cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.fj3a67
title: Occurrence Download

description: A dataset containing 2807 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ruditapes philippinarum",
 "Country is Netherlands",
 "HasCoordinate is true",
 "Year is greater than or equal to 2020",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 2807 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013628-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17984791
title: Wind speed, solar radiation, temperature and demand load time series and other techical characteristics

description: Hourly data for wind speed, solar radiation, temperature and demand load of 20 years length, used within the simulation of a hybrid renewable energy system in Sifnos, Greece.
DOI: 10.5281/zenodo.18493792
title: Wind speed, solar radiation, temperature and demand load time series and other techical characteristics

description: Hourly data for wind speed, solar radiation, temperature and demand load of 20 years length, used within the simulation of a hybrid renewable energy system in Sifnos, Greece.
DOI: 10.15468/dl.8ntbca
title: Occurrence Download

description: A dataset containing 2807 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ruditapes philippinarum",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Country is Netherlands",
 "HasCoordinate is true",
 "Year is greater than or equal to 2020",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 2807 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013631-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18493426
title: A vertical microrod with quasi-ballistic ion transport for efficient electricity generation from low-power-density atmospheric thermal energy
DOI: 10.5281/zenodo.18493427
title: A vertical microrod with quasi-ballistic ion transport for efficient electricity generation from low-power-density atmospheric thermal energy
DOI: 10.15468/dl.935apy
title: Occurrence Download

description: A dataset listing the 34 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-12.04443 36.30387,-11.12360 36.30387,-11.12360 36.75039,-12.04443 36.75039,-12.04443 36.30387))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Annelida"
 ]
}

 The dataset's 34 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013597-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5158/arp/k2489i
title: data for the paper: A Pyrene Hydrazone-CTAB Platform: Achieving Sequential Distinct Sensing for Thorium(IV) and Uranium(VI) in Water

description: Experimental data for materials described in the publication. 1H NMR and FT-IR data. (2026-02-02)
DOI: 10.15468/dl.hvgmxf
title: Occurrence Download

description: A dataset containing 21341 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Human Observation, Machine Observation)",
 "Depth 0.0-500.0m",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "VerbatimScientificName is Dinophyceae",
 "Year 1950-2023"
 ]
}

 The dataset includes 21341 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013633-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/w1dvus
title: UnRootINGStress

description: Rice is the staple food for half the world population and an important crop in Europe, but severely affected by drought. The root system, the first organ to respond to drought, is frequently overlooked in research due to be underground. Nevertheless, many genes have already been described to change the root system architecture (RSA) but most of them lead to growth-defense tradeoffs that hamper yield, due to expression in other organs. UnRootINGStress aims to quantify the root traits of rice plants altered in the expression of a root-specific E3-ubiquitin ligase responsive to drought. These plants exhibit an altered RSA, but not shoot traits. To fully characterize this phenotype, we plan to access a root-phenotyping Installation (Julich) that is able to collect several root (e.g. root length and root system width) and shoot parameters (e.g. apparent leaf area), under control and drought conditions. Given the large amount of data generated, a proper guidance on annotation and raw phenotypic data deposit (.csv files) will be crucial for (re)usability of data by the team or others (data.INRAE.fr). UnRootINGStress will emphasize the importance of root-specific genes (E3-ubiquitin ligases) in RSA plasticity for a sustainable rice production using less water and lower Carbon emissions.
DOI: 10.6084/m9.figshare.31266473
title: Novel insights into the mechanism of formaldehyde-induced lung cancer: a network toxicology and molecular docking approach

description: We explored the complex effects of formaldehyde (FA) on lung cancer through network toxicology and molecular docking techniques, focusing on understanding the molecular mechanisms by which FA affects lung cancer from a biological network perspective. With the information of formaldehyde and lung cancer targets provided by databases (ChEMBL, STITCH, GeneCards, OMIM), we identified key potential targets that are closely related to formaldehyde and lung cancer.Protein-protein interaction(PPI) networks for core targets were constructed using the STRING database, and ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the David database. Subsequently, molecular docking simulations of key target proteins with FA were performed using AutoDock software to visualize binding interactions. Finally, utilizing the PanglaoDB database, we conducted data mining of single-cell RNA sequencing data to find cell types where a certain set of genes are expressed. The results showed that the key genes for formaldehyde effects on lung cancer were mainly concentrated in metabolism-related signaling cascades, including pyruvate metabolism, fatty acid metabolism, and Pantothenate and CoA biosynthesis pathways, and the core genes ADH5, ADH4, ADH1B, ADH6, and ADH7 were screened by PPI with GO and KEGG analysis. Subsequently, molecular docking simulations of the screened key genes with FA confirmed the robust binding interactions between formaldehyde and the core targets in the key genes (binding energies were all less than −1kcal/mol). Single-cell RNA sequencing distribution analysis showed that ADH5 and ADH1B were significantly enriched in Fibroblasts cell clusters, and ADH7 was significantly enriched in Basal cells cell clusters. Our study provides key targets and pathways for understanding the complex molecular mechanisms by which FA affects lung cancer, reveals complex relationships that are difficult to find by traditional toxicological methods, and provides insights into strategies for preventing lung cancer caused by FA exposure.
DOI: 10.6084/m9.figshare.31266473.v1
title: Novel insights into the mechanism of formaldehyde-induced lung cancer: a network toxicology and molecular docking approach

description: We explored the complex effects of formaldehyde (FA) on lung cancer through network toxicology and molecular docking techniques, focusing on understanding the molecular mechanisms by which FA affects lung cancer from a biological network perspective. With the information of formaldehyde and lung cancer targets provided by databases (ChEMBL, STITCH, GeneCards, OMIM), we identified key potential targets that are closely related to formaldehyde and lung cancer.Protein-protein interaction(PPI) networks for core targets were constructed using the STRING database, and ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the David database. Subsequently, molecular docking simulations of key target proteins with FA were performed using AutoDock software to visualize binding interactions. Finally, utilizing the PanglaoDB database, we conducted data mining of single-cell RNA sequencing data to find cell types where a certain set of genes are expressed. The results showed that the key genes for formaldehyde effects on lung cancer were mainly concentrated in metabolism-related signaling cascades, including pyruvate metabolism, fatty acid metabolism, and Pantothenate and CoA biosynthesis pathways, and the core genes ADH5, ADH4, ADH1B, ADH6, and ADH7 were screened by PPI with GO and KEGG analysis. Subsequently, molecular docking simulations of the screened key genes with FA confirmed the robust binding interactions between formaldehyde and the core targets in the key genes (binding energies were all less than −1kcal/mol). Single-cell RNA sequencing distribution analysis showed that ADH5 and ADH1B were significantly enriched in Fibroblasts cell clusters, and ADH7 was significantly enriched in Basal cells cell clusters. Our study provides key targets and pathways for understanding the complex molecular mechanisms by which FA affects lung cancer, reveals complex relationships that are difficult to find by traditional toxicological methods, and provides insights into strategies for preventing lung cancer caused by FA exposure.
DOI: 10.15468/dl.85pp7b
title: Occurrence Download

description: A dataset containing 432 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.decimallatitude &gt;= 20.23898
 AND occurrence.decimallatitude &lt;= 20.37666
 AND (occurrence.decimallongitude &gt;= -99.22684 AND occurrence.decimallongitude &lt;= -99.10216)
 AND GBIF_WITHIN('POLYGON ((-99.20825 20.24335, -99.18027 20.23898, -99.174 20.25972, -99.17345 20.26155, -99.17244 20.26488, -99.17118 20.26903, -99.15549 20.28644, -99.15255 20.30933, -99.12016 20.31931, -99.10216 20.34098, -99.1171 20.37666, -99.16227 20.37293, -99.1694 20.36564, -99.17533 20.36111, -99.2067 20.35711, -99.22684 20.35459, -99.22315 20.3269, -99.21839 20.294, -99.19674 20.27275, -99.20098 20.26193, -99.20825 20.24335))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.classificationdetails['d7dddbf4-2cf0-4f39-9b2a-bb099caae36c']['specieskey'] &lt;&gt; '' AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 432 records were derived from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013596-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yqdgwt
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-1.43938 43.48495,-1.43902 43.48495,-1.43902 43.48531,-1.43938 43.48531,-1.43938 43.48495))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013635-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18329706
title: A Synthetic, Clinically Inspired Neonatal Dataset for Multi-Class Disease Prediction and Trust-Aware Machine Learning

description: This dataset provides a synthetic, clinically inspired collection of neonatal health records designed to support methodological research in machine learning. It focuses on multi-class neonatal disease prediction across nine clinically motivated outcome categories and is intended for studies in trust-aware modeling, uncertainty estimation, confidence-aware decision logic, and calibration analysis.

The dataset is entirely synthetic and does not contain any real patient data. It is designed as a research-enabling resource and is not clinically validated. It must not be used for diagnosis, treatment planning, or real-world medical decision-making.

All features are provided in a single consolidated dataset file. An accompanying metadata file documents clinically inspired feature groupings corresponding to conceptual stages of maternal, birth-related, and postnatal information. These stage definitions are provided for research guidance only and do not impose any constraints on how the dataset must be used.

The dataset has been developed as part of doctoral research focused on advancing trustworthy and transparent machine learning frameworks for neonatal health applications.
DOI: 10.26255/heal.yzsp-89vp
title: LKD2 GNSS RINEX DATA
DOI: 10.5281/zenodo.18234459
title: A Synthetic, Clinically Inspired Neonatal Dataset for Multi-Class Disease Prediction and Trust-Aware Machine Learning

description: This dataset provides a synthetic, clinically inspired collection of neonatal health records designed to support methodological research in machine learning. It focuses on multi-class neonatal disease prediction across nine clinically motivated outcome categories and is intended for studies in trust-aware modeling, uncertainty estimation, confidence-aware decision logic, and calibration analysis.

The dataset is entirely synthetic and does not contain any real patient data. It is designed as a research-enabling resource and is not clinically validated. It must not be used for diagnosis, treatment planning, or real-world medical decision-making.

All features are provided in a single consolidated dataset file. An accompanying metadata file documents clinically inspired feature groupings corresponding to conceptual stages of maternal, birth-related, and postnatal information. These stage definitions are provided for research guidance only and do not impose any constraints on how the dataset must be used.

The dataset has been developed as part of doctoral research focused on advancing trustworthy and transparent machine learning frameworks for neonatal health applications.
DOI: 10.21227/75d3-tc62
title: "SAR Image Denoising and Colorization Dataset"

description: "The dataset under consideration is a set of 10,000 paired Synthetic Aperture Radar (SAR) and optical-style reference images created to aid in the research of SAR image denoising and perceptual colorization. The dataset is made to imitate real Earth-surface patterns, such as agricultural land, city regions, vegetations, water features and barren area while maintaining geographical anonymity and complete syntheticity to make it license-free to public distribution.The data is organized in a way that allows two-stage learning pipeline. During the initial step, clean grayscale optical-like images are offered as luminance (L) references to speckle noise reduction in SAR images. The second stage involves the regression targets of SAR-to-optical colorization using corresponding chrominance (a, b) in the CIE Lab color space. The supervision is done using pixel-level regression targets in place of discrete semantic class labels.All the images are rescaled to 224 224 pixels, and they are kept without logos, watermarks, comments, and artificial interventions. The dataset is accompanied by a structured metadata file that records the identifiers of the images, the data splits, the preprocessing stages, and the information on the specific use of the task.The data can be used to facilitate research and education in the areas of SAR image denoising, SAR-to-optical translation, image colorization and evaluation of two-stage deep learning architectures in remote sensing and perceptual image optimization schemes."
DOI: 10.15468/dl.aa25f3
title: Occurrence Download

description: A dataset containing 35233 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation)",
 "Continent is Europe",
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Juniperus communis"
 ]
}

 The dataset includes 35233 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013639-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7yq577
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alticola albicaudus"
 ]
}

 The dataset includes 23 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013641-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48544/4b3b75b7-c250-48a3-9101-df1e9bd9a3ec
title: Orde op zaken stellen. Een onderzoek naar veiligheidsbeleving in Lombardijen
DOI: 10.48544/1b27ec62-973e-4e47-a047-55f8fca8085a
title: Integrating Sustainable Development Goals into Transdisciplinary Project Based Learning for Environmetal Design
DOI: 10.5517/ccpk31c
title: CCDC 642042: Experimental Crystal Structure Determination

description: Related Article: P.Chandrasekaran, J.T.Mague, R.Venkateswaran, M.S.Balakrishna|2007|Eur.J.Inorg.Chem.|2007|4988|doi:10.1002/ejic.200700554
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: 10.5517/ccdc.csd.cc2jcrvj
title: CCDC 2335008: Experimental Crystal Structure Determination

description: Related Article: Minaho Nakaie, Yuka Nishiyama, Mizuki Takishita, Shuji Shiose, Yuuko Miyaji, Hiromichi Toyota, Toshio Naito|2025|Bull.Chem.Soc.Jpn.|98|uoaf099|doi:10.1093/bulcsj/uoaf099
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: 10.5517/ccdc.csd.cc2jd6rx
title: CCDC 2335439: Experimental Crystal Structure Determination

description: Related Article: Minaho Nakaie, Yuka Nishiyama, Mizuki Takishita, Shuji Shiose, Yuuko Miyaji, Hiromichi Toyota, Toshio Naito|2025|Bull.Chem.Soc.Jpn.|98|uoaf099|doi:10.1093/bulcsj/uoaf099
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: 10.5517/ccdc.csd.cc2k9z2y
title: CCDC 2363070: Experimental Crystal Structure Determination

description: Related Article: Paranimuthu Panjacharam, Sabbasani Rajasekhara Reddy|2025|Asian J.Org.Chem|14|e70106|doi:10.1002/ajoc.70106
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: 10.5517/ccdc.csd.cc2k9z40
title: CCDC 2363072: Experimental Crystal Structure Determination

description: Related Article: Paranimuthu Panjacharam, Sabbasani Rajasekhara Reddy|2025|Asian J.Org.Chem|14|e70106|doi:10.1002/ajoc.70106
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: 10.5517/ccdc.csd.cc2k9z51
title: CCDC 2363073: Experimental Crystal Structure Determination

description: Related Article: Paranimuthu Panjacharam, Sabbasani Rajasekhara Reddy|2025|Asian J.Org.Chem|14|e70106|doi:10.1002/ajoc.70106
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: 10.5517/ccdc.csd.cc2kd1jk
title: CCDC 2365068: Experimental Crystal Structure Determination

description: Related Article: Soheil Mahmoudi, Tim Gruene, Christian Schröder, Khalil D. Ferjaoui, Erik Fröjdh, Aldo Mozzanica, Kiyofumi Takaba, Anatoliy Volkov, Julian Maisriml, Vladimir Paunović, Jeroen A. van Bokhoven, Bernhard K. Keppler |2025|Nature (London)|645|88|doi:10.1038/s41586-025-09405-0
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.