{"id":5241,"date":"2026-03-25T00:10:17","date_gmt":"2026-03-25T00:10:17","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=5241"},"modified":"2026-03-25T00:11:21","modified_gmt":"2026-03-25T00:11:21","slug":"dataset-of-raw-iq-recordings-rf-signals-from-37-uav-drone-types","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=5241","title":{"rendered":"Dataset of raw IQ recordings (RF signals from 37 UAV\/drone types)"},"content":{"rendered":"\n<p><a href=\"https:\/\/grok.com\/share\/c2hhcmQtNA_0c2f8c75-7a93-4883-9d8d-a13ebe5b66dd\">https:\/\/grok.com\/share\/c2hhcmQtNA_0c2f8c75-7a93-4883-9d8d-a13ebe5b66dd<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Hey Grok, I have a Disk Space crunch, how could I use <a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\">https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV<\/a> without downloading to whole 10GB?<\/h3>\n\n\n\n<p><strong>Yes, you can use the <code>kitofrank\/RFUAV<\/code> dataset without downloading the full ~10GB (or larger raw data).<\/strong><\/p>\n\n\n\n<p>The dataset is hosted on Hugging Face and consists of <strong>raw IQ recordings<\/strong> (RF signals from 37 UAV\/drone types), spectrogram images, and some model weights\/preprocessing files. The bulk of the size comes from the raw binary IQ data and image folders.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Best Options for Disk-Constrained Use (Ranked by Convenience)<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">1. <strong>Streaming Mode (Recommended \u2013 Zero Disk Usage)<\/strong><\/h4>\n\n\n\n<p>The Hugging Face <code>datasets<\/code> library can <strong>stream<\/strong> the data on-the-fly. It downloads and processes one example at a time without saving the full dataset to disk.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from datasets import load_dataset\n\n# Stream the dataset (no full download)\ndataset = load_dataset(\"kitofrank\/RFUAV\", streaming=True)\n\n# Example: iterate over train split (or specific config\/split if available)\nfor example in dataset&#91;\"train\"]:\n    # example contains IQ data, spectrogram, labels, etc.\n    iq_samples = example&#91;\"iq\"]          # raw I\/Q signal\n    spectrogram = example&#91;\"image\"]      # PIL Image or array\n    label = example&#91;\"label\"]            # drone type \/ class\n    snr = example.get(\"snr\")            # signal-to-noise ratio if present\n\n    # Process only what you need (e.g., convert IQ to spectrogram on-the-fly)\n    # Then feed into your Gemma-3n reasoning, embeddinggemma, or hypergraph\n    print(f\"Drone type: {label}, SNR: {snr}\")\n\n    # Stop early if you only want a subset\n    if some_condition:\n        break<\/code><\/pre>\n\n\n\n<p><strong>Advantages<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Almost zero persistent disk usage (only temporary buffers).<\/li>\n\n\n\n<li>Perfect for your RF pipeline \u2014 process one UAV signal at a time, extract features, embed with <code>embeddinggemma<\/code>, reason with Gemma-3n, then discard.<\/li>\n\n\n\n<li>Works great with <code>torch.utils.data.DataLoader<\/code> for batching.<\/li>\n<\/ul>\n\n\n\n<p><strong>Tip<\/strong>: If the dataset has multiple splits\/configs (e.g., <code>train<\/code>, <code>valid<\/code>, raw vs. spectrogram), specify them:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>dataset = load_dataset(\"kitofrank\/RFUAV\", \"default\", streaming=True, split=\"train\")<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">2. <strong>Download Only a Subset \/ Slice<\/strong><\/h4>\n\n\n\n<p>If you need a small fixed portion on disk:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># First 500 examples only (downloads only what\u2019s needed)\nsmall_dataset = load_dataset(\"kitofrank\/RFUAV\", split=\"train&#91;:500]\")\n\n# Or percentage (if supported for this dataset)\nsmall_dataset = load_dataset(\"kitofrank\/RFUAV\", split=\"train&#91;:5%]\")<\/code><\/pre>\n\n\n\n<p>You can also specify <code>data_files<\/code> if you know the Parquet\/ file structure, but streaming is usually simpler.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">3. <strong>Download Only Specific Files or Columns<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use the Hugging Face Hub API or <code>hf_hub_download<\/code> to grab individual files (e.g., one drone\u2019s raw data or spectrograms).<\/li>\n\n\n\n<li>If the dataset has Parquet files, you can download only certain Parquet shards.<\/li>\n<\/ul>\n\n\n\n<p>Example:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from huggingface_hub import hf_hub_download\n\n# Download one specific file (replace with actual path from the repo tree)\nhf_hub_download(repo_id=\"kitofrank\/RFUAV\", filename=\"weight\/some_model_weights.bin\", local_dir=\"rfuav_subset\")<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">4. <strong>Hybrid Approach for Your Workflow<\/strong><\/h4>\n\n\n\n<p>Since you\u2019re working with RF fusion, hypergraph, Gemma-3n reasoning, and Three.js viz:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Stream<\/strong> most of the time for exploration and feature extraction.<\/li>\n\n\n\n<li><strong>Download small targeted subsets<\/strong> (e.g., only high-SNR samples from 5\u201310 drone types) when you need offline training data.<\/li>\n\n\n\n<li>Use <code>datasets<\/code> <code>map()<\/code> or <code>filter()<\/code> with streaming to preprocess on-the-fly (e.g., convert raw IQ \u2192 spectrogram \u2192 embedding).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Practical Next Steps for Your RF Scythe Setup<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Start with streaming + a small slice to prototype:<\/li>\n<\/ol>\n\n\n\n<pre class=\"wp-block-code\"><code>   ds = load_dataset(\"kitofrank\/RFUAV\", streaming=True, split=\"train&#91;:1000]\")<\/code><\/pre>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li>Feed examples into your pipeline:<\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Extract IQ or spectrogram \u2192 run local spectrogram analysis (your DASPy code).<\/li>\n\n\n\n<li>Generate text description \u2192 embed with <code>embeddinggemma<\/code>.<\/li>\n\n\n\n<li>Feed to Gemma-3n with one of the RF reasoning templates we discussed earlier.<\/li>\n<\/ul>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li>Store only the <strong>processed features<\/strong> (embeddings, labels, metadata) in your local DuckDB\/Parquet store \u2014 discard raw data immediately.<\/li>\n<\/ol>\n\n\n\n<p>This way you get the value of the RFUAV benchmark (real drone RF signatures for detection\/ID) without the 10GB+ disk hit.<\/p>\n\n\n\n<p>Would you like a ready-to-run code snippet that:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Streams a few examples<\/li>\n\n\n\n<li>Converts IQ to spectrogram<\/li>\n\n\n\n<li>Embeds the description<\/li>\n\n\n\n<li>Runs a Gemma-3n reasoning prompt<\/li>\n\n\n\n<li>Saves only the compact results?<\/li>\n<\/ul>\n\n\n\n<p>Just let me know your preferred output format or next integration point (e.g., hypergraph node creation).<\/p>\n\n\n\n<p><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\" target=\"_blank\" rel=\"noreferrer noopener\">kitofrank\/RFUAV \u00b7 Datasets at Hugging Face<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\" target=\"_blank\" rel=\"noreferrer noopener\">This repository contains the RFUAV dataset , presented in the paper &#8221; RFUAV : A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification&#8221;.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\" target=\"_blank\" rel=\"noreferrer noopener\">Huggingface<\/a><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\/viewer\" target=\"_blank\" rel=\"noreferrer noopener\">kitofrank\/RFUAV \u00b7 Datasets at Hugging FaceWe&#8217;re on a journey to advance and democratize artificial intelligence through open source and open science.Huggingface<\/a><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\/tree\/main\" target=\"_blank\" rel=\"noreferrer noopener\">kitofrank\/RFUAV at mainDatasets : \u00b7 kitofrank . \/. RFUAV . like 19 ; Tasks: Audio Classification ; Modalities: Image ; Formats: imagefolder ; Languages: English ; Size : 10K &#8211; 100K.Huggingface<\/a><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\/blob\/61baf823591346cbae25bc6ab73151010efe17e5\/README.md\" target=\"_blank\" rel=\"noreferrer noopener\">README.md \u00b7 kitofrank\/RFUAV at &#8230;The RFUAV DATASET . Abstract. The official repository for our paper, &#8221; RFUAV : A Benchmark Dataset for Unmanned Aerial Vehicle Detection and &#8230;Huggingface<\/a><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\/tree\/main\/weight\" target=\"_blank\" rel=\"noreferrer noopener\">kitofrank\/RFUAV at mainTasks: Audio Classification ; Modalities: Image ; Formats: imagefolder ; Languages: English ; Size : 10K &#8211; 100K.Huggingface<\/a><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\/resolve\/61baf823591346cbae25bc6ab73151010efe17e5\/README.md?download=true\" target=\"_blank\" rel=\"noreferrer noopener\">961 BytesRFUAV offers a comprehensive benchmark dataset for Radio-Frequency (RF)-based drone detection and identification. ![pic.1](.\/abstract\/profile.png) More detailed &#8230;Huggingface<\/a><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\/blob\/main\/weight\/config-template.yaml\" target=\"_blank\" rel=\"noreferrer noopener\">weight\/config-template.yaml \u00b7 kitofrank\/RFUAV at mainDatasets : \u00b7 kitofrank . \/. RFUAV . like 20 ; Tasks: Audio Classification ; Modalities: Image ; Formats: imagefolder ; Languages: English ; Size : 10K &#8211; &#8230;Huggingface<\/a><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\/tree\/3f9c5d3ca3487f69008ccc9764564a618c1e1ff7\" target=\"_blank\" rel=\"noreferrer noopener\">TasksDatasets : \u00b7 kitofrank . \/. RFUAV . like 19 ; Tasks: Audio Classification ; Modalities: Image ; Formats: imagefolder ; Languages: English ; Size : 10K &#8211; 100K.Huggingface<\/a><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\/blob\/61baf823591346cbae25bc6ab73151010efe17e5\/RAW%20DATA\" target=\"_blank\" rel=\"noreferrer noopener\">RAW DATADatasets : \u00b7 kitofrank . \/. RFUAV . like 20 ; Tasks: Audio Classification ; Modalities: Image ; Formats: imagefolder ; Languages: English ; Size : 10K &#8211; &#8230;Huggingface<\/a><a href=\"https:\/\/github.com\/kitoweeknd\/RFUAV\" target=\"_blank\" rel=\"noreferrer noopener\">kitoweeknd\/RFUAV: This is official &#8230;The official repository for our paper, &#8221; RFUAV : A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification&#8221;, can be accessed here.Github<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Searched webhow to stream or download subset of Hugging Face dataset without full download10<\/h3>\n\n\n\n<p><a href=\"https:\/\/stackoverflow.com\/questions\/75481137\/is-there-is-a-way-that-i-can-download-only-a-part-of-the-dataset-from-huggingfac\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/stackoverflow.com\/questions\/75481137\/is-there-is-a-way-that-i-can-download-only-a-part-of-the-dataset-from-huggingfac\" target=\"_blank\" rel=\"noreferrer noopener\">Is there is a way that I can download only a part &#8230;<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/stackoverflow.com\/questions\/75481137\/is-there-is-a-way-that-i-can-download-only-a-part-of-the-dataset-from-huggingfac\" target=\"_blank\" rel=\"noreferrer noopener\">Did you consider using the stream feature in Datasets ? It allows you to stream data from HuggingFace&#8217;s Hub without having to download the dataset locally.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/stackoverflow.com\/questions\/75481137\/is-there-is-a-way-that-i-can-download-only-a-part-of-the-dataset-from-huggingfac\" target=\"_blank\" rel=\"noreferrer noopener\">Stackoverflow<\/a><a href=\"https:\/\/discuss.huggingface.co\/t\/download-only-a-subset-of-a-split\/16410\" target=\"_blank\" rel=\"noreferrer noopener\">Download only a subset of a split &#8211; \ud83e\udd17DatasetsIf your dataset is too big, please use streaming mode. You can also slice your dataset in streaming mode, see the documentation here: Stream .Discuss.huggingface<\/a><a href=\"https:\/\/discuss.huggingface.co\/t\/how-to-get-the-number-of-samples-in-a-dataset-without-downloading-the-whole-dataset\/53177\" target=\"_blank\" rel=\"noreferrer noopener\">How to get the number of samples in a dataset without &#8230;The only option then is to use the streaming feature to iterate over a dataset&#8217;s samples but without downloading it.Discuss.huggingface<\/a><a href=\"https:\/\/huggingface.co\/docs\/datasets\/en\/stream\" target=\"_blank\" rel=\"noreferrer noopener\">StreamDataset streaming lets you work with a dataset without downloading it . The data is streamed as you iterate over the dataset. This is especially helpful when:.Huggingface<\/a><a href=\"https:\/\/www.youtube.com\/watch?v=1viiT0OlOYo\" target=\"_blank\" rel=\"noreferrer noopener\">How To Download &amp; Process Hugging Face DatasetLet&#8217;s see how we can download and process any hugging face data set . Be it on Google Collab or on your local machine the same code will work.Youtube<\/a><a href=\"https:\/\/discuss.huggingface.co\/t\/how-to-process-the-first-20k-samples-of-a-dataset-without-downloading-all-of-it\/53163\" target=\"_blank\" rel=\"noreferrer noopener\">How to process the first 20k samples of a dataset without &#8230;Just 20k samples per locale is enough for my purpose. But I cannot find a way to select only 20k first samples without downloading the whole dataset first.Discuss.huggingface<\/a><a href=\"https:\/\/discuss.huggingface.co\/t\/loading-a-fraction-of-data\/39373\" target=\"_blank\" rel=\"noreferrer noopener\">Loading a fraction of data &#8211; \ud83e\udd17DatasetsYou can load a subset from a dataset this way: subset = load_dataset (&#8230;, split=&#8221;train[:30%]&#8221;) Note that it still downloads and prepares the full dataset .Discuss.huggingface<\/a><a href=\"https:\/\/github.com\/huggingface\/datasets\/issues\/4114\" target=\"_blank\" rel=\"noreferrer noopener\">Allow downloading just some columns of a dataset #4114Hi @lhoestq, I have an audio dataset of 250GB on the huggingface hub in parquet format. I only wanted to load the text column. It is taking a &#8230;Github<\/a><a href=\"https:\/\/discuss.huggingface.co\/t\/how-can-i-download-a-sizable-subset-of-a-dataset\/78472\" target=\"_blank\" rel=\"noreferrer noopener\">How can I download a sizable subset of a datasetHi, I would like to download 5% of the German dataset from allenai\/c4. It is a huge, 300+ billion-word dataset , so its 5% is also very big.Discuss.huggingface<\/a><a href=\"https:\/\/www.youtube.com\/watch?v=0LUM-CBmyOA\" target=\"_blank\" rel=\"noreferrer noopener\">Simplest Way to Download Models and Datasets from &#8230;This video shares couple of easiest tools to use to download the models and datasets from Hugging Face quickly. Buy Me a Coffee to support &#8230;Youtube<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Searched webkitofrank RFUAV dataset description RF UAV signals5<\/h3>\n\n\n\n<p><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\" target=\"_blank\" rel=\"noreferrer noopener\">kitofrank\/RFUAV \u00b7 Datasets at Hugging Face<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\" target=\"_blank\" rel=\"noreferrer noopener\">This repository contains the RFUAV dataset , presented in the paper &#8220;RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification&#8221;.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV\" target=\"_blank\" rel=\"noreferrer noopener\">Huggingface<\/a><a href=\"https:\/\/arxiv.org\/html\/2503.09033v2\" target=\"_blank\" rel=\"noreferrer noopener\">RFUAV: A Benchmark Dataset for Unmanned Aerial &#8230;In this paper, we propose RFUAV as a new benchmark dataset for radio-frequency based (RF-based) unmanned aerial vehicle (UAV) identification and address the &#8230;Arxiv<\/a><a href=\"https:\/\/github.com\/kitoweeknd\/RFUAV\" target=\"_blank\" rel=\"noreferrer noopener\">kitoweeknd\/RFUAV: This is official &#8230;RFUAV offers a comprehensive benchmark dataset for Radio-Frequency (RF)-based drone detection and identification.Github<\/a><a href=\"https:\/\/www.researchgate.net\/publication\/389786609_RFUAV_A_Benchmark_Dataset_for_Unmanned_Aerial_Vehicle_Detection_and_Identification\" target=\"_blank\" rel=\"noreferrer noopener\">(PDF) RFUAV: A Benchmark Dataset for Unmanned Aerial &#8230;In this paper, we propose RFUAV as a new benchmark dataset for radio-frequency based (RF-based) unmanned aerial vehicle (UAV) identification and address the &#8230;Researchgate<\/a><a href=\"https:\/\/www.emergentmind.com\/papers\/2503.09033\" target=\"_blank\" rel=\"noreferrer noopener\">RFUAV: A Benchmark Dataset for Unmanned Aerial &#8230;The &#8216; RFUAV &#8216; paper presents a benchmark dataset for RF -based UAV detection, featuring unique spectral signatures and a robust two-stage &#8230;Emergentmind<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/grok.com\/share\/c2hhcmQtNA_0c2f8c75-7a93-4883-9d8d-a13ebe5b66dd Hey Grok, I have a Disk Space crunch, how could I use https:\/\/huggingface.co\/datasets\/kitofrank\/RFUAV without downloading to whole 10GB? Yes, you can use the kitofrank\/RFUAV dataset without downloading the full ~10GB (or larger raw data). The dataset is hosted on Hugging Face and consists of raw IQ recordings (RF signals from 37 UAV\/drone types), spectrogram&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=5241\" rel=\"bookmark\"><span class=\"screen-reader-text\">Dataset of raw IQ recordings (RF signals from 37 UAV\/drone types)<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":4570,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-5241","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-the-truben-show"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/5241","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5241"}],"version-history":[{"count":2,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/5241\/revisions"}],"predecessor-version":[{"id":5243,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/5241\/revisions\/5243"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/4570"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5241"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5241"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5241"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}