Results for “datasets”

Manage Apify datasets, key-value stores, and request queues programmatically, and orchestrate multi-Actor pipelines. Use when you need to read or write Apify datasets, export scraped data to CSV/JSON/XLSX, store config or binary artifacts in a key-value store, manage a resumable request queue, chain Actors into a scrape → transform → export pipeline, or monitor Actor run status and cost. Trigger with "apify dataset", "apify key-value store", "apify storage", "export apify data", "apify pipeline", "apify request queue".

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Use whenever datasets, cloud storage buckets, or data pipelines are mentioned — creating, saving, querying, listing, exploring, deleting, or processing data in S3, GCS, Azure Blob, or local storage. Also use when running any script that may create datasets as a side effect. Maintains a knowledge base at dc-knowledge/ (JSON + markdown). ALWAYS use this skill when the user creates a dataset, saves pipeline output, runs a data script, or references any storage bucket.

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Migrate (harvest) datasets between open-data platforms. Reads CKAN, a DCAT-US /data.json catalog (DKAN, ArcGIS Hub, data.gov), a DCAT / DCAT-AP RDF feed (JSON-LD, Turtle, or RDF/XML), Socrata, OpenDataSoft, or an ArcGIS FeatureServer, and writes them to a static PortalJS catalog or pushes them into a CKAN instance over its API. Use when moving datasets from an external open-data platform into a PortalJS portal, or bridging one CKAN instance to another.

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Designs complete research plans that integrate clinical variables with multi-omics data from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a study that combines clinical variables with transcriptomics, proteomics, metabolomics, epigenomics, or related omics layers for mechanism interpretation, biomarker development, risk stratification, treatment-response analysis, or translational use. It should define the clinical use case, alignment across data layers, feature-reduction and fusion logic, modeling route, mechanism-interpretation layer, validation ladder, and four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, platform coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.

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Search all Japanese NLP resources (libraries, models, datasets, tutorials, dictionaries, Hugging Face). Accepts keywords or natural language questions in any language. Use whenever the user asks which Japanese NLP resource to use, or wants to find one: tokenizers / morphological analyzers, BERT or LLM models, embeddings, NER, text classification, datasets / corpora, dictionaries, tutorials, or Hugging Face models. Trigger phrases include '日本語の形態素解析ライブラリ', 'おすすめの日本語tokenizer', '日本語BERTモデル', '日本語の感情分析データセット', '日本語LLM 一覧', 'which Japanese embedding model', 'Japanese NER library'.

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Compare several Japanese NLP libraries, models, or datasets for a keyword (a specific tool name, or a function/task like '形態素解析') across a handful of criteria chosen for that comparison, rendered as a ○/△/✕ table. Use when the user wants a side-by-side comparison of multiple Japanese NLP tools/libraries/datasets, not just the single best one. Trigger phrases include 'X と Y と Z を比較して', '形態素解析ライブラリを比較', 'MeCab と Sudachi どっちがいい', 'どのツールを使うべき', 'compare japanese tokenizers', 'mecab vs sudachi vs janome', 'which embedding model should I use', '日本語NERライブラリの比較表', 'pros and cons of japanese OCR tools'. For a single ranked list use search; for alternatives to one specific tool (or contribution candidates) without a multi-axis table, use discover.

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Use when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a labeled evaluation set. Also use when the user mentions test data design, eval coverage, difficulty stratification, synthetic data generation for eval, or "how to create good evaluation data." Outputs datasets in OpenJudge-compatible format.

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Expert knowledge for Microsoft Planetary Computer Pro development including troubleshooting, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using GeoCatalog/STAC APIs, configuring collections/tiles, securing access, integrating QGIS/ArcGIS, or troubleshooting ingestion, and other Microsoft Planetary Computer Pro related development tasks. Not for Azure Maps (use azure-maps), Azure Open Datasets (use azure-open-datasets), Azure Data Explorer (use azure-data-explorer).

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Read from a UModel object-graph semantic layer with the `umctl` CLI (MCP alternative noted). Three kinds of read: (1) entities & relationships / topology (`.entity`, `.topo`) and (2) the model itself (`.umodel`, and `.entity_set` methods, including entity-linked `list_skills`) return real rows; (3) metrics & logs (`get_metrics` / `get_logs`) return an executable *plan* — PromQL / Elasticsearch DSL with the entity id pre-substituted — that you run against the backend. Against a PaaS endpoint the same calls return data rows instead of a plan. Use to query or read UModel entities, relations, topology, or model metadata; to read a service's metrics or logs; to look up services and dependencies; or to discover what objects, datasets, and methods exist. For root-cause analysis on top of these reads, see the `umodel-rca` skill. Triggers: UModel, object graph, .entity / .topo / .umodel / .entity_set, query entities, read topology, read metrics / logs, get_metrics / get_logs, list services / dependencies / datasets / skills, 实体查询, 关系/拓扑查询, 读模型, 读指标, 读日志, 查指标, 查日志, 查服务依赖.

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PyTorch-based ML platform for drug discovery: graph molecular representation learning, property prediction (ADMET, activity), retrosynthesis, drug-target interaction (DTI), and pretraining on large molecular datasets. Provides GNN layers (GraphConv, GAT, MPNN), pretrained models, and benchmark datasets.

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Use DIG (Dive into Graphs) to load graph-learning datasets, run graph generation, self-supervised learning, GNN explainability, 3D graph learning, GOOD OOD datasets, graph augmentation, fair graph learning, and large-scale graph workflows.

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Routes tasks for discovering NLP datasets in the CLUEDatasetSearch catalogue, including Chinese NLP classification, QA, matching, NER, generation, translation, corpus, and reading-comprehension resources.

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Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.

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Use when the user wants to switch this Codex session to another Cognee dataset, see which datasets are available, or move memory capture to a different dataset. Syncs the current session first, then starts a fresh Cognee session bound to the chosen dataset.

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Switch this session to another Cognee dataset, or back to the previous one, so later saves and recalls use it. Takes a dataset name (created if new) or lists writable datasets to pick from; syncs this session first.

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Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For ready-made ADMET numbers without training a model use admet-prediction; for benchmark datasets and task-aware splits use pytdc.

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Investigate datasets and resolve entities using OpenPlanter methodology — cross-reference heterogeneous sources, build Admiralty/ACH confidence-tiered evidence chains, and apply OSINT tradecraft. Triggers on entity resolution, cross-reference datasets, evidence chain, OSINT investigation, structured investigation.

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Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.

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Search, query, and cross-reference NCBI GEO (Gene Expression Omnibus) datasets with ENCODE experiments. Use when the user wants to find GEO accessions for ENCODE experiments, search GEO for complementary datasets, download GEO metadata or series matrices, cross-reference ENCODE and GEO data, find supplementary files from GEO, or link GEO series to ENCODE experiments for provenance tracking. Also use when the user mentions GEO, GSE, GSM, GPL, GDS, series matrix, SOFT format, or needs to find expression data in GEO that complements their ENCODE analysis.

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Use when deciding whether Salesforce Reports, CRM Analytics, or Tableau is the right level of analytics tooling, and when reviewing or troubleshooting basic CRM Analytics designs. Triggers: 'CRM Analytics', 'Einstein Analytics', 'Tableau CRM', 'lens', 'dataset', 'dataflow', 'analytics dashboard', 'license requirement'. NOT for a detailed CRM Analytics vs Tableau licensing and platform comparison — use architect/crm-analytics-vs-tableau-decision. NOT for building the app, lenses and datasets — use admin/crm-analytics-app-creation.

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Use when creating or configuring a CRM Analytics (Einstein Analytics) app — including app containers, lenses, datasets, data source connections, app sharing, and the row-level security that gates whether users see any data. NOT for choosing Reports vs CRM Analytics vs Tableau — use admin/einstein-analytics-basics. NOT for security predicate syntax, sharing inheritance or Analytics license assignment — use admin/analytics-permission-and-sharing.

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Use when creating or managing CRM Analytics datasets: configuring dataflows, scheduling refreshes, selecting fields, handling date types, managing row-count growth, or troubleshooting stale or broken datasets. Triggers: 'dataset creation', 'dataflow schedule', 'dataflow quota', 'date field not filterable', 'dataset row limit', 'CRM Analytics data refresh', 'field type mismatch in dataset'. NOT for making an existing dataset smaller or faster — use data/analytics-dataset-optimization. NOT for choosing between Reports, CRM Analytics and Tableau in the first place — use admin/einstein-analytics-basics.

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Run read-only SQL against BigQuery public datasets with local result capture, cost safeguards, and reproducibility outputs.

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