{
 "catalogue": {
  "name": "Open Well Data",
  "curator": "Daniel Delgado-Uriarte",
  "orcid": "https://orcid.org/0009-0006-0536-5158",
  "website": "https://danieldelgadouriarte.com",
  "linkedin": "https://www.linkedin.com/in/daniel-delgado-uriarte-b9a523278/",
  "how_to_cite": "Delgado-Uriarte, D. (2026). Open Well Data: an index of public subsurface data sources (Catalogue build 2026-11-04) [Data set]. https://openwelldata.org/",
  "this_record": "SEG (The Leading Edge, tutorial de Brendon Hall). SEG 2016 Machine Learning Contest. https://github.com/seg/2016-ml-contest. Page checked by Open Well Data on 2026-11-04. Found through Open Well Data (Delgado-Uriarte, 2026), https://openwelldata.org/source/seg-2016-ml-contest/."
 },
 "tipo": "ficha",
 "id": "seg-2016-ml-contest",
 "nombre": "SEG 2016 Machine Learning Contest",
 "url": "https://github.com/seg/2016-ml-contest",
 "pais": "no consta",
 "operador": "SEG (The Leading Edge, tutorial de Brendon Hall)",
 "categoria": "dataset_abierto",
 "disciplinas": [
  "wireline_logs",
  "core",
  "otros"
 ],
 "formatos": [
  "csv"
 ],
 "n_pozos": "no consta; se citan STUART, CRAWFORD y SHANKLE",
 "rango_temporal": "concurso cerrado el 31 de enero de 2017; rango de los datos no consta",
 "volumen": "no consta",
 "acceso": "abierto",
 "api": "no consta",
 "requiere_js": "no",
 "licencia": "the dataset is not openly licensed",
 "licencia_url": "https://github.com/seg/2016-ml-contest/blob/master/LICENSE",
 "redistribucion": "restringida",
 "canales_declarados": [
  "GitHub seg/2016-ml-contest"
 ],
 "estado": "verificada_web",
 "evidencia": [
  {
   "url": "https://github.com/seg/2016-ml-contest",
   "cita": "Please note that the dataset is not openly licensed."
  },
  {
   "url": "https://github.com/seg/2016-ml-contest",
   "cita": "It is shared here exclusively for use on this problem, in this contest."
  }
 ],
 "notas": "Contradicción: la barra lateral de GitHub muestra Apache-2.0, pero el README trata los datos como propietarios. Tampoco consta si llegó la noticia prometida para principios de 2017. La disciplina core se infiere del fichero blind_stuart_crawford_core_facies.csv. Ficheros de datos a la vista: training_data.csv, facies_vectors.csv, nofacies_data.csv y validation_data_nofacies.csv. Mejor F1 final: 0,6388 (LA_Team).",
 "verificacion": {
  "estado_web": "ok",
  "que_es": "SEG 2016 machine learning contest repo (facies classification from well logs; blind wells STUART and CRAWFORD), closed (https://github.com/seg/2016-ml-contest)",
  "tipo_web": "dataset",
  "acceso_web": "abierto (descargable)",
  "licencia_web": "«the dataset is not openly licensed» (código Apache-2.0)",
  "formatos_web": "CSV, ipynb, py, R",
  "relevancia": "alta",
  "fecha": "2026-11-04",
  "metodo": "Web reader: one page per source, read without downloading data."
 },
 "tipos_dato": [
  "core",
  "well_headers",
  "wireline_logs"
 ],
 "tipos_dato_metodo": "reglas v1 (scripts/clasifica_tipos.py), 4 nov 2026",
 "variables_declaradas": [],
 "variables_declaradas_metodo": "reglas v1 (scripts/variables_fuentes.py), menciones explícitas en la ficha, 4 nov 2026",
 "country": null,
 "country_inferred": false
}