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+  "unique_name": "5g_energy_consumption",
+  "dataset_year": "2023",
+  "domain_str": "technology & internet",
+  "dataset_source": "HuggingFace",
+  "original_dataset_source_download_link": "https://huggingface.co/datasets/netop/5G-Network-Energy-Consumption",
+  "download_description": "\nWe use the dataset version uploaded by a top solution from the Zindi challenge.\n\nmkdir -p local-data-warehouse/5g_energy_consumption/ \\\n&& wget https://github.com/ITU-AI-ML-in-5G-Challenge/5G-Energy-Consumption-Modelling-Solution-Team-Farzi-Data-Scientists/raw/refs/heads/main/ITU-5G-energy-Consumption-Dataset.zip \\\n&& unzip ITU-5G-energy-Consumption-Dataset.zip -d local-data-warehouse/5g_energy_consumption/ \\\n&& rm ITU-5G-energy-Consumption-Dataset.zip\n",
+  "academic_reference_bibtex": "@misc{huawei_netop_5g_energy_consumption,\n  author       = {{HUAWEI Netop Team}},\n  title        = {5G Network Energy Consumption Dataset},\n  year         = {n.d.},\n  howpublished = {\\url{https://huggingface.co/datasets/netop/5G-Network-Energy-Consumption}},\n  note         = {Dataset hosted on Hugging Face, accessed 2026-04-15}\n}\n",
+  "academic_reference_bibtex_key": "huawei_netop_5g_energy_consumption",
+  "license": "MIT",
+  "data_tags": [
+    "Non-IID",
+    "Grouped"
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+  "curation_comments": "\n- Note: The dataset was used in a Zindi challenge, but also uploaded to Huggingface under a MIT license by the company (Huawei).\n- The corresponding ITU/Zindi challenge explicitly emphasizes generalization to unseen base station products/configurations. We therefore split by base_station (BS).\n- With this setup, the task is development of predictive models for network optimization where models need to generalize to new unseen base station configurations and estimate their energy consumption under similar conditions (during the same time period).\n- For preprocessing, we orient on a top solution from the Zindi challenge: https://github.com/ITU-AI-ML-in-5G-Challenge/5G-Energy-Consumption-Modelling-Solution-Team-Farzi-Data-Scientists/tree/main.\n- Note that the competition also used mostly samples from the known BS as the test set, but weighted unknown base stations higher in evaluation.\n- The data itself is time-series. However, because we predict entirely unseen base stations in a per-sample fashion, the row-wise dependencies resulting from the temporal components cannot be used to improve performance unless the task is treated as transductive learning.\n- Following the Zindi solution, we merge the three given tables and use the Cell0 information only from the cell level table. In addition, we merge the Cell1 information since it contains information as well which might be useful in a grouped split setting.\n- Following the Zindi solution, we derive calendar features (`day`, `hour`, `weekday`) and drop the absolute timestamp.\n- We drop constant columns.\n",
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+  "version_comment": null,
+  "local_data_directory_base": "/home/lennart_priorlabs_ai/code/large_data_ensemble/data-foundry/local-data-warehouse",
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+}

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anes_voting_2026/019db4ba-6124-7a56-a0b5-9df8811adc2f/dataset_metadata.dataset-mold-v1.json

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+  "unique_name": "anes_voting_2026",
+  "dataset_year": "2026",
+  "domain_str": "social science",
+  "dataset_source": "Other",
+  "original_dataset_source_download_link": "https://electionstudies.org/data-center/anes-time-series-cumulative-data-file/",
+  "download_description": "\nNo automatic download supported!\n\nmkdir -p local-data-warehouse/anes_voting_2026/\n\nDownload the February 5, 2026 CSV version, unzip and place the .csv in local-data-warehouse/anes_voting_2026/anes_timeseries_cdf_csv_20260205.csv.\n",
+  "academic_reference_bibtex": "@misc{anes2026timeseries,\n    author       = {{American National Election Studies}},\n    title        = {{ANES Time Series Cumulative Data File [dataset and documentation]}},\n    year         = {2026},\n    month        = feb,\n    note         = {February 5, 2026 version},\n    howpublished = {\\url{https://www.electionstudies.org}}\n}\n",
+  "academic_reference_bibtex_key": "anes2026timeseries",
+  "license": "Use for research or statistical purposes",
+  "data_tags": [
+    "Non-IID",
+    "Temporal"
+  ],
+  "curation_comments": "\n- We remove all samples with missing pre-election data, missing post-election data or a missing target.\n- We drop 327 that are missing from the last 9 years, which will be used for the test splits.\n- We drop all columns with questions that appear only in one year.\n- The question columns partially stem from post-election interviews. However, there is no simple way to tell which is post-election. We therefore drop all questions for which \"no post IW\" is listed as a reason for missing values. Some additional questions are dropped based on common sense.\n- We encode gender as a categorical variable, because Other was introduced in 2016. However there are very few of these samples. This would actually require split-specific preprocessing.\n- For all features, we assign \" \" as missing values. Most features have additional types of missingness with own codes. We keep them as categories. That means NA only is assigned if a question was missing in a survey.\n- We transform only high-cardinality (>10) numeric features to numeric, and leave all other ordinal features as categorical, since they all include at least one value that is out-of-order (e.g., \"Don't know\", \"Refused\", \"Not applicable\", \"Other\", etc.).\n- Although there are several ways to further preprocess features, we leave the preprocessing minimal to enable the development of models that can handle even complex preprocessing on their own.\n- Note: Alternative target could be constructed using who the candidate voted for (VCF0704). This could be a multi-class target with [Democrat, Republican, Other Party, Did not vote, Voted but unknown who]. However, the last category is less useful to predict, and might introduce problems. We therefore stick to predicting whether someone voted or not.\n- Note: While the data was also used in the TableShift benchmark, we define an entirely different task, with a temporal split and more features.\n- Note: Some information is represented across multiple columns, e.g.,  ['VCF0009x', 'VCF0009y', 'VCF0009z', 'VCF0010x', 'VCF0010y', 'VCF0010z', 'VCF0011x', 'VCF0011y', 'VCF0011z'] all correspond to weight. We keep all of these columns, even though some might be redundant, because we want to keep the data as close to the original as possible leaving the challenge to the model.\n- Note: Ideally, we would select different feature sets per data split, depending on feature availability. However, for simplicity, we just keep all features.\n- Note: We considered keeping the unique respondent identifier in the data, because otherwise models that are able to reconstruct this information would unfairly benefit. However, for now we drop the identifier since it is not a desirable predictive feature for the given task, because the goal is generalization to the population, not memorizing individual behavior.\n",
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+  "version_comment": null,
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+  "type_adapter_id": "dataset-mold-v1"
+}

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