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Datasets:
bsebench-org
/
tri-attia-fast-charge-2020-raw

Modalities:
Tabular
Formats:
csv
Size:
10M - 100M
Tags:
bsebench
batteries
fast-charging
raw-mirror
materials-science
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
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akiroussama commited on
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1 Parent(s): 82bdc56

Upload raw TRI fast-charge dataset mirror

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  2. BSEBENCH_SOURCE.json +66 -0
  3. README.md +55 -0
  4. SHA256SUMS.txt +725 -0
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BSEBENCH_SOURCE.json ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bsebench_status": "raw_mirror_pending_validation",
3
+ "dataset_title": "Closed-loop optimization of extreme fast charging for batteries",
4
+ "target_repo": "bsebench-org/tri-attia-fast-charge-2020-raw",
5
+ "source": "TRI Energy & Materials Data / data.matr.io",
6
+ "source_url": "https://data.matr.io/",
7
+ "project_url": "https://data.matr.io/1/projects/5d80e633f405260001c0b60a",
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+ "source_api_base": "https://data.matr.io/1/api/v1",
9
+ "source_project_id": "5d80e633f405260001c0b60a",
10
+ "doi": "10.1038/s41586-020-1994-5",
11
+ "doi_url": "https://doi.org/10.1038/s41586-020-1994-5",
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+ "license": {
13
+ "name": "CC BY 4.0",
14
+ "url": "https://creativecommons.org/licenses/by/4.0/",
15
+ "spdx": "CC-BY-4.0"
16
+ },
17
+ "institution_source_credibility": [
18
+ "Stanford University",
19
+ "MIT",
20
+ "Toyota Research Institute"
21
+ ],
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+ "download_date": "2026-05-08",
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+ "download_method": "wget from official data.matr.io file download endpoints",
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+ "official_download_url_template": "https://data.matr.io/1/api/v1/file/{file_id}/download",
25
+ "transformations": [
26
+ "Organized downloaded files into source_files/<kind>/<batch> directories",
27
+ "Disambiguated duplicate local filenames by appending official file IDs"
28
+ ],
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+ "source_file_count": 718,
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+ "source_total_bytes_actual": 22110649139,
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+ "source_total_bytes_api_reported": 16788439046,
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+ "source_files_by_kind_actual": {
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+ "struct": {
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+ "count": 5,
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+ "bytes": 3975095856
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+ },
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+ "json_struct_zip": {
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+ "count": 5,
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+ "bytes": 6855320161
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+ },
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+ "data_csv": {
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+ "count": 236,
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+ "bytes": 11275069907
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+ },
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+ "metadata": {
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+ "count": 236,
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+ "bytes": 124514
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+ },
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+ "schedule": {
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+ "count": 236,
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+ "bytes": 5038701
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+ }
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+ },
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+ "api_size_mismatch_count": 241,
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+ "verification": {
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+ "source_catalog_license_verified": true,
57
+ "source_project_doi_verified": true,
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+ "nature_data_availability_verified": true,
59
+ "license_verified_before_payload_upload": true,
60
+ "payload_downloaded_only_from_official_source": true
61
+ },
62
+ "residual_risks": [
63
+ "Source API size metadata differs from redirected asset byte counts for 241 files; actual downloaded sizes and SHA-256 checksums are recorded.",
64
+ "Raw mirror is pending independent BSEBench validation; no benchmark conclusions are asserted."
65
+ ]
66
+ }
README.md ADDED
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1
+ ---
2
+ title: Closed-loop optimization of extreme fast charging for batteries
3
+ license: cc-by-4.0
4
+ pretty_name: Closed-loop optimization of extreme fast charging for batteries
5
+ tags:
6
+ - bsebench
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+ - batteries
8
+ - fast-charging
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+ - raw-mirror
10
+ - materials-science
11
+ ---
12
+
13
+ # Closed-loop optimization of extreme fast charging for batteries
14
+
15
+ BSEBench status: `raw_mirror_pending_validation`
16
+
17
+ This dataset repository is a raw mirror of the TRI Energy & Materials Data / data.matr.io project "Closed-loop optimization of extreme fast charging for batteries using machine learning". The source describes commercial lithium-ion phosphate (LFP)/graphite cells cycled under fast-charging conditions for the associated Nature publication.
18
+
19
+ Source page: https://data.matr.io/
20
+ Project URL: https://data.matr.io/1/projects/5d80e633f405260001c0b60a
21
+ DOI: https://doi.org/10.1038/s41586-020-1994-5
22
+ License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
23
+ Source institution/credibility: Stanford University / MIT / Toyota Research Institute
24
+ Download date: 2026-05-08
25
+
26
+ ## Contents
27
+
28
+ The files under `source_files/` are the official source downloads retrieved from `https://data.matr.io/1/api/v1/file/<file_id>/download` and organized by source file kind and batch. No scientific data values were transformed.
29
+
30
+ Actual downloaded source payload:
31
+
32
+ | Kind | Files | Bytes |
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+ |---|---:|---:|
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+ | MATLAB struct files | 5 | 3975095856 |
35
+ | JSON structure ZIP files | 5 | 6855320161 |
36
+ | Raw CSV data files | 236 | 11275069907 |
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+ | Metadata CSV files | 236 | 124514 |
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+ | Schedule files | 236 | 5038701 |
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+ | Total source files | 718 | 22110649139 |
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+
41
+ `SOURCE_FILE_MANIFEST.json` records the official file IDs, source URLs, source API metadata, local paths, and measured local byte counts. `SHA256SUMS.txt` records checksums for uploaded files.
42
+
43
+ ## Provenance Notes
44
+
45
+ The TRI catalog page lists this dataset with license CC BY 4.0, and the project API page includes the citation and DOI for Attia et al. (2020). The Nature article data-availability section links the study datasets to `https://data.matr.io/1/`.
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+
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+ Some `size` values returned by the source API differ from the byte counts of the redirected downloadable assets, especially JSON-structure ZIP archives. This mirror treats the actual downloaded files and SHA-256 checksums as authoritative and records both API-reported and measured sizes in `SOURCE_FILE_MANIFEST.json`.
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+
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+ ## Citation
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+
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+ Attia, P. M., Grover, A., Jin, N. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578, 397-402 (2020). https://doi.org/10.1038/s41586-020-1994-5
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+
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+ ## License
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+
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+ The source page and API configuration identify the dataset license as Creative Commons Attribution 4.0 International (CC BY 4.0). Users should follow the source license and cite the original work.
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