cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/fjoi4f
title: Simulated Forensic Role Interactions Using GPT-4: A Psycholinguistic Dataset

description: This dataset comprises 30 simulated conversational sessions generated by GPT-4 (OpenAI, March 2024 release), each designed to emulate one of three role types commonly observed in forensic contexts: Analyst, Friend, and Witness. Prompts were systematically crafted to evoke role-consistent psycholinguistic behaviors and were used to test the applicability of generative AI for profiling and training in investigative scenarios. For each session, role-specific prompts, AI-generated responses, model metadata, and annotated linguistic features are recorded. This dataset supports the reproducibility and transparency of the findings presented in the article "Generative AI in Forensic Role Simulations as a Human-Computer Interaction Phenomenon".
DOI: 10.6084/m9.figshare.28936802.v1
title: 无标题项目

description: <b>Unlocking High Lithium-Ion Transport in Solid Polymer Electrolytes with Dynamic Hydrogen-Bonded Zinc Adeninate Framework</b>
DOI: 10.15468/dl.d477q6
title: Occurrence Download

description: A dataset containing 74027 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((118.08244 9.10387,118.9189 9.10387,118.9189 9.96294,118.08244 9.96294,118.08244 9.10387))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 74027 records from 227 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017437-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.737frd
title: Occurrence Download

description: A dataset containing 283 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Luvunga scandens (Roxb.) Buch.-Ham. ex Wight & Arn."
 ]
}

 The dataset includes 283 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017456-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y8ftfu
title: Occurrence Download

description: A dataset containing 461 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Machilus odoratissimus Nees"
 ]
}

 The dataset includes 461 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017458-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.knrr6t
title: Occurrence Download

description: A dataset containing 92390 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is True",
 "HasGeospatialIssue is False",
 "OccurrenceStatus is Present",
 "TaxonKey is Notamacropus rufogriseus (Desmarest, 1817)"
 ]
}

 The dataset includes 92390 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017452-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11845991
title: ETHOS.BUILDA: Building Footprint and Height Dataset Germany

description: Introduction

This dataset contains all buildings in Germany with their footprint polygon and height. It is a partial dump of the ETHOS.BUILDA database (version v7_20240429). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset. 

ETHOS.BUILDA is made available under the Open Database License (ODbL). The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point.

Data structure and file overview

Building data is provided per federal state, the files are named according to the NUTS-1 region names. The building data has the following fields:




field name
description


ID
unique identifier of the building


source
the source of the building footprint


footprint
footprint polygon in WKT-format, EPSG:3035


height_m


value: height of the building in [m], 

source: source of the height data,

lineage: height assignment method





A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no source entry for the source "v7_model.json" in the sources.csv file, as this refers to the internally trained machine learning model and not to an external dataset.

Acknowledgements

This work was supported by the Helmholtz Association under the program "Energy System Design". 

Furthermore, the authors would like to express their gratitude to the Federal Ministry for Economic Affairs and Climate Action (BMWK.IIB4) for providing the necessary resources to conduct this study. Our research was supported by the WAAGE Grant Program (Grant No. 03EI1044/03EE 5031D), and we appreciate their financial assistance.
DOI: 10.15468/dl.3ej37j
title: Occurrence Download

description: A dataset containing 1349 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Macaranga denticulata (Blume) Müll.Arg."
 ]
}

 The dataset includes 1349 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017457-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mek6sy
title: Occurrence Download

description: A dataset containing 102 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mammea suriga (Buch.-Ham. ex Roxb.) Kosterm."
 ]
}

 The dataset includes 102 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017459-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u8a5sq
title: Occurrence Download

description: A dataset containing 137 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mangifera caloneura Kurz"
 ]
}

 The dataset includes 137 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017460-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11845992
title: ETHOS.BUILDA: Building Footprint and Height Dataset Germany

description: Introduction

This dataset contains all buildings in Germany with their footprint polygon and height. It is a partial dump of the ETHOS.BUILDA database (version v7_20240429). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset. 

ETHOS.BUILDA is made available under the Open Database License (ODbL). The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point.

Data structure and file overview

Building data is provided per federal state, the files are named according to the NUTS-1 region names. The building data has the following fields:




field name
description


ID
unique identifier of the building


source
the source of the building footprint


footprint
footprint polygon in WKT-format, EPSG:3035


height_m


value: height of the building in [m], 

source: source of the height data,

lineage: height assignment method





A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no source entry for the source "v7_model.json" in the sources.csv file, as this refers to the internally trained machine learning model and not to an external dataset.

Acknowledgements

This work was supported by the Helmholtz Association under the program "Energy System Design". 

Furthermore, the authors would like to express their gratitude to the Federal Ministry for Economic Affairs and Climate Action (BMWK.IIB4) for providing the necessary resources to conduct this study. Our research was supported by the WAAGE Grant Program (Grant No. 03EI1044/03EE 5031D), and we appreciate their financial assistance.
DOI: 10.5281/zenodo.12069754
title: ETHOS.BUILDA: Residential Building TABULA Archetype Dataset Germany

description: Introduction

This dataset contains all residential buildings in Germany with their construction year, size class, refurbishment state, and TABULA archetype. It is a partial dump of the ETHOS.BUILDA database (version v8_20240916). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset. 

ETHOS.BUILDA is made available under the Open Database License (ODbL). The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point.

Data structure and file overview

Building data is provided per federal state, the files are named according to the NUTS-1 region names. The building data has the following fields:




field name
description


ID
unique identifier of the building


position
location of building centroid in WKT-format, EPSG:3035


construction_year


value: construction year, 

source: source of the construction year data,

lineage: construction year assignment method



size_class


value: size class of the building, 

source: source of the size class data,

lineage: size class assignment method



refurbishment_state


value: refurbishment state of the building, 

source: source of the refurbishment state data,

lineage: refurbishment state assignment method



tabula_type


value: TABULA type of the building, 

source: source of the TABULA data,

lineage: TABULA type assignment method





A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no entry for the source "v3_model.json", as it refers to the internally trained machine learning model for the respective attribute and not to an external data source.

The full footprint polygons from which the centroids are derived and the sources of the footprints are found in the related dataset linked as "is supplemented by".

Acknowledgements

This work was supported by the Helmholtz Association under the program "Energy System Design". 

Furthermore, the authors would like to express their gratitude to the Federal Ministry for Economic Affairs and Climate Action (BMWK.IIB4) for providing the necessary resources to conduct this study. Our research was supported by the WAAGE Grant Program (Grant No. 03EI1044/03EE 5031D), and we appreciate their financial assistance.
DOI: 10.5281/zenodo.12069755
title: ETHOS.BUILDA: Residential Building TABULA Archetype Dataset Germany

description: Introduction

This dataset contains all residential buildings in Germany with their construction year, size class, refurbishment state, and TABULA archetype. It is a partial dump of the ETHOS.BUILDA database (version v7_20240429). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset. 

ETHOS.BUILDA is made available under the Open Database License (ODbL). The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point.

Data structure and file overview

Building data is provided per federal state, the files are named according to the NUTS-1 region names. The building data has the following fields:




field name
description


ID
unique identifier of the building


position
location of building centroid in WKT-format, EPSG:3035


construction_year


value: construction year, 

source: source of the construction year data,

lineage: construction year assignment method



size_class


value: size class of the building, 

source: source of the size class data,

lineage: size class assignment method



refurbishment_state


value: refurbishment state of the building, 

source: source of the refurbishment state data,

lineage: refurbishment state assignment method



tabula_type


value: TABULA type of the building, 

source: source of the TABULA data,

lineage: TABULA type assignment method





A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no entry for the source "v3_model.json", as it refers to the internally trained machine learning model for the respective attribute and not to an external data source.

The full footprint polygons from which the centroids are derived and the sources of the footprints are found in the related dataset linked as "is supplemented by".

Acknowledgements

This work was supported by the Helmholtz Association under the program "Energy System Design".
DOI: 10.5281/zenodo.13771740
title: ETHOS.BUILDA: Residential Building TABULA Archetype Dataset Germany

description: Introduction

This dataset contains all residential buildings in Germany with their construction year, size class, refurbishment state, and TABULA archetype. It is a partial dump of the ETHOS.BUILDA database (version v8_20240916). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset. 

ETHOS.BUILDA is made available under the Open Database License (ODbL). The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point.

Data structure and file overview

Building data is provided per federal state, the files are named according to the NUTS-1 region names. The building data has the following fields:




field name
description


ID
unique identifier of the building


position
location of building centroid in WKT-format, EPSG:3035


construction_year


value: construction year, 

source: source of the construction year data,

lineage: construction year assignment method



size_class


value: size class of the building, 

source: source of the size class data,

lineage: size class assignment method



refurbishment_state


value: refurbishment state of the building, 

source: source of the refurbishment state data,

lineage: refurbishment state assignment method



tabula_type


value: TABULA type of the building, 

source: source of the TABULA data,

lineage: TABULA type assignment method





A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no entry for the source "v3_model.json", as it refers to the internally trained machine learning model for the respective attribute and not to an external data source.

The full footprint polygons from which the centroids are derived and the sources of the footprints are found in the related dataset linked as "is supplemented by".

Acknowledgements

This work was supported by the Helmholtz Association under the program "Energy System Design". 

Furthermore, the authors would like to express their gratitude to the Federal Ministry for Economic Affairs and Climate Action (BMWK.IIB4) for providing the necessary resources to conduct this study. Our research was supported by the WAAGE Grant Program (Grant No. 03EI1044/03EE 5031D), and we appreciate their financial assistance.
DOI: 10.13016/lxva-rkcs
title: Dataset for Figures in Robust Wave Splitters Based on Scattering Singularities in Complex non-Hermitian Systems

description: All experimental data in Robust Wave Splitters Based on Scattering Singularities in Complex non-Hermitian Systems were collected using a Keysight model N5242B microwave vector network analyzer (VNA). The theoretical data were created using the random matrix theory model described in supplementary material section VIII of the paper. All raw data collected was the 2x2 scattering matrix S measured over many different parameters, or the voltage values measured at the receivers of the VNA. In the attached data files, the raw S data was processed into det(S) values by calculating the determinant of the scattering matrix for all parameter space measured, and the voltage values measured at the receivers were processed into input/output power ratios and phase differences. The data used to create each figure was saved in the corresponding .mat data file using matlab. Additional details are specified in the paper.
description: This is the dataset used to create all the figures in the paper: Robust Wave Splitters Based on Scattering Singularities in Complex non-Hermitian Systems.
DOI: 10.57760/sciencedb.08284
title: Gender norms and effects on time use and labor supply: evidence from Colombia
title: Gender norms and effects on time use and labor supply: evidence from Colombia

description: Databases, replication materials and supplementary material of the research.
description: Databases, replication materials and supplementary material of the research.
DOI: 10.15468/dl.tmax88
title: Occurrence Download

description: A dataset containing 4210 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Culex tritaeniorhynchus Giles, 1901)"
 ]
}

 The dataset includes 4210 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017462-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.24455
title: Strawberry growth state dataset
title: Strawberry growth state dataset

description: The dataset used in this study is sourced from two public strawberry datasets: StrawDI_Db1 and the Strawberry Dataset for Object Detection. StrawDI_Db1 consists of 3,803 images captured in strawberry plantations in Huelva, Spain, during different harvesting activities. These images have a resolution of 4032×3024 pixels and are stored in 8-bit per channel JPEG format. Upon release, they were resized to 1008×756 pixels and saved in PNG format. The Strawberry Dataset for Object Detection , released by the Natural Resources Institute Finland (Luke) in 2022, contains 813 images for strawberry flower and fruit object detection.After obtaining the datasets, we performed image cleaning, removing blurry and unidentifiable images, and merged them to create a final set of 2,301 images covering multiple growth stages of strawberry flowers and fruits. The merged dataset was split into training, and test sets at a ratio of 8:2, ensuring no overlapping images between subsets, and the test set was not involved in training to ensure the rigor of model evaluation. 
description: The dataset used in this study is sourced from two public strawberry datasets: StrawDI_Db1 and the Strawberry Dataset for Object Detection. StrawDI_Db1 consists of 3,803 images captured in strawberry plantations in Huelva, Spain, during different harvesting activities. These images have a resolution of 4032×3024 pixels and are stored in 8-bit per channel JPEG format. Upon release, they were resized to 1008×756 pixels and saved in PNG format. The Strawberry Dataset for Object Detection , released by the Natural Resources Institute Finland (Luke) in 2022, contains 813 images for strawberry flower and fruit object detection.After obtaining the datasets, we performed image cleaning, removing blurry and unidentifiable images, and merged them to create a final set of 2,301 images covering multiple growth stages of strawberry flowers and fruits. The merged dataset was split into training, and test sets at a ratio of 8:2, ensuring no overlapping images between subsets, and the test set was not involved in training to ensure the rigor of model evaluation.
DOI: 10.57760/sciencedb.24483
title: 三峡水库沉积物微生物氮循环数据集
title: 三峡水库沉积物微生物氮循环数据集

description: 本数据集基于宏基因组测序技术构建，采用Illumina NovaSeq平台进行双端测序，原始数据经Fastp质控后保留平均测序深度10Gb/样本。通过KEGG数据库对功能基因进行注释，识别到固氮、硝化、反硝化、厌氧氨氧化、氮运输、有机氮代谢、同化硝酸盐还原和异化硝酸盐还原等关键基因集，共注释到46个氮循环功能基因，覆盖8种氮转化途径。采样点分布于三峡水库常年回水区，采样时间为2023年9月，样本涵盖表层沉积物（0-30 cm）。
description: 本数据集基于宏基因组测序技术构建，采用Illumina NovaSeq平台进行双端测序，原始数据经Fastp质控后保留平均测序深度10Gb/样本。通过KEGG数据库对功能基因进行注释，识别到固氮、硝化、反硝化、厌氧氨氧化、氮运输、有机氮代谢、同化硝酸盐还原和异化硝酸盐还原等关键基因集，共注释到46个氮循环功能基因，覆盖8种氮转化途径。采样点分布于三峡水库常年回水区，采样时间为2023年9月，样本涵盖表层沉积物（0-30 cm）。
DOI: 10.15468/dl.da3uzx
title: Occurrence Download

description: A dataset containing 705 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mangifera foetida Lour."
 ]
}

 The dataset includes 705 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017461-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.