WW1LOD: an application of CIDOC-CRM to World War 1 linked data

The CIDOC-CRM standard indicates that common events, actors, places and timeframes are important in linking together cultural material, and provides a framework for describing them. However, merely describing entities in this way in two datasets does not yet interlink them. To do that, the identities of instances still need to be either reconciled, or be based on a shared vocabulary. The WW1LOD dataset presented in this paper was created to facilitate both of these approaches for collections dealing with the First World War. For this purpose, the dataset includes events, places, agents, times, keywords, and themes related to the war, based on over ten different authoritative data sources from providers such as the Imperial War Museum. The content is harmonized into RDF, and published as a Linked Open Data service. While generally basing on CIDOC-CRM, some modeling choices used also deviate from it where our experience dictated such. In the article, these deviations are discussed in the hope that they may serve as examples where CIDOC-CRM itself may warrant further examination. As a demonstration of use, the dataset and online service have been used to create a contextual reader application that is able link together and pull in information related to WW1 from e.g. 1914–1918 Online, Wikipedia, WW1 Discovery, Europeana and the Digital Public Library of America

Beacon v2 and Beacon networks: A "lingua franca" for federated data discovery in biomedical genomics, and beyond

Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple "yes" or "no" responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real-world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a "lingua franca" to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects

GA4GH: International policies and standards for data sharing across genomic research and healthcare.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits

Ontologiaperusteisten tapahtumien tunnistus piilevän semantiikan analyysillä

Perinteinen tekstihaku vertaa toisiinsa tekstistä löytyviä merkkijonoja, jolloin esimerkiksi hakusanalla 'Nokia' voidaan tulokseksi saada dokumentteja matkapuhelinvalmistajasta, Nokian kaupungista tai F.E Sillanpään Ihmiset suviyössä teoksen päähenkilöstä. Tässä tutkielmassa esitetään informaation haussa (engl. Information Retrieval, IR) käytettävä menetelmä, jolla on mahdollista hakea tekstidokumentteja tarkasti määritellyllä käsitteellä. Tarkasti määritellyllä käsitteellä tarkoitetaan ontologiassa, koneymmärrettävässä sanastossa, määriteltyä käsitettä. Tässä tutkielmassa keskitytään erityisesti historiaontologiassa määriteltyihin tapahtumiin. Tutkielmassa esitetty menetelmä pyrkii tunnistamaan dokumentissa esiintyvät käsitteet sanoja ympäröivän semantiikan perusteella. Täsmällisesti sanaa ympäröivä semantiikka saadaan niin kutsutusta semanttisesta avaruudesta, joka muodostetaan piilevän semantiikan analyysiksi (engl. Latent Semantic Analysis, LSA) kutsutulla matemaattisella menetelmällä, ja ympäröivää semantiikkaa sovelletaan ontologiseen kyselyn laajentamiseen. Mallin toimivuutta pyrittiin arvioimaan koejärjestelyllä, jossa aineistona käytetään Suomalaista historiaontologiaa ja suomenkielisen Wikipedia-tietosanakirjan artikkeleita. Koejärjestelyssä ilmenneiden vaikeuksien vuoksi toimivuuden arviointi jäi puutteelliseksi. Tutkielman lopussa on pohdittu menetelmän merkitystä informaation haussa yleisesti, sillä tutkielmassa kuvattu menetelmä ontologiassa määriteltyjen käsitteiden kuvaamisesta tekstidokumenttien määräämään semanttiseen avaruuteen on uusi, eikä aiempaa tutkimusta menetelmän toiminnasta tai kehittämisestä ole tehty

WW1LOD

The CIDOC-CRM standard indicates that common events, actors, places and timeframes are important in linking together cultural material, and provides a framework for describing them. However, merely describing entities in this way in two datasets does not yet interlink them. To do that, the identities of instances still need to be either reconciled, or be based on a shared vocabulary. The WW1LOD dataset presented in this paper was created to facilitate both of these approaches for collections dealing with the First World War. For this purpose, the dataset includes events, places, agents, times, keywords, and themes related to the war, based on over ten different authoritative data sources from providers such as the Imperial War Museum. The content is harmonized into RDF, and published as a Linked Open Data service. While generally based on CIDOC-CRM, some modeling choices used also deviate from it where our experience dictated such. In the article, these deviations are discussed in the hope that they may serveas examples where CIDOC-CRM itself may warrant further examination. As a demonstration of use, the dataset and online service have been used to create a contextual reader application that is able to link together and pull in information related to WW1 from, e.g., 1914–1918 Online, Wikipedia, WW1 Discovery, Europeana and the Digital Public Library of America.Peer reviewe

Modular Pre-Ingest Tool for Diverse Needs of Producers

We introduce an open-source pre-ingest tool that assists the generation of Submission Information Packages (SIPs) that are to be submitted to the national digital preservation service in Finland. The pre-ingest tool consists of several independent components that produce the parts of a METS document required by the national preservation service. These components are easy to modify when developing services for different user demands or for different repositories. Users of the tool provide the necessary information as parameters for the tool, which produces the structure and descriptions for the SIP. The pre-ingest tool reduces the need to deeply understand either METS, PREMIS or other metadata formats to be able to preserve digital assets

IOS Press World War 1 as Linked Open Data

Abstract. The WW1LOD dataset is primarily a reference dataset meant to bind together collections dealing with the First World War. For this purpose, the dataset gathers events, places and agents related to the war from various authoritative sources. These are then made available for indexing and other use through a variety of interfaces and APIs. Additional information on the entities is also collected, in order to be able to answer more complex questions relating to them. The approach is being evaluated using a concrete WW1 online collection

Publisher Correction: Federated discovery and sharing of genomic data using Beacons.

In the version of this article initially published, Lena Dolman's second affiliation was given as Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. The correct second affiliation is Ontario Institute for Cancer Research, Toronto, Ontario, Canada. The error has been corrected in the HTML and PDF versions of the article