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CITATION.cff

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+cff-version: 1.2.0
+message: "If you use this dataset, please cite the associated paper."
+title: "DERE Dataset"
+type: dataset
+authors:
+  - family-names: "Xu"
+    given-names: "Shuo"
+
+repository-code: "https://github.com/ai-spatial/DERE"
+url: "https://huggingface.co/datasets/ai-spatial/DERE"
+version: "1.0.0"
+
+preferred-citation:
+  type: conference-paper
+  title: "Knowledge-Guided Learning for Global Carbon Flux Prediction: Integrating High-Level Remote Sensing with Bottom-Up Physical Modeling"
+  authors:
+    - family-names: "Xu"
+      given-names: "Shuo"
+    - family-names: "Wang"
+      given-names: "Zhihao"
+    - family-names: "Li"
+      given-names: "Ruohan"
+    - family-names: "Wang"
+      given-names: "Ruichen"
+    - family-names: "Ma"
+      given-names: "Lei"
+    - family-names: "Hurtt"
+      given-names: "George C."
+    - family-names: "Jia"
+      given-names: "Xiaowei"
+    - family-names: "Xie"
+      given-names: "Yiqun"
+  collection-title: "Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"
+  year: 2026
+  publisher:
+    name: "ACM"
+  conference:
+    name: "32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining"
+  location:
+    name: "Jeju Island, Republic of Korea"
+  doi: "10.1145/3770855.3818927"

README.md

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+---
+language:
+  - en
+pretty_name: DERE Dataset
+tags:
+  - remote-sensing
+  - carbon-flux
+  - time-series
+  - earth-system-science
+  - knowledge-guided-machine-learning
+  - process-based-modeling
+  - gpp
+  - reco
+  - nee
+configs:
+  - config_name: global_mask
+    data_files:
+      - split: train
+        path: viewer/global_mask/train.parquet
+      - split: test
+        path: viewer/global_mask/test.parquet
+  - config_name: above
+    data_files:
+      - split: train
+        path: viewer/insitu_matched/above/train.parquet
+      - split: test
+        path: viewer/insitu_matched/above/test.parquet
+  - config_name: ameriflux
+    data_files:
+      - split: train
+        path: viewer/insitu_matched/ameriflux/train.parquet
+      - split: test
+        path: viewer/insitu_matched/ameriflux/test.parquet
+  - config_name: fluxnet
+    data_files:
+      - split: train
+        path: viewer/insitu_matched/fluxnet/train.parquet
+      - split: test
+        path: viewer/insitu_matched/fluxnet/test.parquet
+  - config_name: icos-ww
+    data_files:
+      - split: train
+        path: viewer/insitu_matched/icos-ww/train.parquet
+      - split: test
+        path: viewer/insitu_matched/icos-ww/test.parquet
+  - config_name: multiple
+    data_files:
+      - split: train
+        path: viewer/insitu_matched/multiple/train.parquet
+      - split: test
+        path: viewer/insitu_matched/multiple/test.parquet
+---
+
+# DERE Dataset
+
+DERE is a processed multi-source ecosystem dataset for global carbon-flux
+prediction. It integrates Ecosystem Demography (ED) simulations, remote-sensing
+products, LiDAR-derived forest-age information, and in-situ flux observations.
+
+The dataset supports the paper:
+
+**Knowledge-Guided Learning for Global Carbon Flux Prediction: Integrating
+High-Level Remote Sensing with Bottom-Up Physical Modeling**
+
+Code repository: `https://github.com/ai-spatial/DERE`
+
+## Dataset Viewer
+
+The Hugging Face Dataset Viewer uses lightweight Parquet summary tables under
+`viewer/`. Each Viewer row corresponds to one sample in the associated NPZ file
+and includes sample identifiers, array shapes, file paths, and compact summary
+statistics.
+
+The complete multidimensional arrays remain in the NPZ files under
+`GlobalMask/` and `InSituMatched/`.
+
+## Dataset organization
+
+```text
+DERE/
+├── README.md
+├── CITATION.cff
+├── GlobalMask/
+│   ├── README.md
+│   ├── train/
+│   │   └── GlobalMask_train.npz
+│   └── test/
+│       └── GlobalMask_test.npz
+├── InSituMatched/
+│   ├── README.md
+│   ├── above/
+│   │   ├── train/
+│   │   │   └── InSituMatched_above_train.npz
+│   │   └── test/
+│   │       └── InSituMatched_above_test.npz
+│   ├── ameriflux/
+│   │   ├── train/
+│   │   │   └── InSituMatched_ameriflux_train.npz
+│   │   └── test/
+│   │       └── InSituMatched_ameriflux_test.npz
+│   ├── fluxnet/
+│   │   ├── train/
+│   │   │   └── InSituMatched_fluxnet_train.npz
+│   │   └── test/
+│   │       └── InSituMatched_fluxnet_test.npz
+│   ├── icos_ww/
+│   │   ├── train/
+│   │   │   └── InSituMatched_icos-ww_train.npz
+│   │   └── test/
+│   │       └── InSituMatched_icos-ww_test.npz
+│   └── multiple/
+│       ├── train/
+│       │   └── InSituMatched_multiple_train.npz
+│       └── test/
+│           └── InSituMatched_multiple_test.npz
+├── metadata/
+│   ├── README.md
+│   ├── dimension_definitions.md
+│   ├── dataset_schema.json
+│   ├── normalization_statistics.npz
+│   ├── feature_names.csv
+│   ├── target_names.csv
+│   ├── pft_names.csv
+│   ├── age_classes.csv
+│   ├── insitu_site_metadata.csv
+│   ├── train_test_mask.md
+│   └── train_test_mask.npy
+└── viewer/
+    ├── README.md
+    ├── global_mask/
+    │   ├── train.parquet
+    │   └── test.parquet
+    └── insitu_matched/
+        ├── above/
+        ├── ameriflux/
+        ├── fluxnet/
+        ├── icos-ww/
+        └── multiple/
+```
+
+## GlobalMask
+
+`GlobalMask` contains globally sampled land-grid cells selected by a fixed
+train/test mask.
+
+| Split | Samples | File |
+|---|---:|---|
+| Training | 3373 | `GlobalMask/train/GlobalMask_train.npz` |
+| Testing | 852 | `GlobalMask/test/GlobalMask_test.npz` |
+
+Each file contains:
+
+- `ed_simulation_x`
+- `ed_simulation_y`
+- `ed_simulation_pft_bl`
+- `ed_simulation_pft_nl`
+- `ed_simulation_pft_gs`
+- `lidar_age_weight_fraction`
+- `esa_cci_bl_fraction`
+- `esa_cci_nl_fraction`
+- `esa_cci_gs_fraction`
+
+## InSituMatched
+
+`InSituMatched` contains ED simulation data and auxiliary variables aligned with
+in-situ carbon-flux observations.
+
+| Subset | Training samples | Testing samples |
+|---|---:|---:|
+| ABoVE | 48 | 12 |
+| AmeriFlux | 67 | 18 |
+| FLUXNET | 68 | 16 |
+| ICOS-WW | 9 | 4 |
+| Multiple-network sites | 58 | 16 |
+
+Each file contains:
+
+- `ed_simulation_x`
+- `ed_simulation_y`
+- `observed_y`
+- `lidar_age_weight_fraction`
+- `esa_cci_bl_fraction`
+- `esa_cci_nl_fraction`
+- `esa_cci_gs_fraction`
+
+The `multiple` subset contains sites occurring in more than one in-situ network.
+
+## Temporal alignment
+
+The complete ED target sequence covers 29 calendar years from 1992 through
+2020.
+
+- December 1992 is used as the initial ED state.
+- Model inputs cover January 1993 through December 2020.
+- Prediction targets cover January 1993 through December 2020.
+- The prediction period contains 28 years, or 336 monthly time steps.
+
+All released arrays use sample-first orientation whenever a sample dimension is
+present. Detailed dimensions and released shapes are documented in
+`metadata/dimension_definitions.md`.
+
+## Metadata
+
+- `feature_names.csv` defines the ordering and index ranges of the 136 ED input features.
+- `target_names.csv` defines the 10 ED simulation targets and 3 observed
+  carbon-flux targets.
+- `pft_names.csv` defines broadleaf, needleleaf, and grass-and-shrub PFTs.
+- `age_classes.csv` defines the 18 representative forest-age classes.
+- `insitu_site_metadata.csv` maps each InSituMatched sample index to its network and split.
+- `train_test_mask.md` documents the GlobalMask spatial mask.
+- `train_test_mask.npy` stores the GlobalMask sampling split.
+- `normalization_statistics.npz` contains:
+  - `x_mean`: shape `[136]`
+  - `x_std`: shape `[136]`
+  - `y_mean`: shape `[10]`
+  - `y_std`: shape `[10]`
+
+## Loading the data
+
+```python
+import numpy as np
+
+file_path = "GlobalMask/train/GlobalMask_train.npz"
+
+with np.load(file_path, allow_pickle=False) as data:
+    for key in data.files:
+        print(key, data[key].shape, data[key].dtype)
+```
+
+Load the normalization statistics with:
+
+```python
+import numpy as np
+
+with np.load(
+    "metadata/normalization_statistics.npz",
+    allow_pickle=False,
+) as stats:
+    x_mean = stats["x_mean"]
+    x_std = stats["x_std"]
+    y_mean = stats["y_mean"]
+    y_std = stats["y_std"]
+```
+
+Standardization is performed as:
+
+```python
+x_normalized = (x - x_mean) / x_std
+y_normalized = (y - y_mean) / y_std
+```
+
+## Intended use
+
+The dataset is intended for research on:
+
+- global carbon-flux prediction
+- knowledge-guided machine learning
+- process-model emulation
+- multi-source data fusion
+- time-series modeling of GPP, RECO, and NEE
+- simulation-to-observation transfer learning
+- reproduction and comparison of DERE and baseline models
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@inproceedings{xu2026knowledge,
+  author    = {Shuo Xu and Zhihao Wang and Ruohan Li and Ruichen Wang and Lei Ma and George C. Hurtt and Xiaowei Jia and Yiqun Xie},
+  title     = {Knowledge-Guided Learning for Global Carbon Flux Prediction: Integrating High-Level Remote Sensing with Bottom-Up Physical Modeling},
+  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
+  year      = {2026},
+  address   = {Jeju Island, Republic of Korea},
+  publisher = {ACM},
+  doi       = {10.1145/3770855.3818927}
+}
+```
+
+The same citation is also provided in `CITATION.cff`.
+
+## License and source terms
+
+The released files combine information derived from multiple upstream sources.
+Users are responsible for following the applicable attribution and
+redistribution terms of those sources.