cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2ng0tz
title: CCDC 2456341: Experimental Crystal Structure Determination

description: Related Article: Ye-Hui Qin, Xiu-Fang Ma, Xinlan Hou, Xin-Da Huang, Song-Song Bao, Yuxi Tian, Yi-Quan Zhang, Li-Min Zheng|2025|Chemical Science|16|19806|doi:10.1039/d5sc04192a
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.cc2ng0z4
title: CCDC 2456346: Experimental Crystal Structure Determination

description: Related Article: Ye-Hui Qin, Xiu-Fang Ma, Xinlan Hou, Xin-Da Huang, Song-Song Bao, Yuxi Tian, Yi-Quan Zhang, Li-Min Zheng|2025|Chemical Science|16|19806|doi:10.1039/d5sc04192a
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.cc2ng128
title: CCDC 2456349: Experimental Crystal Structure Determination

description: Related Article: Ye-Hui Qin, Xiu-Fang Ma, Xinlan Hou, Xin-Da Huang, Song-Song Bao, Yuxi Tian, Yi-Quan Zhang, Li-Min Zheng|2025|Chemical Science|16|19806|doi:10.1039/d5sc04192a
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.cc2nrjdc
title: CCDC 2465473: Experimental Crystal Structure Determination

description: Related Article: Huanyu Sun, Zhiwei Zeng, Chunhui Chen, Xiancheng Mai, Jia Wang, Wei Huang, Yongxing Tang, Yuji Liu|2025|Org.Biomol.Chem.|23|9652|doi:10.1039/d5ob01264c
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.cc2nrmgj
title: CCDC 2465568: Experimental Crystal Structure Determination

description: Related Article: Huanyu Sun, Zhiwei Zeng, Chunhui Chen, Xiancheng Mai, Jia Wang, Wei Huang, Yongxing Tang, Yuji Liu|2025|Org.Biomol.Chem.|23|9652|doi:10.1039/d5ob01264c
description: Related Article: Sun Huanyu|2025|CSD Communication|||
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.cc2p46xz
title: CCDC 2476711: Experimental Crystal Structure Determination

description: Related Article: Shun Dekura, Motohiro Mizuno, Hatsumi Mori|2025|Chemical Science|16|19601|doi:10.1039/d5sc05839b
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.cc2p4769
title: CCDC 2476720: Experimental Crystal Structure Determination

description: Related Article: Shun Dekura, Motohiro Mizuno, Hatsumi Mori|2025|Chemical Science|16|19601|doi:10.1039/d5sc05839b
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.cc2pdzx0
title: CCDC 2485143: Experimental Crystal Structure Determination

description: Related Article: Shipan Xu|2025|CSD Communication|||
description: Related Article: Shipan Xu, Wenping Liu, Yan An, Xuyang Du, Yuanhui Sun, Junfei Tao, Guijiang Zhou, Zhao Chen, Xiaolong Yang|2025|Chemical Science|16|19956|doi:10.1039/d5sc04507j
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.cc2pg32c
title: CCDC 2486202: Experimental Crystal Structure Determination

description: Related Article: Shaotong Qiu, Yangxin Mao, Zhicheng Yang, Jiangan Liu, Yimin Shangguan, Xinlei Wang, Chun Zou, Yun-Hui Zhao, Baishu Zheng, Hu Zhou|2025|Org.Biomol.Chem.|23|9582|doi:10.1039/d5ob01444a
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.cc2pgh7x
title: CCDC 2486579: Experimental Crystal Structure Determination

description: Related Article: Toshiya M. Fukunaga, Kiyofumi Takaba, Satoshi Yoshida, Saori Maki-Yonekura, Koji Yonekura, Hiroyuki Isobe|2025|Chemical Science|16|19594|doi:10.1039/d5sc06999h
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.v428h9
title: Occurrence Download

description: A dataset containing 677 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhododendron nivale"
 ]
}

 The dataset includes 677 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006543-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17456002
title: Antibody CDR Position Control - Trained Model Checkpoints (Mamba + Transformer)

description: Complete trained model checkpoints for the manuscript:"Precise CDR Position Control in Antibody Sequence Generation Using Conditional Deep Generative Models"

This archive contains the full trained PyTorch model weights for both architectures described in the manuscript:

1. Mamba Model (mamba_epoch8_best_model.pt, 1.0 GB)   - Selective State Space Model architecture   - 88.6 million parameters   - Training: 8 epochs, 52 GPU-hours on 6× A100 40GB   - Best validation loss: 0.4636   - CDR position accuracy: 100.0%   - Trained on 10.88M antibody sequences from OAS + SAbDab

2. Transformer Model (transformer_epoch13_best_model.pt, 579 MB)   - Decoder-only Transformer (GPT-style) architecture   - 50.5 million parameters   - Training: 13 epochs, 65 GPU-hours on 6× A100 40GB   - Best validation loss: 0.6187   - CDR position accuracy: 98.0%   - Inference speed: 6.49 seq/s (2.13× faster than Mamba)

3. Configuration Files   - vocab.json: 40-token vocabulary mapping   - config_mamba.yaml: Complete Mamba architecture specification   - config_transformer.yaml: Complete Transformer architecture specification   - README.md: Usage instructions and model details

Key Results:- Both models achieve statistically significant conditional control over CDR3 properties (p<0.001)- Mamba: Superior training quality (25% lower validation loss)- Transformer: Faster inference (2.13× speedup)

These models can be loaded directly in PyTorch for sequence generation and evaluation. All training code, data preprocessing scripts, and usage examples are available in the GitHub repository.

Manuscript Status: Under review at PLOS ONE (October 2025)GitHub Repository: https://github.com/261732506/antibody-cdr-position-control

Upon publication, this record will be updated with full author information and manuscript DOI.
DOI: 10.5281/zenodo.17456001
title: Antibody CDR Position Control - Trained Model Checkpoints (Mamba + Transformer)

description: Complete trained model checkpoints for the manuscript:"Precise CDR Position Control in Antibody Sequence Generation Using Conditional Deep Generative Models"

This archive contains the full trained PyTorch model weights for both architectures described in the manuscript:

1. Mamba Model (mamba_epoch8_best_model.pt, 1.0 GB)   - Selective State Space Model architecture   - 88.6 million parameters   - Training: 8 epochs, 52 GPU-hours on 6× A100 40GB   - Best validation loss: 0.4636   - CDR position accuracy: 100.0%   - Trained on 10.88M antibody sequences from OAS + SAbDab

2. Transformer Model (transformer_epoch13_best_model.pt, 579 MB)   - Decoder-only Transformer (GPT-style) architecture   - 50.5 million parameters   - Training: 13 epochs, 65 GPU-hours on 6× A100 40GB   - Best validation loss: 0.6187   - CDR position accuracy: 98.0%   - Inference speed: 6.49 seq/s (2.13× faster than Mamba)

3. Configuration Files   - vocab.json: 40-token vocabulary mapping   - config_mamba.yaml: Complete Mamba architecture specification   - config_transformer.yaml: Complete Transformer architecture specification   - README.md: Usage instructions and model details

Key Results:- Both models achieve statistically significant conditional control over CDR3 properties (p<0.001)- Mamba: Superior training quality (25% lower validation loss)- Transformer: Faster inference (2.13× speedup)

These models can be loaded directly in PyTorch for sequence generation and evaluation. All training code, data preprocessing scripts, and usage examples are available in the GitHub repository.

Manuscript Status: Under review at PLOS ONE (October 2025)GitHub Repository: https://github.com/261732506/antibody-cdr-position-control

Upon publication, this record will be updated with full author information and manuscript DOI.
DOI: 10.21227/971z-v517
title: "Experimental Dataset for Predicting Temperature Rise in Low-Voltage Switchgear"

description: "This dataset contains experimental data obtained from a low-voltage switchgear prototype designed according to IEC 61439-1 for the analysis of thermal behavior under different operational configurations. Twelve experiments were conducted by varying panel orientation, ventilation conditions (natural, forced, or none), and busbar surface treatment (painted or unpainted), while maintaining a constant current of 800 A.Temperature measurements were acquired using calibrated type-K thermocouples installed at critical points such as busbars and connections, with continuous logging by a PLC at one-minute intervals. Each test represents a distinct configuration, providing comprehensive information on temperature rise and heat exchange characteristics.The dataset is organized into three processing levels: (1) Raw measurement data (minute-by-minute temperature readings, 19 CSV files), (2) Summarized test results (steady-state temperature rise and effective heat transfer coefficients), and (3) Processed input data (structured variables for predictive modeling).This resource supports research on thermal management, component design optimization, and validation of computational or statistical models for low-voltage electrical panels. It serves as the experimental foundation for future studies. "
DOI: 10.15468/dl.sezrh9
title: Occurrence Download

description: A dataset containing 467 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-1.3009 47.06199,-2.05746 46.97074,-2.14366 46.84315,-1.77972 46.49154,-1.22902 46.26689,-0.77566 46.33881,-0.69714 46.42204,-0.6453 46.55758,-0.66875 46.68242,-0.83042 46.89731,-0.91363 46.99653,-1.09325 47.08572,-1.3009 47.06199))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Esox lucius",
 "Year 2000-2025"
 ]
}

 The dataset includes 467 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006544-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/ux19b1
title: Vanilla semi-structured interview database

description: Database of semi-structured interviews on traditional knowledge of Vanilla pompona in selected locations in Mexico.
DOI: 10.5281/zenodo.17475640
title: Novel Prediction Equations for Appendicular Skeletal Muscle Mass in Hemodialysis Patients: Referenced against Bioelectrical Impedance Analysis

description: This dataset contains de-identified baseline anthropometric and bioimpedance information from 111 maintenance hemodialysis (MHD) patients treated at the Kunming (KM) center in Yunnan Province, China. The data were used to evaluate the performance of existing appendicular skeletal muscle mass (ASM) prediction equations against multi-frequency bioelectrical impedance analysis (BIA) and to develop and validate new, hemodialysis-specific prediction models.

The dataset includes demographic data (age, sex), core measurements (height [m], weight [kg]), reference muscle outcomes (ASM by BIA [kg]), limb lengths (arm, upper arm, forearm, leg, thigh, calf; cm), skinfolds (triceps, subscapular, thigh, calf, abdominal; mm), and circumferences (upper arm, forearm, thigh, calf, waist, hip; cm). Variables needed to compute corrected girths are provided—CAG, CTG, and CCG can be derived as measured circumference − π × (corresponding skinfold [mm]/10)—enabling reproduction of the height–weight, height–circumference, and limb length–circumference models as well as the newly developed equations.
DOI: 10.15468/cpqq37
title: Monitoring records of plant species in the Hakone region of Fuji-Hakone-Izu National Park, Japan, 2001-2010

description: The monitoring of species occurrences is a crucial aspect of biodiversity conservation, and regional volunteerism can serve as a powerful tool in such endeavors. The Fuji-Hakone-Izu National Park in the Hakone region of Kanagawa Prefecture, Japan, boasts a volunteer association of approximately 100 members. These volunteers have monitored plant species occurrences from 2001 to the present along several hiking trails in the region. In this paper, I present the annual observation records of plant occurrences in Hakone from 2001 to 2010. This data set includes 1071 species of plants from 151 families. Scientific names follow the Y List. And this data set includes several threatened plant species. Data files are formatted based on the Darwin Core and Darwin Core Archives, which are defined by the Biodiversity Information Standards (BIS) or Biodiversity Information Standards Taxonomic Databases Working Group (TDWG). Data files filled on required and some additional item on Darwin Core. The data set can download from author’s personal Web site as of July 2012. These data will soon be published for the Global Biodiversity Information Facility (GBIF) through GBIF Japan. All users can then access the data from the GBIF portal site.
DOI: 10.15468/dl.dgjts8
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "NetworkKey is 99d66b6c-9087-452f-a9d4-f15f2c2d0e7e",
 "TaxonKey is Chordata"
 ]
 },
 {
 "and" : [
 "InstitutionCode is one of (AMNH, FMNH)",
 "TaxonKey is one of (Antidorcas [GBIF Backbone Taxonomy])",
 "BasisOfRecord is one of (Specimen)"
 ]
 }
 ]
}

 The dataset includes 118 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006545-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/4za7-n155
title: "Event-based Dataset for Insect Trajectories"

description: "This dataset consists of event recordings that were manually labelled to create a representative subset of insect flight trajectories and background activity. It includes ground-truth labels for semantic segmentation as well as for instance segmentation. "
DOI: 10.15468/2kuekk
title: CardObs : Observations naturalistes issues de l'outil CardObs-Données ajoutées SiCEN - Kélian GAUTIER

description: Données téléversées au CEN Auvergne
DOI: 10.5281/zenodo.17474850
title: Dataset of "Hierarchical ground-state crystals underlying Hertzian Quasicrystals"

description: Dataset of "Hierarchical ground-state crystals underlying Hertzian Quasicrystals"
DOI: 10.5281/zenodo.17474851
title: Dataset of "Hierarchical ground-state crystals underlying Hertzian Quasicrystals"

description: Dataset of "Hierarchical ground-state crystals underlying Hertzian Quasicrystals"
DOI: 10.34808/neqr-f154
title: Structure of the register of archeological monuments by primary function and voivodeship - comparison of the 2016, 2024 and 2025

description: The following dataset compares the structure of the register of archeological monuments in Poland regarding the general state of preservation of the objects in connection with the primary function and voivodeship for 2016, 2024 and 2025. The dataset has been compiled based on the data contained in the Report on the State of the Immovable Monuments in Poland – Monuments entered in the Register of Monuments (Register Books A and C) of 2017 and the Registry of archeological monuments as at 04.10 2024 and 20.05.2025.
DOI: 10.34808/wsjm-cz86
title: Structure of the register of archeological monuments by primary function and voivodeship - comparison of the 2016, 2024 and 2025
DOI: 10.15468/abbie7
title: Programme Cybelle Méditerranée - Cybelle Méditerranée. Observation nautique avec effort d'observation

description: Données provenant d'un programme de science participative. Observation des espèces marines au cours d'un trajet nautique avec prise en compte de l'effort d'observation
DOI: 10.17632/jt4gywvwtj
title: Bangla-MedER: Bangla Medical Entity Recognition Dataset

description: The Bangla-MedER dataset is a carefully compiled collection of 2980 annotated Bangla texts, centered on the field of medical entity recognition. This collection has six unique entity types: Medicine/Chemical Name, Organ, Disease, Hormone, Pharmacological Class, and Common Medical Terms. For the Bangla language, which is mostly spoken in Bangladesh and certain parts of India, this dataset intends to support research in natural language processing (NLP) and medical text mining. For training and assessing medical entity identification algorithms, the dataset—which was assembled from a variety of online medical resources, including blogs and websites—is an invaluable tool. This dataset can be used for a range of natural language processing (NLP) applications, including entity extraction, text classification, and information retrieval, which will enhance medical informatics and healthcare data processing.
Here we have also provided the English translated dataset of our prepared Bengali dataset in a separate .csv file.
DOI: 10.15468/dl.uj9wby
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Thymus quinquecostatus magnus"
 ]
}

 The dataset includes 9 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006548-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jt4gywvwtj.2
title: Bangla-MedER: Bangla Medical Entity Recognition Dataset

description: The Bangla-MedER dataset is a carefully compiled collection of 2980 annotated Bangla texts, centered on the field of medical entity recognition. This collection has six unique entity types: Medicine/Chemical Name, Organ, Disease, Hormone, Pharmacological Class, and Common Medical Terms. For the Bangla language, which is mostly spoken in Bangladesh and certain parts of India, this dataset intends to support research in natural language processing (NLP) and medical text mining. For training and assessing medical entity identification algorithms, the dataset—which was assembled from a variety of online medical resources, including blogs and websites—is an invaluable tool. This dataset can be used for a range of natural language processing (NLP) applications, including entity extraction, text classification, and information retrieval, which will enhance medical informatics and healthcare data processing.
Here we have also provided the English translated dataset of our prepared Bengali dataset in a separate .csv file.
DOI: 10.7910/dvn/munjzs
title: Sample data and associated MATLAB code to analyze the data

description: Sample data and associated MATLAB code to analyze the data

Main Function: mainFunction2020.m, processing the OMS data
Read OMS data files that acquired by Scimedia MiCAM: readorginoms2020.m

Sample data: W01C_0p0_2019-12-11-153934_N256 (IF1-CAM1).gsd

Other OMS data were uploaded in DataVerse

https://doi.org/10.7910/DVN/XSA5XT
https://doi.org/10.7910/DVN/W6BYN8
DOI: 10.15468/dl.8dd4da
title: Occurrence Download

description: A dataset containing 92939 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hada plebeja",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "HasCoordinate is true"
 ]
}

 The dataset includes 92939 records from 275 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006513-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/uc2t64
title: Observations aléatoires Vienne Nature [VIENNE NATURE]-VN_observations_aléatoires

description: Observations aléatoires faites par les salarié.e.s et bénévoles de Vienne Nature
DOI: 10.5281/zenodo.17475483
title: Mass Spec Data for Opposing Functions of Gut Immunomodulatory Metabolites on CAR-T Therapy
DOI: 10.5281/zenodo.17475484
title: Mass Spec Data for Opposing Functions of Gut Immunomodulatory Metabolites on CAR-T Therapy