cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/tfj4sdmh6y.1
title: Highspeed footage of an automated inline rewelding process based on detection of process anomalies during laser deep penetration welding

description: During laser deep penetration welding, disturbances of the nominal process can lead to seam imperfections. Due to their possibly impairing characteristics on the mechanical properties of the seam, it is of high interest to reliably detect these defects. This can be achieved by means of sensor based inline process monitoring. While the sensor data can be employed to identify the defect location and perform post process repair work, it can also build the foundation to reweld the detected seam segments inline to enhance the seam quality and possibly eliminate necessary post process work. 

This highspeed footage shows a demonstration of an in-situ rewelding process based on an automized detection of a disturbed welding process. 

The process monitoring is done by means of pyrometry. When the analog output voltage of the pyrometer falls below a predetermined threshold, a process disturbance is registered. As the voltage level exceeds the threshold again, the length of the disturbance is calculated and the laser spot is deflected by the calculated distance. After that, the spot is deflected back to the inital position. The deflection is carried out by a 1D scanner within the welding optics. 

For further information about the process parameters as well as the data processing and controler design, please see the authors published paper "Automated inline rewelding processes based on detection of process anomalies during laser deep penetration welding " in Lasers in Manufacturing Conference 2025
DOI: 10.15468/dl.tyfcjp
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Cinnamomum pittosporoides",
 "Year 1950-2025"
 ]
}

 The dataset includes 34 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021245-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zmsg9k6d69
title: iPS promoter methylation

description: Raw data of the Western blot and Bisulfite sequencing
DOI: 10.17632/zmsg9k6d69.1
title: iPS promoter methylation

description: Raw data of the Western blot and Bisulfite sequencing
DOI: 10.17632/wbbjfds7th
title: Melanoma targeted therapies dabrafenib and trametinib cross the placental barrier in ex vivo human placental perfusions

description: Supplemental files for JAAD
DOI: 10.17632/wbbjfds7th.2
title: Melanoma targeted therapies dabrafenib and trametinib cross the placental barrier in ex vivo human placental perfusions

description: Supplemental files for JAAD
DOI: 10.17632/zh4n5ww42p
title: Database for The Article: Leaf Area Index (LAI) Partitioning in Dryland Forests Using Dual-Sensor Sentinel Imagery

description: Leaf Area Index (LAI) is a key biophysical variable for understanding forest structure and function, particularly in dryland ecosystems where vegetation responses to environmental stressors are highly dynamic. Traditional satellite-derived LAI products are often inaccurate, as they typically represent the effective Plant Area Index (PAIeff), excluding necessary corrections such as consideration of leaf clumping and exclusion of woody elements. Moreover, while most studies report total ecosystem LAI, it is increasingly important to partition LAI into its structural components, such as overstory and understory layers, to better understand forest structure and function. In this study, we developed and validated a novel approach to estimate forest LAI (LAIEcosystem) and partition it into overstory (LAIOverstory) and understory (LAIUnderstory) components using an integrated model based on Sentinel-2 (S2) multispectral and Sentinel-1 SAR data (S1). Field LAI measurements were conducted in two Aleppo pine forests in Israel, HaKedoshim and Yatir, which span an aridity gradient and include multiple thinning treatments. Partial Least Squares Regression (PLS-R) models were calibrated and validated using extensive field data collected between 2018 and 2019. The combined S1+S2 model significantly improved LAIEcosystem predictions (R² = 0.85, RMSE = 0.52) and enabled fine-scale monitoring of vertical forest structure. Notably, S1 data substantially enhanced the prediction of understory LAI (R² = 0.87, RMSE = 0.26), highlighting the value of active sensing in capturing low-canopy structure. Comparisons with commonly used satellite LAI products (i.e., GCOM-C SGLI, Globe PROBA-V, Sentinel-3 OLCI, the MODIS, and the Simplified Level 2 Prototype Processor (SL2P) for S2 at 20 m and 10 m resolutions) revealed strong agreements for PAIeff, underscoring the importance of methodologies for correcting LAI estimation. This study presents an operational approach for LAI partitioning in dryland forests, providing novel insights into the structural responses of forests to climatic aridity and forest management. The methodology holds significant potential for enhancing global forest monitoring and improving the precision of ecosystem models under changing environmental conditions.
DOI: 10.17632/zh4n5ww42p.1
title: Database for The Article: Leaf Area Index (LAI) Partitioning in Dryland Forests Using Dual-Sensor Sentinel Imagery

description: Leaf Area Index (LAI) is a key biophysical variable for understanding forest structure and function, particularly in dryland ecosystems where vegetation responses to environmental stressors are highly dynamic. Traditional satellite-derived LAI products are often inaccurate, as they typically represent the effective Plant Area Index (PAIeff), excluding necessary corrections such as consideration of leaf clumping and exclusion of woody elements. Moreover, while most studies report total ecosystem LAI, it is increasingly important to partition LAI into its structural components, such as overstory and understory layers, to better understand forest structure and function. In this study, we developed and validated a novel approach to estimate forest LAI (LAIEcosystem) and partition it into overstory (LAIOverstory) and understory (LAIUnderstory) components using an integrated model based on Sentinel-2 (S2) multispectral and Sentinel-1 SAR data (S1). Field LAI measurements were conducted in two Aleppo pine forests in Israel, HaKedoshim and Yatir, which span an aridity gradient and include multiple thinning treatments. Partial Least Squares Regression (PLS-R) models were calibrated and validated using extensive field data collected between 2018 and 2019. The combined S1+S2 model significantly improved LAIEcosystem predictions (R² = 0.85, RMSE = 0.52) and enabled fine-scale monitoring of vertical forest structure. Notably, S1 data substantially enhanced the prediction of understory LAI (R² = 0.87, RMSE = 0.26), highlighting the value of active sensing in capturing low-canopy structure. Comparisons with commonly used satellite LAI products (i.e., GCOM-C SGLI, Globe PROBA-V, Sentinel-3 OLCI, the MODIS, and the Simplified Level 2 Prototype Processor (SL2P) for S2 at 20 m and 10 m resolutions) revealed strong agreements for PAIeff, underscoring the importance of methodologies for correcting LAI estimation. This study presents an operational approach for LAI partitioning in dryland forests, providing novel insights into the structural responses of forests to climatic aridity and forest management. The methodology holds significant potential for enhancing global forest monitoring and improving the precision of ecosystem models under changing environmental conditions.
DOI: 10.25345/c5b27q43w
title: MassIVE MSV000098576 - GNPS - Feeding experiments of Xylaria sp. XSP14 with 3Br-phenylalanine
title: Molecular network of Xylaria sp. XSP14 fed with 3Br-phenylalanine (2, 10, 20 mM)
DOI: 10.15468/dl.brybqh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bambusa balcooa",
 "Country is Namibia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021246-250717081556266/datasets/export for details.
DOI: 10.5517/ccdc.csd.cc2grhg6
title: CCDC 2286698: Experimental Crystal Structure Determination

description: Related Article: Xiao-Lin Wang, Kavuri Rupa, Sheng-Cai Zheng, Xiaoming Zhao|2025|Org.Biomol.Chem.|23|6687|doi:10.1039/D5OB00926J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.4bhsqh
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bambusa balcooa",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 24 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021247-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uutsvn
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Buteogallus anthracinus)",
 "Country is one of (Åland Islands, Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Republic of, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021248-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3mm4zu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Buteogallus anthracinus)",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021249-250717081556266/datasets/export for details.
DOI: 10.17031/68664b68381b2
title: 2022 Seasearch Survey of the Scotland North Coast and the Northern Isles

description: A collection of Seasearch Survey and Observation forms from the north coast of Scotland (Cape Wrath to John o' Groats) and Orkney and Shetland.
description: Data archived into DASSH from Marine Recorder Online.
DOI: 10.15468/dl.gdg8ra
title: Occurrence Download

description: A dataset containing 243 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Chinese Taipei)",
 "OccurrenceStatus is Present",
 "TaxonKey is Cinnamomum reticulatum",
 "Year 1950-2025"
 ]
}

 The dataset includes 243 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021250-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qg6q9r
title: Occurrence Download

description: A dataset containing 11684044 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations, Flora-On: occurrence data of the flora of mainland Portugal)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 11684044 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021227-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5gh2mm
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Hong Kong, Chinese Taipei)",
 "TaxonKey is one of (Poria cocos, Wolfiporia cocos)"
 ]
}

 The dataset includes 29 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021251-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/56frb5h97h
title: Cuando el Cerebro Calla, los Signos Hablan: Narrativa de Enfermeria

description: La narrativa de enfermería es una herramienta metodológica y epistemológica que permite visibilizar el conocimiento disciplinar a partir del relato reflexivo de experiencias significativas en la práctica clínica. A través de ella, el profesional de enfermería da cuenta no solo de lo que hace, sino de cómo piensa, decide, siente y cuida, haciendo evidente la complejidad del juicio clínico y la integralidad del cuidado.

La narrativa titulada “Cuando el Cerebro Calla, los Signos Hablan: Narrativa del Cuidado Intensivo Analizada desde el Sistema Conceptual-Teórico-Empírico de Enfermería” es un ejemplo potente de cómo el relato clínico puede convertirse en un medio para articular los conceptos fundamentales de la disciplina, con marcos teóricos que sustentan la práctica avanzada. En este caso, el uso del Sistema Conceptual-Teórico-Empírico permite enlazar una teoría de Situación específica (como la teoría de señal respuesta en lesión cerebral) y los indicadores empíricos (acciones concretas de monitoreo, intervención, comunicación y acompañamiento).

De este modo, esta narrativa no solo representa un testimonio humano y técnico, sino que se consolida como un producto académico y disciplinar que integra teoría, práctica e investigación, reafirmando la enfermería como una ciencia humanista, situada y transformadora.
DOI: 10.17632/56frb5h97h.1
title: Cuando el Cerebro Calla, los Signos Hablan: Narrativa de Enfermeria

description: La narrativa de enfermería es una herramienta metodológica y epistemológica que permite visibilizar el conocimiento disciplinar a partir del relato reflexivo de experiencias significativas en la práctica clínica. A través de ella, el profesional de enfermería da cuenta no solo de lo que hace, sino de cómo piensa, decide, siente y cuida, haciendo evidente la complejidad del juicio clínico y la integralidad del cuidado.

La narrativa titulada “Cuando el Cerebro Calla, los Signos Hablan: Narrativa del Cuidado Intensivo Analizada desde el Sistema Conceptual-Teórico-Empírico de Enfermería” es un ejemplo potente de cómo el relato clínico puede convertirse en un medio para articular los conceptos fundamentales de la disciplina, con marcos teóricos que sustentan la práctica avanzada. En este caso, el uso del Sistema Conceptual-Teórico-Empírico permite enlazar una teoría de Situación específica (como la teoría de señal respuesta en lesión cerebral) y los indicadores empíricos (acciones concretas de monitoreo, intervención, comunicación y acompañamiento).

De este modo, esta narrativa no solo representa un testimonio humano y técnico, sino que se consolida como un producto académico y disciplinar que integra teoría, práctica e investigación, reafirmando la enfermería como una ciencia humanista, situada y transformadora.
DOI: 10.15468/dl.x33yjf
title: Occurrence Download

description: A dataset containing 2654662 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Banco de Datos de la Biodiversidad de la Comunitat Valenciana"
 ]
}

 The dataset includes 2654662 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021229-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16263248
title: promoter methylation after gene editing in iPS cells
DOI: 10.5281/zenodo.16263247
title: promoter methylation after gene editing in iPS cells
DOI: 10.17632/39kyzcp9r6
title: Group Ownership Information from Annual Reports of SIX Swiss Exchange Companies

description: This dataset contains group ownership information extracted from annual reports for the year 2021, published by companies listed on the SIX Swiss Exchange. It is used in the article "Discriminative meets generative: Automated information retrieval from unstructured corporate documents via (large) language models".

The labels for page identification are provided as a CSV file ("labels/page_identification_labels.csv"). Each row corresponds to a single page and includes its label. With a few exceptions, the dataset includes only pages that contain group ownership information (i.e., positive cases). Please note that the page numbers in this dataset were extracted using the Python library doc2data (https://pypi.org/project/doc2data/). They may not correspond to the printed page numbers in the original PDF files. The dataset includes the following attributes:

* file_name: name of the associated annual report file
* company: company name
* symbol: company ticker symbol
* accounting_rules: accounting standards applied in the annual report (i.e., US GAAP, IFRS, Swiss GAAP FER or Bank Law)
* foreign_issuer: boolean indicating whether the company is a foreign issuer
* no_subs: boolean indicating whether the annual report does not contain the target information (i.e., no subsidiaries)
* dual_lang: boolean indicating whether the pages in the report contain content in two languages
* language: language of the annual report (i.e., German, English, French or Italian)
* page_nr: page number within the document to which the label refers
* page_label: label assigned to the specific page - either primary range, secondary range or not relevant (for the information extraction task we only use the primary range).

The labels for information extraction are provided as a JSON file ("labels/bbox_labels.json"). The labels indicate whether a word, which is defined by its literal text and its bounding box on the page, belongs to a specified category of group ownership information. The dataset is structured via nested dictionaries in the following way:

* dictionary of files:
 - [file name as key for each annual report]: dictionary of pages
* dictionary of pages:
 - [page number as key for each relevant page]: list of dictionaries, each representing a word
* dictionary per word (four fixed keys):
 - id: unique identifier for each word on the page
 - bbox: bounding box coordinates as four normalized values [left, top, right, bottom] representing the position of the word on the page
 - text: the extracted text content from that bounding box region
 - label: label assigned to the word

The labels correspond to the following classes: LE (name of the legal entity), C (location of the legal entity), Own (total ownership percentage of the issuer in the legal entity), and Other (non-relevant words).

Finally, we also publish the exact data split ("data_split.csv") that was used for model training and evaluation in the associated article.
DOI: 10.17632/39kyzcp9r6.1
title: Group Ownership Information from Annual Reports of SIX Swiss Exchange Companies

description: This dataset contains group ownership information extracted from annual reports for the year 2021, published by companies listed on the SIX Swiss Exchange. It is used in the article "Discriminative meets generative: Automated information retrieval from unstructured corporate documents via (large) language models".

The labels for page identification are provided as a CSV file ("labels/page_identification_labels.csv"). Each row corresponds to a single page and includes its label. With a few exceptions, the dataset includes only pages that contain group ownership information (i.e., positive cases). Please note that the page numbers in this dataset were extracted using the Python library doc2data (https://pypi.org/project/doc2data/). They may not correspond to the printed page numbers in the original PDF files. The dataset includes the following attributes:

* file_name: name of the associated annual report file
* company: company name
* symbol: company ticker symbol
* accounting_rules: accounting standards applied in the annual report (i.e., US GAAP, IFRS, Swiss GAAP FER or Bank Law)
* foreign_issuer: boolean indicating whether the company is a foreign issuer
* no_subs: boolean indicating whether the annual report does not contain the target information (i.e., no subsidiaries)
* dual_lang: boolean indicating whether the pages in the report contain content in two languages
* language: language of the annual report (i.e., German, English, French or Italian)
* page_nr: page number within the document to which the label refers
* page_label: label assigned to the specific page - either primary range, secondary range or not relevant (for the information extraction task we only use the primary range).

The labels for information extraction are provided as a JSON file ("labels/bbox_labels.json"). The labels indicate whether a word, which is defined by its literal text and its bounding box on the page, belongs to a specified category of group ownership information. The dataset is structured via nested dictionaries in the following way:

* dictionary of files:
 - [file name as key for each annual report]: dictionary of pages
* dictionary of pages:
 - [page number as key for each relevant page]: list of dictionaries, each representing a word
* dictionary per word (four fixed keys):
 - id: unique identifier for each word on the page
 - bbox: bounding box coordinates as four normalized values [left, top, right, bottom] representing the position of the word on the page
 - text: the extracted text content from that bounding box region
 - label: label assigned to the word

The labels correspond to the following classes: LE (name of the legal entity), C (location of the legal entity), Own (total ownership percentage of the issuer in the legal entity), and Other (non-relevant words).

Finally, we also publish the exact data split ("data_split.csv") that was used for model training and evaluation in the associated article.
DOI: 10.17632/7cgjs5rgtm
title: Data for: A steady-state model of the Greek natural gas system analyzing hydrogen injection to absorb excess renewable electricity
DOI: 10.17632/7cgjs5rgtm.1
title: Data for: A steady-state model of the Greek natural gas system analyzing hydrogen injection to absorb excess renewable electricity