A small tool to view real-world ActivityPub objects as JSON! Enter a URL
or username from Mastodon or a similar service below, and we'll send a
request with
the right
Accept
header
to the server to view the underlying object.
{
"@context": [
"https://www.w3.org/ns/activitystreams",
{
"ostatus": "http://ostatus.org#",
"atomUri": "ostatus:atomUri",
"inReplyToAtomUri": "ostatus:inReplyToAtomUri",
"conversation": "ostatus:conversation",
"sensitive": "as:sensitive",
"toot": "http://joinmastodon.org/ns#",
"votersCount": "toot:votersCount",
"litepub": "http://litepub.social/ns#",
"directMessage": "litepub:directMessage",
"blurhash": "toot:blurhash",
"focalPoint": {
"@container": "@list",
"@id": "toot:focalPoint"
}
}
],
"id": "https://synapse.cafe/users/lili/statuses/112321906996127537/replies",
"type": "Collection",
"first": {
"id": "https://synapse.cafe/users/lili/statuses/112321906996127537/replies?page=true",
"type": "CollectionPage",
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"partOf": "https://synapse.cafe/users/lili/statuses/112321906996127537/replies",
"items": [
{
"id": "https://synapse.cafe/users/lili/statuses/112321913987339551",
"type": "Note",
"summary": null,
"inReplyTo": "https://synapse.cafe/users/lili/statuses/112321906996127537",
"published": "2024-04-23T18:19:52Z",
"url": "https://synapse.cafe/@lili/112321913987339551",
"attributedTo": "https://synapse.cafe/users/lili",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://synapse.cafe/users/lili/followers"
],
"sensitive": false,
"atomUri": "https://synapse.cafe/users/lili/statuses/112321913987339551",
"inReplyToAtomUri": "https://synapse.cafe/users/lili/statuses/112321906996127537",
"conversation": "tag:synapse.cafe,2024-04-23:objectId=4168655:objectType=Conversation",
"content": "<p>5/7</p><p>When running simulations outside the range of data, we wanted some way to quantify how "good" the simulated walking looked, compared to real data. Thus, we created a metric to estimate the match in kinematic trajectories in simulation to the space of possible real kinematics. We call this "kinematic similarity".</p>",
"contentMap": {
"en": "<p>5/7</p><p>When running simulations outside the range of data, we wanted some way to quantify how "good" the simulated walking looked, compared to real data. Thus, we created a metric to estimate the match in kinematic trajectories in simulation to the space of possible real kinematics. We call this "kinematic similarity".</p>"
},
"attachment": [
{
"type": "Document",
"mediaType": "image/png",
"url": "https://files.synapse.cafe/media_attachments/files/112/321/913/877/434/805/original/95edee5ac57c110f.png",
"name": "Method for computing kinematic similarity between real data and simulated walking. First, the full set of experimental data (from flies) is used to compute a Gaussian kernel density estimator (KDE). To quantify the similarity (to data) of a given bout of simulated walking, we apply the KDE to evaluate the log probability density function of each bout, a scalar value we refer to as kinematic similarity (KS). High KS indicates that the perturbed walking resembles the unperturbed walking from data, while low KS indicates that the perturbed walking deviates from data.",
"blurhash": "U8RW3j~W%14.s;xZRjx]%2bGofoM%fD*-;IT",
"width": 763,
"height": 350
}
],
"tag": [],
"replies": {
"id": "https://synapse.cafe/users/lili/statuses/112321913987339551/replies",
"type": "Collection",
"first": {
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"items": [
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]
}
}
}
]
}
}