Wiki-Quantities and Wiki-Measurements: Datasets of Quantities and their Measurement Context from Wikipedia

The task of measurement extraction (or quantitative information extraction) is typically approached in a pipeline manner, where 1) quantities are identified before 2) their individual measurement context is extracted (see our review paper). To support the development and evaluation of systems for measurement extraction, we present two large datasets that correspond to the two tasks: Wiki-Quantities, a dataset for identifying quantities, and Wiki-Measurements, a dataset for extracting measurement context for given quantities. The datasets are heuristically generated from Wikipedia articles and Wikidata facts. Citation and reference spans sampled from the S2ORC dataset are randomly added to the examples in Wiki-Quantities to make models trained on the data more robust to numerals appearing outside of quantities in scientific texts. For a detailed description of the datasets, please refer to the upcoming corresponding paper: Wiki-Quantities and Wiki-Measurements: Datasets of Quantities and their Measurement Context from Wikipedia. 2025. Jan Göpfert, Patrick Kuckertz, Jann M. Weinand, and Detlef Stolten. Versions The datasets are released in different versions: Processing level: the pre-processed versions can be used directly for training and evaluating models, while the raw versions can be used to create custom pre-processed versions or for other purposes. Wiki-Quantities is pre-processed for IOB sequence labeling, while Wiki-Measurements is pre-processed for SQuAD-style generative question answering. Filtering level: Wiki-Quantities is available in a raw, large, small, and tiny version: The raw version is the original version, which includes all the samples originally obtained. In the large version, all duplicates and near duplicates present in the raw version are removed. The small and tiny versions are subsets of the large version which are additionally filtered to balance the data with respect to units, properties, and topics. Wiki-Measurements is available in a large`, small, large_strict, small_strict, small_context, and large_strict_context version: The large version contains all examples minus a few duplicates. The small version is a subset of the large version with very similar examples removed. In the context versions, additional sentences are added around the annotated sentence. In the strict versions, the quantitative facts are more strictly aligned with the text. Quality: all data has been automatically annotated using heuristics. In contrast to the silver data, the gold data has been manually curated. Format The datasets are stored in JSON files and are available in a pre-processed version and a raw version. The respective pre-processed version is formatted for direct use in NLP frameworks such as Huggingface Transformers, while the raw version is provided in two formats: one with character offsets and one with emojis to visualize the annotations and facilitate curation. Below, we provide examples of the different formats for both datasets. Wiki-Quantities Example from the raw version in the format that uses character offsets to indicate the start and end of the annotated spans: { "id": 52633, "source": "Suldal (en:178082)", "text": "The finished hydroelectric complex has a total capacity of approximately 2,100 megawatts and produces about 4.8 terawatt-hours yearly (Figure S2).", "quantities": [[73, 88], [108, 126]], "correct": null } source indicates the source Wikipedia page of the example text. correct indicates the curation status: if set to true, the quantity annotations were considered correct during manual curation; if set to false, the quantity annotations were considered incorrect; if set to null, no manual curation was performed. Note that quantity modifiers are not included in the quantity spans. Example from the raw version in the format that uses emojis to mark annotations: { "id": 52633, "source": "Suldal (en:178082)", "text": "The finished hydroelectric complex has a total capacity of approximately 🍏2,100 megawatts🍏 and produces about 🍏4.8 terawatt-hours🍏 yearly (Figure S2).", "correct": null } Here, quantities are enclosed in 🍏 emojis. Example from the version pre-processed for IOB sequence labeling: { "target": "Suldal (en:178082)", "tokens": ["The", "finished", "hydroelectric", "complex", "has", "a", "total", "capacity", "of", "approximately", "2,100", "megawatts", "and", "produces", "about", "4.8", "terawatt", "-", "hours", "yearly", "(", "Figure", "S2", ")", "."], "ner_tags": [ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-Quantity", "I-Quantity", "O", "O", "O", "B-Quantity", "I-Quantity", "I-Quantity", "I-Quantity", "O", "O", "O", "O", "O", "O" ] } Wiki-Measurements Example from the raw version in the format that uses character offsets to indicate the start and end of the annotated spans: { "id": 28066, "source": "Colongra Power Station (en:25606789)", "text": "Colongra Gas Generation Plant (also known as Colongra Power Station) is a 667 MW gas-fired power station located in Colongra, New South Wales, Australia and is the largest gas-fired power station in New South Wales.", "quantitative_statement": { "entity": {"explicit": [[0, 68]]}, "property": {"explicit": [], "implicit": "installed capacity"}, "value": {"explicit": [[74, 77]]}, "unit": {"explicit": [[78, 80]]}, "location": {"explicit": [[116, 152]]} }, "curation": { "quantity_entity_property_correct": true, "spatio_temporal_scope_correct": true, "qualifiers_correct": true } } If the measured property is explicitly stated in the text it is annotated using character offsets and the implicit property field is kept empty. If the measured property is implicit, it is specified as a string in the implicit property field. The implicit property field can optionally be used in conjunction with an explicit property annotation, to correct its spelling, improve its meaningfulness, or adapt its inflection (e.g., "length" instead of "long"). All other annotations are explicitly annotated using character offsets. While the entity, property, value, and unit annotations are mandatory, they can be empty if no corresponding span is found in the text. The annotation of qualifiers and quantity modifiers is optional. If quantity_entity_property_correct is set to true, the quantity, entity, and property annotations were considered correct during manual curation. If spatio_temporal_scope_correct is true, the temporal and spatial scope were considered correct. If qualifiers_correct is true, all qualifiers were considered correct. If any of the three fields is false or null, the corresponding annotations were considered incorrect or no manual curation was performed, respectively. Equivalent example from the raw version in the format that uses emojis to mark annotations: { "id": 28066, "source": "Colongra Power Station (en:25606789)", "text": "🌶️Colongra Gas Generation Plant (also known as Colongra Power Station)🌶️ is a 🍐667🍐 🍓MW🍓 gas-fired power station located in 📍Colongra, New South Wales, Australia📍 and is the largest gas-fired power station in New South Wales.", "implicit_property": "installed capacity", "curation": { "quantity_entity_property_correct": true, "spatio_temporal_scope_correct": true, "qualifiers_correct": true } } The value spans are marked with 🍐, the unit spans with 🍓, the property spans with 🍊, the entity spans with 🌶️, and the spatial scope with 📍. The full mapping from emojis to annotation types is given below. ./Wiki-Measurements/raw/additional_data.json provides additional information about the example, including the Wikidata fact with URIs for its items, as well as the URL of the respective Wikipedia article. Additionally, the text surrounding the example sentence is provided. If not all quality criteria were met when matching the Wikidata fact to the Wikipedia article, the reasons why the example was accepted anyway are provided. The additional data given for the above example is: { "id": 28066, "target": "Colongra Power Station (en:25606789)", "context": { "before": [], "after": ["It will generally be used during peak demand periods in [...]"] }, "wikidata_fact": { "entity": "http://www.wikidata.org/entity/Q5148195", "property": "http://www.wikidata.org/entity/P2109", "value": "667", "value_lowerbound": null, "value_upperbound": null, "unit": "http://www.wikidata.org/entity/Q6982035", "qualifiers": [""], "qualifier_values": [""], "qualifier_lowerbounds": [""], "qualifier_upperbounds": [""], "qualifier_units": [""], "qualifier_time_precisions": [""], "article": "https://en.wikipedia.org/wiki/Colongra_Power_Station" }, "weak_accept_reasons": ["no_property_but_rare_num_exact_match"] } Equivalent examples from the version pre-processed for SQuAD-style, multi-turn, generative question answering: { "id": "1403", "title": "Colongra Power Station (en:25606789)", "context": "Colongra Gas Generation Plant (also known as Colongra Power Station) is a $667 MW$ gas-fired power station located in Colongra, New South Wales, Australia and is the largest gas-fired power station in New South Wales.", "question": "Which property or quality is characterized by 667 MW?", "answers": {"text": ["installed capacity"], "answer_start": [-1]} }, { "id": "1404", "title": "Colongra Power Station (en:25606789)", "context": "Colongra Gas Generation Plant (also known as Colongra Power Station) is a $667 MW$ gas-fired power station located in Colongra, New South Wales, Australia and is the largest gas-fired power station in New South Wales.", "question": "Which entity's installed capacity is characterized by 667 MW?", "answers": {"text": ["Colongra Gas Generation Plant (also known as Colongra Power Station)"], "answer_start": [0]} }, { "id": "1406", "title": "Colongra Power Station (en:25606789)", "context": "[[Colongra Gas Generation Plant (also known as Colongra Power Station)]] is a $667 MW$ gas-fired power station located in Colongra, New South Wales, Australia and is the largest gas-fired power station in New South Wales.", "question": "For which location is the statement true that the installed capacity of the colongra Gas Generation Plant (also known as Colongra Power Station) is 667 MW?", "answers": {"text": ["Colongra, New South Wales, Australia"], "answer_start": [116]} }, ... A separate example with the corresponding formatting, question, and answer is provided for each annotation type. The pre-processed data implies that for a given quantity, first the measured property is asked for, then the measured entity, and finally the qualifiers. Previous answers are highlighted in the context using dollar signs for the quantity, double asterisks for the measured property, and double brackets for the measured entity. For the measured property, the implicit property is taken as the answer if it is not empty. In this case, the answer_start is set to -1. Note that for each version of Wiki-Measurements sample IDs are randomly assigned. Therefore, they are not consistent, e.g., between silver small and silver large. The proportions of train, dev, and test sets are unusual because Wiki-Quantities and Wiki-Measurements are intended to be used in conjunction with other non-heuristically generated data. Emoji format The emoji format uses colorful emojis to visualize the annotations and facilitate curation. The mapping of annotation types to emojis is as follows: Basic quantitative statement: Entity: 🌶️ Property: 🍊 Quantity: 🍏 Value: 🍐 Unit: 🍓 Quantity modifier: ☎️ Qualifier: Temporal scope: 📆 Start time: ⏱️ End time: ⏰️ Location: 📍 Reference: 🙋 Determination method: 🔭 Criterion used: 📏 Applies to part: 🦵 Scope: 🔎 Some qualifier: 🛁 For example: Wiki-Quantities (only quantities annotated): "In a 🍏100-gram🍏 reference amount, almonds supply 🍏579 kilocalories🍏 of food energy." "Extreme heat waves can raise readings to around and slightly above 🍏38 °C🍏, and arctic blasts can drop lows to 🍏−23 °C to 0 °F🍏." "This sail added another 🍏0.5 kn🍏." Wiki-Measurements (measurement context for a single quantity; qualifiers and quantity modifiers are only sparsely annotated): "The 🔭French national census🔭 of 📆2018📆 estimated the 🍊population🍊 of 🌶️Metz🌶️ to be 🍐116,581🍐, while the population of Metz metropolitan area was about 368,000." "🌶️Airbus🌶️ sized the production facilities and supply chain for a 🍊production rate🍊 of 🍐four🍐 🍓A380s per month🍓." "The 🍊surface temperature🍊 of 🌶️Triton🌶️ was 🔭recorded by Voyager 2🔭 as 🍐-235🍐 🍓°C🍓 (-391 °F)." "🙋The Babylonians🙋 were able to find that the 🍊value🍊 of 🌶️pi🌶️ was ☎️slightly greater than☎️ 🍐3🍐, by simply 🔭making a big circle and then sticking a piece of rope onto the circumference and the diameter, taking note of their distances, and then dividing the circumference by the diameter🔭." To facilitate reading and conversion between the different formats, we will soon publish a Python library here. Evaluation The evaluation directories contain the manually validated random samples used for evaluation. The evaluation is based on the large versions of the datasets. Manual validation of 100 samples each of Wiki-Quantities and Wiki-Measurements showed that 100% of the Wiki-Quantities samples and 94% (or 84% if strictly scored) of the Wiki-Measurements samples were correct. License In accordance with Wikipedia's and Wikidata's licensing terms, the datasets are released under the CC BY-SA 4.0 license, except for Wikidata facts in ./Wiki-Measurements/raw/additional_data.json, which are released under the CC0 1.0 license (the texts are still CC BY-SA 4.0). About Us We are the Institute of Climate and Energy Systems (ICE) - Jülich Systems Analysis belonging to the Forschungszentrum Jülich. Our interdisciplinary department's research is focusing on energy-related process and systems analyses. Data searches and system simulations are used to determine energy and mass balances, as well as to evaluate performance, emissions and costs of energy systems. The results are used for performing comparative assessment studies between the various systems. Our current priorities include the development of energy strategies, in accordance with the German Federal Government’s greenhouse gas reduction targets, by designing new infrastructures for sustainable and secure energy supply chains and by conducting cost analysis studies for integrating new technologies into future energy market frameworks. Acknowledgements The authors would like to thank the German Federal Government, the German State Governments, and the Joint Science Conference (GWK) for their funding and support as part of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) – project number: 442146713. Furthermore, this work was supported by the Helmholtz Association under the program "Energy System Design".### 计量抽取任务概述 计量抽取（或量化信息抽取）任务通常采用流水线方式处理，具体分为两步：1）先识别量化实体，2）再抽取各量化实体对应的测量上下文（详见我们的综述论文）。为支撑计量抽取系统的开发与评估，我们构建了对应这两个子任务的两大数据集：分别是用于量化实体识别的**Wiki-Quantities**，以及用于为给定量化实体抽取测量上下文的**Wiki-Measurements**。 上述数据集均通过启发式方法从维基百科（Wikipedia）条目与维基数据（Wikidata）事实中生成。为使基于该数据集训练的模型对科学文本中出现在量化实体之外的数字具有更强鲁棒性，我们从S2ORC数据集采样的引用与引用跨度被随机添加至Wiki-Quantities的样本中。如需了解数据集的详细信息，请参阅即将发表的相关论文：《Wiki-Quantities与Wiki-Measurements：来自维基百科的量化实体及其测量上下文数据集》，2025年，作者Jan Göpfert、Patrick Kuckertz、Jann M. Weinand与Detlef Stolten。 ### 版本 本数据集提供多种版本： 1. **处理层级**：预处理版本可直接用于模型的训练与评估，原始版本则可用于自定义预处理或其他用途。其中，Wiki-Quantities已针对IOB序列标注进行预处理，而Wiki-Measurements则针对SQuAD风格生成式问答任务完成预处理。 2. **过滤层级**： - Wiki-Quantities提供原始版、大尺寸版、小尺寸版与超小尺寸版：原始版为初始获取的全部样本；大尺寸版已移除原始版本中的重复与近重复样本；小尺寸版与超小尺寸版均为大尺寸版的子集，且额外经过过滤以平衡数据在单位、属性与主题维度的分布。 - Wiki-Measurements提供large、small、large_strict、small_strict、small_context与large_strict_context版本：大尺寸版包含除少量重复样本外的全部示例；小尺寸版为大尺寸版的子集，已移除高度相似的样本；上下文版本会在标注句前后添加额外句子；严格版本则会将量化事实与文本进行更严格的对齐。 3. **质量层级**：所有数据均通过启发式方法自动标注。与银标数据（silver data）不同，金标数据（gold data）已经过人工审核。 ### 格式 数据集以JSON文件存储，并分为预处理版本与原始版本两类。对应的预处理版本格式可直接用于Huggingface Transformers等自然语言处理（Natural Language Processing, NLP）框架，而原始版本则提供两种格式：一种带有字符偏移量以标注跨度位置，另一种使用表情符号可视化标注结果以方便审核。下文将展示两个数据集不同格式的示例。 #### Wiki-Quantities ##### 原始版本（字符偏移标注格式）示例 json { "id": 52633, "source": "Suldal (en:178082)", "text": "The finished hydroelectric complex has a total capacity of approximately 2,100 megawatts and produces about 4.8 terawatt-hours yearly (Figure S2).", "quantities": [[73, 88], [108, 126]], "correct": null } 其中，`source`字段表示该示例文本的来源维基百科页面；`correct`字段表示审核状态：若设为`true`，则该量化实体标注在人工审核中被判定为正确；若设为`false`，则判定为错误；若设为`null`，则未进行人工审核。需注意，量化实体修饰词未包含在量化实体跨度内。 ##### 原始版本（表情符号标注格式）示例 json { "id": 52633, "source": "Suldal (en:178082)", "text": "The finished hydroelectric complex has a total capacity of approximately 🍏2,100 megawatts🍏 and produces about 🍏4.8 terawatt-hours🍏 yearly (Figure S2).", "correct": null } 此处，量化实体被🍏表情符号包裹。 ##### IOB序列标注预处理版本示例 json { "target": "Suldal (en:178082)", "tokens": ["The", "finished", "hydroelectric", "complex", "has", "a", "total", "capacity", "of", "approximately", "2,100", "megawatts", "and", "produces", "about", "4.8", "terawatt", "-", "hours", "yearly", "(", "Figure", "S2", ")", "."], "ner_tags": [ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-Quantity", "I-Quantity", "O", "O", "O", "B-Quantity", "I-Quantity", "I-Quantity", "I-Quantity", "O", "O", "O", "O", "O", "O" ] } #### Wiki-Measurements ##### 原始版本（字符偏移标注格式）示例 json { "id": 28066, "source": "Colongra Power Station (en:25606789)", "text": "Colongra Gas Generation Plant (also known as Colongra Power Station) is a 667 MW gas-fired power station located in Colongra, New South Wales, Australia and is the largest gas-fired power station in New South Wales.", "quantitative_statement": { "entity": {"explicit": [[0, 68]]}, "property": {"explicit": [], "implicit": "installed capacity"}, "value": {"explicit": [[74, 77]]}, "unit": {"explicit": [[78, 80]]}, "location": {"explicit": [[116, 152]]} }, "curation": { "quantity_entity_property_correct": true, "spatio_temporal_scope_correct": true, "qualifiers_correct": true } } 若被测属性在文本中显式出现，则通过字符偏移量标注，且隐式属性字段留空；若被测属性为隐式，则以字符串形式填入隐式属性字段。隐式属性字段可配合显式属性标注使用，用于修正拼写、提升语义清晰度或调整屈折形式（例如用"length"替代"long"）。其余标注均通过字符偏移量显式标注。实体、属性、数值与单位标注为必填项，若文本中未找到对应跨度，则可留空。限定词与量化实体修饰词的标注为可选项。若`quantity_entity_property_correct`设为`true`，则表示量化实体、实体与属性标注在人工审核中判定为正确；若`spatio_temporal_scope_correct`为`true`，则表示时空范围标注正确；若`qualifiers_correct`为`true`，则表示所有限定词标注正确。若上述三个字段任一为`false`或`null`，则分别表示对应标注错误或未进行人工审核。 ##### 原始版本（表情符号标注格式）等效示例 json { "id": 28066, "source": "Colongra Power Station (en:25606789)", "text": "🌶️Colongra Gas Generation Plant (also known as Colongra Power Station)🌶️ is a 🍐667🍐 🍓MW🍓 gas-fired power station located in 📍Colongra, New South Wales, Australia📍 and is the largest gas-fired power station in New South Wales.", "implicit_property": "installed capacity", "curation": { "quantity_entity_property_correct": true, "spatio_temporal_scope_correct": true, "qualifiers_correct": true } } 此处，数值跨度以🍐标注，单位跨度以🍓标注，属性跨度以🍊标注，实体跨度以🌶️标注，空间范围以📍标注。完整的表情符号与标注类型映射关系如下： | 标注类型 | 表情符号 | | --- | --- | | 实体 | 🌶️ | | 属性 | 🍊 | | 量化实体 | 🍏 | | 数值 | 🍐 | | 单位 | 🍓 | | 量化实体修饰词 | ☎️ | | 时间范围 | 📆 | | 开始时间 | ⏱️ | | 结束时间 | ⏰️ | | 位置 | 📍 | | 参考文献 | 🙋 | | 测定方法 | 🔭 | | 使用标准 | 📏 | | 适用部分 | 🦵 | | 范围 | 🔎 | | 其他限定词 | 🛁 | 例如： - Wiki-Quantities（仅标注量化实体）： > "In a 🍏100-gram🍏 reference amount, almonds supply 🍏579 kilocalories🍏 of food energy." > "Extreme heat waves can raise readings to around and slightly above 🍏38 °C🍏, and arctic blasts can drop lows to 🍏−23 °C to 0 °F🍏." > "This sail added another 🍏0.5 kn🍏." - Wiki-Measurements（单量化实体的测量上下文；限定词与量化实体修饰词仅稀疏标注）： > "The 🔭French national census🔭 of 📆2018📆 estimated the 🍊population🍊 of 🌶️Metz🌶️ to be 🍐116,581🍐, while the population of Metz metropolitan area was about 368,000." > "🌶️Airbus🌶️ sized the production facilities and supply chain for a 🍊production rate🍊 of 🍐four🍐 🍓A380s per month🍓." > "The 🍊surface temperature🍊 of 🌶️Triton🌶️ was 🔭recorded by Voyager 2🔭 as 🍐-235🍐 🍓°C🍓 (-391 °F)." > "🙋The Babylonians🙋 were able to find that the 🍊value🍊 of 🌶️pi🌶️ was ☎️slightly greater than☎️ 🍐3🍐, by simply 🔭making a big circle and then sticking a piece of rope onto the circumference and the diameter, taking note of their distances, and then dividing the circumference by the diameter🔭." `./Wiki-Measurements/raw/additional_data.json`提供了示例的额外信息，包括带有实体URI的维基数据事实，以及对应维基百科条目的URL。此外，还提供了示例句子周围的上下文文本。若将维基数据事实与维基百科条目匹配时未满足所有质量标准，则会提供该示例仍被接受的原因。上述示例的额外数据如下： json { "id": 28066, "target": "Colongra Power Station (en:25606789)", "context": { "before": [], "after": ["It will generally be used during peak demand periods in [...]"] }, "wikidata_fact": { "entity": "http://www.wikidata.org/entity/Q5148195", "property": "http://www.wikidata.org/entity/P2109", "value": "667", "value_lowerbound": null, "value_upperbound": null, "unit": "http://www.wikidata.org/entity/Q6982035", "qualifiers": [""], "qualifier_values": [""], "qualifier_lowerbounds": [""], "qualifier_upperbounds": [""], "qualifier_units": [""], "qualifier_time_precisions": [""], "article": "https://en.wikipedia.org/wiki/Colongra_Power_Station" }, "weak_accept_reasons": ["no_property_but_rare_num_exact_match"] } ##### SQuAD风格生成式问答预处理版本等效示例 json { "id": "1403", "title": "Colongra Power Station (en:25606789)", "context": "Colongra Gas Generation Plant (also known as Colongra Power Station) is a $667 MW$ gas-fired power station located in Colongra, New South Wales, Australia and is the largest gas-fired power station in New South Wales.", "question": "Which property or quality is characterized by 667 MW?", "answers": {"text": ["installed capacity"], "answer_start": [-1]} }, { "id": "1404", "title": "Colongra Power Station (en:25606789)", "context": "Colongra Gas Generation Plant (also known as Colongra Power Station) is a $667 MW$ gas-fired power station located in Colongra, New South Wales, Australia and is the largest gas-fired power station in New South Wales.", "question": "Which entity's installed capacity is characterized by 667 MW?", "answers": {"text": ["Colongra Gas Generation Plant (also known as Colongra Power Station)"], "answer_start": [0]} }, { "id": "1406", "title": "Colongra Power Station (en:25606789)", "context": "[[Colongra Gas Generation Plant (also known as Colongra Power Station)]] is a $667 MW$ gas-fired power station located in Colongra, New South Wales, Australia and is the largest gas-fired power station in New South Wales.", "question": "For which location is the statement true that the installed capacity of the colongra Gas Generation Plant (also known as Colongra Power Station) is 667 MW?", "answers": {"text": ["Colongra, New South Wales, Australia"], "answer_start": [116]} }, ... 每个标注类型均对应一个单独的示例，包含对应的格式、问题与答案。预处理数据遵循以下逻辑：针对给定量化实体，依次询问被测属性、被测实体与限定词。上下文会使用美元符号标注量化实体、双星号标注被测属性、双括号标注被测实体以高亮过往答案。若被测属性为隐式，则以隐式属性作为答案，此时`answer_start`设为-1。 需注意，Wiki-Measurements各版本的样本ID均为随机分配，因此不同版本（例如银标小尺寸版与银标大尺寸版）之间的样本ID并不一致。训练集、开发集与测试集的比例较为特殊，因为Wiki-Quantities与Wiki-Measurements旨在与其他非启发式生成的数据集结合使用。 ### 评估 评估目录包含用于评估的人工验证随机样本。评估基于数据集的大尺寸版本展开。对Wiki-Quantities与Wiki-Measurements各100个样本的人工验证结果显示，Wiki-Quantities的样本正确率为100%，Wiki-Measurements的样本正确率为94%（若严格评分则为84%）。 ### 许可 根据维基百科与维基数据的许可条款，本数据集采用CC BY-SA 4.0协议发布，但`./Wiki-Measurements/raw/additional_data.json`中的维基数据事实除外，该部分采用CC0 1.0协议发布（文本内容仍采用CC BY-SA 4.0协议）。 ### 关于我们 我们隶属于尤利希研究中心（Forschungszentrum Jülich）的气候与能源系统研究所（ICE）。我们的跨学科团队致力于能源相关的过程与系统分析研究：通过数据检索与系统模拟，确定能量与质量平衡，评估能源系统的性能、排放与成本，并开展各类系统间的比较评估研究。当前我们的研究重点包括：根据德国联邦政府的温室气体减排目标，通过构建可持续且安全的能源供应链基础设施，以及开展将新技术集成至未来能源市场框架的成本分析研究，制定能源战略。 ### 致谢 作者感谢德国联邦政府、德国各州政府以及联合科学会议（GWK）作为NFDI4Ing联盟的一部分提供的资助与支持，该项目由德国研究基金会（DFG）资助，项目编号：442146713。此外，本研究还获得了亥姆霍兹协会"Energy System Design"项目的支持。