# Supercon 2 Dataset

https://mdr.nims.go.jp/datasets/2169a31f-ca73-4391-aceb-7acf15066f78

## File

- [supercon2_1203_papers.csv](https://mdr.nims.go.jp/filesets/b73172bc-233c-4ac0-9c3c-ee92bb54ec36/download) ([Detail](https://mdr.nims.go.jp/filesets/b73172bc-233c-4ac0-9c3c-ee92bb54ec36.md))
- [supercon2_v22.12.03.csv](https://mdr.nims.go.jp/filesets/b737b44a-b07a-4853-9378-8ba63f644e79/download) ([Detail](https://mdr.nims.go.jp/filesets/b737b44a-b07a-4853-9378-8ba63f644e79.md))

## Id

2169a31f-ca73-4391-aceb-7acf15066f78

## Local identifier

identifier: mdr-schema-yaml/4q77fv540

## Visibility

open_to_public

## State

published

## Created at

2022-10-17T10:38:30.771499Z

## Updated at

2022-10-18T01:11:01.728578Z

## Published at

2022-11-10T06:27:13.013979Z

## Doi

https://doi.org/10.48505/nims.3735

## First published url

https://github.com/lfoppiano/supercon

## Date published

2022-09-10

## Recorded date published



## Resource type

dataset

## Manuscript type

na

## Collection



## Title

- title: Supercon 2 Dataset
  title_type: original

## Description

- description: The automatic extraction of materials and related properties from the
    scientific literature is gaining attention in data-driven materials science (Materials
    Informatics). In this paper, we discuss Grobid-superconductors, our solution for
    automatically extracting superconductor material names and respective properties
    from text. Built as a Grobid module, it combines machine learning and heuristic
    approaches in a multi-step architecture that supports input data as raw text or
    PDF documents. Using Grobid-superconductors, we built SuperCon2, a database of
    40324 materials and properties records from 37700 papers. The material (or sample)
    information is represented by name, chemical formula, and material class, and
    is characterised by shape, doping, substitution variables for components, and
    substrate as adjoined information. The properties include the Tc superconducting
    critical temperature and, when available, applied pressure with the Tc measurement
    method.
  description_type: abstract
  lang: und

## Creator

- name: Foppiano, Luca
  role: author
  orcid: https://orcid.org/0000-0002-6114-6164

## Contact agent



## Publisher

organization: National Institute for Materials Science
ror: https://ror.org/026v1ze26

## Managing organization

organization: 0 ~ NIMS

## Keyword

- subject: superconductors
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: dataset
  schema: not_defined
- subject: tdm
  schema: not_defined
- subject: materials science
  schema: not_defined

## Rights

- description: Creative Commons BY Attribution 4.0 International
  identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: informatics_and_data_science

## Embargo



## Journal



## Conference



## Related item

- title: Automatic Extraction of Materials and Properties from Superconductors Scientific
    Literature
  identifier: https://mdr.nims.go.jp/concern/publications/h415pd80f
  identifier_type: URI
  relation_type: is_documented_by
  related_item_type: dataset

## Funding



## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: b73172bc-233c-4ac0-9c3c-ee92bb54ec36
  filename: supercon2_1203_papers.csv
  content_type: text/csv
  size: 2290255
  md5: 36f4c66bdf9649d9dde625f4cafa9236
- id: b737b44a-b07a-4853-9378-8ba63f644e79
  filename: supercon2_v22.12.03.csv
  content_type: text/csv
  size: 14616331
  md5: ed55ea8f43a0984d77b5c29576164e06

## Thumbnail

fileset_id: b73172bc-233c-4ac0-9c3c-ee92bb54ec36
filename: supercon2_1203_papers.csv