii) module 10 of the number of characters of such words, and

                                (iii) a string obtained as an alternation of the function prefix(3) and suffix(3) (and

                                vice

                                versa) o the first 3 words (2 words if the title only has 2). For example, the title

                                “Entity

                                deduplication in big data graphs for scholarly communication” becomes “entity

                                deduplication

                                big data graphs scholarly communication” with two keys key “7.1entionbig” and

                                “7.1itydedbig”

                                (where 1 is module 10 of 54 characters of the normalized title.

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                            To give an idea, this configuration generates around 77Mi blocks, which we limited to 200

                            records each (only 15K blocks are affected by the cut), and entails 260Bi matches. Matches in

                            a

                            block are performed using a “sliding window” set to 80 records. The records are sorted

                            lexicographically on a normalized version of their titles. The 1st record is matched against

                            all

                            the 80 following ones, then the second, etc. for an NlogN complexity.

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                            Once the clusters have been built, the algorithm proceeds with the comparisons.

                            Comparisons are driven by a decisional tree that:

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                              <div>

                                <span class="portal-color">1.</span> Tries to capture equivalence via PIDs: if records

                                share

                                a PID then they are equivalent

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                            </li>

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                              <div>

                                <span class="portal-color">2.</span> Tries to capture difference:

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                                    <span class="portal-color">a.</span>

                                    If record titles contain different “numbers” then they are different (this rule is

                                    subject to different feelings, and should be fine-tuned);

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                                    <span class="portal-color">b.</span>

                                    If record contain different number of authors then they are different;

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                                    <span class="portal-color">c.</span>

                                    Note that different PIDs do not imply different records, as different versions may

                                    have

                                    different PIDs.

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                                </li>

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                              <div><span class="portal-color">3.</span> Measures equivalence:</div>

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                                <li>

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                                    The titles of the two records are normalised and compared for similarity by applying

                                    the

                                    Levenstein distance algorithm.

                                    The algorithm returns a number in the range [0,1], where 0 means “very different” and

                                    1

                                    means “equal”.

                                    If the distance is greater than or equal 0,99 the two records are identified as

                                    duplicates.

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                                </li>

                                <li>

                                  <div>Dates are not regarded for equivalence matching because different versions of the

                                    same records should be merged and may be published on different dates, e.g. pre-print

                                    and published version of an article.

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                                </li>

                              </ul>

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                            Once the equivalence relationships between pairs of records are set, the groups of equivalent

                            records are obtained (transitive closure, i.e. “mesh”).

                            From such sets a new representative object is obtained, which inherits all properties from the

                            merged records and keeps track of their provenance.

                            The ID of the record is obtained by appending the prefix “dedup_” to the MD5 of the first ID

                            (given their lexicographical ordering).

                            A new, more stable function to generate the ID is under development, which exploits the DOI

                            when

                            one of the records to be merged includes a Crossref or a DataCite record.

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                    <li><a>General</a></li>

                    <li><a>Mining</a></li>

                    <li><a>Bulk tagging/ Deduction</a></li>

                    <li><a>Propagation</a></li>

                  </ul>

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                    <li>

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                           src="assets/graph-assets/about/architecture/enrichment.svg" alt="Enrichment">

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                        <p>

                          The aggregation processes are continuously running and apply vocabularies as they are in a given

                          moment of time.

                          It could be the case that a vocabulary changes after the aggregation of one data source has

                          finished,

                          thus the aggregated content does not reflect the current status of the controlled vocabularies.

                          <br><br>

                          In addition, the integration of ScholeXplorer and DOIBooost and some enrichment processes

                          applied

                          on the raw

                          and on the de-duplicated graph may introduce values that do not comply with the current status

                          of

                          the OpenAIRE controlled vocabularies.

                          For these reasons, we included a final step of cleansing at the end of the workflow

                          materialisation.

                          The output of the final cleansing step is the final version of the OpenAIRE Research Graph.

                        </p>

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                            The OpenAIRE Research Graph is enriched by links mined by OpenAIRE’s full-text mining

                            algorithms

                            that scan the plaintexts of publications for funding information, references to datasets,

                            software URIs, accession numbers of bioetities, and EPO patent mentions.

                            Custom mining modules also link research objects to specific research communities, initiatives

                            and infrastructures.

                            In addition, other inference modules provide content-based document classification, document

                            similarity, citation matching, and author affiliation matching.

                            <br><br>

                            <span class="portal-color">Project mining</span>

                            in OpenAIRE text mines the full-texts of publications in order to extract matches to funding

                            project codes/IDs.

                            The mining algorithm works by utilising

                            (i) the grant identifier, and

                            (ii) the project acronym (if available) of each project.

                            The mining algorithm:

                            (1) Preprocesses/normalizes the full-texts using several functions, which depend on the

                            characteristics of each funder (i.e., the format of the grant identifiers), such as stopword

                            and/or punctuation removal, tokenization, stemming, converting to lowercase; then

                            (2) String matching of grant identifiers against the normalized text is done using database

                            techniques; and

                            (3) The results are validated and cleaned using the context near the match by looking at the

                            context around the matched ID for relevant metadata and positive or negative words/phrases, in

                            order to calculate a confidence value for each publication-->project link.

                            A confidence threshold is set to optimise high accuracy while minimising false positives, such

                            as matches with page or report numbers, post/zip codes, parts of telephone numbers, DOIs or

                            URLs, accession numbers.

                            The algorithm also applies rules for disambiguating results, as different funders can share

                            identical project IDs; for example, grant number 633172 could refer to H2020 project EuroMix

                            but

                            also to Australian-funded NHMRC project “Brain activity (EEG) analysis and brain imaging

                            techniques to measure the neurobiological effects of sleep apnea”.

                            Project mining works very well and was the first Text & Data Mining (TDM) service of OpenAIRE.

                            Performance results vary from funder to funder but precision is higher than 98% for all

                            funders

                            and 99.5% for EC projects.

                            Recall is higher than 95% (99% for EC projects), when projects are properly acknowledged using

                            project/grant IDs.

                            <br><br>

                            <span class="portal-color">Dataset extraction</span>

                            runs on publications full-texts as described in “High pass text-filtering for Citation

                            matching”, TPDL 2017[1].

                            In particular, we search for citations to datasets using their DOIs, titles and other metadata

                            (i.e., dates, creator names, publishers, etc.).

                            We extract parts of the text which look like citations and search for datasets using database

                            join and pattern matching techniques.

                            Based on the experiments described in the paper, precision of the dataset extraction module is

                            98.5% and recall is 97.4% but it is also probably overestimated since it does not take into

                            account corruptions that may take place during pdf to text extraction.

                            It is calculated on the extracted full-texts of small samples from PubMed and arXiv.

                            <br><br>

                            <span class="portal-color">Software extraction</span>

                            runs also on parts of the text which look like citations.

                            We search the citations for links to software in open software repositories, specifically

                            github, sourceforge, bitbucket and the google code archive.

                            After that, we search for links that are included in Software Heritage (SH,

                            https://www.softwareheritage.org) and return the permanent URL that SH provides for each

                            software project.

                            We also enrich this content with user names, titles and descriptions of the software projects

                            using web mining techniques.

                            Since software mining is based on URL matching, our precision is 100% (we return a software

                            link

                            only if we find it in the text and there is no need to disambiguate).

                            As for recall rate, this is not calculable for this mining task.

                            Although we apply all the necessary normalizations to the URLs in order to overcome usual

                            issues

                            (e.g., http or https, existence of www or not, lower/upper case), we do not calculate cases

                            where a software is mentioned using its name and not by a link from the supported software

                            repositories.

                            <br><br>

                            <span class="portal-color">For the extraction of bio-entities</span>, we focus on Protein Data

                            Bank (PDB) entries.

                            We have downloaded the database with PDB codes and we update it regularly.

                            We search through the whole publication’s full-text for references to PDB codes.

                            We apply disambiguation rules (e.g., there are PDB codes that are the same as antibody codes

                            or

                            other issues) so that we return valid results.

                            Current precision is 98%.

                            Although it's risky to mention recall rates since these are usually overestimated, we have

                            calculated a recall rate of 98% using small samples from pubmed publications.

                            Moreover, our technique is able to identify about 30% more links to proteins than the ones

                            that

                            are tagged in Pubmed xmls.

                            <br><br>

                            <span class="portal-color">Other text-mining modules</span> include mining for links to EPO

                            patents, or custom mining modules for linking research objects to specific research

                            communities,

                            initiatives and infrastructures, e.g. COVID-19 mining module.

                            Apart from text-mining modules, OpenAIRE also provides a document classification service that

                            employs analysis of free text stemming from the abstracts of the publications.

                            The purpose of applying a document classification module is to assign a scientific text one or

                            more predefined content classes.

                            In OpenAIRE, the currently used taxonomies are arXiv, MeSH (Medical Subject Headings), ACM and

                            DDC (Dewey Decimal Classification, or Dewey Decimal System).

                            <br><br>

                            <hr>

                            [1] Foufoulas, Y., Stamatogiannakis, L., Dimitropoulos, H., & Ioannidis, Y. (2017, September).

                            High-Pass Text Filtering for Citation Matching.

                            In International Conference on Theory and Practice of Digital Libraries (pp. 355-366).

                            Springer,

                            Cham.

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                        <a class="custom-explore-toggle">Read more<span uk-icon="chevron-down"></span></a>

                      </div>

                      <div *ngIf="enrichmentMiningReadMore" class="uk-width-3-5@m uk-text-center clickable"

                           (click)="enrichmentMiningReadMore = false">

                        <a class="custom-explore-toggle">Read less<span uk-icon="chevron-up"></span></a>

                      </div>

                    </li>

                    <li>

                      <img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"

                           src="assets/graph-assets/about/architecture/enrichment.svg" alt="Enrichment">

                      <div class="uk-margin-bottom uk-margin-medium-right uk-text-small">

                        The Deduction process (also known as “bulk tagging”) enriches each record with new information

                        that

                        can be derived from the existing property values.

                        <br><br>

                        As of September 2020, three procedures are in place to relate a research product to a research

                        initiative, infrastructure (RI) or community (RC) based on:

                        <ul class="portal-circle">

                          <li>subjects (2.7M results tagged)</li>

                          <li>Zenodo community (16K results tagged)</li>

                          <li>the data source it comes from (250K results tagged)</li>

                        </ul>

                        The list of subjects, Zenodo communities and data sources used to enrich the products are defined

                        by

                        the managers of the community gateway or infrastructure monitoring dashboard associated with the

                        RC/RI.

                      </div>

                    </li>

                    <li>

                      <img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"

                           src="assets/graph-assets/about/architecture/enrichment.svg" alt="Enrichment">

                      <div

                          [class]="'uk-margin-bottom uk-margin-medium-right uk-text-small '+(enrichmentPropagationReadMore ? '' : 'lines-18 multi-line-ellipsis')">

                        <div>

                          <div>

                            This process “propagates” properties and links from one product to another if between the two

                            there is a “strong” semantic relationship.

                            <br><br>

                            As of September 2020, the following procedures are in place:

                            <ul class="portal-circle">

                              <li>

                                Propagation of the property “country” to results from institutional repositories:

                                e.g. publication collected from an institutional repository maintained by an italian

                                university will be enriched with the property “country = IT”.

                              </li>

                              <li>

                                Propagation of links to projects: e.g. publication linked to project P “is supplemented

                                by”

                                a dataset D.

                                Dataset D will get the link to project P.

                                The relationships considered for this procedure are “isSupplementedBy” and “supplements”.

                              </li>

                              <li>

                                Propagation of related community/infrastructure/initiative from organizations to products

                                via affiliation relationships: e.g. a publication with an author affiliated with

                                organization O.

                                The manager of the community gateway C declared that the outputs of O are all relevant for

                                his/her community C.

                                The publication is tagged as relevant for C.

                              </li>

                              <li>

                                Propagation of related community/infrastructure/initiative to related products: e.g.

                                publication associated to community C is supplemented by a dataset D.

                                Dataset D will get the association to C.

                                The relationships considered for this procedure are “isSupplementedBy” and “supplements”.

                              </li>

                              <li>

                                Propagation of ORCID identifiers to related products, if the products have the same

                                authors:

                                e.g. publication has ORCID for its authors and is supplemented by a dataset D. Dataset D

                                has

                                the same authors as the publication. Authors of D are enriched with the ORCIDs available

                                in

                                the publication.

                                The relationships considered for this procedure are “isSupplementedBy” and “supplements”.

                              </li>

                            </ul>

                          </div>

                        </div>

                      </div>

                      <div *ngIf="!enrichmentPropagationReadMore" class="uk-width-3-5@m uk-text-center clickable"

                           (click)="enrichmentPropagationReadMore = true">

                        <a class="custom-explore-toggle">Read more<span uk-icon="chevron-down"></span></a>

                      </div>

                      <div *ngIf="enrichmentPropagationReadMore" class="uk-width-3-5@m uk-text-center clickable"

                           (click)="enrichmentPropagationReadMore = false">

                        <a class="custom-explore-toggle">Read less<span uk-icon="chevron-up"></span></a>

                      </div>

                    </li>

                  </ul>

                </div>

                <!--              </div>-->

                <!--              <div class="uk-width-expand">-->

                <!--                <img src="assets/graph-assets/about/architecture/enrichment.svg">-->

                <!--              </div>-->

              </div>

            </li>

            <li>

              <div class="uk-text-small uk-margin-large-top">

                <!--              <div class="uk-width-3-5@m">-->

                <img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"

                     src="assets/graph-assets/about/architecture/post_cleaning.svg" alt="Post Cleaning">

                <div class="uk-margin-bottom uk-margin-medium-right">

                  <p>

                    Lorem ipsum...

                  </p>

                </div>

                <!--              </div>-->

                <!--              <div class="uk-width-expand">-->

                <!--                <img src="assets/graph-assets/about/architecture/post_cleaning.svg">-->

                <!--              </div>-->

              </div>

            </li>

            <li>

              <div class="uk-text-small uk-margin-large-top">

                <!--              <div class="uk-width-3-5@m">-->

                <img class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image"

                     src="assets/graph-assets/about/architecture/indexing.svg" alt="Indexing">

                <div class="uk-margin-bottom uk-margin-medium-right">

                  <p>

                    The final version of the OpenAIRE Research Graph is indexed on a Solr server that is used by the

                    OpenAIRE portals (EXPLORE, CONNECT, PROVIDE) and APIs, the latter adopted by several third-party

                    applications and organizations, such as:

                  </p>

                  <ul class="portal-circle">

                    <li class="uk-margin-small-bottom">

                      <span class="portal-color">EOSC</span>

                      --The OpenAIRE Research Graph APIs and Portals will offer to the EOSC an Open Science Resource

                      Catalogue, keeping an up to date map of all research results (publications, datasets, software),

                      services, organizations, projects, funders in Europe and beyond.

                    </li>

                    <li class="uk-margin-small-bottom">

                      <span class="portal-color">DSpace & EPrints</span>

                      repositories can install the OpenAIRE plugin to expose OpenAIRE compliant metadata records via their

                      OAI-PMH endpoint and offer to researchers the possibility to link their depositions to the funding

                      project, by selecting it from the list of project provided by OpenAIRE

                    </li>

                    <li>

                      <span class="portal-color">EC participant portal (Sygma - System for Grant Management)</span>

                      uses the OpenAIRE API in the “Continuous Reporting” section.

                      Sygma automatically fetches from the OpenAIRE Search API the list of publications and datasets in

                      the

                      OpenAIRE Research Graph that are linked to the project.

                      The user can select the research products from the list and easily compile the continuous reporting

                      data of the project.

                    </li>

                  </ul>

                </div>

                <!--              </div>-->

                <!--              <div class="uk-width-expand">-->

                <!--                <img src="assets/graph-assets/about/architecture/indexing.svg">-->

                <!--              </div>-->

              </div>

            </li>

            <li>

              <div class="uk-text-small uk-margin-large-top">

                <!--              <div class="uk-width-3-5@m">-->

                <img

                    class="uk-width-2-5@m uk-align-right@m uk-margin-remove-adjacent tab-image uk-padding-large uk-padding-remove-top uk-padding-remove-horizontal"

                    src="assets/graph-assets/about/architecture/stats_analysis.svg" alt="Stats Analysis">

                <div class="uk-margin-bottom uk-margin-medium-right">

                  <p>

                    The OpenAIRE Research Graph is also processed by a pipeline for extracting the statistics and

                    producing

                    the charts for funders, research initiative, infrastructures, and policy makers that you can see on

                    MONITOR.

                    Based on the information available on the graph, OpenAIRE provides a set of indicators for monitoring

                    the funding and research impact and the uptake of Open Science publishing practices,

                    such as Open Access publishing of publications and datasets, availability of interlinks between

                    research

                    products, availability of post-print versions in institutional or thematic Open Access repositories,

                    etc.

                  </p>

                </div>

                <!--              </div>-->

                <!--              <div class="uk-width-expand">-->

                <!--                <img src="assets/graph-assets/about/architecture/stats_analysis.svg">-->

                <!--              </div>-->

              </div>

            </li>

          </ul>

        </div>

      </div>

      <div class="uk-padding-small uk-margin-top">

        <h6>References</h6>

        <ul class="uk-text-small portal-circle">

          <li>

            <a href="https://aka.ms/msracad" target="_blank">Microsoft Academic Graph</a>

            which is made available under the ODC Attribution License.

            For more information on Microsoft Academic Graph please also read

            <a href="https://docs.microsoft.com/en-us/academic-services/graph/resources-faq" target="_blank">here</a>.

          </li>

          <li>

            <a href="https://www.openaire.eu/aggregation-and-content-provision-workflows" target="_blank">https://www.openaire.eu/aggregation-and-content-provision-workflows</a>

          </li>

        </ul>

      </div>

    </div>

  </div>

  <div id="metrics" class="uk-container uk-container-large uk-section">

    <div class="uk-padding-small">

      <h2 class="uk-text-center">Data & Metrics</h2>

      <h4 class="uk-text-center uk-margin-medium-top portal-color">Coming soon...</h4>

      <!--        <div>-->

      <!--          <h3 class="uk-margin-medium-top portal-color">Data</h3>-->

      <!--          <div></div>-->

      <!--        </div>-->

      <!--        <div>-->

      <!--          <h3 class="uk-margin-medium-top portal-color">Metrics</h3>-->

      <!--          <div></div>-->

      <!--        </div>-->

    </div>

  </div>

  <div id="infrastructure" class="uk-container uk-container-large uk-section">

    <div class="uk-padding-small">

      <h2 class="uk-text-center">Infrastructure</h2>

      <div>

        <div class="uk-flex uk-flex-center">

          <p class="uk-width-3-4@m uk-padding-small">

            The OpenAIRE graph operates based on a vast variety of hardware and software. As of December 2019, the

            hardware infrastructure is the following:

          </p>

        </div>

        <img src="assets/graph-assets/about/infrastructure.png">

      </div>

    </div>

  </div>

  <div id="team" class="uk-container uk-container-large uk-section">

    <div class="uk-padding-small">

      <h2 class="uk-text-center">Team</h2>

      <div>

        <div class="uk-margin-bottom">

          <div class="uk-flex uk-flex-middle uk-grid" uk-grid="">

            <div class="uk-text-center uk-width-1-1@s uk-width-1-3@m uk-first-column">

              <img src="assets/graph-assets/about/team.svg">

            </div>

            <div class="uk-text-center">

              <div class="uk-margin-medium-bottom">

                Key team members contributing to the Research Graph

              </div>

              <div>

                <a class="uk-button portal-button" target="_blank" href="https://www.openaire.eu/research-graph-team">

                  Meet the team

                  <span class="space">

                <svg height="16" viewBox="0 0 16 16" width="16" xmlns="http://www.w3.org/2000/svg">

                  <path

                      d="M12.578,4.244,5.667,11.155V8.167A.833.833,0,1,0,4,8.167v5A.832.832,0,0,0,4.833,14h5a.833.833,0,0,0,0-1.667H6.845l6.911-6.911a.833.833,0,1,0-1.178-1.178h0Z"

                      fill="#fff" id="arrow-down-left2" transform="translate(7.071 19.799) rotate(-135)">

                  </path>

                </svg>

              </span>

                </a>

              </div>

            </div>

          </div>

        </div>

      </div>

    </div>

  </div>

</div>