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En el instante 24 de octubre de 2025, 0:47:19 UTC,
albertogarob:
-
Añadido recurso WhistlerLib: Biblioteca para el Análisis Distribuido de Grandes Conjuntos de Tuits a WhistlerLib: Biblioteca para el Análisis Distribuido de Grandes Conjuntos de Tuits
| f | 1 | { | f | 1 | { |
| 2 | "author": "Alberto Garc\u00eda Robledo, Angelina Espejel Trujillo", | 2 | "author": "Alberto Garc\u00eda Robledo, Angelina Espejel Trujillo", | ||
| 3 | "author_email": null, | 3 | "author_email": null, | ||
| 4 | "creator_user_id": "a3da3ec9-3fd4-47a4-8d04-0a90b09614e0", | 4 | "creator_user_id": "a3da3ec9-3fd4-47a4-8d04-0a90b09614e0", | ||
| 5 | "extras": [ | 5 | "extras": [ | ||
| 6 | { | 6 | { | ||
| 7 | "key": "Identificador hash", | 7 | "key": "Identificador hash", | ||
| 8 | "value": "59b4c925ab4f" | 8 | "value": "59b4c925ab4f" | ||
| 9 | }, | 9 | }, | ||
| 10 | { | 10 | { | ||
| 11 | "key": "Instituciones", | 11 | "key": "Instituciones", | ||
| 12 | "value": "SECIHTI-CentroGeo" | 12 | "value": "SECIHTI-CentroGeo" | ||
| 13 | }, | 13 | }, | ||
| 14 | { | 14 | { | ||
| 15 | "key": "Tipo", | 15 | "key": "Tipo", | ||
| 16 | "value": "Art\u00edculo en l\u00ednea" | 16 | "value": "Art\u00edculo en l\u00ednea" | ||
| 17 | }, | 17 | }, | ||
| 18 | { | 18 | { | ||
| 19 | "key": "URL", | 19 | "key": "URL", | ||
| 20 | "value": | 20 | "value": | ||
| 21 | iblioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/" | 21 | iblioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/" | ||
| 22 | } | 22 | } | ||
| 23 | ], | 23 | ], | ||
| 24 | "groups": [ | 24 | "groups": [ | ||
| 25 | { | 25 | { | ||
| 26 | "description": "", | 26 | "description": "", | ||
| 27 | "display_name": "Art\u00edculos en l\u00ednea", | 27 | "display_name": "Art\u00edculos en l\u00ednea", | ||
| 28 | "id": "8659310a-f66e-46e8-b1e5-3d7e04acd171", | 28 | "id": "8659310a-f66e-46e8-b1e5-3d7e04acd171", | ||
| 29 | "image_display_url": "", | 29 | "image_display_url": "", | ||
| 30 | "name": "articulos-en-linea", | 30 | "name": "articulos-en-linea", | ||
| 31 | "title": "Art\u00edculos en l\u00ednea" | 31 | "title": "Art\u00edculos en l\u00ednea" | ||
| 32 | } | 32 | } | ||
| 33 | ], | 33 | ], | ||
| 34 | "id": "be3c84aa-412d-409e-9e49-c54f35c27eb3", | 34 | "id": "be3c84aa-412d-409e-9e49-c54f35c27eb3", | ||
| 35 | "isopen": false, | 35 | "isopen": false, | ||
| 36 | "license_id": null, | 36 | "license_id": null, | ||
| 37 | "license_title": null, | 37 | "license_title": null, | ||
| 38 | "maintainer": null, | 38 | "maintainer": null, | ||
| 39 | "maintainer_email": null, | 39 | "maintainer_email": null, | ||
| 40 | "metadata_created": "2025-10-24T00:47:19.261601", | 40 | "metadata_created": "2025-10-24T00:47:19.261601", | ||
| n | 41 | "metadata_modified": "2025-10-24T00:47:19.261610", | n | 41 | "metadata_modified": "2025-10-24T00:47:19.816138", |
| 42 | "name": "59b4c925ab4f", | 42 | "name": "59b4c925ab4f", | ||
| 43 | "notes": "WhistlerLib es una nueva biblioteca de Python desarrollada | 43 | "notes": "WhistlerLib es una nueva biblioteca de Python desarrollada | ||
| 44 | en el Observatorio Metropolitano CentroGeo que aprovecha la | 44 | en el Observatorio Metropolitano CentroGeo que aprovecha la | ||
| 45 | computaci\u00f3n distribuida para realizar an\u00e1lisis de redes | 45 | computaci\u00f3n distribuida para realizar an\u00e1lisis de redes | ||
| 46 | sociales en grandes conjuntos de datos de Tweeter. WhistlerLib | 46 | sociales en grandes conjuntos de datos de Tweeter. WhistlerLib | ||
| 47 | proporciona diversas t\u00e9cnicas de an\u00e1lisis de redes sociales | 47 | proporciona diversas t\u00e9cnicas de an\u00e1lisis de redes sociales | ||
| 48 | (SNA) y de procesamiento de lenguaje natural (NLP) para el | 48 | (SNA) y de procesamiento de lenguaje natural (NLP) para el | ||
| 49 | an\u00e1lisis de texto, sentimiento y enlaces que explotan la memoria | 49 | an\u00e1lisis de texto, sentimiento y enlaces que explotan la memoria | ||
| 50 | y la potencia de c\u00f3mputo encontrado en cl\u00fasters | 50 | y la potencia de c\u00f3mputo encontrado en cl\u00fasters | ||
| 51 | multi-n\u00facleo.", | 51 | multi-n\u00facleo.", | ||
| n | 52 | "num_resources": 0, | n | 52 | "num_resources": 1, |
| 53 | "num_tags": 32, | 53 | "num_tags": 32, | ||
| 54 | "organization": { | 54 | "organization": { | ||
| 55 | "approval_status": "approved", | 55 | "approval_status": "approved", | ||
| 56 | "created": "2022-05-19T00:10:30.480393", | 56 | "created": "2022-05-19T00:10:30.480393", | ||
| 57 | "description": "Observatorio Metropolitano CentroGeo", | 57 | "description": "Observatorio Metropolitano CentroGeo", | ||
| 58 | "id": "b3b3a79d-748a-4464-9471-732b6c74ec53", | 58 | "id": "b3b3a79d-748a-4464-9471-732b6c74ec53", | ||
| 59 | "image_url": | 59 | "image_url": | ||
| 60 | "2022-05-19-001030.456616FullColor1280x1024LogoOnly.png", | 60 | "2022-05-19-001030.456616FullColor1280x1024LogoOnly.png", | ||
| 61 | "is_organization": true, | 61 | "is_organization": true, | ||
| 62 | "name": "observatorio-metropolitano-centrogeo", | 62 | "name": "observatorio-metropolitano-centrogeo", | ||
| 63 | "state": "active", | 63 | "state": "active", | ||
| 64 | "title": "Observatorio Metropolitano CentroGeo", | 64 | "title": "Observatorio Metropolitano CentroGeo", | ||
| 65 | "type": "organization" | 65 | "type": "organization" | ||
| 66 | }, | 66 | }, | ||
| 67 | "owner_org": "b3b3a79d-748a-4464-9471-732b6c74ec53", | 67 | "owner_org": "b3b3a79d-748a-4464-9471-732b6c74ec53", | ||
| 68 | "private": false, | 68 | "private": false, | ||
| 69 | "relationships_as_object": [], | 69 | "relationships_as_object": [], | ||
| 70 | "relationships_as_subject": [], | 70 | "relationships_as_subject": [], | ||
| t | 71 | "resources": [], | t | 71 | "resources": [ |
| 72 | { | ||||
| 73 | "cache_last_updated": null, | ||||
| 74 | "cache_url": null, | ||||
| 75 | "created": "2025-10-24T00:47:19.893985", | ||||
| 76 | "datastore_active": false, | ||||
| 77 | "description": "WhistlerLib es una nueva biblioteca de Python | ||||
| 78 | desarrollada en el Observatorio Metropolitano CentroGeo que aprovecha | ||||
| 79 | la computaci\u00f3n distribuida para realizar an\u00e1lisis de redes | ||||
| 80 | sociales en grandes conjuntos de datos de Tweeter. WhistlerLib | ||||
| 81 | proporciona diversas t\u00e9cnicas de an\u00e1lisis de redes sociales | ||||
| 82 | (SNA) y de procesamiento de lenguaje natural (NLP) para el | ||||
| 83 | an\u00e1lisis de texto, sentimiento y enlaces que explotan la memoria | ||||
| 84 | y la potencia de c\u00f3mputo encontrado en cl\u00fasters | ||||
| 85 | multi-n\u00facleo.", | ||||
| 86 | "format": "HTML", | ||||
| 87 | "hash": "", | ||||
| 88 | "id": "2ef2530f-c07b-4d30-bc34-5e8566270562", | ||||
| 89 | "last_modified": null, | ||||
| 90 | "metadata_modified": "2025-10-24T00:47:19.820595", | ||||
| 91 | "mimetype": null, | ||||
| 92 | "mimetype_inner": null, | ||||
| 93 | "name": "WhistlerLib: Biblioteca para el An\u00e1lisis | ||||
| 94 | Distribuido de Grandes Conjuntos de Tuits", | ||||
| 95 | "package_id": "be3c84aa-412d-409e-9e49-c54f35c27eb3", | ||||
| 96 | "position": 0, | ||||
| 97 | "resource_type": null, | ||||
| 98 | "size": null, | ||||
| 99 | "state": "active", | ||||
| 100 | "url": | ||||
| 101 | blioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/", | ||||
| 102 | "url_type": null | ||||
| 103 | } | ||||
| 104 | ], | ||||
| 72 | "state": "active", | 105 | "state": "active", | ||
| 73 | "tags": [ | 106 | "tags": [ | ||
| 74 | { | 107 | { | ||
| 75 | "display_name": "albertogarob", | 108 | "display_name": "albertogarob", | ||
| 76 | "id": "b079b3e9-8dbb-4423-88fb-f095153d314a", | 109 | "id": "b079b3e9-8dbb-4423-88fb-f095153d314a", | ||
| 77 | "name": "albertogarob", | 110 | "name": "albertogarob", | ||
| 78 | "state": "active", | 111 | "state": "active", | ||
| 79 | "vocabulary_id": null | 112 | "vocabulary_id": null | ||
| 80 | }, | 113 | }, | ||
| 81 | { | 114 | { | ||
| 82 | "display_name": "anlisis-de-datos-sociales", | 115 | "display_name": "anlisis-de-datos-sociales", | ||
| 83 | "id": "d7096352-f5b4-4005-aeee-61a0c03970ba", | 116 | "id": "d7096352-f5b4-4005-aeee-61a0c03970ba", | ||
| 84 | "name": "anlisis-de-datos-sociales", | 117 | "name": "anlisis-de-datos-sociales", | ||
| 85 | "state": "active", | 118 | "state": "active", | ||
| 86 | "vocabulary_id": null | 119 | "vocabulary_id": null | ||
| 87 | }, | 120 | }, | ||
| 88 | { | 121 | { | ||
| 89 | "display_name": "anlisis-de-redes-sociales", | 122 | "display_name": "anlisis-de-redes-sociales", | ||
| 90 | "id": "b5c3ef39-6e71-4ed9-bc16-0d4e7997df9d", | 123 | "id": "b5c3ef39-6e71-4ed9-bc16-0d4e7997df9d", | ||
| 91 | "name": "anlisis-de-redes-sociales", | 124 | "name": "anlisis-de-redes-sociales", | ||
| 92 | "state": "active", | 125 | "state": "active", | ||
| 93 | "vocabulary_id": null | 126 | "vocabulary_id": null | ||
| 94 | }, | 127 | }, | ||
| 95 | { | 128 | { | ||
| 96 | "display_name": "anlisis-de-sentimiento", | 129 | "display_name": "anlisis-de-sentimiento", | ||
| 97 | "id": "b54c43ec-3f1c-4b7d-85ae-21d624ac43c0", | 130 | "id": "b54c43ec-3f1c-4b7d-85ae-21d624ac43c0", | ||
| 98 | "name": "anlisis-de-sentimiento", | 131 | "name": "anlisis-de-sentimiento", | ||
| 99 | "state": "active", | 132 | "state": "active", | ||
| 100 | "vocabulary_id": null | 133 | "vocabulary_id": null | ||
| 101 | }, | 134 | }, | ||
| 102 | { | 135 | { | ||
| 103 | "display_name": "anlisis-de-tuits", | 136 | "display_name": "anlisis-de-tuits", | ||
| 104 | "id": "b669bb0d-f07a-4a7e-9e20-a8a7ba242b2a", | 137 | "id": "b669bb0d-f07a-4a7e-9e20-a8a7ba242b2a", | ||
| 105 | "name": "anlisis-de-tuits", | 138 | "name": "anlisis-de-tuits", | ||
| 106 | "state": "active", | 139 | "state": "active", | ||
| 107 | "vocabulary_id": null | 140 | "vocabulary_id": null | ||
| 108 | }, | 141 | }, | ||
| 109 | { | 142 | { | ||
| 110 | "display_name": "anlisis-distribuido", | 143 | "display_name": "anlisis-distribuido", | ||
| 111 | "id": "f710e6b8-f12c-470a-b7b2-c8fe447e2150", | 144 | "id": "f710e6b8-f12c-470a-b7b2-c8fe447e2150", | ||
| 112 | "name": "anlisis-distribuido", | 145 | "name": "anlisis-distribuido", | ||
| 113 | "state": "active", | 146 | "state": "active", | ||
| 114 | "vocabulary_id": null | 147 | "vocabulary_id": null | ||
| 115 | }, | 148 | }, | ||
| 116 | { | 149 | { | ||
| 117 | "display_name": "big-data", | 150 | "display_name": "big-data", | ||
| 118 | "id": "9d010501-9c43-4888-8bde-6e679234a080", | 151 | "id": "9d010501-9c43-4888-8bde-6e679234a080", | ||
| 119 | "name": "big-data", | 152 | "name": "big-data", | ||
| 120 | "state": "active", | 153 | "state": "active", | ||
| 121 | "vocabulary_id": null | 154 | "vocabulary_id": null | ||
| 122 | }, | 155 | }, | ||
| 123 | { | 156 | { | ||
| 124 | "display_name": "clsteres-multincleo", | 157 | "display_name": "clsteres-multincleo", | ||
| 125 | "id": "8763c71a-7694-4d69-9e1d-b8e4860792bd", | 158 | "id": "8763c71a-7694-4d69-9e1d-b8e4860792bd", | ||
| 126 | "name": "clsteres-multincleo", | 159 | "name": "clsteres-multincleo", | ||
| 127 | "state": "active", | 160 | "state": "active", | ||
| 128 | "vocabulary_id": null | 161 | "vocabulary_id": null | ||
| 129 | }, | 162 | }, | ||
| 130 | { | 163 | { | ||
| 131 | "display_name": "cmputo-paralelo", | 164 | "display_name": "cmputo-paralelo", | ||
| 132 | "id": "176cb841-4483-4ee3-a4f4-7851347aba47", | 165 | "id": "176cb841-4483-4ee3-a4f4-7851347aba47", | ||
| 133 | "name": "cmputo-paralelo", | 166 | "name": "cmputo-paralelo", | ||
| 134 | "state": "active", | 167 | "state": "active", | ||
| 135 | "vocabulary_id": null | 168 | "vocabulary_id": null | ||
| 136 | }, | 169 | }, | ||
| 137 | { | 170 | { | ||
| 138 | "display_name": "co-aparicin", | 171 | "display_name": "co-aparicin", | ||
| 139 | "id": "fcd6e068-5bfe-4aac-b5cb-c6cf6fc7afee", | 172 | "id": "fcd6e068-5bfe-4aac-b5cb-c6cf6fc7afee", | ||
| 140 | "name": "co-aparicin", | 173 | "name": "co-aparicin", | ||
| 141 | "state": "active", | 174 | "state": "active", | ||
| 142 | "vocabulary_id": null | 175 | "vocabulary_id": null | ||
| 143 | }, | 176 | }, | ||
| 144 | { | 177 | { | ||
| 145 | "display_name": "co-ocurrencia", | 178 | "display_name": "co-ocurrencia", | ||
| 146 | "id": "c550c273-9f1f-4cac-bfb3-032bc930b642", | 179 | "id": "c550c273-9f1f-4cac-bfb3-032bc930b642", | ||
| 147 | "name": "co-ocurrencia", | 180 | "name": "co-ocurrencia", | ||
| 148 | "state": "active", | 181 | "state": "active", | ||
| 149 | "vocabulary_id": null | 182 | "vocabulary_id": null | ||
| 150 | }, | 183 | }, | ||
| 151 | { | 184 | { | ||
| 152 | "display_name": "computacin-distribuida", | 185 | "display_name": "computacin-distribuida", | ||
| 153 | "id": "d6b68964-e683-49aa-8c90-c6972c742c4d", | 186 | "id": "d6b68964-e683-49aa-8c90-c6972c742c4d", | ||
| 154 | "name": "computacin-distribuida", | 187 | "name": "computacin-distribuida", | ||
| 155 | "state": "active", | 188 | "state": "active", | ||
| 156 | "vocabulary_id": null | 189 | "vocabulary_id": null | ||
| 157 | }, | 190 | }, | ||
| 158 | { | 191 | { | ||
| 159 | "display_name": "dask", | 192 | "display_name": "dask", | ||
| 160 | "id": "c2c782e1-6a4f-4dd9-9e69-f9bae7963573", | 193 | "id": "c2c782e1-6a4f-4dd9-9e69-f9bae7963573", | ||
| 161 | "name": "dask", | 194 | "name": "dask", | ||
| 162 | "state": "active", | 195 | "state": "active", | ||
| 163 | "vocabulary_id": null | 196 | "vocabulary_id": null | ||
| 164 | }, | 197 | }, | ||
| 165 | { | 198 | { | ||
| 166 | "display_name": "emociones", | 199 | "display_name": "emociones", | ||
| 167 | "id": "d023dede-354b-4c0f-8dfe-8df650c40a85", | 200 | "id": "d023dede-354b-4c0f-8dfe-8df650c40a85", | ||
| 168 | "name": "emociones", | 201 | "name": "emociones", | ||
| 169 | "state": "active", | 202 | "state": "active", | ||
| 170 | "vocabulary_id": null | 203 | "vocabulary_id": null | ||
| 171 | }, | 204 | }, | ||
| 172 | { | 205 | { | ||
| 173 | "display_name": "hashtags", | 206 | "display_name": "hashtags", | ||
| 174 | "id": "a1173263-93c8-4582-82ce-4f343c809a7d", | 207 | "id": "a1173263-93c8-4582-82ce-4f343c809a7d", | ||
| 175 | "name": "hashtags", | 208 | "name": "hashtags", | ||
| 176 | "state": "active", | 209 | "state": "active", | ||
| 177 | "vocabulary_id": null | 210 | "vocabulary_id": null | ||
| 178 | }, | 211 | }, | ||
| 179 | { | 212 | { | ||
| 180 | "display_name": "histogramas", | 213 | "display_name": "histogramas", | ||
| 181 | "id": "53eae82d-d4cf-4ff2-8891-24b491392a7f", | 214 | "id": "53eae82d-d4cf-4ff2-8891-24b491392a7f", | ||
| 182 | "name": "histogramas", | 215 | "name": "histogramas", | ||
| 183 | "state": "active", | 216 | "state": "active", | ||
| 184 | "vocabulary_id": null | 217 | "vocabulary_id": null | ||
| 185 | }, | 218 | }, | ||
| 186 | { | 219 | { | ||
| 187 | "display_name": "inteligencia-artificial-aplicada", | 220 | "display_name": "inteligencia-artificial-aplicada", | ||
| 188 | "id": "655f59af-948a-42eb-aaa0-fa3dd355608f", | 221 | "id": "655f59af-948a-42eb-aaa0-fa3dd355608f", | ||
| 189 | "name": "inteligencia-artificial-aplicada", | 222 | "name": "inteligencia-artificial-aplicada", | ||
| 190 | "state": "active", | 223 | "state": "active", | ||
| 191 | "vocabulary_id": null | 224 | "vocabulary_id": null | ||
| 192 | }, | 225 | }, | ||
| 193 | { | 226 | { | ||
| 194 | "display_name": "menciones", | 227 | "display_name": "menciones", | ||
| 195 | "id": "35a17f6c-1e7e-483d-bdb0-9d8b633811c2", | 228 | "id": "35a17f6c-1e7e-483d-bdb0-9d8b633811c2", | ||
| 196 | "name": "menciones", | 229 | "name": "menciones", | ||
| 197 | "state": "active", | 230 | "state": "active", | ||
| 198 | "vocabulary_id": null | 231 | "vocabulary_id": null | ||
| 199 | }, | 232 | }, | ||
| 200 | { | 233 | { | ||
| 201 | "display_name": "minera-de-texto", | 234 | "display_name": "minera-de-texto", | ||
| 202 | "id": "a1599026-f888-4d5a-969e-7448770ca1ba", | 235 | "id": "a1599026-f888-4d5a-969e-7448770ca1ba", | ||
| 203 | "name": "minera-de-texto", | 236 | "name": "minera-de-texto", | ||
| 204 | "state": "active", | 237 | "state": "active", | ||
| 205 | "vocabulary_id": null | 238 | "vocabulary_id": null | ||
| 206 | }, | 239 | }, | ||
| 207 | { | 240 | { | ||
| 208 | "display_name": "n-gramas", | 241 | "display_name": "n-gramas", | ||
| 209 | "id": "36942251-7fc1-4b9c-a83f-5f5eb86bed27", | 242 | "id": "36942251-7fc1-4b9c-a83f-5f5eb86bed27", | ||
| 210 | "name": "n-gramas", | 243 | "name": "n-gramas", | ||
| 211 | "state": "active", | 244 | "state": "active", | ||
| 212 | "vocabulary_id": null | 245 | "vocabulary_id": null | ||
| 213 | }, | 246 | }, | ||
| 214 | { | 247 | { | ||
| 215 | "display_name": "nlp", | 248 | "display_name": "nlp", | ||
| 216 | "id": "c4481d67-d003-47af-b468-5dbf781eb247", | 249 | "id": "c4481d67-d003-47af-b468-5dbf781eb247", | ||
| 217 | "name": "nlp", | 250 | "name": "nlp", | ||
| 218 | "state": "active", | 251 | "state": "active", | ||
| 219 | "vocabulary_id": null | 252 | "vocabulary_id": null | ||
| 220 | }, | 253 | }, | ||
| 221 | { | 254 | { | ||
| 222 | "display_name": "polaridad", | 255 | "display_name": "polaridad", | ||
| 223 | "id": "fc3354a0-f398-46b4-8ad2-977b807e7290", | 256 | "id": "fc3354a0-f398-46b4-8ad2-977b807e7290", | ||
| 224 | "name": "polaridad", | 257 | "name": "polaridad", | ||
| 225 | "state": "active", | 258 | "state": "active", | ||
| 226 | "vocabulary_id": null | 259 | "vocabulary_id": null | ||
| 227 | }, | 260 | }, | ||
| 228 | { | 261 | { | ||
| 229 | "display_name": "procesamiento-de-lenguaje-natural", | 262 | "display_name": "procesamiento-de-lenguaje-natural", | ||
| 230 | "id": "a1ea0b7f-24d7-4a1c-88cb-2675df76427c", | 263 | "id": "a1ea0b7f-24d7-4a1c-88cb-2675df76427c", | ||
| 231 | "name": "procesamiento-de-lenguaje-natural", | 264 | "name": "procesamiento-de-lenguaje-natural", | ||
| 232 | "state": "active", | 265 | "state": "active", | ||
| 233 | "vocabulary_id": null | 266 | "vocabulary_id": null | ||
| 234 | }, | 267 | }, | ||
| 235 | { | 268 | { | ||
| 236 | "display_name": "procesamiento-masivo-de-datos", | 269 | "display_name": "procesamiento-masivo-de-datos", | ||
| 237 | "id": "8000cf2a-8589-4881-9cc8-646341ee511f", | 270 | "id": "8000cf2a-8589-4881-9cc8-646341ee511f", | ||
| 238 | "name": "procesamiento-masivo-de-datos", | 271 | "name": "procesamiento-masivo-de-datos", | ||
| 239 | "state": "active", | 272 | "state": "active", | ||
| 240 | "vocabulary_id": null | 273 | "vocabulary_id": null | ||
| 241 | }, | 274 | }, | ||
| 242 | { | 275 | { | ||
| 243 | "display_name": "python", | 276 | "display_name": "python", | ||
| 244 | "id": "ea0c471b-9b13-4dd3-8381-aed24f2f855c", | 277 | "id": "ea0c471b-9b13-4dd3-8381-aed24f2f855c", | ||
| 245 | "name": "python", | 278 | "name": "python", | ||
| 246 | "state": "active", | 279 | "state": "active", | ||
| 247 | "vocabulary_id": null | 280 | "vocabulary_id": null | ||
| 248 | }, | 281 | }, | ||
| 249 | { | 282 | { | ||
| 250 | "display_name": "redes-conversacionales", | 283 | "display_name": "redes-conversacionales", | ||
| 251 | "id": "a1f8ec9d-cbfb-4588-8122-67095bafa469", | 284 | "id": "a1f8ec9d-cbfb-4588-8122-67095bafa469", | ||
| 252 | "name": "redes-conversacionales", | 285 | "name": "redes-conversacionales", | ||
| 253 | "state": "active", | 286 | "state": "active", | ||
| 254 | "vocabulary_id": null | 287 | "vocabulary_id": null | ||
| 255 | }, | 288 | }, | ||
| 256 | { | 289 | { | ||
| 257 | "display_name": "redes-ponderadas", | 290 | "display_name": "redes-ponderadas", | ||
| 258 | "id": "e9b341a2-f496-4b74-9082-87d8fa95f359", | 291 | "id": "e9b341a2-f496-4b74-9082-87d8fa95f359", | ||
| 259 | "name": "redes-ponderadas", | 292 | "name": "redes-ponderadas", | ||
| 260 | "state": "active", | 293 | "state": "active", | ||
| 261 | "vocabulary_id": null | 294 | "vocabulary_id": null | ||
| 262 | }, | 295 | }, | ||
| 263 | { | 296 | { | ||
| 264 | "display_name": "redes-sociales-digitales", | 297 | "display_name": "redes-sociales-digitales", | ||
| 265 | "id": "21e3b676-3d8a-4742-a1e0-6533b7969444", | 298 | "id": "21e3b676-3d8a-4742-a1e0-6533b7969444", | ||
| 266 | "name": "redes-sociales-digitales", | 299 | "name": "redes-sociales-digitales", | ||
| 267 | "state": "active", | 300 | "state": "active", | ||
| 268 | "vocabulary_id": null | 301 | "vocabulary_id": null | ||
| 269 | }, | 302 | }, | ||
| 270 | { | 303 | { | ||
| 271 | "display_name": "software-om", | 304 | "display_name": "software-om", | ||
| 272 | "id": "622765a2-267e-4392-b44a-2b888ea04798", | 305 | "id": "622765a2-267e-4392-b44a-2b888ea04798", | ||
| 273 | "name": "software-om", | 306 | "name": "software-om", | ||
| 274 | "state": "active", | 307 | "state": "active", | ||
| 275 | "vocabulary_id": null | 308 | "vocabulary_id": null | ||
| 276 | }, | 309 | }, | ||
| 277 | { | 310 | { | ||
| 278 | "display_name": "visualizacin-de-redes", | 311 | "display_name": "visualizacin-de-redes", | ||
| 279 | "id": "814b04e5-b857-4a46-941d-e5974e0322af", | 312 | "id": "814b04e5-b857-4a46-941d-e5974e0322af", | ||
| 280 | "name": "visualizacin-de-redes", | 313 | "name": "visualizacin-de-redes", | ||
| 281 | "state": "active", | 314 | "state": "active", | ||
| 282 | "vocabulary_id": null | 315 | "vocabulary_id": null | ||
| 283 | }, | 316 | }, | ||
| 284 | { | 317 | { | ||
| 285 | "display_name": "whistlerlib", | 318 | "display_name": "whistlerlib", | ||
| 286 | "id": "db2e651c-d995-4bd5-9a00-9dc8b3337a7c", | 319 | "id": "db2e651c-d995-4bd5-9a00-9dc8b3337a7c", | ||
| 287 | "name": "whistlerlib", | 320 | "name": "whistlerlib", | ||
| 288 | "state": "active", | 321 | "state": "active", | ||
| 289 | "vocabulary_id": null | 322 | "vocabulary_id": null | ||
| 290 | }, | 323 | }, | ||
| 291 | { | 324 | { | ||
| 292 | "display_name": "x-twitter", | 325 | "display_name": "x-twitter", | ||
| 293 | "id": "e4c0fa56-a1b6-448b-84a0-d6113614cc77", | 326 | "id": "e4c0fa56-a1b6-448b-84a0-d6113614cc77", | ||
| 294 | "name": "x-twitter", | 327 | "name": "x-twitter", | ||
| 295 | "state": "active", | 328 | "state": "active", | ||
| 296 | "vocabulary_id": null | 329 | "vocabulary_id": null | ||
| 297 | } | 330 | } | ||
| 298 | ], | 331 | ], | ||
| 299 | "title": "WhistlerLib: Biblioteca para el An\u00e1lisis Distribuido | 332 | "title": "WhistlerLib: Biblioteca para el An\u00e1lisis Distribuido | ||
| 300 | de Grandes Conjuntos de Tuits", | 333 | de Grandes Conjuntos de Tuits", | ||
| 301 | "type": "dataset", | 334 | "type": "dataset", | ||
| 302 | "url": | 335 | "url": | ||
| 303 | blioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/", | 336 | blioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/", | ||
| 304 | "version": null | 337 | "version": null | ||
| 305 | } | 338 | } |