The consensus is reached in the same way as fortransactions i.e. using hasgraph consensus algorithm. The onlydifference is, that the concerning events in the hashgraph nowcontain other type of data instead of transactions

No need to construct strings that then need to be deconstructed later.

I believe we need the break free of these anachronistic designs and use event loggers, not message loggers

µ/log's idea is to replace the "3 Pillars of Observability" with a more fundamental concept: "the event"

Event-based data is easy to index, search, augment, aggregate and visualise therefore can easily replace traditional logs, metrics and traces.

In this example, Bigipedia informs us that the DOI is referenced by the article page. Note that because the subject is not a DOI, the metadata must be supplied in the subj key. $ curl "https://bus.eventdata.crossref.org/events" \ --verbose \ -H "Content-Type: application/json" \ -H "Authorization: Token token=591df7a9-5b32-4f1a-b23c-d54c19adf3fe" \ -X POST \ --data '{"id": "dbba925e-b47c-4732-a27b-0063040c079d", "source_token": "b1bba157-ab5b-4cb8-9ac8-4beb2d6405ff", "subj_id": "http://bigipedia.com/pages/Chianto", "obj_id": "https://doi.org/10.3403/30164641u", "relation_type_id": "references", "source_id": "bigipedia", "license: "https://creativecommons.org/publicdomain/zero/1.0/", "subj": {"title": "Chianto", "issued": "2016-01-02", "URL": "http://bigipedia.com/pages/Chianto"}}'

El púlsar binario PSR J0737 como banco de pruebas de la relatividad general Por Francisco R. Villatoro, el 16 diciembre, 2021. Categoría(s): Astronomía • Ciencia • Física • Noticias • Physics • Relatividad • Science ✎ 3

Evidence Record Creates observations of type landing-page-url for annotates relation types. Creates observations of type plaintext for discusses relation types.

Discusses: { "license": "https://creativecommons.org/publicdomain/zero/1.0/", "obj_id": "https://doi.org/10.1146/annurev.earth.32.082503.144359", "source_token": "8075957f-e0da-405f-9eee-7f35519d7c4c", "occurred_at": "2015-05-11T04:03:44Z", "subj_id": "https://hypothes.is/a/qNv_Ei5ZSnWOWO54GXdFPA", "id": "00054d54-7f35-4557-b083-7fa1f028856d", "evidence_record": "https://evidence.eventdata.crossref.org/evidence/20170413-hypothesis-a37bc9bf-1dc0-4c8a-b943-2e14beb4de6f", "terms": "https://doi.org/10.13003/CED-terms-of-use", "action": "add", "subj": { "pid": "https://hypothes.is/a/qNv_Ei5ZSnWOWO54GXdFPA", "json-url": "https://hypothes.is/api/annotations/qNv_Ei5ZSnWOWO54GXdFPA", "url": "https://hyp.is/qNv_Ei5ZSnWOWO54GXdFPA/www.cnn.com/2015/05/05/opinions/sutter-sea-level-climate/#", "type": "annotation", "title": "The various scenarios presented should be specified as being global averages of expected sea level rise. The sea level rise observed locally will vary significantly, due to a lot of different geophysical factors.", "issued": "2015-05-11T04:03:44Z" }, "source_id": "hypothesis", "obj": { "pid": "https://doi.org/10.1146/annurev.earth.32.082503.144359", "url": "https://doi.org/10.1146/annurev.earth.32.082503.144359" }, "timestamp": "2017-04-13T10:40:18Z", "relation_type_id": "discusses" }

Annotates: { "license": "https://creativecommons.org/publicdomain/zero/1.0/", "obj_id": "https://doi.org/10.1007/bfb0105342", "source_token": "8075957f-e0da-405f-9eee-7f35519d7c4c", "occurred_at": "2015-11-04T06:30:10Z", "subj_id": "https://hypothes.is/a/NrIw4KlKTwa7MzbTrMAyjw", "id": "00044ac9-d729-4d3f-a2c8-618bcdf1d252", "evidence_record": "https://evidence.eventdata.crossref.org/evidence/20170412-hypothesis-de560308-e500-4c55-ba28-799d7b272039", "terms": "https://doi.org/10.13003/CED-terms-of-use", "action": "add", "subj": { "pid": "https://hypothes.is/a/NrIw4KlKTwa7MzbTrMAyjw", "json-url": "https://hypothes.is/api/annotations/NrIw4KlKTwa7MzbTrMAyjw", "url": "https://hyp.is/NrIw4KlKTwa7MzbTrMAyjw/arxiv.org/abs/quant-ph/9803052", "type": "annotation", "title": "[This article](http://arxiv.org/abs/quant-ph/9803052) was referenced by [\"Decoherence\"](http://web.mit.edu/redingtn/www/netadv/Xdecoherenc.html) on Sunday, September 25 2005.", "issued": "2015-11-04T06:30:10Z" }, "source_id": "hypothesis", "obj": { "pid": "https://doi.org/10.1007/bfb0105342", "url": "http://arxiv.org/abs/quant-ph/9803052" }, "timestamp": "2017-04-12T07:16:20Z", "relation_type_id": "annotates" }

looks in the text for links to registered content

It looks for two things: the annotation of registered content (for example Article Landing Pages) and the mentioning of registered content (for example DOIs) in the text of annotations.

The Hypothes.is Agent monitors annotations

Crossref Membership rules #7 state that: You must have your DOIs resolve to a page containing complete bibliographic information for the content with a link to — or information about — getting the full text of the content. Where publishers break these rules, we will alert them.

It's always not one-to-one DOIs can be assigned to books and book chapters, articles and figures. Each Agent will do its job as accurately as possible, with minimal cleaning-up, which could affect interpretation. This means that if someone tweets the DOI for a figure within an article, we will record that figure's DOI. If they tweet the landing page URL for that figure, we will do our best to match it to a DOI. Depending on the method used, and what the publisher landing page tells us, we may match the article's DOI or the figure's DOI. Sometimes two pages may claim to be about the same DOI. This could happen if a publisher runs two different sites about the same content. It's also possible that a landing page has no DOI metadata, so we can't match it to an Event. The reverse is true: sometimes two DOIs point to the same landing page. This can happen by accident. It is rare, but does happen. This has no material effect on the current methods for reporting Events.

Matching also varies from publisher to publisher. For some landing page domains we can easily match the DOI. For some we need to do a bit more work. For others, it's impossible.

We maintain a list of domain names that belong to publishers (see the Artifact page for more information) and track and query for those domains. When we see a URL that could be a landing page, we attempt to match it to a DOI.

Every Agent will attempt to match registered content items in as broad a manner as possible by looking for linked and unlinked DOIs and Article Landing Page URLs.

They could use a hyperlinked DOI (one you can click), or a plain-text DOI (one you can't click). They could also use the Article Landing Page (the page you get to when you click on a DOI). Every source is different: we tend to see most people using Article Landing Pages on Twitter, but on Wikipedia DOIs are frequently used.

Event Data Agents are on the look out for links to registered content items, but people on the Web use a variety of methods to refer to them.

www.scientificamerican.com: 6768,

Every Event starts its journey somewhere, usually in an external source. Data from that external source is processed and analyzed

the subject of the event, e.g. Wikipedia article on Fish the type of the relation, e.g. "references" the object of the event, e.g. article with DOI 10.5555/12345678

Every time we notice that there is a new relationship between a piece of registered content and something out in the web, we record that as an individual Event.

Each web source is referred to as a 'data contributor'. The Events, and all original data from the data contributor, are available via an API.

When a relationship is observed

a registered content item (that is, content that has been assigned a DOI by Crossref or DataCite)

This data is of interest to a wide range of people: Publishers may want to know how their articles are being shared, authors might want to know when people are talking about their articles, researchers may want to conduct bibliometrics research. And that's just the obvious uses.

The Event Data service captures this activity and acts as a hub for the storage and distribution of this data. The service provides a record of instances where research has been bookmarked, linked, liked, shared, referenced, commented on etc, beyond publisher platforms. For example, when datasets are linked to articles, articles are mentioned on social media or referenced in Wikipedia.