search

sciety / sciety

Where research is evaluated and curated by the communities you trust
https://sciety.org
MIT License
16 stars 9 forks source link

Where to fetch arXiv preprint expressions #3068

Closed giorgiosironi closed 8 months ago

giorgiosironi commented 8 months ago

https://blog.arxiv.org/2022/02/17/new-arxiv-articles-are-now-automatically-assigned-dois/ states:

As of Feb 2022, all arXiv articles now have DOIs.

and

Making article metadata available in DataCite’s centralized location allows research outputs to be more discoverable and harvestable.

and

What is arXiv’s DOI prefix with DataCite? 10.48550

giorgiosironi commented 8 months ago

The DataCite REST API appears extremely slow at the time of writing:

$ time curl https://api.datacite.org/dois/10.48550/arXiv.2202.07378
{"data":{"id":"10.48550/arxiv.2202.07378","type":"dois","attributes":{"doi":"10.48550/arxiv.2202.07378","prefix":"10.48550","suffix":"arxiv.2202.07378","identifiers":[{"identifier":"2202.07378","identifierType":"arXiv"}],"alternateIdentifiers":[{"alternateIdentifierType":"arXiv","alternateIdentifier":"2202.07378"}],"creators":[{"name":"Hellmuth, Kathrin","nameType":"Personal","givenName":"Kathrin","familyName":"Hellmuth","affiliation":[],"nameIdentifiers":[]},{"name":"Klingenberg, Christian","nameType":"Personal","givenName":"Christian","familyName":"Klingenberg","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Computing Black Scholes with Uncertain Volatility-A Machine Learning Approach"}],"publisher":"arXiv","container":{},"publicationYear":2022,"subjects":[{"lang":"en","subject":"Mathematical Finance (q-fin.MF)","subjectScheme":"arXiv"},{"lang":"en","subject":"Computational Finance (q-fin.CP)","subjectScheme":"arXiv"},{"subject":"FOS: Economics and business","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Economics and business","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"lang":"en","subject":"65N35, 65N75, 91G60, 91G80","subjectScheme":"MSC"}],"contributors":[],"dates":[{"date":"2022-02-15T13:07:08Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2024-01-11T01:38:15Z","dateType":"Updated","dateInformation":"v1"},{"date":"2022-02","dateType":"Available","dateInformation":"v1"},{"date":"2022","dateType":"Issued"}],"language":null,"types":{"ris":"RPRT","bibtex":"article","citeproc":"article-journal","schemaOrg":"ScholarlyArticle","resourceType":"Article","resourceTypeGeneral":"Text"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.3390/math10030489","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Attribution Share Alike 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-sa-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"In financial mathematics, it is a typical approach to approximate financial markets operating in discrete time by continuous-time models such as the Black Scholes model. Fitting this model gives rise to difficulties due to the discrete nature of market data. We thus model the pricing process of financial derivatives by the Black Scholes equation, where the volatility is a function of a finite number of random variables. This reflects an influence of uncertain factors when determining volatility. The aim is to quantify the effect of this uncertainty when computing the price of derivatives. Our underlying method is the generalized Polynomial Chaos (gPC) method in order to numerically compute the uncertainty of the solution by the stochastic Galerkin approach and a finite difference method. We present an efficient numerical variation of this method, which is based on a machine learning technique, the so-called Bi-Fidelity approach. This is illustrated with numerical examples.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://arxiv.org/abs/2202.07378","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-02-16T02:15:14.000Z","registered":"2022-02-16T02:15:15.000Z","published":"2022","updated":"2024-01-11T03:14:14.000Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}},"provider":{"data":{"id":"arxiv","type":"providers"}},"media":{"data":{"id":"10.48550/arxiv.2202.07378","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}
real    1m10.871s
user    0m0.072s
sys     0m0.011s

They mention, like Crossref, a Public and Member API being split across different server pools. This does not indicate that performance is improve in one of the two though.

giorgiosironi commented 8 months ago

A filtered response with properties that seem relevant to front matters or our concept of expression:

{
  "data": {
    "id": "10.48550/arxiv.2202.07378",
    "type": "dois",
    "attributes": {
      "doi": "10.48550/arxiv.2202.07378",
      "creators": [
        {
          "name": "Hellmuth, Kathrin",
          "nameType": "Personal",
          "givenName": "Kathrin",
          "familyName": "Hellmuth",
          "affiliation": [],
          "nameIdentifiers": []
        },
        {
          "name": "Klingenberg, Christian",
          "nameType": "Personal",
          "givenName": "Christian",
          "familyName": "Klingenberg",
          "affiliation": [],
          "nameIdentifiers": []
        }
      ],
      "titles": [
        {
          "title": "Computing Black Scholes with Uncertain Volatility-A Machine Learning Approach"
        }
      ],
      "publisher": "arXiv",
      "publicationYear": 2022,
      "dates": [
        {
          "date": "2022-02-15T13:07:08Z",
          "dateType": "Submitted",
          "dateInformation": "v1"
        },
        {
          "date": "2024-01-11T01:38:15Z",
          "dateType": "Updated",
          "dateInformation": "v1"
        },
        {
          "date": "2022-02",
          "dateType": "Available",
          "dateInformation": "v1"
        },
        {
          "date": "2022",
          "dateType": "Issued"
        }
      ],
      "types": {
        "ris": "RPRT",
        "bibtex": "article",
        "citeproc": "article-journal",
        "schemaOrg": "ScholarlyArticle",
        "resourceType": "Article",
        "resourceTypeGeneral": "Text"
      },
      "relatedIdentifiers": [
        {
          "relationType": "IsVersionOf",
          "relatedIdentifier": "10.3390/math10030489",
          "relatedIdentifierType": "DOI"
        }
      ],
      "descriptions": [
        {
          "description": "In financial mathematics, it is a typical approach to approximate financial markets operating in discrete time by continuous-time models such as the Black Scholes model. Fitting this model gives rise to difficulties due to the discrete nature of market data. We thus model the pricing process of financial derivatives by the Black Scholes equation, where the volatility is a function of a finite number of random variables. This reflects an influence of uncertain factors when determining volatility. The aim is to quantify the effect of this uncertainty when computing the price of derivatives. Our underlying method is the generalized Polynomial Chaos (gPC) method in order to numerically compute the uncertainty of the solution by the stochastic Galerkin approach and a finite difference method. We present an efficient numerical variation of this method, which is based on a machine learning technique, the so-called Bi-Fidelity approach. This is illustrated with numerical examples.",
          "descriptionType": "Abstract"
        }
      ],
      "url": "https://arxiv.org/abs/2202.07378",
      "created": "2022-02-16T02:15:14.000Z",
      "registered": "2022-02-16T02:15:15.000Z",
      "published": "2022",
      "updated": "2024-01-11T03:14:14.000Z"
    },
    "relationships": {
    }
  }
}
davidcmoulton commented 7 months ago

Today we reproduced the curl further up this ticket:

$ time curl https://api.datacite.org/dois/10.48550/arXiv.2202.07378

and it completed in:

real    0m1.024s
user    0m0.023s
sys     0m0.026s

so performance is not consistently slow.