D-Net

          // new MenuItem("api", "API", "", "/resources", false, [], null, {}),

          // new MenuItem("schema", "Metadata Schema", "", "/resources", false, [], null, {}, null, "schema"),

          // new MenuItem("sources", "Sources", "", "/resources", false, [], null, {}, null, "sources"),

        items: [

          // new MenuItem("contact", "Contact", "", "/support", false, [], null, {}),

          // new MenuItem("documentation", "Documentation", "", "/support", false, [], null, {}, null, "documentation"),

          // new MenuItem("faq", "FAQs", "", "/support", false, [], null, {}, null, "faq"),

        ]

.numbers-background {

    background: transparent url('assets/graph-assets/home/5.svg') repeat-x center bottom;

}

    RefineFieldResultsServiceModule,

    DataProvidersServiceModule, SearchResearchResultsServiceModule,

    SEOServiceModule, OtherPortalsModule, EntitiesSelectionModule, QuickSelectionsModule, IconsModule

    this.iconsService.registerIcons([arrow_right, book, database, cog, earth])

      <div class="uk-margin-large-top uk-container uk-margin-bottom uk-text-center">

        <div class="uk-grid uk-child-width-1-3@m">

          <div *ngIf="fundersSize">

            <h3>

              <a href="https://explore.openaire.eu/search/find/projects" target="_blank" class="uk-text-bold number">

                {{fundersSize.number|number}}<span class="number-size">{{fundersSize.size}}</span>

              </a>

            </h3>

            <span class="uk-text-uppercase uk-text-large">Funders</span>

          </div>

          <div *ngIf="datasourcesSize">

            <h3>

              <a href="https://explore.openaire.eu/search/find/dataproviders" target="_blank"

                 class="uk-text-bold number">

                {{datasourcesSize.number|number}}<span class="number-size">{{datasourcesSize.size}}</span>

              </a>

            </h3>

            <span class="uk-text-uppercase uk-text-large">Content providers</span>

          </div>

          <div *ngIf="projectsSize">

            <h3>

              <a href="https://explore.openaire.eu/search/find/projects" target="_blank" class="uk-text-bold number">

                {{projectsSize.number|number}}<span class="number-size">{{projectsSize.size}}</span>

              </a>

            </h3>

            <span class="uk-text-uppercase uk-text-large">Projects</span>

          </div>

        </div>

      </div>

      <div class="numbers-background uk-section">

        <div class="uk-container">

          <div class="uk-grid uk-margin-auto-left uk-margin-auto-right uk-grid-large" uk-grid>

            <div *ngIf="publicationsSize" class="uk-width-1-2@m uk-flex uk-flex-center">

              <div class="number-width">

                <a href="https://explore.openaire.eu/search/find/research-outcomes?type=publications&qf=false"

                   target="_blank" class="number uk-text-bold uk-margin-bottom">{{publicationsSize.count|number}} </a>

                <div class="uk-text-uppercase uk-flex uk-flex-middle">

                  <icon name="book" ratio="1.5" [flex]="true" class="uk-margin-right"

                        customClass="uk-text-secondary"></icon>

                  <span>publications</span>

                </div>

              </div>

            </div>

            <div *ngIf="datasetsSize" class="uk-width-1-2@m uk-flex uk-flex-center">

              <div class="number-width">

                <a href="https://explore.openaire.eu/search/find/research-outcomes?type=datasets&qf=false"

                   target="_blank" class="number uk-text-bold uk-margin-bottom">{{datasetsSize.count|number}} </a>

                <div class="uk-text-uppercase uk-flex uk-flex-middle">

                  <icon name="database" ratio="1.5" [flex]="true" class="uk-margin-right"

                        customClass="uk-text-secondary"></icon>

                  <span>research data</span>

                </div>

            </div>

          </div>

          <div *ngIf="softwareSize" class="uk-width-1-2@m uk-flex uk-flex-center">

            <div class="number-width">

              <a href="https://explore.openaire.eu/search/find/research-outcomes?type=software&qf=false"

                 target="_blank"  class="number uk-text-bold uk-margin-bottom">{{softwareSize.count|number}} </a>

              <div class="uk-text-uppercase uk-flex uk-flex-middle">

                <icon name="cog" ratio="1.5" [flex]="true" class="uk-margin-right"

                      customClass="uk-text-secondary"></icon>

                <span>software</span>

              </div>

            </div>

          </div>

          <div *ngIf="otherSize" class="uk-width-1-2@m uk-flex uk-flex-center">

            <div class="number-width">

              <a href="https://explore.openaire.eu/search/find/research-outcomes?type=other&qf=false"

                 target="_blank" class="number uk-text-bold uk-margin-bottom">{{otherSize.count|number}} </a>

              <div class="uk-text-uppercase uk-flex uk-flex-middle">

                <icon name="earth" ratio="1.5" [flex]="true" class="uk-margin-right"

                      customClass="uk-text-secondary"></icon>

                <span>other research products</span>

              </div>

            </div>

          </div>

        </div>

      </div>

</div>

  public publicationsSize: any = null;

  public datasetsSize: any = null;

  public datasetsLinkedSize: any = null;

  public softwareLinkedSize: any = null;

  public softwareSize: any = null;

  public otherSize: any = null;

  public fundersSize: any = null;

  public projectsSize: any = null;

  public datasourcesSize: any = null;

    private _searchResearchResultsService: SearchResearchResultsService,

    private _searchDataprovidersService: SearchDataprovidersService,

    private _searchProjectsService: SearchProjectsService,

    private _searchOrganizationsService: SearchOrganizationsService,

    private _refineFieldResultsService: RefineFieldResultsService,

      this.getNumbers();

  private getNumbers() {

    this.subs.push(this._searchResearchResultsService.numOfSearchResults("publication", "", this.properties).subscribe(

      data => {

        if (data && data > 0) {

          this.publicationsSize = NumberUtils.roundNumber(data);

        }

      },

      err => {

        this.handleError("Error getting number of publications", err);

      }

    ));

    this.subs.push(this._searchResearchResultsService.numOfSearchResults("dataset", "", this.properties).subscribe(

      data => {

        if (data && data > 0) {

          this.datasetsSize = NumberUtils.roundNumber(data);

        }

      },

      err => {

        //console.log(err);

        this.handleError("Error getting number of research data", err);

      }

    ));

    this.subs.push(this._searchResearchResultsService.numOfSearchResultsLinkedToPub("dataset", this.properties).subscribe(

      data => {

        if (data && data > 0) {

          this.datasetsLinkedSize = NumberUtils.roundNumber(data);

        }

      },

      err => {

        this.handleError("Error getting number of linkedresearch data", err);

      }

    ));

    this.subs.push(this._searchResearchResultsService.numOfSearchResults("software", "", this.properties).subscribe(

      data => {

        if (data && data > 0) {

          this.softwareSize = NumberUtils.roundNumber(data);

        }

      },

      err => {

        this.handleError("Error getting number of software data", err);

      }

    ));

    this.subs.push(this._searchResearchResultsService.numOfSearchResultsLinkedToPub("software", this.properties).subscribe(

      data => {

        if (data && data > 0) {

          this.softwareLinkedSize = NumberUtils.roundNumber(data);

        }

      },

      err => {

        this.handleError("Error getting number of linked software", err);

      }

    ));

    this.subs.push(this._searchResearchResultsService.numOfSearchResults("other", "", this.properties).subscribe(

      data => {

        if (data && data > 0) {

          this.otherSize = NumberUtils.roundNumber(data);

        }

      },

      err => {

        this.handleError("Error getting number of software data", err);

      }

    ));

    this.subs.push(this._refineFieldResultsService.getRefineFieldsResultsByEntityName(["funder"], "project", this.properties).subscribe(

      data => {

        if (data[0] && data[0] > 0) {

          this.projectsSize = NumberUtils.roundNumber(data[0]);

        }

        if (data[1].length > 0 && data[1][0].filterId == "funder" && data[1][0].values) {

          this.fundersSize = NumberUtils.roundNumber(data[1][0].values.length);

        }

      },

      err => {

        this.handleError("Error getting 'funder' field results of projects", err);

      })

    );

    this.subs.push(this._searchDataprovidersService.numOfSearchDataproviders("", this.properties).subscribe(

      data => {

        if (data && data > 0) {

          this.datasourcesSize = NumberUtils.roundNumber(data);

        }

      },

      err => {

        this.handleError("Error getting number of content providers", err);

      }

    ));

  }

  private handleError(message: string, error) {

    console.error("Home Page: " + message, error);

  }

export const faqs = [

/*  {

    question: 'Test',

    answer: 'Test'

  }*/

];

  goTo(id: string) {

    const yOffset = -100;

    const element = document.getElementById(id);

    if(element) {

      const y = element.getBoundingClientRect().top + window.pageYOffset + yOffset;

      window.scrollTo({top: y, behavior: 'smooth'});

    }

  }

    RouterModule.forChild([{

      path: '', component: AboutComponent

    }]),

    Schema2jsonldModule

  declarations: [AboutComponent, ActionPointComponent],

          <circle cx="7.5" cy="7.5" r="7.5" fill="#ee2540"/>

          <circle cx="7.4" cy="6.6" r="5" stroke="#ee2540" stroke-width="2.5" fill="none" />

        <a class="uk-position-absolute uk-transform-center uk-padding" style="left: 27%; top: 48%"

        <a class="uk-position-absolute uk-transform-center uk-padding" style="left: 47%; top: 48%"

        <a class="uk-position-absolute uk-transform-center uk-padding" style="left: 58%; top: 48%"

        <a class="uk-position-absolute uk-transform-center uk-padding" style="left: 70%; top: 48%"

        <a class="uk-position-absolute uk-transform-center uk-padding" style="left: 76%; top: 32%"

        <a class="uk-position-absolute uk-transform-center uk-padding" style="left: 76%; top: 72%"

              <!--            uk-grid-->

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

                <div

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

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

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

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

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

                    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

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

                     (click)="aggregationReadMore = true">

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

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

                     (click)="aggregationReadMore = false">

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

                <!--              </div>-->

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

                <!--                <img src="assets/graph-assets/about/architecture/aggregation.png">-->

                <!--              </div>-->

              <div class="uk-grid">

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

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

                      <div

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

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

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

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

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

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

                           (click)="dedupClusteringReadMore = true">

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

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

                           (click)="dedupClusteringReadMore = false">

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

                      <div

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

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

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

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

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

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

                           (click)="dedupMatchingAndElectionReadMore = true">

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

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

                           (click)="dedupMatchingAndElectionReadMore = false">

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

                <!--              </div>-->

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

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

                <!--              </div>-->

              </div>

              <div class="uk-grid">

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

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

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

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

                        <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.

                          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>

                      </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 '+(enrichmentMiningReadMore ? '' : 'lines-18 multi-line-ellipsis')">

                        <div>

                          <div>

                            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.

                          </div>

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

                           (click)="enrichmentMiningReadMore = true">

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

                      <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>

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

                      <div

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

                        <div>

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

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

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

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

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

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

                        </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 *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>-->

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

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

                <!--              </div>-->

              </div>

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

                    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.

                    In addition, the integration of ScholeXplorer and DOIBoost 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.

                <!--              </div>-->

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

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

                <!--              </div>-->

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

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

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

                <!--              </div>-->

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

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

                <!--              </div>-->

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

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

                <!--              </div>-->

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

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

                <!--              </div>-->

            which is made available under the ODC Attribution License.

      <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>-->

            <p class="uk-width-4-5@m uk-padding-small">

              The OpenAIRE Research Graph is operated and maintained at the <a

              with the facilities and staff guaranteeing robust operation of the whole system.

              Okeanos SuperComputer hosting the graph consists of 26016 cores in total providing 1082 Tflops/s.

              Whole setup is energy efficient with 1.554 Gflops/Watts Power Efficiency resulting in 160th place on the "Top500 by energy-eficiency" list (as of 2019).

            </p>

            <img class="infrastructure-image uk-margin-top uk-margin-bottom" src="assets/graph-assets/about/infrastructure.png">

            <p class="uk-width-4-5@m uk-padding-small">

              ICM supports the continuous operation of the infrastructure including data aggregation, deduplication, inference and provision ensuring seamless 24/7 system uptime and availability.

              System administration activities cover hardware maintenance and provisioning of the new computational resources, providing High Availability solutions to address resilience to failures by service-level redundancy and Load Balancing to distribute workloads uniformly across servers.

              The most crucial parts of the persisted graph are covered with backups along with well defined restore procedures.

              All the monitoring activities rely on an aggregated system-level monitoring accessible via various dashboards giving the better overview of system stability and potential requirements for system elements extension.

              System level monitoring is supplemented with monitoring availability of all the publicly accessible endpoints.

              Hence, the offer of the public API of OpenAIRE to third parties, is of high-standards.

            </p>

            <p class="uk-width-4-5@m uk-padding-small">

              All the maintenance operations undertaken by experienced system administrators are founded on well established routines and emergency maintenance procedures.

            </p>

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