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En el instante 30 de octubre de 2025, 2:20:51 UTC,
albertogarob:
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Eliminada etiqueta 2025 de WhistlerLib: Biblioteca para el Análisis Distribuido de Grandes Conjuntos de Tuits
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Añadida etiqueta 2022 de WhistlerLib: Biblioteca para el Análisis Distribuido de Grandes Conjuntos de Tuits
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Añadido campo
Fecha
con valor14/10/2022
a WhistlerLib: Biblioteca para el Análisis Distribuido de Grandes Conjuntos de Tuits -
Modificado el valor del campo
Año
a2022
(anteriormente2025
) en 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": "A\u00f1o", | 7 | "key": "A\u00f1o", | ||
| n | 8 | "value": "2025" | n | 8 | "value": "2022" |
| 9 | }, | ||||
| 10 | { | ||||
| 11 | "key": "Fecha", | ||||
| 12 | "value": "14/10/2022" | ||||
| 9 | }, | 13 | }, | ||
| 10 | { | 14 | { | ||
| 11 | "key": "Identificador hash", | 15 | "key": "Identificador hash", | ||
| 12 | "value": "59b4c925ab4f" | 16 | "value": "59b4c925ab4f" | ||
| 13 | }, | 17 | }, | ||
| 14 | { | 18 | { | ||
| 15 | "key": "Instituciones", | 19 | "key": "Instituciones", | ||
| 16 | "value": "SECIHTI-CentroGeo" | 20 | "value": "SECIHTI-CentroGeo" | ||
| 17 | }, | 21 | }, | ||
| 18 | { | 22 | { | ||
| 19 | "key": "Tipo", | 23 | "key": "Tipo", | ||
| 20 | "value": "Art\u00edculo en l\u00ednea" | 24 | "value": "Art\u00edculo en l\u00ednea" | ||
| 21 | }, | 25 | }, | ||
| 22 | { | 26 | { | ||
| 23 | "key": "URL", | 27 | "key": "URL", | ||
| 24 | "value": | 28 | "value": | ||
| 25 | iblioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/" | 29 | iblioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/" | ||
| 26 | } | 30 | } | ||
| 27 | ], | 31 | ], | ||
| 28 | "groups": [ | 32 | "groups": [ | ||
| 29 | { | 33 | { | ||
| 30 | "description": "Este grupo re\u00fane los art\u00edculos de | 34 | "description": "Este grupo re\u00fane los art\u00edculos de | ||
| 31 | divulgaci\u00f3n publicados por el Observatorio Metropolitano del | 35 | divulgaci\u00f3n publicados por el Observatorio Metropolitano del | ||
| 32 | CentroGeo. Cada art\u00edculo presenta, en un lenguaje accesible y con | 36 | CentroGeo. Cada art\u00edculo presenta, en un lenguaje accesible y con | ||
| 33 | enfoque metropolitano, los principales hallazgos, metodolog\u00edas y | 37 | enfoque metropolitano, los principales hallazgos, metodolog\u00edas y | ||
| 34 | aplicaciones de los proyectos de investigaci\u00f3n desarrollados por | 38 | aplicaciones de los proyectos de investigaci\u00f3n desarrollados por | ||
| 35 | el observatorio. Los contenidos est\u00e1n hospedados en el portal web | 39 | el observatorio. Los contenidos est\u00e1n hospedados en el portal web | ||
| 36 | del Observatorio Metropolitano y buscan acercar el conocimiento | 40 | del Observatorio Metropolitano y buscan acercar el conocimiento | ||
| 37 | cient\u00edfico y t\u00e9cnico a la sociedad, fomentando la | 41 | cient\u00edfico y t\u00e9cnico a la sociedad, fomentando la | ||
| 38 | comprensi\u00f3n de los fen\u00f3menos urbanos y territoriales desde | 42 | comprensi\u00f3n de los fen\u00f3menos urbanos y territoriales desde | ||
| 39 | una perspectiva interdisciplinaria.", | 43 | una perspectiva interdisciplinaria.", | ||
| 40 | "display_name": "Art\u00edculos en l\u00ednea", | 44 | "display_name": "Art\u00edculos en l\u00ednea", | ||
| 41 | "id": "8659310a-f66e-46e8-b1e5-3d7e04acd171", | 45 | "id": "8659310a-f66e-46e8-b1e5-3d7e04acd171", | ||
| 42 | "image_display_url": "", | 46 | "image_display_url": "", | ||
| 43 | "name": "articulos-en-linea", | 47 | "name": "articulos-en-linea", | ||
| 44 | "title": "Art\u00edculos en l\u00ednea" | 48 | "title": "Art\u00edculos en l\u00ednea" | ||
| 45 | } | 49 | } | ||
| 46 | ], | 50 | ], | ||
| 47 | "id": "be3c84aa-412d-409e-9e49-c54f35c27eb3", | 51 | "id": "be3c84aa-412d-409e-9e49-c54f35c27eb3", | ||
| 48 | "isopen": false, | 52 | "isopen": false, | ||
| 49 | "license_id": null, | 53 | "license_id": null, | ||
| 50 | "license_title": null, | 54 | "license_title": null, | ||
| 51 | "maintainer": null, | 55 | "maintainer": null, | ||
| 52 | "maintainer_email": null, | 56 | "maintainer_email": null, | ||
| 53 | "metadata_created": "2025-10-24T00:47:19.261601", | 57 | "metadata_created": "2025-10-24T00:47:19.261601", | ||
| n | 54 | "metadata_modified": "2025-10-30T02:07:19.664477", | n | 58 | "metadata_modified": "2025-10-30T02:20:51.484025", |
| 55 | "name": "59b4c925ab4f", | 59 | "name": "59b4c925ab4f", | ||
| 56 | "notes": "WhistlerLib es una nueva biblioteca de Python desarrollada | 60 | "notes": "WhistlerLib es una nueva biblioteca de Python desarrollada | ||
| 57 | en el Observatorio Metropolitano CentroGeo que aprovecha la | 61 | en el Observatorio Metropolitano CentroGeo que aprovecha la | ||
| 58 | computaci\u00f3n distribuida para realizar an\u00e1lisis de redes | 62 | computaci\u00f3n distribuida para realizar an\u00e1lisis de redes | ||
| 59 | sociales en grandes conjuntos de datos de Tweeter. WhistlerLib | 63 | sociales en grandes conjuntos de datos de Tweeter. WhistlerLib | ||
| 60 | proporciona diversas t\u00e9cnicas de an\u00e1lisis de redes sociales | 64 | proporciona diversas t\u00e9cnicas de an\u00e1lisis de redes sociales | ||
| 61 | (SNA) y de procesamiento de lenguaje natural (NLP) para el | 65 | (SNA) y de procesamiento de lenguaje natural (NLP) para el | ||
| 62 | an\u00e1lisis de texto, sentimiento y enlaces que explotan la memoria | 66 | an\u00e1lisis de texto, sentimiento y enlaces que explotan la memoria | ||
| 63 | y la potencia de c\u00f3mputo encontrado en cl\u00fasters | 67 | y la potencia de c\u00f3mputo encontrado en cl\u00fasters | ||
| 64 | multi-n\u00facleo.", | 68 | multi-n\u00facleo.", | ||
| 65 | "num_resources": 1, | 69 | "num_resources": 1, | ||
| 66 | "num_tags": 33, | 70 | "num_tags": 33, | ||
| 67 | "organization": { | 71 | "organization": { | ||
| 68 | "approval_status": "approved", | 72 | "approval_status": "approved", | ||
| 69 | "created": "2022-05-19T00:10:30.480393", | 73 | "created": "2022-05-19T00:10:30.480393", | ||
| 70 | "description": "Observatorio Metropolitano CentroGeo", | 74 | "description": "Observatorio Metropolitano CentroGeo", | ||
| 71 | "id": "b3b3a79d-748a-4464-9471-732b6c74ec53", | 75 | "id": "b3b3a79d-748a-4464-9471-732b6c74ec53", | ||
| 72 | "image_url": | 76 | "image_url": | ||
| 73 | "2022-05-19-001030.456616FullColor1280x1024LogoOnly.png", | 77 | "2022-05-19-001030.456616FullColor1280x1024LogoOnly.png", | ||
| 74 | "is_organization": true, | 78 | "is_organization": true, | ||
| 75 | "name": "observatorio-metropolitano-centrogeo", | 79 | "name": "observatorio-metropolitano-centrogeo", | ||
| 76 | "state": "active", | 80 | "state": "active", | ||
| 77 | "title": "Observatorio Metropolitano CentroGeo", | 81 | "title": "Observatorio Metropolitano CentroGeo", | ||
| 78 | "type": "organization" | 82 | "type": "organization" | ||
| 79 | }, | 83 | }, | ||
| 80 | "owner_org": "b3b3a79d-748a-4464-9471-732b6c74ec53", | 84 | "owner_org": "b3b3a79d-748a-4464-9471-732b6c74ec53", | ||
| 81 | "private": false, | 85 | "private": false, | ||
| 82 | "relationships_as_object": [], | 86 | "relationships_as_object": [], | ||
| 83 | "relationships_as_subject": [], | 87 | "relationships_as_subject": [], | ||
| 84 | "resources": [ | 88 | "resources": [ | ||
| 85 | { | 89 | { | ||
| 86 | "cache_last_updated": null, | 90 | "cache_last_updated": null, | ||
| 87 | "cache_url": null, | 91 | "cache_url": null, | ||
| 88 | "created": "2025-10-24T00:47:19.893985", | 92 | "created": "2025-10-24T00:47:19.893985", | ||
| 89 | "datastore_active": false, | 93 | "datastore_active": false, | ||
| 90 | "description": "WhistlerLib es una nueva biblioteca de Python | 94 | "description": "WhistlerLib es una nueva biblioteca de Python | ||
| 91 | desarrollada en el Observatorio Metropolitano CentroGeo que aprovecha | 95 | desarrollada en el Observatorio Metropolitano CentroGeo que aprovecha | ||
| 92 | la computaci\u00f3n distribuida para realizar an\u00e1lisis de redes | 96 | la computaci\u00f3n distribuida para realizar an\u00e1lisis de redes | ||
| 93 | sociales en grandes conjuntos de datos de Tweeter. WhistlerLib | 97 | sociales en grandes conjuntos de datos de Tweeter. WhistlerLib | ||
| 94 | proporciona diversas t\u00e9cnicas de an\u00e1lisis de redes sociales | 98 | proporciona diversas t\u00e9cnicas de an\u00e1lisis de redes sociales | ||
| 95 | (SNA) y de procesamiento de lenguaje natural (NLP) para el | 99 | (SNA) y de procesamiento de lenguaje natural (NLP) para el | ||
| 96 | an\u00e1lisis de texto, sentimiento y enlaces que explotan la memoria | 100 | an\u00e1lisis de texto, sentimiento y enlaces que explotan la memoria | ||
| 97 | y la potencia de c\u00f3mputo encontrado en cl\u00fasters | 101 | y la potencia de c\u00f3mputo encontrado en cl\u00fasters | ||
| 98 | multi-n\u00facleo.", | 102 | multi-n\u00facleo.", | ||
| 99 | "format": "HTML", | 103 | "format": "HTML", | ||
| 100 | "hash": "", | 104 | "hash": "", | ||
| 101 | "id": "2ef2530f-c07b-4d30-bc34-5e8566270562", | 105 | "id": "2ef2530f-c07b-4d30-bc34-5e8566270562", | ||
| 102 | "last_modified": null, | 106 | "last_modified": null, | ||
| 103 | "metadata_modified": "2025-10-30T02:07:19.668272", | 107 | "metadata_modified": "2025-10-30T02:07:19.668272", | ||
| 104 | "mimetype": null, | 108 | "mimetype": null, | ||
| 105 | "mimetype_inner": null, | 109 | "mimetype_inner": null, | ||
| 106 | "name": "WhistlerLib: Biblioteca para el An\u00e1lisis | 110 | "name": "WhistlerLib: Biblioteca para el An\u00e1lisis | ||
| 107 | Distribuido de Grandes Conjuntos de Tuits", | 111 | Distribuido de Grandes Conjuntos de Tuits", | ||
| 108 | "package_id": "be3c84aa-412d-409e-9e49-c54f35c27eb3", | 112 | "package_id": "be3c84aa-412d-409e-9e49-c54f35c27eb3", | ||
| 109 | "position": 0, | 113 | "position": 0, | ||
| 110 | "resource_type": null, | 114 | "resource_type": null, | ||
| 111 | "size": null, | 115 | "size": null, | ||
| 112 | "state": "active", | 116 | "state": "active", | ||
| 113 | "url": | 117 | "url": | ||
| 114 | blioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/", | 118 | blioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/", | ||
| 115 | "url_type": null | 119 | "url_type": null | ||
| 116 | } | 120 | } | ||
| 117 | ], | 121 | ], | ||
| 118 | "state": "active", | 122 | "state": "active", | ||
| 119 | "tags": [ | 123 | "tags": [ | ||
| 120 | { | 124 | { | ||
| t | 121 | "display_name": "2025", | t | 125 | "display_name": "2022", |
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| 124 | "state": "active", | 128 | "state": "active", | ||
| 125 | "vocabulary_id": null | 129 | "vocabulary_id": null | ||
| 126 | }, | 130 | }, | ||
| 127 | { | 131 | { | ||
| 128 | "display_name": "albertogarob", | 132 | "display_name": "albertogarob", | ||
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| 133 | }, | 137 | }, | ||
| 134 | { | 138 | { | ||
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| 136 | "id": "e3c629fa-42f6-413c-bfad-8d9a630c9aab", | 140 | "id": "e3c629fa-42f6-413c-bfad-8d9a630c9aab", | ||
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| 138 | "state": "active", | 142 | "state": "active", | ||
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| 140 | }, | 144 | }, | ||
| 141 | { | 145 | { | ||
| 142 | "display_name": "analisis-de-redes-sociales", | 146 | "display_name": "analisis-de-redes-sociales", | ||
| 143 | "id": "e5fa97c2-3a3f-40ed-8028-17937542257e", | 147 | "id": "e5fa97c2-3a3f-40ed-8028-17937542257e", | ||
| 144 | "name": "analisis-de-redes-sociales", | 148 | "name": "analisis-de-redes-sociales", | ||
| 145 | "state": "active", | 149 | "state": "active", | ||
| 146 | "vocabulary_id": null | 150 | "vocabulary_id": null | ||
| 147 | }, | 151 | }, | ||
| 148 | { | 152 | { | ||
| 149 | "display_name": "analisis-de-sentimiento", | 153 | "display_name": "analisis-de-sentimiento", | ||
| 150 | "id": "f0eeb4a0-f1a9-4088-81d8-f298545c95f8", | 154 | "id": "f0eeb4a0-f1a9-4088-81d8-f298545c95f8", | ||
| 151 | "name": "analisis-de-sentimiento", | 155 | "name": "analisis-de-sentimiento", | ||
| 152 | "state": "active", | 156 | "state": "active", | ||
| 153 | "vocabulary_id": null | 157 | "vocabulary_id": null | ||
| 154 | }, | 158 | }, | ||
| 155 | { | 159 | { | ||
| 156 | "display_name": "analisis-de-tuits", | 160 | "display_name": "analisis-de-tuits", | ||
| 157 | "id": "b9c95c5c-d0a6-45e2-b120-ccfaba6139d0", | 161 | "id": "b9c95c5c-d0a6-45e2-b120-ccfaba6139d0", | ||
| 158 | "name": "analisis-de-tuits", | 162 | "name": "analisis-de-tuits", | ||
| 159 | "state": "active", | 163 | "state": "active", | ||
| 160 | "vocabulary_id": null | 164 | "vocabulary_id": null | ||
| 161 | }, | 165 | }, | ||
| 162 | { | 166 | { | ||
| 163 | "display_name": "analisis-distribuido", | 167 | "display_name": "analisis-distribuido", | ||
| 164 | "id": "2d239367-8254-408b-9e99-6953140e0fc7", | 168 | "id": "2d239367-8254-408b-9e99-6953140e0fc7", | ||
| 165 | "name": "analisis-distribuido", | 169 | "name": "analisis-distribuido", | ||
| 166 | "state": "active", | 170 | "state": "active", | ||
| 167 | "vocabulary_id": null | 171 | "vocabulary_id": null | ||
| 168 | }, | 172 | }, | ||
| 169 | { | 173 | { | ||
| 170 | "display_name": "big-data", | 174 | "display_name": "big-data", | ||
| 171 | "id": "9d010501-9c43-4888-8bde-6e679234a080", | 175 | "id": "9d010501-9c43-4888-8bde-6e679234a080", | ||
| 172 | "name": "big-data", | 176 | "name": "big-data", | ||
| 173 | "state": "active", | 177 | "state": "active", | ||
| 174 | "vocabulary_id": null | 178 | "vocabulary_id": null | ||
| 175 | }, | 179 | }, | ||
| 176 | { | 180 | { | ||
| 177 | "display_name": "clusteres-multinucleo", | 181 | "display_name": "clusteres-multinucleo", | ||
| 178 | "id": "23aa14f5-b6bd-467a-8101-b5830cac365b", | 182 | "id": "23aa14f5-b6bd-467a-8101-b5830cac365b", | ||
| 179 | "name": "clusteres-multinucleo", | 183 | "name": "clusteres-multinucleo", | ||
| 180 | "state": "active", | 184 | "state": "active", | ||
| 181 | "vocabulary_id": null | 185 | "vocabulary_id": null | ||
| 182 | }, | 186 | }, | ||
| 183 | { | 187 | { | ||
| 184 | "display_name": "co-aparicion", | 188 | "display_name": "co-aparicion", | ||
| 185 | "id": "9d66a60e-35af-4410-b9fa-088c20d60b28", | 189 | "id": "9d66a60e-35af-4410-b9fa-088c20d60b28", | ||
| 186 | "name": "co-aparicion", | 190 | "name": "co-aparicion", | ||
| 187 | "state": "active", | 191 | "state": "active", | ||
| 188 | "vocabulary_id": null | 192 | "vocabulary_id": null | ||
| 189 | }, | 193 | }, | ||
| 190 | { | 194 | { | ||
| 191 | "display_name": "co-ocurrencia", | 195 | "display_name": "co-ocurrencia", | ||
| 192 | "id": "c550c273-9f1f-4cac-bfb3-032bc930b642", | 196 | "id": "c550c273-9f1f-4cac-bfb3-032bc930b642", | ||
| 193 | "name": "co-ocurrencia", | 197 | "name": "co-ocurrencia", | ||
| 194 | "state": "active", | 198 | "state": "active", | ||
| 195 | "vocabulary_id": null | 199 | "vocabulary_id": null | ||
| 196 | }, | 200 | }, | ||
| 197 | { | 201 | { | ||
| 198 | "display_name": "computacion-distribuida", | 202 | "display_name": "computacion-distribuida", | ||
| 199 | "id": "558e39e0-f577-4fbc-9e46-4beb5e1e6c02", | 203 | "id": "558e39e0-f577-4fbc-9e46-4beb5e1e6c02", | ||
| 200 | "name": "computacion-distribuida", | 204 | "name": "computacion-distribuida", | ||
| 201 | "state": "active", | 205 | "state": "active", | ||
| 202 | "vocabulary_id": null | 206 | "vocabulary_id": null | ||
| 203 | }, | 207 | }, | ||
| 204 | { | 208 | { | ||
| 205 | "display_name": "computo-paralelo", | 209 | "display_name": "computo-paralelo", | ||
| 206 | "id": "3cde1fd3-3962-40d2-af52-28b43c1e31c9", | 210 | "id": "3cde1fd3-3962-40d2-af52-28b43c1e31c9", | ||
| 207 | "name": "computo-paralelo", | 211 | "name": "computo-paralelo", | ||
| 208 | "state": "active", | 212 | "state": "active", | ||
| 209 | "vocabulary_id": null | 213 | "vocabulary_id": null | ||
| 210 | }, | 214 | }, | ||
| 211 | { | 215 | { | ||
| 212 | "display_name": "dask", | 216 | "display_name": "dask", | ||
| 213 | "id": "c2c782e1-6a4f-4dd9-9e69-f9bae7963573", | 217 | "id": "c2c782e1-6a4f-4dd9-9e69-f9bae7963573", | ||
| 214 | "name": "dask", | 218 | "name": "dask", | ||
| 215 | "state": "active", | 219 | "state": "active", | ||
| 216 | "vocabulary_id": null | 220 | "vocabulary_id": null | ||
| 217 | }, | 221 | }, | ||
| 218 | { | 222 | { | ||
| 219 | "display_name": "emociones", | 223 | "display_name": "emociones", | ||
| 220 | "id": "d023dede-354b-4c0f-8dfe-8df650c40a85", | 224 | "id": "d023dede-354b-4c0f-8dfe-8df650c40a85", | ||
| 221 | "name": "emociones", | 225 | "name": "emociones", | ||
| 222 | "state": "active", | 226 | "state": "active", | ||
| 223 | "vocabulary_id": null | 227 | "vocabulary_id": null | ||
| 224 | }, | 228 | }, | ||
| 225 | { | 229 | { | ||
| 226 | "display_name": "hashtags", | 230 | "display_name": "hashtags", | ||
| 227 | "id": "a1173263-93c8-4582-82ce-4f343c809a7d", | 231 | "id": "a1173263-93c8-4582-82ce-4f343c809a7d", | ||
| 228 | "name": "hashtags", | 232 | "name": "hashtags", | ||
| 229 | "state": "active", | 233 | "state": "active", | ||
| 230 | "vocabulary_id": null | 234 | "vocabulary_id": null | ||
| 231 | }, | 235 | }, | ||
| 232 | { | 236 | { | ||
| 233 | "display_name": "histogramas", | 237 | "display_name": "histogramas", | ||
| 234 | "id": "53eae82d-d4cf-4ff2-8891-24b491392a7f", | 238 | "id": "53eae82d-d4cf-4ff2-8891-24b491392a7f", | ||
| 235 | "name": "histogramas", | 239 | "name": "histogramas", | ||
| 236 | "state": "active", | 240 | "state": "active", | ||
| 237 | "vocabulary_id": null | 241 | "vocabulary_id": null | ||
| 238 | }, | 242 | }, | ||
| 239 | { | 243 | { | ||
| 240 | "display_name": "inteligencia-artificial-aplicada", | 244 | "display_name": "inteligencia-artificial-aplicada", | ||
| 241 | "id": "655f59af-948a-42eb-aaa0-fa3dd355608f", | 245 | "id": "655f59af-948a-42eb-aaa0-fa3dd355608f", | ||
| 242 | "name": "inteligencia-artificial-aplicada", | 246 | "name": "inteligencia-artificial-aplicada", | ||
| 243 | "state": "active", | 247 | "state": "active", | ||
| 244 | "vocabulary_id": null | 248 | "vocabulary_id": null | ||
| 245 | }, | 249 | }, | ||
| 246 | { | 250 | { | ||
| 247 | "display_name": "menciones", | 251 | "display_name": "menciones", | ||
| 248 | "id": "35a17f6c-1e7e-483d-bdb0-9d8b633811c2", | 252 | "id": "35a17f6c-1e7e-483d-bdb0-9d8b633811c2", | ||
| 249 | "name": "menciones", | 253 | "name": "menciones", | ||
| 250 | "state": "active", | 254 | "state": "active", | ||
| 251 | "vocabulary_id": null | 255 | "vocabulary_id": null | ||
| 252 | }, | 256 | }, | ||
| 253 | { | 257 | { | ||
| 254 | "display_name": "mineria-de-texto", | 258 | "display_name": "mineria-de-texto", | ||
| 255 | "id": "d55f2be5-2ae3-4bad-9dc9-8170ca27c79b", | 259 | "id": "d55f2be5-2ae3-4bad-9dc9-8170ca27c79b", | ||
| 256 | "name": "mineria-de-texto", | 260 | "name": "mineria-de-texto", | ||
| 257 | "state": "active", | 261 | "state": "active", | ||
| 258 | "vocabulary_id": null | 262 | "vocabulary_id": null | ||
| 259 | }, | 263 | }, | ||
| 260 | { | 264 | { | ||
| 261 | "display_name": "n-gramas", | 265 | "display_name": "n-gramas", | ||
| 262 | "id": "36942251-7fc1-4b9c-a83f-5f5eb86bed27", | 266 | "id": "36942251-7fc1-4b9c-a83f-5f5eb86bed27", | ||
| 263 | "name": "n-gramas", | 267 | "name": "n-gramas", | ||
| 264 | "state": "active", | 268 | "state": "active", | ||
| 265 | "vocabulary_id": null | 269 | "vocabulary_id": null | ||
| 266 | }, | 270 | }, | ||
| 267 | { | 271 | { | ||
| 268 | "display_name": "nlp", | 272 | "display_name": "nlp", | ||
| 269 | "id": "c4481d67-d003-47af-b468-5dbf781eb247", | 273 | "id": "c4481d67-d003-47af-b468-5dbf781eb247", | ||
| 270 | "name": "nlp", | 274 | "name": "nlp", | ||
| 271 | "state": "active", | 275 | "state": "active", | ||
| 272 | "vocabulary_id": null | 276 | "vocabulary_id": null | ||
| 273 | }, | 277 | }, | ||
| 274 | { | 278 | { | ||
| 275 | "display_name": "polaridad", | 279 | "display_name": "polaridad", | ||
| 276 | "id": "fc3354a0-f398-46b4-8ad2-977b807e7290", | 280 | "id": "fc3354a0-f398-46b4-8ad2-977b807e7290", | ||
| 277 | "name": "polaridad", | 281 | "name": "polaridad", | ||
| 278 | "state": "active", | 282 | "state": "active", | ||
| 279 | "vocabulary_id": null | 283 | "vocabulary_id": null | ||
| 280 | }, | 284 | }, | ||
| 281 | { | 285 | { | ||
| 282 | "display_name": "procesamiento-de-lenguaje-natural", | 286 | "display_name": "procesamiento-de-lenguaje-natural", | ||
| 283 | "id": "a1ea0b7f-24d7-4a1c-88cb-2675df76427c", | 287 | "id": "a1ea0b7f-24d7-4a1c-88cb-2675df76427c", | ||
| 284 | "name": "procesamiento-de-lenguaje-natural", | 288 | "name": "procesamiento-de-lenguaje-natural", | ||
| 285 | "state": "active", | 289 | "state": "active", | ||
| 286 | "vocabulary_id": null | 290 | "vocabulary_id": null | ||
| 287 | }, | 291 | }, | ||
| 288 | { | 292 | { | ||
| 289 | "display_name": "procesamiento-masivo-de-datos", | 293 | "display_name": "procesamiento-masivo-de-datos", | ||
| 290 | "id": "8000cf2a-8589-4881-9cc8-646341ee511f", | 294 | "id": "8000cf2a-8589-4881-9cc8-646341ee511f", | ||
| 291 | "name": "procesamiento-masivo-de-datos", | 295 | "name": "procesamiento-masivo-de-datos", | ||
| 292 | "state": "active", | 296 | "state": "active", | ||
| 293 | "vocabulary_id": null | 297 | "vocabulary_id": null | ||
| 294 | }, | 298 | }, | ||
| 295 | { | 299 | { | ||
| 296 | "display_name": "python", | 300 | "display_name": "python", | ||
| 297 | "id": "ea0c471b-9b13-4dd3-8381-aed24f2f855c", | 301 | "id": "ea0c471b-9b13-4dd3-8381-aed24f2f855c", | ||
| 298 | "name": "python", | 302 | "name": "python", | ||
| 299 | "state": "active", | 303 | "state": "active", | ||
| 300 | "vocabulary_id": null | 304 | "vocabulary_id": null | ||
| 301 | }, | 305 | }, | ||
| 302 | { | 306 | { | ||
| 303 | "display_name": "redes-conversacionales", | 307 | "display_name": "redes-conversacionales", | ||
| 304 | "id": "a1f8ec9d-cbfb-4588-8122-67095bafa469", | 308 | "id": "a1f8ec9d-cbfb-4588-8122-67095bafa469", | ||
| 305 | "name": "redes-conversacionales", | 309 | "name": "redes-conversacionales", | ||
| 306 | "state": "active", | 310 | "state": "active", | ||
| 307 | "vocabulary_id": null | 311 | "vocabulary_id": null | ||
| 308 | }, | 312 | }, | ||
| 309 | { | 313 | { | ||
| 310 | "display_name": "redes-ponderadas", | 314 | "display_name": "redes-ponderadas", | ||
| 311 | "id": "e9b341a2-f496-4b74-9082-87d8fa95f359", | 315 | "id": "e9b341a2-f496-4b74-9082-87d8fa95f359", | ||
| 312 | "name": "redes-ponderadas", | 316 | "name": "redes-ponderadas", | ||
| 313 | "state": "active", | 317 | "state": "active", | ||
| 314 | "vocabulary_id": null | 318 | "vocabulary_id": null | ||
| 315 | }, | 319 | }, | ||
| 316 | { | 320 | { | ||
| 317 | "display_name": "redes-sociales-digitales", | 321 | "display_name": "redes-sociales-digitales", | ||
| 318 | "id": "21e3b676-3d8a-4742-a1e0-6533b7969444", | 322 | "id": "21e3b676-3d8a-4742-a1e0-6533b7969444", | ||
| 319 | "name": "redes-sociales-digitales", | 323 | "name": "redes-sociales-digitales", | ||
| 320 | "state": "active", | 324 | "state": "active", | ||
| 321 | "vocabulary_id": null | 325 | "vocabulary_id": null | ||
| 322 | }, | 326 | }, | ||
| 323 | { | 327 | { | ||
| 324 | "display_name": "software-om", | 328 | "display_name": "software-om", | ||
| 325 | "id": "622765a2-267e-4392-b44a-2b888ea04798", | 329 | "id": "622765a2-267e-4392-b44a-2b888ea04798", | ||
| 326 | "name": "software-om", | 330 | "name": "software-om", | ||
| 327 | "state": "active", | 331 | "state": "active", | ||
| 328 | "vocabulary_id": null | 332 | "vocabulary_id": null | ||
| 329 | }, | 333 | }, | ||
| 330 | { | 334 | { | ||
| 331 | "display_name": "visualizacion-de-redes", | 335 | "display_name": "visualizacion-de-redes", | ||
| 332 | "id": "0cb9d55e-3606-4d95-9cc6-03faa37fbcc8", | 336 | "id": "0cb9d55e-3606-4d95-9cc6-03faa37fbcc8", | ||
| 333 | "name": "visualizacion-de-redes", | 337 | "name": "visualizacion-de-redes", | ||
| 334 | "state": "active", | 338 | "state": "active", | ||
| 335 | "vocabulary_id": null | 339 | "vocabulary_id": null | ||
| 336 | }, | 340 | }, | ||
| 337 | { | 341 | { | ||
| 338 | "display_name": "whistlerlib", | 342 | "display_name": "whistlerlib", | ||
| 339 | "id": "db2e651c-d995-4bd5-9a00-9dc8b3337a7c", | 343 | "id": "db2e651c-d995-4bd5-9a00-9dc8b3337a7c", | ||
| 340 | "name": "whistlerlib", | 344 | "name": "whistlerlib", | ||
| 341 | "state": "active", | 345 | "state": "active", | ||
| 342 | "vocabulary_id": null | 346 | "vocabulary_id": null | ||
| 343 | }, | 347 | }, | ||
| 344 | { | 348 | { | ||
| 345 | "display_name": "x-twitter", | 349 | "display_name": "x-twitter", | ||
| 346 | "id": "e4c0fa56-a1b6-448b-84a0-d6113614cc77", | 350 | "id": "e4c0fa56-a1b6-448b-84a0-d6113614cc77", | ||
| 347 | "name": "x-twitter", | 351 | "name": "x-twitter", | ||
| 348 | "state": "active", | 352 | "state": "active", | ||
| 349 | "vocabulary_id": null | 353 | "vocabulary_id": null | ||
| 350 | } | 354 | } | ||
| 351 | ], | 355 | ], | ||
| 352 | "title": "WhistlerLib: Biblioteca para el An\u00e1lisis Distribuido | 356 | "title": "WhistlerLib: Biblioteca para el An\u00e1lisis Distribuido | ||
| 353 | de Grandes Conjuntos de Tuits", | 357 | de Grandes Conjuntos de Tuits", | ||
| 354 | "type": "dataset", | 358 | "type": "dataset", | ||
| 355 | "url": | 359 | "url": | ||
| 356 | blioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/", | 360 | blioteca-para-el-analisis-distribuido-de-grandes-conjuntos-de-tuits/", | ||
| 357 | "version": null | 361 | "version": null | ||
| 358 | } | 362 | } |