Find research datasets worth reusing

Conflict Event Knowledge Graph based on the ongoing Ukraine-Russia Conflict

Conflict Event Knowledge Graph is a Knowledge Graph that links the tweets and the current events of Russia-Ukraine Conflict portrayed in English Wikipedia from 24th February 2022 to 4th March 2022.  Abstract: In the current situation of Russia-Ukraine Conflict, numerous contents are posted daily to engage in the discourse about different events in this conflict. The goal of this study is to provide a framework to enable analysis of these events and the Twitter data, utilizing entity linking. The relevant tweets and events are integrated into a Knowledge Graph ConflictEventKG, and the resources are made publicly available. Homepage: <a href="https://siebeniris.github.io/ConflictEventKG/">https://siebeniris.github.io/ConflictEventKG/</a> Code: <a href="https://github.com/siebeniris/ConflictEventKG">https://github.com/siebeniris/ConflictEventKG</a>

opencc-zeroMar 2022View details →

Wikipedia Knowledge Graph dataset

Wikipedia is the largest and most read online free encyclopedia currently existing. As such, Wikipedia offers a large amount of data on all its own contents and interactions around them,  as well as different types of open data sources. This makes Wikipedia a unique data source that can be analyzed with quantitative data science techniques. However, the enormous amount of data makes it difficult to have an overview, and sometimes many of the analytical possibilities that Wikipedia offers remain unknown. In order to reduce the complexity of identifying and collecting data on Wikipedia and expanding its analytical potential, after collecting different data from various sources and processing them, we have generated a dedicated Wikipedia Knowledge Graph aimed at facilitating the analysis, contextualization of the activity and relations of Wikipedia pages, in this case limited to its English edition. We share this Knowledge Graph dataset in an open way, aiming to be useful for a wide range of researchers, such as informetricians, sociologists or data scientists. There are a total of 9 files, all of them in tsv format, and they have been built under a relational structure. The main one that acts as the core of the dataset is the page file, after it there are 4 files with different entities related to the Wikipedia pages (category, url, pub and page_property files) and 4 other files that act as "intermediate tables" making it possible to connect the pages both with the latter and between pages (page_category, page_url, page_pub and page_link files). The document Dataset_summary includes a detailed description of the dataset. Thanks to Nees Jan van Eck and the Centre for Science and Technology Studies (CWTS) for the valuable comments and suggestions.

opencc-by-4.0Aug 2021View details →

PID Graph for Moorea Biocode Retrospective

This GIF was created as part of the Moorea Biocode retrospective project. For this project, I created a data management plan using the DMPTool about 10 years after the project ended. I started the DMP using the original application submitted to the UCNRS. From this information, I added title, abstract, people associated with the project and funder. I searched the literature for Moorea Biocode and found publications and datasets, which I linked through related works on the DMP. With these links established, I used the DataCite DMP ID Jupyter Notebook and drew the three graphs - (1) before any works were added (2) after journal articles were added and (3) after datasets were added. This simulates the way that the <a href="https://www.project-freya.eu/en/pid-graph/the-pid-graph">PIDGraph (Persistent Identifier Graph)</a> would evolve over the life of the project.

Kiez Benchmarking Knowledge Graph Embeddings

This upload contains pre-calculated Knowledge Graph Embeddings produced by our study "<a href="https://dbs.uni-leipzig.de/file/KIEZ_KEOD_2021_Obraczka_Rahm.pdf">An Evaluation of Hubness Reduction Methods for Entity Alignment with Knowledge Graph Embeddings</a>"

opencc-by-4.0Jan 2022View details →

EMAKG: an enriched version of the Microsoft Academic Knowledge Graph

The Enhanced MAKG (EMAKG) provides an updated and enriched version of the Microsoft Academic Knowledge Graph (MAKG). The EMAKG is a large dataset of scientific publications and related entities such as authors, affiliations, venues, and fields of study. Data includes authors' careers and networks of collaborations, linguistics features, together with worldwide yearly authors' stocks and flows. The EMAKG data is mainly based on the MAKG - Version 2020-06-19 (March 25, 2021). Methods: <a href="https://github.com/LauraPollacci/EMAKG">https://github.com/LauraPollacci/EMAKG</a> Version 0.0  (reduced version) Version 0.0 provides a set of EMAKG subsets, some of which are in abridged form: 01.AffiliationsGeo: Affiliations subset. 03.ConferenceInstances: Conferences subset. 04.Conference Series: ConferenceSeries subset.   05.Journals: Journals subset.   06.24.PaperAuthorAffiliations_Disambiguated: Relationships between papers and disambiguated authors.  09.PaperResources: URLs and resources of publications. 10.Papers: Papers subset. 12.EntityRelatedEntities: Connections between entities. 13.FieldOfStudyChildren: Field of study kinship relations. 14.FieldOfStudyExtendedAttributes: Fields of study co-references between different datasets. 15.FieldsOfStudy: Fields of study subset. 16.PaperFieldsOfStudy: Relationships between papers and fields of study. 18.RelatedFieldOfStudy: Relationships between symptoms, medical treatments, disease causes and fields of study. 19.PaperCitationContexts: Contexts of citations in CiTo. 20.AbstractsProcessed_Chunk0-14: Chunk of processed abstracts. 22.FieldOfStudyLabeled: Tags and scores of fields of studies. 23.Authors_disambiguated: Disambiguated authors subset. 24.PaperAuthorAffiliation_Disambiguated: Relationships between papers, disambiguated authors and affiliations. 25.AuthorORCID: Authors' ORCIDs. 26.AuthorCareer: Authors' yearly publications. 27.AuthorYearLocation: Authors' yearly locations. 28.AuthorEgoNetworks_2000-2014: Authors' ego networks from 2000 to 2014. 29.CountryAnnualFlowsAggregated: Flows aggregated by country and year. 30.FlowsAnnual: Annual country to country flows. 31.StocksAnnual: Annual stocks aggregated by country. 32.PaperFieldsOfStudyLabeled: Publications tagged with fields of studies. 33.Authors_disambiguated_Hindex: H-index of disambiguated authors.

opencc-by-4.0Feb 2022View details →

Knowledge Graph: tyrolean mining documents 15th and 16th century

The dataset contains a Knowledge Graph (.nq file) of two historical mining documents: “Verleihbuch der Rattenberger Bergrichter” ( Hs. 37, 1460-1463) and “Schwazer Berglehenbuch” (Hs. 1587, approx. 1515) stored by the Tyrolean Regional Archive, Innsbruck (Austria). The user of the KG may explore the montanistic network and relations between people, claims and mines in the late medieval Tyrol. The core regions concern the districts Schwaz and Kufstein (Tyrol, Austria). The ontology used to represent the claims is CIDOC CRM, an ISO certified ontology for Cultural Heritage documentation. Supported by the Karma tool the KG is generated as RDF (Resource Description Framework). The generated RDF data is imported into a Triplestore, in this case GraphDB, and then displayed visually. This puts the data from the early mining texts into a semantically structured context and makes the mutual relationships between people, places and mines visible. Both documents and the Knowledge Graph were processed and generated by the research team of the project “Text Mining Medieval Mining Texts”. The research project (2019-2022) was carried out at the university of Innsbruck and funded by go!digital next generation programme of the Austrian Academy of Sciences. Citeable Transcripts of the historical documents are online available: Hs. 37 DOI: 10.5281/zenodo.6274562 Hs. 1587 DOI: 10.5281/zenodo.6274928

A hierarchical graph-based model for mobility data representation and analysis

Hierarchical representations of transportation networks should provide a better understanding of mobility patterns and the underlying structures at various abstraction levels. A hierarchical graph-based model allows representing moving objects and trajectories according to multiple spatial, temporal and semantic scales. The latter model is implemented here in a Neo4j graph database (version 4.4.0) and experimented with historical maritime data covering Brittany Bay in France.

DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment

The file contains two benchmark sets: Heterodimer-AF2 and Docking benchmark 5.5-AF2 test. Each set includes (1) doecy folder, (2) native folder, and (3) label_info.csv.

Benchmark Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures

This archive contains three benchmark datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.

DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment: MAF2 set

Multimer AF2  set. For more information, please read our paper on biorxiv: <a href="https://www.biorxiv.org/content/10.1101/2022.05.19.492741v2">https://www.biorxiv.org/content/10.1101/2022.05.19.492741v2</a>

Full Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures

This archive contains three datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.

Decomposition, topology, properties, and graphs of woody crown networks of 15 tree species of Cerrado vegetation

Data of decomposition, topology, properties, and the corresponding graphs of 15 adult tree species of Cerrado vegetation, sensu stricto physiognomy. The woody crown networks (WCN) representations in a bidimensional space were obtained by drawing followed the methodology described by Prado et al. (2020, Prado, C.H.B.A., Trovão, D.M.B.M., Souza, J.P., 2020. A network model for determining the woody crown's decomposition, topology, and properties. Journal of Theoretical Biology, v. 499, p. 110318. https://doi.org/<a href="https://www.x-mol.com/paperRedirect/1258515479077781504">10.1016/j.jtbi.2020.110318</a>.). The branching regions were the nodes, and the woody crown segments connecting the nodes or merely emerging from them were the connectors. Those trees grew under natural conditions in a most common (sensu stricto) physiognomy of Cerrado vegetation, in a reservoir of 86 ha, located at 850 m above sea level in São Carlos city, São Paulo state, Brazil, at 21°58'- 22°00'S and 47°51'-47°52'W. Following the Köppen climatic classification, this region is between Aw and Cwa, a tropical climate with dry winter and wet summer. The rainy season occurs between October-March, and the dry season between April and September.

opencc-by-4.0Dec 2021View details →

Data of "Efficient generation of entangled multi-photon graph states from a single atom"

Data published in "Efficient generation of entangled multi-photon graph states from a single atom"

Ramanujan graphs with degree 3, 4, 5, 6, or 7

This data set collects all <ul> <li>Ramanujan graphs <ul> <li>3-regular with at most 20 vertices</li> <li>4-regular with at most 16 vertices</li> <li>5-, 6-, 7-regular with at most 14 vertices</li> </ul> </li> <li>Bipartite Ramanujan graphs <ul> <li>4-regular with at most 22 vertices</li> <li>5-, 6-, 7-regular with at most 20 vertices</li> </ul> </li> <li>Vertex-transitive Ramanujan graphs <ul> <li>4-, 5-, 6-, 7-regular with at most 47 vertices</li> </ul> </li> </ul> All files are in Graph6 data format. If a file for a given order does not exist, there are no graphs with that order and the respective property.

Books from the OpenAIRE Research Graph

This dataset is the subset of the OpenAIRE Research Graph about research products of type "Book". The tar archive contains gz files, each with one json per line. Each json compliant to the schema available at <a href="http://doi.org/10.5281/zenodo.5799514">http://doi.org/10.5281/zenodo.5799514</a>.

PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning

PubMed Graph Benchmark (PGB) aggregates the metadata associated with the biomedical articles from PubMed into a unified source. The benchmark contains metadata including title, abstract, authors, in/out citations, MeSH terms, MeSH hierarchy, venue, publication type, and chemicals.

EB-KG: Knowledge Graph of the first 8 eiditions Encyclopaedia Brittanica (1768-1860)

This Knowlege Graph represents the information of the first eight editions of Encyclopaedia Brittanica (years: 1768 to 1860) in RDF (ttl format). The raw dataset is provided by the NLS in this <a href="https://data.nls.uk/data/digitised-collections/encyclopaedia-britannica/">link</a> , and it comprises of eight editions and a total of 195 volumes with a total size of 44GB. It uses two XMLs schemas: METS  for descriptive, structural, technical and administrative metadata (Title, Author, Publisher, etc); and ALTO  for encoding the OCR text of a page. In this work, we have extracted the information from METS and ALTO XMLS using <a href="https://github.com/francesNLP/defoe">defoe</a> tool and developed <a href="https://github.com/francesNLP/defoe/tree/master/defoe/nlsArticles/queries">novel information extraction heuristics</a>. With the extracted information, we created the EB-KG Knowlege Graph, which  uses the <a href="https://francesnlp.github.io/EB-ontology/doc/index-en.html">EB Ontolgy</a>, to represent such information. Furthermore, during the information extraction phase, we have employed several techniques to mitigate two common OCR errors: long-S and the line-break hyphenation. The EB-KG contains 1,638,239 RDF triples. It has information from 8 editions. Each edition can have several Volumes, references to Books, Supplements; it also has an Editor and a Publisher, which can be a Person or an Organization. A Volume has several Pages, which can contain several Terms. And a Term can be either a Topic (a term described across several pages, often combining text, pictures, and tables.) or an Article (a description of the term in one- or two-paragraph long text (similar to an entry in a dictionary)). The data model of the EB-KG can be found <a href="https://francesnlp.github.io/EB-ontology/doc/dataModel.png">here</a>. The original ALTO files do not indicate the start and end of each EB term, the first part of our work involved the automated extraction of all terms (along with their metadata) across editions, so they can be analysed independently without the surrounding text.

GazetteersScotland-KG: A Knowlege Graph for representing the Gazetteers of Scotland (1803-1901)

This Knowlege Graph represents the information of the "Gazeteers of Scotland" (years: 1803 - 1901) collection in RDF (ttl format). This collection comprises twenty volumes of the most popular descriptive gazetteers of Scotland in the 19th century. Principal places in Scotland, including towns, counties, castles, glens, antiquities and parishes, are listed alphabetically. Each entry includes detailed historical and geographical information about each place.  The raw dataset is provided by the NLS in this <a href="https://data.nls.uk/data/digitised-collections/gazetteers-of-scotland/">link</a>. As  other NLS data collections, they are originally provided using two XMLs schemas: METS  for descriptive, structural, technical and administrative metadata (Title, Author, Publisher, etc); and ALTO  for encoding the OCR text of a page. In this work, we have extracted the information from METS and ALTO XMLS using <a href="https://github.com/francesNLP/defoe">defoe</a> tool and developed a <a href="https://github.com/francesNLP/defoe/blob/master/defoe/nls/queries/write_metadata_pages_yml.py">new information extraction defoe query</a> , and created a new Knowlege Graph called GazetteersScotland-KG.  The GazetteersScotland-KG uses the <a href="https://francesnlp.github.io/NLS-ontology/doc/index-en.html">NLS Ontology </a>to represent the information extracted. Furthermore, during the information extraction phase, we have employed several techniques to mitigate two common OCR errors: long-S and the line-break hyphenation. The GazetteersScotland-KG contains 354,998 RDF triples. It has information from 12 series and 20 volumes: Each serie can have several Volumes. Each serie has an Editorm Publisher, mmsid, Shelf-Locator, publication year, etc.  A Volume has several Pages,  with text in them. The data model of the GazetteersScotland-KG can be found <a href="https://francesnlp.github.io/NLS-ontology/doc/dataModel.png">here</a>. <pre>  </pre>

LadiesDebating-KG: A Knowlege Graph for representing the "Edinburgh Ladies' Debating Society Digital Collection" (1865 - 1880)

This Knowlege Graph represents the information of the "Edinburgh Ladies’ Debating Society" (years: 1865 - 1880) collection in RDF (ttl format). This collection consists of the complete runs of two Edinburgh journals, ‘The Attempt’ (10 volumes, 1865-74) and its successor ‘The Ladies’ Edinburgh Magazine’ (6 volumes, 1875-80). These publications were produced by a leading Edinburgh women’s club, known during the period as the Edinburgh Essay Society or the Ladies’ Edinburgh Essay Society, but subsequently as the Ladies’ Edinburgh Debating Society. The Society existed from 1865 to 1935.  The raw dataset is provided by the NLS in this <a href="https://data.nls.uk/data/digitised-collections/edinburgh-ladies-debating-society/">link</a>. As  other NLS data collections, they are originally provided using two XMLs schemas: METS  for descriptive, structural, technical and administrative metadata (Title, Author, Publisher, etc); and ALTO  for encoding the OCR text of a page. In this work, we have extracted the information from METS and ALTO XMLS using <a href="https://github.com/francesNLP/defoe">defoe</a> tool and developed a <a href="https://github.com/francesNLP/defoe/blob/master/defoe/nls/queries/write_metadata_pages_yml.py">new information extraction defoe query</a> , and created a new Knowlege Graph called LadiesDebating-KG.  The LadiesDebating-KG uses the <a href="https://francesnlp.github.io/NLS-ontology/doc/index-en.html">NLS Ontology </a>to represent the information extracted. Furthermore, during the information extraction phase, we have employed several techniques to mitigate two common OCR errors: long-S and the line-break hyphenation. The LadiesDebating-KG contains 38,279 RDF triples. It has information from 2 series and 16 volumes: 'The attempt' serie has 10 volumes and  'The Ladies' serie has 6 volumes . Each serie has an Editor, mmsid, Shelf-Locator, publication year, etc.  A Volume has several Pages,  with text in them. The data model of the LadiesDebating-KG can be found <a href="https://francesnlp.github.io/NLS-ontology/doc/dataModel.png">here</a>. <pre> </pre>

ChapbooksScotland-KG: A Knowlege Graph for representing the "Chapbooks Printed In Scotland" (1671 - 1893)

This Knowlege Graph represents the information of the "Chapbooks Printed In Scotland" (years: 1671 - 1893) collection in RDF (ttl format). This dataset comprises more than 3,000 chapbooks printed in Scotland from the 17th to 19th century. They form part of the Lauriston Castle Collection, which was bequeathed to the Library in 1926. It includes some 500 chapbook volumes containing around 5,500 individual items, more than half of which were printed in Scotland.  The raw dataset is provided by the NLS in this <a href="https://data.nls.uk/data/digitised-collections/chapbooks-printed-in-scotland/">link</a>. As  other NLS data collections, they are originally provided using two XMLs schemas: METS  for descriptive, structural, technical and administrative metadata (Title, Author, Publisher, etc); and ALTO  for encoding the OCR text of a page. In this work, we have extracted the information from METS and ALTO XMLS using <a href="https://github.com/francesNLP/defoe">defoe</a> tool and developed a <a href="https://github.com/francesNLP/defoe/blob/master/defoe/nls/queries/write_metadata_pages_yml.py">new information extraction defoe query</a> , and created a new Knowlege Graph called ChapbooksScotland-KG.  The ChapbooksScotland-KG uses the <a href="https://francesnlp.github.io/NLS-ontology/doc/index-en.html">NLS Ontology </a>to represent the information extracted. Furthermore, during the information extraction phase, we have employed several techniques to mitigate two common OCR errors: long-S and the line-break hyphenation. The ChapbooksScotland-KG contains 352,270 RDF triples. It has information from 2728 series and 3080 volumes. Each serie can have several Volumes, Suplements, references to Books; it also has an Editor and a Publisher, which can be a Person or an Organization. A Volume has several Pages,  with text in them. The data model of the ChapbooksScotland-KG can be found <a href="https://francesnlp.github.io/NLS-ontology/doc/dataModel.png">here</a>.