diff --git a/src/app/about/about.component.html b/src/app/about/about.component.html index 76fcad3..31e4b96 100644 --- a/src/app/about/about.component.html +++ b/src/app/about/about.component.html @@ -79,33 +79,32 @@
- - - - - - - @@ -122,16 +121,13 @@
  • Indexing
  • Stats Analysis
    • -
    - - - - -
  • -
    - -
    +
    -
    • Enrichment -
      -

      - 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. -

      -
      -
      • -
    • - Enrichment -
      +
      -
      - 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. -

      - Project mining - 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. -

      - Dataset extraction - 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. -

      - Software extraction - 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. -

      - For the extraction of bio-entities, 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. -

      - Other text-mining modules 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). -

      -
      - [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. -
      + 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. +

      + Project mining + 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. +

      + Dataset extraction + 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. +

      + Software extraction + 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. +

      + For the extraction of bio-entities, 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. +

      + Other text-mining modules 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). +

      +
      + [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.
      -
      - Read more +
      + Read more
      -
      - Read less +
      + Read less
    • @@ -521,7 +483,8 @@
    • Zenodo community (16K results tagged)
    • the data source it comes from (250K results tagged)
    - The list of subjects, Zenodo communities and data sources used to enrich the products are defined + 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. @@ -530,11 +493,11 @@
  • Enrichment -
    -
    +
    - This process “propagates” properties and links from one product to another if between the two + This process “propagates” properties and links from one product to another if between the + two there is a “strong” semantic relationship.

    As of September 2020, the following procedures are in place: @@ -549,13 +512,16 @@ by” a dataset D. Dataset D will get the link to project P. - The relationships considered for this procedure are “isSupplementedBy” and “supplements”. + The relationships considered for this procedure are “isSupplementedBy” and + “supplements”.
  • - Propagation of related community/infrastructure/initiative from organizations to products + 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 + 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.
    • @@ -563,7 +529,8 @@ 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”. + The relationships considered for this procedure are “isSupplementedBy” and + “supplements”.
  • Propagation of ORCID identifiers to related products, if the products have the same @@ -573,53 +540,45 @@ 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”. + The relationships considered for this procedure are “isSupplementedBy” and + “supplements”.
    • - -
    - Read more +
    + Read more
    -
    - Read less +
    + Read less
    - - - - -
  • - Post Cleaning

    - 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. + 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. + 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. 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.

    - - - -
  • - Indexing
    @@ -637,7 +596,8 @@
  • DSpace & EPrints - repositories can install the OpenAIRE plugin to expose OpenAIRE compliant metadata records via their + 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
    • @@ -647,20 +607,16 @@ 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 + The user can select the research products from the list and easily compile the continuous + reporting data of the project. - - - -
  • - Stats Analysis @@ -670,7 +626,8 @@ 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 + 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 @@ -678,10 +635,6 @@ etc.

    - - - -
    • @@ -692,7 +645,7 @@ + + See all references +
    -

    Data & Metrics

    -

    Coming soon...

    - - - - - - - - +

    Data & Metrics

    +
    @@ -722,31 +670,35 @@

    Infrastructure

    - -

    - The OpenAIRE Research Graph is operated and maintained at the + The OpenAIRE Research Graph is operated and maintained at the ICM cutting-edge Technology centre - 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). -

    - -

    - 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. -

    -

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

    - - - - - + 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). +

    + +

    + 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. +

    +

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

    @@ -756,11 +708,6 @@

    Team

    - - - - - Team @@ -768,14 +715,12 @@
    Key team members contributing to the Research Graph
    -
    - +
    Meet the team
    -
    diff --git a/src/app/about/about.component.ts b/src/app/about/about.component.ts index ddd6d3a..5b1fbad 100644 --- a/src/app/about/about.component.ts +++ b/src/app/about/about.component.ts @@ -55,15 +55,6 @@ export class AboutComponent implements OnInit { this.subs.push(this._piwikService.trackView(this.properties, this.title).subscribe()); } } - - 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'}); - } - } changeTab(index: number) { UIkit.switcher(this.tabs.nativeElement).show(index); diff --git a/src/app/about/about.module.ts b/src/app/about/about.module.ts index f8c8936..e0f9077 100644 --- a/src/app/about/about.module.ts +++ b/src/app/about/about.module.ts @@ -9,18 +9,22 @@ import {IconsModule} from "../openaireLibrary/utils/icons/icons.module"; import {IconsService} from "../openaireLibrary/utils/icons/icons.service"; import {arrow_right} from "../openaireLibrary/utils/icons/icons"; import {Schema2jsonldModule} from '../openaireLibrary/sharedComponents/schema2jsonld/schema2jsonld.module'; +import {NumbersModule} from '../openaireLibrary/sharedComponents/numbers/numbers.module'; +import {TeamComponent} from './team.component'; @NgModule({ imports: [ CommonModule, - RouterModule.forChild([{ - path: '', component: AboutComponent - }]), + RouterModule.forChild([ + {path: '', component: AboutComponent}, + {path: 'team', component: TeamComponent}, + ]), BreadcrumbsModule, IconsModule, - Schema2jsonldModule + Schema2jsonldModule, + NumbersModule ], - declarations: [AboutComponent, ActionPointComponent], + declarations: [AboutComponent, ActionPointComponent, TeamComponent], exports: [AboutComponent] }) export class AboutModule { diff --git a/src/app/about/action-point.component.ts b/src/app/about/action-point.component.ts index bd73c90..1c992ac 100644 --- a/src/app/about/action-point.component.ts +++ b/src/app/about/action-point.component.ts @@ -7,12 +7,12 @@ import {animate, state, style, transition, trigger} from "@angular/animations";
    - +
    - +
    diff --git a/src/app/about/faqs.ts b/src/app/about/faqs.ts deleted file mode 100644 index b8bf5f8..0000000 --- a/src/app/about/faqs.ts +++ /dev/null @@ -1,6 +0,0 @@ -export const faqs = [ -/* { - question: 'Test', - answer: 'Test' - }*/ -]; diff --git a/src/app/about/team.component.css b/src/app/about/team.component.css new file mode 100644 index 0000000..a90064b --- /dev/null +++ b/src/app/about/team.component.css @@ -0,0 +1,32 @@ +.uk-card .uk-card-flip-inner .front, +.uk-card .uk-card-flip-inner .back { + border-radius: 5px; + box-shadow: 0 2px 5px #0000001a; + border: 1px solid #E0E0E0; + font-size: 14px; + font-family: "Roboto", sans-serif; + line-height: 19px; + color: rgba(26, 26, 26, 0.8); +} + +.uk-card:hover .uk-card-flip-inner .front, +.uk-card:hover .uk-card-flip-inner .back { + box-shadow: 0 6px 15px #0000001A; +} + +.uk-card .front img, +.uk-card .back img { + width: 150px; + height: 150px; + border-radius: 50%; + object-fit: cover; +} + +.uk-card .back img { + width: 100px; + height: 100px; +} + +.uk-card .uk-card-flip-inner .uk-text-muted { + color: rgba(26, 26, 26, 0.6); +} diff --git a/src/app/about/team.component.ts b/src/app/about/team.component.ts new file mode 100644 index 0000000..ffed138 --- /dev/null +++ b/src/app/about/team.component.ts @@ -0,0 +1,85 @@ +import {Component} from '@angular/core'; +import {Breadcrumb} from '../openaireLibrary/utils/breadcrumbs/breadcrumbs.component'; +import {member, team} from './team'; + +@Component({ + selector: 'team', + template: ` +
    +
    +
    + +
    +
    +

    Meet The Team

    +
    +
    +
    +
    +
    +
    +
    + +
    +
    {{member.name}}
    +
    + {{member.role}} +
    +
    +
    +
    +
    + Learn more +
    +
    +
    +
    +
    + +
    +
    {{member.name}}
    +
    +
    +
    + Role: {{member.role}} +
    +
    + Affiliation: {{member.affiliation}} +
    +
    + Country: {{member.country}} +
    + +
    +
    + Back +
    +
    +
    +
    +
    +
    +
    +
    +
    + `, + styleUrls: ['team.component.css'] +}) +export class TeamComponent { + public team: member[] = team; + public breadcrumbs: Breadcrumb[] = [ + { + name: 'home', + route: '/' + }, + { + name: 'about', + route: '/about' + }, + { + name: 'Team' + } + ]; +} diff --git a/src/app/about/team.ts b/src/app/about/team.ts new file mode 100644 index 0000000..5c0d8d9 --- /dev/null +++ b/src/app/about/team.ts @@ -0,0 +1,135 @@ +export interface member { + name: string, + role: string, + affiliation: string, + country: string, + responsibilities: string, + photo: string, + active?: boolean +} + +export const team: member[] = [ + { + name: 'Alessia Bardi', + role: 'Researcher / Product Manager of the OpenAIRE Research Community Dashboard (CONNECT)', + affiliation: 'Institute of Information Science and Technologies, Italian National Research Council', + country: 'Italy', + responsibilities: + 'Responsible for the design and operation of the pipeline for the materialisation and data quality evaluation of the graph\n' + + 'Responsible for the integration of content for the communities using OpenAIRE CONNECT services.', + photo: 'alessia.jpg' + }, + { + name: 'Amelie Bäcker', + role: 'Librarian', + affiliation: 'Bielefeld University Library', + country: 'Germany', + responsibilities: 'Metadata integration (standard cases), helpdesk support (OpenAIRE Guidelines, metadata integration).', + photo: 'amelie.jpg' + }, + { + name: 'Andrea Dell Amico', + role: 'Τechnical Team - Systems Administrator', + affiliation: 'Institute of Information Science and Technologies, Italian National Research Council', + country: 'Italy', + responsibilities: 'Working on the computing and storage infrastructure on the CNR side, maintaining the Hadoop and ElasticSearch clusters.', + photo: 'dell_amico.jpg' + }, + { + name: 'Andrea Mannocci', + role: 'Researcher/Data scientist', + affiliation: 'Institute of Information Science and Technologies, Italian National Research Council', + country: 'Italy', + responsibilities: 'Working on data analysis and quality of data.', + photo: 'mannocci.jpg' + }, + { + name: 'Andreas Czerniak', + role: 'OpenAIRE Project Officer', + affiliation: 'Bielefeld University Library', + country: 'Germany', + responsibilities: 'Responsible with the UniBI team for the aggregation (collection and transformation) of metadata.', + photo: 'czerniak.jpg' + }, + { + name: 'Claudio Atzori', + role: 'Software & Data Engineer / Data quality', + affiliation: 'Institute of Information Science and Technologies, Italian National Research Council', + country: 'Italy', + responsibilities: + 'Responsible for the design of graph processing pipeline, glueing the different stages together, from the content aggregation, to the end of the supply chain at the indexing stage.', + photo: 'claudio.jpg' + }, + { + name: 'Eleni Zacharia-Lamprou', + role: 'Software Engineer - Postdoctoral Researcher', + affiliation: 'Athena Research Center (ARC)', + country: 'Greece', + responsibilities: 'Implementation of Text and Data Mining (TDM) modules, used in the graph Enrichment phase.', + photo: 'eleni.jpg' + }, + { + name: 'Enrico Ottonello', + role: 'Technical Team', + affiliation: 'Institute of Information Science and Technologies, Italian National Research Council', + country: 'Italy', + responsibilities: 'Data engineering, aggregation, data curation along the graph enrichment steps.', + photo: 'enrico.jpg' + }, + { + name: 'Harry Dimitropoulos', + role: 'Senior Scientific Associate', + affiliation: 'Athena Research Center (ARC)', + country: 'Greece', + responsibilities: 'Responsible for the Text and Data Mining (TDM) modules used in the graph Enrichment phase.', + photo: 'dimitropoulos.jpg' + }, + { + name: 'Lampros Smyrnaios', + role: 'Software & Data Engineer / Research Assistant', + affiliation: 'Athena Research Center (ARC)', + country: 'Greece', + responsibilities: 'Development of a software to extract the full-texts from publications\' web-pages. These full-texts are used by the Text and Data Mining (TDM) modules. Implementation of Text and Data Mining (TDM) modules, used in the graph Enrichment phase.', + photo: 'lampros.jpg' + }, + { + name: 'Michele De Bonis', + role: 'Technical Team', + affiliation: 'Institute of Information Science and Technologies, Italian National Research Council', + country: 'Italy', + responsibilities: 'Responsible for the deduplication phase and the creation of algorithms to identify groups of data into the graph.', + photo: 'de_bonis.jpg' + }, + { + name: 'Miriam Baglioni', + role: 'Researcher / Software & Data Engineer / Data quality', + affiliation: 'Institute of Information Science and Technologies, Italian National Research Council', + country: 'Italy', + responsibilities: 'Responsible for the graph enrichment steps not related to mining, and to the production of the graph dumps.', + photo: 'miriam.jpeg' + }, + { + name: 'Paolo Manghi', + role: 'Chief Technical Officer', + affiliation: 'Institute of Information Science and Technologies, Italian National Research Council', + country: 'Italy', + responsibilities: 'Responsible for the design and roadmapping of the OpenAIRE infrastructure services, their operation, evolution, and interaction with third-parties.', + photo: 'paolo.png' + }, + { + name: 'Sandro La Bruzzo', + role: 'Technical Team', + affiliation: 'Institute of Information Science and Technologies, Italian National Research Council', + country: 'Italy', + responsibilities: 'Responsible for the graph enrichment steps including the generation of DOIBoost.', + photo: 'sandro.jpg' + }, + { + name: 'Yannis Foufoulas', + role: 'Software Engineer / Researcher', + affiliation: 'Athena Research Center (ARC)', + country: 'Greece', + responsibilities: 'Implementation of Text and Data Mining (TDM) modules used in the graph Enrichment phase.', + photo: 'yiannis.jpg' + } +] diff --git a/src/app/app.component.ts b/src/app/app.component.ts index 178b476..b64198d 100644 --- a/src/app/app.component.ts +++ b/src/app/app.component.ts @@ -64,18 +64,13 @@ export class AppComponent implements OnInit, OnDestroy { { rootItem: new MenuItem("resources", "Resources", "", "/resources", false, [], null, {}), items: [ - // 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"), + new MenuItem("api", "API", "", "/resources", false, [], null, {}), + new MenuItem("references", "References", "", "/resources/references", false, [], null, {}) ] }, { rootItem: new MenuItem("contact", "Support", "", "/support", false, [], null, {}), - 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"), - ] + items: [] } ]; if(!isHome) { diff --git a/src/app/contact/contact.component.html b/src/app/contact/contact.component.html index 5242c1f..a2be63f 100644 --- a/src/app/contact/contact.component.html +++ b/src/app/contact/contact.component.html @@ -83,9 +83,9 @@
    - How to + Support Material
    diff --git a/src/app/home/home.component.css b/src/app/home/home.component.css index 7adfcdb..25a19b3 100644 --- a/src/app/home/home.component.css +++ b/src/app/home/home.component.css @@ -51,10 +51,6 @@ animation-duration: 1.5s; } -.numbers-background { - background: transparent url('assets/graph-assets/home/5.svg') repeat-x center bottom; -} - .fade-out { visibility: hidden; opacity: 0; diff --git a/src/app/home/home.component.html b/src/app/home/home.component.html index 4a9373f..a8156d7 100644 --- a/src/app/home/home.component.html +++ b/src/app/home/home.component.html @@ -115,6 +115,24 @@
    +
    + Explore +
    +
    +
    +
    +
    +

    Use Cases

    +
    Brief presentations of our success stories
    +
    +
    +
    + +
    +
    +
    +
    +
    @@ -285,88 +303,10 @@

    We believe in Numbers

    -
    -
    - - - -
    -
    -
    -
    -
    -
    -
    - {{publicationsSize.count|number}} -
    - - publications -
    -
    -
    -
    -
    - {{datasetsSize.count|number}} -
    - - research data -
    -
    -
    -
    -
    - {{softwareSize.count|number}} -
    - - software -
    -
    -
    -
    -
    - {{otherSize.count|number}} -
    - - other research products -
    -
    -
    -
    -
    +
    -
    diff --git a/src/app/home/home.component.ts b/src/app/home/home.component.ts index 8e6c853..2b1733d 100644 --- a/src/app/home/home.component.ts +++ b/src/app/home/home.component.ts @@ -27,15 +27,6 @@ import {portals} from "./portals"; }) export class HomeComponent { public pageTitle = "OpenAIRE - Research Graph"; - 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; public portals: any[] = portals; public state: number = 0; public properties: EnvProperties = properties; @@ -45,11 +36,6 @@ export class HomeComponent { constructor( private route: ActivatedRoute, private _router: Router, - private _searchResearchResultsService: SearchResearchResultsService, - private _searchDataprovidersService: SearchDataprovidersService, - private _searchProjectsService: SearchProjectsService, - private _searchOrganizationsService: SearchOrganizationsService, - private _refineFieldResultsService: RefineFieldResultsService, private location: Location, private _piwikService: PiwikService, private config: ConfigurationService, private _meta: Meta, private _title: Title, private seoService: SEOService ) { @@ -70,7 +56,6 @@ export class HomeComponent { if (this.properties.enablePiwikTrack && (typeof document !== 'undefined')) { this.subs.push(this._piwikService.trackView(this.properties, this.pageTitle).subscribe()); } - this.getNumbers(); this.animation(); } } @@ -105,101 +90,4 @@ export class HomeComponent { }); this.state = 0; } - - 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); - } } diff --git a/src/app/home/home.module.ts b/src/app/home/home.module.ts index 384660f..cad3e08 100644 --- a/src/app/home/home.module.ts +++ b/src/app/home/home.module.ts @@ -25,6 +25,7 @@ import {QuickSelectionsModule} from "../openaireLibrary/searchPages/searchUtils/ import {IconsService} from "../openaireLibrary/utils/icons/icons.service"; import {arrow_right, book, cog, database, earth} from "../openaireLibrary/utils/icons/icons"; import {IconsModule} from "../openaireLibrary/utils/icons/icons.module"; +import {NumbersModule} from '../openaireLibrary/sharedComponents/numbers/numbers.module'; @NgModule({ imports: [ @@ -32,14 +33,12 @@ import {IconsModule} from "../openaireLibrary/utils/icons/icons.module"; RouterModule.forChild([{ path: '', component: HomeComponent, }]), - RefineFieldResultsServiceModule, - DataProvidersServiceModule, SearchResearchResultsServiceModule, ProjectsServiceModule, OrganizationsServiceModule, SearchFormModule, PiwikServiceModule, HelperModule, ErrorMessagesModule, - SEOServiceModule, OtherPortalsModule, EntitiesSelectionModule, QuickSelectionsModule, IconsModule + SEOServiceModule, OtherPortalsModule, EntitiesSelectionModule, QuickSelectionsModule, IconsModule, NumbersModule ], declarations: [ HomeComponent @@ -53,6 +52,6 @@ import {IconsModule} from "../openaireLibrary/utils/icons/icons.module"; }) export class HomeModule { constructor(private iconsService: IconsService) { - this.iconsService.registerIcons([arrow_right, book, database, cog, earth]) + this.iconsService.registerIcons([arrow_right]) } } diff --git a/src/app/resources/references.component.css b/src/app/resources/references.component.css new file mode 100644 index 0000000..e69de29 diff --git a/src/app/resources/references.component.ts b/src/app/resources/references.component.ts new file mode 100644 index 0000000..cbaf015 --- /dev/null +++ b/src/app/resources/references.component.ts @@ -0,0 +1,178 @@ +import {Component} from '@angular/core'; +import {Breadcrumb} from '../openaireLibrary/utils/breadcrumbs/breadcrumbs.component'; + +@Component({ + selector: 'references', + template: ` +
    +
    +
    + +
    +
    +

    How to cite the graph

    +
    + +
    +
    +
    +
    +

    References

    +
    + This document gathers the bibliographic references relative to the implementation of the OpenAIRE graph processing pipeline, as + well as the indications on how to cite the graph results. +
    +
    +
    +

    References on the graph pipeline

    +
    +
    +

    Aggregation system

    +
      +
    • Manghi, P., Artini, M., Atzori, C., Bardi, A., Mannocci, A., La Bruzzo, S., Candela, L., Castelli, D. and Pagano, P. + (2014), “The D-NET software toolkit: A framework for the realization, maintenance, and operation of aggregative + infrastructures”, Program: electronic library and information systems, Vol. 48 No. 4, pp. 322-354. + https://doi.org/10.1108/PROG-08-2013-0045 +
      • +
    • + Michele Artini, Claudio Atzori, Alessia Bardi, Sandro La Bruzzo, Paolo Manghi, & Andrea Mannocci. (2016, November 24). The + D-NET software toolkit: dnet-basic-aggregator (Version 1.3.0). Zenodo. + http://doi.org/10.5281/zenodo.168362 +
      • +
    • Atzori, C., Bardi, A., Manghi, P., & Mannocci, A. (2017, January). The OpenAIRE workflows for data management. In Italian + Research Conference on Digital Libraries (pp. 95-107). Springer, Cham. +
      • +
    • + Mannocci, A., & Manghi, P. (2016, September). DataQ: a data flow quality monitoring system for aggregative data + infrastructures. In International Conference on Theory and Practice of Digital Libraries (pp. 357-369). Springer, Cham. +
      • +
    +
    +
    +

    Deduplication

    +
      +
    • Claudio Atzori, & Paolo Manghi. (2017, February 17). gdup: a big graph entity deduplication system (Version 4.0.5). + Zenodo. http://doi.org/10.5281/zenodo.4302081, + https://code-repo.d4science.org/D-Net/dnet-dedup/releases +
      • +
    • + Manghi, Paolo, Marko Mikulicic, and Claudio Atzori. "De-duplication of aggregation authority files." International Journal + of Metadata, Semantics and Ontologies 7.2 (2012): 114-130. 10.1504/IJMSO.2012.050014. +
      • +
    • Manghi, P., Atzori, C., De Bonis, M., & Bardi, A. (2020). Entity deduplication in big data graphs for scholarly + communication. Data Technologies and Applications. 10.1108/DTA-09-2019-0163. +
      • +
    • + Atzori, C., Manghi, P., & Bardi, A. (2018, December). GDup: de-duplication of scholarly communication big graphs. In 2018 + IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT) (pp. 142-151). IEEE. + 10.1109/BDCAT.2018.00025. +
      • +
    • + Atzori, Claudio. "GDup: an Integrated, Scalable Big Graph Deduplication System." (2016). 10.5281/zenodo.1454880. +
      • +
    +
    +
    +

    Mining

    +
      +
    • M. Kobos, Ł. Bolikowski, M. Horst, P. Manghi, N. Manola, J. Schirrwagen, “Information inference in scholarly communication + infrastructures: the OpenAIREplus project experience”, Procedia Computer Science 38, 92-99, + http://dx.doi.org/10.1016/j.procs.2014.10.016 +
      • +
    • + Tkaczyk, D., Szostek, P., Fedoryszak, M. et al. CERMINE: automatic extraction of structured metadata from scientific + literature. IJDAR 18, 317–335 (2015). https://doi.org/10.1007/s10032-015-0249-8 +
      • +
    • Giannakopoulos T., Foufoulas Y., Dimitropoulos H., Manola N. (2019) “Interactive Text Analysis and Information + Extraction”. In: Manghi P., Candela L., Silvello G. (eds) Digital Libraries: Supporting Open Science. IRCDL 2019. + Communications in Computer and Information Science, vol 988. Springer, Cham. + https://doi.org/10.1007/978-3-030-11226-4_27 +
      • +
    • + Foufoulas Y., Stamatogiannakis L., Dimitropoulos H., Ioannidis Y. (2017) “High-Pass Text Filtering for Citation Matching”. + In: Kamps J., Tsakonas G., Manolopoulos Y., Iliadis L., Karydis I. (eds) Research and Advanced Technology for Digital + Libraries. TPDL 2017. Lecture Notes in Computer Science, vol 10450. Springer, Cham. + https://doi.org/10.1007/978-3-319-67008-9_28 +
      • +
    • + T. Giannakopoulos, I. Foufoulas, E. Stamatogiannakis, H. Dimitropoulos, N. Manola, and Y. Ioannidis. 2015. “Visual-Based + Classification of Figures from Scientific Literature”. In Proceedings of the 24th International Conference on World Wide Web + (WWW '15 Companion). Association for Computing Machinery, New York, NY, USA, 1059–1060. + DOI: https://doi.org/10.1145/2740908.2742024 +
      • +
    • Giannakopoulos, T., Foufoulas, I., Stamatogiannakis, E., Dimitropoulos, H., Manola, N., & Ioannidis, Y. (2014). + “Discovering and Visualizing Interdisciplinary Content Classes in Scientific Publications”. D-Lib Mag., Volume 20, Number + 11/12. https://doi.org/10.1045/november14-giannakopoulos +
      • +
    • Giannakopoulos T., Stamatogiannakis E., Foufoulas I., Dimitropoulos H., Manola N., Ioannidis Y. (2014) “Content + Visualization of Scientific Corpora Using an Extensible Relational Database Implementation”. In: Bolikowski Ł., Casarosa V., + Goodale P., Houssos N., Manghi P., Schirrwagen J. (eds) Theory and Practice of Digital Libraries -- TPDL 2013 Selected + Workshops. TPDL 2013. Communications in Computer and Information Science, vol 416. Springer, Cham. + https://doi.org/10.1007/978-3-319-08425-1_10, + also in: Google Books +
      • +
    • Giannakopoulos T., Dimitropoulos H., Metaxas O., Manola N., Ioannidis Y. (2013) “Supervised Content Visualization of + Scientific Publications: A Case Study on the ArXiv Dataset”. In: Kłopotek M.A., Koronacki J., Marciniak M., Mykowiecka A., + Wierzchoń S.T. (eds) Language Processing and Intelligent Information Systems. IIS 2013. Lecture Notes in Computer Science, + vol 7912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38634-3_23 +
      • +
    • Y. Chronis, Y. Foufoulas, V. Nikolopoulos, A. Papadopoulos, L. Stamatogiannakis, C. Svingos, Y. E. Ioannidis, "A + Relational Approach to Complex Dataflows", in Workshop Proceedings of the EDBT/ICDT 2016 (MEDAL 2016) Joint Conference + (March 15, 2016, Bordeaux, France) on CEUR-WS.org (ISSN 1613-0073): http://ceur-ws.org/Vol-1558/paper45.pdf +
      • +
    +
    +
    +

    Portals

    + +
    +
    +

    Broker Service

    +
      +
    • Artini, M., Atzori, C., Bardi, A., La Bruzzo, S., Manghi, P., & Mannocci, A. (2015). The OpenAIRE literature broker + service for institutional repositories. D-Lib Magazine, 21(11/12), 1. +
      • +
    • Manghi, P., Atzori, C., Bardi, A., La Bruzzo, S., & Artini, M. (2016, February). Realizing a Scalable and History-Aware + Literature Broker Service for OpenAIRE. In Italian Research Conference on Digital Libraries (pp. 92-103). Springer, Cham. +
      • +
    +
    +
    +
    +
    + `, + styleUrls: ['references.component.css'] +}) +export class ReferencesComponent { + public breadcrumbs: Breadcrumb[] = [ + { + name: 'home', + route: '/' + }, + { + name: 'Resources', + route: '/resources' + }, + { + name: 'References' + } + ]; +} diff --git a/src/app/resources/resources.component.html b/src/app/resources/resources.component.html index 8b7fd67..979f2d5 100644 --- a/src/app/resources/resources.component.html +++ b/src/app/resources/resources.component.html @@ -17,11 +17,11 @@
    diff --git a/src/app/resources/resources.module.ts b/src/app/resources/resources.module.ts index 3e46869..4e1372f 100644 --- a/src/app/resources/resources.module.ts +++ b/src/app/resources/resources.module.ts @@ -5,17 +5,19 @@ import {RouterModule} from "@angular/router"; import {ResourcesComponent} from "./resources.component"; import {BreadcrumbsModule} from "../openaireLibrary/utils/breadcrumbs/breadcrumbs.module"; import {Schema2jsonldModule} from '../openaireLibrary/sharedComponents/schema2jsonld/schema2jsonld.module'; +import {ReferencesComponent} from './references.component'; @NgModule({ imports: [ CommonModule, - RouterModule.forChild([{ - path: '', component: ResourcesComponent - }]), + RouterModule.forChild([ + {path: '', component: ResourcesComponent}, + {path: 'references', component: ReferencesComponent} + ]), BreadcrumbsModule, Schema2jsonldModule ], - declarations: [ResourcesComponent], + declarations: [ResourcesComponent, ReferencesComponent], exports: [ResourcesComponent] }) export class ResourcesModule { diff --git a/src/assets/graph-assets/about/architecture/aggregation.png b/src/assets/graph-assets/about/architecture/aggregation.png index dc1dd03..bd6dd19 100644 Binary files a/src/assets/graph-assets/about/architecture/aggregation.png and b/src/assets/graph-assets/about/architecture/aggregation.png differ diff --git a/src/assets/graph-assets/about/architecture/aggregation_hover.png b/src/assets/graph-assets/about/architecture/aggregation_hover.png index d8c67d9..5ae2dc5 100644 Binary files a/src/assets/graph-assets/about/architecture/aggregation_hover.png and b/src/assets/graph-assets/about/architecture/aggregation_hover.png differ diff --git a/src/assets/graph-assets/about/architecture/deduplication_hover.png b/src/assets/graph-assets/about/architecture/deduplication_hover.png index 4936900..e34d5c7 100644 Binary files a/src/assets/graph-assets/about/architecture/deduplication_hover.png and 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b/src/styles.css @@ -21,7 +21,7 @@ main { min-height: calc(100vh - 100px); - line-height: 30px; + line-height: 25px; letter-spacing: 0.16px; font-size: 16px; font-family: "Open Sans", sans-serif; @@ -39,7 +39,7 @@ main { /* Unordered list*/ ul.portal-circle { list-style: none; - padding-left: 20px; + padding-left: 30px; } ul.portal-circle li { @@ -146,6 +146,10 @@ ul.uk-text-small.portal-circle li:before { border-color: #eeeeee !important; } +.numbers-background { + background: transparent url('assets/graph-assets/home/5.svg') repeat-x center bottom; +} + .number { color: #1a1a1a; }