federated-learning-ro-crate-profile/example-fl-crate/ro-crate-metadata.json at main · eScienceLab/federated-learning-ro-crate-profile · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
{
    "@context": "https://w3id.org/ro/crate/1.1/context",
    "@graph": [
        {
            "@id": "./",
            "@type": "Dataset",
            "author": {
                "@id": "https://orcid.org/0000-0002-0035-6475"
            },
            "conformsTo": [
                {
                    "@id": "https://w3id.org/ro/wfrun/process/0.5"
                }
            ],
            "datePublished": "2026-02-27T13:50:54+00:00",
            "description": "Training of an image categorization model using Flower and PyTorch. The Flower configuration is based on the quickstart-pytorch tutorial (https://flower.ai/docs/framework/tutorial-quickstart-pytorch.html). This is an example RO-Crate demonstrating the Federated Learning RO-Crate Profile.",
            "hasPart": [
                {
                    "@id": "https://huggingface.co/datasets/uoft-cs/cifar10/"
                },
                {
                    "@id": "quickstart-pytorch/"
                },
                {
                    "@id": "quickstart-pytorch/pytorchexample/"
                },
                {
                    "@id": "quickstart-pytorch/pyproject.toml"
                },
                {
                    "@id": "quickstart-pytorch/README.md"
                },
                {
                    "@id": "quickstart-pytorch/final_model.pt"
                }
            ],
            "identifier": [
                "TODO"
            ],
            "license": {
                "@id": "https://spdx.org/licenses/CC0-1.0"
            },
            "mainEntity": {
                "@id": "quickstart-pytorch/final_model.pt"
            },
            "mentions": [
                {
                    "@id": "#action-dc098066-d4c2-42aa-be36-4a39321aff27"
                }
            ],
            "name": "Training of image categorization model (Federated Learning RO-Crate example)",
            "publisher": {
                "@id": "https://ror.org/027m9bs27"
            }
        },
        {
            "@id": "ro-crate-metadata.json",
            "@type": "CreativeWork",
            "about": {
                "@id": "./"
            },
            "conformsTo": {
                "@id": "https://w3id.org/ro/crate/1.1"
            }
        },
        {
            "@id": "https://spdx.org/licenses/CC0-1.0",
            "@type": "CreativeWork",
            "name": "Creative Commons Zero v1.0 Universal",
            "url": "https://creativecommons.org/publicdomain/zero/1.0/legalcode"
        },
        {
            "@id": "https://ror.org/027m9bs27",
            "@type": "Organization",
            "name": "The University of Manchester",
            "url": "https://www.manchester.ac.uk"
        },
        {
            "@id": "https://orcid.org/0000-0002-0035-6475",
            "@type": "Person",
            "affiliation": {
                "@id": "https://ror.org/027m9bs27"
            },
            "familyName": "Chadwick",
            "givenName": "Eli",
            "name": "Eli Chadwick"
        },
        {
            "@id": "https://huggingface.co/datasets/uoft-cs/cifar10/",
            "@type": "Dataset",
            "conformsTo": "http://mlcommons.org/croissant/1.1",
            "description": "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.",
            "encodingFormat": "git+https",
            "name": "uoft-cs/cifar10 (Hugging Face dataset)"
        },
        {
            "@id": "https://flower.ai",
            "@type": "SoftwareApplication",
            "description": "Flower federated learning framework",
            "name": "Flower",
            "url": "https://flower.ai",
            "version": "1.26.1"
        },
        {
            "@id": "quickstart-pytorch/",
            "@type": "Dataset",
            "description": "A Flower project based on the quickstart-pytorch tutorial",
            "hasPart": [
                {
                    "@id": "quickstart-pytorch/pytorchexample/"
                },
                {
                    "@id": "quickstart-pytorch/pyproject.toml"
                },
                {
                    "@id": "quickstart-pytorch/README.md"
                }
            ],
            "name": "Flower project folder"
        },
        {
            "@id": "quickstart-pytorch/pytorchexample/",
            "@type": "Dataset",
            "description": "Flower scripts written in Python which configure the client app, server app, datasets, and model",
            "encodingFormat": "text/x-python",
            "name": "Flower configuration scripts"
        },
        {
            "@id": "quickstart-pytorch/pyproject.toml",
            "@type": "File",
            "description": "A TOML file which includes the configuration for the Flower project. It's also a Python project configuration file.",
            "encodingFormat": "application/toml",
            "name": "Flower project configuration TOML"
        },
        {
            "@id": "quickstart-pytorch/README.md",
            "@type": "File",
            "about": {
                "@id": "quickstart-pytorch/"
            },
            "description": "A Markdown file containing instructions for how to run the project.",
            "encodingFormat": "text/markdown",
            "name": "Flower project README"
        },
        {
            "@id": "https://docs.python.org/3/library/pickle.html",
            "@type": "WebPage",
            "description": "Pickle Python library documentation. The pickle module implements binary protocols for serializing and de-serializing a Python object structure.",
            "name": "Pickle Python library documentation"
        },
        {
            "@id": "quickstart-pytorch/final_model.pt",
            "@type": "File",
            "description": "Model trained using Flower federated learning process",
            "encodingFormat": {
                "@id": "https://docs.python.org/3/library/pickle.html"
            },
            "name": "Output model"
        },
        {
            "@id": "#action-dc098066-d4c2-42aa-be36-4a39321aff27",
            "@type": "CreateAction",
            "agent": {
                "@id": "https://orcid.org/0000-0002-0035-6475"
            },
            "description": "Execution of the federated learning process using `flwr run`",
            "instrument": {
                "@id": "https://flower.ai"
            },
            "name": "Execution of federated learning process",
            "object": [
                {
                    "@id": "quickstart-pytorch/pytorchexample/"
                },
                {
                    "@id": "quickstart-pytorch/pyproject.toml"
                }
            ],
            "result": [
                {
                    "@id": "quickstart-pytorch/final_model.pt"
                }
            ]
        },
        {
            "@id": "https://w3id.org/ro/wfrun/process/0.5",
            "@type": "CreativeWork",
            "name": "Process Run Crate",
            "version": "0.5"
        }
    ]
}